About his PhD project

Advances in single-cell RNA sequencing (scRNA-seq) have transformed our understanding of cell identity and gene expression heterogeneity during development. However, the static nature of transcriptomic snapshots limits our ability to infer true cell lineages and dynamic transitions over time. My PhD project aims to develop and apply computational methods for integrating scRNA-seq data with high-resolution single-cell tracking and lineage information extracted from live imaging of developing embryos. By linking molecular profiles to lineage trees reconstructed from time-lapse microscopy, we seek to reconstruct dynamic gene expression trajectories along precise developmental lineages. The project will involve: (1) developing algorithms for aligning cells across modalities using spatial, morphological, and lineage cues; (2) creating probabilistic models to reconcile transcriptomic states with observed lineage bifurcations; and (3) applying these tools to real biological systems where high-quality imaging and scRNA-seq data are available. The integration of these modalities will provide a comprehensive view of how gene expression programs unfold along developmental paths, offering new insights into the coordination of fate decisions and the mechanisms underlying robust tissue patterning.