Date: Friday, April 18, 2025
Time: 12:00 -1:00 PM
Room: Yellowstone room in Broad Institute
CSB Ph.D. Candidate: Jide Ezike
Supervisor: Gad Getz
TDC Members: Jonathan Weissman (chair)
Aviv Regev
Caroline Uhler
Mario Suva
Title: Applications of Native and Engineered Genetic Barcodes in Single-Cell RNA-Seq to Study Clonal Evolution and Cellular Phenotypic Diversity
Abstract: 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. Acquired mutations are thus at risk of being clonally expanded and ultimately propagated within cell lineages. Understanding how populations of cells relate to each other phylogenetically aids in the hypothesis generation of drivers of different developmental processes, such as cancer evolution and hematopoietic development. In this thesis, we describe a suite of computational and analytical approaches that enable one to study lineage trajectories within single cells and gene expression programs associated with said lineages. Each chapter describes a different single-cell modality from which either barcode markers or snapshot expression information is used to construct lineage trajectories or identify subclones with shared features.
Chapter 1 describes an atlas single-cell human hematopoiesis study that profiles HSPCs throughout human lifespan (gestation to 77yo). Here, we used various snapshot lineage tracing based methods to quantify the lineage fate biases across human lifetime and identify lineage-specific genes that are both consistently and variably expressed across human lifetime.
Chapter 2 describes a computational pipeline for identifying somatic mutations from full-length single-cell RNA-sequencing data, denoising various technical artifacts that plague mutation calling in RNA-sequencing data. This enables the uncovering of cancer associated mutational signatures from single-cell mutations and detection of clones that are orthogonally supported by shared copy number alterations.
Lastly, chapter 3 describes a study where we build high resolution single-cell phylogenies, using a CRISPR-based lineage tracing system, to study cancer persistence potential. Devising a lineage informed “persistence potential score” per cell, coupled with phylogenetic statistical tests enabled the identification of gene programs that potentially modulate lung cancer cells’ potential to persist under targeted treatment. Upon inhibiting our pathways most associated with persistence potential in combination with osimertinib (EGFR-inhibitor/TKI), we observe synergistic killing of persisters relative to TKI-only treatment.
Collectively, these works demonstrate the utility in leveraging single-cell (native or engineered) barcoding information to identify single-cell lineages, and contribute computational advances for hypothesis generation when using single-cell lineage or mutation data.