Poster abstracts

Poster number 2 submitted by Bryce Guidry

Single-cell transcriptomic explorations of population-level heterogeneity in bacteria

Bryce Guidry (Biophysics Graduate Program, Ohio State University), James Tan (Department of Microbiology and Department of Mechanical and Aerospace Engineering, Ohio State University), Natalie Solonenko (Department of Microbiology, Ohio State University), Marion Urvoy, Karna Gowda (Department of Microbiology, Ohio State University), Matthew Sullivan (Department of Microbiology, Ohio State University)

Abstract:
Population-level heterogeneity is an inherent feature of bacteria where even clonal populations display individual phenotypic differences despite possessing the same genotype and being exposed to the same environment. The mechanisms for how and why these differences occur are not fully understood, and high-throughput, untargeted methods for single-cell biology are an important tool for addressing these questions. My primary research focus is on the development of tools that help with the unique challenges of single-cell RNA sequencing (scRNA-seq) to better understand population-level heterogeneity. These tools will include the improvement of the experimental technique, single-microbe RNA-sequencing (smRandom-seq), to generate new data for studying gene expression of single cells. The computational tool, cellstates, allows for gene expression analysis of cells that is based on the principles of scRNA-seq noise and will be improved to better analyze bacterial single-cell data. These tools will allow for population-level heterogeneity to be explored in the context of community metabolism and phage-host interactions.

References:
Grobecker P, Sakoparnig T, van Nimwegen E (2024) Identifying cell states in single-cell RNA-seq data at statistically maximal resolution. PLOS Computational Biology 20(7): e1012224. https://doi.org/10.1371/journal.pcbi.1012224.

Xu, Z., Wang, Y., Sheng, K. et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat Commun 14, 5130 (2023). https://doi.org/10.1038/s41467-023-40137-9

Keywords: population-level heterogeneity, bacteria, single-cell RNA sequencing