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)

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🧭 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.

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🧪 Source

• NCBI GEO Accession: GSE229022
• Publication DOI

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📂 Files

• GSE229022_expression_sparse.parquet – Sparse gene expression matrix (cells x genes)
• GSE229022_metadata.parquet – Cell-level metadata including sample ID and batch info

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🛠️ 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]

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💡 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

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📚 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

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🙏 Acknowledgments

Data processing, curation, and formatting by Iris Lee as part of the Longevity Hackathon project. ### 🧑‍💻 Team: MultiModalMillenials. Iris Lee (@iris8090)