Protocol for the quantification of mouse fecal IgM, IgG, and IgA utilizing standard curve-based enzyme-linked immunosorbent assays
Bibliographic record
Abstract
Immunoglobulin (Ig) transport into the intestinal lumen is of interest as Igs modulate gut microbial species composition and play protective roles upon exposure to intestinal pathogens. Here, we present a protocol for the quantification of mouse intestinal Igs through enzyme-linked immunosorbent assay (ELISA) measurement. We describe procedures for isolating and processing feces from the whole mouse intestine. We then detail steps for the quantification of mouse fecal IgM, IgG, and IgA using the generated ELISA standard curves. For complete details on the use and execution of this protocol, please refer to Pioli et al. 1 • Details for the isolation and processing of mouse feces into fecal supernatants • Steps for the generation of IgM, IgG, and IgA ELISA standard curves • Guidance for the detection of fecal IgM, IgG, and IgA using ELISA methodology • Instructions for standard curve-based ELISA quantification of fecal IgM, IgG, and IgA Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Immunoglobulin (Ig) transport into the intestinal lumen is of interest as Igs modulate gut microbial species composition and play protective roles upon exposure to intestinal pathogens. Here, we present a protocol for the quantification of mouse intestinal Igs through enzyme-linked immunosorbent assay (ELISA) measurement. We describe procedures for isolating and processing feces from the whole mouse intestine. We then detail steps for the quantification of mouse fecal IgM, IgG, and IgA using the generated ELISA standard curves.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.041 |
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".