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Matson, K. J. E.

Publications and source records attributed to Matson, K. J. E..

4 recordsLinked to original sources

The Human Motoneuron Expression Signature is Defined by ALS-Related Genes

The mammalian spinal cord functions as a community of glial and neuronal cell types to accomplish sensory processing, autonomic control, and movement; conversely, the dysfunction of these cell types following spinal cord injury or disease states can lead to chronic pain, paralysis, and death. While we have made great strides in understanding spinal cellular diversity in animal models, it is crucial to characterize human biology directly to uncover specialized features of basic function and to illuminate 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 observed 29 glial clusters, including rare cell types such as ependymal cells, and 35 neuronal clusters, which we found are organized principally by anatomical location. To demonstrate the potential of this resource for understanding human disease, we analyzed the transcriptome of spinal motoneurons that are prone to degeneration in amyotrophic lateral sclerosis (ALS) and other diseases. We found that, compared with all other spinal neurons, human motoneurons are defined by genes related to cell size, cytoskeletal structure, and ALS, thereby supporting a model of a specialized motoneuron molecular repertoire that underlies their selective vulnerability to disease. We include a publicly available browsable web resource with this work, in the hope that it will catalyze future discoveries about human spinal cord biology.

neuroscience↗

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 manipulation. 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. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

bioinformatics↗

A Harmonized Atlas of Spinal Cord Cell Types and Their Computational Classification

Single cell sequencing is transforming many fields of science but the vast amount of data it creates has the potential to both illuminate and obscure underlying biology. To harness the exciting potential of single cell data for the study of the mouse spinal cord, we have created a harmonized atlas of spinal cord transcriptomic cell types that unifies six independent and disparate studies into one common analysis. With the power of this large and diverse dataset, we reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification of spinal cord cell types based on transcriptomic data. This work provides a comprehensive resource with unprecedented resolution of spinal cord cell types and charts a path forward for how to utilize transcriptomic data to expand our knowledge of spinal cord biology.

neuroscience↗

Cell type prioritization in single-cell data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

genomics↗