longevity-db/GSE229022

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

• —NCBI GEO Accession: GSE229022
• —Publication DOI

📂 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)