By EVOBYTE Your partner in bioinformatics
Introduction
Every effective vaccine leaves a molecular footprint in our immune system. You can see it in antibodies that neutralize pathogens, and you can see it in T cells that coordinate clearance and memory. Today, immunoreceptor sequencing—often called AIRR‑seq—lets us read that footprint directly by profiling B‑cell receptors (BCRs) and T‑cell receptors (TCRs) at scale. When we measure how specific clonotypes expand, mutate, and persist, we gain hard evidence about which antigens work, which doses matter, and how long protection might last. That is the promise of immunoreceptor analysis for modern vaccine development.
From antigen to antibody: how BCRs evolve after vaccination
B cells recognize antigen using BCRs generated through V(D)J recombination. After vaccination, activated B cells enter germinal centers where they undergo somatic hypermutation (SHM) and selection, refining affinity and specificity. Long‑lived plasma cells and memory B cells emerge from this process and sustain humoral immunity.
Work in humans shows how pronounced this can be. Following mRNA vaccination, germinal center B cells targeting spike persist for months, with increasing SHM and rising antibody avidity—direct evidence of ongoing maturation that underpins durable neutralization. These clonal lineages can be tracked from early plasmablasts to memory B cells and bone‑marrow plasma cells using BCR sequencing and single‑cell approaches.
Influenza studies offer a complementary view: most measurable BCR evolution is concentrated in germinal centers rather than blood, so bulk blood sampling alone can miss important maturation. When lymph node aspirates are included, vaccine‑binding B cell lineages clearly continue to evolve, revealing why pairing anatomy‑aware sampling with repertoire sequencing sharpens our understanding of vaccine performance.
TCR clonotypes and vaccine response dynamics
If B cells are the antibody factory, T cells are the factory foremen. TCRs, particularly CD8+ T cells, expand in response to vaccination and then contract to memory, with distinct waves of clonotypes dominating over time. Longitudinal TCR sequencing after mRNA vaccination shows early responders that surge after the first dose, main responders that peak after the second, and additional waves after boosting—together reshaping clonal dominance and diversity. These shifts are visible at clonotype resolution and help explain how boosters refresh cellular immunity.
Two repertoire metrics often used to summarize these patterns are clonal breadth and clonal depth. Breadth reflects how many distinct vaccine‑associated clonotypes are present, while depth captures how much they expanded. Both can correlate with functional readouts such as neutralizing antibody titers or clinical outcomes, making them attractive biomarkers for immunogenicity.
AIRR‑seq in practice: standards, study designs, and tooling
AIRR‑seq spans bulk and single‑cell modalities, each with trade‑offs. Bulk repertoire sequencing from genomic DNA or RNA provides deep, quantitative counts of CDR3‑defined clonotypes across the repertoire. Unique molecular identifiers (UMIs) and synthetic controls reduce amplification bias and enable accurate frequency estimates. Single‑cell V(D)J sequencing pairs receptor chains and links clonal identity to phenotype and transcriptional state, letting you observe, for example, whether expanded BCR clonotypes correspond to plasmablasts or memory subsets.
To make these data reproducible and comparable, the AIRR Community’s MiAIRR standard defines the minimal metadata to report, along with schemas and APIs that underpin the AIRR Data Commons. Following these conventions pays off later, when you want to benchmark your vaccine candidate against public datasets or reuse analysis pipelines across studies.
On the software side, established pipelines handle alignment, clonotyping, and export. For bulk data, tools like MiXCR produce per‑sample tables with clone counts and clone fractions ready for downstream analysis. For single‑cell data, Cell Ranger V(D)J groups cells into clonotypes while accounting for SHM in BCRs; packages such as scirpy in Python add convenient repertoire analytics on top of Scanpy.
Measuring receptor frequencies and clonal change
At the core of immunoreceptor analysis is frequency: how much of the repertoire a clonotype occupies. In bulk AIRR‑seq, frequency is typically the clone’s productive template count normalized by the total productive templates in the sample. Because sequencing depth and sampling vary, comparing time points benefits from diversity‑aware metrics and replication. In TCR studies, clonal depth (summed log2 of template counts for vaccine‑assigned clonotypes, normalized by total templates) summarizes expansion, while breadth captures the fraction of unique clonotypes assigned to the antigen.
Here’s a compact example that reads a MiXCR clones table and computes basic statistics for a pre/post‑vaccine pair:
import pandas as pd
pre = pd.read_csv("pre_vax_clones.tsv", sep="\t")
post = pd.read_csv("day14_clones.tsv", sep="\t")
def summarize(df):
total = df["cloneCount"].sum()
df["freq"] = df["cloneCount"] / total
top10 = df.nlargest(10, "freq")["freq"].sum()
gini = 1 - (df["freq"]**2).sum() # Simpson index proxy
return pd.Series({"top10_freq": top10, "simpson_div": gini})
summary = pd.concat({"pre": summarize(pre), "post": summarize(post)}, axis=1)
print(summary)
For single‑cell V(D)J, you can flag expanded clonotypes and relate them to phenotypes in an AnnData object:
import scirpy as ir
# adata: single-cell object with V(D)J annotations
ir.tl.clonal_expansion(adata, target_col="clone_id", key_added="expansion")
# e.g., compare expansion between plasmablasts and memory B cells
MiXCR exposes cloneCount and cloneFraction out of the box, and 10x Genomics documents how Cell Ranger derives clonotypes and filters artifacts—details that matter when you use frequency as a quantitative endpoint.
From repertoire signals to vaccine decisions
Why does this level of granularity matter? Because immunoreceptor readouts tie directly to choices developers make.
When BCR lineages accumulate SHM and persist in memory, you gain confidence that an antigen is recruiting germinal center reactions rather than only transient plasmablast bursts. Conversely, if most expansion comes from pre‑existing, narrow memory without breadth, you may need to adjust antigen design to widen epitope coverage. Persistent germinal center activity after mRNA vaccination showed how extended maturation improves antibody avidity; repertoire tracking turned that biological intuition into measurable signal.
On the T cell side, shifts in clonotypic dominance across doses help you time boosters and select adjuvants. If clonal depth increases but breadth stays flat, you might be amplifying existing specificities without expanding coverage; if breadth rises, you are likely recruiting new clonotypes that can improve variant cross‑recognition. Designs that integrate peptide pools known to elicit broad TCR responses can be tested directly by tracking breadth and depth across trial arms.
Finally, standards matter for translation. When your AIRR‑seq outputs adhere to MiAIRR and your analysis computes transparent, shareable metrics, regulators and collaborators can reproduce your results. That shortens the path from an immunogenicity signal in phase 1 to a confident go/no‑go for phase 2.
Summary / Takeaways
Immunoreceptor sequencing gives vaccine developers a high‑resolution view of immunity in action. BCR sequencing reveals whether germinal centers are doing their job—driving SHM, affinity maturation, and durable memory—while TCR sequencing shows how cellular responses gain or lose clonal breadth and depth. With standards like MiAIRR and practical pipelines such as MiXCR and Cell Ranger, receptor frequencies and clonal dynamics become robust, quantitative endpoints. If you are designing or benchmarking a vaccine, start treating BCR and TCR repertoires as primary readouts, not just supporting data. The clones will tell you what is working.
Further Reading
- SARS‑CoV‑2 mRNA vaccines induce persistent human germinal centre responses (Nature, 2021)
- CD8+ T cell clonal dynamics after mRNA vaccination (clonal shifts across doses)
- AIRR Community MiAIRR standard and data commons resources
- MiXCR export documentation (cloneCount, cloneFraction, UMIs)
- 10x Genomics Cell Ranger V(D)J clonotyping algorithm overview