Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

ABSTRACT: Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Furthe

SUBMITTER: Bahl E 

PROVIDER: S-EPMC10817898 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature