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Spatially resolved 3D metabolomic profiling in tissues
Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840140/ https://www.ncbi.nlm.nih.gov/pubmed/33571119 http://dx.doi.org/10.1126/sciadv.abd0957 |
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author | Ganesh, Shambavi Hu, Thomas Woods, Eric Allam, Mayar Cai, Shuangyi Henderson, Walter Coskun, Ahmet F. |
author_facet | Ganesh, Shambavi Hu, Thomas Woods, Eric Allam, Mayar Cai, Shuangyi Henderson, Walter Coskun, Ahmet F. |
author_sort | Ganesh, Shambavi |
collection | PubMed |
description | Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub–5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease. |
format | Online Article Text |
id | pubmed-7840140 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78401402021-02-05 Spatially resolved 3D metabolomic profiling in tissues Ganesh, Shambavi Hu, Thomas Woods, Eric Allam, Mayar Cai, Shuangyi Henderson, Walter Coskun, Ahmet F. Sci Adv Research Articles Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub–5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease. American Association for the Advancement of Science 2021-01-27 /pmc/articles/PMC7840140/ /pubmed/33571119 http://dx.doi.org/10.1126/sciadv.abd0957 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Ganesh, Shambavi Hu, Thomas Woods, Eric Allam, Mayar Cai, Shuangyi Henderson, Walter Coskun, Ahmet F. Spatially resolved 3D metabolomic profiling in tissues |
title | Spatially resolved 3D metabolomic profiling in tissues |
title_full | Spatially resolved 3D metabolomic profiling in tissues |
title_fullStr | Spatially resolved 3D metabolomic profiling in tissues |
title_full_unstemmed | Spatially resolved 3D metabolomic profiling in tissues |
title_short | Spatially resolved 3D metabolomic profiling in tissues |
title_sort | spatially resolved 3d metabolomic profiling in tissues |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840140/ https://www.ncbi.nlm.nih.gov/pubmed/33571119 http://dx.doi.org/10.1126/sciadv.abd0957 |
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