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How Does Experimental Psychology Study Behavior?

Purpose, methods, and history

• Why It Matters

What factors influence people's behaviors and thoughts? Experimental psychology utilizes scientific methods to answer these questions by researching the mind and behavior. Experimental psychologists conduct experiments to learn more about why people do certain things.

Overview of Experimental Psychology

Why do people do the things they do? What factors influence how personality develops? And how do our behaviors and experiences shape our character?

These are just a few of the questions that psychologists explore, and experimental methods allow researchers to create and empirically test hypotheses. By studying such questions, researchers can also develop theories that enable them to describe, explain, predict, and even change human behaviors.

For example, researchers might utilize experimental methods to investigate why people engage in unhealthy behaviors. By learning more about the underlying reasons why these behaviors occur, researchers can then search for effective ways to help people avoid such actions or replace unhealthy choices with more beneficial ones.

Why Experimental Psychology Matters

While students are often required to take experimental psychology courses during undergraduate and graduate school , think about this subject as a methodology rather than a singular area within psychology. People in many subfields of psychology use these techniques to conduct research on everything from childhood development to social issues.

Experimental psychology is important because the findings play a vital role in our understanding of the human mind and behavior.

By better understanding exactly what makes people tick, psychologists and other mental health professionals can explore new approaches to treating psychological distress and mental illness. These are often topics of experimental psychology research.

Experimental Psychology Methods

So how exactly do researchers investigate the human mind and behavior? Because the mind is so complex, it seems like a challenging task to explore the many factors that contribute to how we think, act, and feel.

Experimental psychologists use a variety of different research methods and tools to investigate human behavior. Methods in the experimental psychology category include experiments, case studies, correlational research, and naturalistic observations.

Experiments

Experimentation remains the primary standard in psychological research. In some cases, psychologists can perform experiments to determine if there is a cause-and-effect relationship between different variables.

The basics of conducting a psychology experiment involve:

• Randomly assigning participants to groups
• Operationally defining variables
• Developing a hypothesis
• Manipulating independent variables
• Measuring dependent variables

One experimental psychology research example would be to perform a study to look at whether sleep deprivation impairs performance on a driving test. The experimenter could control other variables that might influence the outcome, varying the amount of sleep participants get the night before.

All of the participants would then take the same driving test via a simulator or on a controlled course. By analyzing the results, researchers can determine if changes in the independent variable (amount of sleep) led to differences in the dependent variable (performance on a driving test).

Case Studies

Case studies allow researchers to study an individual or group of people in great depth. When performing a case study, the researcher collects every single piece of data possible, often observing the person or group over a period of time and in a variety of situations. They also collect detailed information about their subject's background—including family history, education, work, and social life—is also collected.

Such studies are often performed in instances where experimentation is not possible. For example, a scientist might conduct a case study when the person of interest has had a unique or rare experience that could not be replicated in a lab.

Correlational Research

Correlational studies are an experimental psychology method that makes it possible for researchers to look at relationships between different variables. For example, a psychologist might note that as one variable increases, another tends to decrease.

While such studies can look at relationships, they cannot be used to imply causal relationships. The golden rule is that correlation does not equal causation.

Naturalistic Observations

Naturalistic observation gives researchers the opportunity to watch people in their natural environments. This experimental psychology method can be particularly useful in cases where the investigators believe that a lab setting might have an undue influence on participant behaviors.

What Experimental Psychologists Do

Experimental psychologists work in a wide variety of settings, including colleges, universities, research centers, government, and private businesses. Some of these professionals teach experimental methods to students while others conduct research on cognitive processes, animal behavior, neuroscience, personality, and other subject areas.

Those who work in academic settings often teach psychology courses in addition to performing research and publishing their findings in professional journals. Other experimental psychologists work with businesses to discover ways to make employees more productive or to create a safer workplace—a specialty area known as human factors psychology .

Experimental Psychology Research Examples

Some topics that might be explored in experimental psychology research include how music affects motivation, the impact social media has on mental health , and whether a certain color changes one's thoughts or perceptions.

History of Experimental Psychology

To understand how experimental psychology got where it is today, it can be helpful to look at how it originated. Psychology is a relatively young discipline, emerging in the late 1800s. While it started as part of philosophy and biology, it officially became its own field of study when early psychologist Wilhelm Wundt founded the first laboratory devoted to the study of experimental psychology.

Some of the important events that helped shape the field of experimental psychology include:

• 1874 - Wilhelm Wundt published the first experimental psychology textbook, "Grundzüge der physiologischen Psychologie" ("Principles of Physiological Psychology").
• 1875 - William James opened a psychology lab in the United States. The lab was created for the purpose of class demonstrations rather than to perform original experimental research.
• 1879 - The first experimental psychology lab was founded in Leipzig, Germany. Modern experimental psychology dates back to the establishment of the very first psychology lab by pioneering psychologist Wilhelm Wundt during the late nineteenth century.
• 1883 - G. Stanley Hall opened the first experimental psychology lab in the United States at John Hopkins University.
• 1885 - Herman Ebbinghaus published his famous "Über das Gedächtnis" ("On Memory"), which was later translated to English as "Memory: A Contribution to Experimental Psychology." In the work, Ebbinghaus described learning and memory experiments that he conducted on himself.
• 1887 - George Truball Ladd published his textbook "Elements of Physiological Psychology," the first American book to include a significant amount of information on experimental psychology.
• 1887 - James McKeen Cattell established the world's third experimental psychology lab at the University of Pennsylvania.
• 1890 - William James published his classic textbook, "The Principles of Psychology."
• 1891 - Mary Whiton Calkins established an experimental psychology lab at Wellesley College, becoming the first woman to form a psychology lab.
• 1893 - G. Stanley Hall founded the American Psychological Association , the largest professional and scientific organization of psychologists in the United States.
• 1920 - John B. Watson and Rosalie Rayner conducted their now-famous Little Albert Experiment , in which they demonstrated that emotional reactions could be classically conditioned in people.
• 1929 - Edwin Boring's book "A History of Experimental Psychology" was published. Boring was an influential experimental psychologist who was devoted to the use of experimental methods in psychology research.
• 1955 - Lee Cronbach published "Construct Validity in Psychological Tests," which popularized the use of construct validity in psychological studies.
• 1958 - Harry Harlow published "The Nature of Love," which described his experiments with rhesus monkeys on attachment and love.
• 1961 - Albert Bandura conducted his famous Bobo doll experiment, which demonstrated the effects of observation on aggressive behavior.

Experimental Psychology Uses

While experimental psychology is sometimes thought of as a separate branch or subfield of psychology, experimental methods are widely used throughout all areas of psychology.

• Developmental psychologists use experimental methods to study how people grow through childhood and over the course of a lifetime.
• Social psychologists use experimental techniques to study how people are influenced by groups.
• Health psychologists rely on experimentation and research to better understand the factors that contribute to wellness and disease.

A Word From Verywell

The experimental method in psychology helps us learn more about how people think and why they behave the way they do. Experimental psychologists can research a variety of topics using many different experimental methods. Each one contributes to what we know about the mind and human behavior.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research Methods in Psychology . McGraw-Hill.

Heale R, Twycross A. What is a case study? . Evid Based Nurs. 2018;21(1):7-8. doi:10.1136/eb-2017-102845

Chiang IA, Jhangiani RS, Price PC.  Correlational research . In: Research Methods in Psychology, 2nd Canadian edition. BCcampus Open Education.

Pierce T.  Naturalistic observation . Radford University.

Kantowitz BH, Roediger HL, Elmes DG. Experimental Psychology . Cengage Learning.

Weiner IB, Healy AF, Proctor RW. Handbook of Psychology: Volume 4, Experimental Psychology . John Wiley & Sons.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

When conducting research, it is important to create an aim and a hypothesis,  click here  to learn more about the formation of aims and hypotheses.

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Definition of laboratory

Examples of laboratory in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'laboratory.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Medieval Latin laboratorium , from Latin laborare to labor, from labor

1592, in the meaning defined at sense 1a

Phrases Containing laboratory

• lab / laboratory rat
• laboratory test
• language laboratory

Dictionary Entries Near laboratory

laboratorial

laboratory school

Cite this Entry

“Laboratory.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/laboratory. Accessed 26 Aug. 2024.

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Laboratory Experiments

Last updated 22 Mar 2021

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Experiments look for the effect that manipulated variables (independent variables, or IVs) have on measured variables (dependent variables, or DVs), i.e. causal effects.

Laboratory experiments pay particular attention to eliminating the effects of other, extraneous variables, by controlling them (i.e. removing or keeping them constant) in an artificial environment. This makes it more likely for researchers to find a causal effect, having confidence that no variables other than changes in an IV can affect a resulting DV. Laboratory experiments are the most heavily controlled form of experimental research.

Participants can also be randomly allocated to experimental conditions, to avoid experimenter bias (i.e. the experimenter cannot be accused of choosing who will be in each experimental condition, which could affect the results).

Evaluation of laboratory experiments:

- High control over extraneous variables means that they cannot confound the results, so a ‘cause and effect’ relationship between the IV and DV is often assumed.

- Results of laboratory experiments tend to be reliable, as the conditions created (and thus results produced) can be replicated.

- Variables can be measured accurately with the tools made available in a laboratory setting, which may otherwise be impossible for experiments conducted ‘in the field’ (field experiments).

- Data collected may lack ecological validity, as the artificial nature of laboratory experiments can cast doubt over whether the results reflect the nature of real life scenarios.

- There is a high risk of demand characteristics, i.e. participants may alter their behaviour based on their interpretation of the purpose of the experiment.

- There is also a risk of experimenter bias, e.g. researchers’ expectations may affect how they interact with participants (affecting participants’ behaviour), or alter their interpretation of the results.

• Laboratory Experiment

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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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Experimentation in Scientific Research: Variables and controls in practice

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that experimental design was developed more than a thousand years ago by a Middle Eastern scientist who studied light? All of us use a form of experimental research in our day to day lives when we try to find the spot with the best cell phone reception, try out new cooking recipes, and more. Scientific experiments are built on similar principles.

Experimentation is a research method in which one or more variables are consciously manipulated and the outcome or effect of that manipulation on other variables is observed.

Experimental designs often make use of controls that provide a measure of variability within a system and a check for sources of error.

Experimental methods are commonly applied to determine causal relationships or to quantify the magnitude of response of a variable.

Anyone who has used a cellular phone knows that certain situations require a bit of research: If you suddenly find yourself in an area with poor phone reception, you might move a bit to the left or right, walk a few steps forward or back, or even hold the phone over your head to get a better signal. While the actions of a cell phone user might seem obvious, the person seeking cell phone reception is actually performing a scientific experiment: consciously manipulating one component (the location of the cell phone) and observing the effect of that action on another component (the phone's reception). Scientific experiments are obviously a bit more complicated, and generally involve more rigorous use of controls , but they draw on the same type of reasoning that we use in many everyday situations. In fact, the earliest documented scientific experiments were devised to answer a very common everyday question: how vision works.

• A brief history of experimental methods

Figure 1: Alhazen (965-ca.1039) as pictured on an Iraqi 10,000-dinar note

One of the first ideas regarding how human vision works came from the Greek philosopher Empedocles around 450 BCE . Empedocles reasoned that the Greek goddess Aphrodite had lit a fire in the human eye, and vision was possible because light rays from this fire emanated from the eye, illuminating objects around us. While a number of people challenged this proposal, the idea that light radiated from the human eye proved surprisingly persistent until around 1,000 CE , when a Middle Eastern scientist advanced our knowledge of the nature of light and, in so doing, developed a new and more rigorous approach to scientific research . Abū 'Alī al-Hasan ibn al-Hasan ibn al-Haytham, also known as Alhazen , was born in 965 CE in the Arabian city of Basra in what is present-day Iraq. He began his scientific studies in physics, mathematics, and other sciences after reading the works of several Greek philosophers. One of Alhazen's most significant contributions was a seven-volume work on optics titled Kitab al-Manazir (later translated to Latin as Opticae Thesaurus Alhazeni – Alhazen's Book of Optics ). Beyond the contributions this book made to the field of optics, it was a remarkable work in that it based conclusions on experimental evidence rather than abstract reasoning – the first major publication to do so. Alhazen's contributions have proved so significant that his likeness was immortalized on the 2003 10,000-dinar note issued by Iraq (Figure 1).

Alhazen invested significant time studying light , color, shadows, rainbows, and other optical phenomena. Among this work was a study in which he stood in a darkened room with a small hole in one wall. Outside of the room, he hung two lanterns at different heights. Alhazen observed that the light from each lantern illuminated a different spot in the room, and each lighted spot formed a direct line with the hole and one of the lanterns outside the room. He also found that covering a lantern caused the spot it illuminated to darken, and exposing the lantern caused the spot to reappear. Thus, Alhazen provided some of the first experimental evidence that light does not emanate from the human eye but rather is emitted by certain objects (like lanterns) and travels from these objects in straight lines. Alhazen's experiment may seem simplistic today, but his methodology was groundbreaking: He developed a hypothesis based on observations of physical relationships (that light comes from objects), and then designed an experiment to test that hypothesis. Despite the simplicity of the method , Alhazen's experiment was a critical step in refuting the long-standing theory that light emanated from the human eye, and it was a major event in the development of modern scientific research methodology.

Comprehension Checkpoint

• Experimentation as a scientific research method

Experimentation is one scientific research method , perhaps the most recognizable, in a spectrum of methods that also includes description, comparison, and modeling (see our Description , Comparison , and Modeling modules). While all of these methods share in common a scientific approach, experimentation is unique in that it involves the conscious manipulation of certain aspects of a real system and the observation of the effects of that manipulation. You could solve a cell phone reception problem by walking around a neighborhood until you see a cell phone tower, observing other cell phone users to see where those people who get the best reception are standing, or looking on the web for a map of cell phone signal coverage. All of these methods could also provide answers, but by moving around and testing reception yourself, you are experimenting.

• Variables: Independent and dependent

In the experimental method , a condition or a parameter , generally referred to as a variable , is consciously manipulated (often referred to as a treatment) and the outcome or effect of that manipulation is observed on other variables. Variables are given different names depending on whether they are the ones manipulated or the ones observed:

• Independent variable refers to a condition within an experiment that is manipulated by the scientist.
• Dependent variable refers to an event or outcome of an experiment that might be affected by the manipulation of the independent variable .

Scientific experimentation helps to determine the nature of the relationship between independent and dependent variables . While it is often difficult, or sometimes impossible, to manipulate a single variable in an experiment , scientists often work to minimize the number of variables being manipulated. For example, as we move from one location to another to get better cell reception, we likely change the orientation of our body, perhaps from south-facing to east-facing, or we hold the cell phone at a different angle. Which variable affected reception: location, orientation, or angle of the phone? It is critical that scientists understand which aspects of their experiment they are manipulating so that they can accurately determine the impacts of that manipulation . In order to constrain the possible outcomes of an experimental procedure, most scientific experiments use a system of controls .

• Controls: Negative, positive, and placebos

In a controlled study, a scientist essentially runs two (or more) parallel and simultaneous experiments: a treatment group, in which the effect of an experimental manipulation is observed on a dependent variable , and a control group, which uses all of the same conditions as the first with the exception of the actual treatment. Controls can fall into one of two groups: negative controls and positive controls .

In a negative control , the control group is exposed to all of the experimental conditions except for the actual treatment . The need to match all experimental conditions exactly is so great that, for example, in a trial for a new drug, the negative control group will be given a pill or liquid that looks exactly like the drug, except that it will not contain the drug itself, a control often referred to as a placebo . Negative controls allow scientists to measure the natural variability of the dependent variable(s), provide a means of measuring error in the experiment , and also provide a baseline to measure against the experimental treatment.

Some experimental designs also make use of positive controls . A positive control is run as a parallel experiment and generally involves the use of an alternative treatment that the researcher knows will have an effect on the dependent variable . For example, when testing the effectiveness of a new drug for pain relief, a scientist might administer treatment placebo to one group of patients as a negative control , and a known treatment like aspirin to a separate group of individuals as a positive control since the pain-relieving aspects of aspirin are well documented. In both cases, the controls allow scientists to quantify background variability and reject alternative hypotheses that might otherwise explain the effect of the treatment on the dependent variable .

• Experimentation in practice: The case of Louis Pasteur

Well-controlled experiments generally provide strong evidence of causality, demonstrating whether the manipulation of one variable causes a response in another variable. For example, as early as the 6th century BCE , Anaximander , a Greek philosopher, speculated that life could be formed from a mixture of sea water, mud, and sunlight. The idea probably stemmed from the observation of worms, mosquitoes, and other insects "magically" appearing in mudflats and other shallow areas. While the suggestion was challenged on a number of occasions, the idea that living microorganisms could be spontaneously generated from air persisted until the middle of the 18 th century.

In the 1750s, John Needham, a Scottish clergyman and naturalist, claimed to have proved that spontaneous generation does occur when he showed that microorganisms flourished in certain foods such as soup broth, even after they had been briefly boiled and covered. Several years later, the Italian abbot and biologist Lazzaro Spallanzani , boiled soup broth for over an hour and then placed bowls of this soup in different conditions, sealing some and leaving others exposed to air. Spallanzani found that microorganisms grew in the soup exposed to air but were absent from the sealed soup. He therefore challenged Needham's conclusions and hypothesized that microorganisms suspended in air settled onto the exposed soup but not the sealed soup, and rejected the idea of spontaneous generation .

Needham countered, arguing that the growth of bacteria in the soup was not due to microbes settling onto the soup from the air, but rather because spontaneous generation required contact with an intangible "life force" in the air itself. He proposed that Spallanzani's extensive boiling destroyed the "life force" present in the soup, preventing spontaneous generation in the sealed bowls but allowing air to replenish the life force in the open bowls. For several decades, scientists continued to debate the spontaneous generation theory of life, with support for the theory coming from several notable scientists including Félix Pouchet and Henry Bastion. Pouchet, Director of the Rouen Museum of Natural History in France, and Bastion, a well-known British bacteriologist, argued that living organisms could spontaneously arise from chemical processes such as fermentation and putrefaction. The debate became so heated that in 1860, the French Academy of Sciences established the Alhumbert prize of 2,500 francs to the first person who could conclusively resolve the conflict. In 1864, Louis Pasteur achieved that result with a series of well-controlled experiments and in doing so claimed the Alhumbert prize.

Pasteur prepared for his experiments by studying the work of others that came before him. In fact, in April 1861 Pasteur wrote to Pouchet to obtain a research description that Pouchet had published. In this letter, Pasteur writes:

Paris, April 3, 1861 Dear Colleague, The difference of our opinions on the famous question of spontaneous generation does not prevent me from esteeming highly your labor and praiseworthy efforts... The sincerity of these sentiments...permits me to have recourse to your obligingness in full confidence. I read with great care everything that you write on the subject that occupies both of us. Now, I cannot obtain a brochure that I understand you have just published.... I would be happy to have a copy of it because I am at present editing the totality of my observations, where naturally I criticize your assertions. L. Pasteur (Porter, 1961)

Pasteur received the brochure from Pouchet several days later and went on to conduct his own experiments . In these, he repeated Spallanzani's method of boiling soup broth, but he divided the broth into portions and exposed these portions to different controlled conditions. Some broth was placed in flasks that had straight necks that were open to the air, some broth was placed in sealed flasks that were not open to the air, and some broth was placed into a specially designed set of swan-necked flasks, in which the broth would be open to the air but the air would have to travel a curved path before reaching the broth, thus preventing anything that might be present in the air from simply settling onto the soup (Figure 2). Pasteur then observed the response of the dependent variable (the growth of microorganisms) in response to the independent variable (the design of the flask). Pasteur's experiments contained both positive controls (samples in the straight-necked flasks that he knew would become contaminated with microorganisms) and negative controls (samples in the sealed flasks that he knew would remain sterile). If spontaneous generation did indeed occur upon exposure to air, Pasteur hypothesized, microorganisms would be found in both the swan-neck flasks and the straight-necked flasks, but not in the sealed flasks. Instead, Pasteur found that microorganisms appeared in the straight-necked flasks, but not in the sealed flasks or the swan-necked flasks.

Figure 2: Pasteur's drawings of the flasks he used (Pasteur, 1861). Fig. 25 D, C, and B (top) show various sealed flasks (negative controls); Fig. 26 (bottom right) illustrates a straight-necked flask directly open to the atmosphere (positive control); and Fig. 25 A (bottom left) illustrates the specially designed swan-necked flask (treatment group).

By using controls and replicating his experiment (he used more than one of each type of flask), Pasteur was able to answer many of the questions that still surrounded the issue of spontaneous generation. Pasteur said of his experimental design, "I affirm with the most perfect sincerity that I have never had a single experiment, arranged as I have just explained, which gave me a doubtful result" (Porter, 1961). Pasteur's work helped refute the theory of spontaneous generation – his experiments showed that air alone was not the cause of bacterial growth in the flask, and his research supported the hypothesis that live microorganisms suspended in air could settle onto the broth in open-necked flasks via gravity .

• Experimentation across disciplines

Experiments are used across all scientific disciplines to investigate a multitude of questions. In some cases, scientific experiments are used for exploratory purposes in which the scientist does not know what the dependent variable is. In this type of experiment, the scientist will manipulate an independent variable and observe what the effect of the manipulation is in order to identify a dependent variable (or variables). Exploratory experiments are sometimes used in nutritional biology when scientists probe the function and purpose of dietary nutrients . In one approach, a scientist will expose one group of animals to a normal diet, and a second group to a similar diet except that it is lacking a specific vitamin or nutrient. The researcher will then observe the two groups to see what specific physiological changes or medical problems arise in the group lacking the nutrient being studied.

Scientific experiments are also commonly used to quantify the magnitude of a relationship between two or more variables . For example, in the fields of pharmacology and toxicology, scientific experiments are used to determine the dose-response relationship of a new drug or chemical. In these approaches, researchers perform a series of experiments in which a population of organisms , such as laboratory mice, is separated into groups and each group is exposed to a different amount of the drug or chemical of interest. The analysis of the data that result from these experiments (see our Data Analysis and Interpretation module) involves comparing the degree of the organism's response to the dose of the substance administered.

In this context, experiments can provide additional evidence to complement other research methods . For example, in the 1950s a great debate ensued over whether or not the chemicals in cigarette smoke cause cancer. Several researchers had conducted comparative studies (see our Comparison in Scientific Research module) that indicated that patients who smoked had a higher probability of developing lung cancer when compared to nonsmokers. Comparative studies differ slightly from experimental methods in that you do not consciously manipulate a variable ; rather you observe differences between two or more groups depending on whether or not they fall into a treatment or control group. Cigarette companies and lobbyists criticized these studies, suggesting that the relationship between smoking and lung cancer was coincidental. Several researchers noted the need for a clear dose-response study; however, the difficulties in getting cigarette smoke into the lungs of laboratory animals prevented this research. In the mid-1950s, Ernest Wynder and colleagues had an ingenious idea: They condensed the chemicals from cigarette smoke into a liquid and applied this in various doses to the skin of groups of mice. The researchers published data from a dose-response experiment of the effect of tobacco smoke condensate on mice (Wynder et al., 1957).

As seen in Figure 3, the researchers found a positive relationship between the amount of condensate applied to the skin of mice and the number of cancers that developed. The graph shows the results of a study in which different groups of mice were exposed to increasing amounts of cigarette tar. The black dots indicate the percentage of each sample group of mice that developed cancer for a given amount cigarette smoke "condensate" applied to their skin. The vertical lines are error bars, showing the amount of uncertainty . The graph shows generally increasing cancer rates with greater exposure. This study was one of the first pieces of experimental evidence in the cigarette smoking debate , and it helped strengthen the case for cigarette smoke as the causative agent in lung cancer in smokers.

Figure 3: Percentage of mice with cancer versus the amount cigarette smoke "condensate" applied to their skin (source: Wynder et al., 1957).

Sometimes experimental approaches and other research methods are not clearly distinct, or scientists may even use multiple research approaches in combination. For example, at 1:52 a.m. EDT on July 4, 2005, scientists with the National Aeronautics and Space Administration (NASA) conducted a study in which a 370 kg spacecraft named Deep Impact was purposely slammed into passing comet Tempel 1. A nearby spacecraft observed the impact and radioed data back to Earth. The research was partially descriptive in that it documented the chemical composition of the comet, but it was also partly experimental in that the effect of slamming the Deep Impact probe into the comet on the volatilization of previously undetected compounds , such as water, was assessed (A'Hearn et al., 2005). It is particularly common that experimentation and description overlap: Another example is Jane Goodall 's research on the behavior of chimpanzees, which can be read in our Description in Scientific Research module.

• Limitations of experimental methods

Figure 4: An image of comet Tempel 1 67 seconds after collision with the Deep Impact impactor. Image credit: NASA/JPL-Caltech/UMD http://deepimpact.umd.edu/gallery/HRI_937_1.html

While scientific experiments provide invaluable data regarding causal relationships, they do have limitations. One criticism of experiments is that they do not necessarily represent real-world situations. In order to clearly identify the relationship between an independent variable and a dependent variable , experiments are designed so that many other contributing variables are fixed or eliminated. For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants' diet by feeding them all the same meals, described in the methods section of their research article in this way:

Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.

While this is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

Design is critical to the success or failure of an experiment . Slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: They had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars, and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

Scientists also have an obligation to adhere to ethical limits in designing and conducting experiments . During World War II, doctors working in Nazi Germany conducted many heinous experiments using human subjects . Among them was an experiment meant to identify effective treatments for hypothermia in humans, in which concentration camp prisoners were forced to sit in ice water or left naked outdoors in freezing temperatures and then re-warmed by various means. Many of the exposed victims froze to death or suffered permanent injuries. As a result of the Nazi experiments and other unethical research , strict scientific ethical standards have been adopted by the United States and other governments, and by the scientific community at large. Among other things, ethical standards (see our Scientific Ethics module) require that the benefits of research outweigh the risks to human subjects, and those who participate do so voluntarily and only after they have been made fully aware of all the risks posed by the research. These guidelines have far-reaching effects: While the clearest indication of causation in the cigarette smoke and lung cancer debate would have been to design an experiment in which one group of people was asked to take up smoking and another group was asked to refrain from smoking, it would be highly unethical for a scientist to purposefully expose a group of healthy people to a suspected cancer causing agent. As an alternative, comparative studies (see our Comparison in Scientific Research module) were initiated in humans, and experimental studies focused on animal subjects. The combination of these and other studies provided even stronger evidence of the link between smoking and lung cancer than either one method alone would have.

• Experimentation in modern practice

Like all scientific research , the results of experiments are shared with the scientific community, are built upon, and inspire additional experiments and research. For example, once Alhazen established that light given off by objects enters the human eye, the natural question that was asked was "What is the nature of light that enters the human eye?" Two common theories about the nature of light were debated for many years. Sir Isaac Newton was among the principal proponents of a theory suggesting that light was made of small particles . The English naturalist Robert Hooke (who held the interesting title of Curator of Experiments at the Royal Society of London) supported a different theory stating that light was a type of wave, like sound waves . In 1801, Thomas Young conducted a now classic scientific experiment that helped resolve this controversy . Young, like Alhazen, worked in a darkened room and allowed light to enter only through a small hole in a window shade (Figure 5). Young refocused the beam of light with mirrors and split the beam with a paper-thin card. The split light beams were then projected onto a screen, and formed an alternating light and dark banding pattern – that was a sign that light was indeed a wave (see our Light I: Particle or Wave? module).

Figure 5: Young's split-light beam experiment helped clarify the wave nature of light.

Approximately 100 years later, in 1905, new experiments led Albert Einstein to conclude that light exhibits properties of both waves and particles . Einstein's dual wave-particle theory is now generally accepted by scientists.

Experiments continue to help refine our understanding of light even today. In addition to his wave-particle theory , Einstein also proposed that the speed of light was unchanging and absolute. Yet in 1998 a group of scientists led by Lene Hau showed that light could be slowed from its normal speed of 3 x 10 8 meters per second to a mere 17 meters per second with a special experimental apparatus (Hau et al., 1999). The series of experiments that began with Alhazen 's work 1000 years ago has led to a progressively deeper understanding of the nature of light. Although the tools with which scientists conduct experiments may have become more complex, the principles behind controlled experiments are remarkably similar to those used by Pasteur and Alhazen hundreds of years ago.

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Experimental Research: Meaning And Examples Of Experimental Research

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.

What Is Experimental Research?

Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.

Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.

Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.  

Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.

The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:

• Dependent variables are manipulated or treated while independent variables are exerted on dependent variables as an experimental treatment. Extraneous variables are variables generated from other factors that can affect the experiment and contribute to change. Researchers have to exercise control to reduce the influence of these variables by randomization, making homogeneous groups and applying statistical analysis techniques.
• Researchers deliberately operate independent variables on the subject of the experiment. This is known as manipulation.
• Once a variable is manipulated, researchers observe the effect an independent variable has on a dependent variable. This is key for interpreting results.
• A researcher may want multiple comparisons between different groups with equivalent subjects. They may replicate the process by conducting sub-experiments within the framework of the experimental design.

Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.

The way a researcher assigns subjects to different groups determines the types of experimental research design .

Pre-experimental Research Design

In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:

• A one-shot case study research design is a study where one dependent variable is considered. It’s a posttest study as it’s carried out after treating what presumably caused the change.
• One-group pretest-posttest design is a study that combines both pretest and posttest studies by testing a single group before and after administering the treatment.
• Static-group comparison involves studying two groups by subjecting one to treatment while the other remains static. After post-testing all groups the differences are observed.

This design is practical but lacks in certain areas of true experimental criteria.

True Experimental Research Design

This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:

• The posttest-only control group design involves randomly selecting and assigning subjects to two groups: experimental and control. Only the experimental group is treated, while both groups are observed and post-tested to draw a conclusion from the difference between the groups.
• In a pretest-posttest control group design, two groups are randomly assigned subjects. Both groups are presented, the experimental group is treated and both groups are post-tested to measure how much change happened in each group.
• Solomon four-group design is a combination of the previous two methods. Subjects are randomly selected and assigned to four groups. Two groups are tested using each of the previous methods.

True experimental research design should have a variable to manipulate, a control group and random distribution.

With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:

• It allows researchers to have a stronghold over variables and collect desired results.
• Results are usually specific.
• The effectiveness of the research isn’t affected by the subject.
• Findings from the results usually apply to similar situations and ideas.
• Cause and effect of a hypothesis can be identified, which can be further analyzed for in-depth ideas.
• It’s the ideal starting point to collect data and lay a foundation for conducting further research and building more ideas.
• Medical researchers can develop medicines and vaccines to treat diseases by collecting samples from patients and testing them under multiple conditions.
• It can be used to improve the standard of academics across institutions by testing student knowledge and teaching methods before analyzing the result to implement programs.
• Social scientists often use experimental research design to study and test behavior in humans and animals.
• Software development and testing heavily depend on experimental research to test programs by letting subjects use a beta version and analyzing their feedback.

Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:

• Human error is a concern because the method depends on controlling variables. Improper implementation nullifies the validity of the research and conclusion.
• Eliminating extraneous variables (real-life scenarios) produces inaccurate conclusions.
• The process is time-consuming and expensive
• In medical research, it can have ethical implications by affecting patients’ well-being.
• Results are not descriptive and subjects can contribute to response bias.

Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences

Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :

• This research method can be used to evaluate employees’ skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment. After training employees on the job, organizations further evaluate them to test impact and improvement. This is a pretest-posttest control group research example where employees are ‘subjects’ and the training is ‘treatment’.
• Educational institutions follow the pre-experimental research design to administer exams and evaluate students at the end of a semester. Students are the dependent variables and lectures are independent. Since exams are conducted at the end and not the beginning of a semester, it’s easy to conclude that it’s a one-shot case study research.
• To evaluate the teaching methods of two teachers, they can be assigned two student groups. After teaching their respective groups on the same topic, a posttest can determine which group scored better and who is better at teaching. This method can have its drawbacks as certain human factors, such as attitudes of students and effectiveness to grasp a subject, may negatively influence results. 

Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.

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A Complete Guide to Experimental Research

Published by Carmen Troy at August 14th, 2021 , Revised On August 25, 2023

A Quick Guide to Experimental Research

Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables. 

The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature, diet, atmosphere, or given a new drug to observe the changes. Experiments can vary from personal and informal natural comparisons. It includes three  types of variables ;

• Independent variable
• Dependent variable
• Controlled variable

Before conducting experimental research, you need to have a clear understanding of the experimental design. A true experimental design includes  identifying a problem , formulating a  hypothesis , determining the number of variables, selecting and assigning the participants,  types of research designs , meeting ethical values, etc.

There are many  types of research  methods that can be classified based on:

• The nature of the problem to be studied
• Number of participants (individual or groups)
• Number of groups involved (Single group or multiple groups)
• Types of data collection methods (Qualitative/Quantitative/Mixed methods)
• Number of variables (single independent variable/ factorial two independent variables)
• The experimental design

Types of Experimental Research

Laboratory Experiment  

It is also called experimental research. This type of research is conducted in the laboratory. A researcher can manipulate and control the variables of the experiment.

Example: Milgram’s experiment on obedience.

Field Experiment

Field experiments are conducted in the participants’ open field and the environment by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.

Natural Experiments

The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.

Examples: Estimating the health condition of the population. Did the increase in tobacco prices decrease the sale of tobacco? Did the usage of helmets decrease the number of head injuries of the bikers?

Quasi-Experiments

A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.

Example: Comparing the academic performance of the two schools.

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How to Conduct Experimental Research?

Step 1. identify and define the problem.

You need to identify a problem as per your field of study and describe your  research question .

Example: You want to know about the effects of social media on the behavior of youngsters. It would help if you found out how much time students spend on the internet daily.

Example: You want to find out the adverse effects of junk food on human health. It would help if you found out how junk food frequent consumption can affect an individual’s health.

Step 2. Determine the Number of Levels of Variables

You need to determine the number of  variables . The independent variable is the predictor and manipulated by the researcher. At the same time, the dependent variable is the result of the independent variable.

In the first example, we predicted that increased social media usage negatively correlates with youngsters’ negative behaviour.

In the second example, we predicted the positive correlation between a balanced diet and a good healthy and negative relationship between junk food consumption and multiple health issues.

Step 3. Formulate the Hypothesis

One of the essential aspects of experimental research is formulating a hypothesis . A researcher studies the cause and effect between the independent and dependent variables and eliminates the confounding variables. A  null hypothesis is when there is no significant relationship between the dependent variable and the participants’ independent variables. A researcher aims to disprove the theory. H0 denotes it.  The  Alternative hypothesis  is the theory that a researcher seeks to prove.  H1or HA denotes it. 

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Step 4. Selection and Assignment of the Subjects

It’s an essential feature that differentiates the experimental design from other research designs . You need to select the number of participants based on the requirements of your experiment. Then the participants are assigned to the treatment group. There should be a control group without any treatment to study the outcomes without applying any changes compared to the experimental group.

Randomisation:  The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.

Stratified sampling : It’s a type of random selection of the participants by dividing them into strata and randomly selecting them from each level. 

Matching:   Even though participants are selected randomly, they can be assigned to the various comparison groups. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.  

What is Replicability?

When a researcher uses the same methodology  and subject groups to carry out the experiments, it’s called ‘replicability.’ The  results will be similar each time. Researchers usually replicate their own work to strengthen external validity.

Step 5. Select a Research Design

You need to select a  research design  according to the requirements of your experiment. There are many types of experimental designs as follows.

Step 6. Meet Ethical and Legal Requirements

• Participants of the research should not be harmed.
• The dignity and confidentiality of the research should be maintained.
• The consent of the participants should be taken before experimenting.
• The privacy of the participants should be ensured.
• Research data should remain confidential.
• The anonymity of the participants should be ensured.
• The rules and objectives of the experiments should be followed strictly.
• Any wrong information or data should be avoided.

Tips for Meeting the Ethical Considerations

To meet the ethical considerations, you need to ensure that.

• Participants have the right to withdraw from the experiment.
• They should be aware of the required information about the experiment.
• It would help if you avoided offensive or unacceptable language while framing the questions of interviews, questionnaires, or Focus groups.
• You should ensure the privacy and anonymity of the participants.
• You should acknowledge the sources and authors in your dissertation using any referencing styles such as APA/MLA/Harvard referencing style.

Step 7. Collect and Analyse Data.

Collect the data  by using suitable data collection according to your experiment’s requirement, such as observations,  case studies ,  surveys ,  interviews , questionnaires, etc. Analyse the obtained information.

Step 8. Present and Conclude the Findings of the Study.

Write the report of your research. Present, conclude, and explain the outcomes of your study .  

Frequently Asked Questions

What is the first step in conducting an experimental research.

The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.

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Effect size in statistics measures how important the difference between group means and the relationship between different variables.

A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies.

Analysis of Variance or ANOVA is a statistical test that uses means of two or more groups to analyse the difference. Here is all you need to know about it.

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Laboratory Experimentation

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• First Online: 01 January 2020
• pp 2559–2562
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• Katrin Bittrich 3 &
• Torsten Schubert 3  

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In laboratory experimentation the causal influence of at least one actively manipulated independent variable on at least on dependent variable is tested in a controlled envorinment.

Introduction

The main objective of the experimental approach is to causally relate changes in one or more independent variables to changes in one or more dependent variables. The condition assignment is usually randomized, and researchers aim to eliminate or control the potential effect(s) of extraneous variables on the data of interest. By analyzing the manifestation of individual differences in the data variability with elaborated methods, the advantages of an experimental approach can be combined with research methods designed to understand the individual realization of the investigated phenomena and their emergence in the corresponding experimental condition.

Experimental Method

Scientific research aims to gather information objectively and systematically such that valid conclusions can be...

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Bittrich, K., & Blankenberger, S. (2011). Experimentelle Psychologie: Experimente planen realisieren, präsentieren . Weinheim: Beltz.

Google Scholar  

Hager, W. (1987). Grundlagen einer Versuchsplanung zur Prüfung empirischer Hypothesen der Psychologie. In G. Lüer (Ed.), Allgemeine Experimentelle Psychologie (pp. 43–253). Stuttgart: Gustav Fischer Verlag.

Iacobucci, D., Posavach, S. S., Kardes, F. R., Schneider, M. J., & Popovich, D. L. (2015). Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology, 25 , 666–678. https://doi.org/10.1016/j.jcps.2015.04.004 .

Article   Google Scholar  

Myers, A., & Hansen, C. (2012). Experimental psychology (7th ed.). Wadsworth: Cengage Learning.

Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10 , 178–192. https://doi.org/10.1037/1082-989X.10.2.178 .

Article   PubMed   Google Scholar  

Revelle, W. (2007). Experimental approaches to the study of personality. In R. W. Robins, R. C. Fraley, & R. F. Krueger (Eds.), Handbook of research methods in personality psychology . New York: The Guilford Press.

Revelle, W., Condon, D. M., & Wilt, J. (2011). Methodological advances in differential psychology. In T. Chamorro-Premuzic, S. von Stumm, & A. Furnham (Eds.), The Wiley-Blackwell handbook of individual differences . Oxford, UK: Wiley-Blackwell.

Rucker, D. D., McShane, B. B., & Preacher, K. J. (2015). A researcher's guide to regression, discretization, and median splits of continuous variables. Journal of Consumer Psychology, 25 (4), 666–678. https://doi.org/10.1016/j.jcps.2015.04.004 .

Rusting, C. L., & Larsen, R. J. (1998). Personality and cognitive processing of affective information. Personality and Social Psychology Bulletin, 24 , 200–2013. https://doi.org/10.1177/0146167298242008 .

Shadish, W. R., Cook T. D., & Campbell D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference (2nd ed.). Wadsworth: Cengage Learning.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Innovation Acceptance Lab

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Laboratory Experiments

The advantage of laboratory experiments is an accurate measurement of the  interrelations of the influencing factor(s) and the measured variables. Therefore, this is the only research method able to reliably evaluate causal relationships.

The downside of controlling the experimental conditions is that the actions of the subjects do not take place in the natural environment and that the artificial setting could cause unnatural behavior. This means that the obtained results could not reflect real life, resulting in a  lower external validity compared to field experiments .

Application

Laboratory experiments can be employed to measure direct reaction to external stimuli, such as experiencing a new technology for the first time. These measures can be carried out highly accurate by employing bio-physiological feedback and by ruling out all extraneous factors. 

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• Published: 26 August 2024

Dimensionality crossover to a two-dimensional vestigial nematic state from a three-dimensional antiferromagnet in a honeycomb van der Waals magnet

• Zeliang Sun 1   na1 ,
• Gaihua Ye 2   na1 ,
• Chengkang Zhou 3   na1 ,
• Mengqi Huang 4 ,
• Nan Huang 5 ,
• Xilong Xu 6 ,
• Qiuyang Li   ORCID: orcid.org/0000-0002-8192-3960 1 ,
• Guoxin Zheng   ORCID: orcid.org/0000-0002-5431-5716 1 ,
• Zhipeng Ye 2 ,
• Cynthia Nnokwe 2 ,
• Lu Li   ORCID: orcid.org/0000-0002-8054-7406 1 ,
• Hui Deng   ORCID: orcid.org/0000-0003-0629-3230 1 ,
• Li Yang 6 ,
• David Mandrus   ORCID: orcid.org/0000-0003-3616-7104 5 , 7 ,
• Zi Yang Meng   ORCID: orcid.org/0000-0001-9771-7494 3 ,
• Kai Sun   ORCID: orcid.org/0000-0001-9595-7646 1 ,
• Chunhui Rita Du   ORCID: orcid.org/0000-0001-8063-7711 4 ,
• Rui He   ORCID: orcid.org/0000-0002-2368-7269 2 &
• Liuyan Zhao   ORCID: orcid.org/0000-0001-9512-3537 1  

Nature Physics ( 2024 ) Cite this article

Metrics details

• Magnetic properties and materials
• Phase transitions and critical phenomena

The effects of fluctuations and disorder, which are substantially enhanced in reduced dimensionalities, can play a crucial role in producing non-trivial phases of matter such as vestigial orders characterized by a composite order parameter. However, fluctuation-driven magnetic phases in low dimensions have remained relatively unexplored. Here we demonstrate a phase transition from the zigzag antiferromagnetic order in the three-dimensional bulk to a Z 3 vestigial Potts nematicity in two-dimensional few-layer samples of van der Waals magnet NiPS 3 . Our spin relaxometry and optical spectroscopy measurements reveal that the spin fluctuations are enhanced over the gigahertz to terahertz range as the layer number of NiPS 3 reduces. Monte Carlo simulations corroborate the experimental finding of threefold rotational symmetry breaking but show that the translational symmetry is restored in thin layers of NiPS 3 . Therefore, our results show that strong quantum fluctuations can stabilize an unconventional magnetic phase after destroying a more conventional one.

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Nie, L., Tarjus, G. & Kivelson, S. A. Quenched disorder and vestigial nematicity in the pseudogap regime of the cuprates. Proc. Natl Acad. Sci. 111 , 7980–7985 (2014).

Article   ADS   Google Scholar  

Fernandes, R. M., Orth, P. P. & Schmalian, J. Intertwined vestigial order in quantum materials: nematicity and beyond. Annu. Rev. Condens. Matter Phys. 10 , 133–154 (2019).

Kivelson, S. A., Fradkin, E. & Emery, V. J. Electronic liquid-crystal phases of a doped Mott insulator. Nature 393 , 550–553 (1998).

Ando, Y., Segawa, K., Komiya, S. & Lavrov, A. Electrical resistivity anisotropy from self-organized one dimensionality in high-temperature superconductors. Phys. Rev. Lett. 88 , 137005 (2002).

Hinkov, V. et al. Electronic liquid crystal state in the high-temperature superconductor YBa 2 Cu 3 O 6.45 . Science 319 , 597–600 (2008).

Article   Google Scholar  

Lawler, M. et al. Intra-unit-cell electronic nematicity of the high- T c copper-oxide pseudogap states. Nature 466 , 347–351 (2010).

Achkar, A. et al. Nematicity in stripe-ordered cuprates probed via resonant X-ray scattering. Science 351 , 576–578 (2016).

Article   ADS   MathSciNet   Google Scholar  

de La Cruz, C. et al. Magnetic order close to superconductivity in the iron-based layered LaO 1− x F x FeAs systems. Nature 453 , 899–902 (2008).

Chu, J.-H., Kuo, H.-H., Analytis, J. G. & Fisher, I. R. Divergent nematic susceptibility in an iron arsenide superconductor. Science 337 , 710–712 (2012).

Fang, C., Yao, H., Tsai, W.-F., Hu, J. & Kivelson, S. A. Theory of electron nematic order in LaFeAsO. Phys. Rev. B 77 , 224509 (2008).

Xu, C., Müller, M. & Sachdev, S. Ising and spin orders in the iron-based superconductors. Phys. Rev. B 78 , 020501 (2008).

Domröse, T. et al. Light-induced hexatic state in a layered quantum material. Nat. Mater . 22 , 1345–1351 (2023).

Cho, C.-W. et al. Z 3 -vestigial nematic order due to superconducting fluctuations in the doped topological insulators Nb x Bi 2 Se 3 and Cu x Bi 2 Se 3 . Nat. Commun. 11 , 3056 (2020).

Fradkin, E., Kivelson, S. A. & Tranquada, J. M. Colloquium: theory of intertwined orders in high temperature superconductors. Rev. Mod. Phys. 87 , 457 (2015).

Fernandes, R., Chubukov, A. & Schmalian, J. What drives nematic order in iron-based superconductors? Nat. Phys. 10 , 97–104 (2014).

Berg, E., Fradkin, E. & Kivelson, S. A. Charge-4 e superconductivity from pair-density-wave order in certain high-temperature superconductors. Nat. Phys. 5 , 830–833 (2009).

Agterberg, D. F. et al. The physics of pair-density waves: cuprate superconductors and beyond. Annu. Rev. Condens. Matter Phys. 11 , 231–270 (2020).

Lee, P. A. Amperean pairing and the pseudogap phase of cuprate superconductors. Phys. Rev. 4 , 031017 (2014).

Berg, E., Fradkin, E., Kivelson, S. A. & Tranquada, J. M. Striped superconductors: how spin, charge and superconducting orders intertwine in the cuprates. New J. Phys. 11 , 115004 (2009).

Jian, S.-K., Huang, Y. & Yao, H. Charge-4 e superconductivity from nematic superconductors in two and three dimensions. Phys. Rev. Lett. 127 , 227001 (2021).

Fernandes, R. M. & Fu, L. Charge-4 e superconductivity from multicomponent nematic pairing: application to twisted bilayer graphene. Phys. Rev. Lett. 127 , 047001 (2021).

Hecker, M., Willa, R., Schmalian, J. & Fernandes, R. M. Cascade of vestigial orders in two-component superconductors: nematic, ferromagnetic, s-wave charge-4 e , and d-wave charge-4 e states. Phys. Rev. B 107 , 224503 (2023).

Wang, Y.-C., Yan, Z., Wang, C., Qi, Y. & Meng, Z. Y. Vestigial anyon condensation in kagome quantum spin liquids. Phys. Rev. B 103 , 014408 (2021).

Wu, F.-Y. The Potts model. Rev. Mod. Phys. 54 , 235 (1982).

Fernandes, R. M. & Venderbos, J. W. Nematicity with a twist: rotational symmetry breaking in a moiré superlattice. Sci. Adv. 6 , eaba8834 (2020).

Christensen, M. H., Birol, T., Andersen, B. M. & Fernandes, R. M. Theory of the charge density wave in AV 3 Sb 5 kagome metals. Phys. Rev. B 104 , 214513 (2021).

Mulder, A., Ganesh, R., Capriotti, L. & Paramekanti, A. Spiral order by disorder and lattice nematic order in a frustrated Heisenberg antiferromagnet on the honeycomb lattice. Phys. Rev. B 81 , 214419 (2010).

Little, A. et al. Three-state nematicity in the triangular lattice antiferromagnet Fe 1/3 NbS 2 . Nat. Mater. 19 , 1062–1067 (2020).

Ni, Z., Huang, N., Haglund, A. V., Mandrus, D. G. & Wu, L. Observation of giant surface second-harmonic generation coupled to nematic orders in the van der Waals antiferromagnet FePS 3 . Nano Lett. 22 , 3283–3288 (2022).

Chakraborty, A. R. & Fernandes, R. M. Strain-tuned quantum criticality in electronic Potts-nematic systems. Phys. Rev. B 107 , 195136 (2023).

Kang, S. et al. Coherent many-body exciton in van der Waals antiferromagnet NiPS 3 . Nature 583 , 785–789 (2020).

Hwangbo, K. et al. Highly anisotropic excitons and multiple phonon bound states in a van der Waals antiferromagnetic insulator. Nat. Nanotechnol. 16 , 655–660 (2021).

Wang, X. et al. Spin-induced linear polarization of photoluminescence in antiferromagnetic van der Waals crystals. Nat. Mater. 20 , 964–970 (2021).

Kim, K. et al. Suppression of magnetic ordering in XXZ-type antiferromagnetic monolayer NiPS 3 . Nat. Commun. 10 , 345 (2019).

DiScala, M. F. et al. Elucidating the role of dimensionality on the electronic structure of the van der Waals antiferromagnet NiPS3. Adv. Phys. Res. 3 , 2300096 (2024).

Huang, B. et al. Layer-dependent ferromagnetism in a van der Waals crystal down to the monolayer limit. Nature 546 , 270–273 (2017).

Gong, C. et al. Discovery of intrinsic ferromagnetism in two-dimensional van der Waals crystals. Nature 546 , 265–269 (2017).

Lee, K. et al. Magnetic order and symmetry in the 2D semiconductor CrSBr. Nano Lett. 21 , 3511–3517 (2021).

Lee, J.-U. et al. Ising-type magnetic ordering in atomically thin FePS 3 . Nano Lett. 16 , 7433–7438 (2016).

Ni, Z. et al. Imaging the Néel vector switching in the monolayer antiferromagnet MnPSe 3 with strain-controlled Ising order. Nat. Nanotechnol. 16 , 782–787 (2021).

Wildes, A. R. et al. Magnetic structure of the quasi-two-dimensional antiferromagnet NiPS 3 . Phys. Rev. B 92 , 224408 (2015).

Lançon, D., Ewings, R., Guidi, T., Formisano, F. & Wildes, A. Magnetic exchange parameters and anisotropy of the quasi-two-dimensional antiferromagnet NiPS 3 . Phys. Rev. B 98 , 134414 (2018).

Wildes, A. et al. Magnetic dynamics of NiPS 3 . Phys. Rev. B 106 , 174422 (2022).

Scheie, A. et al. Spin wave Hamiltonian and anomalous scattering in NiPS 3 . Phys. Rev. B 108 , 104402 (2023).

Rondin, L. et al. Magnetometry with nitrogen-vacancy defects in diamond. Rep. Prog. Phys. 77 , 056503 (2014).

Van der Sar, T., Casola, F., Walsworth, R. & Yacoby, A. Nanometre-scale probing of spin waves using single electron spins. Nat. Commun. 6 , 7886 (2015).

Du, C. et al. Control and local measurement of the spin chemical potential in a magnetic insulator. Science 357 , 195–198 (2017).

Ku, M. J. et al. Imaging viscous flow of the Dirac fluid in graphene. Nature 583 , 537–541 (2020).

Kubo, R. The fluctuation–dissipation theorem. Rep. Prog. Phys. 29 , 255 (1966).

Lemmens, P., Güntherodt, G. & Gros, C. Magnetic light scattering in low-dimensional quantum spin systems. Phys. Rep. 375 , 1–103 (2003).

Jana, D. et al. Magnon gap excitations and spin-entangled optical transition in the van der Waals antiferromagnet NiPS 3 . Phys. Rev. B 108 , 115149 (2023).

Hashemi, A., Komsa, H.-P., Puska, M. & Krasheninnikov, A. V. Vibrational properties of metal phosphorus trichalcogenides from first-principles calculations. J. Phys. Chem. C 121 , 27207–27217 (2017).

Kuo, C.-T. et al. Exfoliation and Raman spectroscopic fingerprint of few-layer NiPS 3 van der Waals crystals. Sci. Rep. 6 , 20904 (2016).

Yao, Y. et al. An electronic nematic liquid in BaNi 2 As 2 . Nat. Commun. 13 , 4535 (2022).

Akram, M. et al. Theory of moiré magnetism in twisted bilayer α-RuCl 3 . Nano Lett. 24 , 890–896 (2024).

Kim, S. Y. et al. Charge-spin correlation in van der Waals antiferromagnet NiPS 3 . Phys. Rev. Lett. 120 , 136402 (2018).

Kim, D. S. et al. Anisotropic excitons reveal local spin chain directions in a van der Waals antiferromagnet. Adv. Mater. 35 , 2206585 (2023).

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Acknowledgements

We acknowledge valuable discussions with R. Fernandes and J. Venderbos. L.Z. acknowledges support from the National Science Foundation (NSF; Grant No. DMR-2103731), the Office of Naval Research (ONR; Grant No. N00014-21-1-2770) and the Gordon and Betty Moore Foundation (Award No. GBMF10694). R.H. acknowledges support from the NSF (Grant Nos. DMR-2104036 and DMR-2300640). C.R.D. acknowledges support from the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (Award No. DE-SC0024870). Z.Y.M. acknowledges support from the Research Grants Council (RGC) of the Hong Kong Special Administrative Region (SAR) of China (Project Nos. 17301721, AoE/P-701/20, 17309822, HKU C7037-22GF and 17302223), the ANR/RGC Joint Research Scheme sponsored by the RGC of the Hong Kong SAR of China and the French National Research Agency (Project No. A_HKU703/22). D.M. acknowledges support from the Gordon and Betty Moore Foundation's EPiQS Initiative (Grant GBMF9069). K.S. acknowledges support from the ONR (Grant No. N00014-21-1-2770) and the Gordon and Betty Moore Foundation (Award No. GBMF10694). Q.L. and H.D. acknowledge support from the ONR (Grant No. N00014-21-1-2770) and the Gordon and Betty Moore Foundation (Award No. GBMF10694). L.L acknowledges support from the DOE (Grant No. DE-SC0020184). X.X. and L.Y. acknowledge support from the NSF (Grant No. DMR-2118779). The ab initio simulation used Anvil at Purdue University through allocation DMR100005 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) programme, which is supported by the NSF (Grant Nos. 2138259, 2138286, 2138307, 2137603 and 2138296).

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These authors contributed equally: Zeliang Sun, Gaihua Ye, Chengkang Zhou.

Authors and Affiliations

Department of Physics, University of Michigan, Ann Arbor, MI, USA

Zeliang Sun, Qiuyang Li, Guoxin Zheng, Lu Li, Hui Deng, Kai Sun & Liuyan Zhao

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA

Gaihua Ye, Zhipeng Ye, Cynthia Nnokwe & Rui He

Department of Physics and HKU-UCAS Joint Institute of Theoretical and Computational Physics, The University of Hong Kong, Hong Kong SAR, China

Chengkang Zhou & Zi Yang Meng

School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

Mengqi Huang & Chunhui Rita Du

Department of Materials Science and Engineering, The University of Tennessee, Knoxville, TN, USA

Nan Huang & David Mandrus

Department of Physics, Washington University in St Louis, St Louis, MO, USA

Xilong Xu & Li Yang

Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

David Mandrus

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Contributions

Z.S., R.H. and L.Z. conceived the idea and initiated this project. Z.S. exfoliated the NiPS 3 thin flakes with different layer numbers. G.Y., Z.Y., C.N. and Z.S. carried out the Raman experiments under the supervision of L.Z. and R.H. M.H. performed the NV spin relaxometry under the supervision of C.D. C.Z. carried out the Monte Carlo simulations under the supervision of K.S. and Z.Y.M. Q.L. and Z.S. carried out the atomic force microscopy measurements and the PL measurements guided by H.D. and L.Z. N.H. grew the high-quality NiPS 3 bulk single crystals under the supervision of D.M. G.Z. performed the susceptibility measurements under the supervision of L.L. X.X. performed the phonon calculations under the supervision of L.Y. Z.S., R.H. and L.Z. analysed the data and wrote the manuscript. All authors participated in discussions about the results.

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Sun, Z., Ye, G., Zhou, C. et al. Dimensionality crossover to a two-dimensional vestigial nematic state from a three-dimensional antiferromagnet in a honeycomb van der Waals magnet. Nat. Phys. (2024). https://doi.org/10.1038/s41567-024-02618-6

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• Published: 23 August 2024

Sand fly blood meal volumes and their relation to female body weight under experimental conditions

• Věra Volfová 1 ,
• Magdalena Jančářová 1 &
• Petr Volf 1  

Parasites & Vectors volume  17 , Article number:  360 ( 2024 ) Cite this article

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Sand fly females require a blood meal to develop eggs. The size of the blood meal is crucial for fecundity and affects the dose of pathogens acquired by females when feeding on infected hosts or during experimental membrane-feeding.

Under standard laboratory conditions, we compared blood meal volumes taken by females of ten sand fly species from four genera: Phlebotomus , Lutzomyia , Migonomyia , and Sergentomyia . The amount of ingested blood was determined using a haemoglobin assay. Additionally, we weighed unfed sand flies to calculate the ratio between body weight and blood meal weight.

The mean blood meal volume ingested by sand fly females ranged from 0.47 to 1.01 µl. Five species, Phlebotomus papatasi , P. duboscqi , Lutzomyia longipalpis , Sergentomyia minuta , and S. schwetzi , consumed about double the blood meal size compared to Migonomyia migonei . The mean body weight of females ranged from 0.183 mg in S. minuta to 0.369 mg in P. duboscqi . In males, the mean body weight ranged from 0.106 mg in M. migonei to 0.242 mg in P. duboscqi . Males were always lighter than females, with the male-to-female weight ratio ranging from 75% (in Phlebotomus argentipes ) to 52% (in Phlebotomus tobbi ).

Conclusions

Females of most species took a blood meal 2.25–3.05 times their body weight. Notably, the relatively tiny females of P. argentipes consumed blood meals 3.34 times their body weight. The highest (Mbl/Mf) ratios were found in both Sergentomyia species studied; females of S. minuta and S. schwetzi took blood meals 4.5–5 times their body weight. This parameter is substantially higher than that reported for mosquitoes and biting midges.

Graphical Abstract

Due to their haematophagous behaviour, phlebotomine sand flies (Diptera: Psychodidae, Phlebotominae) are key vectors in the transmission of medically and veterinary important pathogens, including various Leishmania species, Bartonella bacilliformis , and several phleboviruses such as the Toscana virus [ 1 , 2 ].

Sand fly females require a blood meal to obtain the necessary nutrition for successful reproduction. Feeding behaviour and the size of the ingested blood meal can vary depending on the vertebrate source [ 3 , 4 ]. Under laboratory conditions, host choice is usually limited to commonly used laboratory animals such as mice, hamsters, and rabbits [ 5 , 6 ]. Alternative blood-feeding on artificial membrane feeders often results in lower feeding success compared to feeding on live animals [ 7 , 8 ]. Determining the mean blood volume ingested by a single sand fly female is crucial for specifying the maximum tolerable number of females per host in a feeding trial. Additionally, the volume of the blood meal is essential for calculating infective doses in laboratory experiments as it affects the number of infective agents taken up during xenodiagnoses or membrane feeding in vector competence studies [ 9 ]. For example, in low-susceptible sand fly species the Leishmania infections are dose dependent but in highly susceptible ones, like Phlebotomus argentipes and P. orientalis , even 1–2 Leishmania donovani parasites are enough to initiate mature infections. This low infection dose corresponds to 2 × 10 3 parasites/ml [ 7 ].

Previous studies, often focussing on a single sand fly species, have reported blood meal sizes ranging from 0.4 to > 1.0 µl of ingested blood [ 3 , 7 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, the variety of methods used makes precise interspecific comparison difficult. Notably, data obtained by gravimetry may be underestimated as this method omits the ability of blood-feeding Nematocera to excrete excess water and concentrate the blood meal during feeding [ 16 , 17 ]. This process, known as prediuresis, has been described in various sand fly species [ 3 , 10 , 18 , 19 ]. Therefore, methods based on haemoglobin or protein estimation provide more accurate and reproducible results of the total amount ingested.

In other haematophagous Nematocera, particularly in Aedes and Anopheles mosquitoes, a positive correlation between blood meal volume and female body size has been documented [ 20 , 21 ]. However, extensive prediuretic excretion allows some small-sized species to significantly increase the amount of ingested blood [ 16 ]. In sand flies, previous studies [ 7 , 15 ] suggested that the blood meal volume does not interspecifically correlate with body size, but this relationship has not been closely examined. Similarly, data on sand fly body weight are scarce [ 10 , 22 ].

In this study, we used ten laboratory-reared sand fly species and haemoglobinometry to determine the amount of ingested blood under standard conditions, which are the same conditions used for experimental infections with Leishmania or phleboviruses. Additionally, we compared the body weight of sand fly females and males and analysed the relationship between blood meal size and female body weight.

Sand fly colonies and their maintenance

Ten well-established colonies of ten species from four sand fly genera including three Phlebotomus subgenera were used: Phlebotomus (Euphlebotomus) argentipes Annandale & Brunetti 1908; P. (Phlebotomus) duboscqi Neveu-Lamaire, 1908; P. (Larroussius) orientalis (Parrot, 1936); P. (Larroussius) perniciosus Newstead, 1911; P. (Phlebotomus) papatasi  (Scopoli, 1786); P. (Larroussius) tobbi Adler, Theodor & Lourie, 1930; Lutzomyia longipalpis  (Lutz & Neiva, 1912); Migonemyia migonei (França 1920); Sergentomyia minuta ( Rondani, 1843) and S. schwetzi (Adler, Theodor and Parrot, 1929). The colonies were maintained in the insectary of the Department of Parasitology, Charles University, in Prague, under standard conditions (at 26 °C, 60–70% humidity, and 14 h light/10 h dark photoperiod) as described previously [ 5 ]. Adults were offered 50% sucrose ad libitum. Sand fly females were fed on either anaesthetized BALB/c mice ( P. argentipes, P. duboscqi , P. papatasi, L. longipalpis , S. schwetzi ) or mechanically restrained New Zealand White (NWZ) rabbits ( P. orientalis , P. perniciosus , P. tobbi , M. migonei ) or leopard geckos ( S. minuta ). The blood-feeding was routinely carried out on the animal host for about 60 min in most colonies. Sergentomyia minuta females need more time for full engorgement [ 15 ]; thus, they were allowed to feed on reptiles for 2 h.

Animal maintenance

BALB/c mice originating from AnLab s.r.o. (Harlan Laboratories, USA) were maintained in T3 breeding containers (Velaz) equipped with bedding (German Horse Span, Pferde a.s.) and breeding material (Woodwool) and provided with a standard feed mixture (ST-1, Velaz) and water ad libitum, with a 12 h light/12 h dark photoperiod, at 22–25 °C and 40–60% humidity. NZW rabbits (originating from AnLab s.r.o.) were kept in breeding boxes (Velaz) equipped according to guidelines and legislation, provided with a standard feeding mixture for rabbits (Biopharm), hay, and water ad libitum as described in Ticha et al. [ 15 ]. Leopard geckos, Eublepharis macularius (Blyth, 1854), were kept in glass terraria (60 × 40 × 35 cm) at 24 °C and 32 °C in a basking area, 12/12 light/dark regime and continual access to water. Their feeding was performed three times a week with crickets. The exposure to sand flies was performed once every 3 weeks to allow the animal host to recover.

Body weight of sand flies

While body size of sand flies has been mostly determined using morphometric parameters to date, in this study the body weight of sand fly females was correlated with the weight of ingested blood. Newly emerged adults were released from rearing pots into nylon cages (40 × 40 × 40 cm) with wet cotton wool on the top of the cage to supply sufficient humidity. The adults from at least four pots were used per trial. No sugar meal was provided for 24 h. Then, unfed sand flies were collected, immobilized on ice, carefully transferred into 0.5-ml micro test tubes (Eppendorf ® ), and weighed in batches of 20 flies on the Ohaus ® PR 124/E analytical balance (OHAUS Corp., USA). The measurement was repeated in six independent trials for both males and females of all studied sand fly species. The last measurement was done 12 months after the first one. The mean individual male (M m ) and female (M f ) body weights together with the M m /M f ratio were calculated and statistically compared.

Haemoglobin assay for measuring the blood meal volume

Haemoglobinometry was used to compare the blood meal size in ten sand fly species; the method chosen is independent of prediuresis and diuresis and provides precise assessment of the blood meal volumes ingested by sand fly females [ 7 ]. We used a modification with a commercially available kit, as described previously by Ticha et al. [ 15 ]. Sand fly females (100 per trial, 5–7 days old) were offered a blood meal on a routinely used animal host (same conditions as for maintenance of the colony). One hour post blood meal, fully engorged females were selected and immobilized on ice. Individual guts without Malpighian tubules were dissected in 20-mM TRIS-NaCl under a stereo microscope (Olympus SZH Stereo Microscope), transferred to microtubes with 1 ml dH 2 O, and stored in batches of ten guts per sample at − 70 °C. Sample homogenates were prepared by thorough mechanical homogenization. Afterwards, haemoglobin content was measured using Haemoglobin Assay Kit (MAK115, Sigma-Aldrich) following the manufacturer’s instruction in 96-well plates. The assay was calibrated by diluted calibrator provided in the kit (equivalent to 1 mg/ml haemoglobin). Fifty μicrolitres of the sample homogenate was loaded per well in quadruplicate, mixed with 200 μl reagent, and incubated 5 min at room temperature, and the absorbance was measured at 400 nm by Tecan-Infinite M 200 Fluorometer (Schoeller Instruments). The measurement was performed in three independent trials for each sand fly species in the study. The resulting haemoglobin content was compared to the haemoglobin concentration measured in the host blood (the same animal host individuals as used for experimental feeding) to determine a mean blood meal volume per female (V bl ). In addition, mean blood meal mass (M bl ) was related to the estimated mean weight of the unfed females (M f ) in the experimental cohort.

Statistical analysis

Statistical analysis was performed using standard Excel programme tests for Windows 10 (Microsoft ® Corp., USA) and Real Statistics Resource Pack software (Release 8.8.2) http://www.real-statistics.com . Comparisons of body weights and blood meal volumes in ten sand fly species were analysed using one-way analysis of variance (ANOVA) followed by Tukey-Kramer multiple comparison tests. The intraspecific differences in male and female body weights were tested by Student’s t-test. Shapiro-Wilk tests were used to analyse data for normality and Levene’s tests for homogeneity of variances. P -values < 0.05 were considered statistically significant.

Comparison of blood meal volumes ingested by ten sand fly species

The size of ingested blood meal was determined in ten sand fly species using haemoglobin measurement. Significant interspecific differences were found; mean blood meal volume ranged from 0.47 µl in Migonomyia migonei to 1.01 µl in Sergentomyia minuta (Fig.  1 A) .

Blood meal size ingested by females of ten sand fly species. Volume of ingested blood meal ( A ) and comparison of blood meal weight with the mean body Δ weight of unfed females ( B ). Ten sand fly species were studied: Phlebotomus argentipes (PAR); P. duboscqi (PDU); P. papatasi  (PPA); P. orientalis (POR); P. perniciosus (PPE); P. tobbi (PTO); Lutzomyia longipalpis  (LLO); Migonemyia migonei (MMI); Sergentomyia minuta ( SMI) and S. schwetzi (SSC). The columns represent an average from three independent samples (each sample comprising 10 sand fly specimens). The interspecific differences analysed by one-way ANOVA were highly significant in all studied parameters: the blood meal size ( F (9,20)  = 44.16; P  < 0.0001), unfed body weights ( F (9,20)  = 15.87; P  < 0.0001), and the M bl /M f ratios ( F (9,20)  = 150.94; P  < 0.0001)

Both Sergentomyia species took relatively big blood meals (V bl 0.96 ± 0.07 µl and 1.01 ± 0.03 µl for S. schwetzi and S. minuta , respectively). Within Phlebotomus , similarities occurred between members of the same subgenus. Phlebotomus duboscqi and P. papatasi (both belonging to subgenus Phlebotomus ) took very similar blood meal volume (V bl 0.89 ± 0.06 µl and 0.90 ± 0.04 µl, respectively). Analogously, no statistical difference was not found in blood meal volumes between Larroussius species, P. perniciosus , P. orientalis , and P. tobbi (V bl 0.61 ± 0.05 µl, 0.51 ± 0.07 µl, and 0.54 ± 0.03 µl, respectively). In L. longipalpis , the blood meal volume (V bl 0.89 ± 0.07 µl) was very similar to those in P. papatasi and P. duboscqi (Fig.  1 A). By contrast, the blood meal volume taken by M. migonei females was the lowest among all sand fly species tested (V bl 0.47 ± 0.02 µl).

Data are vizualized in Fig.  1 A. For the Tukey-Kramer multiple comparison test table, see Supplementary information (Additional file 1 : Table S1).

Body weight of sand fly females and males

Mean unfed body weights were measured in ten sand fly species. The interspecific comparison revealed highly significant differences in both male and female body weights (see Fig.  2 ) and in M m /M f ratios ( ANOVA , F (9,50)  = 8.21, P  <  0.0001 ). In Phlebotomus species, differences were smaller among members of the same subgenus ( Phlebotomus and Larroussius , respectively); see Table  1 .

Comparison of body weight of sand fly females and males. The mean unfed body weight of females (white boxes) and males (grey boxes) was measured in ten laboratory-reared sand fly species: Phlebotomus argentipes (PAR); P. duboscqi (PDU); P. papatasi  (PPA); P. orientalis (POR); P. perniciosus (PPE); P. tobbi (PTO); Lutzomyia longipalpis  (LLO); Migonemyia migonei (MMI); Sergentomyia minuta ( SMI) and S. schwetzi (SSC). The data represent an average from six independent trials (each sample comprising 20 sand fly specimens). The interspecific differences analysed by one-way ANOVA were highly significant in both females ( F (9,50)  = 30.40; P  < 0.0001) and males ( F (9,50)  = 24.49; P  < 0.0001)

In all species studied, the weight of males was significantly lower than that of females. The smallest difference between sexes was observed in P. argentipes and L. longipalpis where male weight was about 75% and 70% that of females, respectively. By contrast, in P. tobbi and M. migonei male weight was only about one half that of females (52% and 53%, respectively); see Fig.  2 and Table  1 .

Members of the subgenus Phlebotomus were the biggest species studied : P. duboscqi (M f 0.369 ± 0.046 mg; M m 0.242 ± 0.031 mg) and P. papatasi (M f 0.329 ± 0.022 mg; M m 0.218 ± 0.032 mg). Members of subgenus Larroussius ranked among the middle-sized species: P. orientalis (M f 0.291 ± 0.025 mg; M m 0.160 ± 0.022 mg), P. perniciosus (M f 0.248 ± 0.023 mg; M m 0.135 ± 0.023 mg), and P. tobbi (M f 0.243 ± 0.019 mg; M m 0.126 ± 0.017 mg). Females of P. (Euphlebotomus) argentipes (M f 0.223 ± 0.038 mg) were the smallest ones in the genus Phlebotomus while the males (M m 166 ± 0.025 mg) were slightly heavier than the males of all Larroussius species (Fig.  2 ).

Two species of the genus Sergentomyia , S. minuta (M f 0.183 ± 0.02 mg; M m 0.123 ± 0.015 mg) and S. schwetzi (M f 0.202 ± 0.029 mg; M m 0.127 ± 0.029 mg), did not exhibit any significant difference in either M f and M m or M m /M f ratio. On the other hand, considerable differences were found between two New World species studied: L. longipalpis values (M f 0.317 ± 0.023 mg and M m 0.222 ± 0.021 mg) ranged close to the values of P. papatasi . By contrast, M. migonei grouped among the lowest values measured (M f 0.199 ± 0.021 mg and M m 0.106 ± 0.018 mg) and males of M. migonei were the smallest males in the study. All data are summarized in Table  1 .

Blood meal size versus body weight of females

M bl /M f ratios ranged from 2.25 to 3.05 in most species, with the exception of P. argentipes and both Sergentomyia species. The females of P. argentipes , the tiniest species of the genus Phlebotomus , were able to acquire more blood (V bl 0.72 ± 0.06 µl) than females of three relatively bigger species of the Larroussius subgenus. If related to their unfed weight (M bl /M f 3.34 ± 0.14), they took relatively more blood than the females of all other Phlebotomus species tested. Within the subgenus Larroussius , the relative consumption was significantly higher in P. perniciosus than in the larger females of P. orientalis ( P  = 0.015, 95% CI = [0.064, 0.871]) (see S2).

The highest M bl /M f ratio was found in both Sergentomyia species: 5.18 and 4.55 for S. minuta and S. schwetzi , respectively. Females of the tiniest species, S. minuta (V bl 1.01 ± 0.03 µl) and S. schwetzi (V bl 0.96 ± 0.07 µl), ingested higher volumes than the largest species studied, P. duboscqi (V bl 0.89 ± 0.06 µl) and P. papatasi (V bl 0.90 ± 0.04 µl). Results are summarized in Fig.  1 B and Table  2 . The Tukey-Kramer multiple comparison test table for M bl /M f ratio is provided as “Supplementary information” (Additional file 2 : Table S2).

In ten sand fly species studied, the mean volume of ingested blood meal ranged from 0.47 to 1.01 µl. This size is higher than that in biting midges (Ceratopogonidae) but smaller than in mosquitoes (Culicidae). In biting midges, the mean blood meal size was 0.44 mg for Culicoides variipennis (by ELISA test) [ 17 ] and 0.36 mg for Culicoides arakawae (by chemical analyses) [ 23 ]. For Aedes aegypti , blood intake has been reported by several studies (reviewed in [ 24 ]). For instance, Woke et al. [ 25 ] determined the blood meal range from 1.5 to 3.9 mg by gravimetry, while Briegel quantified [ 20 ] blood meals using excretory haematin measurement, finding a range of 1.3 to 6.6 µl.

In most sand fly species studied (eight Phlebotomus and Lutzomyia species), the relation of blood meal size to the size of females (relative consumption) was similar to that ofmosquitoes but higher than that in biting midges. If fed to repletion, mosquito females can ingest blood meals 2–4 times their body weight [ 26 ], while C. variipennis females fed on horse blood retained 1.2–1.9 times their unfed weight in blood [ 17 ]. In all sand fly species tested, the largest blood intake was documented in females from cohorts with the highest mean weight. However, the highest relative consumption (Mbl/Mf ratio) was observed in cohorts with the lowest weight in most species tested. No positive correlation was found between mean blood volume and mean size of sand fly females by interspecific comparison.

Very high relative consumption was found in both Sergentomyia species studied; they ingested blood meals 4.5–5 times greater than their body weight. This high blood consumption (both absolute and relative) affirms the large volumes reported by previous studies: 0.91 µl on anaesthetized mice and 0.82 µl by artificial feeding for S. schwetzi and 0.97 µl on human arms and 1.02 µl on geckos for S. minuta [ 7 , 15 ]. While S. minuta is mainly herpetophilic [ 15 ], S. schwetzi is an opportunistic feeder [ 27 ] that readily feeds on reptiles; a colony of S. schwetzi fed solely on geckos was successfully maintained in the laboratory for 8 years [ 28 ]. The substantial amounts of blood acquired may reflect an adaptation of Sergentomyia species to the lower haemoglobin content in reptilian erythrocytes [ 29 ], requiring a large gut capacity and highly efficient blood meal concentration during feeding. Additionally, S. minuta feeds on geckos for up to 45 min to full repletion, enabled by the lack of defensive behaviour in reptiles [ 15 ]. In mosquitoes, a similarly long feeding time of up to 40 min has been observed in Culex territans , which primarily feeds on cold-blooded vertebrates [ 30 ]. In contrast, the low consumption observed in M. migonei may reflect its ornithophilic feeding preferences [ 31 ], where a fast feeding strategy reduces the risk of active defensive behaviour by birds.

Among Phlebotomus species, the tiny females of P. argentipes showed the highest relative consumption. Our results align with previous findings by Pruzinova et al. [ 7 ] which reported blood meal volumes of 0.73 µl on anaesthetized mice and 0.63 µl on rabbit blood via a chick-skin membrane. All three Larroussius species studied ( P. perniciosus , P. orientalis , and P. tobbi ) took similar blood volumes while feeding on rabbits (mean Vbl = 0.51–0.61 µl). However, P. perniciosus females showed the highest relative consumption, possibly because they readily feed on hares and wild rabbits [ 32 ], whereas P. orientalis and P. tobbi prefer large livestock and humans [ 33 , 34 ]. Similar volumes were previously described for P. orientalis feeding on mice or through a membrane on rabbit blood (0.53 µl and 0.59 µl) [ 7 ]. For Phlebotomus (Larroussius) langeroni membrane fed on defibrinated human blood, larger blood meals were observed (0.76–0.94 µl and 0.71–0.99 µl, measured by protein content and haemoglobin methods, respectively) [ 13 ].

The blood meal volume of 0.89 µl reported here for L. longipalpis is higher than volumes estimated previously for this species by gravimetry: 0.55 mg (maximum 0.75 mg [ 12 ]. Similar differences, caused by different methodologies, were observed in P. papatasi . Here, the mean blood volume was 0.90 µl, matching previous findings in P. papatasi females feeding on anaesthetized mice and measured by haemoglobinometry [ 7 ]. In contrast, Theodor [ 10 ], using gravimetry for P. papatasi , determined a mean blood meal weight of 0.4–0.5 mg (maximum 0.58 mg). These differences are due to prediuresis: excretion of excessive water and concentration of the blood meal during feeding. Prediuretic excretion is a physiological mechanism used by haematophagous arthropods to control water balance and body temperature and to concentrate the blood meal during feeding [ 35 , 36 ]. Diuresis, initiated after feeding, reduces the flight weight of the freshly fed female [ 16 ]. Very efficient prediuresis was described in Anopheles mosquitoes, where Anopheles stephensi can have a maximum gut capacity of 2–3 µl but mean blood meal consumption can reach 6 µl because of extensive prediuretic excretion [ 16 , 37 ].

In sand flies, prediuretic excretion was previously documented in 100% of P. argentipes [ 18 ], 100% of P. papatasi , and 85% of P. duboscqi females [ 19 ] and in the majority of L. longipalpis females [ 3 ]. Variations in urine production correlated with the length of feeding, with P. papatasi having a significantly longer excretion time and producing more droplets than P. duboscqi [ 19 ]. These interspecific discrepancies in prediuresis patterns correspond with the higher Mbl/Mf ratio of P. papatasi compared to P. duboscqi documented in this study.

Analogously to blood meal size, the mean weight of sand fly females also ranged between the largest biting midges (e.g. 0.2501 ± 0.0587 mg of Culicoides variipennis ) [ 17 ] and small mosquito species (e.g. 0.7 ± 0.1 mg of Anopheles minimus ) [ 21 ]. Similarly to other Nematocera, the body size of sand flies has mostly been determined using morphometric parameters. The only data on their body weight came from studies of Israeli populations of P. papatasi . Adler and Theodor [ 38 ] reported an average female weight of 0.3 mg. The unfed weight of males and females caught in Jerusalem was 0.24–0.28 mg and 0.35–0.4 mg, respectively [ 10 ]. Population differences were described by Jacobson et al. [ 22 ] among four P. papatasi colonies from diverse ecological habitats and seasons. The mean unfed weight (48 h after emergence) was 295 µg (95% CI 0.277–0.312) in females and 223 µg (95% CI 0.213–0.233) in males. Oasis flies were smaller than desert flies, and the autumn line of flies from super arid areas was significantly heavier than for flies from other localities [ 22 ].

This study was conducted under standard laboratory conditions. However, in natural environments, we expect higher variability in the blood meal volumes taken by sand fly females, likely due to the diversity of hosts and their defensive behaviours. Furthermore, some sand fly species or populations are known to be gonotrophically discordant. For example, in various populations of P. papatasi , females have been repeatedly observed taking multiple blood meals (2–4) within a single gonotrophic cycle [ 10 , 39 ]. The implications of this behaviour for the transmission of human pathogens have been extensivelydiscussed for P. papatasi and P. duboscqi [ 40 ].

Sand fly species significantly differ in blood meal volume taken by females under standard conditions. Five species from three genera ( P. papatasi, P. duboscqi, L. longipalpis, S. minuta, and S. schwetzi ) took double the blood meal compared to M. migonei . These intraspecific differences are crucial for determining optimal pathogen doses (e.g. Leishmania or phleboviruses) during experimental infections, such as comparative studies with New World species L. longipalpis and M. migonei [ 41 ]. The relation of blood meal amount to female size (relative consumption) had not been studied in sand flies before to our knowledge. Interestingly, in all Phlebotomus and Lutzomyia species studied, the Mbl/Mf ratio ranged between 2.25 and 3.34. In contrast, both Sergentomyia species studied ingested blood meals 4.5–5 times their body weight. For future research, we recommend testing the blood meal volumes taken by sand fly females under natural conditions. Haemoglobinometry would be an optimal assay for such a study.

Availability of data and materials

Data are provided within the manuscript or supplementary information files.

Maroli M, Feliciangeli MD, Bichaud L, Charrel RN, Gradoni L. Phlebotomine sandflies and the spreading of leishmaniases and other diseases of public health concern. Med Vet Entomol. 2013;27:123–47. https://doi.org/10.1111/j.1365-2915.2012.01034.x .

Article   CAS   PubMed   Google Scholar  

Jancarova M, Polanska N, Volf P, Dvorak V. The role of sand flies as vectors of viruses other than phleboviruses. J Gen Virol. 2023;104:4. https://doi.org/10.1099/jgv.0.001837 .

Article   PubMed   Google Scholar  

Sant’anna MR, Nascimento A, Alexander B, Dilger E, Cavalcante RR, Diaz-Albiter HM, et al. Chicken blood provides a suitable meal for the sand fly Lutzomyia longipalpis and does not inhibit Leishmania development in the gut. Parasit Vectors. 2010;3:3. https://doi.org/10.1186/1756-3305-3-3 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Noguera P, Rondon M, Nieves E. Caloric content of the sand fly Lutzomyia ovallesi (Diptera: Psychodidae) vector of Leishmania . Rev Colomb Entomol. 2006;32:57–60. https://doi.org/10.25100/socolen.v32i1.9359 .

Article   Google Scholar  

Volf P, Volfova V. Establishment and maintenance of sand fly colonies. J Vector Ecol. 2011;36:1–9. https://doi.org/10.1111/j.1948-7134.2011.00106.x .

Lawyer P, Killick-Kendrick M, Rowland T, Rowton E, Volf P. Laboratory colonization and mass rearing of phlebotomine sand flies (Diptera, Psychodidae). Parasite. 2017;24:42. https://doi.org/10.1051/parasite/2017041 .

Article   PubMed   PubMed Central   Google Scholar  

Pruzinova K, Sadlova J, Seblova V, Homola M, Votypka J, Volf P. Comparison of bloodmeal digestion and the peritrophic matrix in four sand fly species differing in susceptibility to Leishmania donovani . PLoS ONE. 2015;2015:e0128203. https://doi.org/10.1371/journal.pone.0128203 .

Article   CAS   Google Scholar  

Denlinger DS, Li AY, Durham SL, Lawyer PG, Anderson JL, Bernhardt SA. Comparison of in vivo and in vitro methods for blood feeding of Phlebotomus papatasi (Diptera: Psychodidae) in the laboratory. J Med Entomol. 2016;53:1112–6. https://doi.org/10.1093/jme/tjw074 .

Venter GJ, Paweska JT, Lunt H, Mellor PS, Carpenter S. An alternative method of blood-feeding Culicoides imicola and other haematophagous Culicoides species for vector competence studies. Vet Parasitol. 2005;131:331–5. https://doi.org/10.1016/j.vetpar.2005.05.002 .

Theodor O. On the relation of Phlebotomus papatasi to the temperature and humidity of the environment. Bull Entomol Res. 1936;27:653–71. https://doi.org/10.1017/S0007485300058739 .

de Freitas J, da Guedes AS. Determination by radioactive iron (59Fe) of the amount of blood ingested by insects. Bull World Health Organ. 1961;25:271–3.

PubMed   PubMed Central   Google Scholar  

Killick-Kendrick R, Leaney AJ, Ready PD. The establishment, maintenance and productivity of a laboratory colony of Lutzomyia longipalpis (Diptera: Psychodidae). J Med Entomol. 1977;13:429–40.

Daba S, Daba A, Shehata MG, El Sawaf BM. A simple micro-assay method for estimating blood meal size of the sand fly, Phlebotomus langeroni (Diptera: Psychodidae). J Egypt Soc parasitol. 2004;34:173–82.

CAS   PubMed   Google Scholar  

da Rocha Silva FB, Miguel DC, Machado VE, Oliveira WHC, Goulart TM, Tosta CD, et al. Influence of Leishmania (Viannia) braziliensis infection on the attractiveness of BALB/c mice to Nyssomyia neivai (Diptera: Psychodidae). PLoS ONE. 2019;14:e0214574. https://doi.org/10.1371/journal.pone.0214574 .

Ticha L, Volfova V, Mendoza-Roldan JA, Bezerra-Santos MA, Maia C, Sadlova J, et al. Experimental feeding of Sergentomyia minuta on reptiles and mammals: comparison with Phlebotomus papatasi . Parasit Vectors. 2023;16:126. https://doi.org/10.1186/s13071-023-05758-5 .

Briegel H, Rezzonico L. Concentration of host blood protein during feeding by anopheline mosquitoes (Diptera: Culicidae). J Med Entomol. 1985;22:612–8. https://doi.org/10.1093/jmedent/22.6.612 .

Leprince DJ, Higgins JA, Church GE, Issel CJ, McManus JM, Foil LD. Body size of Culicoides variipennis (Diptera: Ceratopogonidae) in relation to bloodmeal size estimates and the ingestion of Onchocerca cervicalis (Nematoda: Filarioidea) microfiliariae. J Am Mosq Control Assoc. 1989;5:100–3.

Shortt HE, Swaminath CS. The method of feeding of Phlebotomus argentipes with relation to its bearing on the transmission of kala-azar. Indian J Med Res. 1927;15:827–36.

Google Scholar  

Sadlova J, Reishig J, Volf P. Prediuresis in female Phlebotomus sandflies (Diptera: Psychodidae). Eur J Entomol. 1998;95:643–7.

Briegel H. Metabolic relationship between female body size, reserves, and fecundity od Aedes aegypti . J Insect Physiol. 1990;36:165–72. https://doi.org/10.1016/0022-1910(90)90118-Y .

Phasomkusolsil S, Pantuwattana K, Tawong J, Khongtak W, Kertmanee Y, Monkanna N, et al. The relationship between wing length, blood meal volume, and fecundity for seven colonies of Anopheles species housed at the Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand. Acta Trop. 2015;152:220–7. https://doi.org/10.1016/j.actatropica.2015.09.021 .

Jacobson RL, Studentsky L, Schlein Y. Glycolytic and chitinolytic activities of Phlebotomus papatasi (Diptera: Psychodidae) from diverse ecological habitats. Folia Parasitol. 2007;54:301–9. https://doi.org/10.14411/fp.2007.039 .

Fujisaki K, Kamio T, Kitaoka S, Morii T. Quantitation of the blood meal ingested by Culicoides arakawae (Diptera: Ceratopogonidae). J Med Entomol. 1987;24:702–3. https://doi.org/10.1093/jmedent/24.6.702 .

Clements AN. The biology of mosquitoes. Volume 1. Development nutrition and reproduction. London: Chapman and Hall; 1992.

Book   Google Scholar  

Woke PA, Ally MS, Rosenberger CR. The numbers of eggs developed related to the quantities of human blood ingested in  Aedes aegypti  (L.) (Diptera: Culicidae). Ann Entomol Soc Am. 1956;49:435–41. https://doi.org/10.1093/aesa/49.5.435 .

Nayar JK, Sauerman DM. The effects of nutrition on survival and fecundity in Florida mosquitoes. Part 2. Utilization of a blood meal for survival. J Med Entomol. 1975;12:99–103. https://doi.org/10.1093/jmedent/12.1.99 .

Senghor MW, Niang AA, Depaquit J, Faye MN, Elguero E, Gaye O, et al. Transmission of Leishmania infantum in the canine leishmaniasis focus of Mont-Rolland, Senegal: ecological, parasitological and molecular evidence for a possible role of Sergentomyia sand flies. PLoS Negl Trop Dis. 2016;10:e0004940. https://doi.org/10.1371/journal.pntd.0004940 .

Polanska N, Ishemgulova A, Volfova V, Flegontov P, Votypka J, Yurchenko V, et al. Sergentomyia schwetzi : salivary gland transcriptome, proteome and enzymatic activities in two lineages adapted to different blood sources. PLoS ONE. 2020;15:e0230537. https://doi.org/10.1371/journal.pone.0230537 .

Knotkova Z, Morici M, Oliveri M, Knotek Z. Blood profile in captive adult male leopard geckos ( Euplepharis macularius ). Vet Med-Czech. 2019;64:172–7. https://doi.org/10.17221/164/2018-VETMED .

Reinhold JM, Shaw R, Lahondère C. Beat the heat: Culex quinquefasciatus regulates its body temperature during blood feeding. J Therm Biol. 2021;96:102826. https://doi.org/10.1016/j.jtherbio.2020.102826 .

Carvalho GML, Rêgo FD, Tanure A, Silva ACP, Dias TA, Paz GF, et al. Bloodmeal identification in field-collected sand flies from Casa Branca, Brazil, using the Cytochrome b PCR method. J Med Entomol. 2017;54:1049–54. https://doi.org/10.1093/jme/tjx051 .

Martín-Martín I, Molina R, Rohoušová I, Drahota J, Volf P, Jiménez M. High levels of anti- Phlebotomus perniciosus saliva antibodies in different vertebrate hosts from the re-emerging leishmaniosis focus in Madrid, Spain. Vet Parasitol. 2014;202:207–16. https://doi.org/10.1016/j.vetpar.2014.02.045 .

Lemma W, Tekie H, Abassi I, Balkew M, Gebre-Michael T, Warburg A, et al. Nocturnal activities and host preferences of Phlebotomus orientalis in extra-domestic habitats of Kafta-Humera lowlands, Kala-azar endemic, Northwest Ethiopia. Parasit Vectors. 2014;7:594. https://doi.org/10.1186/s13071-014-0594-3 .

Svobodová M, Alten B, Zídková L, Dvorák V, Hlavacková J, Myskova J, et al. Cutaneous leishmaniasis caused by Leishmania infantum transmitted by Phlebotomus tobbi . Int J Parasitol. 2009;39:251–6. https://doi.org/10.1016/j.ijpara.2008.06.016 .

Benoit JB, Denlinger DL. Meeting the challenges of on-host and off-host water balance in blood-feeding arthropods. J Insect Physiol. 2010;56:1366–76. https://doi.org/10.1016/j.jinsphys.2010.02.014 .

Lahondère C, Lazzari CR. Mosquitoes cool down during blood feeding to avoid overheating. Curr Biol. 2012;22:40–5. https://doi.org/10.1016/j.cub.2011.11.029 .

Vaughan JA, Noden BH, Beier JC. Concentration of human erythrocytes by anopheline mosquitoes (Diptera: Culicidae) during feeding. J Med Entomol. 1991;28:780–6. https://doi.org/10.1093/jmedent/28.6.780 .

Adler S, Theodor O. The mouthparts, alimentary tract and salivary apparatus of the female in Phlebotomus papatasii . Ann Trop Med Parasitol. 1926;20:109–42. https://doi.org/10.1080/00034983.1926.11684482 .

Schmidt JR, Schmidt ML. Observations on the feeding habits of Phlebotomus Papatasi (Scopoli) under simulated natural conditions. J Med Entomol. 1965;2:225–30. https://doi.org/10.1093/jmedent/2.3.225 .

Mukhopadhyay J, Ghosh K. Vector potential of Phlebotomus duboscqi and P. papatasi : a comparison of feeding behaviour, reproductive capacity and experimental infection with Leishmania major . Ann Trop Med Parasitol. 1999;93:309–18. https://doi.org/10.1080/00034989958573 .

Alexandre J, Sadlova J, Lestinova T, Vojtkova B, Jancarova M, Podesvova L, et al. Experimental infections and co-infections with Leishmania braziliensis and Leishmania infantum in two sand fly species, Lutzomyia migonei and Lutzomyia longipalpis . Sci Rep. 2020;10:3566. https://doi.org/10.1038/s41598-020-60600-7 .

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Acknowledgements

The authors thank Lenka Krejcirikova, Lenka Hlubinkova and Kristyna Srstkova for their administrative and technical support.

This study was funded by the project National Institute of Virology and Bacteriology (Programme EXCELES, ID no. LX22NPO5103, European Union, Next Generation EU).

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VV carried out the experimental part, MJ contributed to the revision of the manuscript, PV designed and supervised the study and revised the manuscript. All authors read and approved the final manuscript.

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Volfová, V., Jančářová, M. & Volf, P. Sand fly blood meal volumes and their relation to female body weight under experimental conditions. Parasites Vectors 17 , 360 (2024). https://doi.org/10.1186/s13071-024-06418-y

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Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples

• Aneta Pluta 1 , 13 ,
• Juan Pablo Jaworski 2 ,
• Casey Droscha 3 ,
• Sophie VanderWeele 3 ,
• Tasia M. Taxis 4 ,
• Stephen Valas 5 ,
• Dragan Brnić 6 ,
• Andreja Jungić 6 ,
• María José Ruano 7 ,
• Azucena Sánchez 7 ,
• Kenji Murakami 8 ,
• Kurumi Nakamura 8 ,
• Rodrigo Puentes 9 ,
• MLaureana De Brun 9 ,
• Vanesa Ruiz 2 ,
• Marla Eliana Ladera Gómez 10 ,
• Pamela Lendez 10 ,
• Guillermina Dolcini 10 ,
• Marcelo Fernandes Camargos 11 ,
• Antônio Fonseca 11 ,
• Subarna Barua 12 ,
• Chengming Wang 12 ,
• Aleksandra Giza 13 &
• Jacek Kuźmak 1  

BMC Veterinary Research volume  20 , Article number:  381 ( 2024 ) Cite this article

Metrics details

Bovine leukemia virus (BLV) is the etiological agent of enzootic bovine leukosis and causes a persistent infection that can leave cattle with no symptoms. Many countries have been able to successfully eradicate BLV through improved detection and management methods. However, with the increasing novel molecular detection methods there have been few efforts to standardize these results at global scale. This study aimed to determine the interlaboratory accuracy and agreement of 11 molecular tests in detecting BLV. Each qPCR/ddPCR method varied by target gene, primer design, DNA input and chemistries. DNA samples were extracted from blood of BLV-seropositive cattle and lyophilized to grant a better preservation during shipping to all participants around the globe. Twenty nine out of 44 samples were correctly identified by the 11 labs and all methods exhibited a diagnostic sensitivity between 74 and 100%. Agreement amongst different assays was linked to BLV copy numbers present in samples and the characteristics of each assay (i.e., BLV target sequence). Finally, the mean correlation value for all assays was within the range of strong correlation. This study highlights the importance of continuous need for standardization and harmonization amongst assays and the different participants. The results underscore the need of an international calibrator to estimate the efficiency (standard curve) of the different assays and improve quantitation accuracy. Additionally, this will inform future participants about the variability associated with emerging chemistries, methods, and technologies used to study BLV. Altogether, by improving tests performance worldwide it will positively aid in the eradication efforts.

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Introduction

Bovine leukemia virus (BLV) is a deltaretrovirus from the Orthoretrovirinae subfamily of the Retroviridae family. An essential step in the BLV replication cycle is the integration of DNA copy of its RNA genome into the DNA of a host cell [ 1 ]. Once integrated, the proviral DNA is replicated along with the host’s DNA during cellular divisions, as for any cellular gene. The BLV is the etiologic agent of enzootic bovine leukosis (EBL). BLV causes a persistent infection in cattle, and in most cases this infection is asymptomatic [ 2 ]. In one-third of infected animals the infection progresses to a state of persistent lymphocytosis, and in 1 to 10% of infected cattle it develops into lymphosarcoma [ 2 ]. BLV induces high economic losses due to trade restrictions, replacement cost, reduced milk production, immunosuppression, and increased susceptibility to pneumonia, diarrhea, mastitis, and so on [ 3 , 4 , 5 , 6 ]. BLV is globally distributed with a high prevalence, except for Western Europe and Oceania, where the virus has been successfully eradicated through detection and elimination of BLV-infected animals [ 7 , 8 ]. The agar gel immunodiffusion and ELISA for the detection of BLV-specific antibodies in sera and milk are the World Organization for Animal Health (WOAH, founded as OIE) prescribed tests for serological diagnosis but ELISA, due to its high sensitivity and ability to test many samples at a very low cost, is highly recommended [ 9 ]. Despite the advantages of serologic testing, there are some scenarios in which direct detection of the BLV genomic fragment was important to improve BLV detection. The most frequent cases is the screening of calves with maternal antibodies, acute infection, animals without persistent antibody response and animal subproducts (i.e., semen). In this regard, nucleic acid amplification tests such as real-time quantitative PCR (qPCR) allows for a rapid and highly sensitive detection of BLV proviral DNA (BLV DNA) that can be used to test infected and asymptomatic animals, before the elicitation of anti-BLV specific antibodies and when proviral load (PVL) are still low [ 10 ]. Furthermore, qPCR assays can serve as confirmatory tests for the clarification of inconclusive and discordant serological test results usually associated with these cases [ 11 ]. For these reasons, the inclusion of qPCR in combination with other screening tests might increase control programs efficiency. Additionally, qPCR allows the estimation of BLV PVL which is important for studying the dynamics of BLV infection (i.e., basic research). Further, considering that BLV PVL correlates with the risk of BLV transmission, this feature of qPCR can be exploited for developing rational segregation programs [ 12 , 13 ]. The results of Kobayashi et al. suggest that high PVL is also a significant risk factor for progression to EBL and should therefore be used as a parameter to identify cattle for culling from the herd well before EBL progression [ 14 ]. Several qPCRs have been developed globally for the quantitation of BLV DNA. Although most assays have been properly validated by each developer, a proper standardization and harmonization of such tests is currently lacking. Considering that standardization and harmonization of qPCR methods and results are essential for comparisons of data from BLV laboratories around the world, this could directly impact international surveillance programs and collaborative research. We built a global collaborative network of BLV reference laboratories to evaluate the interlaboratory variability of different qPCRs and sponsored a harmonization of assays to hopefully impact international surveillance programs and research going forward.

In 2018 we conducted the first global trial of this kind to assess the interlaboratory variability of six qPCRs for the detection of BLV DNA [ 15 ]. Since this complex process is a continuous rather than a one-time effort, we now started a second study of this type. In this follow up study, we built a more comprehensive sample panel, accounting for a broader geographical diversification. Additionally, we increased the number of participants to ten collaborating laboratories plus one WOAH reference lab and tested novel methodologies including digital PCR (ddPCR) and FRET-qPCR. Finally, we established the next steps towards the international standardization of molecular assays for the detection of BLV DNA.

Materials and methods

Participants.

The eleven laboratories that took part in the study were:(i) the Auburn University College of Veterinary Medicine (Auburn, Alabama, United States): (ii) AntelBio, a division of CentralStar Cooperative (Michigan, United States); (iii) Laboratórios Federais de Defesa Agropecuária de Minas Gerais (LFDA-MG, Pedro Leopoldo, Brasil); (iv) Centro de Investigación Veterinaria de Tandil (CIVETAN, Buenos Aires, Argentina); (v) the Faculty of Agriculture Iwate University (Iwate, Japan); (vi) Universidad de la República de Uruguay (UdelaR, Montevideo, Uruguay); (vii) the Croatian Veterinary Institute (Zagreb, Croatia); (viii) Instituto Nacional de Tecnología Agropecuaria (INTA, Buenos Aires, Argentina); (ix) Laboratorio Central de Veterinaria (LCV, Madrid, Spain); (x) the National Veterinary Research Institute (NVRI, Puławy, Poland) and (xi) the French Agency for Food, Environmental and Occupational Health and Safety (Anses, Niort, France). All European laboratories participating in this study are acting as national reference laboratories for EBL, NVRI acts as WOAH reference laboratory for EBL, while the remaining laboratories are nationally renowned entities for BLV diagnostics. The eleven participating methods are referred to below as qPCR1 – qPCR5, ddPCR6, qPCR7 – qPCR11, respectively.

Sample collection and DNA extraction

A total of 42 DNA samples obtained from blood of naturally BLV-infected dairy cattle from Poland, Moldova, Pakistan, Ukraine, Canada and United States were used for this study. Thirty-six of them were archival DNA samples obtained between 2012–2018 as described in our previous studies on samples from Poland ( n  = 21) [ 16 , 17 ], Moldova ( n  = 4) [ 18 ], Pakistan ( n  = 5) [ 19 ] and Ukraine ( n  = 6) [ 15 , 20 ]. Between 2020–2021 6 peripheral blood and serum samples from naturally BLV-infected cattle were obtained from three dairy farms of Alberta, Canada and two dairy farms of Michigan, US. Serological testing and sample processing were conducted by the laboratories from which the samples originated. The genomic DNA from Canadian and US samples was extracted from whole blood using a Quick DNA Miniprep Plus kit (Zymo Research) and a DNeasy Blood & Tissue Kit (Qiagen), respectively in University of Calgary and Michigan State University and sent to the NVRI in the form of DNA solutions. Additionally, one plasmid DNA sample (pBLV344) was kindly supplied by Luc Willems (University of Liège, Belgium) and DNA extracted from FLK-BLV cells were included as positive controls. Finally, DNA extracted from PBL of a serologically negative cattle was included as negative control. At the NVRI, the DNA concentration in all samples was estimated by spectrophotometry using a NanoPhotometer (Implen). Each sample was divided into eleven identical aliquots containing between 800 and 4,000 ng of lyophilised genomic DNA. Eleven identical sets of these samples were lyophilized (Alpha 1–4 LSC basic, Martin Christ Gefriertrocknungsanlagen GmbH) and distributed to participating laboratories. At the NVRI, all samples were coded (identification [ 21 ] run numbers 1 to 44) to perform a blinded testing. The samples, together with instructions for their preparation (Additional file 1), were shipped by air at room temperature (RT).

Examination of DNA quality/stability

Since different extraction methods and lyophilization process were employed for the preparation of the DNA samples, it was necessary to test the quality of the DNA at the NVRI laboratory. For that purpose, one complete set of samples ( n  = 44) was tested by Fragment Analyzer (Agilent Technologies), before and after freeze-drying, to assess DNA quality by calculating a Genomic Quality Number (GQN) for every sample. Low GQN value (< 2.5) represents sheared or degraded DNA. A high GQN (> 9) represents undegraded DNA. In addition, quality of DNA was assessed by determination of copy number of the histone H3 family 3A ( H3F3A ) housekeeping gene using quantitative real-time PCR (qPCR) [ 22 ]. The qPCR results were expressed as the number of H3F3A gene copies per 300 ng of DNA in each sample. Grubbs´ test was performed to determine outliers. To test the stability of DNA, samples were stored for 20 days at RT (10 days) and at + 4 °C (10 days) and were retested by Fragment Analyzer and qPCR 21 days later. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1), respectively.

Description of BLV qPCR protocols used by participating laboratories

All participating laboratories performed their qPCR or ddPCR using a variety of different equipment, reagents, and reaction conditions, which had been set up, validated, and evaluated previously and are currently used as working protocols. The specific features of each of these protocols are described below and summarized in Table  1 .

All laboratories applied standard procedures for avoiding false-positive results indicative of DNA contamination, such as the use of separate rooms for preparing reaction mixtures, adding the samples, and performing the amplification reaction. One of the ten BLV qPCRs used LTR region and the remaining nine qPCRs used the pol gene as the target sequence for amplification, while the ddPCR amplified the env gene.

Method qPCR1

The BLV qPCR amplifying a 187-bp pol gene was performed according to a previously published methods [ 23 , 24 ]. A real-time fluorescence resonance energy transfer (FRET) PCR was carried out in a 20-μl PCR mixture containing 10 μl handmade reaction master mix and 10 μl genomic DNA. The PCR buffer was 4.5 mM MgCl2, 50 mM KCl, 20 mM Tris–HCl, pH 8.4, supplemented with 0.05% each Tween20 and Non-idet P-40, and 0.03% acetylated BSA (Roche Applied Science). For each 20 μl total reaction volume, the nucleotides were used at 0.2 mM each and 1.5 U Platinum Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA) was used. Primers were used at 1 μM, LCRed640 probe was used at 0.2 μM, and 6-FAM probe was used at 0.1 μM. Amplification was performed in the Roche Light Cycler 480 II (Roche Molecular Biochemicals) using 10 min denaturation step at 95 °C, followed by 18 high-stringency step-down thermal cycles and 30 low-stringency fluorescence acquisition cycles.

A plasmid containing the BLV-PCR amplicon region was diluted ten-fold from 1 × 10 5 copies to 10 copies per 10 µl and was used as a standard to measure the BLV copy numbers.

Method qPCR2

A BLV proviral load qPCR assay developed by AntelBio, a division of CentralStar Cooperative Inc. on Applied Biosystems 7500 Real-Time PCR system [ 25 , 33 ]. This multiplex assay amplifies the BLV pol gene along with the bovine β-actin gene and an internal amplification control, “Spike”. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 µl of 2X InhibiTaq Multiplex HotStart qPCR MasterMix (Empirical Bioscience), 16 nM each BLV primer, 16 nM each β-actin primer, 8 nM each spike primer, 8 nM BLV FAM-probe, 8 nM β-actin Cy5-probe, 4 nM spike JOE-probe, 1 µl of an internal spike-in control (10,000 copies per µl), 7.25 µl of nuclease-free water and 4 µl of DNA sample for each qPCR reaction. The thermal PCR protocol was as follows: 95 °C for 10 min, 40 × (95 °C for 15 s, 60 °C for 1 min). Copy numbers of both the BLV pol gene and bovine β-Actin were derived using a plasmid containing target sequences, quantified by ddPCR, diluted 1 × 10 6 copies per µl to 10 copies per µl in tenfold dilutions. DNA concentrations of each sample were measured using a Qubit 4 Fluorometer and used in combination with the qPCR copy numbers to calculate BLV copies per 100 ng.

Method qPCR3

The qPCR assays for the BLV LTR gene were performed according to a previously published methods [ 26 ]. Genomic DNA was amplified by TaqMan PCR with 10 μl of GoTaq Probe qPCR Master Mix × 2 (Promega), 0.6 pmol/μl each primer, 0.3 pmol/µl double-quenched probe and 100 ng genomic DNA. Amplification was performed in the CFX96 cycler (BioRad) according to the protocol: 5 min denaturation at 95°C followed by 45 cycles (60 s at 94°C and 60 s at 60°C). The efficiency of each reaction was calculated from the serial dilution of DNA extracted from BLV persistently infected fetal lamb kidney (FLK) cells, starting at a concentration of 100 ng/µl [ 21 ]. The detection limit was tested using a plasmid containing the target of the qPCRs, starting at 10 3 ng/µl.

Method qPCR4

The quantitative real-time PCR was done with the primers for the BLV pol gene as previously described [ 34 ]. The qPCR reaction mix contained 1 × PCR Master Mix with SYBR Green (FastStart Universal SYBR Green Master Rox, Roche), 0.3 μM each primer and 30 ng of extracted genomic DNA. Amplification was performed in QuantStudio 5 Real-Time PCR System (Applied Biosystems) under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 40 cycles of 15 s at 95 °C and 60 s at 60 °C. A standard curve of six tenfold serial dilutions of pBLV, containing 1 × 10 6 to 10 BLV copies, was built and run 3 times for validation of the method. The number of provirus copies per reaction (100 ng) was calculated.

Method qPCR5

BLV PVLs were determined by using qPCR kit, RC202 (Takara Bio, Shiga, Japan) [ 28 , 35 ]. This qPCR assay amplifies the BLV pol gene along with the bovine RPPH1 gene as an internal control. Briefly, 100 ng genomic DNA was amplified by TaqMan PCR with four primers for pol gene and RPPH1 gene according to the manufacturer’s instructions: 30 s denaturation at 95 °C followed by 45 cycles (5 s at 95 °C and 30 s at 60 °C). The qPCR was performed on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific K.K., Tokyo, Japan). Standard curve was generated by creating tenfold serial dilutions of the standard plasmid included in the kit. The standards for calibration ranged from 1 to 10 6 copies/reaction and were run in duplicate. The number of provirus copies per 100 ng was calculated.

Method ddPCR6

The digital droplet PCR (ddPCR) assay for the env gene of the BLV was performed using the protocol previously described by [ 28 , 29 ]. An absolute quantification by TaqMan ddPCR was performed in a typical 20-μl assay, 1 μl of DNA sample was mixed with 1 μl of each primer (10 μM), 0.5 μl of probe (10 μM), and 2 × Supermix emulsified with oil (Bio-Rad). The droplets were transferred to a 96-well plate (Eppendorf). The PCR assay was performed in a thermocycler (C1000 touch cycler; Bio-Rad) with the following parameters: initial denaturation of 10 min at 95 °C, then 40 cycles of 30 s at 94 °C, and 1 min at 58 °C, with final deactivation of the enzyme for 10 min at 98 °C. The presence of fluorescent droplets determined the number of resulting positive events that were analyzed in the software (QuantaSoft v.1.7.4; Bio-Rad), using dot charts. The number of provirus copies per 100 ng were calculated. Each sample was run in duplicate, and results were averaged.

Method qPCR7

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. A quantitative TaqMan PCR was performed in a 20 μl PCR mix containing 10 μl of 2 × ORA qPCR Probe ROX L Mix (highQu, Kraichtal, Germany), 2 μl primer/probe mix (final concentration 400 nM of each of the primers, 200 nM of BLV probe), and 3 μl extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) with an initial denaturation step and polymerase activation at 95 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 10 copies to 1 × 10 1 copies per reaction and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR8

Proviral load quantification was assessed by SYBR Green real-time quantitative PCR (qPCR) using the pol gene as the target sequence [ 36 ]. Briefly, 12-μl PCR mixture contained Fast Start Universal SYBR Green Master Mix (Roche), 800 nM each BLV pol primers and 1 µl DNA as template. The reactions were incubated at 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s, 55 °C for 15 s and 60 °C for 1 min. All samples were tested in duplicate on a StepOne Plus machine (Applied Biosystems). A positive and negative control, as well as a no-template control, were included in each plate. After the reaction was completed, the specificity of the amplicons was checked by analyzing the individual dissociation curves. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 6 to 10 copies per µl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR9

This qPCR method is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The detection of BLV genome was combined with an endogenous control system (Toussaint 2007) in a duplex assay. Briefly, 20-µl qPCR reaction contained AhPath ID™ One-Step RT-PCR Reagents with ROX (Applied Biosystems, CA, USA) – 10 µl of 2 × RT-PCR buffer and 0.8 µl of 25 × RT-PCR enzyme mix, 400 nM each primer for pol gene, 100 nM BLV specific probe, 40 nM each β-actin primer, 40 nM β-actin specific probe and 2 µl DNA sample. All samples were tested in ABI7500 Real-Time PCR System (Applied Biosystems) according to the following protocol: 10 min at 48 °C (reverse transcription), 10 min at 95 °C (inactivation reverse transcriptase / activation Taq polymerase) followed by 45 cycles (15 s at 95 °C and 60 s at 60 °C). As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 4 copies to 0.1 copies per μl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR10

The BLV qPCR was performed as published previously [ 11 ]. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 μl of 2 × QuantiTect Multiplex PCR NoROX master mix (Qiagen), 0.4 μM each primer, 0.2 μM specific BLV probe, and 500 ng of extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) using an initial denaturation step and polymerase activation at 95 °C for 15 min, followed by 50 cycles of 94 °C for 60 s and 60 °C for 60 s. All samples were amplified in duplicate. As a standard, the pBLV1 plasmid (NVRI, Pulawy, PL), containing a 120-bp BLV pol fragment, was used. Tenfold dilutions of this standard were made from 1 × 10 6 copies per μl to 100 copies per μl and were used to estimate the BLV copy numbers per 100 ng.

Method qPCR11

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The reaction mixture contained 400 nM of each primer, 200 nM of probe, 10 µl of 2 × SsoFast probes supermix (Bio-Rad), 5 µl of DNA sample and H 2 O up to 20 µl of the final volume. PCR assays were carried out on a CFX96 thermocycler (Bio-Rad) under the following amplification profile: 98 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were used to generate the standard curve and estimate BLV copy number per 100 ng.

Analysis of BLV pol, env and LTR sequences targeted by particular qPCR/ddPCR assays

In order to assess full-length pol , env and LTR sequence variability among BLV genotypes, all BLV sequences ( n  = 2191) available on 30 September 2023 in GenBank ( https://www.ncbi.nlm.nih.gov/GenBank/ ) repository were retrieved. From the collected sequences, 100 pol , env and LTR sequences, which were characterized by the highest level of sequence variability and divergence, were selected for the further analysis. A pol -based, env -based and LTR-based maximum likelihood (ML) phylogenetic trees (see Additional file 6) was constructed to assign genotypes to the unassigned BLV genomes [ 37 , 38 , 39 ]. For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied. In this analysis, pol sequences were assigned to 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10), while env and LTR sequences were assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10). Phylogeny of the same isolates assigned to particular genotypes by ML method was confirmed by Mr. Bayes analysis [ 40 , 41 , 42 ] (data not shown). From this analysis, a total of 100 full-length pol, env and LTR sequences were used for multiple-sequence alignment (MSA) using ClustalW algorithm, implemented in MEGA X. For all sequences, nucleotide diversity (π), defined as the average number of nucleotide differences per site between two DNA sequences in all possible pairs in the sample population, was estimated using MEGA X. To measure the relative variation in different positions of aligned genes and LTR region the Shannon’s entropy (a quantitative measure of diversity in the alignment, where H = 0 indicates complete conservation) was estimated using BioEdit v. 7.2.5 software 64. The statistical analyses were performed using DATAtab e.U. Graz, Austria and GraphPad Software by Dotmatics, Boston.

Examination of the quality and stability of DNA samples

To test the quality of DNA samples, the H3F3A copy number of each individual sample was assessed by qPCR at the NVRI. Copy numbers were normalized to DNA mass input and results were expressed as copy numbers per 300 ng of total DNA. The respective values were tested by Grubbs' test. The results for 43 DNA samples (sample ID: 42 with BLV genome plasmid was excluded) followed a normal distribution (Shapiro–Wilk 0.97; P  = 0.286), with a mean value of 35,626 copies (95% confidence interval [ 43 ] 33,843 to 37,408 copies), a minimum value of 19,848 copies and a maximum value of 46,951 copies (see Additional file 2). Despite a low value for sample ID: 40 no significant outlier was detected in the dataset ( P  > 0.05). Therefore, it can be assumed that the DNA quality was acceptable for all samples present in the panel. Next, DNA stability was assessed by retesting the H3F3A copy numbers in each sample ( n  = 43) after a combined storage consisting in 10 days at RT and 10 days at + 4°C. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1, respectively), and no significant difference was observed at the 5% level ( P  = 0.187) (Fig.  1 A).

Assessment of the stability of DNA samples. A Shown are copy numbers of the H3F3A housekeeping gene in 43 DNA samples that were stored in 10 days at RT and 10 days at + 4°C and tested twice with a 21-day interval. A Mann–Whitney U-test was used to compare the median values between two groups ( P  = 0.187); B Shown are GQN values ( n  = 43) tested twice with a 21-day interval: `before freeze-drying` and `after freeze-drying`. A Mann–Whitney U-test results between two groups ( P  = 0.236)

In addition, the quality of DNA samples after lyophilization was analyzed. DNA from individual samples ( n  = 43) was assessed with the genomic DNA quality number on the Fragment Analyzer system. The GQN from all lyophilized samples ranged from 4.0 to 9.7—that represented undegraded DNA. There was no significant difference in GQN values between `before freeze-drying` and `after freeze-drying` groups with respect to the corresponding DNA samples ( P  = 0.236) (Fig.  1 B). Altogether, these results suggested that sample storage, lyophilization and shipping has a minimal impact in DNA stability and further testing during the interlaboratory trial.

Detection of BLV proviral DNA by different qPCR assays

A total of 44 DNA samples, including two positive (ID: 42 and 43) and one negative (ID: 32) controls, were blinded and independently tested by eleven laboratories using their own qPCR methods (Table  2 ). All laboratories measured the concentration of DNA in samples (Additional file 3). BLV provirus copy number was normalized to DNA concentration and expressed per 100 ng of genomic DNA for each test.

Except for the positive (pBLV344 and FLK cell line) and the negative controls, all samples had previously shown detectable levels of BLV-specific antibodies (BLV-Abs) by enzyme-linked immunosorbent assays (ELISA). During the current interlaboratory study, both the positive and negative controls were assessed adequately by all eleven PCR tests. Of all 43 positive samples, 43, 35, 37, 36, 40, 32, 40, 42, 42, 42 and 41 samples were detected as positive by the qPCR1, qPCR2, qPCR3, qPCR4, qPCR5, ddPCR6, qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 methods, respectively. Based on these observations, the most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. Comparison of qualitative results (positive versus negative) from all eleven methods revealed 87.33% overall agreement and a kappa value of 0.396 (Cohen's kappa method adapted by Fleiss) [ 44 , 45 ]. The levels of agreement among the results from the eleven methods are represented in Table  3 . The maximum agreement was seen between two methods (qPCR9 and qPCR10 [100% agreement and a Cohen's kappa value of 1.000]) that used similar protocols and targeted the same region of BLV pol .

Analysis of BLV pol, env and LTR sequences targeted by particular PCR assays

Due to differences in performance observed among the pol -based qPCR assays (the qPCR1, qPCR2, qPCR4, qPCR5 and qPCR7- qPCR11 methods), and considering that the env -based ddPCR6 and LTR-based qPCR3 assay showed the lowest sensitivity and the poorest agreement with the other assays, the degree of sequence variability between the pol , env and LTR genes was addressed. From the MSAs for pol , env and LTR, the nucleotide diversity (π) was calculated. The π value for pol gene was lower than that for LTR and env gene (π pol , 0.023 [standard deviation {SD}, 0.018]; π LTR , 0.024 [SD, 0.011]; π env , 0.037 [SD, 0.013]). From this analysis, pol sequences appeared to be less variable than env and LTR sequences. In addition, we performed a Shannon entropy-based per-site variability profile of the pol , env and LTR sequences used in this study (Fig.  2 A-C).

Sequence variability measured as per-site entropy. A Multiple alignment of the pol gene showing the locations of qPCR fragments in regions of the pol gene for the qPCR1 (highlighted in pink), qPCR4 (highlighted in yellow) and for the qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assays (highlighted in orange). B Multiple alignment of the env gene targeted by ddPCR6 (highlighted by blue rectangle). C Multiple alignment of the LTR region by qPCR3 (highlighted in mint)

The all-observed entropy plots were homogeneous along the whole sequences. Considering the three regions of pol gene, the highest entropy (4.67) occurred in the region targeted by the qPCR1 primers, whereas the entropy for qPCR7—qPCR11 and qPCR4 primers were 1.57 and 0.38, respectively. For the LTR region targeted by qPCR3 primers and for env gene targeted by ddPCR6, the total entropy was equal to 4.46 and 7.85, respectively. This analysis showed a marked region of variability for LTR and env fragments. Interestingly, we noted that the qPCR7—qPCR11 targeted the most conserved regions of reverse transcriptase and qPCR4 primers targeted the most-conserved region of virus integrase (Fig.  2 A-C; see also Additional file 7).

Quantitation of BLV proviral DNA by different qPCR/ddPCR assays

To analyze whether the range of copy numbers detected by each qPCR was comparable to those of the others, Kruskal–Wallis one-way analysis of variance (ANOVA) was used. The violin plots were used to visualize the ANOVA results (Fig.  3 A-B).

Comparison of detection of BLV proviral DNA copy numbers by eleven testing methods. Shown is a box plot of data from Kruskal–Wallis ANOVA, a rank test. The DNA copy numbers for 41 samples, determined independently by each of the 11 qPCRs, were used for the variance analysis. In this analysis, the positive controls (sample ID 42 and ID 43) and negative control (sample ID 32) were excluded. A Violin plot for graphical presentation of the ANOVA of proviral copy number values. B Violin plot for ANOVA analysis of variance, copy number values are presented on a logarithmic scale (Log1.2) for better illustration of copy number differences between PCR methods

The grouping variable revealed significant differences among the distributions of proviral DNA copy numbers with the various qPCRs ( P  < 0.001). These results showed that the abilities of qPCRs/ddPCR to determine the proviral DNA copy number differed. A Dunn-Bonferroni test was used to compare the groups in pairs to find out which was significantly different. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2—qPCR4, qPCR3—ddPCR6, qPCR4—qPCR5, qPCR4—ddPCR6, qPCR4—qPCR9, qPCR4—qPCR10, qPCR5—qPCR11, ddPCR6—qPCR11 and qPCR9—qPCR11 have an adjusted P value less than 0.05 and thus, it can be assumed that these groups were significantly different in each pair (see Additional file 4). The Pareto chart was used to show the average copy number values of all methods in descending order. These Pareto charts were prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes [ 46 ]. The methods that generated the highest copy numbers was qPCR3 and qPCR4, on the other hand the lowest copy numbers and/or highest negative results were generated by ddPCR6 (Fig.  4 ).

A Pareto chart with the proviral BLV copy mean values for eleven PCR assay arranged in descending order. Pareto charts was prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes

The correlations between copy numbers detected by different qPCRs and ddPCR assays were calculated. The Kendall's Tau correlation coefficient measured between each pair of the assays was shown in the Additional file 5 and in Fig.  5 as a correlation heatmap. The average correlation for all qPCRs and ddPCR assays was strong (Kendall's tau = 0.748; P  < 0.001).

The heatmap of Kendall’s tau correlation coefficients between copy numbers detected by ten qPCRs and one ddPCR. Statistically significant differences in the distribution of copy numbers, a moderate, strong and very strong correlation between particular qPCRs/ddPCR was observed. The strength of the association, for absolute values of r, 0–0.19 is regarded as very weak, 0.2–0.39 as weak, 0.40–0.59 as moderate, 0.6–0.79 as strong and 0.8–1 as very strong correlation

Since the differences between PCR tests may be influenced by the number of BLV proviral copies present in each sample, we compared the average number of BLV copies between a group of genomic DNA samples that gave concordant results (group I [ n  = 28]) and a group that gave discordant results (group II [ n  = 15]). The mean number of copies was 73,907 (minimum, 0; maximum, 4,286,730) in group I, and 3,479 (minimum, 0; maximum, 218,583) in group II, and this difference was statistically significant ( P  < 0.001 by a Mann–Whitney U- test) (Fig.  6 ).

Impact of BLV proviral copy numbers on the level of agreement. Violin plot for graphical presentation of Mann–Whitney U test. The test was performed to compare BLV provirus copy number in two groups of samples: 28 samples with fully concordant results from all eleven qPCR/ddPCR assays (left) and 15 samples with discordant results from different qPCR/ddPCR assays (right) ( P  < 0.001). Sample ID 42 was excluded from the statistical analysis

The results show that the concordant results group had considerably higher copy numbers (median, 5,549.0) than the discordant results group (median, 6.3).

BLV control and eradication programs consist of correct identification and subsequent segregation/elimination of BLV-infected animals [ 47 ]. Detection of BLV- infected cows by testing for BLV-specific antibodies in serum by agar gel immunodiffusion and ELISA is the key step and standard to be implemented of EBL eradication programs according to WOAH ( https://www.woah.org/en/disease/enzootic-bovine-leukosis/) [ 9 ]. Despite the low cost and high throughput of serological tests, there are several scenarios where highly specific and sensitive molecular assays for the detection of BLV DNA might improve detection and program efficiency.

In this perspective, qPCR assays can detect small quantities of proviral DNA during acute infection, in which animals show very low levels of anti-BLV antibodies [ 43 , 48 , 49 , 50 ]. qPCR methods can also work as confirmatory tests to clarify ambiguous and inconsistent serological test results [ 11 ]. Such quantitative features of qPCRs are crucial when eradication programs progress and prevalence decreases. Moreover, qPCR allows not only the detection of BLV infection but also estimation of the BLV PVL, which directly correlates with the risk of disease transmission [ 51 , 52 ]. This feature of qPCR allows for a rational segregation of animals based on the stratified risk of transmission. These considerations allow for greater precision in the management of BLV within large herds with a high prevalence of BLV ELISA-positive animals to effectively reduce herd prevalence [ 13 , 53 ]. BLV is a global burden and the lack of technical standardization of molecular detection systems remains a huge obstacle to compare surveillance data globally based on the first interlaboratory trial performed in 2018 [ 15 ]. In the 2018 study we observed an adjusted level of agreement of 70% comparing qualitative qPCR results; however, inconsistencies amongst methods were larger when low number of copies of BLV DNA were compared. Samples with low copies of BLV DNA (< 20 copies per 100 ng) accounted for the higher variability and discrepancies amongst tests. We concluded from the first interlaboratory trial that standardizing protocols to improve sensitivity of assays with lower detection rates was necessary.

In this follow up study, we re-tested the TaqMan BLV qPCR developed and validated by NVRI (acting as reference WOAH laboratory) and the one adapted from this original protocol to be used with SYBR Green dye, allowing a significant reduction in costs [ 11 ]. Another 3 laboratories also performed NVRI´s qPCR with slight modifications (i.e., Spain performed a multiplex assay for internal normalization). The remaining 6 labs introduced novel methodologies to the trial including one ddPCR (UY).

To compare different qPCR methods, a more comprehensive sample panel, accounting for a more geographical diversification was used in this trial. The amounts of BLV DNA in these samples were representative of the different BLV proviral loads found in field samples (from 1 to > 10,000 copies of BLV proviral DNA). Of note, 34% of reference samples had less than 100 copies of BLV DNA per 100 ng; samples were lyophilized to grant better preservation and reduced variability during distribution to participants around the globe.

The panel included a single negative control and two positive controls. Diagnostic sensitivity (DxSn) was estimated for each qPCR. Considering the 43 positive samples, the DxSn for the different qPCRs were: qPCR1 = 100%, qPCR2 = 82%, qPCR3 = 86%, qPCR4 = 84%, qPCR5 = 93%, ddPCR6 = 74%, qPCR7 = 93%, qPCR8 = 98%, qPCR9 = 98%, qPCR10 = 98% and qPCR11 = 95%. The most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6 method. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. The comparison of qualitative qPCR results among all raters revealed an overall observed agreement of 87%, indicating strong interrater reliability (Cohen´s kappa = 0.396) [ 54 , 55 ].

There are several factors that contribute to variability in qPCR results (i.e., number of copies of target input, sample acquisition, processing, storage and shipping, DNA purification, target selection, assay design, calibrator, data analysis, etc.). For that reason and as expected, the level of agreement among sister qPCRs (qPCR7, qPCR9-11) sharing similar protocols was higher compared to the rest of assays; this was also true for qPCR8 which targets the same region of BLV pol gene (shares same primers) but has a particular set-up to be used with SYBR Green chemistry. Oppositely, lower sensitivity and larger discrepancy against other tests was observed for the ddPCR6 and qPCR2-4.

Based on these observations we investigated which factors might have accounted for larger assessment variability amongst tests. In the first place, we observed that the use of different chemistries was not detrimental for the sensitivity and agreement among tests; similar DxSn and comparable level of agreement were obtained comparing TaqMan (qPCR7, 10, 11) vs SYBR Green (qPCR8) chemistries while targeting identical BLV sequence and using same standards. Also, when a multiplex qPCR (TaqMan) targeting the same BLV sequence and using the same standard was compared to previous ones, agreement was kept high, indicating that the lower sensitivity described for some multiplex qPCRs did not take place in this comparison. The use of an international calibrator and the efficiency estimation (standard curve) might inform variability associated with different chemistries. In contrast, another multiplex assay targeting another region of BLV pol (qPCR2) showed much lower sensitivity and agreement. As qPCR2 is performed as service by private company and oligonucleotide sequences were not available, we were not able to investigate in which proportion each of these two variables contributed to the lower performance of this assay, but we note the addition of 4 µl genomic DNA to this assay that would have an impact the DxSn. In this regard, there is substantial evidence showing that the variability of target sequence among strains from different geographical areas, might affect the sensitivity of BLV qPCRs. Previous studies comparing the pol , gag , tax and env genes reported that the pol gene was the most suitable region to target for diagnostic purposes, since it provided the most-sensitive assays [ 11 , 15 , 56 , 57 , 58 , 59 ]. This might be due in part to higher sequence conservation of pol among strains from different geographical areas. Supporting this observation, it is noticeable how JPN qPCR improved their performance in the current trial, by targeting pol in place of tax , as it did in the previous interlaboratory trial. Since it is a commercial test, we cannot exclude other factors contributing for the performance upgrade observed for this qPCR. In the current study, qPCR3 and ddPCR6 targeting LTR and env sequences, showed lower performances than other assays. Standardization of DNA input into each qPCR would have likely resulted in higher concordance in results. For instance, qPCR1 added 10 µl of genomic DNA per reaction and ddPCR6 added 1 µl of genomic DNA, impacting the resulting sensitivity differences.

Since the sensitivity of each assay and, consequently, the level of agreement among assays might also be influenced by the number of BLV DNA copies present in each sample [ 48 ], we compared the average number of BLV DNA copies between a group of genomic DNA samples that gave concordant results and a group that gave discordant results, and observed that samples that gave discordant results had significantly lower numbers of BLV DNA copies than samples that gave concordant results. Related to this point, the degradation of target DNA during lyophilization, shipment and resuspension, could have been more significant in low-copy compared to high-copy samples. Consequently, the degradation of target DNA in samples with low copies of BLV DNA might have accounted for the greater level of discrepancy within this subset of samples. The rational of adding a large proportion of such samples (34% samples with less than 100 BLV copies per 100 ng of total DNA) was to mimic what is frequently observed in surveillance programs (i.e., hyperacute infection, chronic asymptomatic infection, etc.).

Quantitative methods for the detection of BLV DNA copies are important for segregation programs based on animal level of BLV PVL, as well as for scientific research and the study of BLV dynamics. When the numbers of copies of BLV DNA detected by different assays were compared, in the present study, we observed that although the ability to quantify BLV DNA differed among qPCRs/ddPCR and there were statistically significant differences in the distribution of copy numbers among assays, a strong average correlation was found for the eleven qPCRs/ddPCR. In this regard, the lack of an international calibrator (standard curve) could be a major contributor to the increment of quantitative variation amongst laboratories. For that reason, plasmid pBLV1 containing pol 120 bp sequence was originally constructed for use as standard for quantification and shared with some collaborators (i.e., qPCR7, qPCR8, qPCR 9, qPCR10 and qPCR11). Remarkably, the laboratories used pBLV1 standard in the current trial obtained the most comparable results, indicating that the use of an international standard may have significant impact on the convergence of results; such standard reference material should be prepared under identical conditions. To avoid further variability a detailed protocol for lyophilized DNA sample resuspension, quantitation and template input into each qPCR should be shared with all participants.

Conclusions

BLV DNA was detected with different level of sensitivity in serologically positive samples from different origin and classified into different BLV genotypes. Overall agreement was high; however, we found significant differences in results for the samples with low BLV DNA copy numbers. This second interlaboratory study demonstrated that differences in target sequence, DNA input and calibration curve standards can increase interlaboratory variability considerably. Next steps should focus on (i) standard unification (international gold standard) to estimate individual test efficiency and improve quantitative accuracy amongst tests; (ii) building a new panel of samples with low BLV DNA copy numbers to re-evaluate sensitivity and quantitation of molecular methods. Since no variation was observed in samples from different genotypes, all samples will be collected in Poland to standardize the collection, purification, lyophilization and shipping steps with precise instructions for suspension and constant input volume for the PCR reaction. Finally, we believe that following this standardization approach we will be able to improve overall agreement amongst tests, improving the diagnostic of BLV around the world.

Availability of data and materials

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

One-way analysis of variance

Bovine leukemia virus

BLV-specific antibodies

Digital PCR

Diagnostic sensitivity

Enzootic bovine leukosis

Enzyme-linked immunosorbent assays

Real-time fluorescence resonance energy transfer PCR

Genomic quality number

Histone H3 family 3A housekeeping gene

Maximum likelihood phylogenetic tree

Multiple-sequence alignment

Peripheral blood leukocytes

Phosphate-buffered saline

Proviral load

Quantitative real-time PCR

Room temperature

World Organisation for Animal Health

Coffin JM, Hughes SH, Varmus HE. (Eds.). 1650–1655 (1997). Retroviruses. Cold Spring Harbor Laboratory Press.

Ghysdael J, Bruck C, Kettmann R, Burny A. Bovine leukemia virus. Curr Top Microbiol Immunol. 1984;112:1–19.

CAS   PubMed   Google Scholar  

Ott SL, Johnson R, Wells SJ. Association between bovine-leukosis virus seroprevalence and herd-level productivity on US dairy farms. Prev Vet Med. 2003;61:249–62.

Article   CAS   PubMed   Google Scholar  

Bartlett PC, et al. Options for the control of bovine leukemia virus in dairy cattle. J Am Vet Med Assoc. 2014;244:914–22.

Article   PubMed   Google Scholar  

Kuczewski A, et al. Economic evaluation of 4 bovine leukemia virus control strategies for Alberta dairy farms. J Dairy Sci. 2019;102:2578–92.

Frie MC, Coussens PM. Bovine leukemia virus: a major silent threat to proper immune responses in cattle. Vet Immunol Immunopathol. 2015;163:103–14.

Panel, E.A. Scientific opinion on enzootic bovine leukosis. EFSA J. 2015;13:4188.

Google Scholar  

OIE. World Animal Health Information Database - Version: 1.4. World Animal Health Information Database. Paris, France: World Organisation for Animal Health; 2009. Available from: http://www.oie.int . Accessed 16 Aug 2024.

Health, W.O.f.A. Manual of diagnostic tests and vaccines for terrestrial animals. Infect Bursal Dis. 2012;12:549–65.

Hutchinson HC, et al. Bovine leukemia virus detection and dynamics following experimental inoculation. Res Vet Sci. 2020;133:269–75.]

Rola-Luszczak M, Finnegan C, Olech M, Choudhury B, Kuzmak J. Development of an improved real time PCR for the detection of bovine leukaemia provirus nucleic acid and its use in the clarification of inconclusive serological test results. J Virol Methods. 2013;189:258–64.

Nakada S, Kohara J, Makita K. Estimation of circulating bovine leukemia virus levels using conventional blood cell counts. J Dairy Sci. 2018;101:11229–36.

Ruggiero VJ, Bartlett PC. Control of Bovine Leukemia Virus in Three US Dairy Herds by Culling ELISA-Positive Cows. Vet Med Int. 2019;2019:3202184.

Article   PubMed   PubMed Central   Google Scholar  

Kobayashi T, et al. Increasing Bovine leukemia virus (BLV) proviral load is a risk factor for progression of Enzootic bovine leucosis: A prospective study in Japan. Prev Vet Med. 2020;178: 104680.

Article   Google Scholar  

Jaworski JP, Pluta A, Rola-Łuszczak M, McGowan SL, Finnegan C, Heenemann K, Carignano HA, Alvarez I, Murakami K, Willems L, Vahlenkamp TW, Trono KG, Choudhury, B, Kuźmak J. Interlaboratory Comparison of Six Real-Time PCR Assays for Detection of Bovine Leukemia Virus Proviral DNA.  J Clin Microbiol. 2018;56(7):e00304-18. https://doi.org/10.1128/JCM.00304-18 .

Pluta A, Rola-Luszczak M, Douville RN, Kuzmak J. Bovine leukemia virus long terminal repeat variability: identification of single nucleotide polymorphisms in regulatory sequences. Virol J. 2018;15:165.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Pluta A, Willems L, Douville RN, Kuźmak J. Effects of Naturally Occurring Mutations in Bovine Leukemia Virus 5'-LTR and Tax Gene on Viral Transcriptional Activity. Pathog. 2020;9(10):836. https://doi.org/10.3390/pathogens9100836 .

Pluta A, et al. Molecular characterization of bovine leukemia virus from Moldovan dairy cattle. Arch Virol. 2017;162:1563–76.

Rola-Łuszczak M, Sakhawat A, Pluta A, Ryło A, Bomba A, Bibi N, Kuźmak J. Molecular Characterization of the env Gene of Bovine Leukemia Virus in Cattle from Pakistan with NGS-Based Evidence of Virus Heterogeneity. Pathogens (Basel, Switzerland). 2021;10(7):910. https://doi.org/10.3390/pathogens10070910 .

Rola-Luszczak M, et al. The molecular characterization of bovine leukaemia virus isolates from Eastern Europe and Siberia and its impact on phylogeny. PLoS ONE. 2013;8: e58705.

Pinheiro de Oliveira TF, et al. Detection of contaminants in cell cultures, sera and trypsin. Biologicals. 2013;41:407–14.

Pluta A, Blazhko NV, Ngirande C, Joris T, Willems L, Kuźmak J. Analysis of Nucleotide Sequence of Tax, miRNA and LTR of Bovine Leukemia Virus in Cattle with Different Levels of Persistent Lymphocytosis in Russia. Pathogens. 2021;10(2):246. https://doi.org/10.3390/pathogens10020246 .

Yang Y, et al. Bovine leukemia virus infection in cattle of China: Association with reduced milk production and increased somatic cell score. J Dairy Sci. 2016;99:3688–97.

DeGraves FJ, Gao D, Kaltenboeck B. High-sensitivity quantitative PCR platform. Biotechniques. 2003;34(106–110):112–105.

Fonseca Junior AA, et al. Evaluation of three different genomic regions for detection of bovine leukemia virus by real-time PCR. Braz J Microbiol. 2021;52:2483–8.

Farias MVN, et al. Toll-like receptors, IFN-gamma and IL-12 expression in bovine leukemia virus-infected animals with low or high proviral load. Res Vet Sci. 2016;107:190–5.

Holland PM, Abramson RD, Watson R, Gelfand DH. Detection of specific polymerase chain reaction product by utilizing the 5’––3’ exonuclease activity of Thermus aquaticus DNA polymerase. Proc Natl Acad Sci U S A. 1991;88:7276–80.

De Brun ML, et al. Development of a droplet digital PCR assay for quantification of the proviral load of bovine leukemia virus. J Vet Diagn Invest. 2022;34:439–47.

Rola-Łuszczak M, Finnegan C, Olech M, Choudhury B, Kuźmak J. Development of an improved real time PCR for the detection of bovine leukaemia provirus nucleic acid and its use in the clarification of inconclusive serological test results. J Virol Methods. 2013;189:258–64.

Petersen MI, Alvarez I, Trono KG, Jaworski JP. Quantification of bovine leukemia virus proviral DNA using a low-cost real-time polymerase chain reaction. J Dairy Sci. 2018;101:6366–74.

Toussaint JF, Sailleau C, Breard E, Zientara S, De Clercq K. Bluetongue virus detection by two real-time RT-qPCRs targeting two different genomic segments. J Virol Methods. 2007;140:115–23.

John EE, et al. Development of a predictive model for bovine leukemia virus proviral load. J Vet Intern Med. 2022;36:1827–36.

Farias MVN, et al. Toll-like receptors, IFN-γ and IL-12 expression in bovine leukemia virus-infected animals with low or high proviral load. Res Vet Sci. 2016;107:190–5.

Yoneyama S, et al. Comparative Evaluation of Three Commercial Quantitative Real-Time PCRs Used in Japan for Bovine Leukemia Virus. Viruses. 2022;14:1182.

Polat M, Takeshima SN, Aida Y. Epidemiology and genetic diversity of bovine leukemia virus. Virol J. 2017;14:209.

Lee E, et al. Molecular epidemiological and serological studies of bovine leukemia virus (BLV) infection in Thailand cattle. Infect Genet Evol. 2016;41:245–54.

Duran-Yelken S, Alkan F. Molecular analysis of the env, LTR, and pX regions of bovine leukemia virus in dairy cattle of Türkiye. Virus Genes. 2024;60:173–85.

Lv G, Wang J, Lian S, Wang H, Wu R. The Global Epidemiology of Bovine Leukemia Virus: Current Trends and Future Implications. Animals. 2024;14(2):297. https://doi.org/10.3390/ani14020297 .

Úsuga-Monroy C, Díaz FJ, Echeverri-Zuluaga JJ, González-Herrera LG, López-Herrera A. Presence of bovine leukemia virus genotypes 1 and 3 in Antioquia, Colombia. Revista UDCA Actualidad & Divulgación Científica. 2018;21:119–26.

Úsuga-Monroy C, Díaz FJ, González-Herrera LG, Echeverry-Zuluaga JJ, López-Herrera A. Phylogenetic analysis of the partial sequences of the env and tax BLV genes reveals the presence of genotypes 1 and 3 in dairy herds of Antioquia. Colombia VirusDisease. 2023;34:483–97.

Martin D, et al. Comparative study of PCR as a direct assay and ELISA and AGID as indirect assays for the detection of bovine leukaemia virus. J Vet Med B Infect Dis Vet Public Health. 2001;48:97–106.

Cohen J. A Coefficient of Agreement for Nominal Scales. Educ Psychol Measur. 1960;20:37–46.

Feinstein AR, Cicchetti DV. High agreement but low kappa: I. The problems of two paradoxes. J Clin Epidemiol. 1990;43:543–9.

Woolhouse MEJ, et al. Heterogeneities in the transmission of infectious agents: Implications for the design of control programs. Proc Natl Acad Sci. 1997;94:338–42.

Ohshima K, Okada K, Numakunai S, Kayano H, Goto T. An eradication program without economic loss in a herd infected with bovine leukemia virus (BLV). Nihon Juigaku Zasshi. 1988;50:1074–8.

Juliarena MA, Gutierrez SE, Ceriani C. Determination of proviral load in bovine leukemia virus-infected cattle with and without lymphocytosis. Am J Vet Res. 2007;68:1220–5.

Mirsky ML, Olmstead CA, Da Y, Lewin HA. The prevalence of proviral bovine leukemia virus in peripheral blood mononuclear cells at two subclinical stages of infection. J Virol. 1996;70:2178–83.

Eaves FW, Molloy JB, Dimmock CK, Eaves LE. A field evaluation of the polymerase chain reaction procedure for the detection of bovine leukaemia virus proviral DNA in cattle. Vet Microbiol. 1994;39:313–21.

Juliarena MA, Barrios CN, Ceriani MC, Esteban EN. Hot topic: Bovine leukemia virus (BLV)-infected cows with low proviral load are not a source of infection for BLV-free cattle. J Dairy Sci. 2016;99:4586–9.

Yuan Y, et al. Detection of the BLV provirus from nasal secretion and saliva samples using BLV-CoCoMo-qPCR-2: Comparison with blood samples from the same cattle. Virus Res. 2015;210:248–54.

Taxis TM, et al. Reducing bovine leukemia virus prevalence on a large midwestern dairy farm by using lymphocyte counts, ELISA antibody testing, and proviral load. The Bovine Practitioner. 2020;54:136–44.

McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22:276–82.

Cicchetti DV, Feinstein AR. High agreement but low kappa: II. Resolving the paradoxes J Clin Epidemiol. 1990;43:551–8.

Heenemann K, et al. Development of a Bovine leukemia virus polymerase gene–based real-time polymerase chain reaction and comparison with an envelope gene–based assay. J Vet Diagn Invest. 2012;24:649–55.

Lew AE, et al. Sensitive and specific detection of proviral bovine leukemia virus by 5′ Taq nuclease PCR using a 3′ minor groove binder fluorogenic probe. J Virol Methods. 2004;115:167–75.

Takeshima SN, Sasaki S, Meripet P, Sugimoto Y, Aida Y. Single nucleotide polymorphisms in the bovine MHC region of Japanese Black cattle are associated with bovine leukemia virus proviral load. Retrovirology. 2017;14:24.

Debacq C, et al. Reduced proviral loads during primo-infection of sheep by Bovine Leukemia virus attenuated mutants. Retrovirology. 2004;1:31.

Kuckleburg CJ, et al. Detection of bovine leukemia virus in blood and milk by nested and real-time polymerase chain reactions. J Vet Diagn Invest. 2003;15:72–6.

Dube S, et al. Degenerate and specific PCR assays for the detection of bovine leukaemia virus and primate T cell leukaemia/lymphoma virus pol DNA and RNA: phylogenetic comparisons of amplified sequences from cattle and primates from around the world. J Gen Virol. 1997;78(Pt 6):1389–98.

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Acknowledgements

The authors thank Luc Willems (University of Liège, Belgium) for plasmid DNA sample pBLV344; Marlena Smagacz and Eliza Czarnecka (National Veterinary Research Institute, Poland) for lyophilizing DNA samples and DNA analysis, respectively; Ali Sakhawat (Animal Quarantine Department, Pakistan), Vitaliy Bolotin (National Scientific Center IECVM, Ukraine), Frank van der Meer and Sulav Shrestha (University of Calgary, Canada) for sharing material.

The APC was funded by the National Veterinary Research Institute, Puławy, Poland.

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Department of Biochemistry, National Veterinary Research Institute, Puławy, 24-100, Poland

Aneta Pluta & Jacek Kuźmak

Instituto de Virología E Innovaciones Tecnológicas (IVIT), Centro de Investigaciones en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA) - CONICET, Buenos Aires, Argentina

Juan Pablo Jaworski & Vanesa Ruiz

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Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, Michigan, 48824, USA

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Contributions

Proposed the conception and design of the study, A.P.; data curation, A.P., J.P.J., C.D., S.V., D.B., A.S., K.M., R.P., G.D., M.F.C. and CH.W.; investigation, A.P., V.R., S.VW., S.V., A.J., M.J.R., K.N., M.L.B., M.L.G., P.L., A.F., A.G. and S.B., formal analysis, A.P.; statistical analysis, A.P.; database analysis, A.P., visualization of the results, A.P.; resources, A.P., T.M.T. and J.K; writing—original draft preparation, A.P., J.P.J.; writing—review and editing, A.P., J.P.J., C.D., S.VW., T.M.T. and J.K; project administration, A.P. All authors read and approved the submitted version.

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Ethics approval and consent to participate.

The study was approved by the Veterinary Sciences Animal Care Committee No. AC21-0210, Canada; the Institutional Animal Care and Use Committee No. PROTO202000096 from 4/13/2020 to 4/14/2023, Michigan State University, United States and the Ethics Review Board, COMSATS Institute of Information Technology, Islamabad, Pakistan, no. CIIT/Bio/ERB/17/26. Blood samples from Polish, Moldovan and Ukrainian cattle, naturally infected with BLV, were selected from collections at local diagnostic laboratories as part of the Enzootic bovine leukosis (EBL) monitoring program between 2012 and 2018 and sent to the National Veterinary Research Institute (NVRI) in Pulawy for confirmation study. The approval for collection of these samples from ethics committee was not required according to Polish regulation (“Act on the Protection of Animals Used for Scientific or Educational Purposes”, Journal of Laws of 2015). All methods were carried out in accordance with relevant guidelines and regulations. The owners of the cattle herds from which the DNA samples originated, the district veterinarians caring for these farms and the ministries of agriculture were informed and consented to the collection of blood from the animals for scientific purposes and the sending of samples to NVRI.

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Supplementary Information

12917_2024_4228_moesm1_esm.pdf.

Additional file 1. Copy of the instruction included with the panel of 44 DNA samples sent to participating laboratories for dilution of the lyophilisates

12917_2024_4228_MOESM2_ESM.png

Additional file 2. Detection of the H3F3A gene copy number in 43 DNA samples; no outlier was found for any samples ( P <0.05) (two-sided).

12917_2024_4228_MOESM3_ESM.docx

Additional file 3. Concentration values of 44 DNA samples measured by the 11 participating laboratories (given in ng per µl)

12917_2024_4228_MOESM4_ESM.pdf

Additional file 4. Post hoc - Dunn-Bonferroni-Tests. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2 - qPCR4, qPCR3 - ddPCR6, qPCR4 - qPCR5, qPCR4 - ddPCR6, qPCR4 - qPCR9, qPCR4 - qPCR10, qPCR5 - qPCR11, ddPCR6 - qPCR11 and qPCR9 - qPCR11 have an adjusted p-value less than 0,05

12917_2024_4228_MOESM5_ESM.docx

Additional file 5. Kendall's Tau correlation coefficient values measured between each pair of assays. The numbers 1 to 11 in the first column and last row of the table indicate the names of the assays qPCR1-qPCR5, ddPCR6, qPCR7-qPCR11 respectively

12917_2024_4228_MOESM6_ESM.png

Additional file 6. Maximum-likelihood phylogenetic analysis of full-length BLV-pol gene sequences representing 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10) (A); (B) env-based sequences assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10); (C) LTR-based sequences representing 10 BLV genotypes (G1-G10). For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied in MEGA X

12917_2024_4228_MOESM7_ESM.pdf

Additional file 7. Multiple sequence alignment of reverse transcriptase, integrase, envelope and LTR sequences in the context of the specific primers used by different qPCR assays. (A) Multiple sequence alignment of reverse transcriptase (pol gene) sequences in the context of qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assay primers. (B) Multiple sequence alignment of integrase (pol gene) sequences in the context of qPCR4 assay primers. (C) Multiple sequence alignment of env gene sequences in the context of ddPCR6. (D) Sequence alignment of LTR region sequences in the context of qPCR3 method primers

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Pluta, A., Jaworski, J.P., Droscha, C. et al. Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples. BMC Vet Res 20 , 381 (2024). https://doi.org/10.1186/s12917-024-04228-z

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DOI : https://doi.org/10.1186/s12917-024-04228-z

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