Journal article
Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data
Nature communications, Vol.15(1), 779
01/26/2024
DOI: 10.1038/s41467-023-44503-5
PMCID: PMC10817898
PMID: 38278804
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.
Details
- Title: Subtitle
- Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data
- Creators
- Ethan Bahl - University of IowaSnehajyoti Chatterjee - Department of Neuroscience & Pharmacology, University of Iowa, Iowa City, IA, USAUtsav Mukherjee - Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, USAMuhammad Elsadany - Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, USAYann Vanrobaeys - Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USALi-Chun Lin - University of IowaMiriam McDonough - Interdisciplinary Graduate Program in Molecular Medicine, University of Iowa, Iowa City, IA, USAJon Resch - University of IowaK Peter Giese - King's College LondonTed Abel - Department of Neuroscience & Pharmacology, University of Iowa, Iowa City, IA, USAJacob J Michaelson - Department of Communication Sciences & Disorders, University of Iowa, Iowa City, IA, USA. jacob-michaelson@uiowa.edu
- Resource Type
- Journal article
- Publication Details
- Nature communications, Vol.15(1), 779
- DOI
- 10.1038/s41467-023-44503-5
- PMID
- 38278804
- PMCID
- PMC10817898
- NLM abbreviation
- Nat Commun
- eISSN
- 2041-1723
- Grant note
- R01 DC 014489 / U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) K99 AG 068306 / U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01 MH 087463 / U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) P50 HD 103556 / U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- Language
- English
- Date published
- 01/26/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Neurology; Communication Sciences and Disorders; Molecular Physiology and Biophysics; Psychiatry; Psychological and Brain Sciences; Iowa Neuroscience Institute; Fraternal Order of Eagles Diabetes Research Center; Neuroscience and Pharmacology; Biochemistry and Molecular Biology
- Record Identifier
- 9984548570402771
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