Long-term high throughput agitation culturing with real-time metabolic profiling
Bibliographic record
Abstract
Abstract Cellular metabolism relies on the dynamic coordination between glycolytic flux in the cytosol and oxidative phosphorylation (OXPHOS) within the mitochondria. To study the metabolic profiles of cells, researchers apply a monitoring system for measurements of critical parameters, e.g., pH and dissolved oxygen (DO), to understand underlying energy production tendencies, dictating the performance, resilience and growth of cells. However, implementing sensitive, non-invasive sensors into long-term culturing environments remains a technical bottleneck. Here, we describe the DolphinQ bioanalyzer, a novel culturing platform designed for high-throughput, real-time monitoring of cellular metabolism states under physiologically relevant conditions. We validate the system across multiple cell types and experimental set-ups, demonstrating its ability to resolve subtle metabolic shifts that are typically obscured in end-point assays. Notably, we utilize the system to characterize the metabolic impact of heteroplasmy in a mitochondrial disease model with affected ATP synthase. Our results underscore the utility of continuous, minimally disruptive monitoring for revealing the complexities of cellular metabolic adaptation. The DolphinQ framework therefore offers a robust tool for optimizing culture conditions across a wide range of applications and advancing fundamental research into metabolic flux and mitochondrial dysfunction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".