Generation of cell type-specific monoclonal antibodies for the planarian and optimization of sample processing for immunolabeling
Bibliographic record
Abstract
BACKGROUND: Efforts to elucidate the cellular and molecular mechanisms of regeneration have required the application of methods to detect specific cell types and tissues in a growing cohort of experimental animal models. For example, in the planarian Schmidtea mediterranea, substantial improvements to nucleic acid hybridization and electron microscopy protocols have facilitated the visualization of regenerative events at the cellular level. By contrast, immunological resources have been slower to emerge. Specifically, the repertoire of antibodies recognizing planarian antigens remains limited, and a more systematic approach is needed to evaluate the effects of processing steps required during sample preparation for immunolabeling. RESULTS: To address these issues and to facilitate studies of planarian digestive system regeneration, we conducted a monoclonal antibody (mAb) screen using phagocytic intestinal cells purified from the digestive tracts of living planarians as immunogens. This approach yielded ten antibodies that recognized intestinal epitopes, as well as markers for the central nervous system, musculature, secretory cells, and epidermis. In order to improve signal intensity and reduce non-specific background for a subset of mAbs, we evaluated the effects of fixation and other steps during sample processing. We found that fixative choice, treatments to remove mucus and bleach pigment, as well as methods for tissue permeabilization and antigen retrieval profoundly influenced labeling by individual antibodies. These experiments led to the development of a step-by-step workflow for determining optimal specimen preparation for labeling whole planarians as well as unbleached histological sections. CONCLUSIONS: We generated a collection of monoclonal antibodies recognizing the planarian intestine and other tissues; these antibodies will facilitate studies of planarian tissue morphogenesis. We also developed a protocol for optimizing specimen processing that will accelerate future efforts to generate planarian-specific antibodies, and to extend functional genetic studies of regeneration to post-transcriptional aspects of gene expression, such as protein localization or modification. Our efforts demonstrate the importance of systematically testing multiple approaches to species-specific idiosyncracies, such as mucus removal and pigment bleaching, and may serve as a template for the development of immunological resources in other emerging model organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".