Abstract
Fully capturing the heterogeneity of biological processes remains a central challenge, as conventional confocal microscopy typically surveys large ensembles of molecules within a small volume. The development of super-resolution imaging enables the capture of images with exceptional detail. When super-resolution is combined with functional imaging, such as fluorescence lifetime and spectral imaging, it provides better separation of image constituents and sensing of environmental properties at the nanoscale. These multidimensional imaging approaches often acquire functional and fluorescence images separately and then merge the resulting datasets, a post hoc approach that risks dynamic range mismatches and the loss of subtle molecular signals. Here, we introduce a computational super-resolution framework based on the deblurring by pixel reassignment (DPR) algorithm, which processes multidimensional data as high-dimensional tensors, integrating spatial axes with fluorescence lifetime or spectral information into a single dataset. Using DPR, high-resolution details can be extracted from conventional fluorescence microscopy images, even from a single capture. We show that this approach enhances the spatial resolution of multidimensional datasets without compromising quantitative fidelity. As a proof of concept, we demonstrate super-resolution multimodal imaging (intensity, lifetime, and spectra), revealing fine-scale details while preserving signals from rare or low-intensity molecular events. This accessible yet powerful method paves the way for quantitative, single-molecule–level insights into complex biological systems.