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Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution
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 spat...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6946663/ https://www.ncbi.nlm.nih.gov/pubmed/31911630 http://dx.doi.org/10.1038/s41467-019-13858-z |
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author | 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. |
author_facet | 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. |
author_sort | Piehowski, Paul D. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6946663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69466632020-01-09 Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution 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. Nat Commun Article 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. Nature Publishing Group UK 2020-01-07 /pmc/articles/PMC6946663/ /pubmed/31911630 http://dx.doi.org/10.1038/s41467-019-13858-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article 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 |
title | Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
title_full | Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
title_fullStr | Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
title_full_unstemmed | Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
title_short | Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
title_sort | automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6946663/ https://www.ncbi.nlm.nih.gov/pubmed/31911630 http://dx.doi.org/10.1038/s41467-019-13858-z |
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