Exploring Piezo1, Piezo2, and TMEM150C in human brain tissues and their correlation with brain biomechanical characteristics
Bibliographic record
Abstract
Unraveling the intricate relationship between mechanical factors and brain activity is a pivotal endeavor, yet the underlying mechanistic model of signaling pathways in brain mechanotransduction remains enigmatic. To bridge this gap, we introduced an in situ multi-scale platform, through which we delineate comprehensive brain biomechanical traits in white matter (WM), grey-white matter junctions (GW junction), and the pons across human brain tissue from four distinct donors. We investigate the three-dimensional expression patterns of Piezo1, Piezo2, and TMEM150C, while also examining their associated histological features and mechanotransduction signaling networks, particularly focusing on the YAP/β-catenin axis. Our results showed that the biomechanical characteristics (including stiffness, spring term, and equilibrium stress) associated with Piezo1 vary depending on the specific region. Moving beyond Piezo1, our result demonstrated the significant positive correlations between Piezo2 expression and stiffness in the WM. Meanwhile, the expression of Piezo2 and TMEM150C was shown to be correlated to viscoelastic properties in the pons and WM. Given the heterogeneity of brain tissue, we investigated the three-dimensional expression of Piezo1, Piezo2, and TMEM150C. Our results suggested that three mechanosensitive proteins remained consistent across different vertical planes within the tissue sections. Our findings not only establish Piezo1, Piezo2, and TMEM150C as pivotal mechanosensors that regulate the region-specific mechanotransduction activities but also unveil the paradigm connecting brain mechanical properties and mechanotransduction activities and the variations between individuals.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".