Immune repertoire sequencing is moving fast. In 2025, labs link TCR and BCR sequences to cell state, location, and function at once. This shift turns static lists of CDR3s into living maps of immunity. For clarity: this article covers immune repertoire sequencing trends, with deep dives into TCR sequencing and BCR sequencing.

1) The State of Immune Repertoire Sequencing in 2025

Immune repertoire sequencing is changing fast. Today, most labs still run bulk TCRβ or IGH studies with short reads. However, long-read and single-cell approaches are now common in discovery projects. Just as important, communities have agreed on clear reporting rules. Those rules make it easier to compare data across labs and across time.

Because of these shifts, research teams can ask sharper questions. For example, you can now link a clone to its state and its function. You can also track that clone across time or tissue sites. This turns "lists of CDR3s" into real biological stories.

In plain terms, three forces drive progress:

• Resolution: full-length receptor reads, better chain pairing, and cleaner lineage trees.
• Context: pairing receptors with cell state and, more often, tissue location.
• Reproducibility: stronger reporting standards and better error control.

Key idea: depth, context, and standards move results from interesting to decision-ready in research settings.

2) Why These Trends Matter for Your Projects

These trends ease three pains that many projects face.

1. From counts to biology. Counting clonotypes is only the start. When you connect TCRs or BCRs to gene programs or surface proteins, you learn which clones are active, tired, or memory-like. This context supports clearer research claims.

2. From noise to truth. Strand-consensus methods reduce PCR and sequencing artifacts. They work by confirming true events across both DNA strands and by building read-family consensus. This is helpful in studies with rare events or heavy mutation loads.

3. From islands to ecosystems. Data standards and shared databases speed up interpretation. For example, VDJdb links many TCRs to known antigens. Observed Antibody Space (OAS) aggregates huge sets of antibody sequences for mining and benchmarking.

3) Platform Snapshot: Short-Read, Long-Read, and Single-Cell

Short-read bulk

Short reads dominate large cohorts. They are cost-effective and well supported by mature tools such as MiXCR for assignment and Change-O for clonal analysis. However, chain pairing is indirect, and heavy mutation in BCRs can stress short amplicons.

Long-read

Long-read workflows now capture near full-length V(D)J and constant regions. This helps with isotype usage, splice forms, and clean lineage trees. A 2024–2025 Clinical Chemistry study (FLIRseq) showed a rolling-circle plus nanopore design that profiles full-length immune receptor transcripts with strong accuracy.

Analysis of the adaptive immune receptor repertoires by FLIRseq. (Luo, X. et al., 2025)

Single-cell multi-omics

Single-cell assays return paired chains and cell states at once. CITE-seq adds protein markers to each cell. TEA-seq goes further by capturing RNA, surface proteins, and chromatin in the same cell. These designs shift the focus from "who is there" to "what they are doing."

Simultaneous profiling of chromatin accessibility and cell surface epitopes. (Swanson et al., 2021, eLife)

How to choose

• Want broad surveys at low cost? Use bulk short-read.
• Need full-length views and isotypes? Use long-read.
• Need function and phenotype per clone? Use single-cell multi-omics.

4) Multi-Omics: Pairing V(D)J with Transcriptomics or Chromatin

Linking receptors to cell state is a major step forward. With CITE-seq, you add tagged antibodies and capture protein markers together with RNA. With TEA-seq, you capture RNA, protein, and chromatin features at once. As a result, you can connect a clone to its activation state, memory status, or exhaustion signature, all in one run.

Planning tip: start with the smallest design that answers your question. If budget is fixed, it is better to reduce the number of conditions than to under-sequence each cell. This will preserve statistical power and clarity.

5) Quality and Reporting Standards You Should Expect

Quality begins at study design. The AIRR Community's recommendations define what to record and how to share. They cover sample metadata, library details, chain assignment, and outputs. Following these guidelines helps future reviewers and collaborators reuse your data with confidence.

Library construction and error control

• Use per-molecule tags to group reads derived from the same starting molecule.
• Consider two-strand consensus logic when you need very low error rates (for example, in heavy mutation settings).

Analysis and interpretation

• Use maintained tools with clear versioning. MiXCR is a common choice for V(D)J assignment; Change-O supports clonal clustering and lineage analysis.
• To enrich interpretation, cross-reference public resources: VDJdb for TCR–antigen links and OAS for antibody sequences.

Reporting

• Deliver AIRR-compliant tables where possible.
• Include pipeline parameters, germline references, and versions.
• When appropriate, deposit to community portals to aid reuse.

6) Oncology Focus: What TCR Data Can Reveal in Research

Repertoire studies in tumor research show two clear patterns.

First, in melanoma, the main T cell clones after therapy are often new. They are not the same clones that dominated before treatment. Researchers call this clonal replacement. This shift was revealed by single-cell RNA-seq paired with TCR data.

Second, in lung cancer research, single-cell tracing revealed clonal revival. After PD-1–based therapy, precursor exhausted T cells expanded in responders, while non-responders did not show this pattern. The study used temporal single-cell RNA-seq and paired TCR tracking across biopsies.

What this means for research teams: track clones across time and sites, and pair TCRs with state markers. This approach can reveal response patterns and failure modes in research models and exploratory human studies (again, not for clinical care).

Characterization of T cells before and during treatment. (Liu, B et al., 2021)

7) B-Cell Focus: From Full-Length Repertoires to Antigen Linking

Full-length BCR profiling

Long-read designs help recover near full-length IG transcripts. They support isotype usage, splice forms, and cleaner lineage trees. FLIRseq is one recent example that uses rolling-circle enrichment plus nanopore sequencing to capture full-length immune receptor transcripts with high accuracy.

Validation of FLIRseq by comparison with BCR/TCR-seq. (Luo, X. et al., 2025)

Antigen-aware discovery

LIBRA-seq links single-cell BCR sequences to specific antigens using DNA-barcoded antigen panels. The original paper mapped thousands of B cells and confirmed predicted specificities, including new broadly neutralizing HIV antibodies.

When to use which

• Need rapid screening across many antigens? Choose antigen-barcoded single-cell assays.
• Need SHM and class-switch detail? Choose long-read designs with read-family consensus.
• Need cell-state context? Choose single-cell V(D)J paired with transcriptomes and proteins.

8) Quick Decision Guide: Choose the Right Modality

Goal: discover tumor-reactive TCRs in a mouse model

• Design: single-cell TCR + scRNA, optional protein tags; include pre/post time points.
• Why: captures paired chains and activation programs; tracks clonal expansion.

Goal: map B cell maturation after antigen exposure in a macaque study

• Design: long-read BCR with molecule-level tagging; sample nodes and blood over time.
• Why: resolves SHM patterns, class switching, and lineage structure.

Goal: survey immune diversity across a large cohort

• Design: bulk short-read TCRβ or IGH with strong error control and full metadata.
• Why: cost-effective breadth and cross-study comparability.

9) Common Pitfalls and How to Avoid Them

Pitfall 1: Inflated clones due to over-amplification

• Fix: limit PCR cycles, use per-molecule tagging, and build read-family consensus to remove artifacts.

Pitfall 2: Guessing chain pairs in bulk data

• Fix: avoid pairing assumptions. If pairing matters, use single-cell V(D)J.

Pitfall 3: Heavy mutation confuses alignment

• Fix: update germline references and use SHM-aware clonal clustering.

Pitfall 4: Weak metadata blocks reuse

• Fix: follow AIRR guidance and record sample handling, primer scheme, and pipeline versions.

Pitfall 5: Under-powered multi-omics

• Fix: right-size cells per group and simplify conditions. It is better to do one thing well than three things poorly.

10) Case Studies You Can Cite in Proposals

• Checkpoint blockade and clonal replacement (melanoma). Single-cell RNA-seq with paired TCRs showed that post-therapy clones were often newly recruited, not reinvigorated, clones.
• Clonal revival during PD-1–based therapy (NSCLC). Temporal single-cell tracing across biopsies found expansion of precursor exhausted T cells in responders.
• Vaccine monitoring with longitudinal TCRβ. Repeated sampling after pneumococcal conjugate vaccination detected modest expansions in a subset of donors and defined practical thresholds for real biological change.
• Antigen-aware BCR discovery. LIBRA-seq mapped BCRs to antigens at scale and confirmed predicted specificities, enabling faster down-selection.
• Field overviews for planning. For a broad 2024 overview of AIRR analysis challenges and methods, see the Nature Reviews Methods Primers article.

11) Practical Checklist for Your Next Study

Scope and question

• Write your primary readout in one sentence. For example: "Track clonal expansion after booster."
• Decide if you must know chain pairs and cell states. This alone may justify single-cell designs.

Sampling and controls

• Include time points if you expect change over time.
• Balance tissues and blood where relevant.
• Add technical controls and, when possible, synthetic standards or spike-ins.

Assay choice and depth

• Bulk short-read for breadth; long-read for full-length detail; single-cell for pairing and state.
• Size libraries to expected diversity. Under-sequencing is the most common cause of weak signals.

Analysis and reporting

• Pre-register the main metrics: diversity, evenness, clonal expansion rules, lineage criteria.
• Use maintained tools and log versions.
• Deliver tables that match AIRR fields.
• Cross-check results with VDJdb or OAS when appropriate.

12) How We Can Help

If you are planning TCR sequencing or BCR sequencing for discovery research, we can design the full path:

• Study design. We align platform choice to your biological question and budget.
• Wet-lab execution. Bulk, long-read, or paired-chain V(D)J workflows with read-family consensus for error suppression and accurate clone/chain pairing.
• Multi-omics options. Add RNA, surface proteins, or chromatin for richer interpretation.
• Bioinformatics. Clonotyping, lineage trees, mutation analysis, and AIRR-compatible deliverables.
• Delivery. Clear figures and a brief research report for internal review.

Next steps:

• Learn more: TCR Sequencing: New Advances in Tumor Immunotherapy
• Compare options: Comparing the Three Platforms for Immune Repertoire Sequencing
• Plan your experiment: Immune Repertoire Sequencing: Methodologies and Experimental Design
• Go multi-omics: Integrating Immune Repertoire Sequencing with Transcriptomics and Epigenetics
• Or simply contact us to start your project.