longevity-db/GSE120180

🧬 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.

📂 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

🔬 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

🛠️ Usage Instructions

import pandas as pd

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

📑 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

🙏 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. ### 🧑‍💻 Team: MultiModalMillenials. Iris Lee (@iris8090)

🧠 Keywords

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