Iterative Spatial Resolution Enhancement in Imaging Mass Spectrometry via Hydrogel Tissue Expansion and Multimodal Image Fusion

Abstract

The pixel size of imaging mass spectrometry (IMS) is fundamentally limited by several factors, including the diameter of the incident probe and the raster step size of the sample stage. We have previously demonstrated that hydrogel-based tissue expansion, originally developed for microscopy (ExM), can also be adapted for imaging mass spectrometry to physically magnify the size of the tissue. Expansion imaging mass spectrometry (ExIMS) uses a superabsorbent hydrogel to isotropically expand thin tissue sections, which can then be sampled via imaging mass spectrometry, resulting in improved effective spatial resolution. Separately, multimodal image fusion has been used to computationally upsample the effective spatial resolution in imaging mass spectrometry by predictively mapping mass spectrometric intensity values to the smaller diameter pixel sizes of a microscopy image of the same tissue section. Here, we present ExFusion, a unified workflow that combines these two approaches by computationally fusing structurally detailed fluorescent ExM and chemically detailed lipid ExIMS data obtained from the same 9.4-fold expanded mouse brain tissue. Following a 10-fold upsampling from image fusion, multimodal expansion image fusion enabled prediction of MS images at a [~]106 nm pixel size on a commercial mass spectrometer using a 10 m raster step size. At this resolution, lipids in the Purkinje cells of the cerebellum are clearly defined with intracellular distributions.