Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling

Abstract

SummaryAssessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holo-tomographic microscopy (HTM) with Artificial Intelligence (AI) to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets (LD) and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs LD, changes mitochondrial shape and dry mass, and separates LD from mitochondria. We then used Bayesian statistics on organelle dry mass states to define organelle cross-regulation (OCR) networks and report modifications of OCR that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides a new AI-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.

Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells

“…Multiplexed imaging of cellular morphology and machine learning are increasingly used to predict the effects of genetic and chemical perturbations (1)(2)(3) from changes in the cellular architecture. High-throughput live cell imaging, combined with deep learning, has enabled systematic analysis of the dynamic mechanisms that underpin healthy (4)(5)(6) and diseased (7)(8)(9) states of cells.…”