Long-term In Vivo Calcium Imaging of Astrocytes Reveals Distinct Cellular Compartment Responses to Sensory Stimulation
Bibliographic record
Abstract
Localized, heterogeneous calcium transients occur throughout astrocytes, but the characteristics and long-term stability of these signals, particularly in response to sensory stimulation, remain unknown. Here, we used a genetically encoded calcium indicator and an activity-based image analysis scheme to monitor astrocyte calcium activity in vivo. We found that different subcellular compartments (processes, somata, and endfeet) displayed distinct signaling characteristics. Closer examination of individual signals showed that sensory stimulation elevated the number of specific types of calcium peaks within astrocyte processes and somata, in a cortical layer-dependent manner, and that the signals became more synchronous upon sensory stimulation. Although mice genetically lacking astrocytic IP3R-dependent calcium signaling (Ip3r2-/-) had fewer signal peaks, the response to sensory stimulation was sustained, suggesting other calcium pathways are also involved. Long-term imaging of astrocyte populations revealed that all compartments reliably responded to stimulation over several months, but that the location of the response within processes may vary. These previously unknown characteristics of subcellular astrocyte calcium signals provide new insights into how astrocytes may encode local neuronal circuit activity.
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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 | 0.000 | 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 | 0.001 |
| 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 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".