Spatially resolved transcriptomics identifies intercellular signaling post-ischemic stroke that controls neural stem cell proliferation
Bibliographic record
Abstract
SUMMARY Stroke is the second leading cause of death and disability worldwide. Ischemic stroke mobilizes adult neural stem cells (NSCs) out of the quiescent state. The multifaceted responses of endogenous NSCs to ischemic stroke involve proliferation, migration, and differentiation. Hence one strategy which could be leveraged for recovery after ischemic stroke is the intrinsic mechanism of endogenous NSC mobilization. However, the survival rate of recruited endogenous NSCs is low. Moreover, the intercellular signals that activate NSCs after ischemic stroke are poorly understood. We hypothesized that after stroke, cells located in the cerebral lesion send signals to the NSC niche to initiate the regenerative response. To test this hypothesis, we used CellChat to computationally infer the cell-cell communication between the ischemic infarct region and ventricular-subventricular zone (V-SVZ) NSC niche from spatial gene expression profiles. We identified ligand-receptor pairs and signaling pathways involved in the signal transduction events at 2, 10, and 21 days after stroke. Out of several candidate genes of interest we identified, here we reported the regulatory function of galectin-9 on the proliferation of NSCs. Our present work portrays galectin-9 as a checkpoint signaling molecule that guards the responses of NSCs under physiological conditions and potentially during the recovery phase post-ischemic stroke. We suggest that TIM-3 mediates the inhibitory effect of galectin-9 on NSC proliferation and propose a working hypothesis that the stroke-induced proinflammatory factors stimulate the Toll-like receptor 4 (TLR4) on ependymal cells and result in the increased secretion of galectin-9, which in turn modulates neighboring NSCs. Our study paves the way for potential therapeutic approaches which leverage the TLR4 and galectin-9/TIM3 signaling pathways.
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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 | 0.001 | 0.001 |
| 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".