Dataset Information

Wide-Window Acquisition Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

ABSTRACT: The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. However, further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we further increase SCP sensitivity by combining efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to extend the achievable proteome coverage for single cells to >3,000 for single-cell data analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40 min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in only a 10% decrease in proteome coverage. We also compared performance of WWA with DIA underperformed relative to WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Ryan Kelly

LAB HEAD: Ryan T Kelly

PROVIDER: PXD037527 | Pride | 2023-07-11

REPOSITORIES: Pride

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Publications

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

Truong Thy T Webber Kei G I KGI Madisyn Johnston S S Boekweg Hannah H Lindgren Caleb M CM Liang Yiran Y Nydegger Alissia A Xie Xiaofeng X Tsang Tsz-Ming TM Jayatunge D A Dasun N DADN Andersen Joshua L JL Payne Samuel H SH Kelly Ryan T RT

Angewandte Chemie (International ed. in English) 20230713 34

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acq ...[more]

PMID: 37380610

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Project description:A comprehensive proteome map is essential to elucidate molecular pathways and protein functions. Although great improvements in sample preparation, instrumentation and data analysis already yield-ed impressive results, current studies suffer from a limited proteomic depth and dynamic range there-fore lacking low abundant or highly hydrophobic proteins. Here, we combine and benchmark advanced micro pillar array columns (µPAC) operated at nanoflow with wide window acquisition (WWA) and the AI-based CHIMERYS search engine for data analysis to maximize chromatographic separation power, sensitivity and proteome coverage. The wide window acquisition method merges the strengths of DDA and DIA. WWA uses a broad isolation window (≥4 Th) for data-dependent precursor selection. Similar to DIA, precursor ions close to those selected are co-fragmented, producing chimeric spectra that boost IDs and improve coverage of low-abundance peptides, which would otherwise be missed. WWA is particularly powerful when combined with the AI-driven CHIMERYS search algorithm, which allows confident identification of up to eleven peptides from a single chimeric spectrum. Developed by MSAID, the CHIMERYS algorithm makes use of accurate fragment spectrum predictions trained on millions of spectra, which allows to utilize additional spectral properties such as relative signal intensities for drastically improved identification rates. Entrapment experiments were conducted searching mouse immunoprecipitation samples with MS Amanda 2.0 and CHIMERYS by applying an additional custom-made decoy database, which demonstrated excellent protein, peptide and PSM FDR control from 1% upwards for both search engines. Combining wide precursor isolation widths of 4-12 Th with the CHIMERYS search engine identified 51-74% more proteins and 59-150% more peptides for single cell, immunoprecipitation and multi-species samples in comparison to a standard proteomics workflow employing MS Amanda 2.0 and an isolation width of 1 Th. Application of different isolation widths revealed that the optimal isolation window size varies according to sample amount and complexity with 12 Th resulting in the highest number of identifications for single cell-equivalent injection amounts, while 4 Th provided best results for classical bulk inputs of 400ng. Evaluation of coefficients of variation also yielded improved results for WWA over classical DDA when assessing single cell, 40 cell and 250pg bulk HeLa lysates. Overall, WWA and CHIMERYS resulted in substantially improved proteomic depth and quantification for all sample types tested and will provide a highly valuable toolset for the proteomics community.

Dataset Information

Wide-Window Acquisition Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

Publications

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

Similar Datasets

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