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Automated cell-type classification in intact tissues by single-cell molecular profiling
A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) wi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802843/ https://www.ncbi.nlm.nih.gov/pubmed/29319504 http://dx.doi.org/10.7554/eLife.30510 |
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author | Nagendran, Monica Riordan, Daniel P Harbury, Pehr B Desai, Tushar J |
author_facet | Nagendran, Monica Riordan, Daniel P Harbury, Pehr B Desai, Tushar J |
author_sort | Nagendran, Monica |
collection | PubMed |
description | A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research. |
format | Online Article Text |
id | pubmed-5802843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-58028432018-02-08 Automated cell-type classification in intact tissues by single-cell molecular profiling Nagendran, Monica Riordan, Daniel P Harbury, Pehr B Desai, Tushar J eLife Cell Biology A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research. eLife Sciences Publications, Ltd 2018-01-10 /pmc/articles/PMC5802843/ /pubmed/29319504 http://dx.doi.org/10.7554/eLife.30510 Text en © 2018, Nagendran et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Cell Biology Nagendran, Monica Riordan, Daniel P Harbury, Pehr B Desai, Tushar J Automated cell-type classification in intact tissues by single-cell molecular profiling |
title | Automated cell-type classification in intact tissues by single-cell molecular profiling |
title_full | Automated cell-type classification in intact tissues by single-cell molecular profiling |
title_fullStr | Automated cell-type classification in intact tissues by single-cell molecular profiling |
title_full_unstemmed | Automated cell-type classification in intact tissues by single-cell molecular profiling |
title_short | Automated cell-type classification in intact tissues by single-cell molecular profiling |
title_sort | automated cell-type classification in intact tissues by single-cell molecular profiling |
topic | Cell Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802843/ https://www.ncbi.nlm.nih.gov/pubmed/29319504 http://dx.doi.org/10.7554/eLife.30510 |
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