CellCelector™ as a platform in isolating primary B cells for antibody discovery
Bibliographic record
Abstract
The most robust strategy in antibody discovery is the use of immunized animals and the ability to isolate and immortalize immune B-cells to hybridoma for further interrogation. However, capturing the full repertoire of an immunized animal is labor intensive, time consuming and limited in throughput. Therefore, techniques to directly mine the antibody repertoire of primary B-cells are of great importance in antibody discovery. In the current study, we present a method to isolate individual antigen-specific primary B-cells using the CellCellector™ single-cell isolation platform from XenoMouse® (XM) immunized with a recombinant therapeutic protein, EGFR. We screened a subset of CD138+ B-cells and identified 238 potential EGFR-specific B-cells from 1189 antibody-secreting cells (ASCs) and isolated 94 by CellCellector. We identified a diverse set of heavy chain complementarity-determining region sequences and cloned and expressed 20 into a standard human immunoglobulin G1 antibody format. We further characterized and identified 13 recombinant antibodies that engage soluble and native forms of EGFR. By extrapolating the method to all 400 000 CD138+ B-cells extracted from one EGFR immunized XM, a potential 1196 unique EGFR-specific antibodies could be discovered. CellCelector allows for interrogating the B-cell pool directly and isolating B-cells specific to the therapeutic target of interest. Furthermore, antibody sequences recovered from isolated B-cells engage the native and recombinant target, demonstrating the CellCellector can serve as a platform in antibody discovery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".