Biotechnology research is evolving rapidly in molecular biology, genomics, cellular science, biochemistry, drug discovery, synthetic biology, and related life-science disciplines. As our experimental systems continue to become more sophisticated, researchers are increasingly reliant on their ability to achieve consistent, interpretable, and reproducible results.
Although reproducibility in biotechnology research often involves repeating an experiment, study, or analysis, there are many sources that can impact the outcome of a research finding. The quality of biological and chemical materials, controls, laboratory methods, analytical testing, and other aspects of research can greatly impact the results of an experimental finding.
When designing an experiment, researchers should therefore consider research quality as an integral element of their research process rather than an afterthought.
Implementing a consistent approach to material selection, experimental design, laboratory methods, analytical testing, and documentation can help a team of researchers identify sources of avoidable variability and improve confidence in their research results.
Why Reproducibility Matters in Biotechnology Research
Reproducibility provides researchers with an opportunity to identify whether an observed finding or effect was associated with a genuine biological phenomenon or could have been influenced by sources of experimental variability.
This is particularly important in biotechnology, because findings in one experiment can lead to new research in related fields, technology development, diagnostic, therapeutic, and industrial research, and other biotechnology-related discoveries.
There are many different sources of variability that can be involved with the reproducibility of a finding, including differences in biological materials, reagent or research-material quality, sample preparation, experimental conditions, controls, analytical procedures, data processing, and other factors affecting research.
Not all variability is avoidable, and biological systems are known to demonstrate a variety of natural sources of variability. The focus of variability control should therefore be on those sources that are controllable and can be systematically managed.
Research-Grade Materials and Experimental Outcomes
One of the primary components of an experiment is the materials used in research. Their identity, purity, concentration, stability, storage, and handling can all impact the outcome of an experiment.
Researchers must carefully evaluate the appropriate information about materials and consider their quality prior to use in laboratory research.
Depending on the application, material information such as identity, purity, concentration or composition, storage requirements, lot or batch, stability, analytical characterization, and Certificate of Analysis (when applicable) should be carefully reviewed.
Although a Certificate of Analysis (COA) can provide a researcher with useful information regarding the tested characteristics of a material, this information should always be interpreted within the context of their specific application.
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The Role of Experimental Controls
High-quality materials are an essential component of reproducible biotechnology research. However, appropriate controls are also vital, because they allow a researcher to identify whether a particular finding was associated with an expected result.
Depending on a research design, biological or non-biological controls, positive or negative controls, untreated or treated controls, vehicle controls, technical controls, or other control types can be utilized in research.
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Through appropriate control strategies, researchers can also answer common questions related to their research:
- Did the system behave as expected?
- Was the observed effect related to treatment?
- Could the observed result be explained by the experimental procedure?
- Did the analytical system operate as expected?
- Were there unexpected background effects?
Depending on the biological system and research question, an appropriate control strategy should be identified.
Standardized Laboratory Methods
Another important component of biotechnology research is the creation of standardized procedures. They reduce the impact of unnecessary experimental variability and ensure that laboratory procedures are repeatable and interpretable.
Standardized procedures do not necessarily require identical protocols to be used across different laboratories. Rather, researchers should define important parameters within a specific procedure and ensure that they are consistent across all samples and conditions.
When developing standardized procedures, researchers should therefore document essential parameters such as sample preparation, reagent concentrations, incubation time and temperature, instrument settings, storage conditions, data-processing parameters, and acceptance criteria.
By thoroughly documenting laboratory procedures, researchers can more easily identify sources of variability and ensure that procedures are consistently followed across all laboratory personnel.
Analytical Testing and Data Quality
Modern biotechnology research is highly dependent on analytical procedures and data generated by chromatography, spectroscopy, mass-spectrometry, electrophoresis, sequencing, and other laboratory techniques.
However, analytical procedures also contribute to the overall variability of a research finding. Sample preparation, analytical standards, calibration, method conditions, system performance, data processing, and interpretation can all influence the outcome of an analytical procedure.
For example, differences in chromatographic mobile-phase preparation, sample concentration, column conditions, temperature, or injection parameters can influence retention time, peak shape, and overall signal response.
Researchers should therefore consider analytical procedures and their quality control as an integral part of a broader experimental workflow rather than an isolated final step.
Documentation Supports Research Quality
Although documentation is often viewed as a mundane task, it is an essential component of any reproducible research study.
Detailed documentation allows a researcher to reconstruct the details of an experiment or study and identify potential sources of variability.
Appropriate documentation can help identify potential sources of variability between different experiments, including variations in materials, procedures, controls, equipment, and samples.
Useful documentation should therefore contain information such as:
- Material name and lot number
- Dates of experiments
- Protocol version
- Sample identification
- Equipment information
- Experimental conditions
- Deviations from standard procedures
- Data-processing methods
- Quality-control observations
Consistent documentation practices are also vital for effective collaboration.
Quality Control Should Be Part of the Workflow
Quality control procedures are most effective when they are an integral part of the research process.
Instead of relying on end-point quality assessments to address issues with reproducibility or research quality, researchers should implement quality checkpoints at different stages of research.
These checkpoints can be implemented at several different points throughout the research process, including before the experiment, during the experiment, during analysis, and after analysis.
Before the Experiment
Before initiating the experiment, researchers can verify material identity and relevant documentation, storage conditions, study design and controls.
During the Experiment
During the experiment, teams can review control conditions and sample handling, verify that all parameters conform to established procedures, and review deviations from standard operating procedures.
During Analysis
During the analytical process, researchers can review analytical performance, data quality, calibration, standards, and acceptance criteria.
After Analysis
The research team can review all results, document all deviations, investigate unexpected findings, and retain relevant documentation of the experimental process.
Practical Steps for Improving Research Consistency
Improving reproducibility in biotechnology research often requires only small adjustments to laboratory practices and research procedures.
1. Identify Critical Variables
Determine experimental parameters that are likely to affect the results and establish appropriate controls.
2. Evaluate Material Quality
Use appropriately characterized research materials and maintain relevant documentation for materials used in experiments.
3. Standardize Important Procedures
Create clear protocols for frequently used laboratory processes and document important deviations.
4. Establish Analytical Quality Checks
Follow appropriate calibration, controls, standards, and performance checks for analytical workflows.
5. Improve Experimental Documentation
Record enough information for another qualified researcher to understand how an experiment was performed and analyzed.
6. Review Sources of Variability
When results differ, investigate whether changes in materials, protocols, controls, instruments, sample preparation, or data analysis could explain the difference.
7. Separate Biological and Technical Variation
Not every difference between experiments represents experimental failure. Researchers should therefore distinguish between expected biological variability and avoidable technical variability.
Connecting Research Quality With Biotechnology Innovation
As the field of biotechnology evolves, research teams are increasingly expected to perform high-quality experimental research within a shorter timeframe.
New disciplines such as precision medicine, genomics, synthetic biology, cell-based technologies, and advanced analytical platforms are all dependent on a researcher’s ability to obtain reliable and reproducible results.
By improving research quality and standardizing procedures, documentation, materials, and analytical methods, researchers can support collaborations between different biotechnology organizations.
Building a Culture of Research Quality
Reproducibility is often not the result of a single laboratory protocol or quality control measure. Rather, it requires a broad culture of research quality within an organization.
A researcher’s consideration of quality should not be limited to ensuring that an experiment produces consistent results. Instead, they should ask:
- Can the result be independently verified?
- Were critical variables controlled?
- Were the materials adequately characterized?
- Were deviations documented?
- Can another researcher understand the workflow?
- Are the analytical methods appropriate for the research question?
These questions can help turn a reactive approach to research quality into a proactive endeavor.
Conclusion
Research quality plays a major role in the reproducibility of biotechnology research.
High-quality materials, appropriate controls, standardized laboratory methods, analytical testing, data practices, and documentation all contribute to a higher level of research quality and reproducibility in research findings.
Although no single intervention can remove all sources of variability in research, addressing variability is often essential for scientific progress.
By incorporating quality control into their research design and practices, biotechnology researchers can increase the value of their findings and ensure that they can be reliably replicated by other research teams.
Ultimately, reproducibility begins before a researcher reviews their final dataset. By focusing on appropriate research design, materials, methods, measurements, and documentation, scientists can ensure better reproducibility of their results.
