Leander Dony

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

CellFlow predicts single-cell phenotypes from complex perturbations using flow matching, demonstrating accuracy in cytokine stimulation, drug treatments, and developmental modeling.

This study shows chronic glucocorticoid exposure alters lineage specification in human neural organoids by priming inhibitory neuron fate through transcription factors like PBX3.

This paper integrates 36 single-cell transcriptomics datasets from human neural organoids into a unified atlas to assess cell type coverage and compare organoid protocols with primary brain tissue.

Pretrained on 57 million dissociated and 53 million spatial human and mouse cells, a 49.3M-parameter transformer predicts niche, region, neighborhood composition and density, and carries spatial labels onto scRNA-seq.

This study shows glucocorticoids increase specific basal progenitors via ZBTB16, leading to more neurons and linking to educational attainment and brain structure.

The paper develops a scalable cortical organoid platform with quality control metrics and identifies early dysregulation of phosphatase and prefoldin genes in FTD-tau neurons.

The paper develops and compares four dynamical models of a pathogen-infected plant gene regulatory network to determine the best model for capturing its dynamics.

The paper uses β-VAE to learn disentangled representations for scRNA-seq data and shows improved out-of-distribution prediction compared to existing methods.

Sfaira provides a data and model zoo for single-cell RNA-seq, enabling fast reuse of pre-trained models and datasets across human and mouse tissues.

The paper investigates how glucocorticoid receptor activation affects neuronal development in fetal human brain organoids using single-cell transcriptomics, finding cell-type specific impacts on differentiation and behavior-related gene expression.

This paper compares parametric and non-parametric methods for gene regulatory network inference using gradient matching and finds parametric methods often provide better results.