Biology subjects

Squair, J. W.

Publications and source records attributed to Squair, J. W..

5 recordsLinked to original sources

bioRxiv2024

Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases

We introduce Vespucci, a machine-learning method to identify perturbation-responsive regions, genes and gene programs within comparative spatial transcriptomics atlases. We validate Vespucci on simulated and published datasets and show that it outperforms 19 published computational methods for spatial transcriptomics. We apply Vespucci to expose the spatial organization of gene programs activated by therapies that guide repair of the injured spinal cord.

bioinformatics↗

bioRxiv2024

THE NEURONAL ARCHITECTURE OF AUTONOMIC DYSREFLEXIA

Autonomic dysreflexia is a life-threatening medical condition characterized by episodes of uncontrolled hypertension that occur in response to sensory stimuli after spinal cord injury (SCI)1-7. The fragmented understanding of the mechanisms underlying autonomic dysreflexia hampers the development of therapeutic strategies to manage this condition, leaving people with SCI at daily risk of heart attack and stroke8-18. Here, we expose the complete de novo neuronal architecture that develops after SCI and causes autonomic dysreflexia. In parallel, we uncover a competing, yet overlapping neuronal architecture activated by epidural electrical stimulation of the spinal cord that safely regulates blood pressure after SCI. The discovery that these adversarial neuronal architectures converge onto a single neuronal subpopulation provided a blueprint for the design of a mechanism-based intervention that reversed autonomic dysreflexia in mice, rats, and humans with SCI. These results establish a path for the effective treatment of autonomic dysreflexia in people with SCI.

neuroscience↗

bioRxiv2021

A Single Cell Atlas of Spared Tissue Below a Spinal Cord Injury Reveals Cellular Mechanisms of Repair

After spinal cord injury (SCI), the "spared" tissue below the lesion contains undamaged cells that could support or augment recovery, but targeting these cells requires a clearer understanding of their injury responses and capacity for repair. Here, we used single nucleus sequencing to profile how each cell type in the lumbar spinal cord changes over time after a thoracic injury. We present an atlas of these dynamic responses and explore two unexpected findings. Amongst neurons, rare cell types expressed a molecular signature of regeneration and amongst microglia, we identified a population of "trauma associated microglia" (TAM). These TAM cells were present in the white matter near degenerating axons and expressed the trophic factors Igf1 and Spp1(OPN). Viral over-expression of Igf1 and Spp1(OPN) expanded the TAM population and promoted the clearance of myelin debris. These findings expose endogenous mechanisms of repair in spared neural tissue, uncovering potential candidates for targeted therapy.

neuroscience↗

bioRxiv2021

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↗

bioRxiv2019

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↗