(694f) Temporal Analysis of Autophagy Rates and Subcellular Morphological Dynamics Using High-Throughput Image-Based Single Cell Profiling

Autophagy is a multi-step intracellular process by which cells recycle their misfolded proteins and damaged organelles into primary building blocks. The cellular materials are sequestered by double-membrane vesicles called autophagosomes, which fuse with lysosomes to form autolysosomes, which then degrade the sequestered material into fatty acids and amino acids. Autophagy is linked to many diseases, including cancer, neurodegenerative diseases, and infectious diseases. The significance of autophagy in various cellular processes is growing, yet comprehensive quantitative characterization of the autophagic process remains a major challenge. Here, we present two new quantitative methods that can lead to a comprehensive characterization of the autophagy pathway.

Established methods to quantify autophagy use steady-state measurements, which provide limited information about the perturbation and the cellular response. We present a theoretical and experimental framework to measure autophagic steps in the form of rates under non-steady state conditions. We use this approach to measure temporal responses to rapamycin and wortmannin treatments, two commonly used autophagy modulators. We quantified changes in autophagy rates in as little as 10 minutes, which can establish direct mechanisms for autophagy perturbation before feedback mechanisms begins. We identified the concentration-dependent effects of rapamycin on the initial and temporal progression of autophagy rates. This new approach enables the quantification of autophagy flux with high sensitivity and temporal resolution and facilitates a comprehensive understanding of this process.

Along with the kinetic rates of the autophagy pathway, cellular morphology is also significantly altered based on the kind of treatment. Profiling cellular morphology can provide additional insights into fundamental mechanisms and would facilitate high-throughput applications like drug screening. We developed an image-based profiling approach to further characterize the autophagy-related phenotypes. We developed a new computational pipeline to segment and track individual cells and extract up to 1000 subcellular morphological features in a time-resolved manner. We identified the key image features that capture the variability and dyanmics of cellular morphology under different autophagy modulators. Moreover, using these image features we can differentiate various kinds of autophagy perturbations. This approach can be used for characterizing phenotypes and understanding the mechanism of action. Furthermore, this work would act as a proof of principle for developing image-based high throughput drug screening technologies. In future work, similar analysis can be expanded for quantifying morphological changes in other organelles such as the mitochondria and lysosomes for characterizing disease state and progression.

In conclusion, these methods would provide a systematic and comprehensive characterization of the autophagy pathway. Additionally, these techniques would assist with high-throughput drug screening and in the development of new therapies to regulate autophagy accurately. As a future direction, we are applying these methods to study various external perturbations such as small molecules drugs and dynamic perturbations such as viruses at the single-cell level.