Abstract
Single-cell RNA-seq and other profiling assays have opened new windows into understanding cells' properties, regulation, dynamics, and function at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking their temporal dynamics. Here, we present Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through Raman microscopy images and domain translation using Generative Adversarial Networks. We demonstrate R2R in reprogramming mouse fibroblasts or differentiating mouse embryonic stem cells and show that their expression profiles can be accurately predicted in live cells. R2R paves the way to understanding gene expression dynamics at scale in vitro and in vivo.
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