A reproducible technique for specific labeling of antigens using preformed fluorescent molecular IgG‐F(ab′)<sub>2</sub> complexes from primary antibodies of the same species
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
Immunolabeling two different antigens using the indirect approach with antibodies from the same species is not possible as secondary antibodies can bind to either primary target antibodies. In this study, we describe how preformed complexes of primary and secondary labeled antibodies can be used in such circumstances. In this situation, the first antigen is labeled using the conventional indirect method followed by incubation with the preformed primary-secondary antibody complex against the second antigen. To prevent unbound secondary antibody from binding the indirectly-labeled antibodies, resulting in a false positive, we quenched excess secondary antibody with nonimmune murine serum from the species of the primary antibody. Before the formation of the preformed complex, the optimum dilution of both primary and secondary antibodies was determined. Once these concentrations were established, the concentration of nonimmune murine serum required to quench excess unbound secondary was determined. This step was accomplished by first incubating the sample with an antibody against an antigen known to be localized away from the antigen of interest, followed by the preformed complex. If specific staining was seen, other than that expected from the preformed complex, then the concentration of the serum was deemed insufficient for quenching, and increased accordingly. We demonstrate that this approach is successful in determining the optimum conditions for the preformation of ascites and purified monoclonal primary IgG with fluorescently conjugated F(ab')(2). Double immunolabelling of two focal adhesion antigens and two cytoskeletal proteins, with two murine primary antibodies, are presented as examples of the methodology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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