Automated Anatomical Interpretation of Ion Distributionsin Tissue: Linking Imaging Mass Spectrometry to Curated Atlases

摘要

Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computationalmethod, called correlation-based querying, gives an elementary demonstrationof the link by delivering basic insight into relationships betweenion images and anatomical structures. Finally, a third algorithm movesfurther beyond both registration and correlation by providing automatedanatomical interpretation of ion images. This task is approached asan optimization problem that deconstructs ion distributions as combinationsof known anatomical structures. We demonstrate that establishing alink between an IMS experiment and an anatomical atlas enables automatedanatomical annotation, which can serve as an important acceleratorboth for human and machine-guided exploration of IMS experiments.