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Prospective identification of hematopoietic lineage choice by deep learning.

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.

SUBMITTER: Buggenthin F

PROVIDER: S-EPMC5376497 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Prospective identification of hematopoietic lineage choice by deep learning.

Buggenthin Felix F Buettner Florian F Hoppe Philipp S PS Endele Max M Kroiss Manuel M Strasser Michael M Schwarzfischer Michael M Loeffler Dirk D Kokkaliaris Konstantinos D KD Hilsenbeck Oliver O Schroeder Timm T Theis Fabian J FJ Marr Carsten C

Nature methods 20170220 4

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 c ...[more]

PMID: 28218899

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