High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
Bibliographic record
Abstract
Highly multiplexed imaging enables the study of functionally diverse cells and their niches within their native tissue environments. Iterative Bleaching Extends Multiplexity (IBEX) is a cyclic immunolabeling and fluorophore inactivation technique that allows for multiple markers to be visualized on the same tissue section. Captured images can be subsequently analyzed to acquire single-cell data to define cell clusters, their localizations, and neighboring cell types. Interpreting these data relies on the ability to distinguish true marker expressions from sources of background inherent to fluorescence microscopy. Spectral IBEX, an adaptation of the IBEX protocol, integrates spectral confocal detection with computational unmixing and incorporates heparin blocking to reduce charge-based off-target binding. This combination improves the signal-to-background ratio, suppresses tissue autofluorescence, and minimizes bleed-through while also reducing acquisition time compared to conventional multi-track confocal imaging. Application to human nasal polyp tissue, a model characterized by high eosinophil content and strong autofluorescence, demonstrated reliable imaging of 26 markers across structural, immune, and cell state compartments over six imaging rounds. The resulting workflow generates high-dimensional, spatially resolved proteomic information that captures complex tissue architecture and cellular niches. Together, this optimized approach provides a robust and broadly applicable strategy for multiplexed imaging, particularly suited to tissues where autofluorescence and non-specific staining limit conventional approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".