Comprehensive single-cell transcriptional profiling of a multicellular organism
To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis el...
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| Veröffentlicht in: | Science (American Association for the Advancement of Science) 2017-08, Vol.357 (6352), p.661-667 |
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| creator | Cao, Junyue Packer, Jonathan S. Ramani, Vijay Cusanovich, Darren A. Huynh, Chau Daza, Riza Qiu, Xiaojie Lee, Choli Furlan, Scott N. Steemers, Frank J. Adey, Andrew Waterston, Robert H. Trapnell, Cole Shendure, Jay |
| description | To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold “shotgun” cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms. |
| doi_str_mv | 10.1126/science.aam8940 |
| format | Article |
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We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold “shotgun” cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.</description><identifier>ISSN: 0036-8075</identifier><identifier>EISSN: 1095-9203</identifier><identifier>DOI: 10.1126/science.aam8940</identifier><identifier>PMID: 28818938</identifier><language>eng</language><publisher>United States: American Association for the Advancement of Science</publisher><subject>Animals ; Biological materials ; Caenorhabditis elegans ; Caenorhabditis elegans - cytology ; Caenorhabditis elegans - genetics ; Caenorhabditis elegans - growth & development ; Cell Nucleus - genetics ; Chromatin ; Chromatin Immunoprecipitation ; Combinatorial analysis ; Gene sequencing ; HEK293 Cells ; Humans ; Immunoprecipitation ; Indexing ; Larva - genetics ; Mice ; Nematodes ; Neurons - metabolism ; NIH 3T3 Cells ; Nuclei (cytology) ; Ribonucleic acid ; RNA ; RNA - genetics ; Sequence Analysis, RNA ; Single-Cell Analysis - methods ; Transcription factors ; Transcription Factors - genetics ; Transcriptome</subject><ispartof>Science (American Association for the Advancement of Science), 2017-08, Vol.357 (6352), p.661-667</ispartof><rights>Copyright © 2017 by the American Association for the Advancement of Science</rights><rights>Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.</rights><rights>Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c592t-94d674477e9ff62dc6dd308a94b608bc1ae646e04b338dd7e0935a269b5ad81d3</citedby><cites>FETCH-LOGICAL-c592t-94d674477e9ff62dc6dd308a94b608bc1ae646e04b338dd7e0935a269b5ad81d3</cites><orcidid>0000-0002-8901-1835 ; 0000-0001-5956-5215 ; 0000-0001-7648-8717 ; 0000-0003-4097-489X ; 0000-0003-3345-5960 ; 0000-0001-6889-0095 ; 0000-0003-4025-0007 ; 0000-0002-8105-4347 ; 0000-0002-1516-1865</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26399645$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26399645$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,776,780,799,881,2871,2872,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28818938$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cao, Junyue</creatorcontrib><creatorcontrib>Packer, Jonathan S.</creatorcontrib><creatorcontrib>Ramani, Vijay</creatorcontrib><creatorcontrib>Cusanovich, Darren A.</creatorcontrib><creatorcontrib>Huynh, Chau</creatorcontrib><creatorcontrib>Daza, Riza</creatorcontrib><creatorcontrib>Qiu, Xiaojie</creatorcontrib><creatorcontrib>Lee, Choli</creatorcontrib><creatorcontrib>Furlan, Scott N.</creatorcontrib><creatorcontrib>Steemers, Frank J.</creatorcontrib><creatorcontrib>Adey, Andrew</creatorcontrib><creatorcontrib>Waterston, Robert H.</creatorcontrib><creatorcontrib>Trapnell, Cole</creatorcontrib><creatorcontrib>Shendure, Jay</creatorcontrib><title>Comprehensive single-cell transcriptional profiling of a multicellular organism</title><title>Science (American Association for the Advancement of Science)</title><addtitle>Science</addtitle><description>To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold “shotgun” cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.</description><subject>Animals</subject><subject>Biological materials</subject><subject>Caenorhabditis elegans</subject><subject>Caenorhabditis elegans - cytology</subject><subject>Caenorhabditis elegans - genetics</subject><subject>Caenorhabditis elegans - growth & development</subject><subject>Cell Nucleus - genetics</subject><subject>Chromatin</subject><subject>Chromatin Immunoprecipitation</subject><subject>Combinatorial analysis</subject><subject>Gene sequencing</subject><subject>HEK293 Cells</subject><subject>Humans</subject><subject>Immunoprecipitation</subject><subject>Indexing</subject><subject>Larva - genetics</subject><subject>Mice</subject><subject>Nematodes</subject><subject>Neurons - metabolism</subject><subject>NIH 3T3 Cells</subject><subject>Nuclei (cytology)</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA - 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| ispartof | Science (American Association for the Advancement of Science), 2017-08, Vol.357 (6352), p.661-667 |
| issn | 0036-8075 1095-9203 |
| language | eng |
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| source | American Association for the Advancement of Science; Jstor Complete Legacy; MEDLINE |
| subjects | Animals Biological materials Caenorhabditis elegans Caenorhabditis elegans - cytology Caenorhabditis elegans - genetics Caenorhabditis elegans - growth & development Cell Nucleus - genetics Chromatin Chromatin Immunoprecipitation Combinatorial analysis Gene sequencing HEK293 Cells Humans Immunoprecipitation Indexing Larva - genetics Mice Nematodes Neurons - metabolism NIH 3T3 Cells Nuclei (cytology) Ribonucleic acid RNA RNA - genetics Sequence Analysis, RNA Single-Cell Analysis - methods Transcription factors Transcription Factors - genetics Transcriptome |
| title | Comprehensive single-cell transcriptional profiling of a multicellular organism |
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