CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice
Abstract Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and th...
Saved in:
Published in | Briefings in bioinformatics Vol. 22; no. 4 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Oxford University Press
01.07.2021
Oxford Publishing Limited (England) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Abstract
Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes. |
---|---|
AbstractList | Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes. Abstract Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes. Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes. |
Author | Lu, Xiaoyan Fan, Xiaohui Cheng, Junyun Liao, Jie Shao, Xin Li, Chengyu |
Author_xml | – sequence: 1 givenname: Xin surname: Shao fullname: Shao, Xin email: xin_shao@zju.edu.cn – sequence: 2 givenname: Jie surname: Liao fullname: Liao, Jie email: liaojie@zju.edu.cn – sequence: 3 givenname: Chengyu surname: Li fullname: Li, Chengyu email: lichengyu3830@qq.com – sequence: 4 givenname: Xiaoyan surname: Lu fullname: Lu, Xiaoyan email: luxy@zju.edu.cn – sequence: 5 givenname: Junyun surname: Cheng fullname: Cheng, Junyun email: 21819061@zju.edu.cn – sequence: 6 givenname: Xiaohui orcidid: 0000-0002-6336-3007 surname: Fan fullname: Fan, Xiaohui email: fanxh@zju.edu.cn |
BookMark | eNp90EtKBDEQBuAgCj5XXiAgiCCtSToP407HJwhudCc0lXRaoz2dMUkvZucdvKEnMcO4EnSVCnxVVP2baHUIg0Nol5IjSnR9bLw5NgaASb2CNihXquJE8NVFLVUluKzX0WZKr4Qwok7oBnqauL5_gP7t4vwUA57CMELfz7EdI2TX4hYyGEgOhw73_hmG9uvjMzrrZjlE7IfsItjsw5DKB7-MZUDCReGpt24brXXQJ7fz826hx6vLh8lNdXd_fTs5u6tsrWmuWs6At4w6bpjgXAijXKeYqQWjmklurZStdFyLE8KBaGJo2xphF9AK2dVb6GA5dxbD--hSbqY-2XIYDC6MqWFcKK1qIlWhe7_oaxjjULZrmNCEcKEpK4oulY0hpei6xvoMizNzBN83lDSLvJuSd_OTd-k5_NUzi34Kcf6H3l_qMM7-hd8CpJIj |
CitedBy_id | crossref_primary_10_1093_nar_gkad914 crossref_primary_10_1016_j_isci_2024_109386 crossref_primary_10_3389_fimmu_2022_884561 crossref_primary_10_1186_s40779_022_00434_8 crossref_primary_10_1016_j_ccell_2023_02_004 crossref_primary_10_1186_s13059_024_03385_6 crossref_primary_10_1109_JBHI_2023_3333828 crossref_primary_10_1093_bib_bbab130 crossref_primary_10_1093_bioinformatics_btac654 crossref_primary_10_1109_TNB_2023_3278685 crossref_primary_10_1038_s41590_021_00920_6 crossref_primary_10_1016_j_mbplus_2022_100122 crossref_primary_10_1016_j_cell_2024_01_021 crossref_primary_10_1038_s41593_023_01334_3 crossref_primary_10_3390_cells12121645 crossref_primary_10_3389_fmicb_2022_846555 crossref_primary_10_7554_eLife_86493 crossref_primary_10_1016_j_celrep_2023_112412 crossref_primary_10_1002_adtp_202300283 crossref_primary_10_1038_s41598_024_78954_7 crossref_primary_10_3390_cancers14194957 crossref_primary_10_3390_cells12151970 crossref_primary_10_1093_bioinformatics_btad596 crossref_primary_10_3389_fgene_2024_1322886 crossref_primary_10_1093_nar_gkab905 crossref_primary_10_3389_fimmu_2022_766852 crossref_primary_10_1038_s42003_023_05283_2 crossref_primary_10_1186_s12964_023_01184_3 crossref_primary_10_3390_cells11213405 crossref_primary_10_1038_s41467_022_32111_8 crossref_primary_10_3389_fcell_2021_703489 crossref_primary_10_1016_j_eng_2023_12_004 crossref_primary_10_1002_alz_13790 crossref_primary_10_1016_j_crmeth_2025_100985 crossref_primary_10_1007_s10142_024_01524_7 crossref_primary_10_1038_s41421_022_00415_0 crossref_primary_10_1016_j_stem_2023_03_016 crossref_primary_10_1186_s13287_024_03982_z crossref_primary_10_1016_j_bbrep_2024_101802 crossref_primary_10_1093_bfgp_elac019 crossref_primary_10_1016_j_csbj_2024_06_020 crossref_primary_10_3389_fmicb_2023_1126896 crossref_primary_10_1093_bioinformatics_btaf027 crossref_primary_10_1186_s13578_021_00635_z crossref_primary_10_1038_s43018_023_00527_w crossref_primary_10_7554_eLife_79585 crossref_primary_10_3390_ijms23137452 crossref_primary_10_1007_s11427_023_2561_0 crossref_primary_10_1093_nargab_lqac069 crossref_primary_10_3390_antiox11122376 crossref_primary_10_1016_j_compbiolchem_2025_108353 crossref_primary_10_1038_s41467_022_33365_y crossref_primary_10_1016_j_asoc_2025_112839 crossref_primary_10_3390_genes14071368 crossref_primary_10_1093_database_baae098 crossref_primary_10_1093_bioinformatics_btac447 crossref_primary_10_1016_j_cels_2023_07_007 crossref_primary_10_1016_j_csbj_2023_06_016 crossref_primary_10_1016_j_cels_2024_10_004 crossref_primary_10_1016_j_isci_2023_106025 crossref_primary_10_1038_s41467_021_21246_9 crossref_primary_10_1016_j_jgg_2023_07_011 crossref_primary_10_1038_s41540_024_00391_z crossref_primary_10_1093_bib_bbac234 crossref_primary_10_3390_ijms26010275 crossref_primary_10_1042_BST20210863 crossref_primary_10_1038_s41467_021_27276_7 crossref_primary_10_18632_aging_205538 crossref_primary_10_1038_s41467_022_34271_z crossref_primary_10_1038_s44161_023_00223_z crossref_primary_10_1126_science_abp9444 crossref_primary_10_1186_s13040_024_00421_w crossref_primary_10_1038_s43856_024_00530_x crossref_primary_10_1186_s13059_024_03435_z crossref_primary_10_1371_journal_ppat_1012565 crossref_primary_10_1038_s41586_024_07746_w crossref_primary_10_1186_s12915_024_01950_w crossref_primary_10_1158_2326_6066_CIR_23_0211 crossref_primary_10_1093_bioinformatics_btad269 crossref_primary_10_1038_s43587_023_00514_x crossref_primary_10_1016_j_cell_2024_10_023 crossref_primary_10_1093_gigascience_giae078 crossref_primary_10_3390_ijms23020946 crossref_primary_10_3390_cancers15010176 crossref_primary_10_1093_gpbjnl_qzae058 crossref_primary_10_1016_j_ebiom_2024_105102 crossref_primary_10_1038_s41588_024_01709_7 crossref_primary_10_1038_s41596_024_01045_4 crossref_primary_10_1093_bib_bbae716 crossref_primary_10_1172_jci_insight_147413 crossref_primary_10_1084_jem_20221437 crossref_primary_10_1016_j_compbiomed_2024_108110 crossref_primary_10_1186_s12864_022_08811_2 crossref_primary_10_1126_sciadv_adj9173 crossref_primary_10_1016_j_ajt_2024_04_015 crossref_primary_10_3389_fphar_2024_1442752 crossref_primary_10_1016_j_crmeth_2024_100758 crossref_primary_10_1038_s41698_025_00841_9 crossref_primary_10_1186_s12964_024_01640_8 crossref_primary_10_1016_j_compbiomed_2023_107137 crossref_primary_10_1093_bioadv_vbae101 crossref_primary_10_3390_cells13110945 crossref_primary_10_3389_fimmu_2023_1123652 crossref_primary_10_1016_j_celrep_2024_114900 crossref_primary_10_1186_s12859_023_05490_y crossref_primary_10_1038_s41586_023_06464_z crossref_primary_10_1038_s41698_024_00660_4 crossref_primary_10_3390_cancers15164188 crossref_primary_10_1002_advs_202310266 crossref_primary_10_1111_jcmm_18372 crossref_primary_10_1016_j_isci_2023_107309 crossref_primary_10_1126_sciadv_adq5842 crossref_primary_10_1093_bioinformatics_btad175 crossref_primary_10_1007_s11427_023_2557_x crossref_primary_10_1093_nar_gkac333 crossref_primary_10_1038_s41597_023_02342_5 crossref_primary_10_1016_j_bsheal_2022_03_001 crossref_primary_10_1093_bib_bbae619 crossref_primary_10_3389_fgene_2021_667382 crossref_primary_10_1038_s41556_024_01403_0 crossref_primary_10_1016_j_xfss_2024_02_001 crossref_primary_10_1038_s41586_024_07715_3 crossref_primary_10_1093_nar_gkad820 crossref_primary_10_1111_pbi_13893 crossref_primary_10_1038_s44320_023_00006_5 crossref_primary_10_1186_s12974_024_03161_0 crossref_primary_10_1002_alz_14419 crossref_primary_10_1038_s41467_022_30755_0 crossref_primary_10_1093_bioinformatics_btab036 crossref_primary_10_3389_fgene_2022_851719 crossref_primary_10_7554_eLife_93326 crossref_primary_10_3389_fimmu_2021_724855 crossref_primary_10_1016_j_phrs_2022_106308 crossref_primary_10_1007_s13238_022_00915_5 crossref_primary_10_1038_s43018_022_00447_1 crossref_primary_10_1186_s12958_021_00818_w crossref_primary_10_1038_s41421_021_00260_7 crossref_primary_10_1016_j_jpha_2023_06_011 |
Cites_doi | 10.1093/nar/gkaa183 10.1093/nar/gky1131 10.1007/s13238-019-0637-9 10.1016/j.celrep.2018.10.047 10.1016/j.cell.2018.09.009 10.1083/jcb.201804101 10.1016/j.neuron.2017.09.026 10.1016/j.cell.2015.04.044 10.1038/s41591-019-0468-5 10.1038/ncomms8866 10.1038/nchembio.2391 10.1074/jbc.M100097200 10.1038/s41592-019-0540-6 10.1183/09031936.93.03060653 10.1016/j.cels.2018.01.014 10.1016/j.celrep.2019.01.112 10.1016/j.isci.2019.10.026 10.1093/nar/gks960 10.1038/ng755 10.1084/jem.181.1.411 10.1038/ni.1984 10.1007/s13238-020-00727-5 10.3389/fimmu.2018.01553 10.1158/0008-5472.CAN-20-1049 10.1126/stke.2003.187.re9 10.1038/s41596-020-0292-x 10.1016/j.isci.2020.100882 10.1016/j.molcel.2019.07.028 10.1016/j.cell.2015.05.002 |
ContentType | Journal Article |
Copyright | The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. |
Copyright_xml | – notice: The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 – notice: The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com – notice: The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. |
DBID | AAYXX CITATION 7QO 7SC 8FD FR3 JQ2 K9. L7M L~C L~D P64 RC3 7X8 |
DOI | 10.1093/bib/bbaa269 |
DatabaseName | CrossRef Biotechnology Research Abstracts Computer and Information Systems Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic |
DatabaseTitle | CrossRef Genetics Abstracts Biotechnology Research Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Engineering Research Database Advanced Technologies Database with Aerospace Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic Genetics Abstracts CrossRef |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Biology |
EISSN | 1477-4054 |
ExternalDocumentID | 10_1093_bib_bbaa269 10.1093/bib/bbaa269 |
GroupedDBID | --- -E4 .2P .I3 0R~ 1TH 23N 2WC 36B 4.4 48X 53G 5GY 5VS 6J9 70D 8VB AAHBH AAIJN AAIMJ AAJKP AAJQQ AAMDB AAMVS AAOGV AAPQZ AAPXW AARHZ AASNB AAUQX AAVAP AAVLN ABDBF ABEUO ABIXL ABJNI ABNKS ABPTD ABQLI ABQTQ ABWST ABXVV ABZBJ ACGFO ACGFS ACGOD ACIWK ACPRK ACUFI ACYTK ADBBV ADEYI ADFTL ADGKP ADGZP ADHKW ADHZD ADOCK ADPDF ADQBN ADRDM ADRIX ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEGXH AEJOX AEKKA AEKSI AELWJ AEMDU AEMOZ AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AFXEN AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AIAGR AIJHB AJEEA AJEUX AKHUL AKVCP AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC APIBT APWMN ARIXL AXUDD AYOIW AZVOD BAWUL BAYMD BCRHZ BEYMZ BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K E3Z EAD EAP EAS EBA EBC EBD EBR EBS EBU EE~ EJD EMB EMK EMOBN EST ESX F5P F9B FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GX1 H13 H5~ HAR HW0 HZ~ IOX J21 K1G KBUDW KOP KSI KSN M-Z M49 MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NU- O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. Q5Y QWB RD5 ROX RPM RUSNO RW1 RXO SV3 TEORI TH9 TJP TLC TOX TR2 TUS W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ZL0 ~91 AAYXX ABEJV ABGNP ABPQP ABXZS ACUHS ACUXJ AHGBF AHQJS ALXQX AMNDL ANAKG CITATION JXSIZ 7QO 7SC 8FD FR3 JQ2 K9. L7M L~C L~D P64 RC3 7X8 |
ID | FETCH-LOGICAL-c391t-d42a4d21e4b254455b7ef72b35219264cc66d6e495804a090b1ddb5c55b7c56f3 |
IEDL.DBID | TOX |
ISSN | 1467-5463 1477-4054 |
IngestDate | Thu Jul 10 16:39:31 EDT 2025 Mon Jun 30 08:58:37 EDT 2025 Tue Jul 01 03:39:31 EDT 2025 Thu Apr 24 23:06:37 EDT 2025 Wed Aug 28 03:20:06 EDT 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 4 |
Keywords | cell–cell-communication ligand–receptor interaction mouse single-cell transcriptomics scRNA-seq human |
Language | English |
License | This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c391t-d42a4d21e4b254455b7ef72b35219264cc66d6e495804a090b1ddb5c55b7c56f3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0002-6336-3007 |
PQID | 2590045912 |
PQPubID | 26846 |
ParticipantIDs | proquest_miscellaneous_2457973067 proquest_journals_2590045912 crossref_citationtrail_10_1093_bib_bbaa269 crossref_primary_10_1093_bib_bbaa269 oup_primary_10_1093_bib_bbaa269 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2021-07-01 |
PublicationDateYYYYMMDD | 2021-07-01 |
PublicationDate_xml | – month: 07 year: 2021 text: 2021-07-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Oxford |
PublicationPlace_xml | – name: Oxford |
PublicationTitle | Briefings in bioinformatics |
PublicationYear | 2021 |
Publisher | Oxford University Press Oxford Publishing Limited (England) |
Publisher_xml | – name: Oxford University Press – name: Oxford Publishing Limited (England) |
References | Shao (2021072112311563600_ref8) 2020; 23 Rochemonteix-Galve (2021072112311563600_ref23) 1990; 3 Macosko (2021072112311563600_ref25) 2015; 161 Marx (2021072112311563600_ref29) 2019; 16 Nguyen (2021072112311563600_ref27) 2018; 9 Torre (2021072112311563600_ref28) 2018; 6 Liu (2021072112311563600_ref17) 2019; 26 Sharman (2021072112311563600_ref13) 2013; 41 Cabello-Aguilar (2021072112311563600_ref10) 2020; 48 Klein (2021072112311563600_ref26) 2015; 161 Graeber (2021072112311563600_ref12) 2001; 29 Islam (2021072112311563600_ref20) 2011; 12 Szklarczyk (2021072112311563600_ref21) 2019; 47 Xiong (2021072112311563600_ref6) 2019; 75 Sheikh (2021072112311563600_ref4) 2019; 21 Ben-Shlomo (2021072112311563600_ref14) 2003; 2003 Zheng (2021072112311563600_ref18) 2019; 218 Wang (2021072112311563600_ref16) 2020; 80 Efremova (2021072112311563600_ref9) 2020 Ramilowski (2021072112311563600_ref11) 2015; 6 Shao (2021072112311563600_ref2) 2020 Kumar (2021072112311563600_ref5) 2018; 25 Gartner (2021072112311563600_ref1) 2017; 13 Cohen (2021072112311563600_ref7) 2018; 175 Yu (2021072112311563600_ref15) 2019; 10 Wu (2021072112311563600_ref24) 2017; 96 Sadahira (2021072112311563600_ref3) 1995; 181 Baldwin (2021072112311563600_ref19) 2001; 276 Liao (2021072112311563600_ref30) 2020; S0167-7799 Vieira Braga (2021072112311563600_ref22) 2019; 25 |
References_xml | – volume: 48 start-page: e55 issue: 10 year: 2020 ident: 2021072112311563600_ref10 article-title: SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics publication-title: Nucleic Acids Res doi: 10.1093/nar/gkaa183 – volume: 47 start-page: D607 issue: D1 year: 2019 ident: 2021072112311563600_ref21 article-title: STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1131 – volume: 10 start-page: 668 issue: 9 year: 2019 ident: 2021072112311563600_ref15 article-title: Core pluripotency factors promote glycolysis of human embryonic stem cells by activating GLUT1 enhancer publication-title: Protein Cell doi: 10.1007/s13238-019-0637-9 – volume: 25 start-page: 1458 issue: 6 year: 2018 ident: 2021072112311563600_ref5 article-title: Analysis of single-cell RNA-Seq identifies cell-cell communication associated with tumor characteristics publication-title: Cell Rep doi: 10.1016/j.celrep.2018.10.047 – volume: 175 start-page: 1031 issue: 4 year: 2018 ident: 2021072112311563600_ref7 article-title: Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting publication-title: Cell doi: 10.1016/j.cell.2018.09.009 – volume: 218 start-page: 1891 issue: 6 year: 2019 ident: 2021072112311563600_ref18 article-title: Somatic autophagy of axonal mitochondria in ischemic neurons publication-title: J Cell Biol doi: 10.1083/jcb.201804101 – volume: 96 start-page: 313 issue: 2 year: 2017 ident: 2021072112311563600_ref24 article-title: Detecting activated cell populations using single-cell RNA-Seq publication-title: Neuron doi: 10.1016/j.neuron.2017.09.026 – volume: 161 start-page: 1187 issue: 5 year: 2015 ident: 2021072112311563600_ref26 article-title: Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells publication-title: Cell doi: 10.1016/j.cell.2015.04.044 – volume: 25 start-page: 1153 issue: 7 year: 2019 ident: 2021072112311563600_ref22 article-title: A cellular census of human lungs identifies novel cell states in health and in asthma publication-title: Nat Med doi: 10.1038/s41591-019-0468-5 – volume: 6 year: 2015 ident: 2021072112311563600_ref11 article-title: A draft network of ligand-receptor-mediated multicellular signalling in human publication-title: Nat Commun doi: 10.1038/ncomms8866 – volume: 13 start-page: 564 issue: 6 year: 2017 ident: 2021072112311563600_ref1 article-title: Unraveling cell-to-cell signaling networks with chemical biology publication-title: Nat Chem Biol doi: 10.1038/nchembio.2391 – volume: 276 start-page: 19166 issue: 22 year: 2001 ident: 2021072112311563600_ref19 article-title: The specificity of receptor binding by vascular endothelial growth factor-d is different in mouse and man publication-title: J Biol Chem doi: 10.1074/jbc.M100097200 – volume: 16 start-page: 809 issue: 9 year: 2019 ident: 2021072112311563600_ref29 article-title: A dream of single-cell proteomics publication-title: Nat Methods doi: 10.1038/s41592-019-0540-6 – volume: 3 start-page: 653 issue: 6 year: 1990 ident: 2021072112311563600_ref23 article-title: Fibroblast-alveolar cell interactions in sarcoidosis and idiopathic pulmonary fibrosis: evidence for stimulatory and inhibitory cytokine production by alveolar cells publication-title: Eur Respir J doi: 10.1183/09031936.93.03060653 – volume: 6 start-page: 171 issue: 2 year: 2018 ident: 2021072112311563600_ref28 article-title: Rare cell detection by single-cell RNA sequencing as guided by single-molecule RNA FISH publication-title: Cell Syst doi: 10.1016/j.cels.2018.01.014 – volume: 26 start-page: 2540 issue: 10 year: 2019 ident: 2021072112311563600_ref17 article-title: The F-BAR domain of Rga7 relies on a cooperative mechanism of membrane binding with a partner protein during fission yeast cytokinesis publication-title: Cell Rep doi: 10.1016/j.celrep.2019.01.112 – volume: 21 start-page: 273 year: 2019 ident: 2021072112311563600_ref4 article-title: Systematic identification of cell-cell communication networks in the developing brain publication-title: iScience doi: 10.1016/j.isci.2019.10.026 – volume: 41 start-page: D1083 issue: Database issue year: 2013 ident: 2021072112311563600_ref13 article-title: IUPHAR-DB: updated database content and new features publication-title: Nucleic Acids Res doi: 10.1093/nar/gks960 – volume: 29 start-page: 295 issue: 3 year: 2001 ident: 2021072112311563600_ref12 article-title: Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles publication-title: Nat Genet doi: 10.1038/ng755 – volume: 181 start-page: 411 issue: 1 year: 1995 ident: 2021072112311563600_ref3 article-title: Very late activation antigen 4-vascular cell adhesion molecule 1 interaction is involved in the formation of erythroblastic islands publication-title: J Exp Med doi: 10.1084/jem.181.1.411 – volume: 12 start-page: 167 issue: 2 year: 2011 ident: 2021072112311563600_ref20 article-title: Mouse CCL8, a CCR8 agonist, promotes atopic dermatitis by recruiting IL-5+ T(H)2 cells publication-title: Nat Immunol doi: 10.1038/ni.1984 – year: 2020 ident: 2021072112311563600_ref2 article-title: New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data publication-title: Protein Cell doi: 10.1007/s13238-020-00727-5 – volume: 9 start-page: 1553 year: 2018 ident: 2021072112311563600_ref27 article-title: Single cell RNA sequencing of rare immune cell populations publication-title: Front Immunol doi: 10.3389/fimmu.2018.01553 – volume: 80 start-page: 3880 issue: 18 year: 2020 ident: 2021072112311563600_ref16 article-title: CHD4 promotes breast cancer progression as a coactivator of hypoxia-inducible factors publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-20-1049 – volume: 2003 start-page: RE9 issue: 187 year: 2003 ident: 2021072112311563600_ref14 article-title: Signaling receptome: a genomic and evolutionary perspective of plasma membrane receptors involved in signal transduction publication-title: Sci STKE doi: 10.1126/stke.2003.187.re9 – year: 2020 ident: 2021072112311563600_ref9 article-title: CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes publication-title: Nat Protoc doi: 10.1038/s41596-020-0292-x – volume: S0167-7799 start-page: 30140 issue: 20 year: 2020 ident: 2021072112311563600_ref30 article-title: Uncovering an Organ's molecular architecture at single-cell resolution by spatially resolved Transcriptomics publication-title: Trends Biotechnol – volume: 23 issue: 3 year: 2020 ident: 2021072112311563600_ref8 article-title: scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data publication-title: iScience doi: 10.1016/j.isci.2020.100882 – volume: 75 start-page: 644 issue: 3 year: 2019 ident: 2021072112311563600_ref6 article-title: Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis publication-title: Mol Cell doi: 10.1016/j.molcel.2019.07.028 – volume: 161 start-page: 1202 issue: 5 year: 2015 ident: 2021072112311563600_ref25 article-title: Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets publication-title: Cell doi: 10.1016/j.cell.2015.05.002 |
SSID | ssj0020781 |
Score | 2.6333032 |
Snippet | Abstract
Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various... Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological... Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological... |
SourceID | proquest crossref oup |
SourceType | Aggregation Database Enrichment Source Index Database Publisher |
SubjectTerms | Biological activity Cell interactions Communications Data mining Gene sequencing Heterogeneity Ligands Protein interaction Proteins Receptors |
Title | CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice |
URI | https://www.proquest.com/docview/2590045912 https://www.proquest.com/docview/2457973067 |
Volume | 22 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3NSsNAEF6kIHgRf7FadYWehNBku7tpvGlVige9tNCDEHZ2N1KMaWnaQ2--g2_ok7iTpIVK0WPYL2SZ_ZmZzMw3hDSN4gkY55aIEP9WKZF4IMLIs6HxQWvAckjMtniWvQF_GophlSCbbwjhR-0WjKAFoBSTWKfn1C9S5Pdfhiu_CvlqyiKi0EN296oM79e7a4pnrZhtefsWKuVxj-xWtiC9LRdvn2zZ7IBsl90hF4fktWvTtK_S9_u7G6roh0L20HRB9RzpHQzF5E5UQnSc0HT0pjLz_fnlLjA7cX40RR6IaVm1kLsHWjTjy6lDUexAf0QGjw_9bs-rmiF4uh0FM89wprhhgeVQsIoJCG0SMnAGlDPSJNdaSiOt83c6Pld-5ENgDAiNQC1k0j4mtWyc2RNCkeWroyHBICQXEYCwVvtRJ2FIlyeDOrleSirWFVM4NqxI4zJi3Y6dWONKrHXSXIEnJUHGZtilE_nfiMZyOeLqHOUxw6ambo4Bq5Or1bA7ARjWUJkdzx2Gu80Voutz-u9HzsgOw6SUIt-2QWqz6dyeO6tiBhfFnvoBpoPK0Q |
linkProvider | Oxford University Press |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=CellTalkDB%3A+a+manually+curated+database+of+ligand-receptor+interactions+in+humans+and+mice&rft.jtitle=Briefings+in+bioinformatics&rft.au=Shao%2C+Xin&rft.au=Liao%2C+Jie&rft.au=Li%2C+Chengyu&rft.au=Lu%2C+Xiaoyan&rft.date=2021-07-01&rft.issn=1477-4054&rft.eissn=1477-4054&rft.volume=22&rft.issue=4&rft_id=info:doi/10.1093%2Fbib%2Fbbaa269&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1467-5463&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1467-5463&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1467-5463&client=summon |