Spatially resolved transcriptomics identifies intercellular signaling post-ischemic stroke that controls neural stem cell proliferation
Bibliographic record
Abstract
Ischemic stroke mobilizes adult neural stem cells (NSCs), which could be leveraged for recovery. However, the intercellular signals that activate NSCs are poorly understood. Cells located in the cerebral lesion may send signals to the NSC niche to initiate the regenerative responses. Here, we computationally inferred cell-cell communication between the ischemic infarct region and ventricular-subventricular zone (V-SVZ) NSC niche from spatial gene expression profiles and identified ligand-receptor pairs in the signal transduction events at three time points. Further, we detailed the regulatory function of galectin-9 on the proliferation of NSCs as a checkpoint molecule that guards the responses of NSCs under physiological conditions. In addition, we show that TIM-3 may mediate the inhibitory effect of galectin-9 on NSC proliferation. Collectively, these data reveal galectin-9 as an important regulator of NSC proliferation under both physiological and injury conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".