worm-aging

Title: Differential Expression Analysis on Aging in C. elegans

Roux et al. published a fascinating paper in 2023 titled "Individual cell types in C. elegans age differently and activate distinct cell-protective responses", in which they perform single-cell RNA sequencing on C. elegans cultures for six different timepoints (Days 1, 3, 5, 8, 11, and 15) in order to explore the effect of aging on gene expression in both a global and tissue-specific manner. We intend to use the dataset provided by these authors on the Gene Expression Omnibus in order to compare genes that are significantly upregulated and downregulated in C. elegans neurons, muscle, and epithelium from day 1 to day 15.

Datasets (Day1 - Day15 samples): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE208154

Software: Seurat v5 (https://satijalab.org/seurat/) and/or Monocle (http://cole-trapnell-lab.github.io/monocle-release/) for clustering and DE analysis. Ggplot2 for plotting.

Plan

1. Use Seurat or Monocle R packages to do initial loading in of the dataset (just the Day1 and Day15 samples) from the GEO page. Next, we will do a basic DE analysis on the combined neuron, epithelium and muscle cells over D1 and D15 to make sure that we can see the age-based expression change reported in the paper. We will visualize this analysis with a volcano plot, such as the one seen below:

From Visualization of RNA-Seq results with Volcano Plot https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/tutorial.html

Once we have the volcano plot, we will do a more focused DE analysis on each tissue type described above. Once we have a set of 15+ genes that are significantly upregulated/downregulated between day 1 and day 15, we will compare the upregulated/downregulated genes that are unique to and shared between each tissue with an upset plot, as shown below:

From Shaver et al 2024 https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1012245#sec022

2. For a "reach" goal (more than 5 hours), we could introduce some other in-between timepoints (day 5, day 10) and potentially find new ways to visualize expression changes over a larger set of timepoints.

3. Another reach goal: perform a GO search for the genes that are differentially expressed between our tissue types, find a way to visualze the 4 or 5 most signfiicant terms that are differentially enriched between our tissue types, and see if those terms are reasonable given the conclusions reached in the paper.