The Capillary‐Pericyte Relationship in Skeletal Muscle: Implications for Blood Flow Control
Bibliographic record
Abstract
Introduction Pericytes, mural cells of the vasculature, are found adjacent to endothelial cells of capillaries and venules. In the brain, pericytes have been shown to be involved in blood flow control to active nerve tissue. In skeletal muscle, capillaries have been found to be important in directing blood flow to active skeletal muscle fibres. Active skeletal muscle fibres signal capillary endothelial cells to initiate signals that are transmitted upstream, along the blood vessel wall, to cause vasodilation of the upstream arteriole controlling the perfusion of the stimulated capillary. Pericytes have been shown to be associated with the microvasculature in skeletal muscle. If pericytes are closely associated with the capillary wall and capillary endothelial cells, they may be involved in the transmission of the upstream arteriolar vasodilatory signals that control capillary perfusion and blood flow to active skeletal muscle fibres. In order for this to be possible pericytes would have to have to form a continuous, connected layer of cells adjacent to the capillary network such that signals could be passed from pericyte to pericyte upstream to associated arterioles. In order to investigate the potential role of pericytes in blood flow control to skeletal muscle capillaries we investigated 1) whether pericytes were associated with capillaries in skeletal muscle and 2) whether pericytes form a continuous layer adjacent to the capillary network. Methods We used histological techniques, sectioning male mouse slow‐oxidative (soleus (SOL)), and fast‐oxidative‐glycolytic muscle (diaphragm (DIA)) both cross sectionally and longitudinally in order to identify capillary endothelial cells (using isolectin) in cross section and to view capillaries along their length. We then double stained these sections with neural‐glial antigen 2 (NG2) to identify the association of pericytes with capillary endothelial cells. Results Cross sectional analysis of 3 SOL muscles (273 capillaries counted) showed that 63.2+/‐4.9% (avg+/‐ std. dev.) of capillaries were associated with pericytes while 36.8+/‐4.9% of capillaries were not associated with pericytes. Cross sectional analysis of 2 DIA muscles (208 capillaries counted) showed that 53.6+/‐7.7% of capillaries were associated with pericytes while 46.4+/‐7.7% of capillaries were not associated with pericytes. In both muscle types, analysis of longitudinal sections, where capillaries could be viewed along their length, showed a lack of consistent association with pericytes. Conclusions We identified that, while there was an association between pericytes and capillary endothelial cells in skeletal muscle, not all capillary endothelial cells were associated with pericytes. Further, when capillaries were viewed along their length, pericytes were not consistently located adjacent to the capillary network. Our data indicate that pericytes may not form a continuous layer alongside the capillary network and may not be able to conduct vasodilatory signals from pericyte to pericyte to communicate with upstream arterioles and, therefore, may not be able to alter blood flow to active skeletal muscle fibres.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".