So much depends on the subtle variations in biospecimens: these variations, when analyzed via genomics, proteomics, metabolomics, and similar studies, are the foundation of both disease-based and drug-based biomarker discovery. And if the unique "-omics" of a sample is to yield results that lead to improvements in medical research and healthcare, such as understanding disease cause/progression (predictive/prognostic biomarker) to PK/PD in clinical trials (surrogate endpoints), then the entire integrity of the specimen must be protected, or biomarker research/study can go astray.
Various types of unwanted pre-analytical variability can be introduced at numerous points in the life of a sample, beginning at the time it is collected from the donor, through shipping, receiving/inventory, laboratory processing, during storage and subsequent retrieval for analysis. Additional insight is available from our cryopreservation/cold storage expert Alex Esmon in his latest post "Need Cryopreservation? Choose From These Five Methods".
Specimens collected for research, have a well defined life cycle, and much of this life is hidden from the investigators who use the sample for research. Yet, what happens during the life of a specimen can profoundly influence assay results. There are more variables, and more opportunities for sample deterioration, than you think.
For instance, was the vial closure screwed on tight? A spill or contamination renders it useless. Was correct temperature maintained on the way to the lab? Was the sample processed within 24 hours to ensure integrity was maintained? Was the equipment calibrated and validated? What about the accuracy of the data associated with the samples? What was the time out of temperature for the samples during the entire lifecycle? A sample is only as good as the data appended to it.
In the video blog below, I would like to provide you a glimpse into how a sample travels through the laboratory in one of our biobank facilities. You’ll get a look at the life of a research specimen and a better understanding of how pre-analytical variability can be managed (or mismanaged!).