High spatial-resolution imaging of label-free<i>in vivo</i>protein aggregates by VISTA
Bibliographic record
Abstract
Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer's disease and Huntington's disease. High-resolution holistic mapping of the fine structures from these aggregates should facilitate our understanding of their pathological roles. Here, we achieved label-free high-resolution imaging of the polyQ and the amyloid-beta (Aβ) aggregates in cells and tissues utilizing a sample-expansion stimulated Raman strategy. We further focused on characterizing the Aβ plaques in 5XFAD mouse brain tissues. 3D volumetric imaging enabled visualization of the whole plaques, resolving both the fine protein filaments and the surrounding components. Coupling our expanded label-free Raman imaging with machine learning, we obtained specific segmentation of aggregate cores, peripheral filaments together with cell nuclei and blood vessels by pre-trained convolutional neural network models. Combining with 2-channel fluorescence imaging, we achieved a 6-color holistic view of the same sample. This ability for precise and multiplex high-resolution imaging of the protein aggregates and their micro-environment without the requirement of labeling would open new biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".