Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel
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Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | e11503 |
| Tidsskrift | Molecular Systems Biology |
| Vol/bind | 19 |
| Udgave nummer | 9 |
| Antal sider | 23 |
| ISSN | 1744-4292 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This study was supported by the Max Planck Society for Advancement of Science, European Union's Horizon 2020 research and innovation program under grant agreement No. 874839 (ISLET), the Deutsche Forschungsgemeinschaft project “Chemical proteomics inside us” (grant No.: 412136960) and by the Bavarian State Ministry of Health and Care through the research project DigiMed Bayern ( www.digimed‐bayern.de ). MT and MW acknowledge support from the International Max Planck Research School for Life Sciences – IMPRS‐LS. SR is supported by the Helmholtz Association under the joint research school “Munich School for Data Science” – MUDS. FAR is an EMBO postdoctoral fellow (ALTF 399‐2021). We are grateful for the FACS support by the Imaging Core Facility at the Max Planck Institute of Biochemistry, in particular Martin Spitaler and Markus Oster. We thank our colleagues in the Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry and at the Center for Protein Research at Copenhagen University, for discussions and support. In particular, we thank Jakob Bader for DIA‐NN library discussions, Igor Paron and Tim Heymann for technical support and column production and Medini Steger for scientific administration support. Open Access funding enabled and organized by Projekt DEAL.
Publisher Copyright:
© 2023 The Authors. Published under the terms of the CC BY 4.0 license.
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