Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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🧬 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

🧠 Keywords

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

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