Microbial Single-Cell Sequencing Services
Strain-resolved genome and transcriptome profiling of individual microbial cells — resolve functional heterogeneity in bacterial and fungal populations, link mobile genetic elements to their host strains, detect rare taxa missed by bulk metagenomics, and map transcriptional states across thousands of single cells in complex microbial communities.
Why CD Genomics for microbial single-cell sequencing:
- Dual-track service — single amplified genome (SAG) sequencing for strain-resolved genome recovery and single-cell RNA sequencing for transcriptional heterogeneity profiling; choose one track or combine both for integrated genomic–transcriptomic analysis
- Direct host–element linkage — physically assign plasmids, phages, antibiotic resistance genes, and other mobile genetic elements to their specific host strains, overcoming the computational limitations of metagenomic binning
- Rare population detection — capture low-abundance taxa and rare functional states (persisters, competent cells, stress-tolerant subpopulations) that are averaged out in bulk metagenomic and metatranscriptomic approaches
- Broad species compatibility — validated across Gram-negative and Gram-positive bacteria (Escherichia coli, Klebsiella pneumoniae, Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, and more) and fungi (Saccharomyces cerevisiae, Candida glabrata)
How Microbial Single-Cell Sequencing Works
Microbial single-cell sequencing profiles the genomes or transcriptomes of individual microbial cells, resolving heterogeneity that bulk metagenomic and metatranscriptomic approaches average across entire populations. The service operates on two coordinated tracks — genomic and transcriptomic — which can be used independently or combined for integrated analysis.
Track 1 — Single Amplified Genome (SAG) Sequencing
Individual microbial cells are isolated using droplet microfluidics, flow cytometry, or micromanipulation. Each cell is lysed and its genomic DNA is amplified via multiple displacement amplification (MDA) to produce sufficient material for library construction and sequencing. The resulting single amplified genomes (SAGs) provide strain-resolved assemblies that capture mobile genetic elements, plasmids, and phages in direct physical linkage with their host chromosome — information that is computationally inferred and often lost in metagenomic binning. SAG sequencing is particularly valuable for recovering genomes from uncultured or low-abundance taxa and for tracing horizontal gene transfer events at the single-cell level.
Track 2 — Microbial Single-Cell RNA Sequencing (scRNA-seq)
Microbial mRNA lacks poly-A tails and exists at femtogram quantities per cell, requiring fundamentally different approaches from eukaryotic scRNA-seq. The microbial scRNA-seq workflow uses random priming or probe-based capture strategies combined with split-pool combinatorial barcoding to label transcripts from individual cells. Cells are fixed, permeabilized, and distributed across multiple rounds of barcoding in multi-well formats — each cell receives a unique barcode combination. Integrated rRNA depletion reduces ribosomal RNA background, enabling detection of >100 median genes per cell. This approach resolves rare transcriptional states — antibiotic persisters, competence, prophage induction, stress responses — that are invisible in bulk RNA-seq.
| Key Platform Specifications | |
|---|---|
| Genome Track — SAG | Droplet microfluidics, FACS, or micromanipulation isolation; MDA amplification; hundreds to thousands of cells per run; >90% genome recovery achievable; long-read sequencing options available |
| Transcriptome Track — scRNA-seq | Split-pool combinatorial barcoding; random priming + PCR; 2,000–10,000+ cells per run; >100 median genes per cell with integrated rRNA depletion |
| Sample types | Bacterial and fungal cultures, enrichment cultures, defined consortia; currently validated for 12 species |
| Sequencing | Illumina platforms (NovaSeq/NextSeq); long-read options for SAG track |
| Data pipeline (SAG) | SPAdes/IDBA-UD assembly → CheckM QC → Prokka annotation |
| Data pipeline (scRNA-seq) | STAR/bwa-mem2 alignment → cell×gene matrix → clustering → differential expression |
Service Workflow
- Study Design and Feasibility Assessment
Target organism, sample type, and research objectives are reviewed. For SAG projects: cell isolation strategy selected. For scRNA-seq: species compatibility confirmed and permeabilization optimized. QC Checkpoint: Species feasibility confirmed; isolation and lysis protocols validated.
- Sample Preparation and Cell Processing
Cultures or enrichment samples processed to clean single-cell suspensions. Viability and concentration assessed. For scRNA-seq, cells fixed and permeabilized under species-optimized conditions. QC Checkpoint: Cell viability, concentration, morphology; absence of aggregates.
- Single-Cell Isolation and Barcoding
SAG track: individual cells encapsulated in droplets or sorted, lysed, and subjected to MDA. scRNA-seq track: fixed cells undergo split-pool combinatorial barcoding, each cell receiving a unique barcode combination. QC Checkpoint: Single-cell isolation efficiency; MDA yield and uniformity (SAG); barcode complexity and collision rate (scRNA-seq).
- Library Preparation and Sequencing
Amplified genomic DNA (SAG) or barcoded cDNA (scRNA-seq) used to construct Illumina libraries. Libraries purified, size-selected, quantified, pooled, and sequenced. QC Checkpoint: Library fragment size distribution, concentration, adapter-dimer levels.
- Primary Data Processing
SAG: reads quality-filtered, assembled into contigs, assessed for completeness and contamination (CheckM). scRNA-seq: reads aligned to reference genomes, barcodes demultiplexed, cell-by-gene count matrices constructed. QC Checkpoint: Genome completeness ≥50%, contamination ≤10% (SAG); valid cell barcode count, median genes per cell, rRNA contamination rate (scRNA-seq).
- Bioinformatics Analysis and Data Delivery
SAG: genome annotation (Prokka), comparative genomics, phylogenetic placement, and mobile element identification. scRNA-seq: dimensionality reduction, clustering, differential expression, and functional state annotation. QC Checkpoint: Final data review against project specifications before delivery.
Sample Requirements
| Requirement | Genome Track (SAG) | Transcriptome Track (scRNA-seq) |
|---|---|---|
| Sample type | Bacterial/fungal cultures, enrichment cultures, FACS-sorted communities | Bacterial/fungal cultures, defined consortia in exponential or early stationary phase |
| Species | Currently validated for 12 species; additional species evaluated case-by-case | |
| Cell input | ≥2×10⁷ cells per sample recommended | |
| Cell viability | >80% viable cells recommended; fixation performed at collection for scRNA-seq | |
| Culture condition | Log-phase or early stationary phase preferred | Log-phase preferred for active transcription |
| Shipping | Pelleted cells or cultures on dry ice; species-specific guidance provided | |
Currently validated species include Escherichia coli, Klebsiella pneumoniae, Bacillus subtilis, Staphylococcus aureus, Acinetobacter baumannii, Salmonella typhimurium, Enterococcus casseliflavus, Pseudomonas aeruginosa, Aeromonas veronii, Vibrio parahaemolyticus (bacteria), and Saccharomyces cerevisiae, Candida glabrata (fungi). For species not on this list, a feasibility assessment is conducted during project planning — contact our team to discuss your target organism.
Bioinformatics Analysis
Both tracks include a standard bioinformatics pipeline. Analysis scope is matched to your experimental design and research objectives.
SAG Genome Track (Included)
- Read quality filtering, adapter trimming, and decontamination
- De novo genome assembly (SPAdes/IDBA-UD) with assembly statistics
- Genome completeness and contamination assessment (CheckM)
- Gene prediction and functional annotation (Prokka, COG, KEGG)
- rRNA and tRNA identification
- Taxonomic classification and phylogenetic placement
- Comparative genomics across multiple SAGs from the same sample
- Mobile genetic element, plasmid, and prophage identification
- Antibiotic resistance gene and virulence factor annotation
Microbial scRNA-seq Track (Included)
- Read quality filtering, alignment to reference genome, and barcode demultiplexing
- Cell-by-gene count matrix construction with rRNA removal
- Low-quality cell filtering (minimum gene threshold, rRNA contamination cutoff)
- Normalization and dimensionality reduction
- Unsupervised clustering and cluster visualization
- Differential expression analysis between clusters or conditions
- Functional state annotation (growth phase markers, stress regulons, metabolic pathway activity)
- QC report with per-cell metrics, clustering summaries, and gene detection statistics
Advanced Analysis (Optional — Both Tracks): Integrated SAG + scRNA-seq analysis linking genomic variants to transcriptional states; time-series and experimental evolution analysis; cross-species comparison in mixed-community experiments; custom functional annotation and pathway enrichment; publication-quality figure generation. All analysis outputs are delivered with documented parameters, software versions, and intermediate files for reproducibility.
Deliverables
| Deliverable | Genome Track (SAG) | Transcriptome Track (scRNA-seq) |
|---|---|---|
| Raw sequencing data | Demultiplexed FASTQ files | Demultiplexed FASTQ files |
| Genome assemblies | Assembled contigs/scaffolds per SAG with QC metrics (completeness, contamination, N50) | — |
| Expression matrix | — | Cell-by-gene count matrix (h5ad, mtx, csv) |
| Annotation tables | Gene prediction, functional annotation (COG, KEGG, GO), ARG/VF annotation | Cluster-specific marker gene tables, differential expression tables |
| Cell metadata | SAG-level QC metrics, taxonomic classification | Cell-level QC metrics, cluster assignments, functional state labels |
| Clustering report | — | Dimensionality reduction plots, cluster heatmaps, gene expression visualizations |
| Comparative analysis | Multi-SAG comparative genomics, phylogenetic trees | Cross-condition differential expression summary |
| MGE/ARG report | Mobile element, plasmid, prophage, and resistance gene identification | — |
| Bioinformatics report | Methods documentation, assembly and annotation summaries | Methods documentation, QC metrics, clustering and DE summaries |
| Data archive | All intermediate files, scripts, and processing logs | |
Applications
Antibiotic resistance and persistence heterogeneity
Resolve which individual cells harbor antibiotic resistance genes or enter a persister state — and link those phenotypes to their genomic context. scRNA-seq identifies transcriptional programs associated with persistence; SAG sequencing physically links resistance genes and mobile elements to their host strain. This combination answers questions that bulk methods cannot: which cells carry the resistance determinant, which express it, and how the element mobilizes across strains.
Microbiome dark matter and novel taxa discovery
Recover genomes from uncultured and low-abundance microbial taxa that are systematically missed by metagenomic binning. SAG sequencing provides the first genomic blueprint of novel phyla and candidate divisions, enabling phylogenetic placement and metabolic prediction for organisms known only from 16S rRNA surveys. See Spatial Transcriptomics for spatially resolved microbiome profiling in tissue contexts.
Mobile genetic element and phage host assignment
Directly link plasmids, transposons, integrons, and bacteriophages to their specific host strains at the single-cell level. This physical linkage evidence — impossible to obtain from metagenomic binning alone — is critical for understanding the flow of antibiotic resistance and virulence genes through microbial communities.
Functional heterogeneity in biofilms and industrial microbiology
Map metabolic specialization within biofilms and bioreactors. scRNA-seq reveals which cells are metabolically active, dormant, or stress-responsive within structurally complex communities. Direct applications include optimizing industrial fermentation, wastewater treatment, and biocatalysis processes.
Host–pathogen interaction and virulence
Profile pathogen transcriptional states during infection at single-cell resolution — identify which virulence factors are co-expressed, which cells are primed for host invasion, and how subpopulations respond to immune pressure. For fungal pathogens, both SAG and scRNA-seq tracks are applicable. See Single-Cell Sequencing Services for host-focused single-cell approaches.
Experimental evolution and population diversification
Track the emergence and dynamics of genetic and transcriptional variants during experimental evolution. SAG sequencing captures de novo mutations and structural variants in individual cells; scRNA-seq reveals how these variants affect gene expression programs. Combined, they provide a complete picture of genotype–phenotype diversification at the single-cell level.
Microbial Single-Cell vs. Bulk Metagenomics — Method Comparison
Microbial single-cell sequencing and bulk metagenomics serve distinct but complementary purposes. The choice depends on whether your question requires population-average profiling or single-cell resolution.
| Feature | Microbial Single-Cell (SAG + scRNA-seq) | Bulk Metagenomics / Metatranscriptomics |
|---|---|---|
| Resolution | Absolute single-cell — strain-level genomes, individual transcriptomes | Population average — consensus genomes and expression profiles |
| Rare taxa detection | High sensitivity — each cell sequenced independently | Often lost in assembly, binning, or abundance filtering |
| MGE/host linkage | Direct physical linkage — plasmid/phage captured with host genome | Computational inference only — error-prone and often ambiguous |
| Functional heterogeneity | Resolves rare states (persisters, competent cells, stress-tolerant subpopulations) | Averages all cells — rare states invisible |
| Novel genome recovery | High — no reference or binning required | Requires assembly and binning; misses low-abundance genomes |
| Throughput | Hundreds to thousands of cells per run | Millions of reads per run, but at population level |
| Best for | Strain-resolution, MGE tracking, functional heterogeneity, novel taxa | Community composition, broad functional potential surveys, abundant taxa |
When to choose microbial single-cell sequencing: Your research questions require strain-level resolution, direct host–element linkage, or detection of rare transcriptional states that bulk methods average across entire populations.
When to choose bulk metagenomics: Your goal is broad community composition profiling, functional potential prediction, or initial screening of microbial diversity in a sample — and single-cell resolution is not required. Both approaches are available at CD Genomics. Contact our team to discuss which approach best fits your research objectives.
Frequently Asked Questions (FAQ)
References
- Kuchina A, Brettner LM, Paleologu L, et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science. 2021;371(6531):eaba5257. DOI: 10.1126/science.aba5257.
- Kawano-Sugaya T, Arikawa K, Saeki T, et al. A single amplified genome catalog reveals the dynamics of mobilome and resistome in the human microbiome. Microbiome. 2024;12:188. DOI: 10.1186/s40168-024-01903-z.
- McNulty R, Sritharan D, Pahng SH, et al. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nature Microbiology. 2023;8(5):934–947. DOI: 10.1038/s41564-023-01348-4.
- Nishimura M, Takahashi K, Hosokawa M. Recent advances in single-cell RNA sequencing of bacteria: Techniques, challenges, and applications. Journal of Bioscience and Bioengineering. 2025;139(5):341–346. DOI: 10.1016/j.jbiosc.2025.01.008.
- Hosokawa M, Nishikawa Y, Kogawa M, Takeyama H. Massively parallel whole genome amplification for single-cell sequencing using droplet microfluidics. Scientific Reports. 2017;7:5199. DOI: 10.1038/s41598-017-05436-4.
