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Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community. 

Selective autophagy is a conserved subcellular process that maintains the health of eukaryotic cells by targeting damaged or toxic cytoplasmic components to the vacuole/lysosome for degradation. A key player in the initiation of selective autophagy in S. Cerevisiae (baker’s yeast) is a large adapter protein called Atg11. Atg11 has multiple predicted coiled-coil domains and intrinsically disordered regions, is known to dimerize, and binds and organizes other essential components of the autophagosome formation machinery, including Atg1 and Atg9. We performed systematic directed mutagenesis on the coiled-coil 2 domain of Atg11 in order to map which residues were required for its structure and function. Using yeast-2-hybrid and coimmunoprecipitation, we found only three residues to be critical: I562, Y565, and I569. Mutation of any of these, but especially Y565, could interfere with Atg11 dimerization and block its interaction with Atg1 and Atg9, thereby inactivating selective autophagy.