Human motor neurons are rare and can be transcriptomically divided into known subtypes

We performed single-nucleus RNA-sequencing on adult human spinal cord using a neuronal nuclei enrichment strategy. We obtained transcriptomic profiles of >14,000 spinal neurons, including a small population of motor neurons that shares similarities with mouse motor neurons and can be subdivided into alpha and gamma subtypes. We sought to compare our results to those from a recent study by Yadav and colleagues, which provides a single-nucleus transcriptomic atlas of the human spinal cord. While most neuronal nuclei from both studies share similar features, our results from motor neurons differ substantially. We reanalyzed their RNA-sequencing data and provide evidence that the authors incorrectly identified cholinergic cellular debris as motor neuron nuclei in their dataset, raising doubts about their conclusions regarding motor neurons. Our findings underscore the challenges associated with transcriptionally profiling motor neurons from the spinal cord because of their rarity. We propose specific enrichment strategies and recommend important quality control measures for future transcriptional profiling studies involving human spinal cord tissue and rare cell types.



Read the preprint on bioRxiv

Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons

The spinal cord is a fascinating structure that is responsible for coordinating movement in vertebrates. Spinal motor neurons control muscle activity by transmitting signals from the spinal cord to diverse peripheral targets. In this study, we profiled 43,890 single-nucleus transcriptomes from the adult mouse spinal cord using fluorescence-activated nuclei sorting to enrich for motor neuron nuclei. We identified 16 sympathetic motor neuron clusters, which are distinguishable by spatial localization and expression of neuromodulatory signaling genes. We found surprising skeletal motor neuron heterogeneity in the adult spinal cord, including transcriptional differences that correlate with electrophysiologically and spatially distinct motor pools. We also provide evidence for a novel transcriptional subpopulation of skeletal motor neuron (γ*). Collectively, these data provide a single-cell transcriptional atlas (http://spinalcordatlas.org) for investigating the organizing molecular logic of adult motor neuron diversity, as well as the cellular and molecular basis of motor neuron function in health and disease.



Read the full paper at Nature Neuroscience

Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord

In vertebrates, motor control relies on cholinergic neurons in the spinal cord that have been extensively studied over the past hundred years, yet the full heterogeneity of these neurons and their different functional roles in the adult remain to be defined. Here, we developed a targeted single nuclear RNA sequencing approach and used it to identify an array of cholinergic interneurons, visceral and skeletal motor neurons. Our data expose markers for distinguishing these classes of cholinergic neurons and their extremely rich diversity. Specifically, visceral motor neurons, which provide autonomic control, could be divided into more than a dozen transcriptomic classes with anatomically restricted localization along the spinal cord. The complexity of the skeletal motor neurons was also reflected in our analysis with alpha, beta, and gamma subtypes clearly distinguished. In combination, our data provide a comprehensive transcriptomic description of this important population of neurons that control many aspects of physiology and movement and encompass the cellular substrates for debilitating degenerative disorders.



Read the paper at Nature Communications

This site hosts data generated by the Gitler lab and the Le Pichon lab

Blum et al. 2021--read the paper here.

Alkaslasi et al. 2021--read the paper here

Gautier, Blum et al. 2023--read the preprint here