Data-driven microscopy allows for automated targeted acquisition of relevant data with higher fidelity

Abstract

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving highquality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM), that uses population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity.