Abstract

Direct lineage reprogramming converts one cell type into another, a process characterized by significant heterogeneity. Single-cell genomic techniques have been instrumental in dissecting this variability, yet they often fail to preserve lineage relationships. Here, I present CellTagging, a sequential, combinatorial indexing method that enables the reconstruction of clonal history and gene expression dynamics at single-cell resolution. This facilitates the construction of multi-level lineage trees, providing insights into cellular reprogramming trajectories. We applied CellTagging to investigate direct lineage reprogramming of mouse embryonic fibroblasts into induced endoderm progenitors. Our analysis revealed two distinct reprogramming trajectories: one leading to successful conversion and another resulting in a ‘dead-end’ state. Notably, the divergence of these trajectories occurs early in the reprogramming process. Additionally, we identified Mettl7a1, a putative RNA methyltransferase, as a pro-reprogramming factor that enhances reprogramming efficiency. These findings highlight the power of CellTagging in resolving the transcriptional and clonal dynamics of direct lineage reprogramming.

Committee Chair

Samantha Morris

Committee Members

Cristina de Guzman Strong; James Skeath; Robi Mitra; Zachary Pincus

Degree

Doctor of Philosophy (PhD)

Author's Department

Biology & Biomedical Sciences (Molecular Genetics & Genomics)

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

5-6-2025

Language

English (en)

Author's ORCID

https://orcid.org/0000-0003-1680-5527

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