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Chen, J. Y.

Publications and source records attributed to Chen, J. Y..

2 recordsLinked to original sources

Lentiviral interleukin-10 gene therapy preserves fine motor circuitry and function after a cervical spinal cord injury in male and female mice

In mammals, spinal cord injuries often result in muscle paralysis through the apoptosis of lower motor neurons and denervation of neuromuscular junctions. Previous research shows that the inflammatory response to a spinal cord injury can cause additional tissue damage after the initial trauma. To modulate this inflammatory response, we delivered lentiviral anti-inflammatory interleukin-10, via loading onto an implantable biomaterial scaffold, into a left-sided hemisection at the C5 vertebra in mice. We hypothesized that improved behavioral outcomes associated with anti-inflammatory treatment are due to the sparing of fine motor circuit components. We examined behavioral recovery using a ladder beam, lower motor neuron apoptosis and muscle innervation using histology, and electromyogram recordings using intraspinal optogenetic stimulation at 2 weeks post-injury. Ladder beam analysis shows interleukin-10 treatment results in significant improvement of behavioral recovery at 2 and 12 weeks post-injury when compared to mice treated with a control virus. Histology shows interleukin-10 results in greater numbers of lower motor neurons and muscle innervation at 2 weeks post-injury. Furthermore, electromyogram recordings suggest that interleukin-10-treated animals have signal-to-noise ratios and peak-to-peak amplitudes more similar to that of uninjured controls than to that of control injured animals at 2 weeks post-injury. These data show that gene therapy using anti-inflammatory interleukin-10 can significantly reduce lower motor neuron loss, muscle denervation, and subsequent motor deficits after a spinal cord injury. Together, these results suggest that early modulation of the injury response can preserve muscle function with long-lasting benefits.

neuroscience

Polar Gini Curve: a Technique to Discover Single-cell Biomarker Using 2D Visual Information

In this work, we design the Polar Gini Curve (PGC) technique, which combines the gene expression and the 2D embedded visual information to detect biomarkers from single-cell data. Theoretically, a Polar Gini Curve characterizes the shape and evenness of cell-point distribution of cell-point set. To quantify whether a gene could be a marker in a cell cluster, we can combine two Polar Gini Curves: one drawn upon the cell-points expressing the gene, and the other drawn upon all cell-points in the cluster. We hypothesize that the closers these two curves are, the more likely the gene would be cluster markers. We demonstrate the framework in several simulation case-studies. Applying our framework in analyzing neonatal mouse heart single-cell data, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for PGC could be found at https://figshare.com/projects/Polar_Gini_Curve/76749.

bioinformatics