🧬 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