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.
MeSH terms
Animals
Area Under Curve
Biomarkers
Cell Differentiation
Cell Lineage
Gene Knock-In Techniques
Hematopoietic Stem Cells
Image Processing, Computer-Assisted
Machine Learning
Male
Mice, Mutant Strains
Neural Networks, Computer
Proto-Oncogene Proteins
Time-Lapse Imaging
Trans-Activators
Authors
Recommend literature
1. Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.
2. Image analysis of neural stem cell division patterns in the zebrafish brain.
3. Advances in spatial transcriptomic data analysis.
4. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
5. A 3D Atlas of Hematopoietic Stem and Progenitor Cell Expansion by Multi-dimensional RNA-Seq Analysis.
Similar data
1. Transcriptional plasticity, priming and commitment in hematopoietic lineages [CRISP-seq]
2. Transcriptional plasticity, priming and commitment in hematopoietic lineages [RNA-seq]