CELLISA: Reporter Cell-Based Immunization and Screening of Hybridomas Specific for Cell Surface Antigens
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) specific for cell surface antigens are an invaluable tool to study immune receptor expression and function. Here, we outline a generalized reporter cell-based approach to the generation and high-throughput screening of mAbs specific for cell surface antigens. Termed CELLISA, this technology hinges upon the capture of hybridoma supernatants in mAb arrays that facilitate ligation of an antigen of interest displayed on BWZ reporter cells in the form of a CD3ζ-fusion chimeric antigen receptor (zCAR); in turn, specific mAb-mediated cross-linking of zCAR on BWZ cells results in the production of β-galactosidase enzyme (β-gal), which can be assayed colorimetrically. Importantly, the BWZ reporter cells bearing the zCAR of interest may be used for immunization as well as screening. In addition, serial immunizations employing additional zCAR- or native antigen-bearing cell lines can be used to increase the frequency of the desired antigen-specific hybridomas. Finally, the use of a cohort of epitope-tagged zCAR (e.g., zCAR(FLAG)) variants allows visualization of the cell surface antigen prior to immunization, and coimmunization using these variants can be used to enhance the immunogenicity of the target antigen. Employing the CELLISA strategy, we herein describe the generation of mAb directed against an uncharacterized natural killer cell receptor protein.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".