Abstract

Biomolecular changes linked to disease can be studied by integrating Imaging Mass Spectrometry (IMS) with histopathology. However, co-registering IMS with optical images of stained tissues is challenging due to sample preparation constraints and resolution discrepancies, particularly as IMS advances toward single-cell resolution. This work evaluates workflows for multimodal tissue analysis (sequential vs. consecutive) by combining IMS with histological imaging and incorporating laser-etched indium tin oxide (ITO) slides to improve image registration at the cellular level.
In this study, coronal mouse brain tissues were analyzed using cluster ion beam secondary ion mass spectrometry (SIMS) and/or matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. For high-resolution MALDI imaging, 1,5-diaminonaphthalene matrix was sublimated on the tissue sections. Traditionally, serial tissue sections have been used for hematoxylin and eosin (H&E) staining and IMS image co-registration. Here, we examine the benefits and caveats of staining the same tissue section post-IMS analysis.
The 35 µm spatial resolution of our TOF-MALDI instrument exceeds the average diameter of a mouse brain cell, limiting single-cell multimodal IMS analysis. To overcome segmentation challenges, we employed Cell Segmentation Globally Optimized (CSGO), an open-source deep learning model specifically optimized for histological images, which enables accurate and automated whole-cell segmentation from optical microscopy of H&E-stained tissue images. While consecutive sections align overall tissue structure, they fail at cellular precision due to misalignment. By combining deep learning-based segmentation with same-section multimodal co-registration using laser-etched fiducial markers, we achieved improved spatial alignment for high-resolution molecular mapping, advancing disease characterization and biomarker discovery.