Biology subjects

Fernandez Vallone, V.

Publications and source records attributed to Fernandez Vallone, V..

3 recordsLinked to original sources

bioRxiv2025

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

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.

cell biology↗

bioRxiv2025

An autologous human iPSC-derived 3D organoid infection model for preclinical testing of antiviral T cells

Immunocompromised patients, such as those undergoing hematopoietic stem cell or solid organ transplantation, are highly susceptible to viral complications. Given the limitations and side effects of available antiviral therapies, adoptive transfer of antiviral T cells offers a promising alternative by restoring immune defense. However, existing models for evaluating antiviral T cell therapies lack physiological relevance, limiting accurate predictions of efficacy and safety. There is a critical need for in vitro human infection platforms that support personalized assessment of therapeutic responses. To address this, we developed antiviral T cell products (TCPs) targeting Influenza A virus (IAV)-infected cells, alongside an autologous human induced pluripotent stem cell (iPSC)-derived 3D lung organoid infection platform. This model recapitulates key immunological responses and is compatible with a new 3D high-throughput, high-content imaging pipeline. Our study provides the first proof-of-concept for assessing T cell-mediated cytotoxicity in a 3D in vitro lung infection model, advancing personalized antiviral immunotherapy development.

immunology↗

bioRxiv2025

SteMClass: A Novel DNA Methylation-Based Classifier for iPSC In Vitro Differentiation States.

Human induced pluripotent stem cells (iPSCs) hold great promise for regenerative medicine, disease modelling, and drug discovery, but most downstream applications require differentiation into specialised cell types not covered by current quality control assays. Here, we present "SteMClass", a proof-of-concept DNA methylation-based classifier that standardises iPSC differentiation state identification across protocols with one test. We curated a reference cohort of 15 iPSC lines differentiated into seven distinct states (n = 97), performed array-based DNA methylation profiling, and trained a random forest model to classify the eight distinct differentiation states. In nested cross-validation, SteMClass achieved a Brier score of 0.018, and on an independent cohort (n = 58) attained 96.5% accuracy (Cohens K = 0.959) with a 3% rejection rate. Applied to external data (n = 249), SteMClass achieved 85.1% overall accuracy (Cohens K = 0.687) with a 12.9% rejection rate. Among classified samples (n = 217), accuracy was 97.7% (Cohens K = 0.93). SteMClass is compatible with all Illumina methylation array versions, and accessible via an interactive web interface that supports classification and exploration of DNA methylation profiles. By providing a harmonised, single-assay framework for iPSC-derived differentiation state characterisation, SteMClass improves reproducibility and comparability across studies, paving the way for robust quality control standards and accelerating clinical translation.

cell biology↗