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Filtration Chromatography Protocol

Gel Filtration Chromatography Protocol

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Introduction

Gel-filtration chromatography, also known as size exclusion chromatography, is a versatile technique that permits the separation of proteins and other biological molecules. The gel filtration chromatography separates the proteins solely based on molecular size differences. For this, a porous matrix is used to which the molecules, for steric reasons, have different degrees of access. The matrix is enclosed in a chromatographic column, and the separation is accomplished by passing an aqueous buffer through the column. The molecules, confined outside the matrix beads, sweeps through the column by the mobile phase. An in-line UV monitor detects the separated protein zones and the fractions of the sample are collected for subsequent specific analysis. The gel-filtration chromatography has numerous applications including the fractionation of proteins and other water-soluble polymers, size determination and analysis, desalting, and buffer exchange.

 

Principle

The gel filtration chromatography is based on the molecular size and the hydrodynamic volume of the components. The molecules are separated by the differential exclusion or inclusion of solutes as they pass through the stationary phase containing heterosporous cross-linked polymeric gel or beads. Different permeation rates of the solute molecules cause them to sift in the interior of the gel particles. A column of the porous matrix is in equilibrium with the mobile phase for the separation of the molecules. Large molecules are entirely excluded from the pores and come first in the effluent. Smaller molecules get distributed between the mobile phase and the outside of the sieve. Then, they pass through the column at a slower rate and appear later in the effluent.

 

Apparatus

The gel filtration medium is enclosed in a column making a packed bed. The medium consists of a porous matrix selected on the basis of inertness and physical and chemical properties. The pores of the matrix are filled with the buffer equilibrated with the packed bed. The liquid inside the pores is referred to as the stationary phase and the liquid outside as the mobile phase. The signals from the column are taken by the detector, and the calculations are made.

 

Protocol (Irvine, 2001)

 

Group separation (desalting)

Gel preparation

  1. Add calculated amount of dry [amazon link=”B00KIRS1G0″ link_icon=”amazon” /] to a volume of gel-filtration desalting buffer equal to twice the final gel volume.
  2. Carefully stir the solution with a glass rod. Let the gel swell overnight at room temperature or for 3 hours in a 90°C water bath.
  3. Allow the gel bed to settle and decant the solution to remove fines and broken beads. Repeat the decantation (4 or 5 times if necessary) and then dilute to get the final slurry with 50% (v/v) settled gel and 50% (v/v) GF buffer.

Pack column

  1. Ensure that the column is clean and check the nets for any damage.
  2. Mount the column vertically on a stable laboratory stand. Equip the column with an extension to hold the complete volume of the gel slurry.
  3. Inject the gel-filtration (GF) buffer in outlet tubing until the support net is covered with 0.5 cm of the buffer.
  4. Inject GF buffer into the inlet tubing of the adaptor until the net is wetted.
  5. Swirl the gel slurry from step 3 and pour it into the column. Fill the remaining space with GF buffer. And, put lid on the column extension (or put the top adaptor on column).
  6. Fill the buffer reservoir with GF buffer. Connect the reservoir to the pump with the help of a large tube.
  7. Purge the pump with GF buffer and attach the outlet from the pump to the inlet of the column. Open the column outlet and start the pump at the flow rate. Continue the flow until the height of the gel bed becomes constant.
  8. Turn the pump off, close the column outlet, remove the extension, adjust the bed height, and adjust the inlet adaptor.
  9. Reattach the column to pump, open the column outlet, and resume flow conditions used in step 10 for 1 hour to stabilize the bed height.
  10. Inspect the packed bed visually for cracks, trapped air, and particle aggregates. Determine the zones produced on the chromatogram.

Prepare and test the system

  1. Calculate the amount of GF buffer necessary for the run and filter it plus a 50% excess through a 0.22-μm filter.
  2. Assemble the GF system, placing the detector and recorder in line but leaving the column offline. Attach the buffer reservoir to pump and purge it with GF buffer.
  3. Connect the outlet of the pump to the column via the injection valve, and run the system with GF buffer at a flow-rate set for separation.
  4. Collect fractions with the fraction collector, and note the actual flow rate of the pump.

Determine the separating volume

  1. Determine the void volume (Vo) by running a void marker and obtaining the elution volume.
  2. Ascertain the total liquid volume (Vt) by running a total liquid volume marker and obtaining the elution volume.
  3. Calculate the separating volume of the column (Vi) by subtracting the void volume from the total volume (Vt – Vo).

Chromatograph the sample

  1. Dissolve the sample to be desalted in a gel-filtration buffer. Filter it through a 0.22-μm protein-compatible filter.
  2. Open the outlet from the column, start the pump, and let two-bed volumes of the buffer pass through the column. Turn on the detector and stabilize the baseline.
  3. Load sample applicator with the required volume of sample for desalting and switch the sample application valve to the load position.
  4. Pass the buffer through the system at the appropriate flow rate and collect the fractions. Construct a chromatogram and calculate the elution volume as the time from the apex of the peak for the protein multiplied by the flow rate.
  5. Wash the column with ≥1 column volume of the buffer containing an antibacterial agent. Close the column outlet and store it.

 

Protein fractionation

Column preparation

  1. Prepare gel filtration matrix as indicated above except using the gel-filtration (GF) fractionation buffer wherever GF buffer is indicated.
  2. Pack the column following steps 4-13 mentioned above.
  3. Check the quality of the column (see step 14).
  4. Assemble and test the system by following steps 15-17.

Evaluate the column

  1. Chromatograph a colored marker (2 mg/ml Blue Dextran 2000 or 0.2 mg/ml vitamin B12 following steps 20 to 23) and determine the zones produced.
  2. Chromatograph a low-molecular-weight marker (e.g., 5 mg/ml acetone following steps 20 to 23) and determine the column efficiency by constructing the chromatogram.
  3. Calculate the number of theoretical plates per column using the equation N = 5.54(Vr/Wh)2 (where N = number of plates per column, Vr = elution volume of a peak, and Wh = width of the peak at half peak height).
  4. Calculate the asymmetry factor of the peak according to the equation As = (b/a) (where a is the width of the leading part and b is the width of the tailing part of the peak).
  5. Compare the calculated plate number and asymmetry factor for the column with the acceptance limits for these parameters.

Chromatography

  1. Dissolve, apply, and chromatograph the protein sample to be fractionated.
  2. Evaluate the collected fractions for purity.

 

Molecular size determination

  1. Prepare the gel, pack the column, then assemble and test the GF system following steps 1-16 using GF fractionation buffer in place of the GF buffer.
  2. Determine the void volume (Vo) and the total liquid volume (Vt) (follow steps 17 and 18).
  3. Calibrate the column and check the flow rate during calibration by sampling effluent and weighing fractions.
  4. Apply, elute, and chromatograph the sample.
  5. Calculate the molecular size of the sample components using the calibration graph.

 

Applications

 

Assessment of the anticoagulant properties of heparin (Andersson. et al., 1976)

Heparin is an essential anti-coagulation protein that is activated by antithrombin III. Heparin could also affect coagulation and is critical for the studies regarding coagulation and anti-coagulation processes. In addition to thrombin, it inhibits the activated forms of other coagulation factors such as IX, X, and XI. In the study, a sample of heparin was fractionated using affinity chromatography on matrix-bound antithrombin III. The obtained fractions and sub-fractions were separated by gel filtration based on their molecular weights. It was found that about one-third of the heparin was bound to antithrombin and this fraction is responsible for 85% of the total anti-coagulation activity, measured in terms of thrombin inactivation. The results suggested that the antithrombin in the presence of heparin blocks several stages in the coagulation cascade. The fractionation of protein by gel-filtration chromatography enables the assessment of protein cascades involved in physiological processes.

 

Measurement of the plant cell wall permeability (Tepfer. & Taylor., 1981)

The permeability of the plant cell wall determines the ability of enzymes, polysaccharides, and extracellular glycoproteins to penetrate and alter the cell wall. The cell wall permeability could limit the macromolecules to alter the biochemical and physical properties of the wall. Gel-filtration chromatography enabled the detection of permeation of macromolecules into the cell wall matrix even at much lower concentrations. Using proteins of known size, a column packed with isolated cell wall fragments was calibrated, and the degree of protein penetration was measured.  The results showed that the proteins having a molecular weight, of 40,000 to 60,000 could penetrate a substantial portion of the cell wall space. Gel-filtration chromatography is a powerful separation technique for the estimation of plant cell wall permeability.

 

Proteomic characterization of human plasma high-density lipoprotein (Gordon., Deng., Lu., & Davidson., 2010)

The plasma levels of high-density lipoprotein (HDL) cholesterol are crucial to the incidence of cardiovascular disease. The modern proteomic technologies have identified 50 distinct proteins associated with HDL particles implicating the role of HDL in non-lipid transport processes. High-resolution size exclusion chromatography was used to fractionate normal human plasma to 17 phospholipid-containing sub-fractions. Then, the proteins were identified using a phospholipid-binding resin by electrospray ionization mass spectrometry. The identified proteins along with the HDL were found to be involved in complement regulation and protease inhibition. The technique allowed the visualization of HDL protein distribution across particle size with a higher resolution.

 

Analysis of protein biotherapeutics and their aggregates (Hong., Koza., & Bouvier., 2012)

Gel-filtration chromatography has been widely used not only to purify proteins but also to determine the sizes and relative distribution of macromolecules. The size-exclusion chromatography (SEC) is mainly used for routine and validated analyses because of the speed and reproducibility of the technique. The introduction of biologic-based therapeutics and associated protein aggregates have also been studied using the SEC. It has enabled the quantitation of dimers, trimers, and higher-order aggregates for biologic-based therapies including insulin, recombinant human growth hormone, and monoclonal antibodies. The size-exclusion chromatography has been found the most accurate technique for the analysis of proteins, aggregates, and biotherapeutics.

 

Precautions
  • The separation process is based on the particle sizes so any damage in the particle could lead to false results.
  • The buffers and the matrix should be degassed as air bubbles could lead to poor resolution.
  • Preparation of the gel from thin suspension and column packing in stages could result in a poorly packed column.
  • Avoid disturbing the bed as an uneven bed surface may lead to uneven separation.
  • Maintain the experimental setup with proper care.

 

Strengths and limitations
  • Gel-filtration chromatography is generally used to separate organic molecules and to determine their molecular weights and molecular weight distributions.
  • Gel-filtration chromatography is an excellent technique for removing low-molecular-size contaminants from a purified protein sample for structural and functional analysis.
  • The process can be conducted under mild conditions: from 37oC to cold room temperature.
  • The molecules can be separated with a high resolution and greater efficiency.
  • The absence of a molecule-matrix binding prevents unnecessary damage to fragile molecules.
  • Proteolysis is an issue while separating the proteins by gel-filtration chromatography.
  • Because of the large size of the columns, large volumes of eluent are required which may lead to excessive running costs.

 

References
  1. Irvine, B. G. (2001). Determination of molecular size by size-exclusion chromatography (gel filtration). Curr Protoc Cell Biol, Chapter 5: Unit 5.5.
  2. S. Gordon., J. Deng., J. L. Lu., & Davidson., S. W. (2010). Proteomic characterization of human plasma high density lipoprotein fractionated by gel filtration chromatography. J Proteome Res, 9(10), 5239-49.
  3. Tepfer., & Taylor., E. I. (1981). The permeability of plant cell walls as measured by gel filtration chromatography. Science, 213(4509), 761-3.
  4. L. Andersson., W. T. Barrowcliffe., E. Holmer., A. E. Johnson., & Sims., G. E. (1976). Anticoagulant properties of heparin fractionated by affinity chromatography on matrix-bound antithrombin iii and by gel filtration. Thromb Res, 9(6), 575-83.
  5. Hong., S. Koza., & Bouvier., S. E. (2012). Size-Exclusion Chromatography for the Analysis of Protein Biotherapeutics and their Aggregates. J Liq Chromatogr Relat Technol, 35(20), 2923-2950.

 

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Introduction

In behavioral neuroscience, the Open Field Test (OFT) remains one of the most widely used assays to evaluate rodent models of affect, cognition, and motivation. It provides a non-invasive framework for examining how animals respond to novelty, stress, and pharmacological or environmental manipulations. Among the test’s core metrics, the percentage of time spent in the center zone offers a uniquely normalized and sensitive measure of an animal’s emotional reactivity and willingness to engage with a potentially risky environment.

This metric is calculated as the proportion of time spent in the central area of the arena—typically the inner 25%—relative to the entire session duration. By normalizing this value, researchers gain a behaviorally informative variable that is resilient to fluctuations in session length or overall movement levels. This makes it especially valuable in comparative analyses, longitudinal monitoring, and cross-model validation.

Unlike raw center duration, which can be affected by trial design inconsistencies, the percentage-based measure enables clearer comparisons across animals, treatments, and conditions. It plays a key role in identifying trait anxiety, avoidance behavior, risk-taking tendencies, and environmental adaptation, making it indispensable in both basic and translational research contexts.

Whereas simple center duration provides absolute time, the percentage-based metric introduces greater interpretability and reproducibility, especially when comparing different animal models, treatment conditions, or experimental setups. It is particularly effective for quantifying avoidance behaviors, risk assessment strategies, and trait anxiety profiles in both acute and longitudinal designs.

What Does Percentage of Time in the Centre Measure?

This metric reflects the relative amount of time an animal chooses to spend in the open, exposed portion of the arena—typically defined as the inner 25% of a square or circular enclosure. Because rodents innately prefer the periphery (thigmotaxis), time in the center is inversely associated with anxiety-like behavior. As such, this percentage is considered a sensitive, normalized index of:

  • Exploratory drive vs. risk aversion: High center time reflects an animal’s willingness to engage with uncertain or exposed environments, often indicative of lower anxiety and a stronger intrinsic drive to explore. These animals are more likely to exhibit flexible, information-gathering behaviors. On the other hand, animals that spend little time in the center display a strong bias toward the safety of the perimeter, indicative of a defensive behavioral state or trait-level risk aversion. This dichotomy helps distinguish adaptive exploration from fear-driven avoidance.

  • Emotional reactivity: Fluctuations in center time percentage serve as a sensitive behavioral proxy for changes in emotional state. In stress-prone or trauma-exposed animals, decreased center engagement may reflect hypervigilance or fear generalization, while a sudden increase might indicate emotional blunting or impaired threat appraisal. The metric is also responsive to acute stressors, environmental perturbations, or pharmacological interventions that impact affective regulation.

  • Behavioral confidence and adaptation: Repeated exposure to the same environment typically leads to reduced novelty-induced anxiety and increased behavioral flexibility. A rising trend in center time percentage across trials suggests successful habituation, reduced threat perception, and greater confidence in navigating open spaces. Conversely, a stable or declining trend may indicate behavioral rigidity or chronic stress effects.

  • Pharmacological or genetic modulation: The percentage of time in the center is widely used to evaluate the effects of pharmacological treatments and genetic modifications that influence anxiety-related circuits. Anxiolytic agents—including benzodiazepines, SSRIs, and cannabinoid agonists—reliably increase center occupancy, providing a robust behavioral endpoint in preclinical drug trials. Similarly, genetic models targeting serotonin receptors, GABAergic tone, or HPA axis function often show distinct patterns of center preference, offering translational insights into psychiatric vulnerability and resilience.

Critically, because this metric is normalized by session duration, it accommodates variability in activity levels or testing conditions. This makes it especially suitable for comparing across individuals, treatment groups, or timepoints in longitudinal studies.

A high percentage of center time indicates reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance. reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance.

Behavioral Significance and Neuroscientific Context

1. Emotional State and Trait Anxiety

The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity, where animals exhibit persistent avoidance of the center due to heightened emotional reactivity. This metric can also distinguish between acute anxiety responses and enduring trait anxiety, especially in longitudinal or developmental studies. Its normalized nature makes it ideal for comparing across cohorts with variable locomotor profiles, helping researchers detect true affective changes rather than activity-based confounds.

2. Exploration Strategies and Cognitive Engagement

Rodents that spend more time in the center zone typically exhibit broader and more flexible exploration strategies. This behavior reflects not only reduced anxiety but also cognitive engagement and environmental curiosity. High center percentage is associated with robust spatial learning, attentional scanning, and memory encoding functions, supported by coordinated activation in the prefrontal cortex, hippocampus, and basal forebrain. In contrast, reduced center engagement may signal spatial rigidity, attentional narrowing, or cognitive withdrawal, particularly in models of neurodegeneration or aging.

3. Pharmacological Responsiveness

The open field test remains one of the most widely accepted platforms for testing anxiolytic and psychotropic drugs. The percentage of center time reliably increases following administration of anxiolytic agents such as benzodiazepines, SSRIs, and GABA-A receptor agonists. This metric serves as a sensitive and reproducible endpoint in preclinical dose-finding studies, mechanistic pharmacology, and compound screening pipelines. It also aids in differentiating true anxiolytic effects from sedation or motor suppression by integrating with other behavioral parameters like distance traveled and entry count (Prut & Belzung, 2003).

4. Sex Differences and Hormonal Modulation

Sex-based differences in emotional regulation often manifest in open field behavior, with female rodents generally exhibiting higher variability in center zone metrics due to hormonal cycling. For example, estrogen has been shown to facilitate exploratory behavior and increase center occupancy, while progesterone and stress-induced corticosterone often reduce it. Studies involving gonadectomy, hormone replacement, or sex-specific genetic knockouts use this metric to quantify the impact of endocrine factors on anxiety and exploratory behavior. As such, it remains a vital tool for dissecting sex-dependent neurobehavioral dynamics.
 The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity. Because it is normalized, this metric is especially helpful for distinguishing between genuine avoidance and low general activity.

Methodological Considerations

  • Zone Definition: Accurately defining the center zone is critical for reliable and reproducible data. In most open field arenas, the center zone constitutes approximately 25% of the total area, centrally located and evenly distanced from the walls. Software-based segmentation tools enhance precision and ensure consistency across trials and experiments. Deviations in zone parameters—whether due to arena geometry or tracking inconsistencies—can result in skewed data, especially when calculating percentages.

     

  • Trial Duration: Trials typically last between 5 to 10 minutes. The percentage of time in the center must be normalized to total trial duration to maintain comparability across animals and experimental groups. Longer trials may lead to fatigue, boredom, or habituation effects that artificially reduce exploratory behavior, while overly short trials may not capture full behavioral repertoires or response to novel stimuli.

     

  • Handling and Habituation: Variability in pre-test handling can introduce confounds, particularly through stress-induced hypoactivity or hyperactivity. Standardized handling routines—including gentle, consistent human interaction in the days leading up to testing—reduce variability. Habituation to the testing room and apparatus prior to data collection helps animals engage in more representative exploratory behavior, minimizing novelty-induced freezing or erratic movement.

     

  • Tracking Accuracy: High-resolution tracking systems should be validated for accurate, real-time detection of full-body center entries and sustained occupancy. The system should distinguish between full zone occupancy and transient overlaps or partial body entries that do not reflect true exploratory behavior. Poor tracking fidelity or lag can produce significant measurement error in percentage calculations.

     

  • Environmental Control: Uniformity in environmental conditions is essential. Lighting should be evenly diffused to avoid shadow bias, and noise should be minimized to prevent stress-induced variability. The arena must be cleaned between trials using odor-neutral solutions to eliminate scent trails or pheromone cues that may affect zone preference. Any variation in these conditions can introduce systematic bias in center zone behavior. Use consistent definitions of the center zone (commonly 25% of total area) to allow valid comparisons. Software-based segmentation enhances spatial precision.

Interpretation with Complementary Metrics

Temporal Dynamics of Center Occupancy

Evaluating how center time evolves across the duration of a session—divided into early, middle, and late thirds—provides insight into behavioral transitions and adaptive responses. Animals may begin by avoiding the center, only to gradually increase center time as they habituate to the environment. Conversely, persistently low center time across the session can signal prolonged anxiety, fear generalization, or a trait-like avoidance phenotype.

Cross-Paradigm Correlation

To validate the significance of center time percentage, it should be examined alongside results from other anxiety-related tests such as the Elevated Plus Maze, Light-Dark Box, or Novelty Suppressed Feeding. Concordance across paradigms supports the reliability of center time as a trait marker, while discordance may indicate task-specific reactivity or behavioral dissociation.

Behavioral Microstructure Analysis

When paired with high-resolution scoring of behavioral events such as rearing, grooming, defecation, or immobility, center time offers a richer view of the animal’s internal state. For example, an animal that spends substantial time in the center while grooming may be coping with mild stress, while another that remains immobile in the periphery may be experiencing more severe anxiety. Microstructure analysis aids in decoding the complexity behind spatial behavior.

Inter-individual Variability and Subgroup Classification

Animals naturally vary in their exploratory style. By analyzing percentage of center time across subjects, researchers can identify behavioral subgroups—such as consistently bold individuals who frequently explore the center versus cautious animals that remain along the periphery. These classifications can be used to examine predictors of drug response, resilience to stress, or vulnerability to neuropsychiatric disorders.

Machine Learning-Based Behavioral Clustering

In studies with large cohorts or multiple behavioral variables, machine learning techniques such as hierarchical clustering or principal component analysis can incorporate center time percentage to discover novel phenotypic groupings. These data-driven approaches help uncover latent dimensions of behavior that may not be visible through univariate analyses alone.

Total Distance Traveled

Total locomotion helps contextualize center time. Low percentage values in animals with minimal movement may reflect sedation or fatigue, while similar values in high-mobility subjects suggest deliberate avoidance. This metric helps distinguish emotional versus motor causes of low center engagement.

Number of Center Entries

This measure indicates how often the animal initiates exploration of the center zone. When combined with percentage of time, it differentiates between frequent but brief visits (indicative of anxiety or impulsivity) versus fewer but sustained center engagements (suggesting comfort and behavioral confidence).

Latency to First Center Entry

The delay before the first center entry reflects initial threat appraisal. Longer latencies may be associated with heightened fear or low motivation, while shorter latencies are typically linked to exploratory drive or low anxiety.

Thigmotaxis Time

Time spent hugging the walls offers a spatial counterbalance to center metrics. High thigmotaxis and low center time jointly support an interpretation of strong avoidance behavior. This inverse relationship helps triangulate affective and motivational states.

Applications in Translational Research

  • Drug Discovery: The percentage of center time is a key behavioral endpoint in the development and screening of anxiolytic, antidepressant, and antipsychotic medications. Its sensitivity to pharmacological modulation makes it particularly valuable in dose-response assessments and in distinguishing therapeutic effects from sedative or locomotor confounds. Repeated trials can also help assess drug tolerance and chronic efficacy over time.
  • Genetic and Neurodevelopmental Modeling: In transgenic and knockout models, altered center percentage provides a behavioral signature of neurodevelopmental abnormalities. This is particularly relevant in the study of autism spectrum disorders, ADHD, fragile X syndrome, and schizophrenia, where subjects often exhibit heightened anxiety, reduced flexibility, or altered environmental engagement.
  • Hormonal and Sex-Based Research: The metric is highly responsive to hormonal fluctuations, including estrous cycle phases, gonadectomy, and hormone replacement therapies. It supports investigations into sex differences in stress reactivity and the behavioral consequences of endocrine disorders or interventions.
  • Environmental Enrichment and Deprivation: Housing conditions significantly influence anxiety-like behavior and exploratory motivation. Animals raised in enriched environments typically show increased center time, indicative of reduced stress and greater behavioral plasticity. Conversely, socially isolated or stimulus-deprived animals often show strong center avoidance.
  • Behavioral Biomarker Development: As a robust and reproducible readout, center time percentage can serve as a behavioral biomarker in longitudinal and interventional studies. It is increasingly used to identify early signs of affective dysregulation or to track the efficacy of neuromodulatory treatments such as optogenetics, chemogenetics, or deep brain stimulation.
  • Personalized Preclinical Models: This measure supports behavioral stratification, allowing researchers to identify high-anxiety or low-anxiety phenotypes before treatment. This enables within-group comparisons and enhances statistical power by accounting for pre-existing behavioral variation. Used to screen anxiolytic agents and distinguish between compounds with sedative vs. anxiolytic profiles.

Enhancing Research Outcomes with Percentage-Based Analysis

By expressing center zone activity as a proportion of total trial time, researchers gain a metric that is resistant to session variability and more readily comparable across time, treatment, and model conditions. This normalized measure enhances reproducibility and statistical power, particularly in multi-cohort or cross-laboratory designs.

For experimental designs aimed at assessing anxiety, exploratory strategy, or affective state, the percentage of time spent in the center offers one of the most robust and interpretable measures available in the Open Field Test.

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References

  • Prut, L., & Belzung, C. (2003). The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: a review. European Journal of Pharmacology, 463(1–3), 3–33.
  • Seibenhener, M. L., & Wooten, M. C. (2015). Use of the open field maze to measure locomotor and anxiety-like behavior in mice. Journal of Visualized Experiments, (96), e52434.
  • Crawley, J. N. (2007). What’s Wrong With My Mouse? Behavioral Phenotyping of Transgenic and Knockout Mice. Wiley-Liss.
  • Carola, V., D’Olimpio, F., Brunamonti, E., Mangia, F., & Renzi, P. (2002). Evaluation of the elevated plus-maze and open-field tests for the assessment of anxiety-related behavior in inbred mice. Behavioral Brain Research, 134(1–2), 49–57.

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