SFB 1243 Cancer Evolution

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Prospective identification of hematopoietic lineage choice by deep learning

Buggenthin et al. (2017) Nature Methods. doi:10.1038/nmeth.4182


Authors (SFB 1243 members/associates): Felix Buggenthin, Florian Buettner, Philipp S. Hoppe, Max Endele, Manuel Kroiss, Michael Strasser, Michael Schwarzfischer, Dirk Loeffler, Konstantinos D. Kokkaliaris, Oliver Hilsenbeck, Timm Schroeder, Fabian J. Theis & Carsten Marr

Nature Methods (2017) DOI: 10.1038/nmeth.4182

See press release from the Helmholtz Zentrum (English/ deutsch).

Citation from the 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.