Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data

Patrick M Wehrli,Wojciech Michno,Henrik Zetterberg,Jörg Hanrieder,Kaj Blennow

doi:10.1007/s13361-019-02327-y

Abstract

Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology.

Highlights

Imaging mass spectrometry (IMS) allows delineation of histological features based on complex chemical fingerprints
Multivariate image analysis has been widely accepted as first step for unbiased interrogation of complex, chemical imaging data generated with imaging mass spectrometry
Set out to develop a chemometric strategy based on multivariate data analysis workflows for evaluating different IMS data processing methods and their suitability for robust and sensitive image segmentation and region of interest (ROI) annotation as well as to quantify the chemical information associated with histological regions of interest

Summary

Introduction

Imaging mass spectrometry (IMS) allows delineation of histological features based on complex chemical fingerprints This in turn provides comprehensive insight in molecular mechanisms associated with histopathological processes [1]. A further critical issue when analyzing IMS data is the accurate outlining of these histological features as ROI for subsequent extraction of the associated chemical content This ROI annotation and classification step is commonly done by outlining these ROI manually, a variety of methods such as thresholding and clustering methods have been presented for this purpose [9, 16]. We here describe a chemometric strategy for interrogation complex IMS data, to accurately identify anatomical regions of interest (ROIs) and quantify the chemical colocalization associated with these regions For this we, evaluate IMS raw data processing and its consequences for multivariate image analysis. Q2Y refers to the cumulative predicted fraction of variation in the Y-block, according to cross-validation

Results and Discussion

Conclusions

Funding Information