(334av) Developing Experimental and Computational Single-Cell Techniques to Uncover Biological Insights

Single-cell genomics is a rapidly advancing field and is generating new knowledge of complex biological systems, ranging from microbial ecosystems diversity, through whole-organism clone tracing, to understanding early mammalian embryogenesis. During my graduate school, I have contributed to the field in two ways: developing a mathematical model to reconstruct cellular lineage in pre-implantation mouse embryos and creating an efficient and cost-effective mRNA enrichment method for prokaryotic cells.

Cellular lineage reconstruction: Lineage reconstruction is central to understanding tissue development and maintenance. While various tools to infer cellular relationships have been established, these methods typically involve genetic modification and have a clonal resolution. I created scPECLR, a probabilistic algorithm to endogenously infer lineages at a single cell-division resolution using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell mouse embryos, scPECLR predicts the full lineage trees with greater than 95% accuracy. Furthermore, I developed a protocol to detect both 5hmC and genomic DNA from the same single cell. Information from genomic DNA, in combination with scPECLR, could allow us to identify cellular lineages more accurately and expand the reconstruction to even larger trees, where the number of possible tree topologies increase to more than 10²⁶. In addition, I showed that scPECLR can be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the controversial “immortal strand” hypothesis in stem cell biology.

Low-input bacterial mRNA sequencing: RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain understanding of cellular functions and phenotypes. However, compared to mammalian cells, mRNA sequencing of bacterial samples is more challenging due to their 100-fold lower RNA quantities and the absence of a poly-A tail that typically enables an enrichment of mRNA. To overcome these limitations, I conceived an economical and effective mRNA enrichment method called EMBR-seq. The method results in greater than 80% of the sequenced E. coli RNA molecules deriving from mRNA, which originally contributes to lower than 5% of total RNA in a cell. Moreover, EMBR-seq successfully quantifies mRNA from 20 picogram total RNA, a level 500-fold lower than required in existing commercial kits, without introducing technical biases and at a fraction of the cost. Lastly, due to its simplicity and efficiency, EMBR-seq could potentially be extended to a single-cell resolution to advance developments in bacterial mRNA sequencing and improve understanding of microbial systems.

Research Interests: bioengineering, molecular and computational biology, and bioinformatics