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| Item Type: | Article |
|---|---|
| Title: | A framework for ultra-low-input spatial tissue proteomics |
| Creators Name: | Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., König, J. and Coscia, F. |
| Abstract: | Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information. |
| Keywords: | Mass Spectrometry, Proteomics, Spatial Proteomics, Histopathology, FFPE, Single-Cell Proteomics, Deep Visual Proteomics, Animals, Mice |
| Source: | Cell Systems |
| ISSN: | 2405-4712 |
| Publisher: | Cell Press / Elsevier |
| Volume: | 14 |
| Number: | 11 |
| Page Range: | 1002-1014 |
| Date: | 15 November 2023 |
| Official Publication: | https://doi.org/10.1016/j.cels.2023.10.003 |
| PubMed: | View item in PubMed |
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