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 high-quality 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 singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity.