The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

Visualization of Small Intact Proteins in Breast Cancer FFPE Tissue

Molecular visualization of metabolites, lipids, and proteins by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is becoming an in-demand analytical approach to aid the histopathological analysis of breast cancer. Particularly, proteins seem to play a role in cancer progression, and specific proteins are currently used in the clinic for staging. Formalin-fixed paraffin-embedded (FFPE) tissues are ideal for correlating the molecular markers with clinical outcomes due to their long-term storage. So far, to obtain proteomic information by MSI from this kind of tissue, antigen retrieval and tryptic digestion steps are required. In this chapter, we present a protocol to spatially detect small proteins in tumor and necrotic regions of patient-derived breast cancer xenograft FFPE tissues without employing any on-tissue digestion. This protocol can be used for other kinds of FFPE tissue following specific optimization of the sample preparation phases.

Evaluation of Formalin-Fixed and FFPE Tissues for Spatially Resolved Metabolomics and Drug Distribution Studies

Fixation of samples is broadly used prior to the histological evaluation of tissue samples. Though recent reports demonstrated the ability to use fixed tissues for mass spectrometry imaging (MSI) based proteomics, glycomics and tumor classification studies, to date comprehensive evaluation of fixation-related effects for spatially resolved metabolomics and drug disposition studies is still missing. In this study we used matrix assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI to investigate the effect of formalin-fixation and formalin-fixation combined with paraffin embedding on the detectable metabolome including xenobiotics. Formalin fixation was found to cause significant washout of polar molecular species, including inorganic salts, amino acids, organic acids and carnitine species, oxidation of endogenous lipids and formation of reaction products between lipids and fixative ingredients. The slow fixation kinetics under ambient conditions resulted in increased lipid hydrolysis in the tissue core, correlating with the time-dependent progression of the fixation. Paraffin embedding resulted in subsequent partial removal of structural lipids resulting in the distortion of the elucidated biodistributions.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

Spatially Resolved Neuropeptide Characterization from Neuropathological Formalin-Fixed, Paraffin-Embedded Tissue Sections by a Combination of Imaging MALDI FT-ICR Mass Spectrometry Histochemistry and Liquid Extraction Surface Analysis-Trapped Ion Mobility Spectrometry-Tandem Mass Spectrometry

To make the vast collections of well-documented human clinical samples archived in biobanks accessible for mass spectrometry imaging (MSI), recent developments have focused on the label-free top-down MS analysis of neuropeptides in sections of formalin-fixed, paraffin-embedded (FFPE) tissues. In analogy to immunohistochemistry (IHC), this variant of MSI has been designated MSHC (mass spectrometry histochemistry). Besides the detection and localization of neuropeptide and other biomolecular MS signals in these FFPE samples, there is great interest in their molecular identification and full characterization. We here used matrix assisted laser desorption ionization (MALDI) MSI employing ultrahigh-resolution FT-ICR MS on 2,5-dihydroxybenzoic acid (DHB) coated five-micron sections of human FFPE pituitary to demonstrate clear isotope patterns and elemental composition assignment of neuropeptides (with ∼1 ppm mass accuracy). Besides tandem MS fragmentation pattern analysis to deduce or confirm amino acid sequence information (Arg-vasopressin for the case presented here), there is a need for orthogonal primary structure characterization of the peptide-like MS signals of biomolecules desorbed directly off FFPE tissue sections. In the present work, we performed liquid extraction surface analysis (LESA) extractions on consecutive (uncoated) tissue slices. This enables the successful characterization by ion mobility MS of vasopressin present in FFPE material. Differences in sequence coverage are discussed on the basis of the mobility selected collision induced dissociation (CID), electron capture dissociation (ECD), and UV photodissociation (UVPD) MS/MS. Using Arg-vasopressin as model case (a peptide with a disulfide bridged ring structure), we illustrate the use of LESA in combination with a reduction agent for effective sequencing using mobility selected CID, ECD, and UVPD MS/MS.

Mass Spectrometry-Based Differentiation of Oral Tongue Squamous Cell Carcinoma and Nontumor Regions With the SpiderMass Technology

Oral cavity cancers are the 15th most common cancer with more than 350,000 new cases and ~178,000 deaths each year. Among them, squamous cell carcinoma (SCC) accounts for more than 90% of tumors located in the oral cavity and on oropharynx. For the oral cavity SCC, the surgical resection remains the primary course of treatment. Generally, surgical margins are defined intraoperatively using visual and tactile elements. However, in 15–30% of cases, positive margins are found after histopathological examination several days postsurgery. Technologies based on mass spectrometry (MS) were recently developed to help guide surgical resection. The SpiderMass technology is designed for in-vivo real-time analysis under minimally invasive conditions. This instrument achieves tissue microsampling and real-time molecular analysis with the combination of a laser microprobe and a mass spectrometer. It ultimately acts as a tool to support histopathological decision-making and diagnosis. This pilot study included 14 patients treated for tongue SCC (T1 to T4) with the surgical resection as the first line of treatment. Samples were first analyzed by a pathologist to macroscopically delineate the tumor, dysplasia, and peritumoral areas. The retrospective and prospective samples were sectioned into three consecutive sections and thaw-mounted on slides for H&E staining (7 μm), SpiderMass analysis (20 μm), and matrix-assisted laser desorption ionization (MALDI) MS imaging (12 μm). The SpiderMass microprobe collected lipidometabolic profiles of the dysplasia, tumor, and peritumoral regions annotated by the pathologist. The MS spectra were then subjected to the multivariate statistical analysis. The preliminary data demonstrate that the lipidometabolic molecular profiles collected with the SpiderMass are significantly different between the tumor and peritumoral regions enabling molecular classification to be established by linear discriminant analysis (LDA). MALDI images of the different samples were submitted to segmentation for cross instrument validation and revealed additional molecular discrimination within the tumor and nontumor regions. These very promising preliminary results show the applicability of the SpiderMass to SCC of the tongue and demonstrate its interest in the surgical treatment of head and neck cancers.