Longitudinal whole transcriptomic profiling of live cells through domain adaptation

However, longitudinal molecular profiling at the global tran- scriptome level remains a major challenge, as RNA sequencing fundamentally alters or destroys cells. To overcome these limitations, we developed PENNE, a deep-learning framework that infers whole-transcriptomic profiles directly from live-cell images.

Using gated attention mechanisms, PENNE trains on spatial transcriptomic datasets to align morphological features with gene expression. To enable inferences from images, our model performs domain adaptation to eliminate discrepancies between stained and unstained tissue images, effectively transferring molecular information from tis- sue sections to live-cell imaging. PENNE accurately identifies cell-type-specific and radiation-response markers via imputed expression.