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Massively parallel nanowell-based single-cell gene expression profiling
BACKGROUND: Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. RESULTS: Here we report a novel nanowell-...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501953/ https://www.ncbi.nlm.nih.gov/pubmed/28687070 http://dx.doi.org/10.1186/s12864-017-3893-1 |
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author | Goldstein, Leonard D. Chen, Ying-Jiun Jasmine Dunne, Jude Mir, Alain Hubschle, Hermann Guillory, Joseph Yuan, Wenlin Zhang, Jingli Stinson, Jeremy Jaiswal, Bijay Pahuja, Kanika Bajaj Mann, Ishminder Schaal, Thomas Chan, Leo Anandakrishnan, Sangeetha Lin, Chun-wah Espinoza, Patricio Husain, Syed Shapiro, Harris Swaminathan, Karthikeyan Wei, Sherry Srinivasan, Maithreyan Seshagiri, Somasekar Modrusan, Zora |
author_facet | Goldstein, Leonard D. Chen, Ying-Jiun Jasmine Dunne, Jude Mir, Alain Hubschle, Hermann Guillory, Joseph Yuan, Wenlin Zhang, Jingli Stinson, Jeremy Jaiswal, Bijay Pahuja, Kanika Bajaj Mann, Ishminder Schaal, Thomas Chan, Leo Anandakrishnan, Sangeetha Lin, Chun-wah Espinoza, Patricio Husain, Syed Shapiro, Harris Swaminathan, Karthikeyan Wei, Sherry Srinivasan, Maithreyan Seshagiri, Somasekar Modrusan, Zora |
author_sort | Goldstein, Leonard D. |
collection | PubMed |
description | BACKGROUND: Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. RESULTS: Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells. CONCLUSIONS: Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3893-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5501953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55019532017-07-10 Massively parallel nanowell-based single-cell gene expression profiling Goldstein, Leonard D. Chen, Ying-Jiun Jasmine Dunne, Jude Mir, Alain Hubschle, Hermann Guillory, Joseph Yuan, Wenlin Zhang, Jingli Stinson, Jeremy Jaiswal, Bijay Pahuja, Kanika Bajaj Mann, Ishminder Schaal, Thomas Chan, Leo Anandakrishnan, Sangeetha Lin, Chun-wah Espinoza, Patricio Husain, Syed Shapiro, Harris Swaminathan, Karthikeyan Wei, Sherry Srinivasan, Maithreyan Seshagiri, Somasekar Modrusan, Zora BMC Genomics Methodology Article BACKGROUND: Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. RESULTS: Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells. CONCLUSIONS: Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3893-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-07 /pmc/articles/PMC5501953/ /pubmed/28687070 http://dx.doi.org/10.1186/s12864-017-3893-1 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Goldstein, Leonard D. Chen, Ying-Jiun Jasmine Dunne, Jude Mir, Alain Hubschle, Hermann Guillory, Joseph Yuan, Wenlin Zhang, Jingli Stinson, Jeremy Jaiswal, Bijay Pahuja, Kanika Bajaj Mann, Ishminder Schaal, Thomas Chan, Leo Anandakrishnan, Sangeetha Lin, Chun-wah Espinoza, Patricio Husain, Syed Shapiro, Harris Swaminathan, Karthikeyan Wei, Sherry Srinivasan, Maithreyan Seshagiri, Somasekar Modrusan, Zora Massively parallel nanowell-based single-cell gene expression profiling |
title | Massively parallel nanowell-based single-cell gene expression profiling |
title_full | Massively parallel nanowell-based single-cell gene expression profiling |
title_fullStr | Massively parallel nanowell-based single-cell gene expression profiling |
title_full_unstemmed | Massively parallel nanowell-based single-cell gene expression profiling |
title_short | Massively parallel nanowell-based single-cell gene expression profiling |
title_sort | massively parallel nanowell-based single-cell gene expression profiling |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501953/ https://www.ncbi.nlm.nih.gov/pubmed/28687070 http://dx.doi.org/10.1186/s12864-017-3893-1 |
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