Finding differentially expressed genes between cell fates predicted by image-based deep learning.

ABSTRACT: Finding early trigger genes involved in cell-fate-determining processes is important for understanding molecular mechanisms of, e.g., differentiation and disease progression. One of the powerful tools for the finding is hypothesis-free omics measurements, e.g., gene expression analysis (transcriptome analysis) by RNA sequencing (RNA-seq). However, because whole single-cell RNA-seq requires cell disruption and the fate of the disrupted cell is generally unknown, it is difficult to find fate-related genes by single-cell RNA-seq profiles, especially in the early stages of cell-fate determination. Meanwhile, deep learning has successfully predicted cell fates using individual cell images. Here, we developed an approach by integrating image-based cell-fate prediction using deep learning and sin

SUBMITTER: Okaniwa T 

PROVIDER: S-EPMC12582640 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature