Erick Armingol

This study shows that TCR-ligand affinity quantitatively modulates transcriptome remodelling in vivo, affecting cell division and interferon response.

This paper creates an integrated multimodal atlas of the female reproductive system across the lifespan, identifying cell types and linking them to gynaecological pathologies.

This study creates a comprehensive spatial atlas of the human ovarian cortex, revealing molecular mechanisms of primordial follicle activation and follicle niche organization.

The paper develops human ME-Models that integrate gene expression and metabolism, showing improved growth-rate predictions and biological plausibility in cancer cell lines.

Tensor-cell2cell v2 jointly analyzes protein- and metabolite-mediated cell-cell communication over time, revealing coordinated dynamics in brain organoid development.

The paper introduces scCellFie, a framework to infer metabolic activities from single-cell and spatial transcriptomics data, revealing organ-specific metabolic programs and disease-associated alterations.

This study creates a developmental cell atlas of the human thyroid gland using single-cell sequencing, revealing cell type heterogeneity and its implications in thyroid disorders and cancer.

The paper trains a hybrid neural network to predict N-glycosylation events and structures from protein sequences using glycosite-flanking data, demonstrating its ability to predict glycan composition and abundance.

The paper identifies a relationship between protein structure and glycan structure, introducing a glycoimpact matrix to predict glycosylation patterns and validate it through disease variants and engineered antibodies.

This paper integrates LIANA and Tensor-cell2cell to analyze cell-cell communication across multiple samples, enabling robust and flexible identification of communication programs.

This study creates a single-cell gene expression atlas of C. elegans, identifying cell types, housekeeping genes, and intercellular interactions using scRNA-seq and ChIP-seq data.

The paper introduces a computational framework to infer the spatial code of cell-cell interactions using transcriptomic data from C. elegans, validated with experimental confirmation.

Tensor-cell2cell improves cell-cell communication analysis by incorporating cellular context, identifying communication modules linked to diseases like COVID-19 and Autism Spectrum Disorder.