Impact of Preanalytical Variations in Blood-Derived Biospecimens on Omics Studies: Toward Precision Biobanking?

Abstract

Research data and outcomes do vary across populations and persons, but this is not always due to experimental or true biological variation. Preanalytical components of experiments, be they biospecimen acquisition, preparation, storage, or transportation to the laboratory, may all contribute to apparent variability in research data, outcomes, and interpretation. The present review article and biobanking innovation analysis offer new insights with a summary of such preanalytical variables, for example, the type of blood collection tube, centrifugation conditions, long-term sample storage temperature, and duration, on output of omics analyses of blood-derived biospecimens: whole blood, serum, plasma, buffy coat, and peripheral blood mononuclear cells. Furthermore, we draw parallels from the field of precision medicine in this study, with a view to the future of "precision biobanking" wherein such preanalytical variations are carefully taken into consideration so as to minimize their influence on outcomes of omics data, analyses, and sensemaking, particularly in clinical omics applications. We underscore the need for using broadly framed, critical, independent, social and political science, and humanities research so as to understand the multiple possible future trajectories of, and the motivations and values embedded in, precision biobanking that is increasingly relevant in the current age of Big Data.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

“…Therefore, the most common procedure for long‐term storage of blood‐derived samples and urine is deep freezing at −80 °C, although some authors have described that samples stored at –20 °C can also be stable for short periods (i.e., a few weeks or months) . Finally, some studies investigated the stability of plasma samples at −80 °C over multiple years (5 to 17 years), concluding that some metabolites, especially amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, vitamins, and hexoses suffer from long term storage, with changes in concentration varying between +13.7% and –14.5%, while analysis of plasma samples after 14 to 17 years showed no significant differences using an untargeted ultra performance liquid chromatography (UPLC)‐TOF approach …”

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

“…References on protein and RNA search strings accounted for a large proportion in PBMC-and buffy coat-related publications. Preanalytical variations depend on the analyte type (such as DNA, RNA, protein, and metabolite) as well as the processing conditions of biosamples (Lee and Kim, 2017).…”

“…To obtain accurate information from biosamples, it is necessary to minimize preanalytical variations in the collection, processing, and storage stages of biosamples. The preanalytical variations can be different depending on the type of biosample, the type of analyte, and the method of analysis (Araujo et al, 2017;Betsou et al, 2016;Lee and Kim, 2017). Therefore, it is important to establish conditions for collection, processing, and storage of biosamples suitable for research purposes.…”

“…It also is necessary to collect information about conditions for collection, processing, and storage of biosamples when securing and managing biosamples; in case of blood samples, these considerations include the type of blood collection tube, precentrifuge and postcentrifuge conditions (time and temperature) of whole blood, and long-term storage conditions (period and temperature). We have previously defined this biobanking approach as the ''precision biobanking'' (Lee and Kim, 2017). Analyzing the trends in scientific publications by the type of biosample can help the biobanks decide how best to prioritize different types of biosample collections.…”

A Noninvasive Ocular (Tear) Sampling Method for Genetic Ascertainment of Transgenic Mice and Research Ethics Innovation

“…For example, the amount, quality, and feasibility of DNA biosample collection can be influential on the type and validity of Big Data generated by omics system sciences (Stephens et al, 2015;Sung et al, 2012). Attention to data provenance and biosample collection methodologies is therefore both a scientific and a research ethics issue (Lee and Kim, 2017).…”