Comparative Proteome Signatures of Trace Samples by Multiplexed Data-Independent Acquisition
| Autor: | Sasha Mendjan, Gabriela Krššáková, Claudia Ctortecka, Johannes Stadlmann, Karel Stejskal, Josef M. Penninger, Karl Mechtler |
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| Rok vydání: | 2022 |
| Předmět: |
RT
retention time Proteomics large sample cohorts Proteome ultralow peptide input Single cell transcriptomics DIA data-independent acquisition TMT tandem-mass-tag Computational biology Biology identification-free data aggregation Biochemistry NCE normalized collision energy Analytical Chemistry Cell Line Transcriptome Tandem Mass Spectrometry data-independent acquisition Humans Data-independent acquisition Molecular Biology TRACE (psycholinguistics) AGC automatic gain control PSM peptide-to-spectrum match Technological Innovation and Resources Missing data RI reporter ion Integrative data analysis DDA data-dependent acquisition isobaric multiplexing Protein Processing Post-Translational |
| Zdroj: | Mol Cell Proteomics Molecular & Cellular Proteomics : MCP |
| ISSN: | 1535-9484 |
| DOI: | 10.1016/j.mcpro.2021.100177 |
| Popis: | 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 cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultralow input samples. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. These proteome signatures overcome the need to impute quantitative data due to accumulating detrimental amounts of missing data in standard multibatch TMT experiments. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. Our data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultralow input samples. Graphical Abstract Highlights • DIA-TMT provides reproducible, quantitative proteome signatures at high throughput. • Proteome signature inferred cell type characterization is highly accurate. • Proteome signatures accurately highlight underrepresented cell types. • ID-independent DIA-TMT is more reproducible than standard DDA acquisition strategies. In Brief Proteomics faces the challenge of reproducibly comparing the protein expression profiles across large sample cohorts. Here, we combined two hitherto opposing analytical strategies, DIA and isobaric labeling to generate highly reproducible, quantitative “proteome signatures”. These signatures decouple peptide identification from quantification to quantitatively compare hundreds of samples. DIA-TMT data provides complete quantitative signatures independent of peptide identification that distinguish cell types down to single protein knockouts in high-throughput even at ultralow input. |
| Databáze: | OpenAIRE |
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