An unsupervised MVA method to compare specific regions in human breast tumor tissue samples using ToF-SIMS

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.

Successive High-Resolution (H2O)n-GCIB and C60-SIMS Imaging Integrates Multi-Omics in Different Cell Types in Breast Cancer Tissue

The temporo-spatial organization of different cells in the tumor microenvironment (TME) is the key to understanding their complex communication networks and the immune landscape that exists within compromised tissues. Multi-omics profiling of single-interacting cells in the native TME is critical for providing further information regarding the reprograming mechanisms leading to immunosuppression and tumor progression. This requires new technologies for biomolecular profiling of phenotypically heterogeneous cells on the same tissue sample. Here, we developed a new methodology for comprehensive lipidomic and metabolomic profiling of individual cells on frozen-hydrated tissue sections using water gas cluster ion beam secondary ion mass spectrometry ((H2O)n-GCIB-SIMS) (at 1.6 μm beam spot size), followed by profiling cell-type specific lanthanide antibodies on the same tissue section using C60-SIMS (at 1.1 μm beam spot size). We revealed distinct variations of distribution and intensities of >150 key ions (e.g., lipids and important metabolites) in different types of the TME individual cells, such as actively proliferating tumor cells as well as infiltrating immune cells. The demonstrated feasibility of SIMS imaging to integrate the multi-omics profiling in the same tissue section at the single-cell level will lead to new insights into the role of lipid reprogramming and metabolic response in normal regulation or pathogenic discoordination of cell-cell interactions in a variety of tissue microenvironments.

Machine-learning-enhanced time-of-flight mass spectrometry analysis

Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.

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A machine-learning method provides reliable atomic/molecular labels for ToF-MS

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No human labeling or prior information required

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The training dataset is artificially generated based on isotopic abundances

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Method validated on a variety of materials and two ToF-MS-based techniques

Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data.

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the “gold” standard for tissue classification. “All peaks” and “significantly different peaks” datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Glioblastoma (GBM) is one of the most aggressive human cancers with a median survival of less than two years. A distinguishing pathological feature of GBM is a high degree of inter- and intratumoral heterogeneity. Intertumoral heterogeneity of GBM has been extensively investigated on genomic, methylomic, transcriptomic, proteomic and metabolomics levels, however only a few studies describe intratumoral heterogeneity because of the lack of methods allowing to analyze GBM samples with high spatial resolution. Here, we applied TOF-SIMS (Time-of-flight secondary ion mass spectrometry) for the analysis of single cells and clinical samples such as paraffin and frozen tumor sections obtained from 57 patients. We developed a technique that allows us to simultaneously detect the distribution of proteins and metabolites in glioma tissue with 800 nm spatial resolution. Our results demonstrate that according to TOF-SIMS data glioma samples can be subdivided into clinically relevant groups and distinguished from the normal brain tissue. In addition, TOF-SIMS was able to elucidate differences between morphologically distinct regions of GBM within the same tumor. By staining GBM sections with gold-conjugated antibodies against Caveolin-1 we could visualize border between zones of necrotic and cellular tumor and subdivide glioma samples into groups characterized by different survival of the patients. Finally, we demonstrated that GBM contains cells that are characterized by high levels of Caveolin-1 protein and cholesterol. This population may partly represent a glioma stem cells. Collectively, our results show that the technique described here allows to analyze glioma tissues with a spatial resolution beyond reach of most of other omics approaches and the obtained data may be used to predict clinical behavior of the tumor.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Label-Free Time-of-Flight Secondary Ion Mass Spectrometry Imaging of Sulfur-Producing Enzymes inside Microglia Cells following Exposure to Silver Nanowires

There are no methods sensitive enough to detect enzymes within cells, without the use of analyte labeling. Here we show that it is possible to detect protein ion signals of three different H₂S-synthesizing enzymes inside microglia after pretreatment with silver nanowires (AgNW) using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Protein fragment ions, including the fragment of amino acid (C₄H₈N⁺ = 70 amu), fragments of the sulfur-producing cystathionine-containing enzymes, and the Ag⁺ ion signal could be detected without the use of any labels; the cells were mapped using the C₄H₈N⁺ amino acid fragment. Scanning electron microscopy imaging and energy-dispersive X-ray chemical analysis showed that the AgNWs were inside the same cells imaged by TOF-SIMS and transformed chemically into crystalline Ag₂S within cells in which the sulfur-producing proteins were detected. The presence of these sulfur-producing cystathionine-containing enzymes within the cells was confirmed by Western blots and confocal microscopy images of fluorescently labeled antibodies against the sulfur-producing enzymes. Label-free TOF-SIMS is very promising for the label-free identification of H₂S-contributing enzymes and their cellular localization in biological systems. The technique could in the future be used to identify which of these enzymes are most contributory.

Correlated Materials Characterization via Multimodal Chemical and Functional Imaging

Multimodal chemical imaging simultaneously offers high-resolution chemical and physical information with nanoscale and, in select cases, atomic resolution. By coupling modalities that collect physical and chemical information, we can address scientific problems in biological systems, battery and fuel cell research, catalysis, pharmaceuticals, photovoltaics, medicine, and many others. The combined systems enable the local correlation of material properties with chemical makeup, making fundamental questions of how chemistry and structure drive functionality approachable. In this Review, we present recent progress and offer a perspective for chemical imaging used to characterize a variety of samples by a number of platforms. Specifically, we present cases of infrared and Raman spectroscopies combined with scanning probe microscopy; optical microscopy and mass spectrometry; nonlinear optical microscopy; and, finally, ion, electron, and probe microscopies with mass spectrometry. We also discuss the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for the interpretation of multidimensional data acquired from multimodal studies.

Multicenter Study Using Desorption-Electrospray-Ionization-Mass-Spectrometry Imaging for Breast-Cancer Diagnosis

The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios ( m/ z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (>89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.

Imaging mass spectrometry for metabolites: technical progress, multimodal imaging, and biological interactions

Imaging mass spectrometry (IMS) allows the study of the spatial distribution of small molecules in biological samples. IMS is able to identify and quantify chemicals in situ from whole tissue sections to single cells. Both vacuum mass spectrometry (MS) and ambient MS systems have advanced considerably over the last decade; however, some limitations are still hard to surmount. Sample pretreatment, matrix or solvent choices, and instrument improvement are the key factors that determine the successful application of IMS to different samples and analytes. IMS with innovative MS analyzers, powerful MS spectrum databases, and analysis tools can efficiently dereplicate, identify, and quantify natural products. Moreover, multimodal imaging systems and multiple MS-based systems provide additional structural, chemical, and morphological information and are applied as complementary tools to explore new fields. IMS has been applied to reveal interactions between living organisms at molecular level. Recently, IMS has helped solve many previously unidentifiable relations between bacteria, fungi, plants, animals, and insects. Other significant interactions on the chemical level can also be resolved using expanding IMS techniques. WIREs Syst Biol Med 2017, 9:e1387. doi: 10.1002/wsbm.1387 For further resources related to this article, please visit the WIREs website.

Biomedical surface analysis: Evolution and future directions (Review)

This review describes some of the major advances made in biomedical surface analysis over the past 30-40 years. Starting from a single technique analysis of homogeneous surfaces, it has been developed into a complementary, multitechnique approach for obtaining detailed, comprehensive information about a wide range of surfaces and interfaces of interest to the biomedical community. Significant advances have been made in each surface analysis technique, as well as how the techniques are combined to provide detailed information about biological surfaces and interfaces. The driving force for these advances has been that the surface of a biomaterial is the interface between the biological environment and the biomaterial, and so, the state-of-the-art in instrumentation, experimental protocols, and data analysis methods need to be developed so that the detailed surface structure and composition of biomedical devices can be determined and related to their biological performance. Examples of these advances, as well as areas for future developments, are described for immobilized proteins, complex biomedical surfaces, nanoparticles, and 2D/3D imaging of biological materials.

Lipid Heterogeneity Resulting from Fatty Acid Processing in the Human Breast Cancer Microenvironment Identified by GCIB-ToF-SIMS Imaging

Breast cancer is an umbrella term used to describe a collection of different diseases with broad inter- and intratumor heterogeneity. Understanding this variation is critical in order to develop, and precisely prescribe, new treatments. Changes in the lipid metabolism of cancerous cells can provide important indications as to the metabolic state of the cells but are difficult to investigate with conventional histological methods. Due to the introduction of new higher energy (40 kV) gas cluster ion beams (GCIBs), time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is now capable of providing information on the distribution of hundreds of molecular species simultaneously on a cellular to subcellular scale. GCIB-ToF-SIMS was used to elucidate changes in lipid composition in nine breast cancer biopsy samples. Improved molecular signal generation by the GCIB produced location-specific information that revealed elevated levels of essential lipids to be related to inflammatory cells in the stroma, while cancerous areas were dominated by nonessential fatty acids and a variety of phosphatidylinositol species with further in-tumor variety arising from decreased desaturase activity. These changes in lipid composition due to different enzyme activity are seemingly independent of oxygen availability and can be linked to favorable cell membrane properties for either proliferation/invasion or drug resistance/survival.