Title: "Developing experimental and computational single-cell sequencing techniques to study complex mammalian and bacterial systems" 

Abstract: Single-cell sequencing is a rapidly advancing field that is leading to unprecedented new insights into complex biological systems, ranging from diverse microbial ecosystems to early mammalian embryogenesis. During graduate school, I have contributed to the field in two ways: developing a generalized probabilistic mathematical framework to reconstruct cellular lineage trees to understand mammalian development and developing an efficient mRNA enrichment method to efficiently sequence the transcriptome of prokaryotic cells.  

Lineage reconstruction is central to understanding tissue development and maintenance. However, current tools to infer cellular relationships typically involve genetic modifications and have a clonal resolution. I developed scPECLR, a probabilistic algorithm to endogenously infer lineages at a single cell-division resolution by sequencing the epigenetic mark 5-hydroxymethylcytosine (5hmC) in single cells. When applied to 8-cell mouse embryos, scPECLR predicts the full lineage trees with greater than 95% accuracy. The high accuracy of the method, using only endogenous mark, suggests that it can be directly extended to study human development.    

Compared to mammalian cells, mRNA sequencing of bacterial samples is more challenging as they typically contain 100-fold lower total RNA and lack a poly-A tail, which enables an enrichment of mRNA. I developed an effective mRNA enrichment method, EMBR-seq, that increases mRNA detection from 5% present in total RNA to 90% of sequencing reads derived from mRNA. Moreover, EMBR-seq successfully quantifies mRNA from 20 picogram total RNA, starting quantities that are 500-fold lower than required in existing commercial kits at an order-of-magnitude lower costs. Therefore, EMBR-seq provides a powerful approach to investigate gene expression patterns in non-model microbial species.