A Modified Bleaching Method for Multiplex Immunofluorescence Staining of FFPE Tissue Sections
Bibliographic record
Abstract
Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".