Rapid single B cell antibody discovery using nanopens and structured light
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it