Measurement of Calcium Fluctuations Within the Sarcoplasmic Reticulum of Cultured Smooth Muscle Cells Using FRET-based Confocal Imaging
Bibliographic record
Abstract
Maintenance of steady-state calcium (Ca(2+)) levels in the sarcoplasmic reticulum (SR) of vascular smooth muscle cells (VSMCs) is vital to their overall health. A significant portion of intracellular Ca(2+) content is found within the SR stores in VSMCs. As the only intracellular organelle with a close association to the surrounding extracellular space through plasma membrane-SR junctions, the SR can be considered to constitute the first line of response to any irregularity in Ca(2+) transients, or stress experienced by the cell. Among its many functions, one of the most important is its role in the transmission of Ca(2+)-regulated signals throughout the cell to induce further cell-wide reactions downstream. The more common use of cytoplasmic Ca(2+) indicators in this regard is overall insufficient for research into the highly dynamic changes to the intraluminal SR Ca(2+) store that have yet to be fully characterized. Here, we provide a detailed protocol for the direct and clear measurement of luminal SR Ca(2+). This tool is useful for investigation into the nuanced changes in SR Ca(2+) that have significant subsequent effects on the normal function and health of the cell. Fluctuations in SR Ca(2+) content specifically can provide us with a significant amount of information pertaining to cellular responses to disease or stress conditions experienced by the cell. In this method, a modified version of a SR-targeted Ca(2+) indicator, D1SR, is used to detect Ca(2+) fluctuations in response to the introduction of agents to cultured rat aortic smooth muscle cells (SMCs). Following incubation with the D1SR indicator, confocal fluorescence microscopy and fluorescence resonance energy transfer (FRET)-based imaging are used to directly observe changes to intraluminal SR Ca(2+) levels under control conditions and with the addition of agonist agents that function to induce intracellular Ca(2+) movement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".