Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Abstract

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii ) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches. M ass spectrometry imaging (MSI) of biological tissue sections can provide topographically localized biochemical information to supplement conventional histopathological classification systems (1-3). Together with emerging metabolomicsbased profiling approaches, MSI represents a highly promising approach in molecular systems oncology (4, 5) and is increasingly being used for the discovery of next-generation cancer biomarker panels (6, 7). Among the MSI techniques currently available, the three most commonly used are matrix-assisted laser desorption ionization (MALDI) (2, 6), secondary ion mass spectrometry (SIMS) (8, 9), and desorption electrospray ionization (DESI) (10, 11). With each of these described approaches, operating characteristics and experimental parameters can be modulated to suit specific analytical objectives and can be customized for the identification of particular biomolecular species. Here, we have opted to use the DESI technique as there are several practical advantages with this method for metabolome-wide imaging studies, primarily attributable to lack of requirement for matrix deposition and ambient ionization, which requires minimal sample preparation (11,12).Currently MSI is likely to exert greatest influence at the prognostic and therapeutic stages of the disease continuum (Fig. 1), with three fundamental areas of application in cancer phenotyping. First, it offers a mea...

Spatially Resolved Metabolic Phenotyping of Breast Cancer by Desorption Electrospray Ionization Mass Spectrometry

“…Preprocessed data were subjected to multivariate statistical data analysis. Recursive maximum margin criterion (RMMC) analysis was used for supervised discrimination and classification (25,26). Tissue types in each sample and their spatial distribution were determined by histologic examination by an independent consultant histopathologist.…”

“…The most intense peaks, with a P value < 2 Â 10 À5 , are summarized in Table 3. Very high fold changes (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37) were found for signals associated with lactate and calcidiol. Lactate was found as a dimer and in form of several sodium chloride clusters (MþNa 4 Cl 4 , MþNa 3 Cl 3 , MþNa 2 Cl 2 ).…”

Imaging of Esophageal Lymph Node Metastases by Desorption Electrospray Ionization Mass Spectrometry

“…Multivariate classification of the test set pixels was based on a combination of linear discriminant analysis with recursive maximum margin criterion previously described in detail (35). The primary aim was to classify each pixel (mass spectrum) of the test set as either healthy, tumor or glass slide.…”

XMS: Cross-Platform Normalization Method for Multimodal Mass Spectrometric Tissue Profiling

“…3.0.4468 64-bit) was used to transform Thermo Xcalibur and Waters .raw files/folders of porcine and REIMS colorectal samples to mzXML format using 64-bit precision for m/z and intensity dimension [31]. Colorectal DESI images were converted to imzML file format and co-registered with H&E stained sections for detection of mucosa tissue type by a histologist [10]. Mass spectra from the selected regions of interest (healthy and cancerous mucosa) were subjected to XMS.…”

“…Well established techniques for MSI of biological tissue include SIMS [2], desorption electrospray ionization (DESI) [3], matrix assisted laser desorption/ionization (MALDI) [4,5], and laser desorption/ionization (LDI) among others. These techniques capture lipid species in the low molecular weight range, which have been shown to be closely associated with histologic or histopathologic tissue types [6][7][8][9][10]. Thus, MSI has the potential to provide fully automated, MSbased tissue identification systems for next generation chemical histology.…”