Beyond phase signals: digital holographic microscopy and AI revealing disease-specific cell phenotypes

Digital holographic microscopy (DHM) has emerged as a powerful quantitative phase imaging technique offering label-free, non-invasive visualization of cell structures and dynamics. Specifically, DHM provides a quantitative phase signal (QPS) that is highly sensitive, particularly to dry mass, which has led to the development of attractive applications in cell biology. QPS contains, in an intricate way, a large amount of information about the cell content and morphology. Its interferometric detection gives it a high degree of sensitivity, but at the same time it is contaminated with a coherent noise that makes a precise analysis of cell information it contains difficult. I’ll present a series of technical developments that have enabled us to obtain a quasi-coherent noise-free QPS from which we can extract a set of cellular parameters. This paves the way for high-content, label-free screening to identify disease-specific cell phenotypes. Some applications related to neuropsychiatric diseases will be presented. Finally, it will be shown how AI can take advantage of these technical developments to enable label-free cell phenotyping without the need for cumbersome instrumentation.