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Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Author

Listed:
  • Valdemaras Petrosius

    (Technical University of Denmark)

  • Pedro Aragon-Fernandez

    (Technical University of Denmark)

  • Nil Üresin

    (Technical University of Denmark
    University of Copenhagen
    University of Copenhagen)

  • Gergo Kovacs

    (Technical University of Denmark)

  • Teeradon Phlairaharn

    (Technical University of Denmark
    University of Copenhagen
    Max-Planck Institute of Biochemistry
    MaxPlanck Institute of Biochemistry)

  • Benjamin Furtwängler

    (Technical University of Denmark
    University of Copenhagen
    University of Copenhagen)

  • Jeff Op De Beeck

    (Thermo Fisher Scientific)

  • Sarah L. Skovbakke

    (Technical University of Denmark)

  • Steffen Goletz

    (Technical University of Denmark)

  • Simon Francis Thomsen

    (University of Copenhagen)

  • Ulrich auf dem Keller

    (Technical University of Denmark)

  • Kedar N. Natarajan

    (Technical University of Denmark)

  • Bo T. Porse

    (University of Copenhagen
    University of Copenhagen
    University of Copenhagen)

  • Erwin M. Schoof

    (Technical University of Denmark)

Abstract

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41602-1
    DOI: 10.1038/s41467-023-41602-1
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    3. 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.
    4. 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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