Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.

CODA: quantitative 3D reconstruction of large tissues at cellular resolution

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

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Mass spectrometry imaging (MSI) is a powerful molecular imaging technique.

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Integration with other imaging modalities is essential in clinical MSI.

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Image integration is performed by image registration techniques.

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Technical potential of image registration in MSI has not been fully exploited.

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Roadmap proposed to improve registration accuracy.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.

High-resolution MRI of the human palatine tonsil and its schematic anatomic 3D reconstruction

The palatine tonsils form an important part of the human immune system. Together with the other lymphoid tonsils of Waldeyer's tonsillar ring, they act as the first line of defense against ingested or inhaled pathogens. Although histologically stained sections of the palatine tonsil are widely available, they represent the tissue only in two dimensions and do not provide reference to three‐dimensional space. Such a representation of a tonsillar specimen based on imaging data as a 3D anatomical reconstruction is lacking both in scientific publications and especially in textbooks. As a first step in this direction, the objective of the present work was to image a resected tonsil specimen with high spatial resolution in a 9.4 T small‐bore pre‐clinical MRI and to combine these data with data from the completely sectioned and H&E stained same palatine tonsil. Based on the information from both image modalities, a 3D anatomical sketch was drawn by a scientific graphic artist. In perspective, such studies could help to overcome the difficulty of capturing the spatial extent and arrangement of anatomical structures from 2D images and to establish a link between three‐dimensional anatomical preparations and two‐dimensional sections or illustrations, as they have been found so far in common textbooks and anatomical atlases.

The New Era of Three-Dimensional Histoarchitecture of the Human Endometrium

The histology of the endometrium has traditionally been established by observation of two-dimensional (2D) pathological sections. However, because human endometrial glands exhibit coiling and branching morphology, it is extremely difficult to obtain an entire image of the glands by 2D observation. In recent years, the development of three-dimensional (3D) reconstruction of serial pathological sections by computer and whole-mount imaging technology using tissue clearing methods with high-resolution fluorescence microscopy has enabled us to observe the 3D histoarchitecture of tissues. As a result, 3D imaging has revealed that human endometrial glands form a plexus network in the basalis, similar to the rhizome of grass, whereas mouse uterine glands are single branched tubular glands. This review summarizes the relevant literature on the 3D structure of mouse and human endometrium and discusses the significance of the rhizome structure in the human endometrium and the expected role of understanding the 3D tissue structure in future applications to systems biology.

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

Research on athlete training behavior based on improved support vector algorithm and target image detection

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometry

Identifying the spatial distributions of biomolecules in tissue is crucial for understanding integrated function. Imaging mass spectrometry (IMS) allows simultaneous mapping of thousands of biosynthetic products such as lipids but has needed a means of identifying specific cell-types or functional states to correlate with molecular localization. We report, here, advances starting from identity marking with a genetically encoded fluorophore. The fluorescence emission data were integrated with IMS data through multimodal image processing with advanced registration techniques and data-driven image fusion. In an unbiased analysis of spleens, this integrated technology enabled identification of ether lipid species preferentially enriched in germinal centers. We propose that this use of genetic marking for microanatomical regions of interest can be paired with molecular information from IMS for any tissue, cell-type, or activity state for which fluorescence is driven by a gene-tracking allele and ultimately with outputs of other means of spatial mapping.

Developing a Drug Screening Platform: MALDI-Mass Spectrometry Imaging of Paper-Based Cultures

Many potential chemotherapeutics fail to reach patients. One of the key reasons is that compounds are tested during the drug discovery stage in two-dimensional (2D) cell cultures, which are often unable to accurately model in vivo outcomes. Three-dimensional (3D) in vitro tumor models are more predictive of chemotherapeutic effectiveness than 2D cultures, and thus, their implementation during the drug screening stage has the potential to more accurately evaluate compounds earlier, saving both time and money. Paper-based cultures (PBCs) are an emerging 3D culture platform in which cells suspended in Matrigel are seeded into paper scaffolds and cultured to generate a tissue-like environment. In this study, we demonstrate the potential of matrix-assisted laser desorption/ionization-mass spectrometry imaging with PBCs (MALDI-MSI-PBC) as a drug screening platform. This method discriminated regions of the PBCs with and without cells and/or drugs, indicating that coupling PBCs with MALDI-MSI has the potential to develop rapid, large-scale, and parallel mass spectrometric drug screens.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Identification of Proteomic Markers in Head and Neck Cancer Using MALDI-MS Imaging, LC-MS/MS, and Immunohistochemistry

PURPOSE

The heterogeneity of squamous cell carcinoma tissue greatly complicates diagnosis and individualized therapy. Therefore, characterizing the heterogeneity of tissue spatially and identifying appropriate biomarkers is crucial. MALDI-MS imaging (MSI) is capable of analyzing spatially resolved tissue biopsies on a molecular level.

EXPERIMENTAL DESIGN

MALDI-MSI is used on snap frozen and formalin-fixed and paraffin-embedded (FFPE) tissue samples from patients with head and neck cancer (HNC) to analyze m/z values localized in tumor and nontumor regions. Peptide identification is performed using LC-MS/MS and immunohistochemistry (IHC).

RESULTS

In both FFPE and frozen tissue specimens, eight characteristic masses of the tumor's epithelial region are found. Using LC-MS/MS, the peaks are identified as vimentin, keratin type II, nucleolin, heat shock protein 90, prelamin-A/C, junction plakoglobin, and PGAM1. Lastly, vimentin, nucleolin, and PGAM1 are verified with IHC.

CONCLUSIONS AND CLINICAL RELEVANCE

The combination of MALDI-MSI, LC-MS/MS, and subsequent IHC furnishes a tool suitable for characterizing the molecular heterogeneity of tissue. It is also suited for use in identifying new representative biomarkers to enable a more individualized therapy.

Fourier Transform Infrared Microscopy Enables Guidance of Automated Mass Spectrometry Imaging to Predefined Tissue Morphologies

Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Acquisition, Visualization and Potential Applications of 3D Data in Anatomic Pathology

Although human anatomy and histology are naturally three-dimensional (3D), commonly used diagnostic and educational tools are technologically restricted to providing two-dimensional representations (e.g. gross photography and glass slides). This limitation may be overcome by employing techniques to acquire and display 3D data, which refers to the digital information used to describe a 3D object mathematically. There are several established and experimental strategies to capture macroscopic and microscopic 3D data. In addition, recent hardware and software innovations have propelled the visualization of 3D models, including virtual and augmented reality. Accompanying these advances are novel clinical and non-clinical applications of 3D data in pathology. Medical education and research stand to benefit a great deal from utilizing 3D data as it can change our understanding of complex anatomical and histological structures. Although these technologies are yet to be adopted in routine surgical pathology, forensic pathology has embraced 3D scanning and model reconstruction. In this review, we intend to provide a general overview of the technologies and emerging applications involved with 3D data.