Tsolias, A. | Search bioRxiv

bioRxiv2025

Transcriptional and functional profiles of muscarinic receptor-expressing neurons in primate lateral prefrontal and anterior cingulate cortices

Acetylcholine modulates anterior cingulate (ACC) and lateral prefrontal (LPFC) cortices for cognitive-motivational integration, via specific m1-m4 muscarinic receptors (mAChR) encoded by CHRM1-4 genes. Single-nucleus RNA sequencing and mRNA-protein histology in macaques revealed CHRM3 to be the most enriched mAChR gene in neurons, while m1 predominates at the protein level, likely due to nuclear retention of CHRM3 and cytoplasmic trafficking of CHRM1. CHRM3 and CHRM1 showed strong co-expression and functional overlap, and were transcriptomically-distinct from CHRM2, which was uniquely enriched in deep layer excitatory and PVALB+ inhibitory neurons. Although CHRM+ cell distributions were similar between areas, CHRM1-3+ excitatory neurons in ACC exhibited upregulation of synaptic plasticity genes relative to LPFC. Functional in vitro experiments confirm a more robust cholinergic-mediated decrease in excitatory:inhibitory synaptic ratio in ACC than in LPFC neurons, accompanied by compensatory changes in spine morphology. These findings highlight region-specific acetylcholine signaling essential for flexible processing, learning and memory.

neuroscience↗

bioRxiv2021

Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits. SignificanceHow different cell types sculpt activity patterns in brain areas associated with decision-making is a fundamentally unresolved problem in neuroscience. In monkeys, and other species where transgenic access is not yet possible, identifying physiological types in vivo relies on only a few discrete user-specified features of extracellular waveforms to identify cell types. Here, we show that non-linear dimensionality reduction with graph clustering applied to the entire extracellular waveform can delineate many different putative cell types and does so in an interpretable manner. We show that this method reveals previously undocumented physiological, functional, and laminar diversity in the dorsal premotor cortex of monkeys, a key brain area implicated in decision-making.

neuroscience↗