scholarly journals Revisiting Neuronal Cell Type Classification in Caenorhabditis elegans

2016 ◽  
Vol 26 (22) ◽  
pp. R1197-R1203 ◽  
Author(s):  
Oliver Hobert ◽  
Lori Glenwinkel ◽  
John White
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Christopher A. Brosnan ◽  
Alexander J. Palmer ◽  
Steven Zuryn

AbstractMulticellularity has coincided with the evolution of microRNAs (miRNAs), small regulatory RNAs that are integrated into cellular differentiation and homeostatic gene-regulatory networks. However, the regulatory mechanisms underpinning miRNA activity have remained largely obscured because of the precise, and thus difficult to access, cellular contexts under which they operate. To resolve these, we have generated a genome-wide map of active miRNAs in Caenorhabditis elegans by revealing cell-type-specific patterns of miRNAs loaded into Argonaute (AGO) silencing complexes. Epitope-labelled AGO proteins were selectively expressed and immunoprecipitated from three distinct tissue types and associated miRNAs sequenced. In addition to providing information on biological function, we define adaptable miRNA:AGO interactions with single-cell-type and AGO-specific resolution. We demonstrate spatial and temporal dynamicism, flexibility of miRNA loading, and suggest miRNA regulatory mechanisms via AGO selectivity in different tissues and during ageing. Additionally, we resolve widespread changes in AGO-regulated gene expression by analysing translatomes specifically in neurons.


Genetics ◽  
2021 ◽  
Vol 217 (1) ◽  
Author(s):  
Kenneth Pham ◽  
Neda Masoudi ◽  
Eduardo Leyva-Díaz ◽  
Oliver Hobert

Abstract We describe here phase-separated subnuclear organelles in the nematode Caenorhabditis elegans, which we term NUN (NUclear Nervous system-specific) bodies. Unlike other previously described subnuclear organelles, NUN bodies are highly cell type specific. In fully mature animals, 4–10 NUN bodies are observed exclusively in the nucleus of neuronal, glial and neuron-like cells, but not in other somatic cell types. Based on co-localization and genetic loss of function studies, NUN bodies are not related to other previously described subnuclear organelles, such as nucleoli, splicing speckles, paraspeckles, Polycomb bodies, promyelocytic leukemia bodies, gems, stress-induced nuclear bodies, or clastosomes. NUN bodies form immediately after cell cycle exit, before other signs of overt neuronal differentiation and are unaffected by the genetic elimination of transcription factors that control many other aspects of neuronal identity. In one unusual neuron class, the canal-associated neurons, NUN bodies remodel during larval development, and this remodeling depends on the Prd-type homeobox gene ceh-10. In conclusion, we have characterized here a novel subnuclear organelle whose cell type specificity poses the intriguing question of what biochemical process in the nucleus makes all nervous system-associated cells different from cells outside the nervous system.


2019 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zhongjie Ma ◽  
Michael Gleicher ◽  
Colin N. Dewey

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract


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