Single-Cell Transcriptome and Targeted RNA Sequencing Service
Which selected genes need greater detection sensitivity without giving up the whole-transcriptome context used to define cell types and states? This service combines a standard single-cell transcriptome library with a targeted RNA-enriched library, preserving cell identity across both readouts for integrated analysis.
Why combine whole-transcriptome and targeted RNA profiling:
- Use the whole transcriptome to discover and annotate cell populations
- Direct additional sequencing information toward selected low-abundance or high-priority transcripts
- Compare broad cell states and targeted gene expression within the same cell-resolved framework
- Build a custom target panel around a defined mechanism, pathway, or validation objective
Figure 1. Whole-transcriptome context and targeted RNA sensitivity linked by cell identity.
What the Combined Service Adds to Standard scRNA-Seq
Standard single-cell RNA sequencing distributes sequencing information across thousands of genes. That breadth is essential for unbiased cell discovery, but selected transcripts may remain sparse when they are weakly expressed, transient, or confined to rare populations. A targeted enrichment library focuses additional measurement on a predefined gene set while the paired whole-transcriptome library preserves discovery context.
Whole-Transcriptome Readout
- Unbiased cell clustering and annotation
- Broad marker discovery
- Cell-state and pathway context
- Unexpected population detection
Targeted RNA Readout
- Focused detection of selected transcripts
- Cell-resolved target expression
- Panel-specific quality assessment
- Direct comparison with whole-transcriptome counts
This service is designed for gene-expression measurement. It should not be assumed to provide validated mutation, fusion, transgene, or isoform calls unless those endpoints are separately reviewed and a fit-for-purpose assay is confirmed.
How Whole-Transcriptome and Targeted Libraries Work Together
RNA molecules are captured and labeled with a cell-specific barcode during the single-cell workflow. Amplified cDNA supports preparation of the standard whole-transcriptome library and a second library enriched for the approved target panel. Because both outputs retain cell identity, targeted expression can be interpreted within clusters and cell states defined by the whole transcriptome.
| Design Element | Question It Answers | Planning Consideration |
|---|---|---|
| Whole-transcriptome library | What cell types and states are present? | Requires sufficient breadth for clustering and annotation |
| Targeted RNA library | How are selected genes distributed across those cells? | Targets require sequence and expression feasibility review |
| Shared cell barcode | Which targeted signals belong to each transcriptomic cell? | Barcode retention is checked before integration |
| Panel controls | Is the targeted assay specific and informative? | Positive, negative, and expression-range controls are selected as applicable |
Panel design begins with the biological decision the data must support. Targets are reviewed for transcript annotation, sequence specificity, expected abundance, co-expression structure, and compatibility with the selected enrichment strategy. A pilot may be recommended when the panel contains difficult or poorly characterized targets.
Service Workflow
Figure 2. Integrated workflow from panel definition to paired cell-level results.
- Study and Panel Definition
We define the cell populations, comparison groups, target genes, decision criteria, and the role of whole-transcriptome versus targeted evidence.
- Target Feasibility Review
Transcript annotations, target regions, sequence specificity, expected expression, and multiplex compatibility are assessed before panel approval.
- Sample Receipt and Single-Cell QC
Cell concentration, viability, debris, aggregation, sample history, and expected cell composition are reviewed before processing.
- Cell Capture and cDNA Preparation
Qualified cells are processed for cell-specific barcoding, reverse transcription, and cDNA amplification.
- Parallel Library Construction
A standard whole-transcriptome library and a targeted RNA-enriched library are prepared from cell-barcoded material and assessed independently.
- Sequencing and Quality Review
Sequencing allocation is balanced between discovery breadth and target sensitivity. Library performance is evaluated before joint analysis.
- Integrated Analysis and Delivery
Targeted signals are mapped back to transcriptome-defined cells, followed by group comparisons, pathway interpretation, and documented delivery.
Sample Requirements
Fresh tissue, qualified single-cell suspensions, and cultured cells can be evaluated. Human and mouse projects are common starting points; other species require reference annotation and target-design review. Final input and shipping requirements are issued after the sample type and processing route are confirmed.
| Item | Project Planning Requirement |
|---|---|
| Starting material | Fresh tissue, qualified single-cell suspension, or cultured cells after feasibility review |
| Species | Human and mouse; other species reviewed for transcript annotation and reference quality |
| Cell quality | Concentration, viability, aggregation, debris, and handling history assessed at receipt |
| Target list | Gene identifiers, transcript annotations, biological rationale, and priority ranking supplied before panel design |
| Study groups | Biological replicates and comparison groups planned before sample processing |
| Shipping | Collection, preservation, and shipment instructions supplied after sample review |
Projects needing a standard droplet-based transcriptome workflow can be planned with our 10x Genomics Chromium X Single-Cell RNA-Seq Service. For a microwell-based whole-transcriptome option, see the BD Rhapsody scWTA Service.
Bioinformatics Analysis
Whole-transcriptome and targeted libraries are evaluated separately before they are joined. This makes it possible to distinguish biological absence from insufficient transcriptome coverage, target-panel behavior, or barcode-level quality issues.
Standard Analysis
- Raw-read and library QC
- Cell filtering and gene quantification
- Dimensionality reduction and clustering
- Cell-type and cell-state annotation
- Target-panel mapping and count matrix
- Barcode-level library integration
- Target detection by cell type and group
Optional Analysis
- Differential expression analysis
- Pathway and gene-set enrichment
- Target co-expression modules
- Rare population-focused comparisons
- Pseudotime and state-transition analysis
- Cell–cell communication analysis
- Custom validation and visualization modules
Figure 3. Representative clustering, target detection, and integrated comparison outputs.
All filtering decisions, software versions, target definitions, and analysis parameters are documented. Additional custom support is available through our Single-Cell RNA-Seq Data Analysis Service.
Deliverables
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed sequencing files for whole-transcriptome and targeted libraries |
| Whole-transcriptome matrices | Filtered and unfiltered cell-by-gene matrices with cell-level metadata |
| Targeted RNA matrix | Cell-by-target matrix with panel and mapping QC fields |
| Integrated cell metadata | Clusters, annotations, study groups, and target-detection summaries |
| Differential results | Group- and cell-type-aware expression tables when included in scope |
| Figures and tables | Publication-oriented plots and machine-readable result tables |
| Analysis report | Methods, QC, integrated findings, limitations, and reproducibility details |
Research Applications
Combining whole-transcriptome profiling with targeted RNA enrichment gives you broad cell-state context together with stronger measurement of predefined transcripts. This design is particularly useful when standard single-cell RNA sequencing can identify the relevant populations but does not provide sufficient information for the genes that drive the study decision.
Immunology and inflammatory-response research
For immune and inflammation studies, this service increases information for selected cytokines, receptors, transcription factors, and signaling genes while retaining whole-transcriptome cell annotation. It helps you compare pathway activity across immune subsets, detect low-abundance response markers, and characterize rare responsive populations that may be missed because of transcript dropout.
Oncology and rare cell-state characterization
In heterogeneous tumor or model-system samples, the whole transcriptome defines malignant, immune, and stromal populations, while the targeted library provides focused measurements of pathway genes, therapeutic targets, or resistance-associated transcripts. This combination helps you characterize rare target-positive populations and determine whether a selected signature is confined to a specific cell type or shared across the microenvironment.
Functional genomics and perturbation screening
For CRISPR or other pooled perturbation studies with predefined response hypotheses, targeted measurement concentrates sequencing information on the genes needed to evaluate each perturbation. Cell-level linkage between the perturbation design, selected transcripts, and broader cell state helps you detect weak effects, compare multiple response programs, and prioritize perturbations for functional follow-up.
Drug response and time-course studies
In pharmacologic, environmental, stimulation, or time-course experiments, this service measures predefined response genes across transcriptome-defined cell populations. It helps you distinguish population shifts from changes within the same cell type, identify early or persistent response states, and compare targeted response signatures consistently across experimental groups.
Discovery-signature validation
When discovery studies have produced a candidate gene set, this service converts those findings into a focused panel without losing the transcriptome context needed to recognize unexpected cell states. It helps you evaluate whether a signature is reproducible across additional samples, determine the cell populations that contribute to it, and refine candidates for orthogonal validation. For projects centered on a predefined immune panel, compare the BD Rhapsody Targeted Immune Multiplex Sequencing Service.
Choose the Readout Around the Study Decision
| Approach | Discovery Breadth | Selected-Target Sensitivity | Best Fit |
|---|---|---|---|
| Standard scRNA-seq | High | Expression-dependent | Unbiased discovery and cell atlas studies |
| Whole transcriptome + targeted RNA | Retained | Focused on approved panel | Discovery plus hypothesis-focused measurement |
| Targeted-only single-cell RNA | Restricted to panel | Focused on panel | Defined biology with a stable marker set |
| Single-cell full-length RNA | Transcript-structure focused | Depends on strategy | Isoform and splicing questions |
Figure 4. Selection framework based on discovery breadth and target focus.
Best fit: select the combined workflow when cell discovery and focused target measurement are both necessary. Use targeted-only profiling when the biology is already well defined, or consider our Single-Cell Full-Length RNA Sequencing Service when transcript structure is the primary endpoint.
Literature Case Study: Target Enrichment With Transcriptome Context
Figure 5. Original conceptual summary of the literature case; not a reproduced paper panel.
Source: Moro et al., Nature Communications, 2025.
Background: Standard single-cell transcriptomes can miss selected low-abundance transcripts or specific transcript regions, while a targeted-only assay can lose broad cell-state context.
Methods: The researchers developed a workflow that combined whole-transcriptome profiling with multiplexed enrichment of selected transcripts and regions while retaining cell-level linkage.
Results: The study demonstrated stronger detection of selected targets while maintaining a transcriptome readout for cell annotation, and applied the approach to cell lines, mouse colon, and leukemia-related transcript questions.
Conclusion: Pairing a broad transcriptome with a focused target readout can support both unbiased cell classification and hypothesis-driven measurement. Panel size, specificity, off-target behavior, and the exact biological endpoint still require validation.
Frequently Asked Questions (FAQ)
References
- Moro G, Mallona I, Barz MJ, et al. RoCK and ROI: single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing. Nature Communications. 2025;16:10991. DOI: 10.1038/s41467-025-66248-z.
- Saikia M, Burnham P, Keshavjee SH, et al. Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells. Nature Methods. 2019;16(1):59–62. DOI: 10.1038/s41592-018-0259-9.
- Schraivogel D, Gschwind AR, Milbank JH, et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nature Methods. 2020;17(6):629–635. DOI: 10.1038/s41592-020-0837-5.
- Vallejo AF, Davies J, Grover A, et al. Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering. iScience. 2021;24(3):102147. DOI: 10.1016/j.isci.2021.102147.
- Moro G, Brunner E, Basler K. A practical guide to targeted single-cell RNA sequencing technologies. Communications Biology. 2026;9:250. DOI: 10.1038/s42003-026-09675-y.