bioRxivCancer BiologyPreprint
AURORA: a longitudinal single-cell framework for resolving autophagy-associated remodeling under nutrient stress
Joel D. Posligua-GarcíaiD, Maria del Carmen Banqueri PegalajariD, Carlos Ulises Cárdenas-VelaiD, Ana Belén Martínez-PadillaiD, James Richard PerkinsiD, Carlos Rodríguez Caso, José Luis UrdialesiD, Juan A. G. RaneaiD, Miguel Ángel MedinaiD, Manuel BernaliD
Abstract
Nutrient stress induces dynamic and heterogeneous cellular responses that are often reduced to population averages or endpoint measurements. AURORA is a longitudinal single-cell phenotyping framework integrating high-content time-lapse imaging, trajectory-aware quality control, multi-compartment morphology, reference phenotypic states, and cross-cell-line aggregation of state-occupancy behaviors. AURORA is applied to MCF-7, MDA-MB-231, A-549, and HT-1080 cancer cells during 4 h exposure to Earle's balanced salt solution (EBSS). Population-level Total Autophagy shows sustained EBSS-associated elevation in MCF-7, A-549, and HT-1080, but a stronger early response followed by attenuation in MDA-MB-231. Longitudinal analysis shows that these averages arise from cell-line- and time-dependent changes in single-cell distributions rather than uniform displacement. Feature-level analysis further identifies distinct combinations of nuclear, whole-cell, and autophagosome-associated organization, compactness, and shape in each model. Projection of EBSS-treated cells into Complete-defined phenotypic landscapes reveals line-specific redistribution among reference states. Despite this specificity, dominant state-occupancy signatures converge into six recurrent higher-order programs, linking different cellular routes to partially shared remodeling architectures. Because DAPGreen is not paired with a flux inhibitor or ratiometric reporter, these findings describe an autophagy-associated imaging phenotype rather than autophagic flux. AURORA therefore distinguishes population-average change from heterogeneous single-cell adaptation during dynamic stress responses.
- organism: human
- assay: imaging
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: clustering
- research focus: trajectory inference
- approach: deep learning
- approach: statistical model
- Citations
- 1
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