# High-throughput single-cell RNA sequencing of bacteria to uncover cell states involved in pathogenesis

> **NIH NIH R01** · DANA-FARBER CANCER INST · 2024 · $832,772

## Abstract

Bacterial cells with the same genetic material can have different transcriptional states. These distinct
transcriptional states are crucial to many important physiological functions, such as sporulation, motility,
metabolic adaptation, antibiotic resistance, and biofilm formation. To identify the distinct transcriptional cell states
that are present in a population of bacteria, we need to measure the expression levels of all genes in individual
bacterial cells across a large number of cells. To do so, we have recently developed a method, called proBac-
seq, which extends the power of commercial microfluidic platforms for single-cell RNA sequencing of mammalian
cells to bacterial cells. Our method uses DNA probes with poly-A tails to tag individual transcripts in fixed bacteria.
The tagged bacteria are then processed with a commercial platform for single-cell RNA sequencing (such as
10X) where the DNA probes are captured and quantified as if they were transcripts of a mammalian cell. In proof-
of-principle experiments, we have applied our method to E. coli, Bacillus, and C. perfringens, identifying known
and new transcriptional cell states. Here, we propose to extend proBac-seq to enable profiling of hundreds of
thousands of individual bacterial cells in a single run and apply it to create an atlas of transcriptional cell states
of Salmonella enterica during infection. Salmonella is an important human pathogen and a major cause of
foodborne illnesses. Intriguingly, pathogenic strains of Salmonella often exist asymptomatically in people. In
addition, Salmonella can infect a wide variety of cell types, colonize different niches, and interact with the immune
cells and gut microbiome. Therefore, to understand pathogenicity of Salmonella and devise effective therapies,
we need to identify what cell states are present during infection, how cells transition between these states, and
at what rates. To answer this question, we will develop a high through-put platform for cost-effective single-cell
RNA sequencing of bacteria (Aim 1). We will incorporate multiplexing into proBac-seq to increase cell numbers,
implement protein readout using DNA-tagged antibodies, and develop a method for enriching for a particular
species of bacteria from a mixture of species prior to single-cell profiling. Critically, we will build the computational
frameworks needed for data analysis. Our platform will be broadly useful. We will therefore openly share
reagents, protocols, and computational pipelines. In Aim 2, we will apply our platform to create an atlas of
transcriptional cell states of Salmonella during infection in culture and using an in vitro model of intestinal
organoids. We will infer how distinct cell states interact with each other, how they are regulated, and the dynamics
of transitions between them. We will identify perturbations that disrupt the virulent cell state or hinder transitions
into that state. In Aim 3, we will validate our findings in an in vivo mode...

## Key facts

- **NIH application ID:** 10978324
- **Project number:** 1R01AI179839-01A1
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Sahand Hormoz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $832,772
- **Award type:** 1
- **Project period:** 2024-06-20 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978324

## Citation

> US National Institutes of Health, RePORTER application 10978324, High-throughput single-cell RNA sequencing of bacteria to uncover cell states involved in pathogenesis (1R01AI179839-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10978324. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*