Spatial transcriptomics ofC. elegansmales and hermaphrodites identifies novel fertility genes

Abstract

To advance our understanding of the genetic programs that drive cell and tissue specialization, it is necessary to obtain a comprehensive overview of gene expression patterns. Here, we have used RNA tomography to generate the first high-resolution, anteroposterior gene expression maps of C. elegans males and hermaphrodites. To explore these maps, we have developed computational methods for discovering region and tissuespecific genes. Moreover, by combining pattern-based analysis with differential gene expression analysis, we have found extensive sex-specific gene expression differences in the germline and sperm. We have also identified genes that are specifically expressed in the male reproductive tract, including a group of uncharacterized genes that encode small secreted proteins that are required for male fertility. We conclude that spatial gene expression maps provide a powerful resource for identifying novel tissue-specific gene functions in C. elegans. Importantly, we found that expression maps from different animals can be precisely aligned, which opens up new possibilities for transcriptome-wide comparisons of gene expression patterns.

Tracking extracellular vesicle (EV) cargo as a platform for studying EVomics, signaling, and targeting in vivo

“…To identify EV cargo originating from the EVNs, we used cell-specific transcriptomic datasets reporting on EVN-enriched transcripts [10,31]. We found 99 gene products (Figure 2C, Table S3) in our EVome that are also enriched in EVNs, including previously identified and confirmed EV cargo such as the polycystins PKD-2 and LOV-1 (a homolog of human PKD1), a myristoylated coiled-coil protein CIL-7 required for environmental EV biogenesis [32], CWP-5 (a protein coexpressed with polycystins), acid-sensing sodium channels ASIC-2 and EGAS-1, and a transmembrane cysteine-rich protein F14D7.11 that belongs to a family of proteins with CYSTM-like domains presumably involved in stress-resistance mechanisms [33].…”

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

“…Given its foundation in the generic behavior of random networks near their percolation critical point, we expect our method to be broadly applicable beyond the specific context explored here, for example to cluster genes according to spatial, rather than between-individual variation (19,36), and -beyond gene expression -to any noisy, high-dimensional data that sample variation across multiple measurements.…”

“…For each read, the worm-specific barcode and unique molecular identified are detected in the first 8 nucleotides and 4 nucleotides, respectively, of the first read. Second reads were aligned to the C. elegans reference transcriptome, which was compiled from the C. elegans reference genome WS249 (36). Only reads that uniquely mapped to the transcriptome and that have a proper worm-specific barcode are used for downstream analysis.…”

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

“…3A). A tissue-specific annotation is assigned to each of these clusters by mining the expansive literature on C. elegans, with particularly extensive use of the compendium of annotated gene data on WormBase (37,38) as well as a recently published spatially resolved RNA tomography (tomoseq) data set by Ebbing et al (36). The tomoseq data provide spatial maps of gene expression in individual slices along the worm.…”

“…Given its foundation in the generic behavior of random 440 networks near their percolation critical point, we expect our 441 method to be broadly applicable beyond the specific context 442 explored here, for example to cluster genes according to spa-443 tial, rather than between-individual variation (19,36), and 444 -beyond gene expression -to any noisy, high-dimensional 445 data that sample variation across multiple measurements.…”