# Tracking single-cell gene expression heterogeneity and its consequences in bacterial biofilms

> **NIH NIH DP2** · YALE UNIVERSITY · 2024 · $1,005,000

## Abstract

Project Summary
Bacterial biofilms are surface-attached communities of bacterial cells enclosed in an extracellular matrix.
Biofilms are a concern in health and in industrial operations because of persistent infections, clogging of flows,
and surface fouling. Recent advances in single-cell live imaging have revealed well-defined cell ordering in
individual biofilm clusters and the underlying biomechanical principles that shape them. However, we have little
understanding of which genes are activated in each biofilm-dwelling cell and how cell organization is
determined by the gene expression pattern at the single-cell level. We do not know whether and how gene
expression profiles vary from cell to cell in biofilms, and what consequences such heterogeneity has on biofilm
development. Cell-to-cell variation in biofilms could underly the notorious difficulty in eradicating biofilms in
chronic infections because of the differential response of biofilm cells to antibiotic treatment. In this proposal
we put forward ideas to address this challenge by developing new imaging platforms to capture the biofilm
growth dynamics and associated gene expression pattern at the single-cell level. Using these imaging
platforms, we will uncover the intricate interplay between single-cell gene expression, individual cell behavior,
and local cell organization that underlies the developmental program of bacterial biofilms. Specifically, we will
use deep learning algorithms to push the temporal and spatial resolution limits in single-cell biofilm imaging,
and develop an innovative, coupled segmentation-tracking method to generate a robust three-dimensional
lineage tracing algorithm in growing biofilms. By combining lineage tracing and fluorescent reporters, we will
follow the spatiotemporal expression pattern of each individual cell throughout biofilm development. By
focusing on matrix production, degradation, and cell dispersion, we will create concrete examples of gene
expression heterogeneity at the single-cell level and the associated consequences in 3D biofilms. In addition,
we will investigate heterogeneity in genes involved in intra- and intercellular signaling, in motility and
attachment, and in cell shape regulation to broaden our finding. With these efforts, we will reveal how individual
gene expression, cell behavior, and local cell ordering reciprocally interact with each other to define biofilm
architecture and development. The knowledge obtained in the current proposal offers new strategies for
manipulating complex bacterial communities and has the potential to change the clinical procedure of treating
biofilm-related diseases, for example by developing new chemicals that specifically induce dispersal or target
the recalcitrant cell populations.

## Key facts

- **NIH application ID:** 11014624
- **Project number:** 4DP2GM146253-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jing Yan
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,005,000
- **Award type:** 4N
- **Project period:** 2021-09-23 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11014624

## Citation

> US National Institutes of Health, RePORTER application 11014624, Tracking single-cell gene expression heterogeneity and its consequences in bacterial biofilms (4DP2GM146253-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11014624. Licensed CC0.

---

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