Wide Window Acquisition and AI-based data analysis to reach deep proteome coverage for a wide sample range, including single cell proteomic inputs

Abstract

A comprehensive proteome map is essential to elucidate molecular pathways and protein functions. Although great improvements in sample preparation, instrumentation and data analysis already yielded impressive results, current studies suffer from a limited proteomic depth and dynamic range therefore lacking low abundant or highly hydrophobic proteins. Here, we combine and benchmark advanced micro pillar array columns (μPACTM) operated at nanoflow with Wide Window Acquisition (WWA) and the AI-based CHIMERYSTM search engine for data analysis to maximize chromatographic separation power, sensitivity and proteome coverage. Our data shows that μPACTM columns clearly outperform classical packed bed columns boosting peptide IDs by up to 140%. Already at classical narrow isolation widths CHIMERYSTM boosted ID rates by a factor of 2.6 compared to the conventional search engine MS Amanda 2.0. By combining CHIMERYSTM with WWA, even a 4.6-fold increase in ID rates could be achieved. Using our optimized workflow, we were further able to identify more than 10,000 proteins from a single 2 h gradient shotgun analysis. We further investigated the applicability of WWA for single cell inputs and found that the choice of the optimal isolation window width depends on sample input and complexity. Using a short 5.5 cm column and very high flow rates during loading and column equilibration we improved sample throughput to ~100 samples per day while maintaining high protein ID numbers. We believe that this is especially important for the single cell field where throughput is one of the most limiting factors. Finally, we applied our optimized workflow on immunoprecipitations of Smarca5/SNF2H and found additional interaction partners compared to the original workflow utilizing a packed bed column. These additional interaction partners include previously described interaction partners of Smarca5 like Baz2b as well as undescribed interactors including Arid1a, which is also involved in chromatin remodeling and has been described as key player in neurodevelopmental and malignant disorders.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

“…Still, identifying up to an average of 10,789 unique peptides with 1% FDR and without MBR is ~28% greater than our DDA-based coverage (average 8,399) when using the same separation and MS instrumentation (Figure 2B). Peptide coverage was consistently greatest for isolation windows of 8 or 12 Th, which is in agreement with the findings of Mayer et al [16], and which points to a compromise between maximizing the number of isolated precursors and avoiding overly complex MS2 spectra. At the protein level, the number of MS2-identified high-confidence master proteins from 0.2 ng aliquots of HeLa digest increased 39% to 2,396 for WWA compared to 1,721 identified on average using standard DDA (Figure 2C).…”

“…We evaluated WWA for rapid label-free single-cell proteome profiling of single cells prepared using nanoPOTS [6], and found that isolation windows in the range of 8 -12 Th provide greatest peptide and protein coverage when identification is based solely on MS2 spectra (i.e., without the use of MS1-based feature matching such as the Match Between Runs (MBR) algorithm [15]). These optimized isolation windows agree with the findings of Mayer et al [16], who have concurrently explored WWA for small aliquots of bulk-prepared samples. Optimized WWA provides a ~30% increase in MS2-identified peptides relative to standard DDA, although peptide and proteome coverage are similar when including MBR identifications.…”

Robust and easy-to-use one pot workflow for label free single cell proteomics

“…We showed that the proteome depth of our original DDA method, which uses a precursor isolation window of 2 m/z, can be improved by widening up the isolation window to 12 m/z. Using such a wide window acquisition (WWA 18 ) on purpose generates chimeric spectra which can however be identified by the AI based search engine CHIMERYS TM leading to identification of more than one peptide from a single spectrum on average. With DIA we go even a step further and found that variable windows of up to 25 m/z can significantly increase identification numbers to >1,100 proteins from 250 pg HeLa.…”

“…In a first step we compared two column types for their chromatographic performance: A classical packed bed column (CSH C18 Column, 130Å, 1.7 µm, 75 µm X 250 mm, Waters) that was previously used in our lab and a µPAC TM column (brick shape pillars, 5.5 cm, prototype column, Thermo Fisher Scientific) which we recently successfully tested for low input amounts as well as short gradients. 18 Although the µPAC TM column is only 5.5 cm long, its internal flow path is roughly 50 cm leading to a sufficient surface area enabling powerful separation. The median FWHM peak width obtained using the packed bed column was 3.84 sec vs 4.77 sec for the µPAC TM .…”

“…The proteome depth obtained using DDA was improved by widening the precursor isolation window from 2 to 12 m/z. Using such a WWA 18 on purpose generated chimeric spectra and led to the identification of more than one peptide from a single spectra when the CHIMERYS TM intelligent search algorithm was used. Variable DIA windows of up to 25 m/z for co-fragmentation of ions significantly increased protein IDs to more than 1100 from 250 pg HeLa.…”