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StemCNV-check: a pipeline for human pluripotent stem cell (hPSC) genomic integrity control using SNP array data and copy number variant scoring

Authors

  • Nicolai von Kuegelgen
  • Valeria Fernandez Vallone
  • Katarzyna A. Ludwik
  • Mariia Babaeva
  • Sebastian Diecke
  • Dieter Beule
  • Harald Stachelscheid

Journal

  • bioRxiv

Citation

  • bioRxiv

Abstract

  • Human pluripotent stem cells (hPSCs) and other continuously cultured cell lines are prone to acquiring
    mutations and genomic aberrations over time, even when derived from well-characterized cell banks. To
    ensure experimental reproducibility and maintain cell line integrity, routine monitoring for genomic
    abnormalities is essential. Single nucleotide polymorphism (SNP) arrays represent a cost-effective and
    widely accessible method for detecting copy number variations (CNVs) with genome-wide resolution,
    making them particularly suitable for quality control (QC) in cell culture systems.
    Despite the established utility of SNP arrays for CNV detection, there remains a lack of comprehensive,
    user-friendly software solutions that support end-to-end analysis tailored to hPSC line quality assessment.
    Existing tools are either limited to discrete analysis steps requiring specialized bioinformatics expertise or
    are proprietary solutions that do not adequately address the specific needs of cell line monitoring.
    To bridge this gap, we developed an accessible and integrated analysis pipeline for SNP array-based QC of
    hPSC lines. The pipeline facilitates all stages of analysis—from raw data processing to the generation of
    interpretable reports—and includes specialized features such as sample-to-reference comparison, a CNV
    scoring system according to CNV biological impact, single nucleotide variation (SNV) evaluation and
    identity verification via SNP genotyping profiles, all tailored to hPSC. We benchmarked the pipeline against
    established methodologies and implemented strategies to enhance CNV detection reliability through
    expert-guided improvement process.


DOI

doi:10.64898/2025.12.01.691615