Burns

Skin wound healing is a dynamic, tightly coordinated biological process involving inflammation, new tissue growth, and matrix remodeling. When this choreography breaks down, it can lead to devastating chronic non-healing ulcers or excessive fibrotic scarring.

Modern multi-omics technologies now allow us to measure gene expression, chromatin accessibility, and tissue spatial architecture at single-cell resolution. However, turning these vast, high-dimensional datasets into a clear understanding of tissue regeneration remains a major computational bottleneck. This project aims to develop an end-to-end data science pipeline that integrates temporal single-cell and spatial omics data to discover the molecular drivers and cell–cell communication networks that govern successful skin repair.

Objectives

1. Implement multi-modal integration methods (such as Multi-Omics Factor Analysis and variational autoencoders) to jointly embed single-cell RNA-seq and ATAC-seq data, identifying latent cell states and continuous trajectories as skin transitions from injury to repair.
2. Apply spatial graph neural networks and spatial transcriptomics modelling to reconstruct the localized tissue niche, mapping how immune cells, fibroblasts, and keratinocytes physically coordinate during re-epithelialization.
3. Develop predictive machine learning models to identify key regulatory genes and signalling pathways that differentiate regenerative healing from chronic wound failure and pathological fibrosis.

References

1. Liu, J., et al. "Spatiotemporal single-cell roadmap of human skin wound healing." Cell Stem Cell 32.3 (2025): 479–498.
2. Foster, D. S., et al. "Integrated spatial multiomics reveals fibroblast fate during tissue repair." PNAS 118.41 (2021).
3. Mascharak, S., et al. "Multi-omic analysis reveals divergent molecular events in scarring and regenerative wound healing." Cell Stem Cell 29.2 (2022): 315–327.

Work environment

The student will work with interdisciplinary researchers bridging data science, artificial intelligence, and biomedical research from UvA and AmsterdamUMC. You will gain hands-on experience in cutting-edge computational biology, high-dimensional data integration, and machine learning while working on real-world datasets with direct relevance to regenerative medicine.