Biology subjects

Kendrick, R. M.

Publications and source records attributed to Kendrick, R. M..

2 recordsLinked to original sources

bioRxiv2026

Adolescent development accelerates responses to input in human neocortical neurons

Through childhood and adolescence, profound changes to the physiology of individual neurons accompany large-scale network changes in the mammalian neocortex. These physiological changes are well understood in rodent models but far less is known in the human neocortex. Here we combine patch-clamp electrophysiology and single-cell sequencing (Patch-Seq) in neurosurgically-resected pediatric human brain slices and age-matched mouse brain slices to elucidate the unique developmental trajectory of human neurons. We find that human Layer 2/3 pyramidal neurons show distinctive postnatal changes in neuronal physiology that align with the more directed, feedforward network architecture of human neocortex relative to the mouse. Human-specific changes to spike train dynamics include faster spike latencies and selective acceleration of early spiking. By applying linear modeling to our Patch-Seq data, we identify genes that predict physiological variation across single cells. This unbiased approach unexpectedly identifies BK-type calcium-activated potassium channels as key drivers of human postnatal changes in spike train dynamics between childhood and adolescence. We further test this pathway through pharmacology and computational modeling. Together, our results reveal novel mechanisms of postnatal maturation in human neocortical neurons and demonstrate a new application of Patch-Seq to uncover gene-physiology relationships at single-cell resolution.

neuroscience↗

bioRxiv2024

Transcriptomically-measured gene expression predicts physiological variation across single neurons in humans and mice

Single-cell transcriptomics measures the molecular landscape of individual neurons with unprecedented efficiency and scale. This insight has the potential to advance our understanding of the molecular basis of neuronal function, and to identify druggable targets for disease treatments. However, transcriptomics also suffers from greater measurement noise than traditional techniques (e.g., RT-PCR), which raises questions about its ability to offer insight into function at true single-cell resolution. We tested if transcriptomic data could yield insight into function of individual neurons in human and mouse neocortex by analyzing two datasets collected via Patch-Seq, a powerful technique for obtaining transcriptomic and physiology data from the same neuron. We found that computational models trained on single-cell transcriptomic data robustly predicted physiology of individual neurons. Critically, models trained on single cells outperformed those trained on cell type averages when predicting single-cell physiology. Thus, the standard approach of denoising single-cell transcriptomic data by averaging on cell types sacrifices functionally-relevant information. Our analysis also revealed novel relationships between gene expression and physiology, including a potential molecular substrate of human- mouse cross-species differences in the speed of single-neuron computation. Broadly, our findings highlight the promise of Patch-Seq for generating new insight into the molecular basis of neuronal function.

neuroscience↗