Service Overview

Our Microbial Single Cell Transcriptomics Service is a microbial single-cell transcriptome profiling and analysis platform that measures gene expression in individual microbial cells, rather than averaging signals across bulk metatranscriptomes. In a single run, thousands of bacterial or fungal cells can be profiled to resolve cell-to-cell heterogeneity and rare subpopulations that bulk RNA-seq cannot distinguish.

The workflow integrates microbe-optimized sample preparation, rRNA depletion, and droplet-based single-cell capture to deliver high-quality transcriptome data, with optional bioinformatics analysis.

Applications of Microbial Single Cell Transcriptomics

Microbial single-cell RNA sequencing, or microbial single-cell transcriptome analysis, is most valuable when bulk RNA-seq or metatranscriptomics cannot resolve important subpopulations.

Antibiotic resistance and tolerance

Identify persister-like, tolerant, or resistant subpopulations and define their transcriptional programs under drug exposure.

Stress response and environmental adaptation

Dissect how individual cells respond to heat, pH, nutrient limitation, oxidative stress, or other environmental challenges.

Industrial strain optimization and fermentation

Compare high- and low-producing cells within the same culture to guide strain engineering and process optimization.

Microbial communities and co-culture systems

Resolve transcriptional states of different members in simple communities or co-culture models, where feasible.

Experimental evolution and long-term adaptation

Track emerging transcriptional phenotypes over passages to understand how populations diversify and adapt over time.

Technology Highlights for Microbial Single Cell Transcriptomics

Because microbial cells are wall-protected with low mRNA and high rRNA background, reliable end-to-end microbial single-cell transcriptomics is available from only a handful of providers worldwide.

our microbial single cell transcriptomics platform is engineered specifically for microorganisms and underpins our single-cell microbial transcriptome profiling service, with three core technical pillars.

🧫Microbe-Optimized Cell Handling

Microbial cells are small, wall-protected, and contain fragile mRNA. We apply fixation and permeabilization conditions validated for bacteria and selected fungi, which preserve RNA while allowing reagents to enter. This increases the number of single cells that pass QC and contribute usable transcriptomes.

🎯Integrated rRNA Depletion

Ribosomal RNA dominates microbial RNA and wastes sequencing capacity. Our library preparation incorporates rRNA depletion so that more reads map to mRNA rather than ribosomal background. The result is deeper coverage of functional genes per cell at a given sequencing depth.

💧Droplet-Based High-Throughput Capture

We use droplet-based microfluidics adapted to microbial cells to barcode thousands of single cells per run. This high-throughput design enables you to resolve rare tolerant, high-producing, or stress-adapted subpopulations that bulk RNA-seq cannot distinguish.

Technology Comparison: Microbial Transcriptome Approaches

Different methods answer slightly different questions. The table below helps you see where our droplet-based microbial single cell transcriptomics service sits compared with common alternatives.

Method / Category What it measures Key strengths Main limitations Typical use
Bulk metatranscriptomics Average gene expression across many cells Simple, cost-effective, good for global pathway shifts Masks intraspecies heterogeneity and rare subpopulations Overall community activity and pathway screening
Droplet-based microbial single cell transcriptomics (our service) Whole-transcriptome profiles of thousands of individual microbial cells High throughput, good balance of per-cell depth and cost; compatible with rRNA depletion and microbe-optimized prep Requires dedicated droplet microfluidics and robust sample handling Detecting rare subpopulations, mapping functional states across conditions, discovery studies
Plate-based / flow-sorting single cell transcriptomics Single-cell transcriptomes from FACS-sorted cells in plates High coverage per cell; can pre-enrich defined subpopulations Lower throughput, higher cost per cell, more labor-intensive Deep profiling of specific subpopulations rather than whole communities
Imaging-based methods (FISH, MERFISH, etc.) Spatially resolved expression of selected mRNAs in situ Preserves spatial context in tissues and biofilms; precise localization of transcripts Limited gene panels per experiment; custom probe design; high technical barrier Spatial organization questions at specific sites (biofilms, host–microbe interfaces)

Microbial Single Cell Transcriptomics Workflow

our microbial single cell transcriptomics projects follow a streamlined, end-to-end workflow managed by a single technical team.

1. Project consultation and study design

We define target organisms, conditions, cell numbers, and sequencing depth to match your biological questions.

2. Sample preparation and shipment

You prepare cultures according to our guidelines (growth phase, optional fixation) and ship samples with a completed information sheet under controlled temperature.

3. Sample quality control (QC)

On receipt, we check cell density and integrity, and perform additional QC (e.g. RNA and permeabilization checks) before proceeding.

4. Single-cell capture and barcoding

Microbial cells are encapsulated with barcoded beads using droplet-based microfluidics adapted to microbial size and properties.

5. Library preparation with rRNA depletion

cDNA libraries are generated and rRNA is selectively depleted, enriching mRNA-derived fragments for more informative reads.

6. Illumina sequencing

Libraries are sequenced on Illumina platforms with read length and depth tailored to the agreed study design.

7. Bioinformatics analysis and reporting (optional)

We can deliver processed count matrices, clustering, differential expression, and summary figures in a concise analysis report.

Stepwise microbial single cell transcriptomics workflow from study design through sample preparation, quality control, single-cell capture, library construction, sequencing, and analysis

Bioinformatics Analysis

our microbial single cell transcriptomics service can include a complete downstream analysis package so your team receives ready-to-use results, not just raw data.

Standard Analysis

Standard analysis focuses on delivering clean, interpretable single-cell data for downstream biology:

  • Read quality control and alignment to the reference genome.
  • Molecular barcode–based counting and generation of cell × gene expression matrices.
  • Cell filtering, normalization, clustering, and low-dimensional embedding (e.g. UMAP).

This gives you a robust foundation to explore microbial subpopulations, functional states, and responses to experimental conditions without building your own pipeline.

Advanced / Custom Analysis (Optional)

For projects that require deeper interpretation, we can extend the analysis to:

  • Differential expression between clusters or experimental conditions.
  • Pseudotime or trajectory analysis for dynamic or time-course studies.
  • Pathway and gene set enrichment to link expression changes to functions and pathways.

These layers help connect transcriptional patterns to concrete hypotheses around resistance, adaptation, productivity, or community function.

Results Display

The plots below illustrate selected analysis outputs from microbial single cell transcriptomics projects.

Microbial single-cell clustering result Cell clustering & subpopulation identification

Microbial single-cell differential gene expression result Differential gene expression analysis

Microbial single-cell pseudotime analysis result Pseudotime analysis

Deliverables

At the end of your microbial single cell transcriptomics project, you receive data and reports that are ready for downstream analysis and interpretation.

  • Raw sequencing data (FASTQ files)
    Full raw reads for archiving, re-analysis, or integration with other datasets.
  • Processed expression matrices (cell × gene)
    Barcode-based count tables per sample, suitable for direct use in R, Python, or other analysis platform.
  • Quality control summary
    Key metrics such as read counts, mapping rates, rRNA fraction, genes per cell, and usable cell numbers.
  • Clustering and visualization outputs
    Cluster labels, reduced-dimension coordinates (e.g. UMAP), and basic plots to explore microbial subpopulations.
  • Differential expression and enrichment results (if analysis ordered)
    Tables of marker genes, condition-specific changes, and pathway or gene set enrichment where applicable.
  • Concise project report
    A short methods and results summary outlining the workflow used, main QC outcomes, and headline biological findings.

Summary of microbial single cell transcriptomics deliverables including FASTQ raw reads, cell–gene matrices, quality control summary, clustering results, differential expression tables, and a concise project report

Sample Requirements and Shipping

Use this checklist once your project is confirmed.

Yeast Samples

  • Mix 0.5 mL yeast culture with 0.5 mL 50% glycerol in sterile PBS in a 1.5 mL tube (final ~25% glycerol).
  • Prepare 2 tubes per sample, label clearly.
  • Ship on dry ice with a sample information sheet.

Bacterial Samples (Recommended Fixed Prep)

  1. Harvest 10⁹–10¹⁰ cells, centrifuge at 7,000 g, 5 min, 4°C, discard supernatant.
  2. Resuspend in 4 mL 4% formaldehyde in 1× PBS, rotate at 4°C overnight (≤16 h).
  3. Centrifuge (7,000 g, 5 min, 4°C), discard supernatant; wash pellet twice with 1 mL PBS-RI (1× PBS + RNase inhibitor 0.1 U/µL).
  4. Resuspend final pellet in 0.5–1 mL PBS-RI with 25% glycerol + 25% ethanol.
  5. Seal, label, and ship on dry ice; and avoid prolonged transport or thaw–freeze cycles.

Tip: For routine cultures, we recommend collecting cells in exponential phase (OD₆₀₀ = 0.3–0.5) and removing obvious debris/clumps.

Alternative Bacterial Submission Options

  • Fixed log-phase cells in Tris-HCl-RI:
    Send 2 × 1 mL cold 0.1 M Tris-HCl + RNase inhibitor per sample.
  • Unfixed cultures or plates:
    Send 2 × 20 mL culture or streaked plates plus medium and culture conditions.
  • Perturbed cultures (e.g. antibiotic or stress exposure):
    Complete the exposure step, then prepare as above and clearly record the exposure conditions on the sample sheet.

Packing

Why Choose CD Genomics for Microbial Single Cell Transcriptomics?

Choosing a microbial single cell transcriptomics partner is about more than access to a platform; it is about experience, optimization, and consistent data quality.

Microbial and single-cell expertise

We focus on microbial sequencing and have adapted single-cell workflows specifically for bacteria, yeasts, and selected fungi, rather than simply repurposing mammalian protocols.

Microbe-optimized wet lab

From fixation and permeabilization to rRNA depletion, each step is tuned for microbial cells to improve the fraction of usable cells and informative reads.

Integrated sequencing and analysis

The same technical team manages library preparation, sequencing, and downstream analysis, reducing handoff errors and shortening project timelines.

Transparent QC and documentation

You receive clear QC reports and method descriptions, making it easier to interpret results and to reproduce or extend the work in future studies.

Flexible support for diverse study designs

Whether you are running a small pilot or a larger multi-condition experiment, we adapt cell numbers, sequencing depth, and analysis scope to your goals and budget.

To discuss a tailored microbial single-cell RNA sequencing study design, please contact us for a project-specific consultation.

Case Study: Single Cell Transcriptomics in Microbial Eukaryotes

This case study summarizes findings from a published study and illustrates how microbial single-cell transcriptomics can be applied in practice.

Reference:
Onsbring H. et al. "An efficient single-cell transcriptomics workflow for microbial eukaryotes benchmarked on Giardia intestinalis cells." BMC Genomics 2020, 21:448.

Most microbial eukaryotes (protists) lack high-quality transcriptome data because they are difficult to culture and often have low RNA content. The authors set out to build a cost-efficient single-cell transcriptomics workflow that works reliably on individual Giardia intestinalis cells.

Single Giardia trophozoites were sorted by FACS, and seven variants of a Smart-seq2–based protocol were tested, focusing on freeze–thaw lysis, primer changes, and reduced reagent volumes to improve lysis and lower costs.

The optimized Smart-seq2 + freeze–thaw protocol detected on average 4,524–4,992 genes per cell, covering ~70–77% of protein-coding genes, and significantly outperformed unmodified Smart-seq2 for medium and highly expressed transcripts. Halving reagent volumes reduced gene detection, highlighting a trade-off between cost and sensitivity.

Single-cell transcriptomics of Giardia intestinalis showing higher gene detection with an optimized Smart-seq2 workflow Figure. Single-cell transcriptome quality in microbial eukaryotes using an optimized Smart-seq2 workflow.

The study demonstrates that with appropriate lysis and protocol tuning, single-cell transcriptomics is feasible and informative for microbial eukaryotes, providing deep gene coverage from individual protist cells and offering a realistic template for microbial single-cell workflows beyond classical mammalian systems.

This published example illustrates design principles that are also relevant when planning microbial single cell transcriptomics projects with our service.

Frequently Asked Questions (FAQ)

In most microbial single cell transcriptomics projects we profile on the order of 2,000–10,000+ single cells per experiment, depending on organism, sample quality, and study design.
We require at least 2 × 10⁷ cells per sample. This allows for QC, optimization, and final single-cell capture without exhausting material.
Yes. Fixed log-phase cultures are often preferred. We will share a simple fixation and resuspension protocol (using cold Tris-HCl-based buffer) once your project is confirmed.
Yes. Many projects involve drug or stress exposures. Complete the exposure step first, then prepare samples following our culture guidelines and record the exposure conditions on the sample sheet.
No. All services and data are provided for research use only and are not intended for clinical diagnostic applications.
We routinely accept fixed bacterial cultures, yeast, and selected microbial samples prepared according to our sample requirements and shipping guide.
A. Microbial cells are small and wall-protected with low mRNA and high rRNA background. Reliable results require validated permeabilization, integrated rRNA depletion, droplet microfluidics adaptation, and strict QC—capabilities that only a limited number of labs offer end-to-end.
A. Yes. We have delivered microbial single-cell transcriptomics projects across multiple study types. De-identified case summaries can be shared upon request (NDA available).

References

  1. Pountain, Andrew W., and Itai Yanai.,"Dissecting Microbial Communities with Single-Cell Transcriptome Analysis." Science, 2025.
  2. Homberger, Christina, Lars Barquist, and Jörg Vogel., "Ushering in a New Era of Single-Cell Transcriptomics in Bacteria." microLife, 2022.
  3. Nishimura, M., K. Takahashi, and M. Hosokawa., "Recent Advances in Single-Cell RNA Sequencing of Bacteria: Techniques, Challenges, and Applications." Journal of Bioscience and Bioengineering, 2025.
  4. Ma, Peijun, et al., "Bacterial Droplet-Based Single-Cell RNA-Seq Reveals Antibiotic-Associated Heterogeneous Cellular States." Cell, 2023.
  5. Yan, Xiaodan, et al., "An Improved Bacterial Single-Cell RNA-Seq Reveals Biofilm Heterogeneity." eLife, 2024.
  6. Onsbring, Henning, et al., "An Efficient Single-Cell Transcriptomics Workflow for Microbial Eukaryotes Benchmarked on Giardia intestinalis Cells." BMC Genomics, 2020.