Ricard Argelaguet

The paper identifies epigenetically primed enhancers in early embryonic development and shows their role in regulating developmental gene networks across species.

The paper identifies Eomes's role in directing the formation of distinct extra-embryonic hematovascular tissues in mice, revealing functional and mechanistic diversity in their development.

The paper uses scRNA-Seq and computational methods to predict and validate differentiation trajectories during early mammalian organogenesis, showing agreement between computational predictions and experimental outcomes.

The paper provides a Bioconductor package with curated landmark datasets for single-cell multimodal data, enabling easy access and analysis of seven different technologies.

The paper uses single-cell RNA-sequencing to study heterogeneity in mouse gastruloids over time, revealing distinct mesodermal and neural differentiation trajectories and their implications for perturbation studies.

A mouse embryo multiome atlas (E7.5 to E8.75) and an unsupervised transcription factor binding predictor; a Brachyury knockout supports its network predictions in neuromesodermal progenitors.

MEFISTO enables spatio-temporal dimensionality reduction and pattern separation in multi-modal genomic data, revealing conserved and diverged developmental programs and microbiome heterogeneity.

This paper identifies 8-cell like cells (8CLCs) in human naive stem cells that model zygotic genome activation, expressing ZGA markers and transposable elements, and can be manipulated to enhance ZGA-like transcription.

This paper uses spatial transcriptomics to study mouse organogenesis, revealing spatial gene expression patterns and cell fate decisions not captured by single-cell RNA sequencing.

scMET quantifies DNA methylation heterogeneity at single-cell resolution using a Bayesian model, identifying variable features and enabling differential analysis between cell groups.

MOFA+ integrates structured single-cell multi-modal data by modeling group and view structures, revealing coordinated gene expression and epigenetic changes during mouse gastrulation.

The paper uses single cell triple-omics analysis to reveal how epigenetic changes drive germ layer specification in mouse embryos, showing distinct epigenetic priming and remodeling in different lineages.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

This paper introduces scNMT-seq, a method for joint profiling of chromatin accessibility, DNA methylation, and transcription in single cells, and validates it in differentiating mouse embryonic stem cells.