The immune system is both a guardian against disease and a key player in cancer progression, autoimmunity, and infection. Understanding how immune cells develop, communicate, and malfunction at the molecular level requires powerful computational tools and deep biological expertise. At BioinformaticsNext, we provide expert bioinformatics support for immunology and immuno-oncology research — from immune cell characterisation and repertoire profiling to tumour microenvironment analysis and checkpoint biology.
Bioinformatics for Immunology & Immuno-Oncology
End-to-end immune bioinformatics — from single cells to the tumour microenvironment.
Immunology research generates some of the most complex and multidimensional data in modern biology. Single-cell technologies now allow us to profile millions of individual immune cells, spatial transcriptomics preserves their tissue context, and multi-omics approaches reveal the regulatory networks that control immune identity and function.
Our Immunology & Immuno-Oncology service delivers end-to-end bioinformatics analysis from raw data processing to interpretable, publication-ready results across the full range of immune research applications.
What We Analyse
Comprehensive immune profiling across bulk, single-cell, spatial, and repertoire data.
- Immune cell population composition and transcriptional states at bulk and single-cell resolution
- T cell and B cell receptor (TCR / BCR) repertoires and clonal dynamics
- Tumour microenvironment (TME) immune infiltration and immune evasion mechanisms
- Checkpoint molecule expression including PD-1, PD-L1, CTLA-4, TIM-3, and LAG-3
- Cytokine and chemokine signalling networks and cell-cell communication
- Epigenetic regulation of immune cell identity and activation states
- HLA typing, neoantigen prediction, and antigen presentation pathway analysis
- Autoimmune and inflammatory disease molecular mechanisms
Our Immunology & Immuno-Oncology Services
Comprehensive bioinformatics support across the full spectrum of immunology and immuno-oncology data types.
All pipelines follow established best-practice guidelines and are fully version-controlled for reproducibility.
1. Bulk RNA-seq for Immune Profiling DEG · Deconvolution · Signatures
Bulk RNA sequencing remains a powerful and cost-effective approach for profiling immune gene expression across large cohorts. Our bulk RNA-seq immunology service provides robust differential expression analysis, immune deconvolution, and pathway-level interpretation.
- Alignment and quantification — STAR 2-pass alignment; Salmon / RSEM transcript-level quantification; gene-level count matrix generation with featureCounts
- Differential gene expression — DESeq2, edgeR, and limma-voom; immune-focused gene set enrichment with MSigDB immunology gene sets and KEGG immune pathways
- Immune cell deconvolution — Estimating cell-type proportions from bulk RNA-seq using CIBERSORT, TIMER2, quanTIseq, and EPIC; benchmarking against matched flow cytometry data where available
- Immune gene signature scoring — TIS (tumour inflammation signature), IFN-gamma signature, T cell exhaustion score, cytolytic activity score, and custom signature scoring using ssGSEA and GSVA
- Cytokine and chemokine pathway analysis — Differential expression of immune mediators; upstream regulator analysis and network reconstruction
2. Single-Cell RNA Sequencing for Immunology scRNA-seq · CITE-seq · 10x
Single-cell RNA sequencing has transformed immunology by enabling the unbiased discovery of immune cell subsets, activation states, and rare populations at unprecedented resolution. Our scRNA-seq immunology service covers the complete analytical workflow.
- Platform support — 10x Genomics Chromium (3 prime and 5 prime gene expression), Smart-seq2, and CITE-seq (protein and RNA co-measurement)
- Alignment & QC — Cell Ranger / STARsolo; ambient RNA removal (SoupX, DecontX); doublet detection (Scrublet, DoubletFinder); per-sample and per-batch QC metrics
- Immune cell clustering and annotation — UMAP / t-SNE; Louvain / Leiden clustering; SingleR with immune reference atlases (DICE, HCA Immune Cell Atlas) and manual curation
- Differential abundance testing — Milo and scCODA for identifying shifts in immune cell proportions between conditions, timepoints, or treatment groups
- Trajectory and activation state analysis — Monocle3, scVelo, and Palantir for T cell differentiation, exhaustion trajectories, and activation dynamics
- RNA velocity — Inferring the direction and kinetics of immune cell state transitions from spliced / unspliced RNA ratios
- CITE-seq analysis — Joint RNA and surface protein (ADT) analysis with Seurat WNN; protein-based cell-type annotation complementing transcriptomic clustering
- Cell-cell communication — CellChat, NicheNet, and LIANA for mapping ligand-receptor interactions across immune, stromal, and tumour cell populations
3. TCR and BCR Repertoire Analysis MiXCR · scRepertoire · VDJtools
The diversity and clonality of T cell receptor (TCR) and B cell receptor (BCR) repertoires reflects the history of antigen exposure and the magnitude of adaptive immune responses. Our immune repertoire analysis service provides deep characterisation of clonal dynamics from bulk and single-cell data.
- Bulk TCR / BCR sequencing — Amplicon-based immune repertoire sequencing analysis with MiXCR, IMGT/HighV-QUEST, and VDJtools
- Single-cell V(D)J sequencing — 10x Genomics Chromium immune profiling: Cell Ranger VDJ assembly; clonotype calling and annotation with scRepertoire and Immunarch
- Clonotype analysis — Clonal expansion, contraction, and persistence across timepoints; top clone tracking; diversity indices (Shannon, Simpson, D50)
- CDR3 sequence analysis — Length distribution, amino acid composition, physicochemical property profiling, and motif identification
- Paired chain analysis — Alpha-beta TCR and heavy-light BCR chain pairing from single-cell data; full receptor reconstruction
- Public clonotype identification — Sharing of clonotypes across donors or timepoints; database cross-referencing with VDJdb, McPAS-TCR, and IEDB
- Antigen specificity prediction — TCR-antigen interaction prediction using ERGO, NetTCR, and TITAN for neoantigen-reactive T cell identification
4. Tumour Microenvironment (TME) Profiling TILs · TAMs · Spatial
The tumour microenvironment is a complex ecosystem of cancer cells, immune infiltrates, stromal components, and soluble mediators that collectively determine whether a tumour will respond to immunotherapy. Our TME profiling service provides a comprehensive molecular portrait of the immune landscape within tumours.
- Immune infiltration scoring — TIMER, CIBERSORT-ABS, MCP-counter, and ESTIMATE for bulk RNA-seq-based immune cell quantification and tumour purity estimation
- Single-cell TME decomposition — scRNA-seq-based resolution of TILs, myeloid cells, cancer-associated fibroblasts (CAFs), endothelial cells, and malignant cells
- T cell exhaustion analysis — Identification of exhausted, progenitor-exhausted, and effector-like T cell states; TOX, TCF7, and checkpoint marker expression profiling
- Myeloid cell characterisation — Tumour-associated macrophage (TAM) polarisation (M1/M2-like); dendritic cell subtype identification; monocyte differentiation trajectories
- Spatial immune mapping — 10x Visium spatial transcriptomics for preserving tissue context of immune infiltrates; tertiary lymphoid structure (TLS) identification
- Immune exclusion and evasion mechanisms — Stromal barrier gene signatures; TGF-beta pathway activity; WNT/beta-catenin immune exclusion scoring
5. Checkpoint Biology and Immunotherapy Biomarkers TMB · MSI · Neoantigen · HLA
Immune checkpoint inhibitors have transformed cancer treatment, yet many patients do not respond. Identifying predictive biomarkers of response and resistance is one of the most pressing challenges in immuno-oncology. Our checkpoint and immunotherapy biomarker service provides the computational tools to address this challenge.
- Checkpoint molecule expression profiling — PD-1, PD-L1, CTLA-4, TIM-3, LAG-3, and TIGIT expression quantification across bulk and single-cell RNA-seq datasets
- Tumour mutational burden (TMB) — Genome-wide TMB calculation from WGS / WES data as a predictive biomarker for checkpoint inhibitor response
- Microsatellite instability (MSI) — MSI-H / MSS status determination; correlation with immune infiltration and immunotherapy eligibility
- Neoantigen prediction pipeline — Somatic variant calling to peptide generation to MHC class I and II binding prediction with NetMHCpan4.1 and MHCflurry; neoantigen burden and quality scoring
- HLA typing — HLA class I and II allele typing from WGS, WES, or RNA-seq using POLYSOLVER, OptiType, and HLA-HD; loss of heterozygosity (LOH) at the HLA locus
- Immunotherapy response signatures — IFN-gamma gene expression signature, T cell-inflamed GEP score, and published response / resistance gene signatures from clinical trial datasets
6. Epigenomics of Immune Cells ATAC · ChIP · scATAC · Methylation
Epigenetic regulation is central to immune cell identity, activation, and memory. Chromatin accessibility and DNA methylation define the gene regulatory programmes that drive T cell differentiation, B cell maturation, and macrophage polarisation.
- ATAC-seq — Open chromatin profiling in immune cells; peak calling (MACS3), differential accessibility between cell states, TF footprinting with TOBIAS, and chromatin accessibility-based cell-type annotation with ArchR
- ChIP-seq — Histone modification profiling (H3K27ac, H3K4me3, H3K27me3) in immune cells; enhancer landscape mapping and super-enhancer identification
- Single-cell ATAC-seq (scATAC-seq) — Chromatin accessibility at single-cell resolution; joint RNA + ATAC multi-modal analysis with Seurat WNN and ArchR
- DNA methylation — EPIC array and WGBS analysis; immune cell-type deconvolution from methylation profiles using EpiDISH and MethylCIBERSORT; DMR calling
7. Microarray and Legacy Immune Data Analysis Affymetrix · Illumina · Meta-analysis
Many immunology datasets, particularly from older clinical studies, exist as Affymetrix or Illumina microarray data. We provide expert re-analysis of legacy microarray datasets alongside integration with modern RNA-seq data.
- Microarray QC and normalisation — RMA, MAS5, and quantile normalisation; batch effect correction with ComBat and limma
- Differential expression analysis — limma-based testing with appropriate multiple testing correction; gene set enrichment against immune-focused gene sets
- Cross-platform integration — Harmonisation of microarray and RNA-seq data from GEO and ArrayExpress for meta-analysis and cross-cohort comparison
- Immune deconvolution from arrays — CIBERSORT and TIMER2 applied to microarray expression data for retrospective immune infiltration estimation
Key Applications
Research and clinical questions across immunology and immuno-oncology.
- Immune cell atlas construction for tissues and disease states
- Immunotherapy biomarker discovery and validation
- T cell exhaustion and dysfunction characterisation
- TCR / BCR repertoire dynamics in infection, cancer, and autoimmunity
- Neoantigen identification for personalised cancer vaccines
- Spatial mapping of immune infiltrates within tumours
- HLA typing and antigen presentation pathway analysis
- Autoimmune and inflammatory disease immune profiling
- Vaccine immunogenicity and immune response monitoring
- Immune correlates of protection in clinical trial samples
Our Analytical Workflow
A structured, reproducible process from initial data assessment to final interpreted report.
Step 1 — Project Scoping Free
We discuss your experimental design, sample types, data modalities, and research questions to define the most appropriate analytical strategy and deliverables.
Step 2 — Data Receipt & QC
Secure encrypted transfer of raw FASTQ, BAM, count matrices, or VCF files. Comprehensive QC reporting before analysis begins.
Step 3 — Pipeline Configuration
Version-controlled pipeline setup using Snakemake or Nextflow; tool selection matched to your data type and immunological research question.
Step 4 — Primary Analysis
Alignment, quantification, cell clustering, repertoire assembly, or peak calling as appropriate; all steps logged and reproducible.
Step 5 — Immunological Analysis
Immune cell deconvolution, differential abundance, checkpoint scoring, clonotype analysis, or TME characterisation depending on project type.
Step 6 — Visualisation
Publication-ready figures including UMAP plots, heatmaps, clonotype bubble charts, repertoire diversity plots, spatial immune maps, and Kaplan–Meier survival curves.
Step 7 — Interpretation & Report
Structured written report with methods, results, immunological interpretation, limitations, and suggested follow-up experiments.
Step 8 — Manuscript Support Optional
Methods section drafting, figure legends, supplementary table preparation, and response to peer-reviewer comments.
Tools & Technologies
Validated, peer-reviewed, and actively maintained tools across all immunology pipelines.
- scRNA-seq: Seurat, Scanpy, Cell Ranger, STARsolo
- Immune Deconvolution: CIBERSORT, TIMER2, quanTIseq, EPIC, MCP-counter
- TCR / BCR Repertoire: MiXCR, scRepertoire, Immunarch, VDJtools
- Trajectory Analysis: Monocle3, scVelo, Palantir
- Cell Communication: CellChat, NicheNet, LIANA
- Spatial Transcriptomics: SpaceRanger, Squidpy, cell2location
- HLA Typing: POLYSOLVER, OptiType, HLA-HD
- Neoantigen Prediction: NetMHCpan4.1, MHCflurry, pVACtools
- Epigenomics: MACS3, TOBIAS, ArchR, chromVAR, bismark
- Microarray: limma, affy, oligo, ComBat
- Differential Expression: DESeq2, edgeR, limma-voom
- Pathway and Signature Scoring: GSEA, GSVA, ssGSEA, clusterProfiler
- Visualisation: ggplot2, ComplexHeatmap, Seaborn, ggtree
- Workflow Management: Snakemake, Nextflow, CWL
Reference Databases We Use
All major immunology reference resources to contextualise your findings within the broader landscape of immune biology.
- Human Cell Atlas (HCA) — Reference single-cell immune atlases for cell-type annotation across tissues and disease states
- IEDB (Immune Epitope Database) — T cell and B cell epitope data for antigen specificity and neoantigen cross-referencing
- VDJdb and McPAS-TCR — Curated databases of TCR sequences with known antigen specificity for public clonotype identification
- IMGT — International ImMunoGeneTics information system for V(D)J gene nomenclature and reference sequences
- GTEx v8 — Tissue-specific gene expression for immune gene context and eQTL colocalisation
- GEO / ArrayExpress — Public immune transcriptomics datasets for meta-analysis, benchmarking, and cross-cohort comparison
- TCGA / GDC — Tumour immune infiltration data and immune gene expression across 33 cancer types
- MSigDB Immunology Gene Sets — C7 immunology collection for immune-focused gene set enrichment analysis
- IPD-IMGT/HLA — HLA allele sequences and frequency data for HLA typing and population genetics
Project Deliverables
A complete, structured set of outputs ready for publication, grant submission, or clinical reporting.
- Quality control report (MultiQC HTML + PDF) for all samples
- Processed data files: count matrices, aligned BAMs, clonotype tables
- Annotated results tables (TSV / Excel): DEGs, cell-type proportions, clonotypes, enriched pathways
- Publication-ready figures (PDF, SVG, PNG at 300 dpi)
- Full written report: methods, results, immunological interpretation, and recommendations
- Pipeline scripts and configuration files for full reproducibility
- Post-delivery consultation call for results walkthrough and Q&A
- Manuscript methods section and figure legends (journal-formatted)
- Supplementary data tables and extended figure sets
- Custom R Shiny interactive immune data explorer
- Neoantigen report formatted for clinical or vaccine development use
- Training and knowledge transfer sessions for your team
- Long-term retainer support for clinical trial immune monitoring projects
Why Choose BioinformaticsNext?
Immunology domain expertise, validated pipelines, and results that are accurate, reproducible, and publication-ready.
Immunology Domain Expertise
Our analysts have hands-on experience in T cell biology, tumour immunology, repertoire analysis, and immune epigenomics. We understand the biology behind the data.
End-to-End Service
From raw FASTQ to interpreted immune landscape report — every step handled in-house with no need for your own bioinformatics infrastructure.
Validated Pipelines
All workflows follow published best-practice guidelines. Every tool version is recorded and results are fully reproducible.
Fast Turnaround
Most projects are delivered within 2–4 weeks. Rush turnarounds are available for grant deadlines and trial reporting.
Flexible Engagement
Project-based, hourly, or long-term retainer arrangements with no minimum commitment. Scale to your timeline and budget.
Data Security
Encrypted data transfer and storage. NDAs and GDPR-compliant Data Processing Agreements available upon request.
Clinical Trial Support
Experienced in immune monitoring for oncology and infectious disease clinical trials; familiar with regulatory data handling requirements.
Global Reach
UK-headquartered with clients across Europe, North America, the Middle East, and Asia-Pacific.
Frequently Asked Questions
Common questions from immunology and immuno-oncology clients.
We work with a wide range of sample types including PBMCs, tumour-infiltrating lymphocytes (TILs), lymph node biopsies, bone marrow aspirates, bronchoalveolar lavage (BAL), and tissue-dissociated single-cell suspensions. We advise on optimal cell viability thresholds, storage conditions, and minimum cell numbers during the project scoping call.
Yes. When using 10x Genomics 5 prime gene expression with V(D)J enrichment, we jointly analyse transcriptomic and immune receptor data using scRepertoire and Seurat. This allows us to link clonal identity with transcriptional state — for example identifying the gene expression profile of expanded tumour-reactive T cell clones.
For 10x Genomics Chromium, a minimum of 1,000 high-quality cells per sample is generally recommended for meaningful clustering and cell-type annotation, though the optimal target depends on the expected cell-type diversity. We advise on cell number targets and sequencing depth requirements during the free project scoping consultation.
Yes. We regularly process data from both fresh and cryopreserved samples. Frozen samples may show increased ambient RNA and lower overall quality, which we account for with appropriate QC thresholds and ambient RNA correction using SoupX or DecontX.
Yes. We process 10x Visium spatial transcriptomics data from immune and tumour tissues, including immune cell-type deconvolution of spatial spots, spatially variable gene identification, and tertiary lymphoid structure (TLS) mapping. Spatial data can be integrated with matched scRNA-seq atlases for high-resolution cell-type positioning.
Absolutely. We assist with the bioinformatics and immune profiling sections of grant applications and can help design immune monitoring protocols for clinical trials including choice of assay, sample collection strategy, and analytical plan. Please contact us as early as possible in the planning process.
Related Research Areas & Services
Immunology and immuno-oncology intersects with multiple other research domains we support.
- Cancer & Oncogenomics — Somatic variant calling, TME profiling, neoantigen prediction, and multi-omics integration for oncology research
- Genetics & Genomics — HLA typing, germline immune gene variation, and genetic predisposition to autoimmune and inflammatory conditions
- Microbiology & Metagenomics — Host immune response to infection; microbiome-immune axis; pathogen-specific immune profiling
- Metabolism & Endocrinology — Immunometabolism; metabolic reprogramming of immune cells in tumours and inflammatory disease
- Custom Software & Pipeline Development — Bespoke immune monitoring dashboards, neoantigen reporting tools, and clinical trial data management pipelines
Ready to Advance Your Immunology Research?
Tell us about your samples, your data, and your research questions. Our immunology and immuno-oncology bioinformatics team will design a tailored analytical plan — typically within 48 hours of your enquiry. Whether you are profiling PBMCs from a clinical trial, mapping the tumour immune microenvironment, or characterising TCR repertoires in infection, we are here to support you from day one.
