Prospective identification of hematopoietic lineage choice by deep learning.
IF: 47.990
Cited by: 140


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.

MeSH terms

Area Under Curve
Cell Differentiation
Cell Lineage
Gene Knock-In Techniques
Hematopoietic Stem Cells
Image Processing, Computer-Assisted
Machine Learning
Mice, Mutant Strains
Neural Networks, Computer
Proto-Oncogene Proteins
Time-Lapse Imaging


Buggenthin, Felix
Buettner, Florian
Hoppe, Philipp S
Endele, Max
Kroiss, Manuel
Strasser, Michael
Schwarzfischer, Michael
Loeffler, Dirk
Kokkaliaris, Konstantinos D
Hilsenbeck, Oliver
Schroeder, Timm
Theis, Fabian J
Marr, Carsten

Recommend literature

Similar data