STochastic Optical Reconstruction Microscopy (STORM) reveals the nanoscale organization of pathological aggregates in human brain
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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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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 | 0.001 |
| 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 it