🧬 GSE120180 – Single-Cell Transcriptomics of Aging Human Skin

This dataset contains single-cell RNA-seq profiles from aging human skin, originally published as part of the GEO Series GSE120180. The dataset has been converted to .parquet format for faster I/O and compatibility with machine learning pipelines.

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📂 Dataset Overview

• Original Source: GEO: GSE120180
• Species: Homo sapiens
• Tissue: Human skin
• Technique: 10x Genomics scRNA-seq
• Format: .parquet (converted from original .txt.gz)

Each .parquet file contains gene expression matrices with:

• Rows: Gene identifiers (ENSEMBL or gene symbols)
• Columns: Cell barcodes

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🔬 Use Cases

• Build or validate skin-specific aging clocks
• Study age-related changes in gene expression at single-cell resolution
• Explore cell-type-specific aging signatures in skin
• Benchmark de-noising or imputation models for sparse single-cell data
• Integrate with multi-tissue atlases or multi-omics aging datasets

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🛠️ Usage Instructions

import pandas as pd

# Load one of the files
df = pd.read_parquet("GSM#####_expression.parquet")
print(df.shape)
df.head()

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📑 Citation

If you use this dataset, please cite:

Solé-Boldo, L. et al. (2020). Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Cell Stem Cell, 27(3), 387–402.e7.
DOI: 10.1016/j.stem.2020.07.009

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🙏 Acknowledgments

• Original data generated by Solé-Boldo et al. and hosted on GEO under accession GSE120180
• Converted and curated by Iris Lee for use in aging and longevity research

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🧠 Keywords

single-cell, scRNA-seq, aging, skin, GSE120180, longevity, parquet, machine learning, biomarkers