Comprehensive characterization of lipid species and composition across specific brain regions is a significant biological challenge. Mass Spectrometry Imaging (MSI) can provide molecular distribution and spatial information, but most current tools do not allow for lipid annotation to biologically relevant regions of the brain.<br/>The multi-modal combination of MSI and anatomic structure presents a powerful approach for addressing this challenge. The Allen Brain Atlas (ABA) is an anatomical reference that allows for the registration of regio-specific molecular data with mouse brain sections at high-resolution contained within the database. Although there is a growing use of the ABA for anatomical registration, there is no easy to use and uniform workflow for registering MSI data to the ABA.<br/>This study addresses this gap in two phases. First, a workflow is presented using a custom-built Shiny R app which simplifies the data processing and statistical analysis of MSI data. Sections of brain tissue were imaged using desorption electrospray ionization MS (DESI-MS) and gross regional segments were identified using UMAP (Uniform Manifold Approximation and Projection). However, the metabolic variation could not discern specific nuclei within these regions.<br/>In the second phase, the tissues were Nissl stained to obtain high-resolution histological images to accurately identify the metabolite location in different regions of the brain. The images were registered to the ABA in Fiji. Subsequent registration of the MSI ion images to the same high-res histopathological image allows for transferring of the regional annotation information and quantitation of individual ions in a regio-specific manner.<br/>The integration of these workflows progress measurement science by providing tools for enhancing spatial precision and measurements of lipid compositions in specific brain regions, for a better understanding of neurological disease.

Enhancement of spatial precision for annotation of lipids in the brain

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ACS Fall 2022

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