LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Van Puyvelde, B. R.; Devreese, R.; Chiva, C.; Sabido, E.; Pfammatter, S.; Panse, C.; Rijal, J. B.; Keller, C.; Batruch, I.; Pribil, P.; Vincendet, J.-B.; Fontaine, F.; Lefever, L.; Magalhaes, P.; Deforce, D.; Nanni, P.; Ghesquiere, B.; Perez-Riverol, Y.; Martens, L.; Carapito, C.; Bouwmeester, R.; Dhaenens, M.

doi:10.64898/2026.01.29.702266

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bioRxiv · 10.64898/2026.01.29.702266

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

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Abstract

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

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Van Puyvelde, B. R., Devreese, R., Chiva, C., Sabido, E., Pfammatter, S., Panse, C., Rijal, J. B., Keller, C., Batruch, I., Pribil, P., Vincendet, J.-B., Fontaine, F., Lefever, L., Magalhaes, P., Deforce, D., Nanni, P., Ghesquiere, B., Perez-Riverol, Y., Martens, L., Carapito, C., Bouwmeester, R., Dhaenens, M.. 2026-02-02. LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients. https://doi.org/10.64898/2026.01.29.702266

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