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Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular Hierarchies

View ORCID ProfileErwin M. Schoof, Benjamin Furtwängler, Nil Üresin, Nicolas Rapin, Simonas Savickas, Coline Gentil, Eric Lechman, Ulrich auf dem Keller, John E. Dick, View ORCID ProfileBo T. Porse
doi: https://doi.org/10.1101/745679
Erwin M. Schoof
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
3Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
4Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
5Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
6Department of Molecular Genetics, University of Toronto, Toronto, Canada
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  • For correspondence: erws@dtu.dk bo.porse@finsenlab.dk
Benjamin Furtwängler
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
4Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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Nil Üresin
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
4Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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Nicolas Rapin
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
4Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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Simonas Savickas
3Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
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Coline Gentil
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
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Eric Lechman
5Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
6Department of Molecular Genetics, University of Toronto, Toronto, Canada
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Ulrich auf dem Keller
3Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
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John E. Dick
5Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
6Department of Molecular Genetics, University of Toronto, Toronto, Canada
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Bo T. Porse
1The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Denmark
2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
4Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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  • For correspondence: erws@dtu.dk bo.porse@finsenlab.dk
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Abstract

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses.

By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying approximately 1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we developed a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a solid foundation for implementing global single-cell proteomics studies across the world.

Competing Interest Statement

The authors have declared no competing interest.

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 12, 2021.
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Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular Hierarchies
Erwin M. Schoof, Benjamin Furtwängler, Nil Üresin, Nicolas Rapin, Simonas Savickas, Coline Gentil, Eric Lechman, Ulrich auf dem Keller, John E. Dick, Bo T. Porse
bioRxiv 745679; doi: https://doi.org/10.1101/745679
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Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular Hierarchies
Erwin M. Schoof, Benjamin Furtwängler, Nil Üresin, Nicolas Rapin, Simonas Savickas, Coline Gentil, Eric Lechman, Ulrich auf dem Keller, John E. Dick, Bo T. Porse
bioRxiv 745679; doi: https://doi.org/10.1101/745679

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