SNCA and TPPP transcripts increase in oligodendroglial cytoplasmic inclusions in multiple system atrophy
Bibliographic record
Abstract
Multiple system atrophy (MSA) is characterized by glial cytoplasmic inclusions (GCIs) containing aggregated α-synuclein (α-syn) in oligodendrocytes. The origin of α-syn accumulation in GCIs is unclear, in particular whether abnormal α-syn aggregates result from the abnormal elevation of endogenous α-syn expression in MSA or ingested from the neuronal source. Tubulin polymerization promoting protein (TPPP) has been reported to play a crucial role in developing GCI pathology. Here, the total cell body, nucleus, and cytoplasmic area density of SNCA and TPPP transcripts in neurons and oligodendrocytes with and without various α-syn pathologies in the pontine base in autopsy cases of MSA (n = 4) and controls (n = 2) were evaluated using RNAscope with immunofluorescence. Single-nucleus RNA-sequencing data for TPPP was evaluated using control frontal cortex (n = 3). SNCA and TPPP transcripts were present in the nucleus and cytoplasm of oligodendrocytes in both controls and diseased, with higher area density in GCIs and glial nuclear inclusions in MSA. Area densities of SNCA and TPPP transcripts were lower in neurons showing cytoplasmic inclusions in MSA. Indeed, TPPP transcripts were unexpectedly found in neurons, while the anti-TPPP antibody failed to detect immunoreactivity. Single-nucleus RNA-sequencing revealed significant TPPP transcript expression predominantly in oligodendrocytes, but also in excitatory and inhibitory neurons. This study addressed the unclear origin of accumulated α-syn in GCIs, proposing that the elevation of SNCA transcripts may supply templates for misfolded α-syn. In addition, the parallel behavior of TPPP and SNCA transcripts in GCI development highlights their potential synergistic contribution to inclusion formation. In conclusion, this study advances our understanding of MSA pathogenesis, offers insights into the dynamics of SNCA and TPPP transcripts in inclusion formation, and proposes regulating their transcripts for future molecular therapy to MSA.
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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 | 0.001 | 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".