A Proposal for a Machine Learning Classifier for Viral Infection in Living Cells Based on Mitochondrial Distribution
The study of viral infections using live cell imaging (LCI) is an important area with multiple opportunities for new developments in computational cell biology. Here, this point is illustrated by the analysis of the sub cellular distribution of mitochon drium in cell cultures infected by Dengue virus (DENV) and in uninfected cell cul tures (Mock infections) Several videos were recorded from the overnight experiments performed in a confocal microscopy of spinning disk. The density dis tribution of mitochondrium around the nuclei as a function of time and space r(r th, t) was numerically modeled as a smooth interpolation function from the image data and used in further analysis. A graphical study shows that the behavior of the mitochondrial density is substantially different when the infection is present. The DENV infected cells show a more diffuse distribution and a stronger angular variation on it. This behavior can be quantified by using some usual image process ing descriptors called entropy and uniformity. Interestingly, the marked difference found in the mitochondria density distribution for mock and for infected cell is present in every frame and not an evidence of time dependence was found, which indicate that from the start of the infections the cells are showing an altered subcel lular pattern in mitochondrium distribution. Ulteriorly, it would be important to study by analysis of time series for clearing if there is some tendency or approxi mate cycles. Those findings are suggesting that using the image descriptors entropy and uniformity it is possible to create a machine learning classifier that could recog nize if a single selected cell in a culture has been infected or not.