Project Summary Revealing the lineage relations among cells has the potential to illustrate evolutionary patterns in tumor initiation and resistance to therapies and shed light on the processes which give rise to developmentally complex organisms. However, studying these relationships in cancer biology and developmental biology is currently limited by the difficulty of cell lineage tracing and reconstruction in-vivo. Complete reconstructions of cell lineages require non-invasive live cell imaging, which is restricted to relatively small numbers of cells and impractical for physiologically-relevant studies. Although recent systems for CRISPR-mediated genome barcoding have emerged as a promising alternative, they suffer limitations in the diversity and generation rate of lineage- identifying information which render them unsuited to studies of clonal evolution and lineage development, especially in cancer. Moreover, these methods fail to explicitly capture cell divisions, a basic parameter of somatic evolution which could impact our understanding of a cancer's response to intervention. To improve recovery of population dynamics and cell lineage information from cancer models, we propose a cell- cycle inducible genetic construct that will record, in a single locus, the sequence of divisions and lineage of each cell. Our approach uses cell-cycle regulated ubiquitination to control the activity of a donor-free CRISPR-based genome recording device which barcodes cells upon each division. In this study, we will evaluate the ability of our device to track lineage information and cell division dynamics in clonal human and mouse cell populations. To demonstrate its investigative potential in developmental studies, we will combine our cell-division barcoding strategy with single-cell RNA sequencing to identify healthy and abnormal differentiation in cancer- and hiPSC- derived organoids. If successful, our improvements to recovery of cell division, clonality, and lineage will improve our understanding of development, explain evolutionary sources of resistance in cancer, and guide practitioners toward more effective treatments in translational research.

Our approach is to combine synthetic biology and advanced gene editing technologies to accelerate our ability to capture cell-division dynamics, cell-fate decisions, and subclonal diversity in live animals. We will construct a genetic recording device which reliably records the sequence of divisions of each cell and its lineage in the cell's own genome. This will help us understand how organisms as well as tumors develop, and how cancers respond to therapy.