Prospective identification of hematopoietic lineage choice by deep learning
Buggenthin F, Buettner F, Hoppe PS, Endele M, Kroiss M, Strasser M, Schwarzfischer M, Loeffler D, Kokkaliaris KD, Hilsenbeck O, Schroeder T, Theis FJ, Marr C (2017)
Publication Type: Journal article
Publication year: 2017
Journal
Book Volume: 14
Pages Range: 403-406
Journal Issue: 4
DOI: 10.1038/nmeth.4182
Abstract
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.
Involved external institutions
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
Germany (DE)
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
How to cite
APA:
Buggenthin, F., Buettner, F., Hoppe, P.S., Endele, M., Kroiss, M., Strasser, M.,... Marr, C. (2017). Prospective identification of hematopoietic lineage choice by deep learning. Nature methods, 14(4), 403-406. https://doi.org/10.1038/nmeth.4182
MLA:
Buggenthin, Felix, et al. "Prospective identification of hematopoietic lineage choice by deep learning." Nature methods 14.4 (2017): 403-406.
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