Mass spectrometry (MS)-based proteomics allows the comprehensive identification and quantification of proteins in various biological specimen such as cell lines, body fluids or tissues. To study complex samples for applications like biomarker discovery usually requires large sample cohorts to achieve sufficient statistical power. Labor-intensive manual sample preparation, coupled with long high-performance liquid chromatography (HPLC) gradients, restricts throughput and introduces technical variability. Workflows with increased throughput, standardized sample processing and cost-effectiveness are required to facilitate large-scale discovery proteomics and yield generalizable results. We present the integration of the latest technological advancements in sample preparation along with short gradient LC-MS acquisitions to achieve remarkable speed, depth, and reproducibility in proteomic studies.

An automated end-to-end sample preparation workflow designed to process diverse biological specimen as input to MS-ready peptide output is showcased. An integrated liquid handling platform is utilized, enabling efficient parallelized processing of up to two 96-well plates in a single run. The automated sample preparation is combined with optimized dia-PASEF® LC-MS/MS measurements on the timsTOF high-throughput (HT) mass spectrometer achieved a throughput of 38 samples per day. The data was analyzed using Spectronaut's® directDIA+® workflow.

As proof-of-concept ten distinct human cell lines were processed in analytical triplicates. The entire study, from initiating the sample preparation to concluding LC-MS acquisitions, was completed in 31 hours while reducing human input by almost 90% compared to manual processing. This workflow has the potential to be scaled up to a processing throughput of 1000 samples within a single month.

The analyzed cell lines resulted in a total of 11"789 identified protein groups with an average of 9,547 proteins identified per sample demonstrating a remarkable coverage with ultra-short gradients. The median coefficient of variation (CV) for the detected protein groups was calculated at 7.9%, with 87% of all identified proteins exhibiting a CV below 20%, indicating an outstanding quantitative precision and reproducibility.

In this comprehensive dataset biologically relevant proteins for cancer cell lines which are common targets for pharmaceutical intervention, such as KRAS, were consistently detected in all samples with high quality peptide profiles.

Taken together, the workflow described facilitates proteomics experiments at scale by unlocking optimal synergies between sample preparation, LC-MS acquisition and data analysis. It enables to process and measure a substantial number of samples in a short time frame at reduced costs per sample while retaining high depth and precision. Consequently, the presented approach further extends the limits of large-scale studies enabling the systematic exploration of the proteome.