Schaer, S. | bioRxiv Science

bioRxiv2025

A flexible end-to-end automated sample preparation workflow enables reproducible large-scale bottom-up proteomics

Bottom-up proteomics holds significant promise for clinical applications due to its high sensitivity and precision, but is limited by labor-intensive, low-throughput sample preparation methods. Advanced automation is essential to enhance throughput, reproducibility, and accuracy and to allow standardization to make bottom-up proteomics amenable for large-scale studies. We developed a fully integrated, automated sample preparation platform that covers the entire process from biological sample input to mass spectrometry-ready peptide output and can be applied on a multitude of biological samples. With this end-to-end solution, we achieved high intra- and inter-plate reproducibility, as well as longitudinal consistency, resulting in precise and reproducible workflows. We showed that our automated workflow surpasses established manual and semi-automated workflows, while improving time efficiency. Finally, we demonstrated the suitability of our automated sample preparation platform for drug development by performing a high-content compound characterization for targeted protein degradation, where high throughput and quantitative accuracy are indispensable. For this, we coupled application-specific workflows to perform proteome profiling and confirm target degradation by precise protein quantification. Overall, our results highlight the selective degradation of specific proteins of interest for ten selected compounds across two cell lines. Thus, the automated sample preparation platform facilitates rapid adaptation to emerging developments in proteomics sample preparation, combining standardization, flexibility, and high-throughput capabilities to drive significant advancements in clinical assays and proteomics research.

biochemistry↗

bioRxiv2025

Enhanced Identifications and Quantification through Retention Time Down-Sampling in Fast-Cycling diagonal-PASEF Methods

Data-independent acquisition mass spectrometry is essential for comprehensive quantification of proteomes, enabling deeper insights into cellular processes and disease mechanisms. On the timsTOF platform, diagonal-PASEF acquisition methods have recently been introduced to directly and continuously cover the observed diagonal shape of the peptide precursor ion distributions. Although diagonal-PASEF has shown great promise, its broad adoption as a routine workflow has been hampered by a lack of available algorithmic solutions and a paucity of demonstrated real-world applications. Here, we conducted a systematic and comprehensive optimization of diagonal-PASEF for 17-minute gradients on the timsTOF HT in conjunction to Spectronaut 19. We demonstrate that Spectronaut 19 fully supports all tested diagonal-PASEF methods independent of the number of slices or overlaps and with minimal user intervention required. Using our optimized analysis strategy we coupled diagonal-PASEF acquisitions to retention time down-sampling by summation (RTsum), thereby providing a novel mode of analysis of this data that exploits the fast-cycling nature of diagonal-PASEF methods. Through the combination of RTsum with diagonal-PASEF, we demonstrate that this strategy yields higher signal-to-noise ratios while retaining the peak shape for analytes of interest. Importantly, combining RTsum with diagonal-PASEF improved overall identifications and quantitative precision when compared to dia-PASEF with static or variable quadrupole isolation widths and across different input amounts for cell line injections. We also tested the performance of diagonal-PASEF in controlled quantitative experiments where diagonal-PASEF outperformed dia-PASEF in the overall number of retained candidates, quantitative precision and identifications on peptide level. These data indicate that RTsum demonstrates a positive use case of the high sampling rate of diagonal-PASEF. Collectively, our findings imply that diagonal-PASEF methods are superior for peptide and protein level analyses especially at lower input amounts.

biochemistry↗