Protocol to monitor live-cell, real-time, mitochondrial respiration in mouse muscle cells using the Resipher platform
Bibliographic record
Abstract
Mitochondrial function is typically assessed by measuring oxygen consumption at a given time point. However, this approach cannot monitor respiratory changes that occur over time. Here, we present a protocol to measure mitochondrial respiration in freshly isolated muscle stem cells, primary skeletal muscle, and immortalized C2C12 myoblasts in real time using the Resipher platform. We describe steps for preparing and plating cells, performing media changes, setting up the software and device, and analyzing data. This method can be adapted to other cell types. For complete details on the use and execution of this protocol, please refer to Triolo et al. 1 • Protocol to measure cellular oxygen consumption in real time using Resipher • Procedures for culturing myoblasts and muscle stem cells to assess oxygen consumption • Instructions to quantify and represent oxygen consumption data Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Mitochondrial function is typically assessed by measuring oxygen consumption at a given time point. However, this approach cannot monitor respiratory changes that occur over time. Here, we present a protocol to measure mitochondrial respiration in freshly isolated muscle stem cells, primary skeletal muscle, and immortalized C2C12 myoblasts in real time using the Resipher platform. We describe steps for preparing and plating cells, performing media changes, setting up the software and device, and analyzing data. This method can be adapted to other cell types.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.040 |
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".