PXD037527 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Wide-Window Acquisition Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics |
Description | 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. |
HostingRepository | PRIDE |
AnnounceDate | 2023-11-14 |
AnnouncementXML | Submission_2023-11-14_09:04:12.350.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | Ryan Kelly |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | iodoacetamide derivatized residue |
Instrument | Orbitrap Exploris 480 |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2022-10-18 08:02:48 | ID requested | |
1 | 2023-07-11 13:29:33 | announced | |
2 | 2023-07-24 01:52:30 | announced | 2023-07-24: Updated project metadata. |
3 | 2023-11-14 07:09:33 | announced | 2023-11-14: Updated project metadata. |
⏵ 4 | 2023-11-14 09:04:20 | announced | 2023-11-14: Updated project metadata. |
Publication List
Truong T, Webber KGI, Madisyn Johnston S, Boekweg H, Lindgren CM, Liang Y, Nydegger A, Xie X, Tsang TM, Jayatunge DADN, Andersen JL, Payne SH, Kelly RT, Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics. Angew Chem Int Ed Engl, 62(34):e202303415(2023) [pubmed] |
Keyword List
submitter keyword: Label-Free, Proteome, Single-Cell Proteomics,Wide-Window Acquisition |
Contact List
Ryan T Kelly |
contact affiliation | Department of Chemistry and Biochemistry, Brigham Young University. |
contact email | ryan@chem.byu.edu |
lab head | |
Ryan Kelly |
contact affiliation | Brigham Young University |
contact email | ryan.kelly@byu.edu |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
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PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD037527
- Label: PRIDE project
- Name: Wide-Window Acquisition Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics