1. webbased tool
1.1. Viewer
1.1.1. data4fitting
1.1.2. knock-down data
1.1.3. live-cell data
1.2. Analyser
1.2.1. Input: a zip file containing metainfo and images Output: csv file for mitochondrial features
1.3. Loader4Viewer
1.3.1. Input: a csv file containing analysis outcome
2. Figure Flow
3. Summary
3.1. This summary also includes questions
4. Manuscript
4.1. Methods and Materials
4.1.1. Data Generation
4.1.2. Image segmentation
4.1.2.1. Nucleus
4.1.2.1.1. segmentation
4.1.2.2. MitoTracker
4.1.2.2.1. segmentation
4.1.2.3. customize the
4.1.3. Quantification of the segmented objects
4.1.3.1. Nucleus content
4.1.3.1.1. mass
4.1.3.1.2. count
4.1.3.2. Mitochondrial content
4.1.3.2.1. mass
4.1.3.2.2. count
4.1.3.2.3. morphological features
4.1.3.3. Classification of the mitochondria based on morphological features
4.1.3.3.1. fitting
4.1.3.3.2. prediction
4.2. Results
4.2.1. Image analysis outcome
4.2.1.1. Nucleus segmentation
4.2.1.2. Mitochondria segmenatation
4.2.1.3. Mitochondria classification
4.2.2. Fitting Data
4.2.2.1. discover the compounds to activate the morphological processes
4.2.3. find which morphological processes can be activated
4.2.3.1. DMSO +siRNA seems to activate the processes
4.2.3.2. choose the Mock as control condition. difference for fraction of fragmented mito is significant different, suggesting that the effect of siRNA, DRP1 OMA1, TFAM1. (still not sure the difference at 72hr is caused by the transition effect or not.) However, the difference is not significant change compared to DMSO after 24hr oligomycin (0.5uM) exposure. it may suggest that oligomycin activate some morphological processes to balance the effects from purly siRNA treatment.
4.2.4. discover concentration-time landscape
4.3. Discussion
4.4. Motivation
4.4.1. a figure to motivate why to consider sub-population
4.4.2. a resource to backup the selection of four features
4.4.3. why to choose two sub-populations for sake of simplicity