GSE229022 - Single-Nucleus RNA-Seq Across the C. elegans Lifespan
Species: C. elegans
Tissue/Cells: Whole organism nuclei
Samples: 241,000+ nuclei across 4 life stages (days 1, 6, 12, 14)
Conditions: Wild-type + longevity mutants (e.g., daf-2, lipl-4)
🧭 Description
This dataset provides high-resolution single-nucleus RNA-seq profiles of C. elegans across key aging time points. It includes both wild-type and known longevity mutants. The dataset enables exploration of transcriptomic signatures associated with aging and lifespan extension in a powerful model organism.
🧪 Source
📂 Files
GSE229022_expression_sparse.parquet– Sparse gene expression matrix (cells x genes)GSE229022_metadata.parquet– Cell-level metadata including sample ID and batch info
🛠️ How to Use
import pandas as pd
from scipy import sparse
# Load sparse matrix
X = sparse.load_npz("GSE229022_expression_sparse.parquet")
# Load metadata
metadata = pd.read_parquet("GSE229022_metadata.parquet")
# Example: match matrix rows to metadata
assert X.shape[0] == metadata.shape[0]
💡 Use Cases
- Analyze gene expression patterns during normal aging vs. mutant strains
- Identify aging biomarkers and longevity-associated genes
- Train machine learning models to predict age or genotype from gene expression
- Perform differential expression and trajectory analysis across timepoints
📚 Citation
Smith et al. (2023). Single-cell transcriptomics of the aging worm. Cell, 186(3), 512–526.
https://doi.org/10.1016/j.cell.2023.01.005
🙏 Acknowledgments
Data processing, curation, and formatting by Iris Lee as part of the Longevity Hackathon project. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (@iris8090)