Quantifying Oligomer Populations in Real Time during Protein Aggregation Using Single-Molecule Mass Photometry
Bibliographic record
Abstract
Protein aggregation is a hallmark of many neurodegenerative diseases. The early stages of the aggregation cascade are crucial because small oligomers are thought to be key neurotoxic species, but they are difficult to study because they feature heterogeneous mixtures of transient states. We show how the populations of different oligomers can be tracked as they evolve over time during aggregation using single-molecule mass photometry to measure individually the masses of the oligomers present in solution. By applying the approach to tau protein, whose aggregates are linked to diseases including Alzheimer's and frontotemporal dementia, we found that tau existed in an equilibrium between monomers, dimers, and trimers before aggregation was triggered. Once aggregation commenced, the monomer population dropped continuously, paired first with a rise in the population of the smallest oligomers and then a steep drop as the protein was incorporated into larger oligomers and fibrils. Fitting these populations to kinetic models allowed different models of aggregation to be tested, identifying the most likely mechanism and quantifying the microscopic rates for each step in the mechanism. This work demonstrates a powerful approach for the characterization of previously inaccessible regimes in protein aggregation and building quantitative mechanistic models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".