Daniela Basurto-Lozada

The paper builds a spatiotemporal map of human pilosebaceous unit development using single-cell multiomics and machine learning, showing how epithelial-mesenchymal interactions drive organogenesis and how sebaceous gland programs are disrupted in tumors.

The paper benchmarks dopaminergic stem cell models and cell therapy candidates using a human brain atlas to identify off-target populations and lineage plasticity.

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.

Tripso, a self-supervised transformer model, identifies gene program-specific embeddings and discovers novel gene programs, revealing biological insights in hematopoietic development and disease.

This study creates a spatially-resolved molecular atlas of human skin and uses deep learning to identify 26 niches, revealing novel immune memory structures in inflammatory skin diseases.

PerturbGen predicts how genetic perturbations affect cellular trajectories across time in human datasets, showing effects on immune responses, hematopoiesis, and skin development.

This study maps the spatiotemporal cellular dynamics of human skin wound healing using single-cell and spatial transcriptomics, identifying FOSL1 as critical for re-epithelialization and highlighting differences in chronic wounds.

WebAtlas integrates single cell and spatial transcriptomic data into a cloud-optimised format for interactive exploration.

The paper identifies Th2-like malignant cells in CTCL supported by a B cell-rich microenvironment through single-cell and spatial transcriptomics analysis.

This paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

The paper maps the multi-organ functions of the human yolk sac during early development, revealing its roles in hematopoiesis, nutrition, and oxygen regulation.