Journal
NATURE METHODS
Volume 14, Issue 4, Pages 403-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/nmeth.4182
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Funding
- German Federal Ministry of Education and Research (BMBF)
- European Research Council [259294]
- BioSysNet (Bavarian Research Network for Molecular Biosystems)
- German Research Foundation (DFG) [SPP 1395, SPP 1356]
- UK Medical Research Council [MR/M01536X/1]
- Swiss National Science Foundation [31003A_156431, 2014/244]
- MRC [MR/M01536X/1] Funding Source: UKRI
- Swiss National Science Foundation (SNF) [31003A_156431] Funding Source: Swiss National Science Foundation (SNF)
- Medical Research Council [MR/M01536X/1] Funding Source: researchfish
- European Research Council (ERC) [259294] Funding Source: European Research Council (ERC)
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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.
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