Leveraging natural and engineered genetic barcodes from single cell RNA sequencing to investigate cellular evolution, clonal expansion, and associations between cellular genotypes and phenotypes

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PROJECT SUMMARY Cells are constantly altering their states, whether due to physiological stress or exogenous forces. Clonal expansion is a well-defined process that contributes to this alteration and indiscriminately occurs in all types of tissue throughout the body, irrespective of the malignant or disease potential of that tissue. Any mutations or epigenetic changes that one sustains over the course of a lifetime are thus at risk of being clonally expanded and ultimately propagated within cell lineages15,16. However, questions still remain as to why some of these expansions result in cancer while others remain benign and as to how the specific steps that individual cells take genetically and transcriptionally to become pathogenic and ultimately evolve and embody different phenotypic states. These phenotypes include expression cell state, activity of mutational processes (e.g., endogenous APOBEC DNA/RNA deamination mutagenesis), and propensity to persist under treatment. Understanding how cells change their states provides insight into how to control cell fate, which can have ramifications on our understanding of cell plasticity, development, evolution, and disease progression. Computational analysis of single-cell genomes offers an opportunity to provide insight into these questions in biology, but there is a gap in the current ability of existing methods to extract confident variant calls from single- cell RNA sequencing data. Research to date has relied on laborious, inefficient methods limited to mostly cell lines or inherently noisy single-cell DNA data to attempt to understand this interplay between cell lineages, acquired mutations and genomic features (i.e., creating artificially-induced genetic barcodes or using natural DNA mutations)17-20. This project focuses on the development of a more robust genomic tool for building these single cell phylogenies and associating them with cellular phenotypes by leveraging the cell’s transcriptional machinery with full length scRNA-seq. The specific aims of this project can be summarized as follows: 1. Utilize scRNA-seq and CRISPR-based lineage tracing data to reconstruct phylogenies and identify specific genomic associations at the single cell level. 2. Investigate the role mutational processes have on clonal expansion and disease progression across tissues at single-cell resolution. To achieve these project goals as well as my own career objectives to becoming a successful independent genomic scientist, my training plan includes training in machine learning, phylogenetics, and mechanistic biology, as well as further training in scientific communication skills such as manuscript writing and grant writing. My excellent research environment includes the Broad Institute of MIT and Harvard, where my home lab of Dr. Gad Getz is located. This is a world-class institution for genomics research rich in people resources and all other necessary resources needed to perform my proposed research.