In the intricate world of microbiology, not all bacterial cells conform to a single, static phenotype. Within populations of identical species, individual bacteria can exhibit a remarkable range of physiological states, from preparing for cell division to initiating responses against environmental stressors. This cellular heterogeneity, especially evident in pathogenic bacteria, poses challenges for treatment strategies and drives the ongoing quest for technologies capable of dissecting bacterial behavior at the single-cell level. Recent advances offer a powerful window into this microscopic diversity through an innovative approach called single-cell transcriptomics, enabling researchers to unravel the active gene expression profiles of individual bacterial cells with unprecedented resolution.
The essence of single-cell transcriptomics lies in its capacity to capture and analyze the messenger RNA (mRNA) molecules within each bacterial cell. Since mRNA reflects the genes currently active, profiling these transcripts provides a dynamic snapshot of cellular function and response under particular conditions. Unlike bulk RNA sequencing, which averages signals across thousands or millions of cells, single-cell technologies can discern variations within the bacterial community that might otherwise remain obscured. For microbiologists studying antibiotic resistance, pathogenesis, or metabolic adaptation, this tool can reveal how subpopulations react uniquely, potentially exposing vulnerabilities ripe for therapeutic intervention.
Pioneering this frontier, researchers at Julius-Maximilians-Universität (JMU) Würzburg, in cooperation with the Helmholtz Institute for RNA-based Infection Research (HIRI), have developed and refined a cutting-edge method known as bacterial MATQ-seq (Multiple Annealing and dC-Tailing-based Quantitative single-cell RNA sequencing). First introduced in 2020, MATQ-seq represents a major step forward in bacterial single-cell transcriptomics, addressing the challenges posed by the low RNA content and resilient cell walls typical of bacterial cells. This technique combines meticulous cell isolation with sensitive amplification protocols, ensuring the faithful capture of mRNA from individual bacterial cells.
What sets MATQ-seq apart is its remarkable efficiency and robustness. Whereas earlier bacterial single-cell RNA sequencing methods suffered from high cell loss—sometimes up to 70% of input cells are lost during processing—MATQ-seq boasts a retention and successful library construction rate of approximately 95%. This means that nearly every bacterial cell isolated at the beginning of the experiment is represented in the final dataset. Such efficiency not only saves valuable experimental resources but also enhances the statistical power and reliability of downstream analyses, especially when sample sizes are limited.
Moreover, the resolution provided by MATQ-seq is impressive, with the ability to detect active expression of between 300 and 600 genes per bacterial cell. Given that many bacterial genomes harbor only a few thousand genes, identifying several hundred transcripts offers a deep insight into cellular processes, far surpassing other contemporary methodologies that often detect fewer than 100 genes per cell. As a result, researchers can decipher detailed bacterial states such as metabolic activity, stress responses, or virulence factor expression, directly from individual cells.
While the entire MATQ-seq protocol—from the initial single-cell isolation step to the generation of raw sequencing data—can be completed in roughly five days, it proves especially suited to studies involving hundreds to a few thousand cells. This scale balances throughput with resolution, enabling nuanced characterization of bacterial populations without the trade-offs seen in high-throughput platforms, which tend to sacrifice transcript detection per cell and suffer greater sample loss when applied at million-cell scales.
Recognizing the broad utility of MATQ-seq, the JMU Würzburg team recently published an exhaustive, step-by-step protocol in the prestigious journal Nature Protocols. This publication provides not only detailed experimental guidelines but also comprehensive computational workflows to analyze and interpret single-bacterial-cell transcriptomic data. By doing so, the researchers empower laboratories worldwide to adopt and adapt the method for diverse research questions in microbiology, infection biology, and microbial ecology.
Beyond the advancement of the technique itself, this work underpins the establishment of the Center for Microbial Single-Cell RNA-seq (MICROSEQ) at Würzburg—a globally unique platform consolidating expertise and enabling collaborative access to cutting-edge technologies for bacterial single-cell transcriptomics. Led by Professor Jörg Vogel, director of HIRI and the Institute of Molecular Infection Biology, MICROSEQ aims to transform how researchers dissect bacterial heterogeneity, integrating MATQ-seq with other high-throughput approaches to deliver comprehensive and scalable solutions.
This initiative dovetails with the existing Würzburg Single-Cell Center, a hub already renowned for its single-cell RNA-seq capabilities focused on eukaryotic cells. By extending single-cell approaches into microbiology, MICROSEQ positions itself at the vanguard of infection biology, harnessing transcriptomic insights to tackle challenges—from elucidating mechanisms of antibiotic resistance to unraveling pathogen-host interactions at the single-bacterium level.
The fundamental impact of distinguishing transcriptomes within bacterial populations extends beyond pure science. Understanding gene expression variability informs on phenotypic heterogeneity, a phenomenon linked to bacterial persistence and the emergence of drug tolerance. Consequently, technologies like MATQ-seq do not merely catalog cellular states; they pave the way for precision therapeutics designed to target elusive subpopulations that underlie chronic infections and treatment failures.
Technically, MATQ-seq’s success hinges on several innovations. Its RNA capture strategy leverages multiple annealing steps coupled with dC-tailing to enable the efficient reverse transcription of short bacterial mRNAs. This overcomes the notorious obstacle of bacterial RNA degradation and low abundance. Following cDNA synthesis, amplification cycles produce libraries rich enough in material for high-throughput sequencing, which feed into computational pipelines that filter noise, align reads to reference genomes, and quantify gene expression per cell.
Importantly, this method maintains integrity across diverse bacterial species, including model organisms like Salmonella enterica, suggesting broad applicability. Its robustness across species and conditions opens avenues for ecological studies, antibiotic-perturbation experiments, and investigations into microbial community dynamics under stress.
In summary, bacterial single-cell transcriptomics, as exemplified by MATQ-seq, revolutionizes our capacity to resolve the bacterial “black box.” It reveals a dynamic mosaic of gene activity within populations previously viewed as homogeneous. The detailed, stepwise protocol published in Nature Protocols democratizes access to this technology, promising breakthroughs in microbiology, infectious disease research, and antibiotic development. As MICROSEQ gains momentum, the microbial sciences community stands poised to decode bacterial individuality, illuminating the subtle yet profound ways single cells shape population behavior and impact human health.
Subject of Research: Cells
Article Title: Transcriptomic profiling of individual bacteria by MATQ-seq
News Publication Date: 9-Apr-2025
Web References: Würzburg Single-Cell Center
References: DOI 10.1038/s41596-025-01157-5
Image Credits: Scigraphix
Keywords: Transcriptomics, Messenger RNA, Cells, Bacteria
Tags: antibiotic resistance mechanismsbacterial gene expression analysiscellular heterogeneity in bacteriadynamic cellular responses in bacteriaenvironmental stress responses in bacteriainnovative microbiological techniquesmicrobiology research advancementsmRNA profiling in bacteriapathogenic bacterial behaviorsingle-cell RNA sequencing technologysingle-cell transcriptomicstherapeutic targets in bacterial populations