Autophagy is a fundamental cellular process that enables adaptation to metabolic stress and has emerged as a critical modulator of cancer progression. However, how autophagy contributes to phenotypic heterogeneity at the single-cell level remains poorly understood. Here, we leverage High-Content Screening (HCS) coupled with time-lapse imaging and advanced single-cell segmentation to systematically dissect autophagic dynamics in two breast cancer models: MDA-MB-231 (triple-negative) and MCF-7 (estrogen receptor-positive) under nutrient deprivation.
By tracking individual cells over time, we quantified key morphological and fluorescence-based autophagic markers, revealing distinct subpopulations with differential autophagic responses. Notably, we observed a significant temporal shift in the proportion of cells transitioning from near-average autophagic activity to outlier states, a phenomenon more pronounced in MDA-MB-231 cells (3.7% to 8.6%) than in MCF-7 cells (3.1% to 4.7%). This dynamic heterogeneity would remain undetectable in conventional endpoint assays, underscoring the power of real-time single-cell analysis. Our approach further identified rare cell subsets with unique autophagic profiles, shedding light on cellular plasticity and adaptive mechanisms that may drive tumor progression and therapeutic resistance.
By integrating HCS with single-cell tracking, this study provides a novel framework for resolving the complexity of autophagy in cancer. Our findings highlight the necessity of temporally resolved analyses to capture dynamic cellular behaviors that static measurements overlook. These insights advance our understanding of tumor heterogeneity and stress adaptation, offering a refined perspective on autophagy as a potential therapeutic target in breast cancer.