Prospective identification of hematopoietic lineage choice by deep learning

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

Research output: Contribution to journalArticlepeer-review

131 Scopus citations


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.

Original languageEnglish
Pages (from-to)403-406
Number of pages4
JournalNature Methods
Issue number4
StatePublished - 2017


Dive into the research topics of 'Prospective identification of hematopoietic lineage choice by deep learning'. Together they form a unique fingerprint.

Cite this