Quality control for results you can rely on

The nature of biological assays necessitates performing quality control (QC) checks throughout the workflow. Biological samples like nucleic acids vary greatly in many aspects and are sensitive to outside influences such as chemicals, nucleases, temperature and light. The procedures for extracting information from these samples are often complex and include many steps that can be prone to human errors, material failures and variation in experimental conditions.

Robust and relevant sample quality control measures can maximize experiment success and reliable interpretation of results. Sample QC gives you valuable information that lets you take preventive or corrective measures early, saving costs and reducing the risk of jeopardizing your experiments, results and ultimately, your reputation as a scientist.

Sample QC includes any relevant and necessary measures to prevent and detect changes in key sample parameters, such as quantity, integrity, purity and sequence (in the case of nucleic acids). These four parameters are key performance indicators, and variations in these indicators have potential to impact your experiments and the quality of your results and data interpretation.

Experimental results can only be converted to valuable scientific insight if you are certain that the sample quality has been maintained and the biological information has been unaltered throughout your analysis workflows.

Sometimes, small variations within sample quality can have huge consequences, making the difference between success and failure of an experiment. These variations can be easily detected and prevented using proper sample QC. Save time and money and gain peace of mind by dealing only with the highest quality samples.

QC can require just a few minutes and a little bit of money to perform but has potential to save you significant time and costs and give you peace of mind that you're processing only the samples of highest quality.

Validating and streamlining routine nucleic acid QC procedures can be a profitable investment. Using high-performance tools to analyze nucleic acid quantity, purity and size distribution takes just a few minutes and costs only around $1–2 USD per sample.

In certain cases, QC can also be resource intensive and time-consuming, but the benefits of appropriate QC still often outweigh the risks. Today’s detection technologies are much more sensitive than before, but they are not necessarily more robust with respect to tolerating variations in sample quality. Analyzing low-quality samples wastes significant research costs and time and even worse, can lead to data misinterpretation and incorrect results.

The more complex and expensive your workflow is, the more time and money you might lose processing poor-quality samples, but the more you stand to gain from performing relevant QC procedures.

The high incidence of irreproducible scientific research results is a growing concern, with approximately $28 billion USD spent each year in the United States alone on biomedical research that cannot be reproduced (1). Implementing quality control procedures at key steps in laboratory workflows can help standardize sample parameters and the quality of the data they generate.

Together with performing QC at key steps of your workflows, choosing high quality chemistries with high-performing and well-maintained tools and instruments increases the reproducibility of your experiments, bringing you confidence in results interpretation to turn your samples into insights.

References

• Federation of American Societies for Experimental Biology. (2016) Enhancing Research Reproducibility: Recommendations from the Federation of American Societies for Experimental Biology. http://www.faseb.org/Portals/2/PDFs/opa/2016/FASEB_Enhancing Research Reproducibility.pdf
• Frye, S.V. et al. Tackling reproducibility in academic preclinical drug discovery. (2015) Nat. Rev. Drug Discov. 14.
• Bustin, S.A. The reproducibility of biomedical research: Sleepers awake! (2014) Biomol. Detect. Quantif. 2, 35–42.
• Special: Challenges in irreproducible research. Nature website http://www.nature.com/news/reproducibility-1.17552.
• The Academy of Medical Sciences. Reproducibility and reliability of biomedical research: improving research practice. (2015) Symposium report.
• Betsou, F. et al. Identification of evidence-based biospecimen quality-control tools: A report of the international society for biological and environmental repositories (ISBER) biospecimen science working group. (2013) J. Mol. Diagnostics 15, 3–16.
• Freedman, L.P., Cockburn, I.M. and Simcoe, T.S. The Economics of Reproducibility in Preclinical Research. (2015) PLOS Biol. 13, e1002165.