Robust Bacterial scRNA-seq Protocol Developed

Not all bacteria in a population are the same—some are dividing, some are differentiating, and others are adapting to environmental changes. For researchers, especially those studying pathogens, understanding this diversity is essential for developing more effective therapies. Single-cell transcriptomics is a technology that helps reveal this diversity by analyzing the messenger molecules (mRNA) within individual bacterial cells, providing insight into which genes are active at a given time. This approach allows scientists to observe how individual bacteria respond to antibiotics or other environmental shifts, even within complex bacterial communities.

In 2020, researchers from Julius-Maximilians-Universität (JMU) Würzburg and the Helmholtz Institute for RNA-based Infection Research (HIRI) developed an advanced version of bacterial single-cell transcriptomics called bacterial MATQ-seq. The method has since been refined, and a detailed step-by-step protocol for creating single bacterial transcriptomes, including both experimental and computational data analysis, has been published in Nature Protocols.

According to first author Christina Homberger, “We have developed a robust bacterial scRNA-seq protocol based on quantitative single-cell RNA sequencing.” Homberger adds that MATQ-seq is highly efficient, achieving a cell retention rate of 95%, meaning nearly all cells used at the start yield individual gene libraries. This is a significant improvement over other protocols, which can lose up to 70% of cells.

The method reliably detects the activity of 300 to 600 genes per bacterial cell, which is more than most current methods. “On average, this is also significantly more than other methods currently achieve,” says co-author Fabian Imdahl. By recording gene activity, researchers can readily determine what an individual bacterium is doing or how it is adapting to its environment.

The entire MATQ-seq process, from single-cell isolation to raw data generation, takes about five days and is most efficient for samples of hundreds of cells. For larger-scale studies involving hundreds of thousands to millions of cells, other protocols are more suitable, though these typically result in higher cell loss and detect fewer genes per cell.