Organism-wide single-cell transcriptomics of long-lived<i>C. elegans daf</i>-2<sup>-/-</sup>mutants reveals tissue-specific reprogramming of gene expression networks

Preston, Jessica L.; Stiffler, Nicholas; Weitzman, Maggie

doi:10.1101/509992

Cited by 3 publications

(4 citation statements)

References 99 publications

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“…The final dataset contained 47,423 cells quantifying 20,305 genes across the full time series. Our method yielded more reads per cell than previous scRNA-seq in C. elegans adults (2,175 unique molecular identifiers (UMIs) vs. 156 UMIs per cell, 644 genes vs. 52 genes per cell, comparing our data to that of Preston et al (Preston et al, 2019). However, we note that at the last time point (d15), we recovered fewer cells and fewer UMIs per cell (Figure S1A-C).…”

Section: Single-cell Rna Sequencing Of Aging C Elegansmentioning

confidence: 60%

The complete cell atlas of an aging multicellular organism

Roux

Yuan

Podshivalova

et al. 2022

Preprint

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Here we describe a single-cell atlas of aging for the nematode Caenorhabditis elegans. This unique resource describes the expression across adulthood of over 20,000 genes among 211 groups of cells that correspond to virtually every cell type in this organism. Our findings suggest that C. elegans aging is not random and stochastic in nature, but rather characterized by coordinated changes in functionally related metabolic and stress-response genes in a highly cell-type specific fashion. Aging signatures of different cell types are largely different from one another, downregulation of energy metabolism being the only nearly universal change. Some biological pathways, such as genes associated with translation, DNA repair and the ER unfolded protein response, exhibited strong (in some cases opposite) changes in subsets of cell types, but many more were limited to a single cell type. Similarly, the rates at which cells aged, measured as genome-wide expression changes, differed between cell types; some of these differences were tested and validated in vivo by measuring age-dependent changes in mitochondrial morphology. In some, but not all, cell types, aging was characterized by an increase in cell-to-cell variance. Finally, we identified a set of transcription factors whose activities changed coordinately across many cell types with age. This set was strongly enriched for stress-resistance TFs known to influence the rate of aging. We tested other members of this set, and discovered that some, such as GEI-3, likely also regulate the rate of aging. Our dataset can be accessed and queried at c.elegans.aging.atlas.research.calicolabs.com/.

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Section: Single-cell Rna Sequencing Of Aging C Elegansmentioning

confidence: 60%

The complete cell atlas of an aging multicellular organism

Roux

Yuan

Podshivalova

et al. 2022

Preprint

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“…Terms were sorted based on the number of upregulated terms. (e) NES of transcriptomic changes for biomarkers elicited by various daf‐16 mutant alleles in indicated cells (Preston et al., 2019). NES range from −3 (blue) to +3 (red).…”

Section: Resultsmentioning

confidence: 99%

“…We additionally conducted GSEA using single cell RNA‐seq data obtained with wild‐type and daf‐2 mutants (Preston et al., 2019), to analyze the effects of multiple daf‐16 mutant alleles on the transcriptome of daf‐2 mutants at a single cell level (Figure 3e). We commonly detected the enrichment of the cell type “001_Sperm” in almost all the comparisons.…”

Section: Resultsmentioning

confidence: 99%

“…Global expression changes of the WormCat terms and target genes of certain transcription factors caused by the genetic inhibition of different components were represented as normalized enrichment scores (NES) by using GSEA (v.3.0) (Subramanian et al., 2005). Biomarkers of individual cell types defined by single cell RNA‐seq data analysis of wild‐type and daf‐2 mutants (Preston et al., 2019) were used for the single cell level transcriptome analysis. Cumulative fractions with expression levels of genes of interest were also used for the global comparisons.…”

Section: Methodsmentioning

confidence: 99%

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Combinatorial transcriptomic and genetic dissection of insulin/IGF‐1 signaling‐regulated longevity in Caenorhabditis elegans

Ham,

Kim,

Park

et al. 2024

Aging Cell

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Classical genetic analysis is invaluable for understanding the genetic interactions underlying specific phenotypes, but requires laborious and subjective experiments to characterize polygenic and quantitative traits. Contrarily, transcriptomic analysis enables the simultaneous and objective identification of multiple genes whose expression changes are associated with specific phenotypes. Here, we conducted transcriptomic analysis of genes crucial for longevity using datasets with daf‐2/insulin/IGF‐1 receptor mutant Caenorhabditis elegans. Our analysis unraveled multiple epistatic relationships at the transcriptomic level, in addition to verifying genetically established interactions. Our combinatorial analysis also revealed transcriptomic changes associated with longevity conferred by daf‐2 mutations. In particular, we demonstrated that the extent of lifespan changes caused by various mutant alleles of the longevity transcription factor daf‐16/FOXO matched their effects on transcriptomic changes in daf‐2 mutants. We identified specific aging‐regulating signaling pathways and subsets of structural and functional RNA elements altered by different genes in daf‐2 mutants. Lastly, we elucidated the functional cooperation between several longevity regulators, based on the combination of transcriptomic and molecular genetic analysis. These data suggest that different biological processes coordinately exert their effects on longevity in biological networks. Together our work demonstrates the utility of transcriptomic dissection analysis for identifying important genetic interactions for physiological processes, including aging and longevity.

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Detecting gene expression in Caenorhabditis elegans

Calarco,

Taylor,

Miller

2024

Genetics

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Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

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Organism-wide single-cell transcriptomics of long-livedC. elegans daf-2^-/-mutants reveals tissue-specific reprogramming of gene expression networks

Cited by 3 publications

References 99 publications

The complete cell atlas of an aging multicellular organism

The complete cell atlas of an aging multicellular organism

Combinatorial transcriptomic and genetic dissection of insulin/IGF‐1 signaling‐regulated longevity in Caenorhabditis elegans

Detecting gene expression in Caenorhabditis elegans

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Organism-wide single-cell transcriptomics of long-livedC. elegans daf-2-/-mutants reveals tissue-specific reprogramming of gene expression networks

Cited by 3 publications

References 99 publications

The complete cell atlas of an aging multicellular organism

The complete cell atlas of an aging multicellular organism

Combinatorial transcriptomic and genetic dissection of insulin/IGF‐1 signaling‐regulated longevity in Caenorhabditis elegans

Detecting gene expression in Caenorhabditis elegans

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Organism-wide single-cell transcriptomics of long-livedC. elegans daf-2^-/-mutants reveals tissue-specific reprogramming of gene expression networks