Multiphoton ANS fluorescence microscopy as an in vivo sensor for protein misfolding stress
Bibliographic record
Abstract
The inability of cells to maintain protein folding homeostasis is implicated in the development of neurodegenerative diseases, malignant transformation, and aging. We find that multiphoton fluorescence imaging of 1-anilinonaphthalene-8-sulfonate (ANS) can be used to assess cellular responses to protein misfolding stresses. ANS is relatively nontoxic and enters live cells and cells or tissues fixed in formalin. In an animal model of Alzheimer's disease, ANS fluorescence imaging of brain tissue sections reveals the binding of ANS to fibrillar deposits of amyloid peptide (Aβ) in amyloid plaques and in cerebrovascular amyloid. ANS imaging also highlights non-amyloid deposits of glial fibrillary acidic protein in brain tumors. Cultured cells under normal growth conditions possess a number of ANS-binding structures. High levels of ANS fluorescence are associated with the endoplasmic reticulum (ER), Golgi, and lysosomes-regions of protein folding and degradation. Nuclei are virtually devoid of ANS binding sites. Additional ANS binding is triggered by hyperthermia, thermal lesioning, proteasome inhibition, and induction of ER stress. We also use multiphoton imaging of ANS binding to follow the in vivo recovery of cells from protein-damaging insults over time. We find that ANS fluorescence tracks with the binding of the molecular chaperone Hsp70 in compartments where Hsp70 is present. ANS highlights the sensitivity of specific cellular targets, including the nucleus and particularly the nucleolus, to thermal stress and proteasome inhibition. Multiphoton imaging of ANS binding should be a useful probe for monitoring protein misfolding stress in cells.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".