Optimization of differential filtration-based mitochondrial isolation for mitochondrial transplant to cerebral organoids
Bibliographic record
Abstract
BACKGROUND: Mitochondrial dysfunction is involved in several diseases ranging from genetic mitochondrial disorders to chronic metabolic diseases. An emerging approach to potentially treat mitochondrial dysfunction is the transplantation of autologous live mitochondria to promote cell regeneration. We tested the differential filtration-based mitochondrial isolation protocol established by the McCully laboratory for use in cellular models but found whole cell contaminants in the mitochondrial isolate. METHODS: Therefore, we explored alternative types of 5-μm filters (filters A and B) for isolation of mitochondria from multiple cell lines including HEK293 cells and induced pluripotent stem cells (iPSCs). MitoTracker™ staining combined with flow cytometry was used to quantify the concentration of viable mitochondria. A proof-of-principle mitochondrial transplant was performed using mitoDsRed2-tagged mitochondria into a H9-derived cerebral organoid. RESULTS: We found that filter B provided the highest quality mitochondria as compared to the 5-μm filter used in the original protocol. Using this method, mitochondria were also successfully isolated from induced pluripotent stem cells. To test for viability, mitoDsRed2-tagged mitochondria were isolated and transplanted into H9-derived cerebral organoids and observed that mitochondria were engulfed as indicated by immunofluorescent co-localization of TOMM20 and MAP2. CONCLUSIONS: Thus, use of filter B in a differential filtration approach is ideal for isolating pure and viable mitochondria from cells, allowing us to begin evaluating long-term integration and safety of mitochondrial transplant using cellular sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".