Tetramer Immunization and Selection Followed by CELLISA Screening to Generate Monoclonal Antibodies against the Mouse Cytomegalovirus m12 Immunoevasin
Bibliographic record
Abstract
The generation of reliable mAb of unique and desired specificities serves as a valuable technology to study protein expression and function. However, standard approaches to mAb generation usually involve large-scale protein purification and intensive screening. In this study, we describe an optimized high-throughput proof-of-principle method for the expanded generation, enrichment, and screening of mouse hybridomas secreting mAb specific for a protein of interest. Briefly, we demonstrate that small amounts of a biotinylated protein of interest can be used to generate tetramers for use as prime-boost immunogens, followed by selective enrichment of Ag-specific B cells by magnetic sorting using the same tetramers prior to hybridoma generation. This serves two purposes: 1) to effectively expand both low- and high-affinity B cells specific for the antigenic bait during immunization and 2) to minimize subsequent laborious hybridoma efforts by positive selection of Ag-specific, Ab-secreting cells prior to hybridoma fusion and validation screening. Finally, we employ a rapid and inexpensive screening technology, CELLISA, a high-throughput validation method that uses a chimeric Ag fused to the CD3ζ signaling domain expressed on enzyme-generating reporter cells; these reporters can detect specific mAb in hybridoma supernatants via plate-bound Ab-capture arrays, thereby easing screening. Using this strategy, we generated and characterized novel mouse mAb specific for a viral immunoevasin, the mouse CMV m12 protein, and suggest that these mAb may protect mice from CMV infection via passive immunity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".