Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells
Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive rest...
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Veröffentlicht in: | Nucleic acids research 2017-06, Vol.45 (10), p.e77-e77 |
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creator | Han, Lin Wu, Hua-Jun Zhu, Haiying Kim, Kun-Yong Marjani, Sadie L Riester, Markus Euskirchen, Ghia Zi, Xiaoyuan Yang, Jennifer Han, Jasper Snyder, Michael Park, In-Hyun Irizarry, Rafael Weissman, Sherman M Michor, Franziska Fan, Rong Pan, Xinghua |
description | Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population. |
doi_str_mv | 10.1093/nar/gkx026 |
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Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.</description><identifier>ISSN: 0305-1048</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkx026</identifier><identifier>PMID: 28126923</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Cell Line ; Cell Line, Tumor ; Chromosome Mapping ; CpG Islands ; DNA Methylation ; DNA Restriction Enzymes - chemistry ; Epigenesis, Genetic ; Fibroblasts - cytology ; Fibroblasts - metabolism ; Genetic Variation ; Genome, Human ; High-Throughput Nucleotide Sequencing ; Humans ; Induced Pluripotent Stem Cells - cytology ; Induced Pluripotent Stem Cells - metabolism ; K562 Cells ; Lymphocytes - cytology ; Lymphocytes - metabolism ; Methods Online ; Promoter Regions, Genetic ; Single-Cell Analysis - methods</subject><ispartof>Nucleic acids research, 2017-06, Vol.45 (10), p.e77-e77</ispartof><rights>The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><rights>The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c378t-feb7f6a446f05462dea895a0ab830f499377b83c13f4b956ba30b76432a051193</citedby><cites>FETCH-LOGICAL-c378t-feb7f6a446f05462dea895a0ab830f499377b83c13f4b956ba30b76432a051193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605247/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605247/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28126923$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Han, Lin</creatorcontrib><creatorcontrib>Wu, Hua-Jun</creatorcontrib><creatorcontrib>Zhu, Haiying</creatorcontrib><creatorcontrib>Kim, Kun-Yong</creatorcontrib><creatorcontrib>Marjani, Sadie L</creatorcontrib><creatorcontrib>Riester, Markus</creatorcontrib><creatorcontrib>Euskirchen, Ghia</creatorcontrib><creatorcontrib>Zi, Xiaoyuan</creatorcontrib><creatorcontrib>Yang, Jennifer</creatorcontrib><creatorcontrib>Han, Jasper</creatorcontrib><creatorcontrib>Snyder, Michael</creatorcontrib><creatorcontrib>Park, In-Hyun</creatorcontrib><creatorcontrib>Irizarry, Rafael</creatorcontrib><creatorcontrib>Weissman, Sherman M</creatorcontrib><creatorcontrib>Michor, Franziska</creatorcontrib><creatorcontrib>Fan, Rong</creatorcontrib><creatorcontrib>Pan, Xinghua</creatorcontrib><title>Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells</title><title>Nucleic acids research</title><addtitle>Nucleic Acids Res</addtitle><description>Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.</description><subject>Cell Line</subject><subject>Cell Line, Tumor</subject><subject>Chromosome Mapping</subject><subject>CpG Islands</subject><subject>DNA Methylation</subject><subject>DNA Restriction Enzymes - chemistry</subject><subject>Epigenesis, Genetic</subject><subject>Fibroblasts - cytology</subject><subject>Fibroblasts - metabolism</subject><subject>Genetic Variation</subject><subject>Genome, Human</subject><subject>High-Throughput Nucleotide Sequencing</subject><subject>Humans</subject><subject>Induced Pluripotent Stem Cells - cytology</subject><subject>Induced Pluripotent Stem Cells - metabolism</subject><subject>K562 Cells</subject><subject>Lymphocytes - cytology</subject><subject>Lymphocytes - metabolism</subject><subject>Methods Online</subject><subject>Promoter Regions, Genetic</subject><subject>Single-Cell Analysis - methods</subject><issn>0305-1048</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkU9rGzEQxUVJaRy3l36AsMdQ2Eb_Vru6BBKTuIVALu1ZzK5Hjhqt1tlZl_rbR8aJSS8awfvNmxkeY18F_y64VZcJxsv10z8uzQc2E8rIUlsjT9iMK16VguvmlJ0R_eFcaFHpT-xUNkIaK9WMxZtA2-jDhGVIK9xgftJUQIK4o0DF4IvFZlkEipBWRY_T4y7CFIZUYII2IhVrTEOPJXUQsaBpzKoP3YHJ3RTSOgsdxkif2UcPkfDLa52z33e3vxY_yvuH5c_F9X3ZqbqZSo9t7Q1obTyvtJErhMZWwKFtFPfaWlXX-dsJ5XVrK9OC4m1ttJLAKyGsmrOrg-9m2_a46vJFI0S3GUMP484NENz_SgqPbj38dZXhldR1Nrh4NRiH5y3S5PpA-xMg4bAlJxoj80ArREa_HdBuHIhG9Mcxgrt9PC7H4w7xZPj8_WJH9C0P9QKgBI8Q</recordid><startdate>20170602</startdate><enddate>20170602</enddate><creator>Han, Lin</creator><creator>Wu, Hua-Jun</creator><creator>Zhu, Haiying</creator><creator>Kim, Kun-Yong</creator><creator>Marjani, Sadie L</creator><creator>Riester, Markus</creator><creator>Euskirchen, Ghia</creator><creator>Zi, Xiaoyuan</creator><creator>Yang, Jennifer</creator><creator>Han, Jasper</creator><creator>Snyder, Michael</creator><creator>Park, In-Hyun</creator><creator>Irizarry, Rafael</creator><creator>Weissman, Sherman M</creator><creator>Michor, Franziska</creator><creator>Fan, Rong</creator><creator>Pan, Xinghua</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170602</creationdate><title>Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells</title><author>Han, Lin ; Wu, Hua-Jun ; Zhu, Haiying ; Kim, Kun-Yong ; Marjani, Sadie L ; Riester, Markus ; Euskirchen, Ghia ; Zi, Xiaoyuan ; Yang, Jennifer ; Han, Jasper ; Snyder, Michael ; Park, In-Hyun ; Irizarry, Rafael ; Weissman, Sherman M ; Michor, Franziska ; Fan, Rong ; Pan, Xinghua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c378t-feb7f6a446f05462dea895a0ab830f499377b83c13f4b956ba30b76432a051193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Cell Line</topic><topic>Cell Line, Tumor</topic><topic>Chromosome Mapping</topic><topic>CpG Islands</topic><topic>DNA Methylation</topic><topic>DNA Restriction Enzymes - chemistry</topic><topic>Epigenesis, Genetic</topic><topic>Fibroblasts - cytology</topic><topic>Fibroblasts - metabolism</topic><topic>Genetic Variation</topic><topic>Genome, Human</topic><topic>High-Throughput Nucleotide Sequencing</topic><topic>Humans</topic><topic>Induced Pluripotent Stem Cells - cytology</topic><topic>Induced Pluripotent Stem Cells - metabolism</topic><topic>K562 Cells</topic><topic>Lymphocytes - cytology</topic><topic>Lymphocytes - metabolism</topic><topic>Methods Online</topic><topic>Promoter Regions, Genetic</topic><topic>Single-Cell Analysis - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Lin</creatorcontrib><creatorcontrib>Wu, Hua-Jun</creatorcontrib><creatorcontrib>Zhu, Haiying</creatorcontrib><creatorcontrib>Kim, Kun-Yong</creatorcontrib><creatorcontrib>Marjani, Sadie L</creatorcontrib><creatorcontrib>Riester, Markus</creatorcontrib><creatorcontrib>Euskirchen, Ghia</creatorcontrib><creatorcontrib>Zi, Xiaoyuan</creatorcontrib><creatorcontrib>Yang, Jennifer</creatorcontrib><creatorcontrib>Han, Jasper</creatorcontrib><creatorcontrib>Snyder, Michael</creatorcontrib><creatorcontrib>Park, In-Hyun</creatorcontrib><creatorcontrib>Irizarry, Rafael</creatorcontrib><creatorcontrib>Weissman, Sherman M</creatorcontrib><creatorcontrib>Michor, Franziska</creatorcontrib><creatorcontrib>Fan, Rong</creatorcontrib><creatorcontrib>Pan, Xinghua</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Lin</au><au>Wu, Hua-Jun</au><au>Zhu, Haiying</au><au>Kim, Kun-Yong</au><au>Marjani, Sadie L</au><au>Riester, Markus</au><au>Euskirchen, Ghia</au><au>Zi, Xiaoyuan</au><au>Yang, Jennifer</au><au>Han, Jasper</au><au>Snyder, Michael</au><au>Park, In-Hyun</au><au>Irizarry, Rafael</au><au>Weissman, Sherman M</au><au>Michor, Franziska</au><au>Fan, Rong</au><au>Pan, Xinghua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2017-06-02</date><risdate>2017</risdate><volume>45</volume><issue>10</issue><spage>e77</spage><epage>e77</epage><pages>e77-e77</pages><issn>0305-1048</issn><eissn>1362-4962</eissn><abstract>Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>28126923</pmid><doi>10.1093/nar/gkx026</doi><oa>free_for_read</oa></addata></record> |
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subjects | Cell Line Cell Line, Tumor Chromosome Mapping CpG Islands DNA Methylation DNA Restriction Enzymes - chemistry Epigenesis, Genetic Fibroblasts - cytology Fibroblasts - metabolism Genetic Variation Genome, Human High-Throughput Nucleotide Sequencing Humans Induced Pluripotent Stem Cells - cytology Induced Pluripotent Stem Cells - metabolism K562 Cells Lymphocytes - cytology Lymphocytes - metabolism Methods Online Promoter Regions, Genetic Single-Cell Analysis - methods |
title | Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells |
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