Single-Cell Sequencing Services Supporting Spatial Resolution Mapping
CD Genomics provides a comprehensive portfolio of single-cell sequencing services — from high-throughput droplet-based scRNA-seq to targeted immune profiling, single-cell epigenomics, and integrated multi-omics — built on validated platforms and backed by bioinformatics that bridges single-cell resolution to spatial context. Single-cell sequencing resolves cellular heterogeneity within dissociated samples, identifying discrete cell types, states, and lineages that bulk approaches average away. When paired with spatial transcriptomics from matched specimens, single-cell data serves as a deconvolution reference — anchoring cell-type identities to tissue coordinates and enabling spatially resolved cell-type mapping at near-single-cell resolution across the tissue architecture.
Why CD Genomics for single-cell sequencing:
- Full portfolio covering transcriptomic, genomic, epigenomic, and immune profiling — select one modality or combine multiple, with single-cell data formatted for spatial transcriptomics deconvolution
- Platform-agnostic coverage across 10x Chromium, BD Rhapsody, and plate-based Smart-seq — matched to your sample type, cell number, and biological question
- Multi-omics integration — single-cell RNA + ATAC, transcriptome + immune repertoire, transcriptome + surface proteome from the same single cells
- End-to-end service from tissue dissociation through sequencing and publication-grade bioinformatics, managed by a single team
Services are provided for research use only.
Single-Cell Sequencing Service Portfolio
Our single-cell sequencing services are organized by molecular analyte. Each service is available individually or as part of an integrated multi-omics study design.
Single-Cell RNA Sequencing (Transcriptomics)
Single-cell transcriptomics resolves gene expression programs at individual cell resolution — identifying cell types, states, lineages, and differentially expressed genes across conditions. We offer three complementary scRNA-seq modalities:
Droplet-based 3' or 5' gene expression profiling. Captures 500–10,000 cells per sample with high throughput and low cost per cell. Best for cell atlas generation, tissue heterogeneity surveys, and large-cohort studies. Compatible with fresh and frozen single-cell suspensions.
Smart-seq Full-Length Transcriptome
Plate-based full-length transcript coverage with isoform-level resolution. Captures splice variants, allele-specific expression, and low-abundance transcripts. Best for detailed transcript characterization, isoform analysis, and studies requiring full gene body coverage.
Full-Length Isoform Sequencing
Long-read-enabled full-length isoform sequencing at single-cell resolution. Resolves transcript isoforms, fusion transcripts, and alternative splicing events that short-read methods cannot capture. Best for neuroscience, developmental biology, and cancer transcriptome complexity.
Additional transcriptomic services:
- snRNA Sequencing Services — Single-nucleus RNA-seq for frozen and archived tissues where intact cell dissociation is challenging. Compatible with FFPE specimens and serves as a reference for spatial transcriptomics deconvolution.
- CROP-seq Services — Pooled CRISPR screen with single-cell transcriptomic readout, linking genetic perturbations to transcriptional consequences at single-cell resolution.
- BD Rhapsody scWTA Service — Microwell-based whole transcriptome analysis with BD Rhapsody AbSeq compatibility for simultaneous protein detection.
Single-Cell DNA Sequencing (Genomics)
Single-cell DNA sequencing resolves genomic heterogeneity — copy number alterations, somatic mutations, and clonal architecture — that bulk sequencing averages across cell populations.
Genome-wide copy number and structural variant detection from individual cells. Resolves clonal architecture in tumors, lineage tracing, and mosaicism with unbiased whole-genome coverage.
Exome-focused single-cell sequencing for somatic mutation detection, clonal evolution tracking, and targeted coding-region analysis. Higher depth per cell than WGS for mutation-level resolution.
Additional genomic services include 10x Single-Cell CNV Detection for droplet-based copy number profiling linked to transcriptomic cell identity.
Single-Cell Epigenomics
Single-cell epigenomic services map chromatin accessibility and DNA methylation at individual cell resolution, revealing the regulatory mechanisms that define cellular identity and state transitions.
Genome-wide chromatin accessibility profiling at single-cell resolution. Identifies cell-type-specific regulatory elements, transcription factor binding footprints, and enhancer-promoter interactions. Paired with spatial ATAC-seq for spatial epigenomic mapping.
Additional epigenomic services include BD Rhapsody Single-Cell ATAC-seq (microwell-based, sample multiplexing, paired ATAC+WTA), BD Rhapsody Single-Cell CUT&Tag (antibody-guided histone modification and TF profiling), Microfluidic Single-Cell CUT&Tag (droplet-based Paired-Tag dual-modality), Single-Cell WGBS (genome-wide methylation), and Single-Cell RRBS (reduced-representation methylation).
Single-Cell Immune Profiling
Immune repertoire and targeted immune sequencing services resolve the adaptive immune landscape — T-cell receptor (TCR) and B-cell receptor (BCR) clonality, diversity, and antigen specificity — alongside transcriptomic cell state.
Single-Cell Immune Repertoire Sequencing
Full-length paired TCR α/β or BCR heavy/light chain sequencing from individual cells. Resolves clonotype expansion, diversity, and V(D)J usage — linked to transcriptomic cell state for integrated immune profiling.
BD Rhapsody Targeted Immune Panel
Targeted immune gene expression panel with simultaneous protein detection via BD AbSeq. Profiles immune cell subsets, activation states, and cytokine signatures from limited input material.
Whole transcriptome analysis combined with oligonucleotide-conjugated antibody panels for simultaneous mRNA and surface protein detection from the same single cells.
Multi-Omics Integration Services
Integrated multi-omics services profile multiple molecular layers from the same single cells, revealing the relationships between genome, epigenome, transcriptome, and proteome.
- Single-Cell ATAC + RNA-seq — Simultaneous chromatin accessibility and gene expression profiling from the same single cells. Links regulatory element activity to transcriptomic output for gene regulatory network inference.
- 10x Single-Cell + Spatial Transcriptome — Matched single-cell and spatial transcriptomic data from the same or adjacent tissue specimens. Single-cell data serves as a high-resolution reference for spatial deconvolution, anchoring cell-type identities to tissue coordinates. This is the recommended workflow for studies requiring spatially resolved cell-type maps.
- Single-Cell Transcriptome + Immune Repertoire — Paired gene expression and full-length TCR/BCR sequencing from individual cells. Identifies which transcriptomic cell states correspond to expanded clonotypes in tumor-infiltrating lymphocytes, autoimmune lesions, or vaccine responses.
- Single-Cell Transcriptome + Surface Proteome — Simultaneous mRNA and cell-surface protein detection via oligonucleotide-conjugated antibodies. Adds protein-level validation to transcriptomic cell-type annotations.
Microbial Single-Cell Sequencing
- Microbial Single-Cell Sequencing Services — Dual-track SAG (genome) + scRNA-seq (transcriptome) for bacterial and fungal single-cell profiling. Resolves strain-level heterogeneity, mobile element host linkage, and rare transcriptional states in 12 validated species.
Data Analysis Services
- Single-Cell RNA-Seq Data Analysis Service — Standalone bioinformatics for existing scRNA-seq data. Includes QC filtering, normalization, dimensionality reduction, clustering, differential expression, cell-type annotation, trajectory inference, and publication-ready visualization. Accepts FASTQ or count matrix input.
Service Workflow
Our single-cell sequencing workflow spans from sample preparation through bioinformatics — each step aligned with platform-specific protocols and quality checkpoints.
- Study Design and Sample Preparation
Consultative planning phase: we align tissue type, cell number targets, sequencing modality (RNA, DNA, ATAC, immune, multi-omics), and platform choice with your experimental goals. Fresh or frozen tissue is dissociated into single-cell or single-nucleus suspensions. QC Checkpoint: Cell viability (>85% target), concentration, and debris assessment before proceeding.
- Single-Cell Capture and Barcoding
Single cells are partitioned into droplets (10x Chromium), microwells (BD Rhapsody), or plates (Smart-seq) and assigned cell-specific barcodes. QC Checkpoint: Cell recovery rate, multiplet rate estimation, and barcode distribution across captured cells.
- Library Preparation
Cell-barcoded cDNA or DNA is amplified and converted into Illumina-compatible sequencing libraries. Modality-specific steps apply: fragmentation and adapter ligation (RNA-seq), transposase tagmentation (ATAC-seq), or targeted enrichment (immune repertoire). QC Checkpoint: Library fragment size distribution and concentration (Bioanalyzer or TapeStation).
- Sequencing
Libraries are sequenced on Illumina platforms (NovaSeq or NextSeq series) at depths calibrated to modality and cell number — typically 20,000–50,000 read pairs per cell for scRNA-seq, adjusted upward for ATAC, WGS, or multi-omics libraries. QC Checkpoint: Q30 scores, sequencing saturation, and per-cell read distribution.
- Primary Data Processing
Raw sequencing data are processed through platform-specific pipelines (Cell Ranger for 10x, BD Rhapsody pipeline, or custom workflows): read alignment, cell barcode demultiplexing and error correction, molecular tag counting, and feature-barcode matrix generation. QC Checkpoint: Valid barcodes, fraction reads in cells, and mitochondrial read percentage.
- Bioinformatics Analysis and Data Delivery
Secondary analysis includes normalization, dimensionality reduction, clustering, differential expression, cell-type annotation, and publication-ready figure generation. All analysis parameters are logged. QC Checkpoint: Final data review against project specifications before delivery.
Sample Requirements
| Requirement | Specification |
|---|---|
| Sample type | Fresh tissue, frozen tissue, OCT-embedded tissue, or pre-prepared single-cell suspensions |
| Species | Human, mouse, rat, and additional species (case-by-case evaluation) |
| Cell viability | >85% viable cells for standard workflows; lower viability specimens may require protocol optimization or single-nucleus RNA-seq |
| Cell concentration | 700–1,200 cells/µL for 10x Chromium; platform-specific ranges apply |
| Minimum cell input | ~10,000 viable cells for standard droplet-based workflows; plate-based methods can work from fewer cells |
| Cell size range | Typically 10–30 µm diameter; larger cells (>40 µm) or smaller cells (<5 µm) require protocol adjustment |
| Clumping and debris | Cell suspensions must be free of visible clumps, fibers, and debris; filtration is performed in-lab as part of standard preparation |
| Shipping | Fresh tissue in transport medium on wet ice; frozen tissue or cell pellets on dry ice; pre-prepared suspensions under optimized conditions |
Tissue types successfully processed include brain, spleen, lymph node, lung, liver, kidney, skin, tumor biopsies, PBMCs, and sorted cell populations. For complex or fibrous tissues, optimized enzymatic dissociation protocols are applied per tissue type. Contact our team during project planning for tissue-specific guidance.
Bioinformatics Analysis
All single-cell sequencing projects include a standard bioinformatics pipeline. The analysis scope is matched to your modality and biological question.
Standard Analysis (Included)
- Read alignment to reference genome and cell barcode demultiplexing
- Feature-barcode matrix generation
- Cell-level QC filtering (molecular barcodes counts, gene counts, mitochondrial fraction)
- Normalization and variance stabilization (SCTransform or LogNormalize)
- Dimensionality reduction (PCA, UMAP, t-SNE)
- Unsupervised clustering with resolution optimization
- Cluster marker gene identification and visualization
- Automated and manual cell-type annotation
- Differential expression analysis between user-defined groups or clusters
- QC report with sequencing metrics, cell statistics, and sample-level summaries
Optional Advanced Analysis
- Trajectory inference and pseudotime analysis (Monocle, Slingshot, RNA velocity)
- Cell-cell communication inference (CellChat, NicheNet, CellPhoneDB)
- Gene regulatory network inference (SCENIC, pySCENIC)
- Multi-sample integration with batch correction (Harmony, scVI, CCA)
- Spatial deconvolution using matched spatial transcriptomics data (RCTD, Cell2location, Tangram)
- Multi-omics integration (scRNA + scATAC, scRNA + immune repertoire, scRNA + surface proteome)
- Single-cell CNV calling and clonal analysis from scRNA-seq or scDNA-seq data
- Custom visualization — interactive HTML reports, publication-quality figures
Analysis scope is agreed during study design. All data are delivered in formats compatible with R (Seurat, Signac, ArchR), Python (Scanpy, Squidpy, Cell2location), and Loupe Browser (10x Genomics).
For standalone bioinformatics on existing single-cell data, see Single-Cell RNA-Seq Data Analysis Service.
Representative Data Output
Below is an illustrative example of the analysis outputs delivered in a standard single-cell RNA-seq bioinformatics report — cell-type clustering, marker gene expression patterns, and differential expression analysis.
Representative single-cell RNA-seq analysis outputs: cell-type clustering (UMAP), marker gene expression patterns, and differential expression results — indicative of the visualizations delivered in a standard bioinformatics report.
Single-Cell to Spatial — Deconvolution Reference Mapping
One of the most productive uses of single-cell sequencing in the spatial omics context is as a deconvolution reference for spatial transcriptomics.
Spatial transcriptomics captures gene expression across tissue architecture, but each spatial feature (spot or bin) typically contains multiple cells. Cell-type deconvolution uses a single-cell reference dataset — typically scRNA-seq or snRNA-seq from the same tissue type — to estimate the cell-type composition of each spatial feature. This produces spatially resolved cell-type maps that neither modality alone can generate.
| Approach | What it provides | Limitation |
|---|---|---|
| Single-cell RNA-seq alone | High-resolution cell types and states | No spatial context — tissue architecture is lost during dissociation |
| Spatial transcriptomics alone | Gene expression mapped to tissue coordinates | Each spot contains multiple cells — cell types are mixed |
| Single-cell + Spatial (deconvolution) | Cell-type proportions at every tissue coordinate | Requires matched or reference scRNA-seq from comparable tissue |
Single-cell to spatial deconvolution concept: single-cell RNA-seq cell-type signatures (left) serve as a reference to estimate cell-type proportions at each spatial transcriptomics spot (center), producing spatially resolved cell-type maps (right).
Recommended integrated workflow:
- Generate single-cell RNA-seq (or snRNA-seq) from a representative sample of your tissue to build a cell-type reference
- Run spatial transcriptomics on tissue sections from the same or adjacent specimens
- Deconvolve spatial features using the single-cell reference via Cell2location, RCTD, or Tangram
- Validate cell-type assignments using mIHC or spatial proteomics on adjacent sections
This workflow delivers spatially resolved cell-type maps — the foundation for spatially informed differential expression, cell-cell communication analysis, and tissue neighborhood characterization.
For direct multi-omics integration from the same tissue, see 10x Single-Cell + Spatial Transcriptome.
Deliverables
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed FASTQ files for all libraries |
| Processed expression matrix | Feature × cell barcode count matrix in standard formats (h5ad, h5, mtx) |
| Cell metadata | Cell-level QC metrics, cluster assignments, and cell-type annotations |
| Clustering report | UMAP/t-SNE visualizations, cluster marker gene tables, annotation summaries |
| Differential expression tables | Differentially expressed genes between cell types or conditions, with fold changes and adjusted p-values |
| Trajectory analysis (if ordered) | Pseudotime ordering, RNA velocity plots, and lineage branch point analysis |
| Spatial deconvolution results (if ordered) | Cell-type proportion matrices per spatial feature, spatial cell-type maps |
| Bioinformatics report | Methods documentation, QC metrics, analysis parameter logs, and publication-ready figures |
| Data archive | All intermediate files, analysis scripts, and processing logs for reproducibility |
Applications
Single-cell sequencing supports discovery across research areas:
Tumor heterogeneity and cancer biology
Resolve clonal architecture, identify rare drug-tolerant persister populations, and characterize tumor microenvironment cell states. Single-cell RNA-seq, WGS, CNV, and immune repertoire profiling — alone or in combination — map the cellular landscape of tumors at diagnosis, treatment, and relapse.
Immunology and immunotherapy
Profile immune cell subsets, activation states, and clonotype expansion across tissues and conditions. Paired transcriptome + immune repertoire sequencing links T-cell and B-cell clonality to functional states. Single-cell data integrates with spatial immune profiling for tissue-contextualized immune analysis.
Neuroscience
Define neuronal and glial subtypes, map developmental lineages, and characterize synapse-associated gene programs. Single-nucleus RNA-seq from frozen or archived brain tissue captures neuronal populations that resist intact-cell dissociation. Full-length isoform sequencing resolves neuron-specific splicing.
Developmental biology
Trace lineage specification, characterize progenitor-to-differentiated trajectories, and map single-cell epigenomic landscapes during organogenesis. Multi-omics (RNA + ATAC) from the same cells links chromatin remodeling to gene expression during cell fate decisions.
Drug development and target discovery
Identify cell-type-specific drug targets, characterize mechanism-of-action at single-cell resolution, and detect rare cell populations that drive resistance or relapse. CROP-seq enables pooled CRISPR screens with single-cell transcriptomic readout for functional target validation.
Spatial multi-omics integration
Single-cell sequencing provides the cellular-resolution reference layer for spatial transcriptomics, spatial epigenomics, and spatial proteomics — enabling spatially resolved cell-type mapping when single-cell and spatial data from matched specimens are integrated computationally.
Frequently Asked Questions (FAQ)
References
- Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–1902.e21. DOI: 10.1016/j.cell.2019.05.031.
- Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–3587.e29. DOI: 10.1016/j.cell.2021.04.048.
- Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology. 2022;40(5):661–671. DOI: 10.1038/s41587-021-01139-4.
- Cable DM, Murray E, Zou LS, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology. 2022;40(4):517–526. DOI: 10.1038/s41587-021-00830-w.
- Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nature Methods. 2021;18(11):1352–1362. DOI: 10.1038/s41592-021-01264-7.
