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AI-driven Deep Visual Proteomics defines cell identity and heterogeneity

View ORCID ProfileAndreas Mund, View ORCID ProfileFabian Coscia, Réka Hollandi, Ferenc Kovács, András Kriston, Andreas-David Brunner, Michael Bzorek, Soraya Naimy, Lise Mette Rahbek Gjerdrum, Beatrice Dyring-Andersen, Jutta Bulkescher, Claudia Lukas, Christian Gnann, Emma Lundberg, Peter Horvath, Matthias Mann
doi: https://doi.org/10.1101/2021.01.25.427969
Andreas Mund
1Proteomics Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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  • ORCID record for Andreas Mund
  • For correspondence: andreas.mund@cpr.ku.dk
Fabian Coscia
1Proteomics Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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  • ORCID record for Fabian Coscia
Réka Hollandi
4Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary
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Ferenc Kovács
4Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary
5Single-Cell Technologies Ltd, Szeged 6726, Hungary
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András Kriston
4Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary
5Single-Cell Technologies Ltd, Szeged 6726, Hungary
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Andreas-David Brunner
6Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, 82152 Martinsried, Germany
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Michael Bzorek
7Department of Pathology, Zealand University Hospital, Roskilde, Denmark
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Soraya Naimy
7Department of Pathology, Zealand University Hospital, Roskilde, Denmark
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Lise Mette Rahbek Gjerdrum
7Department of Pathology, Zealand University Hospital, Roskilde, Denmark
13Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Beatrice Dyring-Andersen
1Proteomics Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
8Department of Dermatology and Allergy, Herlev and Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
14Leo Foundation Skin Immunology Research Center, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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Jutta Bulkescher
3Protein Imaging Platform, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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Claudia Lukas
2Protein Signaling Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
3Protein Imaging Platform, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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Christian Gnann
9Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
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Emma Lundberg
9Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
10Department of Genetics, Stanford University, Stanford, CA 94305, USA
11Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA
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Peter Horvath
4Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary
5Single-Cell Technologies Ltd, Szeged 6726, Hungary
12Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki 00014, Finland
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  • For correspondence: andreas.mund@cpr.ku.dk
Matthias Mann
1Proteomics Program, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
6Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, 82152 Martinsried, Germany
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  • For correspondence: andreas.mund@cpr.ku.dk
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ABSTRACT

The systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. Applied to archival tissue of salivary gland carcinoma, our generic workflow characterized proteomic differences between normal-appearing and adjacent cancer cells, without admixture of background from unrelated cells or extracellular matrix. In melanoma, DVP revealed immune system and DNA replication related prognostic markers that appeared only in specific tumor regions. Thus, DVP provides unprecedented molecular insights into cell and disease biology while retaining spatial information.

Competing Interest Statement

P.H. is the founder and a shareholder of Single-cell technologies Ltd., a biodata analysis company that owns and develops the BIAS software.

Footnotes

  • ↵§ Lead contact: mmann{at}biochem.mpg.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 27, 2021.
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AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
Andreas Mund, Fabian Coscia, Réka Hollandi, Ferenc Kovács, András Kriston, Andreas-David Brunner, Michael Bzorek, Soraya Naimy, Lise Mette Rahbek Gjerdrum, Beatrice Dyring-Andersen, Jutta Bulkescher, Claudia Lukas, Christian Gnann, Emma Lundberg, Peter Horvath, Matthias Mann
bioRxiv 2021.01.25.427969; doi: https://doi.org/10.1101/2021.01.25.427969
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AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
Andreas Mund, Fabian Coscia, Réka Hollandi, Ferenc Kovács, András Kriston, Andreas-David Brunner, Michael Bzorek, Soraya Naimy, Lise Mette Rahbek Gjerdrum, Beatrice Dyring-Andersen, Jutta Bulkescher, Claudia Lukas, Christian Gnann, Emma Lundberg, Peter Horvath, Matthias Mann
bioRxiv 2021.01.25.427969; doi: https://doi.org/10.1101/2021.01.25.427969

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