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Spatially resolved proteomics via tissue expansion
Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a s...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712279/ https://www.ncbi.nlm.nih.gov/pubmed/36450705 http://dx.doi.org/10.1038/s41467-022-34824-2 |
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author | Li, Lu Sun, Cuiji Sun, Yaoting Dong, Zhen Wu, Runxin Sun, Xiaoting Zhang, Hanbin Jiang, Wenhao Zhou, Yan Cen, Xufeng Cai, Shang Xia, Hongguang Zhu, Yi Guo, Tiannan Piatkevich, Kiryl D. |
author_facet | Li, Lu Sun, Cuiji Sun, Yaoting Dong, Zhen Wu, Runxin Sun, Xiaoting Zhang, Hanbin Jiang, Wenhao Zhou, Yan Cen, Xufeng Cai, Shang Xia, Hongguang Zhu, Yi Guo, Tiannan Piatkevich, Kiryl D. |
author_sort | Li, Lu |
collection | PubMed |
description | Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer’s disease in a mouse model by comparative proteomic analysis of brain subregions. |
format | Online Article Text |
id | pubmed-9712279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97122792022-12-02 Spatially resolved proteomics via tissue expansion Li, Lu Sun, Cuiji Sun, Yaoting Dong, Zhen Wu, Runxin Sun, Xiaoting Zhang, Hanbin Jiang, Wenhao Zhou, Yan Cen, Xufeng Cai, Shang Xia, Hongguang Zhu, Yi Guo, Tiannan Piatkevich, Kiryl D. Nat Commun Article Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer’s disease in a mouse model by comparative proteomic analysis of brain subregions. Nature Publishing Group UK 2022-11-30 /pmc/articles/PMC9712279/ /pubmed/36450705 http://dx.doi.org/10.1038/s41467-022-34824-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Lu Sun, Cuiji Sun, Yaoting Dong, Zhen Wu, Runxin Sun, Xiaoting Zhang, Hanbin Jiang, Wenhao Zhou, Yan Cen, Xufeng Cai, Shang Xia, Hongguang Zhu, Yi Guo, Tiannan Piatkevich, Kiryl D. Spatially resolved proteomics via tissue expansion |
title | Spatially resolved proteomics via tissue expansion |
title_full | Spatially resolved proteomics via tissue expansion |
title_fullStr | Spatially resolved proteomics via tissue expansion |
title_full_unstemmed | Spatially resolved proteomics via tissue expansion |
title_short | Spatially resolved proteomics via tissue expansion |
title_sort | spatially resolved proteomics via tissue expansion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712279/ https://www.ncbi.nlm.nih.gov/pubmed/36450705 http://dx.doi.org/10.1038/s41467-022-34824-2 |
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