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Tritschler, S. ; Büttner, M. ; Fischer, D.S. ; Lange, M. ; Bergen, V. ; Lickert, H. ; Theis, F.J.

Concepts and limitations for learning developmental trajectories from single cell genomics.

Development 146:dev170506 (2019)
Verlagsversion DOI PMC
Closed
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Computational Approaches ; Developmental Trajectories ; Pseudotime ; Single Cell Genomics ; Trajectory Inference; Regulatory Network Inference; Rna-sequencing Data; Spatial Transcriptomics; Blood Stem; Dynamics; Reveals; Reconstruction; Heterogeneity; Information; Mechanisms
Sprache
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 0950-1991
e-ISSN 1477-9129
Zeitschrift Development / Company of Biologists
Quellenangaben Band: 146, Heft: 12, Seiten: , Artikelnummer: dev170506 Supplement: ,
Verlag Company of Biologists
Verlagsort Bidder Building, Station Rd, Histon, Cambridge Cb24 9lf, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Diabetes and Regeneration Research (IDR)
POF Topic(s) 30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-503800-001
G-502300-001
DOI 10.1242/dev.170506
WOS ID WOS:000473330400006
Scopus ID 85069002369
PubMed ID 31249007
Erfassungsdatum 2019-07-01
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