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Quantitative single-cell proteomics as a tool to characterize cellular hierarchies

Author

Listed:
  • Erwin M. Schoof

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen
    Technical University of Denmark
    University of Copenhagen)

  • Benjamin Furtwängler

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen
    University of Copenhagen)

  • Nil Üresin

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen
    University of Copenhagen)

  • Nicolas Rapin

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen
    University of Copenhagen)

  • Simonas Savickas

    (Technical University of Denmark)

  • Coline Gentil

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen)

  • Eric Lechman

    (Princess Margaret Cancer Centre, University Health Network
    University of Toronto)

  • Ulrich auf dem Keller

    (Technical University of Denmark)

  • John E. Dick

    (Princess Margaret Cancer Centre, University Health Network
    University of Toronto)

  • Bo T. Porse

    (University of Copenhagen
    Biotech Research and Innovation Centre (BRIC), University of Copenhagen
    University of Copenhagen)

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 ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.

Suggested Citation

  • 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, 2021. "Quantitative single-cell proteomics as a tool to characterize cellular hierarchies," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23667-y
    DOI: 10.1038/s41467-021-23667-y
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    Cited by:

    1. Kim Theilgaard-Mönch & Sachin Pundhir & Kristian Reckzeh & Jinyu Su & Marta Tapia & Benjamin Furtwängler & Johan Jendholm & Janus Schou Jakobsen & Marie Sigurd Hasemann & Kasper Jermiin Knudsen & Jack, 2022. "Transcription factor-driven coordination of cell cycle exit and lineage-specification in vivo during granulocytic differentiation," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Anna Cioce & Beatriz Calle & Tatiana Rizou & Sarah C. Lowery & Victoria L. Bridgeman & Keira E. Mahoney & Andrea Marchesi & Ganka Bineva-Todd & Helen Flynn & Zhen Li & Omur Y. Tastan & Chloe Roustan &, 2022. "Cell-specific bioorthogonal tagging of glycoproteins," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    3. Claudia Ctortecka & Natalie M. Clark & Brian W. Boyle & Anjali Seth & D. R. Mani & Namrata D. Udeshi & Steven A. Carr, 2024. "Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Yudong Gao & Daichi Shonai & Matthew Trn & Jieqing Zhao & Erik J. Soderblom & S. Alexandra Garcia-Moreno & Charles A. Gersbach & William C. Wetsel & Geraldine Dawson & Dmitry Velmeshev & Yong-hui Jian, 2024. "Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. Christina Bligaard Pedersen & Søren Helweg Dam & Mike Bogetofte Barnkob & Michael D. Leipold & Noelia Purroy & Laura Z. Rassenti & Thomas J. Kipps & Jennifer Nguyen & James Arthur Lederer & Satyen Har, 2022. "cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Benjamin C. Orsburn & Yuting Yuan & Namandjé N. Bumpus, 2022. "Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Yu Wang & Zhi-Ying Guan & Shao-Wen Shi & Yi-Rong Jiang & Jie Zhang & Yi Yang & Qiong Wu & Jie Wu & Jian-Bo Chen & Wei-Xin Ying & Qin-Qin Xu & Qian-Xi Fan & Hui-Feng Wang & Li Zhou & Ling Wang & Jin Fa, 2024. "Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    9. Jongmin Woo & Sarah M. Williams & Lye Meng Markillie & Song Feng & Chia-Feng Tsai & Victor Aguilera-Vazquez & Ryan L. Sontag & Ronald J. Moore & Dehong Hu & Hardeep S. Mehta & Joshua Cantlon-Bruce & T, 2021. "High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    10. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    11. Manuel Matzinger & Anna Schmücker & Ramesh Yelagandula & Karel Stejskal & Gabriela Krššáková & Frédéric Berger & Karl Mechtler & Rupert L. Mayer, 2024. "Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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