Clonal analysis at single cell level helps to understand positional codes during development

Development is a dynamic process, with cells continuously changing their gene expression as they proliferate, differentiate, and migrate. Single-cell transcriptomics allows for the capture of these dynamic changes in gene expression over time, providing insights into the temporal regulation of developmental processes and the mechanisms by which cells transition from one state to another. The current state of single cell technology enables reconstructing the transcriptional portraits of developing systems, including trajectories and fate bifurcations. However, this approach is largely plagued by our inability to discern convergent transcriptional states that belong to different genealogical origins. In my talk, I will outline our unique machine learning approach to clonal barcoding for eliminating this convergence challenge and for revealing sub-trajectories with common or divergent clonal behavior. Surprisingly, it resulted in a novel way for discovering positional information guiding location-specific development and multipotency.