<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Tabula Muris Consortium</submitter><funding>BLRD VA</funding><funding>NIDDK NIH HHS</funding><funding>NIA NIH HHS</funding><funding>RRD VA</funding><funding>NCI NIH HHS</funding><funding>NLM NIH HHS</funding><pagination>367-372</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC6642641</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>562(7727)</volume><pubmed_abstract>Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.</pubmed_abstract><journal>Nature</journal><pubmed_title>Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.</pubmed_title><pmcid>PMC6642641</pmcid><funding_grant_id>P30 DK026743</funding_grant_id><funding_grant_id>DP1 AG053015</funding_grant_id><funding_grant_id>I01 RX001222</funding_grant_id><funding_grant_id>P30 DK116074</funding_grant_id><funding_grant_id>I01 BX002324</funding_grant_id><funding_grant_id>R01 CA157877</funding_grant_id><funding_grant_id>K08 DK101603</funding_grant_id><funding_grant_id>DP1 LM012179</funding_grant_id><pubmed_authors>Travaglini KJ</pubmed_authors><pubmed_authors>Zhang F</pubmed_authors><pubmed_authors>Kershner AM</pubmed_authors><pubmed_authors>Lehallier B</pubmed_authors><pubmed_authors>Nabhan AN</pubmed_authors><pubmed_authors>George BM</pubmed_authors><pubmed_authors>Weinberg K</pubmed_authors><pubmed_authors>Castro P</pubmed_authors><pubmed_authors>Wyss-Coray T</pubmed_authors><pubmed_authors>Tato C</pubmed_authors><pubmed_authors>Xue S</pubmed_authors><pubmed_authors>Peng WC</pubmed_authors><pubmed_authors>Kumar ME</pubmed_authors><pubmed_authors>May AP</pubmed_authors><pubmed_authors>Baghel AS</pubmed_authors><pubmed_authors>Metzger RJ</pubmed_authors><pubmed_authors>Cirolia G</pubmed_authors><pubmed_authors>Lo A</pubmed_authors><pubmed_authors>Beachy PA</pubmed_authors><pubmed_authors>Overall coordination</pubmed_authors><pubmed_authors>Nusse R</pubmed_authors><pubmed_authors>Chan CKF</pubmed_authors><pubmed_authors>Hang Y</pubmed_authors><pubmed_authors>Iram T</pubmed_authors><pubmed_authors>Mignardi M</pubmed_authors><pubmed_authors>Lee DP</pubmed_authors><pubmed_authors>Library preparation and sequencing</pubmed_authors><pubmed_authors>Espinoza FH</pubmed_authors><pubmed_authors>Wu SM</pubmed_authors><pubmed_authors>McKay M</pubmed_authors><pubmed_authors>Hosseinzadeh S</pubmed_authors><pubmed_authors>Rando TA</pubmed_authors><pubmed_authors>Gulati GS</pubmed_authors><pubmed_authors>Sonnenburg J</pubmed_authors><pubmed_authors>Botvinnik O</pubmed_authors><pubmed_authors>Tabula Muris Consortium</pubmed_authors><pubmed_authors>Bilen B</pubmed_authors><pubmed_authors>Cain C</pubmed_authors><pubmed_authors>Clarke MF</pubmed_authors><pubmed_authors>Nguyen PK</pubmed_authors><pubmed_authors>Ives F</pubmed_authors><pubmed_authors>Zhou L</pubmed_authors><pubmed_authors>Supplemental text writing group</pubmed_authors><pubmed_authors>Computational data analysis</pubmed_authors><pubmed_authors>Kiss BM</pubmed_authors><pubmed_authors>Green F</pubmed_authors><pubmed_authors>Lee SE</pubmed_authors><pubmed_authors>Wang BM</pubmed_authors><pubmed_authors>de Morree A</pubmed_authors><pubmed_authors>May OL</pubmed_authors><pubmed_authors>Penland L</pubmed_authors><pubmed_authors>Noh J</pubmed_authors><pubmed_authors>Dulgeroff LBT</pubmed_authors><pubmed_authors>van Weele LJ</pubmed_authors><pubmed_authors>Maynard A</pubmed_authors><pubmed_authors>Ng KM</pubmed_authors><pubmed_authors>Zanini F</pubmed_authors><pubmed_authors>du Bois H</pubmed_authors><pubmed_authors>Quake SR</pubmed_authors><pubmed_authors>Sit RV</pubmed_authors><pubmed_authors>Bansal I</pubmed_authors><pubmed_authors>Chen MB</pubmed_authors><pubmed_authors>Szade K</pubmed_authors><pubmed_authors>Karnam G</pubmed_authors><pubmed_authors>Brownfield D</pubmed_authors><pubmed_authors>Ebadi H</pubmed_authors><pubmed_authors>Kong W</pubmed_authors><pubmed_authors>Tan SY</pubmed_authors><pubmed_authors>Pisco AO</pubmed_authors><pubmed_authors>Bakerman I</pubmed_authors><pubmed_authors>Youngyunpipatkul J</pubmed_authors><pubmed_authors>Krasnow MA</pubmed_authors><pubmed_authors>Webber JT</pubmed_authors><pubmed_authors>Berdnik D</pubmed_authors><pubmed_authors>Yousef H</pubmed_authors><pubmed_authors>Huang KC</pubmed_authors><pubmed_authors>Chen S</pubmed_authors><pubmed_authors>Huang A</pubmed_authors><pubmed_authors>Kim SK</pubmed_authors><pubmed_authors>Darmanis S</pubmed_authors><pubmed_authors>Li G</pubmed_authors><pubmed_authors>Sikandar SS</pubmed_authors><pubmed_authors>Cell type annotation</pubmed_authors><pubmed_authors>Batson J</pubmed_authors><pubmed_authors>Weissman IL</pubmed_authors><pubmed_authors>Kao KS</pubmed_authors><pubmed_authors>Li Q</pubmed_authors><pubmed_authors>Manjunath A</pubmed_authors><pubmed_authors>Zardeneta ME</pubmed_authors><pubmed_authors>Principal investigators</pubmed_authors><pubmed_authors>Demir K</pubmed_authors><pubmed_authors>Isobe T</pubmed_authors><pubmed_authors>Writing group</pubmed_authors><pubmed_authors>Sinha R</pubmed_authors><pubmed_authors>Organ collection and processing</pubmed_authors><pubmed_authors>Tan W</pubmed_authors><pubmed_authors>Croote D</pubmed_authors><pubmed_authors>Conley SD</pubmed_authors><pubmed_authors>Min D</pubmed_authors><pubmed_authors>Jones RC</pubmed_authors><pubmed_authors>Barres BA</pubmed_authors><pubmed_authors>Schaum N</pubmed_authors><pubmed_authors>Tropini C</pubmed_authors><pubmed_authors>Liu L</pubmed_authors><pubmed_authors>Xiang J</pubmed_authors><pubmed_authors>Lam JY</pubmed_authors><pubmed_authors>Lu WJ</pubmed_authors><pubmed_authors>Waldburger L</pubmed_authors><pubmed_authors>Tellez K</pubmed_authors><pubmed_authors>Kuo CS</pubmed_authors><pubmed_authors>Fish M</pubmed_authors><pubmed_authors>Gillich A</pubmed_authors><pubmed_authors>Cho M</pubmed_authors><pubmed_authors>Wosczyna MN</pubmed_authors><pubmed_authors>DeRisi JL</pubmed_authors><pubmed_authors>Gu X</pubmed_authors><pubmed_authors>Neff NF</pubmed_authors><pubmed_authors>Rulifson EJ</pubmed_authors><pubmed_authors>Puccinelli R</pubmed_authors><pubmed_authors>Divita T</pubmed_authors><pubmed_authors>May KL</pubmed_authors><pubmed_authors>Genetiano G</pubmed_authors><pubmed_authors>Demers A</pubmed_authors><pubmed_authors>Logistical coordination</pubmed_authors><pubmed_authors>Gan Q</pubmed_authors><pubmed_authors>Patkar R</pubmed_authors><pubmed_authors>Stanley GM</pubmed_authors><pubmed_authors>Karkanias J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.</name><description>Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.</description><dates><release>2018-01-01T00:00:00Z</release><publication>2018 Oct</publication><modification>2020-11-19T08:28:27Z</modification><creation>2019-07-25T07:17:54Z</creation></dates><accession>S-EPMC6642641</accession><cross_references><pubmed>30283141</pubmed><doi>10.1038/s41586-018-0590-4</doi></cross_references></HashMap>