Fig. 1: Overall study design and implementation.
a The MSCohort quality control (QC) system extracts 81 metrics (58 intra-experiment metrics and 23 inter-experiment metrics) and supports both intra-experiment analysis and inter-experiment analysis, to facilitate the comprehensive quality evaluation of individual experiments and cohort DIA datasets, and assists users in monitoring the entire workflow performance, detecting potential problems, providing optimizing direction, flagging low-quality experiments, and improving experimental outcomes for subsequent analyses. b The standard operating procedure (SOP) for urinary proteomics, which integrates the optimized strategies at each step, including the 96DRA-Urine sample preparation strategy, a monolithic column with 30-min gradient strategy, balance Npercusor_per_MS2 and Rprecursor DIA-based MS method, and comprehensive MSCohort QC system. Npercusor_per_MS2 is the spectra complexity of MS2 scans, Rprecursor is the precursor duplicate identification rate. c A 20 LC-MS platforms analysis under a comprehensive quality control system of urinary proteomics was performed to analyze the variation and the consistency among different LC-MS platforms. d Benchmarking samples were prepared containing known ratios of peptide digestions from human, yeast and E. coli organisms, to mimic differential expressed biological samples and provide proof of the quantitative robustness and reproducibility of the different LC-MS platforms under unified SOP. e Clinical colorectal cancer (CRC) urinary proteome datasets derived from 3 different LC-MS platforms were performed to analyze the performance of urinary proteomics from multi-platform in biomarker discovery.
