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Nelson, M. D.

Publications and source records attributed to Nelson, M. D..

2 recordsLinked to original sources

A screen for stress-induced sleep genes in C. elegans reveals a role for glutamate signaling

Sleep is an essential behavioral state that evolved early in animals, possibly with the advent of the nervous system. The complexity of sleep neural networks varies significantly across phylogeny, yet common signaling molecules exist. Stress-induced sleep (SIS) of Caenorhabditis elegans is controlled by two sleep interneurons (ALA and RIS), within a 302-celled nervous system. Even in this simple framework, a complex array of signaling molecules is expressed. Here, we surveyed some of these genes for roles in SIS. These included neuropeptides, g-protein coupled receptors, a two-pored potassium channel, and glutamate signaling components. We found that multiple genes are required for sleep maintenance (i.e., amounts), and/or the precise timing of sleep initiation. In particular, we identified an important role for glutamate signaling. The conserved ionotropic glutamate receptor glr-5, regulates sleep maintenance and timing, and is required in a 3-celled circuit of interneurons connected by gap junctions and chemical synapses with RIS. This work suggests that numerous redundant and/or parallel mechanisms have evolved to modulate a simple sleep-regulating circuit in C. elegans, and we speculate that conserved pathways may play similar roles in animals with more complex systems.

genetics↗

MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis

Imaging-based spatially resolved transcriptomics (SRT) technologies, such as 10X Xenium, MERSCOPE, and CosMx, have revolutionized our ability to study gene expression within the spatial context of tissues at single-cell resolution. The acquisition of such data relies heavily on cell segmentation algorithms, which often produce imperfect boundaries, leading to transcript misassignment. These misassignments can significantly affect downstream analyses, including cell type identification, differential expression analysis, cell-cell communication, and RNA localization. We present MisTIC (Missegmented Transcript Inference Correction), a variational Bayesian model designed to correct transcript misassignment errors without requiring resegmentation. In benchmarking analyses using synthetic data with simulated transcript misassignment, MisTIC demonstrated high sensitivity and specificity in removing misassigned transcripts. In real data applications, MisTIC effectively enhances cell type identification, reduces ambiguity in differential expression analysis, and improves the detection of cell-cell communication. Furthermore, RNA localization analysis based on data corrected by MisTIC revealed that, in T cells located near cancer-associated fibroblasts compared to those farther away, genes involved in T cell activation, inflammation, and cytotoxicity were depleted from cytoplasmic regions despite not being differentially expressed between the two T cell subsets. In conclusion, MisTIC is a powerful tool for correcting transcript misassignment in SRT data. It not only improves the accuracy of routine analyses but also enables novel investigations that provide deeper insights into the dynamics of gene expression.

bioinformatics↗