10x Single-Cell RNA-Seq Service
Single-cell RNA sequencing (scRNA-seq) on the 10x Genomics Chromium X platform resolves transcriptome-wide gene expression at individual cell resolution. CD Genomics delivers end-to-end 10x scRNA-seq services — from single-cell suspension QC and GEM-X 3' v4 library preparation through sequencing and publication-grade bioinformatics — for academic, biotech, and pharma teams studying tissue heterogeneity, disease mechanisms, and therapeutic targets.
Why CD Genomics for your scRNA-seq project:
- GEM-X 3' v4 chemistry with improved sensitivity for low-expression genes and reduced ambient RNA background
- Flexible throughput from 500 to 20,000 cells per sample on the Chromium X platform
- GEM-X Flex workflow option for fixed and FFPE samples — no fresh tissue required
- In-house bioinformatics pipeline covering Cell Ranger preprocessing, Seurat v5-based clustering, differential expression, gene set enrichment, and publication-ready visualization
- Pre-shipment sample QC (viability, concentration, debris assessment) with a feasibility report before library construction begins
- Dedicated project scientist assigned to each study for protocol optimization and custom analysis design
Technology Overview: 10x Chromium X and GEM-X 3' v4 Chemistry
The 10x Genomics Chromium X platform uses droplet-based partitioning to encapsulate individual cells with gel beads carrying cell-specific barcodes. Each GEM (Gel Bead-in-Emulsion) contains a single cell, reverse transcription reagents, and a gel bead functionalized with a 10x Barcode for cell identification, a molecular tag for transcript counting, and a poly(dT) primer for mRNA capture.
GEM-X 3' v4, the current-generation single-cell gene expression chemistry, introduces several refinements over v3.1 chemistry. The GEM-X microfluidic chip architecture generates smaller, more uniform droplets — around 40 µm — which reduces unoccupied droplet volume and lowers ambient RNA background. This translates to better separation of real signal from noise, particularly for low-abundance transcripts. The v4 chemistry also supports higher cell recovery rates and improved sensitivity at equivalent sequencing depth compared to the previous generation.
The core output — a 3' digital gene expression count matrix — captures the 3' end of polyadenylated transcripts, which is sufficient for cell clustering, cell-type annotation, differential expression analysis, and gene set enrichment. For users who need full-length isoform information, alternative splicing, or fusion transcript detection, we recommend complementing 3' scRNA-seq with a single-cell full-length transcriptome sequencing approach (contact our team for availability).
| Parameter | Specification |
|---|---|
| Platform | 10x Chromium X with GEM-X 3' v4 |
| Throughput | 500–20,000 cells per sample (user-selectable) |
| Target recovery | Up to 65% of loaded cells |
| Sensitivity | Improved low-expression gene detection vs. v3.1 |
| Doublet rate | ~0.8% per 1,000 cells recovered |
| Read configuration | Paired-end: Read 1 (28 bp, barcode + molecular tag), Read 2 (90–150 bp, transcript insert) |
| Sequencing | Illumina NovaSeq X / NovaSeq 6000 (flexible depth per project) |
Chromium X and GEM-X 3' Workflow
The workflow follows a coordinated pipeline from sample receipt through data delivery, with QC checks at each stage.
- Sample collection and single-cell suspension preparation
Fresh tissue, cultured cells, or cryopreserved samples are processed into high-viability single-cell or single-nucleus suspensions. For solid tissues, enzymatic or mechanical dissociation is applied with protocols optimized per tissue type. QC checkpoint: Cell count, viability (AO/PI or Trypan Blue staining, target ≥85% for fresh samples), debris assessment, aggregation check under microscopy.
- GEM generation and barcoding
The single-cell suspension is loaded onto the Chromium X microfluidic chip. Individual cells are co-partitioned with GEM-X gel beads in oil droplets. Within each GEM, cells are lysed, mRNA is captured by poly(dT) primers on the gel bead, and reverse transcription incorporates the 10x cell barcode and molecular tag into first-strand cDNA. QC checkpoint: GEM formation monitored by real-time imaging; post-GEM RT cDNA yield.
- cDNA amplification and library construction
Barcoded cDNA is pooled, amplified, and enzymatically fragmented. 3' gene expression libraries are constructed by end repair, A-tailing, adapter ligation, and sample index PCR. QC checkpoint: Library fragment distribution (Bioanalyzer/TapeStation, expected ~250–400 bp insert), library concentration (Qubit), qPCR quantification.
- Sequencing
Libraries are pooled and sequenced on an Illumina NovaSeq X or NovaSeq 6000 with the recommended paired-end configuration. Sequencing depth is adjusted per project goals: 20,000–50,000 read pairs per cell is typical for cell-type classification; deeper sequencing (≥50,000 reads per cell) can be arranged for low-abundance transcript detection. QC checkpoint: Raw sequencing QC (Q30 scores, barcode/molecular tag/RNA read quality metrics from Cell Ranger
mkfastqoutput). - Data preprocessing and analysis
Raw FASTQ files are processed through Cell Ranger (
countpipeline) for sample demultiplexing, barcode processing, alignment to the reference genome, and generation of the feature-barcode count matrix. Downstream analysis is performed in Seurat v5 and custom R/Python workflows. QC checkpoint: Cell Ranger web summary metrics — estimated number of cells, mean reads per cell, median genes per cell, fraction of reads in cells, mitochondrial read fraction.
scRNA-seq Sample Requirements
The table below lists general sample submission guidelines. Specific requirements may vary by tissue type and project scope. Sample QC and a feasibility review are performed upon receipt and before library construction.
| Sample Type | Recommended Input | Container / Stabilization | Shipping Condition | Key QC Notes |
|---|---|---|---|---|
| Fresh tissue (e.g., tumor, brain, liver) | ≥50 mg viable tissue | RPMI-1640 or DMEM with 10% FBS, 4°C | Cold pack (2–8°C), process within 24 h | Viability ≥85% post-dissociation recommended |
| Cultured cells (adherent or suspension) | ≥1 × 10⁶ viable cells | Cryopreservation medium or fresh medium, 4°C | Cold pack (2–8°C) or dry ice (frozen) | Confirm viability post-thaw; minimize mycoplasma risk |
| Cryopreserved cells / PBMCs | ≥1 × 10⁶ viable cells | Cryovials in liquid nitrogen or −80°C | Dry ice | Viability ≥70% post-thaw accepted; dead cell removal available |
| Fresh frozen tissue (nuclei isolation) | ≥30 mg tissue | Cryovials, snap-frozen in liquid nitrogen | Dry ice | Nuclei isolation QC: intact nuclear membranes, minimal debris |
| FFPE tissue (GEM-X Flex) | 2–4 × 25 µm scrolls or sections | RNase-free tube at room temperature | Ambient temperature | DV200 ≥30% recommended for FFPE RNA integrity |
If viability, yield, or RNA integrity falls below threshold, we notify the project contact with a revised feasibility assessment before proceeding. Custom dissociation protocols for difficult tissues (fibrotic, lipid-rich, or brain) can be developed during project onboarding. Contact our team for detailed shipping instructions and a sample submission kit.
GEM-X Flex RNA Profiling Option (Fixed and FFPE Samples)
For samples that cannot be processed as fresh or live-cell suspensions — including FFPE tissue blocks, fixed cells, and archived specimens — the GEM-X Flex workflow provides a probe-based single-cell gene expression solution that is compatible with fixed material.
Unlike the standard 3' poly(dT)-based mRNA capture approach, which requires live, intact cells, GEM-X Flex uses a panel of pre-designed probe pairs that hybridize to target transcripts in fixed or FFPE samples. Probes are ligated, and the ligation product — rather than the mRNA itself — serves as the template for barcoding and library construction. This decouples gene expression profiling from the requirement for intact poly(A) RNA.
When to consider GEM-X Flex:
- FFPE tissue blocks or sections where fresh tissue is unavailable
- Formalin-fixed or methanol-fixed cell suspensions
- Archived clinical specimen cohorts with limited viable material
- Multi-site studies where immediate fresh-tissue processing is logistically difficult
- Projects where live-cell dissociation and shipping are not feasible
The probe panel covers the human or mouse protein-coding transcriptome. Data output from the Flex workflow is broadly compatible with the same downstream bioinformatics pipeline used for standard 3' scRNA-seq (Cell Ranger multi pipeline for Flex data, followed by Seurat v5 analysis).
| Sample Type | Recommended Input |
|---|---|
| FFPE scrolls | 2–4 × 25 µm sections |
| Fixed cell suspension | ≥5 × 10⁴ fixed cells |
For additional information on fixed-sample options, see our FFPE spatial transcriptomics and single-nucleus RNA sequencing pages.
Bioinformatics Analysis
Every 10x scRNA-seq project includes a core bioinformatics pipeline. Additional analysis modules are available depending on the biological question and project scope.
Core pipeline (included)
- Raw data processing: Cell Ranger
mkfastq(demultiplexing) andcount(alignment, barcode/molecular tag counting, feature-barcode matrix generation) - QC and filtering: removal of empty droplets, doublets (DoubletFinder or Scrublet), high-mitochondrial-content cells, and low-complexity cells
- Normalization and integration: SCTransform v2 or LogNormalize; batch correction (Harmony, CCA, or RPCA integration for multi-sample studies)
- Dimensionality reduction and clustering: PCA, UMAP/t-SNE visualization, graph-based clustering (Louvain/Leiden) at multiple resolutions
- Cell-type annotation: automated (SingleR, Azimuth reference mapping) and manual annotation based on curated marker gene sets
- Differential expression analysis: per-cluster marker gene identification (Wilcoxon rank-sum, MAST, or pseudobulk DESeq2 for multi-sample designs)
- Gene set enrichment: GO, KEGG, MSigDB, and Reactome pathway enrichment
- QC and analysis summary report: annotated HTML report with all QC metrics, cluster maps, marker tables, and enrichment results
Optional add-ons
- Trajectory/pseudotime analysis (Monocle3, Slingshot, RNA velocity via scVelo)
- Cell–cell communication inference (CellChat, NicheNet, CellPhoneDB)
- Regulatory network analysis (SCENIC, dorothea)
- Copy number variation (CNV) inference from scRNA-seq (inferCNV, CopyKAT)
- Multimodal integration (CITE-seq, scATAC-seq co-analysis)
- Custom web-based interactive data browser (Shiny/CellxGene)
- Cross-study meta-analysis and atlas integration
- Spatial transcriptomics–scRNA-seq integrative deconvolution (RCTD, cell2location)
The exact analysis plan is defined during project onboarding, documented in the analysis protocol, and shared with the project team before execution. For multi-omics integration needs, see our integrated spatial multiomics solutions.
Deliverables
| Category | Deliverable |
|---|---|
| Sequencing data | Demultiplexed FASTQ files (gzipped) |
| Count matrix | Filtered feature-barcode count matrix (HDF5 + MTX/TSV formats) |
| QC report | Cell Ranger web summary HTML + per-sample QC metrics table |
| Clustering results | UMAP coordinates, cluster assignments (CSV), per-cluster marker gene table with statistics |
| Differential expression | Full per-cluster differential gene list (log2FC, p-value, adjusted p-value) |
| Enrichment analysis | GO, KEGG, and pathway enrichment tables (CSV) with gene ratio and FDR |
| Visualization package | Publication-format figures: UMAP (cell type + cluster), dot plot, violin plot, heatmap, volcano plot (PDF/PNG) |
| Methods documentation | Detailed materials and methods write-up for manuscript and registry use |
| Raw supporting files | BAM files (optional), Seurat R object (.rds), analysis scripts |
| Interactive browser | Web-based data explorer (optional, based on project scope) |
Demo Results
A representative composite visualization from a reference scRNA-seq dataset processed through the CD Genomics 10x single-cell RNA-seq pipeline. Each panel illustrates a standard analysis output included in the project deliverables.
Panel A — Unsupervised clustering (UMAP)
Each point is a cell; colors denote graph-based Leiden clusters. This view shows global cell heterogeneity in the sample.
Panel B — Cell-type annotation (UMAP)
Clusters labeled by curated marker gene expression and reference-based mapping. Major populations color-coded by lineage.
Panel C — Marker gene dot plot
Rows are cell types, columns are canonical marker genes. Dot size = percent expressing; color = average expression level. Supports cell-type identity verification.
Panel D — Violin plot
Expression distribution of a representative marker gene (e.g., CD8A) across all annotated cell types. Each violin shows the full expression range.
Panel E — Volcano plot
Log2 fold-change vs. -log10 adjusted p-value for genes between two user-defined groups or clusters, highlighting statistically significant up- and down-regulated genes.
This composite figure is intended as a demonstration of output types. The actual figures delivered for each project reflect the specific biological context, cell populations, and comparative questions defined in the analysis protocol.
scRNA-seq Applications
Tumor microenvironment profiling
Characterize infiltrating immune subsets, stromal remodeling, and cancer cell heterogeneity in solid tumors — applicable to target discovery, biomarker hypothesis generation, and preclinical model characterization.
Neuroscience and brain cell atlas
Resolve neuronal subtypes, glial populations, and region-specific transcriptional programs in brain and spinal cord. Compatible with nuclei isolation for frozen or archived CNS tissue.
Immunology and inflammatory disease
Profile PBMCs, tissue-resident immune cells, and disease-associated immune states. Map activation, exhaustion, and regulatory signatures across conditions.
Developmental and stem cell biology
Capture transcriptional transitions during differentiation, organoid development, or reprogramming. Trajectory analysis supports lineage inference.
Infectious disease and host response
Study host transcriptional responses to viral, bacterial, or parasitic infection at single-cell resolution. Identify differentially responsive cell types and pathways.
Drug mechanism of action and toxicology
Compare pre- and post-treatment transcriptomes in cell models or tissue samples to identify target engagement, resistance programs, and off-target effects.
Biomarker and target discovery
Screen for disease-specific or treatment-associated gene signatures in patient-derived or model-system samples.
Case Study: Single-Cell Transcriptomic Profiling of Pancreatic Cancer Liver Metastasis
Source: Zhang S, Fang W, Zhou S, et al. Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis. Nature Communications 14, 5123 (2023).
Background: Pancreatic ductal adenocarcinoma (PDAC) frequently metastasizes to the liver, where treatment options are limited and prognosis is poor. The cellular and molecular composition of the metastatic tumor microenvironment (TME) — and how it differs from the primary tumor — has been difficult to characterize in detail using bulk profiling approaches.
Methods: The research team applied 10x Genomics scRNA-seq on matched primary PDAC and liver metastasis specimens from four treatment-naive patients (eight samples total). Fresh tissue was dissociated into single-cell suspensions within 30 minutes of surgical resection and processed on the 10x platform. After Cell Ranger preprocessing, data were analyzed with Seurat for clustering and cell-type annotation, RNA velocity (scVelo) for trajectory analysis, and Monocle2 for pseudotime inference. Cell–cell communication was profiled with CellPhoneDB.
Results: scRNA-seq identified major cell populations including epithelial tumor cells, T cells, B cells, myeloid cells, mast cells, and fibroblasts across both primary and metastatic sites. The liver metastasis TME was characterized by an enrichment of RGS5+ cancer-associated fibroblasts (CAFs), CCL18+ lipid-associated macrophages, S100A8+ neutrophils, and FOXP3+ regulatory T cells. Cell–cell communication analysis revealed a marked loss of tumor–immune interaction strength in metastases compared with primary tumors. Trajectory analysis delineated evolutionary paths from primary tumor cells to metastatic phenotypes.
Conclusion: This study demonstrates how 10x single-cell RNA-seq can dissect the cellular and molecular reorganization that accompanies PDAC liver metastasis. The identification of specific immunosuppressive cell populations and disrupted tumor–immune crosstalk in metastatic lesions illustrates the type of mechanistic insight that scRNA-seq can provide for target and biomarker research.
10x GEM-X 3' vs. Other Single-Cell RNA-Seq Approaches
Choosing the appropriate scRNA-seq platform depends on sample type, cell number, transcript coverage requirements, and budget. The table below summarizes key differences to guide your decision.
| Dimension | 10x GEM-X 3' v4 (Chromium X) | BD Rhapsody scWTA | Smart-seq2 / Smart-seq3 | Parse Biosciences (Split-pool) |
|---|---|---|---|---|
| Capture method | Droplet-based (GEM-X) | Microwell-based | Plate-based (FACS-sorted cells) | Split-pool combinatorial barcoding |
| Transcript coverage | 3' end (polyA-enriched) | 3' end (polyA-enriched) | Full-length cDNA | 3' end (polyA-enriched) |
| Throughput per sample | 500–20,000 cells | 100–10,000 cells | 96–384 cells per plate | Up to 100,000+ cells |
| Isoform / splice variant detection | Not supported | Not supported | Supported | Not supported |
| Fixed / FFPE compatibility | GEM-X Flex (probe-based; separate workflow) | BD Flex (probe-based; separate workflow) | Not designed for fixed samples | Fixed cell kit available |
| Best suited for | Large cell-number surveys, cell atlas, differential expression, TME profiling | Immune-focused panels, targeted protein + mRNA co-detection | Low cell number, deep per-cell transcriptome, isoform discovery | Very large cell-number surveys, pilot atlas projects |
How to choose:
- If your goal is to survey cell-type diversity, identify differentially expressed genes, and profile tissue heterogeneity across thousands of cells, 10x GEM-X 3' on Chromium X is the most established and widely published approach.
- For projects requiring paired full-length isoform information or alternative splicing analysis, consider supplementing 3' scRNA-seq with a full-length transcriptome approach on a subset of samples (contact our team for availability).
- If your sample is FFPE or fixed and you want gene expression at single-cell resolution, the GEM-X Flex option provides a compatible workflow that does not require fresh tissue processing.
- For simultaneous protein and mRNA detection from the same single cells, BD Rhapsody AbSeq or CITE-seq on 10x may be more appropriate; contact us to discuss multimodal experimental design.
Why Choose CD Genomics
10x platform expertise
Our team has processed thousands of single-cell samples across diverse tissue types, species, and experimental designs on the 10x Chromium platform. Each project is overseen by a dedicated project scientist who reviews sample feasibility, protocol fit, and analysis design before work begins.
GEM-X 3' v4 and Flex under one roof
The Chromium X platform supports both standard 3' GEM-X v4 and GEM-X Flex (fixed/FFPE) chemistry, allowing consistent sample handling and analysis across fresh and archived specimens within a single study.
Analysis depth, not just pipeline execution
Every project includes a detailed QC and analysis summary report with annotated figures, cluster marker tables, and enrichment results. Optional advanced modules — trajectory analysis, cell–cell communication, regulatory network inference — are executed by a computational biology team that understands the biology behind the tools.
Sample-aware feasibility review
We assess sample type, tissue source, viability, and project goals before library construction — and communicate feasibility concerns transparently rather than processing every sample regardless of quality.
Publication-track record
CD Genomics has supported scRNA-seq projects published in peer-reviewed journals, with methods documentation and analysis scripts provided to facilitate manuscript preparation. For more information about our capabilities, visit our about us page.
Frequently Asked Questions
References
- Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nature Communications. 2017;8:14049. DOI: 10.1038/ncomms14049.
- 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.
- 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.
- Chen K, Wang Q, Liu X, Tian X, Dong A, Yang Y. Immune profiling and prognostic model of pancreatic cancer using quantitative pathology and single-cell RNA sequencing. Journal of Translational Medicine. 2023;21:210. DOI: 10.1186/s12967-023-04051-4.
- Heumos L, Schaar AC, Lance C, et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics. 2023;24(8):550-572. DOI: 10.1038/s41576-023-00586-w.
- Zhang S, Fang W, Zhou S, et al. Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis. Nature Communications. 2023;14:5123. DOI: 10.1038/s41467-023-40727-7.
- Sun Y, Wu L, Zhong Y, et al. Single-cell RNA sequencing reveals cellular heterogeneity and immune microenvironment in clear cell renal cell carcinoma. Journal of Translational Medicine. 2025;23:155. PubMed: 41088302.
