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Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

Author

Listed:
  • Paul D. Piehowski

    (Pacific Northwest National Laboratory)

  • Ying Zhu

    (Pacific Northwest National Laboratory)

  • Lisa M. Bramer

    (Pacific Northwest National Laboratory)

  • Kelly G. Stratton

    (Pacific Northwest National Laboratory)

  • Rui Zhao

    (Pacific Northwest National Laboratory)

  • Daniel J. Orton

    (Pacific Northwest National Laboratory)

  • Ronald J. Moore

    (Pacific Northwest National Laboratory)

  • Jia Yuan

    (Cincinnati Children’s Hospital Medical Center)

  • Hugh D. Mitchell

    (Pacific Northwest National Laboratory)

  • Yuqian Gao

    (Pacific Northwest National Laboratory)

  • Bobbie-Jo M. Webb-Robertson

    (Pacific Northwest National Laboratory)

  • Sudhansu K. Dey

    (Cincinnati Children’s Hospital Medical Center)

  • Ryan T. Kelly

    (Pacific Northwest National Laboratory
    Brigham Young University)

  • Kristin E. Burnum-Johnson

    (Pacific Northwest National Laboratory)

Abstract

Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

Suggested Citation

  • Paul D. Piehowski & Ying Zhu & Lisa M. Bramer & Kelly G. Stratton & Rui Zhao & Daniel J. Orton & Ronald J. Moore & Jia Yuan & Hugh D. Mitchell & Yuqian Gao & Bobbie-Jo M. Webb-Robertson & Sudhansu K. , 2020. "Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13858-z
    DOI: 10.1038/s41467-019-13858-z
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    Cited by:

    1. Min Ma & Shihan Huo & Ming Zhang & Shuo Qian & Xiaoyu Zhu & Jie Pu & Sailee Rasam & Chao Xue & Shichen Shen & Bo An & Jianmin Wang & Jun Qu, 2022. "In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP)," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Simon Davis & Connor Scott & Janina Oetjen & Philip D. Charles & Benedikt M. Kessler & Olaf Ansorge & Roman Fischer, 2023. "Deep topographic proteomics of a human brain tumour," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Lu Li & Cuiji Sun & Yaoting Sun & Zhen Dong & Runxin Wu & Xiaoting Sun & Hanbin Zhang & Wenhao Jiang & Yan Zhou & Xufeng Cen & Shang Cai & Hongguang Xia & Yi Zhu & Tiannan Guo & Kiryl D. Piatkevich, 2022. "Spatially resolved proteomics via tissue expansion," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Yunhao Bai & Bokai Zhu & John-Paul Oliveria & Bryan J. Cannon & Dorien Feyaerts & Marc Bosse & Kausalia Vijayaragavan & Noah F. Greenwald & Darci Phillips & Christian M. Schürch & Samuel M. Naik & Edw, 2023. "Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. 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.

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