Immunolabeling of multiple components or live cell dynamic recording can be readily accomplished using fluorescence microscopy, while retrieval of high resolution structural data requires advanced electron microscopy.
Which, happily, means no roaming data required.
Gathering the data requires painstaking care.
Integrating data requires semantically richinformation.
The movement of data in high-performance computing and across storage devices is well known, with hierarchies of transmission latency; therefore, analyzing scanning and electron microscopy data on these platforms will require physics-based algorithms that are customized to exploit parallel work while minimizing communication cost [89].
In agreement with our microscopy data that Atg17-induced autophagy requires Atg1, coexpression of kinase-dead Atg1 blocked Atg17-induced Atg13 hyperphosphorylation in fat bodies of well-fed larvae (Fig. 7G; Fig. S4I), suggesting that Atg17 induces autophagy at least in part by activating Atg1.
GFP-LC3 G120A failed to relocalize in cells infected with WT virus, confirming the electron microscopy data and indicating that the autophagy machinery is required for IAV to redistribute LC3 in infected cells.
Our method maybe potentially promising for many cell biologists who want to extract quantitative data from their SR microscopy data, imposing a great advantage that it can be run on fully automated mode via CellProfiler and does not require special computer programming skills.
The inference of features predictive of this state transition rate requires robust statistical analysis, and thus a large number of time-lapse microscopy data, which is in particular for mammalian systems still a challenging and labor intensive task [ 12, 13].
To obtain genealogies from time-lapse microscopy data, manual [Schwarzfischer et al., in revision] or automatic tracking (for an overview of current methods, see [ 30]) is required.
