Dual-probe fluorescence spectroscopy for sensitive quantitation of Alzheimer’s amyloid pathology
Bibliographic record
Abstract
Protein misfolding is a prominent pathological hallmark of neurodegenerative disorders, including Alzheimer's disease (AD). Studies have shown that the diversity of β sheet-rich protein deposits (such as amyloid β plaques and neurofibrillary tangles), present across different brain regions, might underlie different disease phenotypes and only certain types of aggregates might be associated with cognitive decline. Conformationally sensitive fluorescent amyloid probes have the ability to report different structures of protein aggregates by virtue of their shifting emission spectra. Here we defined the binding affinity of the fluorescent amyloid probes BSB and MCAAD to disease-relevant protein aggregates, and combined the two probes to examine formalin-fixed paraffin-embedded mouse and human brain samples. Coupled with quantitative spectral phasor analysis, the dual-probe staining approach revealed remarkable heterogeneity of protein aggregates across the samples. Distinct emission spectra were consistent with certain types of deposits present in the mouse and human brain sections. The sensitivity of this staining, imaging and analysis approach outperformed conventional immunohistochemistry with the detected spectral differences between the greater parenchyma of cognitively normal and AD cases indicating a subtle yet widespread proteopathy associated with disease. Our method offers more sensitive, objective, and quantitative examination of protein misfolding pathology using conventional tissue sections.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".