Cargando…

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-...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
_version_ 1783248883432816640
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
work_keys_str_mv AT goldsteinleonardd massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT chenyingjiunjasmine massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT dunnejude massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT miralain massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT hubschlehermann massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT guilloryjoseph massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT yuanwenlin massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT zhangjingli massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT stinsonjeremy massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT jaiswalbijay massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT pahujakanikabajaj massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT mannishminder massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT schaalthomas massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT chanleo massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT anandakrishnansangeetha massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT linchunwah massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT espinozapatricio massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT husainsyed massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT shapiroharris massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT swaminathankarthikeyan massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT weisherry massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT srinivasanmaithreyan massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT seshagirisomasekar massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling
AT modrusanzora massivelyparallelnanowellbasedsinglecellgeneexpressionprofiling