STochastic Optical Reconstruction Microscopy (STORM) reveals the nanoscale organization of pathological aggregates in human brain
Bibliographic record
Abstract
AIMS: Histological analysis of brain tissue samples provides valuable information about the pathological processes leading to common neurodegenerative disorders. In this context, the development of novel high-resolution imaging approaches is a current challenge in neuroscience. METHODS: To this end, we used a recent super-resolution imaging technique called STochastic Optical Reconstruction Microscopy (STORM) to analyse human brain sections. We combined STORM cell imaging protocols with neuropathological techniques to image cryopreserved brain samples from control subjects and patients with neurodegenerative diseases. RESULTS: This approach allowed us to perform 2D-, 3D- and two-colour-STORM in neocortex, white matter and brainstem samples. STORM proved to be particularly effective at visualizing the organization of dense protein inclusions and we imaged with a <50 nm resolution pathological aggregates within the central nervous system of patients with Alzheimer's disease, Parkinson's disease, Lewy body dementia and fronto-temporal lobar degeneration. Aggregated Aβ branches appeared reticulated and cross-linked in the extracellular matrix, with widths from 60 to 240 nm. Intraneuronal Tau and TDP-43 inclusions were denser, with a honeycomb pattern in the soma and a filamentous organization in the axons. Finally, STORM imaging of α-synuclein pathology revealed the internal organization of Lewy bodies that could not be observed by conventional fluorescence microscopy. CONCLUSIONS: STORM imaging of human brain samples opens further gates to a more comprehensive understanding of common neurological disorders. The convenience of this technique should open a straightforward extension of its application for super-resolution imaging of the human brain, with promising avenues to current challenges in neuroscience.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".