Piloting and implementation of quality assessment and quality control procedures in RBC-Omics: a large multi-center study of red blood cell hemolysis during storage.

BACKGROUND: The major aims of the RBC-Omics study were to evaluate the genomic and metabolomic determinants of spontaneous and stress-induced hemolysis during RBC storage. This study was unique in scale and design to allow evaluation of RBC donations from a sufficient number of donors across the spectrum of race, ethnicity, sex, and donation intensity. Study procedures were carefully piloted, optimized, and controlled to enable high-quality data collection. METHODS: The enrollment goal of 14,000 RBC donors across four centers, with characterization of RBC hemolysis across two testing laboratories, required rigorous piloting and optimization and establishment of a quality assurance (QA) and quality control (QC) program. Optimization of WBC elution from leukoreduction (LR) filters, development and validation of small-volume transfer bags, impact of manufacturing and sample-handling procedures on hemolysis parameters, and testing consistency across laboratories and technicians and over time were part of this quality assurance/quality control program. RESULTS: LR filter elution procedures were optimized for obtaining DNA for analysis. Significant differences between standard and pediatric storage bags led to use of an alternative LR-RBC transfer bag. The impact of sample preparation and freezing methods on metabolomics analyses was evaluated. Proficiency testing monitored and documented testing consistency across laboratories and technicians. CONCLUSION: Piloting and optimization, and establishment of a robust quality assurance/quality control program documented process consistency throughout the study and was essential in executing this large-scale multicenter study. This program supports the validity of the RBC-Omics study results and a sample repository that can be used in future studies.

Spectral index for assessment of differential protein expression in shotgun proteomics.

Detecting differentially expressed proteins is a key goal of proteomics. We describe a label-free method, the spectral index, for analyzing relative protein abundance in large-scale data sets derived from biological samples by shotgun proteomics. The spectral index is comprised of two biochemically plausible features: relative protein abundance (assessed by spectral counts) and the number of samples within a group with detectable peptides. We combined the spectral index with permutation analysis to establish confidence intervals for assessing differential protein expression in bronchoalveolar lavage fluid from cystic fibrosis and control subjects. Significant differences in protein abundance determined by the spectral index agreed well with independent biochemical measurements. When used to analyze simulated data sets, the spectral index outperformed four other statistical tests (Student's t-test, G-test, Bayesian t-test, and Significance Analysis of Microarrays) by correctly identifying the largest number of differentially expressed proteins. Correspondence analysis and functional annotation analysis indicated that the spectral index improves the identification of enriched proteins corresponding to clinical phenotypes. The spectral index is easily implemented and statistically robust, and its results are readily interpreted graphically. Therefore, it should be useful for biomarker discovery and comparisons of protein expression between normal and disease states.