Broadly reactive anti-VHH antibodies for characterizing, blocking, or activating nanobody-based CAR-T cells
Bibliographic record
Abstract
Background: Production of chimeric antigen receptor T cell (CAR-T) therapies depends on antibody reagents to label, isolate, and expand T cell products. We sought to create antibody tools specific for the variable domain of heavy-chain only antibodies (VHHs), also known as nanobodies, used in some CARs. Methods: We immunized a mouse with VHH and selected two murine monoclonal antibodies (mAbs) that bind to distinct epitopes in conserved framework regions of llama-derived VHHs, and not to human VH domains. Anti-VHH mAbs were characterized by enzyme-linked immunosorbent assay, surface plasmon resonance, and hydrogen-deuterium exchange mass spectrometry; were then tested for cell/tissue labeling and for modulating cellular activity in VHH-CAR-T cells. Results: We produced a high-quality dual-clonal anti-VHH antibody product and confirmed reactivity to over 98% of VHH proteins regardless of their antigenic specificity, with no reactivity to human or mouse IgG and reduced reactivity to conventional llama or alpaca IgG. Anti-VHH binding did not disrupt VHH/antigen interaction, and thus was appropriate for secondary labeling to assess cellular or tissue reactivity of VHH molecules. Despite not interfering with antigen binding, anti-VHH antibodies (Abs) potently blocked VHH-CAR-T activation and cytolytic killing of target cells. When immobilized, anti-VHH Abs induced strong activation and expansion of VHH CAR-T cells; with 730-fold mean expansion, >94% CAR purity, and retained CD8/CD4 heterogeneity. Functionally, anti-VHH antibody-expanded CAR-T cells maintained strong antigen-specific activity without functional exhaustion. Conclusions: Overall, these data identify useful anti-VHH mAbs that can be applied to better understand and manipulate VHH-based CAR-T cells or other VHH-based immunotherapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".