A cellular taxonomy of the adult human spinal cord.

The mammalian spinal cord functions as a community of cell types for sensory processing, autonomic control, and movement. While animal models have advanced our understanding of spinal cellular diversity, characterizing human biology directly is important to uncover specialized features of basic function and human pathology. Here, we present a cellular taxonomy of the adult human spinal cord using single-nucleus RNA sequencing with spatial transcriptomics and antibody validation. We identified 29 glial clusters and 35 neuronal clusters, organized principally by anatomical location. To demonstrate the relevance of this resource to human disease, we analyzed spinal motoneurons, which degenerate in amyotrophic lateral sclerosis (ALS) and other diseases. We found that compared with other spinal neurons, human motoneurons are defined by genes related to cell size, cytoskeletal structure, and ALS, suggesting a specialized molecular repertoire underlying their selective vulnerability. We include a web resource to facilitate further investigations into human spinal cord biology.

The neurons that restore walking after paralysis.

A spinal cord injury interrupts pathways from the brain and brainstem that project to the lumbar spinal cord, leading to paralysis. Here we show that spatiotemporal epidural electrical stimulation (EES) of the lumbar spinal cord1-3 applied during neurorehabilitation4,5 (EESREHAB) restored walking in nine individuals with chronic spinal cord injury. This recovery involved a reduction in neuronal activity in the lumbar spinal cord of humans during walking. We hypothesized that this unexpected reduction reflects activity-dependent selection of specific neuronal subpopulations that become essential for a patient to walk after spinal cord injury. To identify these putative neurons, we modelled the technological and therapeutic features underlying EESREHAB in mice. We applied single-nucleus RNA sequencing6-9 and spatial transcriptomics10,11 to the spinal cords of these mice to chart a spatially resolved molecular atlas of recovery from paralysis. We then employed cell type12,13 and spatial prioritization to identify the neurons involved in the recovery of walking. A single population of excitatory interneurons nested within intermediate laminae emerged. Although these neurons are not required for walking before spinal cord injury, we demonstrate that they are essential for the recovery of walking with EES following spinal cord injury. Augmenting the activity of these neurons phenocopied the recovery of walking enabled by EESREHAB, whereas ablating them prevented the recovery of walking that occurs spontaneously after moderate spinal cord injury. We thus identified a recovery-organizing neuronal subpopulation that is necessary and sufficient to regain walking after paralysis. Moreover, our methodology establishes a framework for using molecular cartography to identify the neurons that produce complex behaviours.

Single cell atlas of spinal cord injury in mice reveals a pro-regenerative signature in spinocerebellar neurons.

After spinal cord injury, tissue distal to the lesion contains undamaged cells that could support or augment recovery. Targeting these cells requires a clearer understanding of their injury responses and capacity for repair. Here, we use single nucleus RNA sequencing to profile how each cell type in the lumbar spinal cord changes after a thoracic injury in mice. We present an atlas of these dynamic responses across dozens of cell types in the acute, subacute, and chronically injured spinal cord. Using this resource, we find rare spinal neurons that express a signature of regeneration in response to injury, including a major population that represent spinocerebellar projection neurons. We characterize these cells anatomically and observed axonal sparing, outgrowth, and remodeling in the spinal cord and cerebellum. Together, this work provides a key resource for studying cellular responses to injury and uncovers the spontaneous plasticity of spinocerebellar neurons, uncovering a potential candidate for targeted therapy.

A harmonized atlas of mouse spinal cord cell types and their spatial organization.

Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.

Confronting false discoveries in single-cell differential expression.

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

Cell type prioritization in single-cell data.

We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation.

Cerebellospinal Neurons Regulate Motor Performance and Motor Learning.

To understand the neural basis of behavior, it is important to reveal how movements are planned, executed, and refined by networks of neurons distributed throughout the nervous system. Here, we report the neuroanatomical organization and behavioral roles of cerebellospinal (CeS) neurons. Using intersectional genetic techniques, we find that CeS neurons constitute a small minority of excitatory neurons in the fastigial and interpositus deep cerebellar nuclei, target pre-motor circuits in the ventral spinal cord and the brain, and control distinct aspects of movement. CeS neurons that project to the ipsilateral cervical cord are required for skilled forelimb performance, while CeS neurons that project to the contralateral cervical cord are involved in skilled locomotor learning. Together, this work establishes CeS neurons as a critical component of the neural circuitry for skilled movements and provides insights into the organizational logic of motor networks.

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing.

Probing an individual cell's gene expression enables the identification of cell type and cell state. Single-cell RNA sequencing has emerged as a powerful tool for studying transcriptional profiles of cells, particularly in heterogeneous tissues such as the central nervous system. However, dissociation methods required for single cell sequencing can lead to experimental changes in the gene expression and cell death. Furthermore, these methods are generally restricted to fresh tissue, thus limiting studies on archival and bio-bank material. Single nucleus RNA sequencing (snRNA-Seq) is an appealing alternative for transcriptional studies, given that it accurately identifies cell types, permits the study of tissue that is frozen or difficult to dissociate, and reduces dissociation-induced transcription. Here, we present a high-throughput protocol for rapid isolation of nuclei for downstream snRNA-Seq. This method enables isolation of nuclei from fresh or frozen spinal cord samples and can be combined with two massively parallel droplet encapsulation platforms.

Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior.

To understand the cellular basis of behavior, it is necessary to know the cell types that exist in the nervous system and their contributions to function. Spinal networks are essential for sensory processing and motor behavior and provide a powerful system for identifying the cellular correlates of behavior. Here, we used massively parallel single nucleus RNA sequencing (snRNA-seq) to create an atlas of the adult mouse lumbar spinal cord. We identified and molecularly characterized 43 neuronal populations. Next, we leveraged the snRNA-seq approach to provide unbiased identification of neuronal populations that were active following a sensory and a motor behavior, using a transcriptional signature of neuronal activity. This approach can be used in the future to link single nucleus gene expression data with dynamic biological responses to behavior, injury, and disease.