Feeling the vibes: Vibrational spectroscopic imaging of biomolecular assemblies in their natural environment
Bibliographic record
Abstract
Biomolecular assemblies form via intramolecular interactions and serve important biological functions. The most characterized biomolecular assemblies are amyloid fibrils, which are associated with neurodegenerative diseases. Advances in microscopy techniques enabled characterization of the morphology of these assemblies, but so far, failed in detailed structural characterizations. Vibrational spectroscopic imaging presents unique advantages to studying biomolecular assemblies in their natural environment due to the sensitivity of vibrational spectra to protein structural changes, especially β-sheet enrichment in amyloid fibrils. High-resolution hyperspectral images originating from distinct vibrations of chemical bonds provide label-free characterizations of biomolecules, including proteins, lipids, and nucleic acids. In this review, we first briefly introduce infrared and Raman-based spectroscopy and their biological interpretation. We then review applications adopting Fourier transform Infrared-based, mid-infrared photothermal-based, and Raman-based approaches in tissue and cells, especially live cells. Finally, we discuss how these technologies are evolving to study biomolecular assemblies beyond amyloid fibrils.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".