PuSH - Publication Server of Helmholtz Zentrum München

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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Haematopoietic stem cells; Image processing; Machine learning; Optical imaging
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 14, Issue: 4, Pages: 403–406 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Stem Cell Research (ISF)