Biology subjects

Mollandin, F.

Publications and source records attributed to Mollandin, F..

3 recordsLinked to original sources

bioRxiv2025

Latent plasticity of the human pancreas across development, health, and disease.

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

genomics↗

bioRxiv2025

Guiding eQTL mapping and genomic prediction of gene expression in three pig breeds with tissue-specific epigenetic annotations from early development

Gene expression is a dynamic phenotype influenced by tissue-specific regulatory mechanisms, which can modulate expression directly or indirectly through cis or trans factors. Identifying genetic variants in these regulatory regions can improve both expression quantitative trait locus (eQTL) mapping and gene expression prediction. Whole genome sequences offer the possibility for enhanced eQTL mapping accuracy, but detecting causal variants remains challenging. Here, we evaluate the potential added-value of integrating tissue-specific epigenetic annotations, such as chromatin accessibility and methylation status, into within-breed genomic predictions of expression for three pig breeds. Functional annotations from early developmental stages improved eQTL mapping interpretability as shown by the enrichment of trait-relevant QTLs. However, despite the use of functional annotations, predictions across breeds remain challenging due to differences in genetic architectures. Our work contributes to the understanding of gene expression regulation in livestock and highlights the value of functional annotations, despite continued challenges for predictions across breeds. HighlightsO_LIMatched whole-genome sequences and expressions reveal transcriptional regulatory variants. C_LIO_LIEarly epigenetic marks can guide transcriptome prediction and eQTL mapping. C_LIO_LIAnnotation-guided models can enhance the interpretability of eQTL mapping. C_LIO_LIPorting predictions across pig breeds remains challenging even with annotations. C_LIO_LIA case study linked IGF2 liver eQTLs with annotations and cholesterol QTLs. C_LI

genomics↗

bioRxiv2020

An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

Technological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

genomics↗