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Liska, D.

Publications and source records attributed to Liska, D..

2 recordsLinked to original sources

Deep Transcriptomics Reveals Cell-Specific Isoforms of Pan-Neuronal Genes

Profiling gene expression in single neurons using single-cell RNA-Seq is a powerful method for understanding the molecular diversity of the nervous system. Profiling alternative splicing in single neurons using these methods is more challenging, however, due to low capture efficiency and sensitivity. As a result, we know much less about splicing patterns and regulation across neurons than we do about gene expression. Here we leverage unique attributes of the C. elegans nervous system to investigate deep cell-specific transcriptomes complete with biological replicates generated by the CeNGEN consortium, enabling high-confidence assessment of splicing across neuron types even for lowly-expressed genes. Global splicing maps reveal several striking observations, including pan-neuronal genes that harbor cell-specific splice variants, abundant differential intron retention across neuron types, and a single neuron highly enriched for upstream alternative 3 splice sites. We develop an algorithm to identify unique cell-specific expression patterns and use it to discover both cell-specific isoforms and potential regulatory RNA binding proteins that establish these isoforms. Genetic interrogation of these RNA binding proteins in vivo identifies three distinct regulatory factors employed to establish unique splicing patterns in a single neuron. Finally, we develop a user-friendly platform for spatial transcriptomic visualization of these splicing patterns with single-neuron resolution.

genomics↗

VISTA: Visualizing the Spatial Transcriptome of the C. elegans Nervous System

SummaryProfiling the transcriptomes of single cells without sacrificing spatial information is a major goal of the field of spatial transcriptomics, but current technologies require tradeoffs between single-cell resolution and whole-transcriptome coverage. In one animal species, the nematode worm C. elegans, a comprehensive spatial transcriptome with single-cell resolution is attainable using existing datasets, thanks to the worms invariant cell lineage and a series of recently-generated single cell transcriptomes. Here we present VISTA, which leverages these datasets to provide a visualization of the worm spatial transcriptome, focusing specifically on the nervous system. VISTA allows users to input a query gene and visualize its expression across all neurons in the form of a "spatial heatmap" in which the color of a cell reports the expression level. Underlying gene expression values (in Transcripts Per Million) are displayed when an individual cell is selected. We provide examples of the utility of VISTA for identifying striking new gene expression patterns in specific neurons, and for resolving cellular identities of ambiguous expression patterns generated from in vivo reporter genes. The ability to easily obtain gene-level snapshots of the neuronal spatial transcriptome should facilitate studies on neuron-specific gene expression and regulation, and provide a template for the high-resolution spatial transcriptomes the field hopes to obtain for various animal species in the future. Availability and ImplementationVISTA is freely available at the following URL: https://public.tableau.com/app/profile/smu.oit.data.insights/viz/VISTA_16814210566130/VISTA

bioinformatics↗