Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution
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.
- Authors:
-
[1];
[1];
[1];
[2];
[1];
[3];
[1]
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH (United States)
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Brigham Young Univ., Provo, UT (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH); March of Dimes
- OSTI Identifier:
- 1595747
- Report Number(s):
- PNNL-SA-138884
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- AC05-76RLO1830; SC0019620; R21-HD084788; UG3HL145593; R33-CA225248; R01HD068524; DA006668; 22-FY17- 889
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Piehowski, Paul D., Zhu, Ying, Bramer, Lisa M., Stratton, Kelly G., Zhao, Rui, Orton, Daniel J., Moore, Ronald J., Yuan, Jia, Mitchell, Hugh D., Gao, Yuqian, Webb-Robertson, Bobbie-Jo M., Dey, Sudhansu K., Kelly, Ryan T., and Burnum-Johnson, Kristin E. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. United States: N. p., 2020.
Web. doi:10.1038/s41467-019-13858-z.
Piehowski, Paul D., Zhu, Ying, Bramer, Lisa M., Stratton, Kelly G., Zhao, Rui, Orton, Daniel J., Moore, Ronald J., Yuan, Jia, Mitchell, Hugh D., Gao, Yuqian, Webb-Robertson, Bobbie-Jo M., Dey, Sudhansu K., Kelly, Ryan T., & Burnum-Johnson, Kristin E. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. United States. https://doi.org/10.1038/s41467-019-13858-z
Piehowski, Paul D., Zhu, Ying, Bramer, Lisa M., Stratton, Kelly G., Zhao, Rui, Orton, Daniel J., Moore, Ronald J., Yuan, Jia, Mitchell, Hugh D., Gao, Yuqian, Webb-Robertson, Bobbie-Jo M., Dey, Sudhansu K., Kelly, Ryan T., and Burnum-Johnson, Kristin E. Tue .
"Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution". United States. https://doi.org/10.1038/s41467-019-13858-z. https://www.osti.gov/servlets/purl/1595747.
@article{osti_1595747,
title = {Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution},
author = {Piehowski, Paul D. and Zhu, Ying and Bramer, Lisa M. and Stratton, Kelly G. and Zhao, Rui and Orton, Daniel J. and Moore, Ronald J. and Yuan, Jia and Mitchell, Hugh D. and Gao, Yuqian and Webb-Robertson, Bobbie-Jo M. and Dey, Sudhansu K. and Kelly, Ryan T. and Burnum-Johnson, Kristin E.},
abstractNote = {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.},
doi = {10.1038/s41467-019-13858-z},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Tue Jan 07 00:00:00 EST 2020},
month = {Tue Jan 07 00:00:00 EST 2020}
}
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