Unbiased profiling of multipotency landscapes reveals spatial modulators of clonal fate biases

Abstract

Abstract The proportion of cell types varies systematically across the body, but it remains unclear how individual progenitor cells integrate positional information to establish patterns of cellular composition. In this study we profile the clonal landscape of the embryo, using single-cell lineage tracing of mouse embryos from neurulation until mid-gestation. To analyze the complex clonal patterns derived from highly multipotent progenitors, we developed clone2vec , which uses unsupervised learning to categorize individual clones into lineages based on shared transcriptional context. This revealed a body-wide gradient of clonal fate biases, in which anatomical position and clonal composition are mutually predictive. Comparison of clonal lineages revealed spatial transcription factor programs associated with dynamic cell biasing towards skeletal versus non-skeletal fates. Mosaic combinatorial perturbations targeting the Hedgehog pathway generated clones in which positional identity was mismatched with clonal composition, suggesting a potential signaling influence on somite patterning. We explore the effects of position and heterochrony on fate biases in cranial, trunk, and caudal neural crest clones. Altogether, our work demonstrates an effective practical approach for dissecting mechanisms of lineage specification.