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Buggenthin, F. ; Buettner, F. ; Hoppe, P.S.* ; Endele, M. ; Kroiss, M. ; Strasser, M. ; Schwarzfischer, M. ; Loeffler, D. ; Kokkaliaris, K.D. ; Hilsenbeck, O. ; Schroeder, T. ; Theis, F.J. ; Marr, C.

Prospective identification of hematopoietic lineage choice by deep learning.

Nat. Methods 14, 403–406 (2017)
Postprint DOI PMC
Open Access Green
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Haematopoietic stem cells; Image processing; Machine learning; Optical imaging
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 14, Heft: 4, Seiten: 403–406 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Stem Cell Research (ISF)