A Mathematical Model of Plasma Membrane Electrophysiology of a Brain Capillary Pericyte: Investigating Pericyte Contribution to the Electrical Properties of the Capillary Network
Bibliographic record
Abstract
Introduction Pericyte cells (PCs) are specialized cells found primarily on the abluminal surface of capillary blood vessels alongside endothelial cells (ECs). They play a role in angiogenesis, formation of the blood‐brain barrier, as well as in the regulation of blood flow. Due to their contractile nature, PCs can modulate capillary diameter and perfusion, however, little is known regarding their contribution to the electrical properties of the capillary network. Furthermore, recent studies show that capillaries can sense neural activity in the brain and transmit electrical signals to upstream feeding arterioles and arteries to match blood supply to local demand. Although passive electrotonic spread underlies signal propagation along the endothelium in larger vessels, evidence for amplification of transmitted signals and preferential upstream propagation have been presented in vessels as branching order increases. We use mathematical modeling to test under what conditions a PC can affect the electrical properties of the capillary network and whether it can act as a sink or an amplifier of conducted hyperpolarization. Methods A PC and an EC model incorporate the dynamic behavior of known plasma membrane currents, release and uptake of Ca 2+ by intracellular stores and dynamic tracking of ionic (Ca 2+ , K + , Na + , and Cl − ) concentrations in the cytosol (Fig. 1). The PC is electrically coupled to capillary ECs through gap junctions and a multicellular model of a capillary network is created. Electrical signal attenuation is examined following hyperpolarizing stimuli at capillary ends in the presence as well as absence of PCs at network bifurcations. Results Cell models predict physiological resting membrane potential and ionic concentrations. Model predictions were compared against experimental responses to agonist stimulation and K + challenge for validation. Simulations show that a sufficient inwardly rectifying K + , Kir, channel current density may allow PCs to amplify conducted hyperpolarization. Increased coupling between a PC with the parent relative to the daughter capillaries at a bifurcation may allow preferential conduction of an electrical signal upstream the vascular network. Conclusion A detailed model of a brain capillary pericyte was developed and validated against experimental data. Model simulations suggest that with sufficient Kir density, pericytes can amplify conducted hyperpolarization and regulate the distance and direction of propagating vasodilation and thus blood flow distribution in the vascular network. Support or Funding Information This work was supported by the Ronald E. McNair Post‐Baccalaureate Achievement Program. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".