Home > Publications database > Single-cell time series analysis reveals the dynamics of HSPC response to inflammation. > print |
001 | 286281 | ||
005 | 20240229155124.0 | ||
024 | 7 | _ | |2 doi |a 10.26508/lsa.202302309 |
024 | 7 | _ | |2 pmid |a pmid:38110222 |
024 | 7 | _ | |a altmetric:157589175 |2 altmetric |
037 | _ | _ | |a DKFZ-2023-02768 |
041 | _ | _ | |a English |
082 | _ | _ | |a 570 |
100 | 1 | _ | |a Bouman, Brigitte J |b 0 |
245 | _ | _ | |a Single-cell time series analysis reveals the dynamics of HSPC response to inflammation. |
260 | _ | _ | |a Heidelberg |b EMBO Press |c 2023 |
336 | 7 | _ | |2 DRIVER |a article |
336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |b journal |m journal |s 1703152421_4066 |
336 | 7 | _ | |2 BibTeX |a ARTICLE |
336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
500 | _ | _ | |a DKFZ–ZMBH Alliance / #EA:A011#LA:A011# |
520 | _ | _ | |a Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs. |
536 | _ | _ | |0 G:(DE-HGF)POF4-311 |a 311 - Zellbiologie und Tumorbiologie (POF4-311) |c POF4-311 |f POF IV |x 0 |
588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
650 | _ | 2 | |2 MeSH |a Humans |
650 | _ | 2 | |2 MeSH |a Time Factors |
650 | _ | 2 | |2 MeSH |a Hematopoietic Stem Cells |
650 | _ | 2 | |2 MeSH |a Cell Differentiation: genetics |
650 | _ | 2 | |2 MeSH |a Hematopoiesis: genetics |
650 | _ | 2 | |2 MeSH |a Inflammation: metabolism |
700 | 1 | _ | |0 P:(DE-He78)5dd8fe881be96755fdff6576c9f8c11f |a Demerdash, Yasmin |b 1 |e First author |u dkfz |
700 | 1 | _ | |0 P:(DE-He78)dbe2fd9578ec0bf59e0e17592b04f22f |a Sood, Shubhankar |b 2 |u dkfz |
700 | 1 | _ | |0 P:(DE-He78)6b879b5f67d8b3bcdffc7c01cbf8d1f1 |a Grünschläger, Florian |b 3 |u dkfz |
700 | 1 | _ | |0 P:(DE-He78)fc5931eb2c72c84c56b289eeedc6758c |a Pilz, Franziska |b 4 |u dkfz |
700 | 1 | _ | |0 P:(DE-He78)95e6d7666dcf19dcfa2b9a9a6ada3d86 |a Itani, Abdul Rahman |b 5 |u dkfz |
700 | 1 | _ | |0 P:(DE-He78)fa2049c5d1b3a53b145d94b5830b3c47 |a Kuck, Andrea |b 6 |u dkfz |
700 | 1 | _ | |0 0000-0002-8362-9634 |a Marot-Lassauzaie, Valérie |b 7 |
700 | 1 | _ | |0 P:(DE-He78)a5ec4e2fef99022a37a6b07c2fdd6325 |a Haas, Simon |b 8 |u dkfz |
700 | 1 | _ | |0 0000-0001-9280-9170 |a Haghverdi, Laleh |b 9 |
700 | 1 | _ | |0 P:(DE-He78)ba3fae49054b6bfaaa289b05ecd936d6 |a Essers, Marieke |b 10 |e Last author |u dkfz |
773 | _ | _ | |0 PERI:(DE-600)2948687-7 |a 10.26508/lsa.202302309 |g Vol. 7, no. 3, p. e202302309 - |n 3 |p e202302309 |t Life science alliance |v 7 |x 2575-1077 |y 2024 |
909 | C | O | |o oai:inrepo02.dkfz.de:286281 |p VDB |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)5dd8fe881be96755fdff6576c9f8c11f |a Deutsches Krebsforschungszentrum |b 1 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)dbe2fd9578ec0bf59e0e17592b04f22f |a Deutsches Krebsforschungszentrum |b 2 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)6b879b5f67d8b3bcdffc7c01cbf8d1f1 |a Deutsches Krebsforschungszentrum |b 3 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)fc5931eb2c72c84c56b289eeedc6758c |a Deutsches Krebsforschungszentrum |b 4 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)95e6d7666dcf19dcfa2b9a9a6ada3d86 |a Deutsches Krebsforschungszentrum |b 5 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)fa2049c5d1b3a53b145d94b5830b3c47 |a Deutsches Krebsforschungszentrum |b 6 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)a5ec4e2fef99022a37a6b07c2fdd6325 |a Deutsches Krebsforschungszentrum |b 8 |k DKFZ |
910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)ba3fae49054b6bfaaa289b05ecd936d6 |a Deutsches Krebsforschungszentrum |b 10 |k DKFZ |
913 | 1 | _ | |0 G:(DE-HGF)POF4-311 |1 G:(DE-HGF)POF4-310 |2 G:(DE-HGF)POF4-300 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |a DE-HGF |b Gesundheit |l Krebsforschung |v Zellbiologie und Tumorbiologie |x 0 |
914 | 1 | _ | |y 2023 |
915 | _ | _ | |0 StatID:(DE-HGF)0100 |2 StatID |a JCR |b LIFE SCI ALLIANCE : 2022 |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0200 |2 StatID |a DBCoverage |b SCOPUS |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0300 |2 StatID |a DBCoverage |b Medline |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0320 |2 StatID |a DBCoverage |b PubMed Central |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0501 |2 StatID |a DBCoverage |b DOAJ Seal |d 2022-02-08T19:01:09Z |
915 | _ | _ | |0 StatID:(DE-HGF)0500 |2 StatID |a DBCoverage |b DOAJ |d 2022-02-08T19:01:09Z |
915 | _ | _ | |0 StatID:(DE-HGF)0030 |2 StatID |a Peer Review |b DOAJ : Anonymous peer review |d 2022-02-08T19:01:09Z |
915 | _ | _ | |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |a Creative Commons Attribution CC BY (No Version) |b DOAJ |d 2022-02-08T19:01:09Z |
915 | _ | _ | |0 StatID:(DE-HGF)0199 |2 StatID |a DBCoverage |b Clarivate Analytics Master Journal List |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)1050 |2 StatID |a DBCoverage |b BIOSIS Previews |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0113 |2 StatID |a WoS |b Science Citation Index Expanded |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0150 |2 StatID |a DBCoverage |b Web of Science Core Collection |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)1030 |2 StatID |a DBCoverage |b Current Contents - Life Sciences |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)1190 |2 StatID |a DBCoverage |b Biological Abstracts |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0160 |2 StatID |a DBCoverage |b Essential Science Indicators |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)9900 |2 StatID |a IF < 5 |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0561 |2 StatID |a Article Processing Charges |d 2023-10-27 |
915 | _ | _ | |0 StatID:(DE-HGF)0700 |2 StatID |a Fees |d 2023-10-27 |
920 | 2 | _ | |0 I:(DE-He78)A011-20160331 |k A011 |l A011 Stressinduzierte Aktivierung von Hämatopeotischen Stammzellen |x 0 |
920 | 1 | _ | |0 I:(DE-He78)A011-20160331 |k A011 |l A011 Stressinduzierte Aktivierung von Hämatopeotischen Stammzellen |x 0 |
920 | 1 | _ | |0 I:(DE-He78)A010-20160331 |k A010 |l A010 Stammzellen und Krebs |x 1 |
920 | 1 | _ | |0 I:(DE-He78)BE01-20160331 |k BE01 |l DKTK Koordinierungsstelle Berlin |x 2 |
920 | 0 | _ | |0 I:(DE-He78)A011-20160331 |k A011 |l A011 Stressinduzierte Aktivierung von Hämatopeotischen Stammzellen |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-He78)A011-20160331 |
980 | _ | _ | |a I:(DE-He78)A010-20160331 |
980 | _ | _ | |a I:(DE-He78)BE01-20160331 |
980 | _ | _ | |a UNRESTRICTED |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|