Project Summary
Antimicrobial resistance (AMR) is one of the biggest threats to public health. The complex heterogeneous nature
of bacterial communities poses a fundamental challenge in understanding the mechanisms of AMR. Even
genetically homogenous bacterial populations can exhibit differential susceptibility to antibiotics, a phenomenon
known as antibiotic heteroresistance. Pre-existing variation in gene expression states is a fundamentally
important mechanism that underlies heteroresistance. Also, it has been shown that antibiotics themselves could
induce transcriptional responses in a small subpopulation of cells that protect them from drug attack.
Remarkably, studies have shown that repressing these responses with small molecule inhibitors leads to a
substantial reduction of multidrug resistance. These findings highlight how understanding transcriptional
heterogeneity could be the foundation for development of novel effective antimicrobial strategies. However,
systematic investigation of how transcriptional heterogeneity affects antibiotic sensitivity has been lacking, due
to unavailability of suitable tools and approaches. Recent work has clearly demonstrated the utility of high-
throughput single-cell RNA sequencing (scRNA-seq) technology to explore gene expression states of
eukaryotes. However, comparable tools for bacteria do not exist due to numerous challenges. We have recently
overcome these challenges by developing Prokaryotic Expression-profiling by Tagging RNA In Situ and
sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq technology. PETRI-seq can
capture single-cell bacterial transcriptomes with high purity and low capture bias, enabling robust discrimination
of transcriptional states of various subpopulations including those that represent as rare as 0.05% of the
population. Here, we propose strategies to further improve the sensitivity of PETRI-seq, and apply it to profile
the heterogeneous transcriptional responses of isogenic Escherichia coli to antibiotic challenge at single-cell
resolution. Using three different classes of antibiotics, we will study how different antibiotics cause cells to
differentiate into subpopulations with distinct transcriptional states. We will study how these transcriptional states
change over the course of antibiotic treatment and contribute to survival. Finally, we propose to determine which
transcriptional states induced by antibiotics are important for survival. Utilizing two functionally-complementing
screening platforms – systematic over-expression and CRISPR interference, we will interrogate how the
expression of every gene in the E. coli genome affects antibiotic sensitivity. We will validate the discovered genes
and pathways whose expression enhance survival, and determine whether their inhibition potentiates the effect
of antibiotics and prevents resistance. In sum, we expect that the combination of our scRNA-seq and functional
genomic strategies will reveal novel transcriptional determinants of antibiotic resistance in small subpopulations
that have been masked by previous bulk methods. These resistance determinants will constitute promising
candidate drug targets for maximizing the efficacy of current antibiotics.