Information-rich localization microscopy through machine learning

Abstract

While current single-molecule localization microscopy (SMLM) methods often rely on the target-specific alteration of the point spread function (PSF) to encode the multidimensional contents of single fluorophores, we argue that the details of the PSF in an unmodified microscope already contain rich, multidimensional information. We introduce a data-driven approach in which artificial neural networks (ANNs) are trained to make a direct link between an experimental PSF image and its underlying parameters. To demonstrate this concept in real systems, we decipher in fixed cells both the colors and the axial positions of single molecules in regular SMLM data.