Cell GenomicsNeurosciencePeer-reviewed journal article
Adult Single-nucleus Neuronal Transcriptomes of Insulin Signaling Mutants Reveal Regulators of Behavior and Learning
Jonathan St. AngeiD, Yifei WengiD, Rachel L. KaletskyiD, Morgan E. StevensoniD, Rebecca S. MooreiD, Shiyi ZhouiD, Coleen Tara MurphyiD
Abstract
The insulin/insulin-like signaling (IIS) pathway regulates many of C. elegans adult functions, including learning and memory1. While whole-worm and tissue-specific transcriptomic analyses have identified IIS targets2,3, a higher-resolution single-cell approach is required to identify changes that confer neuron-specific improvements in the long-lived insulin receptor mutant, daf-2. To understand how behaviors that are controlled by a small number of neurons change in daf-2 mutants, we used the deep resolution of single-nucleus RNA sequencing to define each neuron types transcriptome in adult wild-type and daf-2 mutants. First, we found surprising differences between wild-type L4 larval neurons and young adult neurons in chemoreceptor expression, synaptic genes, and learning and memory genes. These Day 1 adult neuron transcriptomes allowed us to identify adult AWC-specific regulators of chemosensory function and to predict neuron-to-neuron peptide/receptor pairs. We then identified gene expression changes that correlate with daf-2s improved cognitive functions, particularly in the AWC sensory neuron that controls learning and associative memory4, and used behavioral assays to test their roles in cognitive function. Combining deep single-neuron transcriptomics, genetic manipulation, and behavioral analyses enabled us to identify genes that may function in a single adult neuron to control behavior, including conserved genes that function in learning and memory. One-Sentence SummarySingle-nucleus sequencing of adult wild-type and daf-2 C. elegans neurons reveals functionally relevant transcriptional changes, including regulators of chemosensation, learning, and memory.
- organism: C. elegans
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 20
- FWCI
- 3.50
- Field percentile
- top 6%
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