A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy.

Area of Science:

• Computational Biology
• Virology
• Microscopy Imaging

Background:

• Detecting virus-infected cells in light microscopy typically relies on reporter signals from immunohistochemistry or genetic engineering.
• Existing machine learning methods for cell infection detection offer classification but lack continuous signal nuance.
• Virtual staining presents an opportunity for continuous signal detection but is underexplored for virus-infected cells.

Purpose of the Study:

• To establish a benchmark and curated datasets for the novel task of virus infection reporter virtual staining (VIRVS).
• To explore the application of deep learning models for generating continuous virtual staining signals in virus-infected cells.
• To define a new research challenge at the intersection of data science and virology.

Main Methods:

• Collation of diverse microscopy datasets featuring various viruses and imaging modalities.
• Implementation and evaluation of U-Net and pix2pix architectures for virtual staining.
• Exploration of both regressive and generative model approaches for the VIRVS task.

Main Results:

• The study provides a comprehensive benchmark for the VIRVS task.
• Demonstrated the feasibility of using U-Net and pix2pix for virtual staining of virus-infected cells.
• Established datasets and methodologies for future research in this domain.

Conclusions:

• The proposed benchmark and datasets facilitate the advancement of virtual staining techniques for virology.
• This work opens new avenues for quantitative analysis of viral infections using microscopy.
• The study defines a significant challenge in applying data science to virological imaging.