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Radiomics in radiooncology - Challenging the medical physicist.
Phys. Med. 48, 27-36 (2018)
Flatworms of the species Schmidtea mediterranea are immortal-adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
2.240
1.115
41
63
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Review
Schlagwörter
Radiomics ; Radiogenomics ; Machine Learning ; Neural Networks ; Convolutional Neural Network; Planarian Schmidtea-mediterranea; Stem-cells; Rna-seq; Gene-expression; Regeneration; Pluripotency; Trajectories; Organism; Tissues; System
Sprache
Veröffentlichungsjahr
2018
HGF-Berichtsjahr
2018
ISSN (print) / ISBN
1120-1797
e-ISSN
1724-191X
Quellenangaben
Band: 48,
Seiten: 27-36
Verlag
Elsevier
Verlagsort
1200 New York Ave, Nw, Washington, Dc 20005 Usa
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Radiation Medicine (IRM)
POF Topic(s)
30203 - Molecular Targets and Therapies
Forschungsfeld(er)
Radiation Sciences
PSP-Element(e)
G-501300-001
WOS ID
WOS:000432710400005
Scopus ID
85044469463
PubMed ID
29728226
Erfassungsdatum
2018-06-25