Rapid single B cell antibody discovery using nanopens and structured light
Bibliographic record
Abstract
Accelerated development of monoclonal antibody (mAb) tool reagents is an essential requirement for the successful advancement of therapeutic antibodies in today's fast-paced and competitive drug development marketplace. Here, we describe a direct, flexible, and rapid nanofluidic optoelectronic single B lymphocyte antibody screening technique (NanOBlast) applied to the generation of anti-idiotypic reagent antibodies. Selectively enriched, antigen-experienced murine antibody secreting cells (ASCs) were harvested from spleen and lymph nodes. Subsequently, secreted mAbs from individually isolated, single ASCs were screened directly using a novel, integrated, high-content culture, and assay platform capable of manipulating living cells within microfluidic chip nanopens using structured light. Single-cell polymerase chain reaction-based molecular recovery on select anti-idiotypic ASCs followed by recombinant IgG expression and enzyme-linked immunosorbent assay (ELISA) characterization resulted in the recovery and identification of a diverse and high-affinity panel of anti-idiotypic reagent mAbs. Combinatorial ELISA screening identified both capture and detection mAbs, and enabled the development of a sensitive and highly specific ligand binding assay capable of quantifying free therapeutic IgG molecules directly from human patient serum, thereby facilitating important drug development decision-making. The ASC import, screening, and export discovery workflow on the chip was completed within 5 h, while the overall discovery workflow from immunization to recombinantly expressed IgG was completed in under 60 days.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".