Probing Peroxisome Dynamics and Biogenesis by Fluorescence Imaging
Bibliographic record
Abstract
Peroxisomes are the most recently discovered classical organelles, and only lately have their diverse functions been truly recognized. Peroxisomes are highly dynamic structures, changing both morphologically and in number in response to both extracellular and intracellular signals. This metabolic organelle came to prominence due to the many genetic disorders caused by defects in its biogenesis or enzymatic functions. There is now growing evidence that suggests peroxisomes are involved in lipid biosynthesis, innate immunity, redox homeostasis, and metabolite scavenging, among other functions. Therefore, it is important to have available suitable methods and techniques to visualize and quantify peroxisomes in response to various cellular signals. This unit includes a number of protocols that will enable researchers to image, qualify, and quantify peroxisome numbers and morphology-with both steady-state and time-lapse imaging using mammalian cells. The use of photoactivatable fluorescent proteins to detect and measure peroxisome biogenesis is also described. Altogether, the protocols described here will facilitate understanding of the dynamic changes that peroxisomes undergo in response to various cellular signals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".