Cristina Chiva (Barcelona / ES), Roger Olivella (Barcelona / ES), An Staes (Ghent / BE), Teresa Mendes Maia (Ghent / BE), Francis Impens (Ghent / BE), Simon Devos (Ghent / BE), Christian Panse (Zurich / CH), Karel Stejskal (Vienna / AT), Karl Mechtler (Vienna / AT), Thibaut Douché (Paris / FR), Bérangère Lombard (Paris / FR), Damarys Loew (Paris / FR), Mandy Rettel (Heidelberg / DE), Dominic Helm (Heidelberg / DE), Andrea Schuhmann (Dresden / DE), Anna Shevchenko (Dresden / DE), Mariette Matondo (Paris / FR), Paolo Nanni (Zurich / CH), Eduard Sabidó (Barcelona / ES)
Introduction
Common quality control procedures in proteomics core facilities ensure data quality, reproducibility, and comparability, benefiting proteomics infrastructures, the scientific community, and their users. The Proteomics Working Group of Core for Life (https://coreforlife.eu) conducted a four-year harmonization study to enhance quality control comparability, identify community reference values, monitor intra-laboratory performance, and find improvement opportunities. Unlike standardization, harmonization does not impose uniform methods but agrees on practices for data collection. This flexible approach accommodates diverse setups and instrumentation, overcoming the challenges faced by many standardization efforts.
Methods
Eight different proteomics core facilities (CRG, EMBL, FGCZ, Institute Curie, IMP, Institute Pasteur, MPI, and VIB) have incorporated 30 LC-MS systems corresponding to 5 types of mass spectrometer (Orbitrap Eclipse, Fusion Lumos, Q Exactive HF, Q Exactive HF-X, and Exploris 480) and 4 liquid chromatography systems (UltiMate 3000 RSLC, EASY-nLC 1200, ACQUITY UPLC M-Class, Evosep One) into an harmonization study that spanned over four years (2019-2022). A commercially available QC4Life standard has been developed and analyzed with each LC-MS setup using a DDA method with HCD fragmentation (and ETD, EThcD and CID when available) and a flexible chromatographic gradient (30-60 min). The QCloud platform was established as a common software platform to evaluate the results and minimize data analysis heterogeneity.
Results
To improve quality control consistency, we first established a common standard using a sample of 25 ng K562 cells proteome extract spiked with an isotopologue mix at five different concentrations, now available as the QC4Life Standard Mix (Promega, CS30240). We also agreed on using QCloud for automated, reproducible, and centralized analysis of quality control raw files from various instruments across participating sites. Over four years, 9054 raw files were generated, yielding 826,696 quality control data points from 109 parameters per file. Each laboratory accessed its own and other sites' data through QCloud, promoting discussions to address performance differences. This collaboration identified issues like under-performing instruments or misconfigurations, leading to reduced variation in parameter measurements across all instruments. We established average reference values and ranges for each quality control parameter by instrument type. These values, reflecting diverse configurations across Core for Life laboratories, are available in the QCloud interface to help other laboratories assess their instruments, methods, and standard control samples.