five

Transcriptomic comparison of long- vs. short-lived C. elegans

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE283419
下载链接
链接失效反馈
官方服务:
资源简介:
Variability in lifespan among individuals of the same species is a poorly understood phenomenon. Even among isogenic C. elegans reared in identical environments, we observe considerable variance in lifepsan. To better understand the differences in gene expression that underly this variability, we characterized the transcriptomes associated with long vs. short lifespan. We isolated the longest- and shortest-lived members from their respective populations using biomarkers of aging--predictive markers whose expression levels correlate with an individual's future lifespan. Specifically, we sorted animals by the expression of lin-4p::GFP, mir-243p::GFP, mir-240/786p::GFP, and autofluorescence. All populations were chronologically five days old at time of sorting. We subsequently compared the transcriptomes of future lifespan within and between different biomarkers to identify a gene expression signature of future lifespan. Transgenic C. elegans strains were grown containing lifespan-predictive biomarkers lin-4p::GFP, mir-243p::GFP, and mir-240/786p::GFP. To sterilize animals, all strains also contained a temperature sensitive mutation in the spermatogenesis gene spe-9. Populations were synchronized by hypochlorite treatment and cultured at the restrictive temperature of 25 degrees C on NGM plates seeded with E. coli OP50. After five days, worms were sorted by the fluorescence intensity of each transgenic biomarker, as well as by autofluorescence in the “red” channel. Between 3-6 sorts were carried out for each biomarker, with “high” and “low” fluorescence defined as the top or bottom tenth percentile relative to the rest of the populations. Animals in these high and low categories were recovered, lysed, and total RNA was collected.
创建时间:
2025-04-16
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作