Implementation of Mass Spectrometry Imaging in Pathology: Advances and Challenges

Differential distribution of characteristic constituents in peel and pulp of Aurantii Fructus Immaturus (Citrus aurantium L.) using MALDI mass spectrometry imaging

Aurantii Fructus Immaturus (AFI) was structurally divided into two parts named "peel" and "pulp". The exocarp and mesocarp of materials named "peel". The endocarp separated into multiple compartments and the cystic hair attached to it named "pulp". In order to explore the distribution and content of constituents in AFI, an efficient method to explore the distribution of constituents was developed based on matrix assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). After simple processing, thirty-two constituents with distinct localization in the mass range of 101-1200 Da were identified by MALDI-FTICR-MSI. In addition, the identified four flavnoids (poncirin, sinensetin, 3,5,6,7,8,3',4'-heptemthoxyflavone, and tangeritin) were analyzed for differences between using LC-MS. Quantitative analysis results supported the quantitative results from MALDI-FT-ICR-MSI. The results implied that different parts had different constituents in AFI, and demonstrated MALDI-MSI have high potential in the direct analysis of constituents.

Unlocking the Molecular Variations of a Micron-Scale Amyloid Plaque in an Early Stage Alzheimer's Disease by a Cellular-Resolution Mass Spectrometry Imaging Platform

Uncovering the molecular changes at the site where Aβ is deposited plays a critical role in advancing the diagnosis and treatment of Alzheimer's disease. However, there is currently a lack of a suitable label-free imaging method with a high spatial resolution for brain tissue analysis. In this study, we propose a modified desorption electrospray ionization (DESI) mass spectrometry imaging (MSI) method, called segmented temperature-controlled DESI (STC-DESI), to achieve high-resolution and high-sensitivity spatial metabolomics observation by precisely controlling desorption and ionization temperatures. By concentrating the spray plume and accelerating solvent evaporation at different temperatures, we achieved an impressive spatial resolution of 20 μm that enables direct observation of the heterogeneity around a single cell or an individual Aβ plaque and an exciting sensitivity that allows a variety of low-abundance metabolites and less ionizable neutral lipids to be detected. We applied this STC-DESI method to analyze the brains of transgenic AD mice and identified molecular changes associated with individual Aβ aggregates. More importantly, our study provides the first evidence that carnosine is significantly depleted and 5-caffeoylquinic acid (5-CQA) levels rise sharply around Aβ deposits. These observations highlight the potential of carnosine as a sensitive molecular probe for clinical magnetic resonance imaging diagnosis and the potential of 5-CQA as an efficient therapeutic strategy for Aβ clearance in the early AD stage. Overall, our findings demonstrate the effectiveness of our STC-DESI method and shed light on the potential roles of these molecules in AD pathology, specifically in cellular endocytosis, gray matter network disruption, and paravascular Aβ clearance.

Chemical QuantArray: A Quantitative Tool for Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a powerful analytical technique that provides spatially preserved detection and quantification of analytes in tissue specimens. However, clinical translation still requires improved throughput, precision, and accuracy. To accomplish this, we created "Chemical QuantArray", a gelatin tissue microarray (TMA) mold filled with serial dilutions of isotopically labeled endogenous metabolite standards. The mold is then cryo-sectioned onto a tissue homogenate to produce calibration curves. To improve precision and accuracy, we automatically remove pixels outside of each TMA well and investigated several intensity normalizations, including the utilization of a second stable isotope internal standard (IS). Chemical QuantArray enables the quantification of several endogenous metabolites over a wide dynamic range and significantly improve over current approaches. The technique reduces the space needed on the MALDI slides for calibration standards by approximately 80%. Furthermore, removal of empty pixels and normalization to an internal standard or matrix peak provided precision (<20% RSD) and accuracy (<20% DEV). Finally, we demonstrate the applicability of Chemical QuantArray by quantifying multiple purine metabolites in 14 clinical tumor specimens using a single MALDI slide. Chemical QuantArray improves the analytical characteristics and practical feasibility of MALDI-MSI metabolite quantification in clinical and translational applications.

Self-supervised clustering analysis of colorectal cancer biomarkers based on multi-scale whole slides image and mass spectrometry imaging fused images

Mass spectrometry imaging (MSI) is widely used for unlabeled molecular co-localization in biological samples and is also commonly used for screening cancer biomarkers. The main issues affecting the screening of cancer biomarkers are: 1) low-resolution MSI and pathological slices cannot be accurately matched; 2) a large amount of MSI data cannot be directly analyzed without manual annotation. This paper proposes a self-supervised cluster analysis method for colorectal cancer biomarkers based on multi-scale whole slide images (WSI) and MSI fusion images without manual annotation, which can accurately determine the correlation between molecules and lesion areas. This paper uses the combination of WSI multi-scale high-resolution and MSI high-dimensional data to obtain high-resolution fusion images. This method can observe the spatial distribution of molecules in pathological slices and use this method as an evaluation index for self-supervised screening of cancer biomarkers. The experimental results show that the method proposed in this chapter can train the image fusion model with a small amount of MSI and WSI data, and the mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) evaluation metrics of the fused images can reach 0.9587 and 0.8745. And self-supervised clustering using MSI features and fused image features can obtain good classification results, and the precision, recall, and F1-score values of the self-supervised model reach 0.9074, 0.9065, and 0.9069, respectively. This method effectively combines the advantages of WSI and MSI, which will significantly expand the application scenarios of MSI and facilitate the screening of disease markers.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.

A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer

Simple Summary

Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient.

Abstract

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.