Integration of K<sup>+</sup>and Cl<sup>–</sup>currents regulate steady‐state and dynamic membrane potentials in cultured rat microglia
Bibliographic record
Abstract
The role of ion channels and membrane potential (V(m)) in non-excitable cells has recently come under increased scrutiny. Microglia, the brain's resident immune cells, express voltage-gated Kv1.3 channels, a Kir2.1-like inward rectifier, a swelling-activated Cl(-) current and several other channels. We previously showed that Kv1.3 and Cl(-) currents are needed for microglial cell proliferation and that Kv1.3 is important for the respiratory burst. Although their mechanisms of action are unknown, one general role for these channels is to maintain a negative V(m). An impediment to measuring V(m) in non-excitable cells is that many have a very high electrical resistance, which makes them extremely susceptible to leak-induced depolarization. Using non-invasive V(m)-sensitive dyes, we show for the first time that the membrane resistance of microglial cells is several gigaohms; much higher than the seal resistance during patch-clamp recordings. Surprisingly, we observed that small current injections can evoke large V(m) oscillations in some microglial cells, and that injection of sinusoidal currents of varying frequency exposes a strong intrinsic electrical resonance in the 5- to 20-Hz frequency range in all microglial cells tested. Using a dynamic current clamp that we developed to actively compensate for the damage done by the patch-clamp electrode, we found that the V(m) oscillations and resonance were more prevalent and larger. Both types of electrical behaviour required Kv1.3 channels, as they were eliminated by the Kv1.3 blocker, agitoxin-2. To further determine how the ion currents integrate in these cells, voltage-clamp recordings from microglial cells displaying these behaviours were used to analyse the biophysical properties of the Kv1.3, Kir and Cl(-) currents. A mathematical model that incorporated only these three currents reproduced the observed V(m) oscillations and electrical resonance. Thus, the electrical behaviour of this 'non-excitable' cell type is much more complex than previously suspected, and might reflect a more common oversight in high resistance cells.
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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 | 0.000 | 0.000 |
| 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".