Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis

Abstract

Rationale Identification of subregions under different pathological conditions on cancerous tissue is of great significance for understanding cancer progression and metastasis. Infrared matrix‐assisted laser desorption electrospray ionization mass spectrometry (IR‐MALDESI‐MS) can be potentially used for diagnostic purposes since it can monitor spatial distribution and abundance of metabolites and lipids in biological tissues. However, the large size and high dimensionality of hyperspectral data make analysis and interpretation challenging. To overcome these barriers, multivariate methods were applied to IR‐MALDESI data for the first time, aiming at efficiently resolving mass spectral images, from which these results were then used to identify normal regions within cancerous tissue. Methods Molecular profiles of healthy and cancerous hen ovary tissues were generated by IR‐MALDESI‐MS. Principal component analysis (PCA) combined with color‐coding built a single tissue image which summarizes the high‐dimensional data features. Pixels with similar color indicated similar composition. PCA results from healthy tissue were further used to test each pixel in cancerous tissue to determine if it is healthy. Multivariate curve resolution‐alternating least squares (MCR‐ALS) was used to obtain major spatial features existing in ovary tissues, and group molecules with the same distribution patterns simultaneously. Results PCA as the predominating dimensionality reduction approach captured over 90% spectral variances by the first three PCs. The PCA images show the cancerous tissue is more chemically heterogeneous than healthy tissue, where at least four regions with different m/z profiles can be differentiated. PCA modeling assigns top regions of cancerous tissue as healthy‐like. MCR‐ALS extracted three and four major compounds from healthy and cancerous tissue, respectively. Evaluating similarities of resolved spectra uncovered the chemical components that were distinct in some regions on cancerous tissue, serving as a supplementary way to differentiate healthy and cancerous regions. Conclusions Two unsupervised chemometric methods including PCA and MCR‐ALS were applied for resolving and visualizing IR‐MALDESI‐MS data acquired from hen ovary tissues, improving the interpretation of mass spectrometry imaging results. Then possible normal regions were differentiated from cancerous tissue sections. No prior knowledge is required using either chemometric method, so our approach is readily suitable for unstained tissue samples, which allows one to reveal the molecular events happening during disease progression.

Laser Desorption/Ablation Postionization Mass Spectrometry: Recent Progress in Bioanalytical Applications

“…A tutorial review describing the use of IR‐MALDESI for untargeted metabolic MSI analyses has been published (Bokhart & Muddiman, 2016). Recent reports on metabolic profiling using IR‐MALDESI include the following: optimization of the instrument parameters for mapping lipids, cholesterol, and small metabolites in mouse brain sections with improved ion abundances (Robichaud et al, 2014); identification of triterpenoid lipids and study of their spatial distribution in fermented cucumber with high salt concentration (Ekelöf et al, 2017); discriminating healthy and cancerous hen ovarian tissue from distribution and relative abundance of metabolites via polarity switching MSI (Nazari & Muddiman, 2016b) and from principal component analysis and multivariate curve resolution—alternating least squares resolved MSI data (Akbari Lakeh et al, 2019); again quantitative MSI of glutathione in healthy and cancerous hen ovarian, which revealed a ~2‐fold increase of glutathione concentration in the disease state (Nazari et al, 2018a); profiling underivatized neurotransmitters in rat brain tissues exposed to tetrabromobisphenol A, with MS images showing gender‐specific neurotransmitter distribution and level differences (Bagley et al, 2018); analysis of terpenes with optimized electrospray solvent composition and injection times (Nazari et al, 2018b). These reports demonstrate the feasibility of IR‐MALDESI to high throughput identification/quantification of biomolecules in complex matrixes.…”

Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry

“…Desorption electrospray ionization mass spectrometry (DESI-MS), the technique utilized in this work, uses charged solvent droplets to rapidly extract analytes, including metabolites, from the tissue surface [19]. The metabolic profile recorded by MS allows differentiation between cancerous tissue and normal tissue [20,21] and identification of tumor grade and subtype [22]. While still an experimental modality, DESI-MS has been used to distinguish between cancerous and normal tissue in a variety of human cancers including pancreatic [23], breast [24], brain [25][26][27], ovarian [21,28], and gastric cancers [29].…”

“…The metabolic profile recorded by MS allows differentiation between cancerous tissue and normal tissue [20,21] and identification of tumor grade and subtype [22]. While still an experimental modality, DESI-MS has been used to distinguish between cancerous and normal tissue in a variety of human cancers including pancreatic [23], breast [24], brain [25][26][27], ovarian [21,28], and gastric cancers [29]. To facilitate its potential intraoperative application, morphologically friendly spray solvents have been used so that the same specimen can be subjected to histopathologic analysis following MS measurement [30].…”

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

“…Spatial distributions of biomolecules within tissues enable changes in expression to be visualized between healthy and diseased regions of interest within two or three-dimensional specimens. − Mass spectrometry imaging (MSI) is an invaluable tool for obtaining these spatial distributions of biomolecules while still gaining a considerable amount of molecular depth. Existing MSI platforms have explored a variety of fields such as metabolomics, lipidomics, glycomics, and proteomics .…”