UC Davis

Autophagy is a multistep dynamic degradative process that is essential for maintaining cellular homeostasis. Autophagy is linked to a wide range of diseases, including cancer, metabolic diseases, and aging. Therefore, autophagy is emerging as a promising therapeutic target for many diseases. Systematically quantifying autophagy is critical for gaining fundamental insights and effectively modulating this dysregulated process during diseases. However, current methods do not quantitatively capture the dynamic nature of autophagy with high sensitivity and scalability. In this work, we proposed two approaches to address these limitations and characterize autophagy comprehensively.

Established methods to quantify individual autophagy steps are restricted to 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 small-molecule drugs and nutrient-deprived conditions. We quantified changes in autophagy rates in as little as 10 min, which can establish direct mechanisms for autophagy perturbation before feedback begins. In summary, this new approach enables the quantification of autophagy flux with high sensitivity and temporal resolution and facilitates a comprehensive understanding of this process.

Dynamic autophagy rate measurements are useful but resource-intensive and limited by the throughput and number of phenotypic measurements. High-throughput methods to characterize autophagy are essential for accelerating the drug discovery process. We developed a highly scalable image-based profiling approach to characterize ~900 morphological features at a single-cell level with high temporal resolution. We differentiated drug treatments based on morphological profiles using a random forest classifier with ~90% accuracy and identified the key morphological features that govern the classification. Additionally, temporal morphological profiles accurately predicted complex changes in autophagy after perturbation, such as total cargo degradation. This approach can characterize the mechanism of action of perturbations with less resource-intensive measurements. Therefore, this study acts as proof of principle for using image-based profiling in high-throughput autophagy characterization and to identify biologically relevant phenotypes, which can accelerate drug discovery.