Label-free microscopy for virus infections

Abstract

Microscopy has been essential to elucidate micro- and nano-scale processes in space and time, and has provided insight into cell and organismic function. It is widely employed in cell biology, microbiology, physiology, clinical sciences and virology. While label-dependent microscopy, such as fluorescence microscopy, provides molecular specificity, it has remained difficult to multiplex in live samples. In contrast, label-free microscopy reports on overall features of the specimen at minimal perturbation. Here, we discuss modalities of label-free imaging at the molecular, cellular and tissue levels, including transmitted light microscopy, quantitative phase imaging, cryogenic electron microscopy or tomography, and atomic force microscopy. We highlight how label-free microscopy is used to probe the structural organization and mechanical properties of viruses, including virus particles and infected cells across a wide range of spatial scales. We discuss the working principles of imaging procedures and analyses, and showcase how they open new avenues in virology. Finally, we discuss orthogonal approaches that enhance and complement label-free microscopy techniques. Mini Abstract Label-free imaging gives unprecedented insight into viruses at macroscopic, molecular and atomic levels. We present the major label-free imaging techniques and discuss how they are used for virus particles and infected cells. The power of label-free microscopy promises to enhance discovery of unknown aspects of infectious and therapeutic agents.

Machine learning for cross-scale microscopy of viruses

“…The development of imaging technology has been crucial to illuminate virus infections. 6 , 7 , 8 Enormous progress in optics and photonics has led to the development of microscopes capable of imaging at a multitude of spatial and temporal scales. Recently, new advances in super-resolution microscopy (SRM) have enabled particle resolution below the diffraction limit.…”

A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence

“…Light microscopy is suitable to study infected cells in live mode. It monitors changes in shape, morphology, and physiological state of individual cells or population of cells, and is suitable to assess infection variability [13][14][15][16] . In the past decade, automatic interpretation of microscopy images has been increasingly enhanced by deep learning (DL) and convolutional neural networks (CNNs) and enabled numerous applications in cell and infection biology 17,18 .…”