'In silico labelling': Exploring the biological landscape at the nanoscale using intelligent multimodal optical microscopy.

Multimodal optical microscopy has recently converged with artificial intelligence (AI) to enable in silico labelling-the computational prediction of fluorescence-like molecular contrast from label-free measurements. Fluorescence plays a crucial role in linking microscopy and spectroscopy at molecular level, enabling image formation by means of linear and non-linear investigation modalities. Over the past two decades, fluorescence optical microscopy has progressed into optical nanoscopy and single molecule localisation methods that operate at the nano- and even Ångstrom level under ambient conditions. Methods such as Stimulated Emission Depletion (STED), Photo-Activated Localisation (PALM), Stochastic Optical Reconstruction (STORM), super-resolution fluorescence lifetime imaging microscopy (FLIM) and image scanning microscopy (ISM), Minimal Fluorescence photon Flux (MINFLUX) microscopy allows us to investigate living cells at the molecular level. A significant and challenging development in this field is the coupling of fluorescence with label-free polarisation and phase optical methods-for example, Mueller-matrix (MM) microscopy-with the aim of extracting specific molecular information from label-free datasets. In this review, we discuss how this convergence, together with modern generative modelling for in silico labelling, is turning the optical microscope into an intelligent instrument.