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The living microarray: a high-throughput platform for measuring transcription dynamics in single cells
BACKGROUND: Current methods of measuring transcription in high-throughput have led to significant improvements in our knowledge of transcriptional regulation and Systems Biology. However, endpoint measurements obtained from methods that pool populations of cells are not amenable to studying time-dep...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3050818/ https://www.ncbi.nlm.nih.gov/pubmed/21324195 http://dx.doi.org/10.1186/1471-2164-12-115 |
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author | Rajan, Saravanan Djambazian, Haig Dang, Huan Chu Pham Sladek, Rob Hudson, Thomas J |
author_facet | Rajan, Saravanan Djambazian, Haig Dang, Huan Chu Pham Sladek, Rob Hudson, Thomas J |
author_sort | Rajan, Saravanan |
collection | PubMed |
description | BACKGROUND: Current methods of measuring transcription in high-throughput have led to significant improvements in our knowledge of transcriptional regulation and Systems Biology. However, endpoint measurements obtained from methods that pool populations of cells are not amenable to studying time-dependent processes that show cell heterogeneity. RESULTS: Here we describe a high-throughput platform for measuring transcriptional changes in real time in single mammalian cells. By using reverse transfection microarrays we are able to transfect fluorescent reporter plasmids into 600 independent clusters of cells plated on a single microscope slide and image these clusters every 20 minutes. We use a fast-maturing, destabilized and nuclear-localized reporter that is suitable for automated segmentation to accurately measure promoter activity in single cells. We tested this platform with synthetic drug-inducible promoters that showed robust induction over 24 hours. Automated segmentation and tracking of over 11 million cell images during this period revealed that cells display substantial heterogeneity in their responses to the applied treatment, including a large proportion of transfected cells that do not respond at all. CONCLUSIONS: The results from our single-cell analysis suggest that methods that measure average cellular responses, such as DNA microarrays, RT-PCR and chromatin immunoprecipitation, characterize a response skewed by a subset of cells in the population. Our method is scalable and readily adaptable to studying complex systems, including cell proliferation, differentiation and apoptosis. |
format | Text |
id | pubmed-3050818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30508182011-04-06 The living microarray: a high-throughput platform for measuring transcription dynamics in single cells Rajan, Saravanan Djambazian, Haig Dang, Huan Chu Pham Sladek, Rob Hudson, Thomas J BMC Genomics Methodology Article BACKGROUND: Current methods of measuring transcription in high-throughput have led to significant improvements in our knowledge of transcriptional regulation and Systems Biology. However, endpoint measurements obtained from methods that pool populations of cells are not amenable to studying time-dependent processes that show cell heterogeneity. RESULTS: Here we describe a high-throughput platform for measuring transcriptional changes in real time in single mammalian cells. By using reverse transfection microarrays we are able to transfect fluorescent reporter plasmids into 600 independent clusters of cells plated on a single microscope slide and image these clusters every 20 minutes. We use a fast-maturing, destabilized and nuclear-localized reporter that is suitable for automated segmentation to accurately measure promoter activity in single cells. We tested this platform with synthetic drug-inducible promoters that showed robust induction over 24 hours. Automated segmentation and tracking of over 11 million cell images during this period revealed that cells display substantial heterogeneity in their responses to the applied treatment, including a large proportion of transfected cells that do not respond at all. CONCLUSIONS: The results from our single-cell analysis suggest that methods that measure average cellular responses, such as DNA microarrays, RT-PCR and chromatin immunoprecipitation, characterize a response skewed by a subset of cells in the population. Our method is scalable and readily adaptable to studying complex systems, including cell proliferation, differentiation and apoptosis. BioMed Central 2011-02-16 /pmc/articles/PMC3050818/ /pubmed/21324195 http://dx.doi.org/10.1186/1471-2164-12-115 Text en Copyright ©2011 Rajan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Rajan, Saravanan Djambazian, Haig Dang, Huan Chu Pham Sladek, Rob Hudson, Thomas J The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title | The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title_full | The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title_fullStr | The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title_full_unstemmed | The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title_short | The living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
title_sort | living microarray: a high-throughput platform for measuring transcription dynamics in single cells |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3050818/ https://www.ncbi.nlm.nih.gov/pubmed/21324195 http://dx.doi.org/10.1186/1471-2164-12-115 |
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