Dataset Information

Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

ABSTRACT: Single cell transcriptomics have revolutionized fundamental understanding of basic biology and disease. Since transcripts often do not correlate with protein expression, it is paramount to complement transcriptomics approaches with proteome analysis at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not to take full advantage of isotope-encoded sample multiplexing. We also developed a novel, identification-independent proteomics data analysis pipeline to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Claudia Ctortecka

LAB HEAD: Karl Mechtler

PROVIDER: PXD023574 | Pride | 2021-05-17

REPOSITORIES: Pride

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Publications

Comparative Proteome Signatures of Trace Samples by Multiplexed Data-Independent Acquisition.

Ctortecka Claudia C Krššáková Gabriela G Stejskal Karel K Penninger Josef M JM Mendjan Sasha S Mechtler Karl K Stadlmann Johannes J

Molecular & cellular proteomics : MCP 20211115 1

Single-cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single-cell resolution. Despite continuous technological improvements in sensitivity, mass-spectrometry-based single-cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cel ...[more]

PMID: 34793982

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Project 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.

Dataset Information

Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

Publications

Comparative Proteome Signatures of Trace Samples by Multiplexed Data-Independent Acquisition.

Similar Datasets

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