Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling, 2023, Saunders et al.

[SNT Gatchaman]

Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling
Nell Saunders; Blandine Monel; Nadege Cayet; Lorenzo Archetti; Hugo Moreno; Alexandre Jeanne; Agathe Marguier; Timothy Wai; Olivier Schwartz; Mathieu Frechin

Assessing 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.


Link | PDF (Preprint: BioRxiv)

 

[SNT Gatchaman]

A massive increase in LD [lipid droplet] number and size was observed in SARS-CoV-2 Wuhan- or Omicron-infected cells but not in control or Syncytin-1-expressing cells. These observations are in line with a SARS-CoV-2-induced remodeling of lipid metabolism to support pro-virus signaling and provide material for the formation of double membrane vesicles.

The obvious effect of SARS-CoV-2 on LD indicated a broad metabolic impact and thus led to the question of mitochondrial alterations.

We next took advantage of our capacity to localize organelles in space and relative to each other’s to measure the distance between LD and mitochondria, a marker of the rate of fatty acid oxidation and thus of energy production. In uninfected or Syncytin-1 expressing cells, the proportion of LD in proximity (< 400 nm) of mitochondria increased over time. In contrast, this ratio stayed stable or even decreased in Wuhan and Omicron-infected cells. Therefore, infection separates LDs from mitochondria, reflecting a probable impact of SARS-CoV-2 on cell metabolism.

In summary, we have developed a novel pipeline of analysis combing HTM, AI assisted analysis and causality inferences using Bayesian statistics, to assess the impact of SARS-CoV-2 on the dynamics of cellular organelles and OCR. We report that the virus directly alters LDs, mitochondria, nuclei, and nucleoli as well as how those organelles influence each other. [...] This approach opens exciting possibilities to analyze any pathogen, drug effects, and physiological or pathological events affecting the cell life, including nutrient variations, metabolic adaptation, and malignant transformation. It holds promise to lead to new insights into the dynamics a vast range of biological processes.

 

[SNT Gatchaman]

Published as —

Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling (2024)
Saunders, Nell; Monel, Blandine; Cayet, Nadège; Archetti, Lorenzo; Moreno, Hugo; Jeanne, Alexandre; Marguier, Agathe; Buchrieser, Julian; Wai, Timothy; Schwartz, Olivier; Fréchin, Mathieu

Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence 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 and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation.

Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.

Link | PDF (Nature Communications) [Open Access]