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Mitra, R. D.

Publications and source records attributed to Mitra, R. D..

2 recordsLinked to original sources

Single cell RNAseq uncovers a robust transcriptional response to morphine by oligodendrocytes

Molecular and behavioral responses to opioids are thought to be primarily mediated by neurons, although there is accumulating evidence that other cell types also play a role in drug addiction. To investigate cell-type-specific opioid responses, we performed single-cell RNA sequencing of the nucleus accumbens of mice following acute morphine treatment. Differential expression analysis uncovered robust morphine-dependent changes in gene expression in oligodendrocytes. We examined the expression of selected genes, including Cdkn1a and Sgk1, by FISH, confirming their induction by morphine in oligodendrocytes. Further analysis using RNAseq of FACS-purified oligodendrocytes revealed a large cohort of morphine-regulated genes. Importantly, the affected genes are enriched for roles in cellular pathways intimately linked to oligodendrocyte maturation and myelination, including the unfolded protein response. Altogether, our data shed light on a novel, morphine-dependent transcriptional response by oligodendrocytes that may contribute to the myelination defects observed in human opioid addicts.

neuroscience

Accurate typing of class I human leukocyte antigen by Oxford nanopore sequencing

Oxford Nanopore Technologies MinION has expanded the current DNA sequencing toolkit by delivering long read lengths and extreme portability. The MinION has the potential to enable expedited point-of-care human leukocyte antigen (HLA) typing, an assay routinely used to assess the immunological compatibility between organ donors and recipients, but the platforms high error rate makes it challenging to type alleles with clinical-grade accuracy. Here, we developed and validated Athlon, an algorithm that iteratively scores nanopore reads mapped to a hierarchical database of HLA alleles to arrive at a consensus diploid genotype; Athlon achieved a 100% accuracy in class I HLA typing at high resolution.

bioinformatics