A Simple and Efficient Genetic Immunization Protocol for the Production of Highly Specific Polyclonal and Monoclonal Antibodies against the Native Form of Mammalian Proteins
Bibliographic record
Abstract
We have generated polyclonal and monoclonal antibodies by genetic immunization over the last two decades. In this paper, we present our most successful methodology acquired over these years and present the animals in which we obtained the highest rates of success. The technique presented is convenient, easy, affordable, and generates antibodies against mammalian proteins in their native form. This protocol requires neither expensive equipment, such as a gene gun, nor sophisticated techniques such as the conjugation of gold microspheres, electroporation, or surgery to inject in lymph nodes. The protocol presented uses simply the purified plasmid expressing the protein of interest under a strong promoter, which is injected at intramuscular and intradermal sites. This technique was tested in five species. Guinea pigs were the animals of choice for the production of polyclonal antibodies. Monoclonal antibodies could be generated in mice by giving, as a last injection, a suspension of transfected cells. The antibodies detected their antigens in their native forms. They were highly specific with very low non-specific background levels, as assessed by immune-blots, immunocytochemistry, immunohistochemistry and flow cytometry. We present herein a detailed and simple procedure to successfully raise specific antibodies against native proteins.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".