Single-nucleus resolution mapping of the adult C. elegans and its application to elucidate inter- and trans-generational response to alcohol.

Single-cell transcriptomic platforms provide an opportunity to map an organism's response to environmental cues with high resolution. Here, we applied single-nucleus RNA sequencing (snRNA-seq) to establish the tissue and cell type-resolved transcriptome of the adult C. elegans and characterize the inter- and trans-generational transcriptional impact of ethanol. We profiled the transcriptome of 41,749 nuclei resolving into 31 clusters, representing a diverse array of adult cell types including syncytial tissues. Following exposure to human-relevant doses of alcohol, several germline, striated muscle, and neuronal clusters were identified as being the most transcriptionally impacted at the F1 and F3 generations. The effect on germline clusters was confirmed by phenotypic enrichment analysis as well as by functional validation, which revealed a remarkable inter- and trans-generational increase in germline apoptosis, aneuploidy, and embryonic lethality. Together, snRNA-seq represents a valuable approach for the detailed examination of an adult organism's response to environmental exposures.

WormBase single-cell tools.

: We present two web apps for interactively performing common tasks with single-cell RNA sequencing data: scdefg for differential expression and wormcells-viz for visualization of gene expression. We deployed these tools with public Caenorhabditis elegans datasets curated by WormBase at https://single-cell.wormbase.org. Source code for deploying these tools with other datasets is available at https://github.com/WormBase/scdefg and https://github.com/WormBase/wormcells-viz. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

WormBase in 2022-data, processes, and tools for analyzing Caenorhabditis elegans.

WormBase (www.wormbase.org) is the central repository for the genetics and genomics of the nematode Caenorhabditis elegans. We provide the research community with data and tools to facilitate the use of C. elegans and related nematodes as model organisms for studying human health, development, and many aspects of fundamental biology. Throughout our 22-year history, we have continued to evolve to reflect progress and innovation in the science and technologies involved in the study of C. elegans. We strive to incorporate new data types and richer data sets, and to provide integrated displays and services that avail the knowledge generated by the published nematode genetics literature. Here, we provide a broad overview of the current state of WormBase in terms of data type, curation workflows, analysis, and tools, including exciting new advances for analysis of single-cell data, text mining and visualization, and the new community collaboration forum. Concurrently, we continue the integration and harmonization of infrastructure, processes, and tools with the Alliance of Genome Resources, of which WormBase is a founding member.

Modular, efficient and constant-memory single-cell RNA-seq preprocessing.

We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

A curated database reveals trends in single-cell transcriptomics.

The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. Database URL: www.nxn.se/single-cell-studies/gui.

3D Printing of Biomolecular Models for Research and Pedagogy.

The construction of physical three-dimensional (3D) models of biomolecules can uniquely contribute to the study of the structure-function relationship. 3D structures are most often perceived using the two-dimensional and exclusively visual medium of the computer screen. Converting digital 3D molecular data into real objects enables information to be perceived through an expanded range of human senses, including direct stereoscopic vision, touch, and interaction. Such tangible models facilitate new insights, enable hypothesis testing, and serve as psychological or sensory anchors for conceptual information about the functions of biomolecules. Recent advances in consumer 3D printing technology enable, for the first time, the cost-effective fabrication of high-quality and scientifically accurate models of biomolecules in a variety of molecular representations. However, the optimization of the virtual model and its printing parameters is difficult and time consuming without detailed guidance. Here, we provide a guide on the digital design and physical fabrication of biomolecule models for research and pedagogy using open source or low-cost software and low-cost 3D printers that use fused filament fabrication technology.