Unraveling the determinants of stem cell heterogeneity through multiplexed single-cell lineage tracing
Scientific session by Alejo E. Rodriguez-Fraticelli, Aging and Metabolism Programme, IRB Barcelona
Abstract
For decades, biologists have noted that stem-cells in adult tissues show extensive functional heterogeneity. Observations of clonal heritability in hematopoietic stem cell (HSCs) lineage biases have suggested that this heterogeneity is determined by intrinsic and heritable properties. However, a detailed understanding of the mechanisms driving the variation in tissue stem cell behaviors has remained elusive. We have previously shown that expressed barcodes can enable simultaneous read-out of transcriptome and clonal information from the same single cell.
We have used these expressed barcodes to carry out dynamic analysis of states and fates for thousands of differentiating clones in parallel and revealed novel regulators of fate decisions.
Long-term clonal analysis across serial transplantation experiments revealed intrinsic and heritable states driving differences in self-renewal properties. We applied in vivo CRISPR screening to functionally test these signatures and discovered novel determinants of HSC self-renewal. We have recently extended these studies to understand the process of aging and malignancy. We have found clone-intrinsic programs that characterize aged HSCs, as well as preleukemic mutant HSCs. Both aged and preleukemic HSCs are characterized by small subsets of clones that expand and self-renew massively with reduced differentiation capacity. Intriguingly, our results indicate that age-related and mutation-driven clonal HSC states drive selective self-renewal advantages through different mechanisms. In sum, we show that connecting cellular states and cellular fates through high-resolution lineage tracing can be used to describe drivers for a variety of relevant stem cell properties. This could be highly impactful in other in vivo stem cell systems where clonal bottlenecks make conventional functional genomics approaches more difficult.