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Updated: Apr 11, 2026

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
Published on: November 30, 2022
Biobanking in genomic medicine
Jane H Zhou1, Aysegul A Sahin, Jeffrey N Myers
1From the Departments of Pathology (Drs Zhou and Sahin) and Head and Neck Surgery (Dr Myers), University of Texas MD Anderson Cancer Center, Houston. Dr Zhou is now with Human Genome Sequencing Center, Baylor College of Medicine, Houston.
Preanalytic variables in biobanking significantly impact biospecimen quality and gene expression profiling. Further research is needed to standardize preanalytic parameters for reliable genomic medicine applications.
Area of Science:
- Biobanking and Genomic Medicine
- Biomarker Discovery
- Molecular Diagnostics
Background:
- High-quality biospecimens are crucial for genomic medicine, requiring standardized preanalytic variable control in biobanking.
- Current biobanking practices often lack control over preanalytic variables, affecting biospecimen integrity and gene expression data.
- Preanalytic variations demonstrably impact biospecimen quality and downstream genomic analyses.
Purpose of the Study:
- To identify evidence-based preanalytic parameters for biobanking.
- To determine which preanalytic parameters require further investigation for standardization.
Main Methods:
- Systematic literature review of Biospecimen Research and PubMed databases.
- Inclusion criteria applied to 212 retrieved articles, with 58 selected for review.
- Analysis focused on preanalytic variables affecting biospecimen quality and gene expression.
Main Results:
- Preanalytic variables significantly degrade biospecimen quality and alter gene expression profiling.
- Identified key preanalytic variables impacting biospecimen integrity.
- Highlighted areas requiring further research for optimization.
Conclusions:
- Standardizing preanalytic variables in biobanking is essential for reliable genomic medicine.
- Further research is critical for parameters like surgical manipulation, warm ischemia, processing delays, preservation methods, and long-term storage.
- A fit-for-purpose approach is recommended for optimizing preanalytic variables based on intended use.
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