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Spatial characterization of single tumor cells by proteomics

By quantifying thousands of proteins in tumor cells in an unbiased manner, Deep Visual Proteomics uncovers mechanisms that drive tumor evolution and reveals new therapeutic targets. The method incorporates advanced microscopy, artificial intelligence and ultra-high-sensitivity proteomics to characterize individual cell identities.

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Fig. 1: Concept of deep visual proteomics.

References

  1. Lewis, S. M. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 18, 997–1012 (2021). A Review that presents spatial and molecular technologies, including computational methods, available to investigate tumor heterogeneity.

    CAS  Article  Google Scholar 

  2. Brunner, A. et al. Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation. Mol. Sys. Biol. 18, e10798 (2022). This paper reports a new ultra-high-sensitivity liquid chromatography–mass spectrometry workflow that enables true single-cell proteome analysis.

    CAS  Google Scholar 

  3. Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat Biotechnol. 40, 308–318 (2022). A Review that reports on spatial components of molecular tissue biology, challenges in spatial tissue analysis and related computational algorithms.

    CAS  Article  Google Scholar 

  4. Kiemen, A. et al. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.416909 (2020). This preprint reports on the development of CODA, an AI-based imaging method to reconstruct human tissue in 3D with single-cell resolution.

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This is a summary of: Mund, A. et al. Deep Visual Proteomics defines single-cell identity and heterogeneity. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01302-5 (2022).

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Spatial characterization of single tumor cells by proteomics. Nat Biotechnol 40, 1186–1187 (2022). https://doi.org/10.1038/s41587-022-01321-2

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