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The development of optical imaging methods has revolutionized the understanding of modern biology because they provide unprecedented spatial and temporal information of targets across different scales, from subcellular proteins to whole organs1. Among them, fluorescence microscopy is the most well-established, with a large palette of organic dyes with high extinction coefficients and quantum yields2, easy-to-use genetic-encoded fluorescent proteins3, and super-resolution methods such as STED, PALM, and STORM for imaging nanometer-scale structures4,5. In addition, recent advancements in sample engineering and preservation chemistry, which expand specimens embedded in swellable polymer hydrogels6,7,8, enable sub-diffraction limited resolution on conventional fluorescence microscopes. For instance, typical expansion microscopy (ExM) effectively enhances the image resolution by four times with fourfold isotropic sample expansion7.
Despite its advantages, super-resolution fluorescence microscopy shares limitations that originate from fluorophore labeling. First, photobleaching and inactivation of fluorophores compromise the capacity for repetitive and quantitative fluorescence evaluations. Photobleaching is an inevitable event when light keeps pumping electrons into electronically excited states9. Second, labeling the fluorophores to the desired targets is not always a straightforward task. For instance, immunostaining demands a long and laborious sample preparation process and hinders imaging throughput10. It could also introduce artifacts due to inhomogeneous antibody-labeling, especially deep inside tissues11. Moreover, proper labeling strategies that target fluorophores for the desired proteins might be underdeveloped. For example, extensive screenings were required to find effective antibodies for Aβ plaques12. Smaller organic dyes, such as Congo red, often have limited specificity, only staining the core of the Aβ plaque. It is, therefore, highly desirable to develop a label-free super-resolution modality that circumvents the drawbacks of fluorophore-labeling and provides complementary high-resolution imaging from cells to tissues, and even to large-scale human samples.
Raman microscopy provides label-free contrast for chemical-specific structures and maps out the distribution of otherwise invisible chemical bonds by looking at the excited vibrational transitions13. In particular, stimulated Raman scattering (SRS) imaging on label-free or tiny-labeled samples has been demonstrated to have similar speed and resolution to fluorescence microscopy14,15. For example, healthy brain region has been readily differentiated from tumor-infiltrated region in human and mouse tissues16,17. Aβ plaques were also clearly imaged by targeting protein CH3 vibration (2940 cm−1) and amide I (1660 cm−1) on a fresh-frozen brain slice without any labeling18. Raman scattering, therefore, offers robust label-free contrast that overcomes the limitations of fluorophores. The question then became how one can accomplish super-resolution capacity using Raman scattering, which could reveal nanoscale structural details and functional implications in biological samples.
Although extensive efforts have been made to achieve super-resolution for Raman microscopy with elegant optic instrumentations, the resolution enhancement on biological samples has been rather limited19,20,21. Here, based on the recent works22,23, we present a protocol that combines a sample-expansion strategy with stimulated Raman scattering for super-resolution label-free vibrational imaging, named Vibrational Imaging of Swelled Tissues and Analysis (VISTA). First, cells and tissues were embedded in hydrogel matrixes through an optimized protein-hydrogel hybridization protocol. The hydrogel tissue hybrids were then incubated in detergent-rich solutions for delipidation, followed by expansion in water. The expanded samples were then imaged by a regular SRS microscope by targeting CH3 vibrations from retained endogenous proteins. VISTA, owing to its label-free imaging feature, bypasses photobleaching and inhomogeneous labeling arising from fluorophore labeling, with much higher sample processing throughput. This is also the first sub-100 nm (down to 78 nm) label-free imaging reported. No additional optical instrumentation besides typical SRS setup22,24 is required, making it readily applicable. With correlative VISTA and immunofluorescence images, an established machine-learning image-segmentation algorithm was trained25,26 to generate protein-specific multiplex images from single-channel images. The method was further applied to investigate Aβ plaques in mouse brain tissues, providing a holistic image suited for sub-phenotyping based on the fine views of the plaque core and peripheral filaments surrounded by cell nuclei and blood vessels.