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

Ethan Bahl, Snehajyoti Chatterjee, Utsav Mukherjee, Muhammad Elsadany, Yann Vanrobaeys, Li-Chun Lin, Miriam McDonough, Jon Resch, K. Peter Giese, Ted Abel and Jacob J. Michaelson ()
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Ethan Bahl: University of Iowa
Snehajyoti Chatterjee: University of Iowa
Utsav Mukherjee: University of Iowa
Muhammad Elsadany: University of Iowa
Yann Vanrobaeys: University of Iowa
Li-Chun Lin: University of Iowa
Miriam McDonough: University of Iowa
Jon Resch: University of Iowa
K. Peter Giese: King’s College London
Ted Abel: University of Iowa
Jacob J. Michaelson: University of Iowa

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

Date: 2024
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DOI: 10.1038/s41467-023-44503-5

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