Plate IV.431 · Publications
Prospective identification of hematopoietic lineage choice by deep learning
Article in Nature Methods, 20 February 2017.
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.
Cite
@article{buggenthin2017prospective,
title = {{Prospective identification of hematopoietic lineage choice by deep learning}},
author = {Buggenthin, Felix and Buettner, Florian and Hoppe, Philipp S and Endele, Max and Kroiss, Manuel and Strasser, Michael and Schwarzfischer, Michael and Loeffler, Dirk and Kokkaliaris, Konstantinos D and Hilsenbeck, Oliver and Schroeder, Timm and Theis, Fabian J and Marr, Carsten},
journal = {Nature Methods},
publisher = {Springer Science and Business Media LLC},
year = {2017},
month = feb,
doi = {10.1038/nmeth.4182},
url = {https://doi.org/10.1038/nmeth.4182},
pmid = {28218899},
pmcid = {PMC5376497}
}