Fluorescence ImmunoPrecipitation (FLIP): a Novel Assay for High-Throughput IP
Bibliographic record
Abstract
BACKGROUND: The immunoprecipitation (IP) assay is a valuable molecular biology tool applied across a breadth of fields. The standard assay couples IP to immunoblotting (IP/IB), a procedure severely limited as it is not easily scaled for high-throughput analysis. RESULTS: Here we describe and characterize a new methodology for fast and reliable evaluation of an immunoprecipitation reaction. FLIP (FLuorescence IP) relies on the expression of the target protein as a chromophore-tagged protein and couples IP with the measurement of fluorescent signal coating agarose beads. We show here that FLIP displays similar sensitivity to the standard IP/IB procedure but is amenable to high-throughput analysis. We applied FLIP to the screening of mouse monoclonal antibodies of unknown behavior in IP procedures. The parallel analysis of the considered antibodies using FLIP and IP/western shows good correlation between the two procedures. We also show application of FLIP using unpurified antibodies (hybridoma supernatant) and we developed a publicly available tool for the easy analysis and quantification of FLIP signals. CONCLUSIONS: Altogether, our characterizations of this new methodology show that FLIP is an appealing and reliable tool for any application of high-throughput IP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".