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Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

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Published on: February 2, 2024

Transcriptomic profiling of individual bacteria by MATQ-seq.

Christina Homberger¹, Fabian Imdahl^2,3, Regan J Hayward³

¹Institute of Molecular Infection Biology (IMIB), University of Würzburg, Würzburg, Germany.

Nature Protocols

|April 9, 2025

Summary

This summary is machine-generated.

We developed a robust bacterial single-cell RNA sequencing (scRNA-seq) protocol using MATQ-seq, achieving 95% cell retention and detecting 300-600 genes per cell. This method enables detailed gene expression profiling in bacteria.

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RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

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Massively Parallel Reporter Assays in Cultured Mammalian Cells

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Published on: August 17, 2014

Area of Science:

Microbiology
Genomics
Molecular Biology

Background:

Bacterial single-cell transcriptomics offers insights into cell-to-cell variation.
Existing methods face challenges with cell retention and limited input material.

Purpose of the Study:

To develop and present a robust protocol for bacterial single-cell RNA sequencing (scRNA-seq).
To enable gene expression profiling in complex microbial communities.

Main Methods:

Adapted the eukaryotic MATQ-seq approach for bacteria.
Integrated index sorting, random priming, and rRNA depletion.
Utilized fluorescence-activated cell sorting (FACS) for single-cell isolation.
Employed robotic liquid handling for cDNA amplification.
Included computational analysis for transcriptomic data.

Main Results:

Achieved a 95% cell retention rate, surpassing existing protocols.
Successfully detected 300-600 genes per cell, indicating broad transcriptomic capture.
Demonstrated robustness across bacterial species like Salmonella enterica.
Completed the process from cell isolation to raw data generation in approximately 5 days.

Conclusions:

The developed bacterial scRNA-seq protocol is highly efficient and reliable.
It offers a significant improvement for studying bacterial populations at the single-cell level.
This method holds promise for a streamlined microbial scRNA-seq platform.