Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity
Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250 pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.
Authors
- Peter Pichler (ORCID: https://orcid.org/0000-0001-8340-0197)
- Karl Mechtler (ORCID: https://orcid.org/0000-0002-3392-9946)
- Jana Slováková (ORCID: https://orcid.org/0000-0003-3724-9830)
- Manuel Matzinger (ORCID: https://orcid.org/0000-0002-9765-7951)
- Julia A. Bubis (ORCID: https://orcid.org/0000-0002-4895-6146)
- Theresa Maria Sommer (ORCID: https://orcid.org/0000-0002-9751-1143)
- Tabiwang N. Arrey (ORCID: https://orcid.org/0009-0001-2888-2055)
- Eugen Damoc (ORCID: https://orcid.org/0000-0002-4422-2866)
- Harunobu Kagawa (ORCID: https://orcid.org/0000-0003-0436-3805)
- Bernard Delanghe (ORCID: https://orcid.org/0000-0002-9932-3138)
- Nicolas Rivron (ORCID: https://orcid.org/0000-0003-1590-5964)
Institutions
- Institute of Molecular Biotechnology (AT)
- Gregor Mendel Institute of Molecular Plant Biology (AT)
- Austrian Academy of Sciences (AT)
- Research Institute of Molecular Pathology (AT)
- Vienna Biocenter (AT)
- Thermo Fisher Scientific (Germany) (DE)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Nature Methods
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1038/s41592-024-02559-1
- Citations
- 71
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 112.74
Funders
- International Society for Stem Cell Research
- Vienna Science and Technology Fund
- Österreichischen Akademie der Wissenschaften
- Austrian Science Fund
- Österreichische Forschungsförderungsgesellschaft