Biobanking and omics
David T. Harris
Biobanking and omics
BACKGROUND: The “Era of Big Data” and “Precision Medicine” is now upon us. That is, interrogation of large data sets obtained from groups of similar patients or from the patient themselves over time will now hypothetically permit therapies to be designed to provide maximal efficacy with minimal side effects. However, such discoveries depend upon recruitment of very large numbers of subjects (tens of thousands) along with their associated biospecimens and medical records. When considering the establishment of a biobank or the refocusing of an existing repository for the purpose of “omics” research (i.e., genomics, metabolomics, proteomics, microbiomics, etc.) and/or precision medicine, there are a number of considerations to ponder. Each of these facets is discussed.
OBJECTIVE: The objective of this review is to describe best practices for the establishment and operations of a biobank that will be used for omics (genomics, proteomics, metabolomics, microbiomics) analyses based on published literature and our own practical experiences.
METHODS: We describe the most commonly described approaches to a variety of biobanking issues, including our own practical experiences over the past 5 years.
RESULTS: Based on the particular biobanking situation and downstream application, we have described best practices based on the literature and own experience, taking into consideration ease of application and costs.
CONCLUSIONS: The banking of various types of clinical biospecimens has many valuable uses but often depends on overall costs versus sample utility. In addition, specimen flexibility is important but is influenced by the ease or difficulty of the application. It is always preferable to collect and stored a biospecimen in a format that allows for multiple types of downstream analyses, but that often requires additional expertise, equipment and reagents that can increase overall costs. We have described the methodologies most successfully applied to many situations.
biobanking / genomics / metabolomics / microbiomics / methodology / precision medicine / big data
[1] |
Börnigen D, Morgan X C, Franzosa E A, Ren B, Xavier R J, Garrett W S, Huttenhower C (2013). Functional profiling of the gut microbiome in disease-associated inflammation. Genome Med, 5(7): 65
CrossRef
Pubmed
Google scholar
|
[2] |
Cho I, Blaser M J (2012). The human microbiome: at the interface of health and disease. Nat Rev Genet, 13(4): 260–270
CrossRef
Pubmed
Google scholar
|
[3] |
Gopalakrishnan V, Spencer C N, Nezi L, Reuben A, Andrews M C, Karpinets T V, Prieto P A, Vicente D, Hoffman K, Wei S C, Cogdill A P, Zhao L, Hudgens C W, Hutchinson D S, Manzo T, Petaccia de Macedo M, Cotechini T, Kumar T, Chen W S, Reddy S M, Szczepaniak Sloane R, Galloway-Pena J, Jiang H, Chen P L, Shpall E J, Rezvani K, Alousi A M, Chemaly R F, Shelburne S, Vence L M, Okhuysen P C, Jensen V B, Swennes A G, McAllister F, Marcelo Riquelme Sanchez E, Zhang Y, Le Chatelier E, Zitvogel L, Pons N, Austin-Breneman J L, Haydu L E, Burton E M, Gardner J M, Sirmans E, Hu J, Lazar A J, Tsujikawa T, Diab A, Tawbi H, Glitza I C, Hwu W J, Patel S P, Woodman S E, Amaria R N, Davies M A, Gershenwald J E, Hwu P, Lee J E, Zhang J, Coussens L M, Cooper Z A, Futreal P A, Daniel C R, Ajami N J, Petrosino J F, Tetzlaff M T, Sharma P, Allison J P, Jenq R R, Wargo J A (2018). Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science, 359(6371): 97–103
CrossRef
Pubmed
Google scholar
|
[4] |
Gowda G A N, Djukovic D (2014). Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges. In: Raftery D. (eds) Mass Spectrometry in Metabolomics. Methods in Molecular Biology (Methods and Protocols), vol 1198. Humana Press, New York, NY.
|
[5] |
Griffiths R I, Whiteley A S, O’Donnell A G, Bailey M J (2000). Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl Environ Microbiol, 66(12): 5488–5491
CrossRef
Pubmed
Google scholar
|
[6] |
Gu H, Zhang P, Zhu J, Raftery D (2015). Globally optimized targeted mass spectrometry: Reliable metabolomics analysis with broad coverage. Anal Chem, 87(24): 12355–12362
CrossRef
Pubmed
Google scholar
|
[7] |
Hummon A B, Lim S R, Difilippantonio M J, Ried T (2007). Isolation and solubilization of proteins after TRIzol extraction of RNA and DNA from patient material following prolonged storage. Biotechniques, 42(4): 467–470, 472
CrossRef
Pubmed
Google scholar
|
[8] |
Langille M G I, Zaneveld J, Caporaso J G, McDonald D, Knights D, Reyes J A, Clemente J C, Burkepile D E, Vega Thurber R L, Knight R, Beiko R G, Huttenhower C (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol, 31(9): 814–821
CrossRef
Pubmed
Google scholar
|
[9] |
Mutter G L, Zahrieh D, Liu C, Neuberg D, Finkelstein D, Baker H E, Warrington J A (2004). Comparison of frozen and RNALater solid tissue storage methods for use in RNA expression microarrays. BMC Genomics, 5(1): 88
CrossRef
Pubmed
Google scholar
|
[10] |
Parekh P J, Balart L A, Johnson D A (2015). The Influence of the Gut Microbiome on Obesity, Metabolic Syndrome and Gastrointestinal Disease. Clin Transl Gastroenterol, 6(6): e91–e102
CrossRef
Pubmed
Google scholar
|
[11] |
Rainen L, Oelmueller U, Jurgensen S, Wyrich R, Ballas C, Schram J, Herdman C, Bankaitis-Davis D, Nicholls N, Trollinger D, Tryon V (2002). Stabilization of mRNA expression in whole blood samples. Clin Chem, 48(11): 1883–1890
Pubmed
|
[12] |
Segata N, Haake S K, Mannon P, Lemon K P, Waldron L, Gevers D, Huttenhower C, Izard J (2012). Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol, 13(6): R42
CrossRef
Pubmed
Google scholar
|
[13] |
Strauss W M (1998) . Preparation of Genomic DNA from Mammalian Tissue. Curr Prot Mol Biol, 42: 2.2.1–2.2.3
|
[14] |
Yao Y, Liu R, Shin M S, Trentalange M, Allore H, Nassar A, Kang I, Pober J S, Montgomery R R (2014). CyTOF supports efficient detection of immune cell subsets from small samples. J Immunol Methods, 415(15): 1–5
CrossRef
Pubmed
Google scholar
|
[15] |
Young V B (2017). The role of the microbiome in human health and disease: an introduction for clinicians. BMJ, 356: j831
CrossRef
Pubmed
Google scholar
|
[16] |
Zarco M F, Vess T J, Ginsburg G S (2012). The oral microbiome in health and disease and the potential impact on personalized dental medicine. Oral Dis, 18(2): 109–120
CrossRef
Pubmed
Google scholar
|
/
〈 | 〉 |