Chiva, C.; Olivella, R.; Staes, A.; Maia, T. M.; Panse, C.; Stejskal, K.; Douche, T.; Lombard, B.; Schuhmann, A.; Loew, D.; Mechtler, K.; Matondo, M.; Rettel, M.; Helm, D.; Impens, F.; Devos, S.; Shevchenko, A.; Nanni, P.; Sabido, E.

Quality control procedures play a pivotal role in ensuring the reliability and consistency of data generated in mass spectrometry-based proteomics laboratories. However, the lack of standardized quality control practices across laboratories poses challenges for data comparability and reproducibility. In response, we conducted a harmonization study within proteomics laboratories of the Core for Life alliance with the aim to establish a common quality control framework, which facilitates comprehensive quality assessment and identification of potential sources of performance drift. Through collaborative efforts, we developed a consensus quality control standard for longitudinal assessment and we adopted a common processing software. We generated a 4-year longitudinal dataset from multiple instruments and laboratories, which enabled us to assess intra- and inter-laboratory variability, to identify causes of performance drift, and to establish community reference values for several quality control parameters. Our study enhances data comparability and reliability and fosters a culture of collaboration and continuous improvement within the proteomics community to ensure the integrity of proteomics data.