Single-Cell Host Transcriptome and Bacterial 16S Profiling Service
Which host cell types carry or associate with bacterial signals, and how do their transcriptional programs differ? This service combines single-cell host gene expression profiling with targeted bacterial 16S enrichment, retaining a shared cell identity across the two readouts for cell-linked host–bacterium analysis.
Why use this cell-linked workflow:
- Connect bacterial 16S signals to annotated host cell types instead of comparing two disconnected bulk datasets
- Recover bacterial ribosomal RNA signals that standard poly(A)-focused single-cell workflows may underrepresent
- Generate separate host-transcriptome and 16S-enriched libraries from the same cell-barcoded material
- Apply negative-control-aware filtering before interpreting low-biomass bacterial signals
Figure 1. Cell-linked host transcriptome and bacterial 16S readouts from one study workflow.
What This Service Measures
Conventional single-cell RNA sequencing is designed around polyadenylated host RNA, while bacterial ribosomal RNA is not efficiently represented by that capture logic. Our workflow adds a dedicated 16S readout to the cell-barcoded host transcriptome workflow. The result is a host expression matrix and a bacterial 16S signal matrix that can be aligned by host-cell identity.
Evidence the Service Can Provide
- Host cell clusters, identities, and transcriptional states
- Bacterial 16S taxonomic signals associated with retained host-cell barcodes
- Host expression differences between bacterial-signal-positive and comparison cells
- Cell-type-specific host–bacterium association patterns after background filtering
Evidence It Does Not Provide Alone
- A complete bacterial transcriptome or strain-resolved genome
- A whole-microbiome survey covering fungi and viruses
- Proof that every detected bacterial signal is intracellular
- Clinical identification of an infectious agent or a diagnostic result
For bacterial-cell genomics rather than host-cell-associated 16S signals, consider our Microbial Single-Cell Sequencing Service. For a broader research program, see our Spatial Omics Solutions for Microbiology.
How Cell-Linked Host RNA and 16S Profiling Works
Qualified single-cell suspensions are partitioned so that host RNA receives a cell-specific barcode. A bacterial 16S-targeting component supports recovery of bacterial ribosomal RNA signals associated with the same partition. After reverse transcription and amplification, the material is used to prepare a host gene-expression library and a 16S-enriched library. The shared cell barcode provides the link between the two data layers.
| Service Component | Primary Readout | Interpretation |
|---|---|---|
| Host transcriptome | Cell-by-gene expression matrix | Cell types, cell states, marker genes, and response programs |
| Bacterial 16S enrichment | Cell-linked 16S signal matrix | Taxonomic signals associated with host-cell barcodes |
| Shared cell identity | Matched barcodes across libraries | Comparison of host programs by bacterial signal and taxonomic assignment |
| Controls and filtering | Negative controls, prevalence patterns, and abundance context | Reduction of ambient and reagent-associated background |
Low-biomass microbial measurements are sensitive to background. Conclusions are therefore based on the full evidence chain: sample and process controls, retained host-cell barcodes, taxonomic assignment quality, signal prevalence, and consistency across biological replicates. Bacterial 16S detection should be interpreted as cell association unless an orthogonal assay establishes intracellular localization.
Service Workflow
Figure 2. End-to-end workflow with sample, library, and data-quality checkpoints.
- Study Design
We define the host system, biological groups, bacterial question, expected biomass, controls, and the evidence needed to support the study conclusion.
- Sample Receipt and Feasibility QC
Sample condition, cell concentration, viability, debris, aggregation, and microbial-study suitability are reviewed before processing.
- Single-Cell Preparation and Barcoding
Qualified cells are prepared for partitioning and cell-specific barcode assignment while handling conditions are selected to preserve host RNA and relevant bacterial associations.
- Host RNA Capture and 16S Enrichment
Host polyadenylated RNA and bacterial 16S signals are captured from cell-barcoded material, followed by amplification and separate library construction.
- Library QC and Sequencing
Host and 16S-enriched libraries are assessed independently. Sequencing design is confirmed from library complexity, sample count, and project objectives.
- Two-Layer Data Processing
Host expression and bacterial 16S data are processed in parallel, then connected by retained cell identity after quality filtering.
- Biological Interpretation and Delivery
Cell-type-aware bacterial associations and host response programs are reviewed with control results, limitations, and reproducible analysis outputs.
Sample Requirements and Study Controls
Fresh tissue, freshly prepared single-cell suspensions, and cultured-cell preparations can be evaluated. Human and mouse projects are the most direct starting points; additional species require reference-genome and 16S-primer feasibility review. Exact input targets are confirmed for each tissue, study design, and processing route rather than applied as a universal threshold.
| Item | Project Planning Requirement |
|---|---|
| Accepted starting materials | Fresh tissue, qualified single-cell suspension, or cultured cells after feasibility review |
| Species | Human and mouse; other species assessed for host reference and primer compatibility |
| Sample quality | Cell concentration, viability, aggregation, debris, and handling history assessed at receipt |
| Biological design | Replicates and comparison groups planned around the host-cell and bacterial question |
| Negative controls | Process blanks and relevant biological controls included where feasible |
| Shipping | Collection, preservation, and shipment instructions issued after sample review |
Because dissociation can alter both host transcription and host–bacterium associations, collection and transport should be discussed before samples are prepared. When a matched conventional single-cell dataset is required, our 10x Genomics Chromium X Single-Cell RNA-Seq Service can support the study design.
Bioinformatics Analysis
The analysis is organized as two quality-controlled data streams joined by cell identity. This keeps host expression QC, bacterial signal QC, and cross-layer interpretation visible rather than collapsing them into a single score.
Standard Analysis
- Raw-read and library-level QC
- Host alignment, gene quantification, and cell filtering
- Dimensionality reduction, clustering, and cell annotation
- 16S read QC and taxonomic assignment
- Cell-linked host–bacterial signal matrix
- Negative-control-aware background assessment
- Cell-type and condition summaries
Optional Analysis
- Differential host expression by bacterial signal
- Pathway and gene-set enrichment
- Taxon-by-cell-type association testing
- Host response module scoring
- Condition-specific interaction comparisons
- Integration with spatial or matched bulk data
- Custom figures and analysis modules
Figure 3. Representative host clustering, bacterial signal, and cell-type association outputs.
Analysis parameters, reference databases, software versions, filtering decisions, and control findings are documented. Additional support is available through our Single-Cell RNA-Seq Data Analysis Service.
Deliverables
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed sequencing files for host and 16S-enriched libraries |
| Host expression matrix | Filtered and unfiltered cell-by-gene matrices with cell-level metadata |
| Bacterial 16S matrix | Cell-linked bacterial signal table with taxonomic assignments and QC fields |
| Cell annotations | Cluster assignments, marker summaries, and host cell-type labels |
| Association results | Cell-type, group, and taxon-aware summaries with control context |
| Figures and tables | Publication-oriented plots and machine-readable result tables |
| Analysis report | Methods, QC, findings, limitations, parameters, and interpretation notes |
Research Applications
Single-cell host transcriptome and bacterial 16S profiling connects bacterial signals with the identity and transcriptional state of individual host cells. This cell-linked evidence can help you move beyond sample-level correlations and determine where host–bacterium associations occur and how the relevant host populations respond.
Intratumoral microbiome and the tumor microenvironment
For bacteria-positive tumor studies, this service provides cell-resolved maps of bacterial signals across malignant, epithelial, macrophage, and other immune or stromal populations. It helps you identify the host-cell populations associated with selected bacterial taxa and determine whether those cells exhibit distinct inflammatory, invasion, cell-dormancy, DNA-repair, or immune-response programs. These results can support mechanistic hypotheses about how intratumoral bacteria contribute to tumor heterogeneity and microenvironmental states.
Host-cell tropism in microbe-rich tissues
In fresh mucosal or barrier-tissue studies, this service shows whether bacterial signals are broadly distributed or concentrated in particular epithelial or immune-cell subsets. Comparison of bacteria-associated and matched bacteria-negative cells helps you characterize cell-type preference, detect rare responsive populations, and define tissue-specific host programs that may be obscured in bulk measurements. Tissue suitability and background-control strategies are reviewed before the project begins.
Controlled infection and co-culture models
For studies using defined bacterial strains, cultured cells, or ex vivo co-culture systems, this service distinguishes bacteria-associated host cells from bystander cells while retaining the host transcriptome. It helps you evaluate how strain, exposure condition, or sampling time affects cellular association and inflammatory, stress, metabolic, or barrier-response programs. The resulting evidence can guide mechanism validation and inform the design of subsequent tissue studies.
Preclinical intervention and response research
In antimicrobial, microbiome-perturbation, or host-directed intervention studies, this service measures changes in the frequency and identity of bacteria-associated cells together with cell-type-specific transcriptional responses. It helps you distinguish shifts in cell composition from state changes within the same population, identify pathways associated with response or persistence, and prioritize findings for functional or spatial validation. Biological replicates and prespecified comparison groups are incorporated into project planning.
Interpretation boundary: A cell-linked 16S signal supports bacterial association with a captured host cell, but it does not by itself establish bacterial viability, intracellular localization, or causality. Microscopy, culture, spatial profiling, or targeted validation should be added when those conclusions are central to the study.
Choose the Assay Around the Primary Evidence Needed
| Approach | Host Cell States | Bacterial Taxonomic Signals | Cell-Level Linkage |
|---|---|---|---|
| Standard scRNA-seq | Yes | Limited and incidental | No validated 16S linkage |
| Bulk 16S sequencing | No | Community-level | No |
| Matched bulk RNA-seq + 16S | Bulk average | Community-level | Sample-level only |
| Host transcriptome + bacterial 16S | Single-cell | Targeted 16S | Host-cell barcode level |
| Microbial single-cell sequencing | No host-cell profile | Bacterial-cell focused | Not host-cell linked |
Figure 4. Evidence-level comparison of bulk, single-cell, and cell-linked strategies.
Best fit: use the combined service when the biological question requires both host-cell identity and bacterial 16S association in the same cell-resolved framework. Use conventional scRNA-seq when host heterogeneity is the only endpoint, or bulk 16S when community composition is the primary endpoint.
Literature Case Study: Intratumoral Bacteria and Host Cell Programs
Figure 5. Original conceptual summary of the literature case; not a reproduced paper panel.
Source: Galeano Niño et al., Nature, 2022.
Background: Bulk profiling could show that bacteria were present in tumors but could not resolve which host cells carried bacterial signals or how those cells differed transcriptionally.
Methods: The investigators combined single-cell host transcriptome profiling with targeted bacterial 16S capture and retained cell barcodes, supported by spatial profiling and functional experiments.
Results: The study associated bacterial signals with specific epithelial and immune populations and identified host programs related to inflammation, cell migration, and tumor microenvironment organization.
Conclusion: Cell-linked host and bacterial profiling can move a study from sample-level coexistence toward testable, cell-type-specific host–bacterium hypotheses. Results remain dependent on controls and orthogonal validation.
Frequently Asked Questions (FAQ)
References
- Galeano Niño JL, Wu H, LaCourse KD, et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature. 2022;611(7937):810–817. DOI: 10.1038/s41586-022-05435-0.
- Galeano Niño JL, Wu H, LaCourse KD, et al. INVADEseq to identify cell-adherent or invasive bacteria and the associated host transcriptome at single-cell-level resolution. Nature Protocols. 2023;18(11):3355–3389. DOI: 10.1038/s41596-023-00888-7.
- Lötstedt B, Stražar M, Xavier R, et al. Spatial host–microbiome sequencing reveals niches in the mouse gut. Nature Biotechnology. 2024;42(9):1394–1403. DOI: 10.1038/s41587-023-01988-1.
- Davis NM, Proctor DM, Holmes SP, et al. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226. DOI: 10.1186/s40168-018-0605-2.
- Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods. 2016;13(7):581–583. DOI: 10.1038/nmeth.3869.