Abstract
Abstract Autophagy is a process in which normal cellular components that accumulate during growth and differentiation are degraded via the lysosome; it is a survival mechanism that reallocates nutrients from unnecessary processes to more vital processes in the cell. Basal levels of autophagy are usually low but can be up-regulated by numerous stimuli including starvation, physiological stress, pharmacological agents and infections. In addition, autophagy suppression has been associated with cancer, neurodegenerative disorders, infectious diseases and inflammation. During autophagy, cytoplasmic LC3 is processed and recruited to the outer membrane of forming autophagosomes, and cells undergoing autophagy can be identified by visualizing LC3 puncta by immunofluorescence microscopy. While manual microscopy allows visual per-cell identification of autophagy, objective statistical analysis of autophagy can be challenging with this method owing to its time-consuming and generally subjective nature. To overcome these problems, we used the ImgeStreamX imaging cytometry platform, which objectively collects and quantifies statistically large numbers of images per sample, to measure autophagy in K562 cells, a human CML line. In this study, we demonstrate a method for determining the best image-based parameter for quantifying LC3 puncta in these cells, and apply that parameter to large data sets to measure compound-induced autophagy in an objectively and statistically significant manner.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call