Module A — Sample preparation and QC
We review sample type, organism, and study design early. This improves success rates and reduces avoidable reruns.
Microbial Single-Cell Sequencing
From "who's there" to "what they're doing" with SAG sequencing and microbial scRNA-seq
Microbial single-cell sequencing moves beyond population averages. It resolves strain diversity and cell-state heterogeneity, including rare subpopulations that bulk meta-omics can miss.
Choose SAG (Single Amplified Genome) sequencing for strain-resolved genomes, or microbial single-cell RNA sequencing (scRNA-seq) for expression programs under changing conditions. Projects can run as genome-only, RNA-only, or integrated designs.
Fast reasons teams use this service
- Detect rare functional states linked to tolerance or stress.
- Separate strain-level genetic differences without community averaging.
- Convert samples into analysis-ready outputs with fewer handoffs.
Service Overview
Microbial single-cell sequencing profiles individual microbial cells, not population averages. It helps you resolve strain diversity and cell-state heterogeneity, including rare subpopulations that bulk meta-omics can miss.
We deliver two coordinated tracks through one streamlined service workflow:
- Microbial Single-Cell RNA Sequencing (scRNA-seq): captures mRNA across thousands of microbial cells. It is best for mapping functional programs, stress responses, tolerance/rare states, and condition-to-condition comparisons.
- Microbial Single-Cell Genome Sequencing (SAG): reconstructs strain-resolved genomes from single cells. It is best for novel or low-abundance members, genomic heterogeneity, and comparative analyses, including mobile element or HGT signals (project-dependent).
If you are comparing community-level RNA activity, our metatranscriptomics service may be a better baseline method.
For bulk expression profiling in controlled systems, see microbial transcriptomics.
Modular workflow components
We structure projects as a modular pipeline, so you can select genome, RNA, or both.
Module B — Single-cell partitioning and isolation
Isolation is chosen by sample constraints. Options include FACS, microfluidics, or micromanipulation (sample-dependent). This flexibility helps control multiplets and supports clean single-cell inputs.
Module C — Library preparation (Genome or RNA)
For SAG projects, we apply single-cell lysis plus whole-genome amplification options (MDA- or MALBAC-like). For microbial scRNA-seq, we use microbe-optimised handling designed for challenging cell walls and low-input RNA.
Module D — Optional bioinformatics and reporting
We deliver analysis-ready outputs aligned to your question. RNA projects typically include a cell×gene matrix plus state summaries. Genome projects include assemblies/contigs, annotation, and genome QC summaries where feasible.
Not sure which track fits?
Use our Sample Quick Check. Send: sample type | goal (genome/RNA/both) | #samples/conditions. We will recommend an approach and quote-ready scope.
Choose Your Track
Select the pathway that matches your scientific question. We can also scope a combined design.
Track 1 — Microbial Single-Cell Genome Sequencing (SAG)
SAG sequencing reconstructs strain-resolved microbial genomes from individual cells. This reduces population averaging and supports clearer genotype-level interpretation.
Best for- Strain-level genomes and novel or low-abundance members
- Comparative genomics and phylogenetic context
- Mobile elements, phage signals, or HGT clues (project-dependent)
- Assemblies/contigs, annotation, and genome QC summaries (where feasible)
- Comparative summaries aligned to your study goals
SAG projects are supported on platforms such as MobiNova™-M1 (or equivalent), selected based on sample and study design.
Track 2 — Microbial Single-Cell RNA Sequencing (microbial scRNA-seq)
Microbial scRNA-seq captures mRNA from thousands of microbial cells. It reveals expression heterogeneity across conditions and identifies rare functional states.
Best suited for- Stress-response programs and condition-to-condition comparisons
- Tolerance or rare states that may drive phenotype
- Cell-to-cell variability in microbial function and metabolism
- Cell × gene matrix with QC metrics
- Clustering/embedding results, marker features, and differential expression tables
This workflow is delivered using a MobiMicrobe™ scRNA-seq workflow on MobiNova™-100 (or equivalent), depending on project fit.
When to run Genome + RNA
If rare subpopulations likely drive outcomes, an integrated plan can link genetic potential to functional state. For complex or host-associated samples, we recommend a feasibility review first.
Key Advantages (Microbe-Ready, End-to-End, Scalable)
Microbial single-cell sequencing is harder than mammalian workflows. Microbes have tough cell walls and low transcript abundance. CD Genomics delivers an end-to-end service designed for these constraints.
Microbe-optimised handling
tailored cell wall processing improves usable signal and reduces dropouts.
Scalable profiling
supports thousands of microbial cells per run to capture rare states.
Flexible isolation options
FACS, microfluidics, or micromanipulation (sample-dependent) to control multiplets.
Integrated delivery
wet lab plus optional bioinformatics reduces handoffs and speeds interpretation.
Decision-ready reporting
structured outputs, key figures, and summary tables aligned to study goals.
Microbial Single-Cell Sequencing Service Workflow
Our microbial single-cell sequencing workflow is designed to minimise avoidable failure points. It converts samples into analysis-ready results through a clear, reviewable process.
1
Study design
We align the plan to your goal: genome, RNA, or both. We define samples, conditions, and key comparisons.
2
Sample QC
We review sample type and organism constraints early. This helps prevent low-quality inputs from limiting interpretation.
3
Single-cell partitioning or isolation
We apply the most suitable route for your sample. Options include FACS, microfluidics, or micromanipulation (sample-dependent).
4
Library preparation (Genome or RNA)
SAG projects use single-cell lysis plus whole-genome amplification options (MDA- or MALBAC-like). Microbial scRNA-seq uses microbe-optimised handling to support low-input RNA.
5
NGS sequencing
We sequence to a depth matched to your question and design. This supports robust downstream analysis.
6
Optional bioinformatics and delivery
We provide structured results aligned to your reporting goals. Outputs include QC summaries, key figures, and analysis-ready files.
Bioinformatics (Optional)
We offer optional bioinformatics so you receive results that are easier to interpret. The analysis plan is matched to your study goal and organism constraints. Outputs and scope can vary by sample type and design.
Microbial Single-Cell RNA Sequencing (scRNA-seq)
Included analysis- QC metrics and filtering recommendations
- Clustering and state profiling with standard visualisations
- Marker feature identification and differential expression tables
- GO/KEGG enrichment where applicable
Microbial Single-Cell Genome Sequencing (SAG)
Included analysis- Genome assembly with QC summaries (species-level where feasible)
- Genome annotation
- Phylogenetic tree analysis
- Strain comparison and SNP calling
- Horizontal gene transfer (HGT) analysis
- Mobile element and phage-context screening
- ARG and VF screening
Deliverables
You receive a structured delivery folder with raw data and analysis-ready results. Deliverables vary by track and study design.
Standard package (all projects)- Raw sequencing files (FASTQ)
- Analysis-ready results aligned to your goal
- Key figures and summary tables
- Short project report (methods, QC, main findings)
- Microbial scRNA-seq: cell × gene matrix, QC metrics, clustering/embedding, markers, differential expression (plus GO/KEGG where applicable)
- SAG genomics: assemblies/contigs, annotation, genome QC summaries (where feasible), comparative summaries (project-dependent)
Project Specifications (Typical Ranges)
Project specifications depend on organism, sample type, and study design. We confirm scope during study design and the Sample Quick Check.
- Throughput: scalable profiling of thousands of microbial cells per run (project-dependent).
- Tracks supported: microbial scRNA-seq, SAG genomics, or an integrated plan.
- Single-cell isolation options: FACS, microfluidics, or micromanipulation (sample-dependent).
- Genome preparation options (SAG): single-cell lysis plus whole-genome amplification strategies, including MDA- or MALBAC-like options.
- RNA workflow considerations: microbe-optimised handling for challenging cell walls and low transcript abundance.
- Reporting scope: deliverables and analyses vary by sample complexity and project goals.
Sample Requirements & Shipping
Strong microbial single-cell results start with clean samples and clear metadata. Requirements depend on organism, matrix, and study design. We will align handling and workflow during project setup.
Input guidance (typical)
- Microbial scRNA-seq: ≥ 2 × 10⁷ cells per sample is recommended for robust capture and QC.
- SAG genomics: single cells or a small number of cells can be submitted in 4 µL 1× PBS in a PCR tube. Include a negative control.
- If submitting DNA for QC purposes, target OD260/280 between 1.8 and 2.2.
Packaging and shipping
- Use sterile, DNA-free consumables and minimise environmental exposure.
- Ship with sufficient dry ice or cold-chain protection, based on sample format.
- Label tubes clearly and include a sample sheet with conditions and replicates
At present, CD Genomics' microbial single-cell transcriptomics (scRNA-seq) workflow has been developed for a limited set of bacterial and fungal species. Currently supported species include:
| No. | Species (Latin) | Type |
|---|---|---|
| 1 | Escherichia coli | Bacterium |
| 2 | Klebsiella pneumoniae | Bacterium |
| 3 | Bacillus subtilis | Bacterium |
| 4 | Staphylococcus aureus | Bacterium |
| 5 | Acinetobacter baumannii | Bacterium |
| 6 | Salmonella typhimurium | Bacterium |
| 7 | Enterococcus casseliflavus | Bacterium |
| 8 | Pseudomonas aeruginosa | Bacterium |
| 9 | Aeromonas veronii | Bacterium |
| 10 | Vibrio parahaemolyticus | Bacterium |
| 11 | Saccharomyces cerevisiae | Fungus |
| 12 | Candida glabrata | Fungus |
Applications
Microbial single-cell sequencing is most useful when the biology is heterogeneous. It helps you detect rare states, separate strains, and interpret condition-driven responses.
- Antibiotic resistance and tolerance: reveal rare functional states linked to survival and recovery.
- Stress-response programs: map responses to nutrient shifts, oxidative stress, pH, and temperature changes.
- Experimental evolution: track population diversification across passages and conditions.
- Controlled pathogen and industrial systems: support pathogen biology studies and strain optimisation workflows.
- Phage–microbe and mobile element signals: explore mobilome context and HGT clues (project-dependent).
Case Study
Source: McNulty R. et al. "Probe-based bacterial single-cell RNA sequencing predicts toxin regulation." Nature Microbiology (2023).
Bulk transcriptomics averages bacterial populations and can miss rare, high-impact states. In Clostridium perfringens, toxin expression can be heterogeneous, meaning a minority of cells may drive the phenotype. This study used bacterial single-cell RNA sequencing to resolve toxin-linked subpopulations and test whether acetate shifts toxin expression at the single-cell level.
The authors developed ProBac-seq, a probe-based bacterial single-cell RNA-seq approach compatible with droplet microfluidic barcoding. They profiled thousands of single bacterial cells, clustered cells by expression state, and compared ± acetate conditions to quantify state composition and toxin-linked expression differences.
Single-cell analysis resolved distinct C. perfringens subpopulations and showed that netB toxin expression concentrated in a specific cluster. With acetate supplementation, toxin-linked expression decreased and the fraction of cells in the dominant toxin-associated state shifted, demonstrating a condition-dependent change that is difficult to quantify from population averages alone.
Single-cell RNA-seq identifies a toxin-associated subpopulation in Clostridium perfringens and shows reduced toxin-linked signal and altered state composition after acetate supplementation. Adapted from McNulty et al. Nature Microbiology (2023), Fig. 4 (panels a–c).
This case shows why microbial single-cell transcriptomics is valuable when phenotypes are driven by minority states. Single-cell resolution can quantify both state composition and state-specific expression changes under perturbations, supporting clearer decisions in microbial pathogenesis and controlled industrial microbiology studies.
FAQ
Microbial single-cell sequencing profiles individual microbial cells for genomes (SAG) and/or gene expression (microbial scRNA-seq). It reduces bulk averaging and helps resolve strain diversity and rare cell states.
A SAG is a genome reconstructed from one microbial cell after isolation, lysis, whole-genome amplification, sequencing, and assembly. It supports strain-resolved genome analysis where community binning is limiting.
Choose SAG when you need strain-level genomes, novel members, or genetic potential. Choose microbial scRNA-seq when you need functional programs, stress responses, or tolerance states across conditions.
Yes, in some designs. Combined plans can connect genetic potential to functional state, but feasibility depends on sample type, organism, and study structure.
Typical outputs include a cell × gene matrix, QC metrics, clustering/embedding results, marker features, and differential expression tables. Enrichment outputs are provided where applicable.
Typical outputs include assemblies/contigs, annotation, genome QC summaries where feasible, and project-aligned comparative summaries. Mobile element or HGT context is project-dependent.
The current workflow has been developed for a limited set of bacterial and fungal species, including E. coli, K. pneumoniae, B. subtilis, S. aureus, and S. cerevisiae. Other organisms can be assessed case by case.
No. This service is provided for research use only and is not intended for clinical diagnosis or patient management.
References
- McNulty, Ryan, et al. "Probe-based bacterial single-cell RNA sequencing predicts toxin regulation." Nature Microbiology, vol. 8, no. 5, 2023, pp. 934–946.
- Blattman, Samuel B., et al. "Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing." Nature Microbiology, 2020.
- Kuchina, Anna, et al. "Microbial single-cell RNA sequencing by split-pool barcoding." Science, 2021.
- Bowers, Robert M., et al. "Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea." Nature Biotechnology, vol. 35, no. 8, 2017, pp. 725–731.
- Lasken, Roger S. "Genomic sequencing of uncultured microorganisms from single cells." Nature Reviews Microbiology, 2012.
* For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.
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