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Palla, G. ; Fischer, D.S. ; Regev, A.* ; Theis, F.J.

Spatial components of molecular tissue biology.

Nat. Biotechnol. 40, 308–318 (2022)
Postprint Forschungsdaten DOI PMC
Open Access Green
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Gene-expression; Resolved Transcriptomics; Analysis Strategies; Power Analysis; Cell Atlas; Seq; Identification; Microenvironment; Reconstruction; Technologies
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Quellenangaben Band: 40, Heft: 3, Seiten: 308–318 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Joachim Herz Stiftung
German Research Foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich
sparse2big
Networking Fund through Helmholtz AI
Helmholtz Association's Initiative
BMBF
Helmholtz Association under the joint research school 'Munich School for Data Science-MUDS'
DOI 10.1038/s41587-021-01182-1
WOS ID WOS:000769954200013
WOS ID WOS:000752215600001
Scopus ID 85124347270
PubMed ID 35132261
Erfassungsdatum 2022-02-09
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