MALDI-MSI of Immunotherapy: Mapping the EGFR-Targeting Antibody Cetuximab in 3D Colon-Cancer Cell Cultures

Morphological and molecular preservation through universal preparation of fresh-frozen tissue samples for multimodal imaging workflows

The landscape of tissue-based imaging modalities is constantly and rapidly evolving. While formalin-fixed, paraffin-embedded material is still useful for histological imaging, the fixation process irreversibly changes the molecular composition of the sample. Therefore, many imaging approaches require fresh-frozen material to get meaningful results. This is particularly true for molecular imaging techniques such as mass spectrometry imaging, which are widely used to probe the spatial arrangement of the tissue metabolome. As high-quality fresh-frozen tissues are limited in their availability, any sample preparation workflow they are subjected to needs to ensure morphological and molecular preservation of the tissues and be compatible with as many of the established and emerging imaging techniques as possible to obtain the maximum possible insights from the tissues. Here we describe a universal sample preparation workflow, from the initial step of freezing the tissues to the cold embedding in a new hydroxypropyl methylcellulose/polyvinylpyrrolidone-enriched hydrogel and the generation of thin tissue sections for analysis. Moreover, we highlight the optimized storage conditions that limit molecular and morphological degradation of the sections. The protocol is compatible with human and plant tissues and can be easily adapted for the preparation of alternative sample formats (e.g., three-dimensional cell cultures). The integrated workflow is universally compatible with histological tissue analysis, mass spectrometry imaging and imaging mass cytometry, as well as spatial proteomic, genomic and transcriptomic tissue analysis. The protocol can be completed within 4 h and requires minimal prior experience in the preparation of tissue samples for multimodal imaging experiments.

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.

Lipidomic Phenotyping Of Human Small Intestinal Organoids Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

In the past decade, interest in organoids for biomedical research has surged, resulting in a higher demand for advanced imaging techniques. Traditional specimen embedding methods pose challenges, such as analyte delocalization and histological assessment. Here, we present an optimized sample preparation approach utilizing an Epredia M-1 cellulose-based embedding matrix, which preserves the structural integrity of fragile small intestinal organoids (SIOs). Additionally, background interference (delocalization of analytes, nonspecific (histological) staining, matrix ion clusters) was minimized, and we demonstrate the compatibility with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). With our approach, we can conduct label-free lipid imaging at the single-cell level, thereby yielding insights into the spatial distribution of lipids in both positive and negative ion modes. Moreover, M-1 embedding allows for an improved coregistration with histological and immunohistochemical (IHC) stainings, including MALDI-IHC, facilitating combined untargeted and targeted spatial information. Applying this approach, we successfully phenotyped crypt-like (CL) and villus-like (VL) SIOs, revealing that PE 36:2 [M - H]- (m/z 742.5) and PI 38:4 [M - H]- (m/z 885.5) display higher abundance in CL organoids, whereas PI 36:1 [M - H]- (m/z 863.6) was more prevalent in VL organoids. Our findings demonstrate the utility of M-1 embedding for advancing organoid research and unraveling intricate biological processes within these in vitro models.

Spatially Resolved Metabolomics Combined with the 3D Tumor-Immune Cell Coculture Spheroid Highlights Metabolic Alterations during Antitumor Immune Response

The metabolic cross-talk between tumor and immune cells plays key roles in immune cell function and immune checkpoint blockade therapy. However, the characterization of tumor immunometabolism and its spatiotemporal alterations during immune response in a complex tumor microenvironment is challenging. Here, a 3D tumor-immune cell coculture spheroid model was developed to mimic tumor-immune interactions, combined with mass spectrometry imaging-based spatially resolved metabolomics to visualize tumor immunometabolic alterations during immune response. The inhibition of T cells was simulated by coculturing breast tumor spheroids with Jurkat T cells, and the reactivation of T cells can be monitored through diminishing cancer PD-L1 expressions by berberine. This system enables simultaneously screening and imaging discriminatory metabolites that are altered during T cell-mediated antitumor immune response and characterizing the distributions of berberine and its metabolites in tumor spheroids. We discovered that the transport and catabolism of glutamine were significantly reprogrammed during the antitumor immune response at both metabolite and enzyme levels, corresponding to its indispensable roles in energy metabolism and building new biomass. The combination of spatially resolved metabolomics with the 3D tumor-immune cell coculture spheroid visually reveals metabolic interactions between tumor and immune cells and possibly helps decipher the role of immunometabolic alterations in tumor immunotherapy.

Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids

Three-dimensional (3D) cell spheroid models combined with mass spectrometry imaging (MSI) enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions. Herein, airflow-assisted desorption electrospray ionization-MSI (AFADESI-MSI) was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone (AMI). High-coverage imaging of >1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI. Following AMI treatment at different times, 15 metabolites of AMI involved in N-desethylation, hydroxylation, deiodination, and desaturation metabolic reactions were identified, and according to their spatiotemporal dynamics features, the metabolic pathways of AMI were proposed. Subsequently, the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis. The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism, providing considerable evidence for the mechanism of AMI hepatotoxicity. In addition, a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI. The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs, drug metabolites, and endogenous metabolites after AMI treatment, providing an effective tool for in vitro drug hepatotoxicity evaluation.

Cuproplasia characterization in colon cancer assists to predict prognosis and immunotherapeutic response

Introduction

Colon cancer is the 3rd most prevalent cancer worldwide, with more than 900,000 deaths annually. Chemotherapy, targeted treatment, and immunotherapeutic treatment are the three cornerstones of colon cancer treatment; however, the occurrence of immune therapy resistance is the most pressing problem to solve. Copper is a mineral nutrient that is both beneficial and potentially toxic to cells and is increasingly implicated in cell proliferation and death pathways. Cuproplasia is characterized by copper-dependent cell growth and proliferation. This term encompasses both neoplasia and hyperplasia and describes the primary and secondary effects of copper. The connection between copper and cancer has been noted for decades. However, the relationship between cuproplasia and colon cancer prognosis remains unclear.

Method

In this study, we applied bioinformatics approaches including WGCNA, GSEA and etc. to delineate cuproplasia characterization of colon cancer, set up a robust Cu_riskScore model based on cuproplasia-relevant genes and found its relevant biological processes use qRT-pCR to validate our results on our cohort.

Result

The Cu_riskScore is found to be relevant to Stage and MSI-H subtype, and some biological processes including MYOGENESIS and MYC TARGETS. The Cu_riskScore high and low groups also showed different immune infiltration pattern and genomic traits. Finally, the result of our cohort showed the Cu_riskScore gene RNF113A has a marked effect in predicting immunotherapy response.

Discussion

In conclusion, we identified a cuproplasia-related gene expression signature consisting of six genes and studied the landscape of the clinical and biological characterization of this model in Colon Cancer. Furthermore, the Cu_riskScore was demonstrated to be a robust prognostic indicator and predictive factor for the benefits of immunotherapy.

Recent advances in top-down proteome sample processing ahead of MS analysis

Top-down proteomics is emerging as a preferred approach to investigate biological systems, with objectives ranging from the detailed assessment of a single protein therapeutic, to the complete characterization of every possible protein including their modifications, which define the human proteoform. Given the controlling influence of protein modifications on their biological function, understanding how gene products manifest or respond to disease is most precisely achieved by characterization at the intact protein level. Top-down mass spectrometry (MS) analysis of proteins entails unique challenges associated with processing whole proteins while maintaining their integrity throughout the processes of extraction, enrichment, purification, and fractionation. Recent advances in each of these critical front-end preparation processes, including minimalistic workflows, have greatly expanded the capacity of MS for top-down proteome analysis. Acknowledging the many contributions in MS technology and sample processing, the present review aims to highlight the diverse strategies that have forged a pathway for top-down proteomics. We comprehensively discuss the evolution of front-end workflows that today facilitate optimal characterization of proteoform-driven biology, including a brief description of the clinical applications that have motivated these impactful contributions.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

A simple preparation step to remove excess liquid lipids in white adipose tissue enabling improved detection of metabolites via MALDI-FTICR imaging MS

Matrix-assisted laser desorption ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) imaging mass spectrometry (MS) is a powerful technology used to analyze metabolites in various tissues. However, it faces significant challenges in studying adipose tissues. Poor matrix distribution and crystallization caused by excess liquid lipids on the surface of tissue sections hamper m/z species detection, an adverse effect that particularly presents in lipid-rich white adipose tissue (WAT). In this study, we integrated a simple and low-cost preparation step into the existing MALDI-FTICR imaging MS pipeline. The new method—referred to as filter paper application—is characterized by an easy sample handling and high reproducibility. The aforementioned filter paper is placed onto the tissue prior to matrix application in order to remove the layer of excess liquid lipids. Consequently, MALDI-FTICR imaging MS detection was significantly improved, resulting in a higher number of detected m/z species and higher ion intensities. After analyzing various durations of filter paper application, 30 s was found to be optimal, resulting in the detection of more than 3700 m/z species. Apart from the most common lipids found in WAT, other molecules involved in various metabolic pathways were detected, including nucleotides, carbohydrates, and amino acids. Our study is the first to propose a solution to a specific limitation of MALDI-FTICR imaging MS in investigating lipid-rich WAT. The filter paper approach can be performed quickly and is particularly effective for achieving uniform matrix distribution on fresh frozen WAT while maintaining tissue integrity. It thus helps to gain insight into the metabolism in WAT.

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

Cell and Tissue Imaging by TOF-SIMS and MALDI-TOF: An Overview for Biological and Pharmaceutical Analysis

The potential of mass spectrometry imaging (MSI) has been demonstrated in cell and tissue research since 1970. MSI can reveal the spatial distribution of a wide range of atomic and molecular ions detected from biological sample surfaces, it is a powerful and valuable technique used to monitor and detect diverse chemical and biological compounds, such as drugs, lipids, proteins, and DNA. MSI techniques, notably matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) and time of flight secondary ion mass spectrometry (TOF-SIMS), witnessed a dramatic upsurge in studying and investigating biological samples especially, cells and tissue sections. This advancement is attributed to the submicron lateral resolution, the high sensitivity, the good precision, and the accurate chemical specificity, which make these techniques suitable for decoding and understanding complex mechanisms of certain diseases, as well as monitoring the spatial distribution of specific elements, and compounds. While the application of both techniques for the analysis of cells and tissues is thoroughly discussed, a briefing of MALDI-TOF and TOF-SIMS basis and the adequate sampling before analysis are briefly covered. The importance of MALDI-TOF and TOF-SIMS as diagnostic tools and robust analytical techniques in the medicinal, pharmaceutical, and toxicology fields is highlighted through representative published studies.

Anti-osteosarcoma effect of antiserum against cross antigen TPD52 between osteosarcoma and Trichinella spiralis

BACKGROUND

Trichinella spiralis (T. spiralis) is a parasite occurring worldwide that has been proven to have antitumour ability. However, studies on the antitumour effects of cross antigens between the tumour and T. spiralis or antibodies against cross antigens between tumours and T. spiralis are rare.

METHODS

To study the role of cross antigens between osteosarcoma and T. spiralis, we first screened the cDNA expression library of T. spiralis muscle larvae to obtain the cross antigen gene tumour protein D52 (TPD52), and prepared fusion protein TPD52 and its antiserum. The anti-osteosarcoma effect of the anti-TPD52 antiserum was studied using cell proliferation and cytotoxicity assays as well as in vivo animal models; preliminary data on the mechanism were obtained using western blot and immunohistochemistry analyses.

RESULTS

Our results indicated that TPD52 was mainly localized in the cytoplasm of MG-63 cells. Anti-TPD52 antiserum inhibited the proliferation of MG-63 cells and the growth of osteosarcoma in a dose-dependent manner. The tumour inhibition rate in the 100 μg treatment group was 61.95%. Enzyme-linked immunosorbent assay showed that injection of anti-TPD52 antiserum increased the serum levels of IFN-γ, TNF-α, and IL-12 in nude mice. Haematoxylin and eosin staining showed that anti-TPD52 antiserum did not cause significant pathological damage. Apoptosis of osteosarcoma cells was induced by anti-TPD52 antiserum in vivo and in vitro.

CONCLUSIONS

Anti-TPD52 antiserum exerts an anti-osteosarcoma effect by inducing apoptosis without causing histopathological damage.

MS imaging of multicellular tumor spheroids and organoids as an emerging tool for personalized medicine and drug discovery

MS imaging (MSI) is a powerful tool in drug discovery because of its ability to interrogate a wide range of endogenous and exogenous molecules in a broad variety of samples. The impressive versatility of the approach, where almost any ionizable biomolecule can be analyzed, including peptides, proteins, lipids, carbohydrates, and nucleic acids, has been applied to numerous types of complex biological samples. While originally demonstrated with harvested organs from animal models and biopsies from humans, these models are time consuming and expensive, which makes it necessary to extend the approach to 3D cell culture systems. These systems, which include spheroid models, prepared from immortalized cell lines, and organoid cultures, grown from patient biopsies, can provide insight on the intersection of molecular information on a spatial scale. In particular, the investigation of drug compounds, their metabolism, and the subsequent distribution of their metabolites in 3D cell culture systems by MSI has been a promising area of study. This review summarizes the different ionization methods, sample preparation steps, and data analysis methods of MSI and focuses on several of the latest applications of MALDI-MSI for drug studies in spheroids and organoids. Finally, the application of this approach in patient-derived organoids to evaluate personalized medicine options is discussed.

The emergence of imaging mass spectrometry in drug discovery and development: Making a difference by driving decision making

The pharmaceutical industry is a dynamic, science-driven business constantly under pressure to innovate and morph into a higher performing organization. Innovations can include the implementation of new technologies, adopting new scientific methods, changing the decision-making process, compressing timelines, or making changes to the organizational structure. The drivers for the constant focus on performance improvement are the high cost of R&D as well as the lengthy timelines required to deliver new medicines for unmet needs. Successful innovations are measured against both the quality and quantity of potential new medicines in the pipeline and the delivery to patients. In this special feature article, we share our collective experience implementing matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology as an innovative approach to better understand the tissue biodistribution of drugs in the early phases of drug discovery to establish pharmacokinetic-pharmacodynamic (PK-PD) relationships, as well as in the development phase to understand pharmacology, toxicology, and disease pathogenesis. In our experience, successful implementation of MALDI IMS in support of therapeutic programs can be measured by the impact IMS studies have on driving decision making in pipeline progression. This provides a direct quantifiable measurement of the return to the organization for the investment in IMS. We have included discussion not only on the technical merits of IMS study conduct but also the key elements of setting study objectives, building collaborations, data integration into the medicine progression milestones, and potential pitfalls when trying to establish IMS in the pharmaceutical arena. We categorized IMS study types into five groups that parallel pipeline progression from the earliest phases of discovery to late stages of preclinical development. We conclude the article with some perspectives on how we see MALDI IMS maintaining relevance and becoming further embedded as an essential tool in the constantly changing environment of the pharmaceutical industry.

[Mass spectrometry imaging technology and its application in breast cancer research]

乳腺癌是女性最常见的恶性肿瘤,其发病率在世界范围内呈现上升趋势,是威胁女性健康的重要疾病之一。随着现代医学技术的快速发展,早期有效的诊断和筛查方法能够改善乳腺癌患者生存率和提高其生活质量。由于乳腺癌肿瘤具有非常显著的异质性,这对于诊断和筛查带来了较大困难,亟须在肿瘤演进时间信息中,继续引入生物分子的空间信息,从而对其异质性、肿瘤微环境等进行准确的追踪。质谱成像技术,可在免标记的前提下利用离子质荷比的特性发现生物组织中的各种分子,并研究这些分子的时间和空间信息,对其进行准确的定性、定量和空间定位。目前,通过质谱成像技术可直接获取药物及其代谢物、内源性代谢物、脂质、多肽和蛋白质等在组织中的空间分布信息,为肿瘤分子分型诊断和确认以及相关抗肿瘤药物的筛选提供了新的思路和研究方向。该综述以乳腺癌相关的生物样品制备和研究进展为主要内容,从小分子样本、大分子样本、石蜡包埋样本、基质喷涂方式、常用离子源等方面阐述质谱成像中样本制备的重要性以及样品制备过程中存在的难点问题。同时,以细胞模型、动物模型和临床肿瘤标本为研究对象,汇总了质谱成像技术在乳腺癌方面的应用进展,并进行了展望,为开展癌症精准分型研究和药物药效的快速筛查提供了重要依据。

Macrophages, as a Promising Strategy to Targeted Treatment for Colorectal Cancer Metastasis in Tumor Immune Microenvironment

The tumor immune microenvironment plays a vital role in the metastasis of colorectal cancer. As one of the most important immune cells, macrophages act as phagocytes, patrol the surroundings of tissues, and remove invading pathogens and cell debris to maintain tissue homeostasis. Significantly, macrophages have a characteristic of high plasticity and can be classified into different subtypes according to the different functions, which can undergo reciprocal phenotypic switching induced by different types of molecules and signaling pathways. Macrophages regulate the development and metastatic potential of colorectal cancer by changing the tumor immune microenvironment. In tumor tissues, the tumor-associated macrophages usually play a tumor-promoting role in the tumor immune microenvironment, and they are also associated with poor prognosis. This paper reviews the mechanisms and stimulating factors of macrophages in the process of colorectal cancer metastasis and intends to indicate that targeting macrophages may be a promising strategy in colorectal cancer treatment.

Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures

Spatially resolved metabolomics offers unprecedented opportunities for elucidating metabolic mechanisms during cancer progression. It facilitated the discovery of aberrant cellular metabolism with clinical application potential. Here, we developed a novel strategy to discover cancer tissue relevant metabolic signatures by high spatially resolved metabolomics combined with a multicellular tumor spheroid (MCTS) in vitro model. Esophageal cancer MCTS were generated using KYSE-30 human esophageal cancer cells to fully mimic the 3D microenvironment under physiological conditions. Then, the spatial features and temporal variation of metabolites and metabolic pathways in MCTS were accurately mapped by using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with a spatial resolution at ∼12 μm. Metabolites, such as glutamate, tyrosine, inosine and various types of lipids displayed heterogeneous distributions in different microregions inside the MCTS, revealing the metabolic heterogenicity of cancer cells under different proliferative states. Subsequently, through joint analysis of metabolomic data of clinical cancer tissue samples, cancer tissue relevant metabolic signatures in esophageal cancer MCTS were identified, including glutamine metabolism, fatty acid metabolism, de novo synthesis phosphatidylcholine (PC) and phosphatidylethanolamine (PE), etc. In addition, the abnormal expression of the involved metabolic enzymes, i.e., GLS, FASN, CHKA and cPLA2, was further confirmed and showed similar tendencies in esophageal cancer MCTS and cancer tissues. The MALDI-MSI combined with MCTS approach offers molecular insights into cancer metabolism with real-word relevance, which would potentially benefit the biomarker discovery and metabolic mechanism studies.

A versatile deep learning architecture for classification and label-free prediction of hyperspectral images

Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist’s ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30 year old statistical approach to address the problem. The solution, “local-balanced model,” relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

3D Culture Systems for Exploring Cancer Immunology

Simple Summary

To study any disease, researchers need convenient and relevant disease models. In cancer, the most commonly used models are two-dimensional (2D) culture models, which grow cells on hard, rigid, plastic surfaces, and mouse models. Cancer immunology is especially difficult to model because the immune system is exceedingly complex; it contains multiple types of cells, and each cell type has several subtypes and a spectrum of activation states. These many immune cell types interact with cancer cells and other components of the tumor, ultimately influencing disease outcomes. 2D culture methods fail to recapitulate these complex cellular interactions. Mouse models also suffer because the murine and human immune systems vary significantly. Three-dimensional (3D) culture systems therefore provide an alternative method to study cancer immunology and can fill the current gaps in available models. This review will describe common 3D culture models and how those models have been used to advance our understanding of cancer immunology.

Abstract

Cancer immunotherapy has revolutionized cancer treatment, spurring extensive investigation into cancer immunology and how to exploit this biology for therapeutic benefit. Current methods to investigate cancer-immune cell interactions and develop novel drug therapies rely on either two-dimensional (2D) culture systems or murine models. However, three-dimensional (3D) culture systems provide a potentially superior alternative model to both 2D and murine approaches. As opposed to 2D models, 3D models are more physiologically relevant and better replicate tumor complexities. Compared to murine models, 3D models are cheaper, faster, and can study the human immune system. In this review, we discuss the most common 3D culture systems—spheroids, organoids, and microfluidic chips—and detail how these systems have advanced our understanding of cancer immunology.

3D Tumor Models and Their Use for the Testing of Immunotherapies

Over the past decade, immunotherapy has become a powerful and evident tool in the fight against cancers. Notably, the rise of checkpoint blockade using monoclonal antibodies (anti-CTLA4, anti-PD1) to avoid interaction between inhibitory molecules allowed the betterment of patient care. Indeed, immunotherapies led to increased overall survival in forms of cutaneous melanoma or lung cancer. However, the percentage of patients responding varies from 20 to 40% depending on the type of cancer and on the expression of the target molecules by the tumor. This is due to the tumor microenvironment which allows the acquisition of resistance mechanisms to immunotherapies by tumor cells. These are closely linked to the architecture and cellular composition of the tumor microenvironment. This one acts on different parameters such as the immune cells infiltrate its composition and therefore, favors the recruitment of immunosuppressive cells as well as the tumor expression of checkpoint inhibitors such as Programmed Death Ligand-1 (PD-L1). Therefore, the analysis and modeling of the complexity of the microenvironment is an important parameter to consider, not only in the search for new therapies but also for the identification and stratification of patients likely to respond to immunotherapy. This is why the use of 3D culture models, reflecting the architecture and cellular composition of a tumor, is essential in immuno-oncology studies. Nowadays, there are several 3-D culture methods such as spheroids and organoids, which are applicable to immuno-oncology. In this review we evaluate 3D culture models as tools for the development of treatments in the field of immuno-oncology.

Role of MALDI-MSI in combination with 3D tissue models for early stage efficacy and safety testing of drugs and toxicants

Introduction: Three-dimensional (3D) cell cultures have become increasingly important materials to investigate biological processes and drug efficacy and toxicity. The ability of 3D cultures to mimic the physiology of primary tissues and organs in the human body enables further insight into cellular behavior and is hence highly desirable in early-stage drug development. Analyzing the spatial distribution of drug compounds and endogenous molecules provides an insight into the efficacy of a drug whilst simultaneously giving information on biological responses. Areas Covered: In this review we will examine the main 3D cell culture systems employed and applications, which describe their integration with mass spectrometry imaging (MSI). Expert Opinion: MSI is a powerful technique that can map a vast range of molecules simultaneously in tissues without the addition of labels that can provide insights into the efficacy and safety of a new drug. The combination of MSI and 3D cell cultures has emerged as a promising tool in early-stage drug analysis. However, the most common administration route for pharmaceutical drugs is via oral delivery. The use of MSI in combination with models of the GI tract is an area that has been little explored to date, the reasons for this are discussed.

Recent progress in the molecular imaging of therapeutic monoclonal antibodies

Therapeutic monoclonal antibodies have become one of the central components of the healthcare system and continuous efforts are made to bring innovative antibody therapeutics to patients in need. It is equally critical to acquire sufficient knowledge of their molecular structure and biological functions to ensure the efficacy and safety by incorporating new detection approaches since new challenges like individual differences and resistance are presented. Conventional techniques for determining antibody disposition including plasma drug concentration measurements using LC-MS or ELISA, and tissue distribution using immunohistochemistry and immunofluorescence are now complemented with molecular imaging modalities like positron emission tomography and near-infrared fluorescence imaging to obtain more dynamic information, while methods for characterization of antibody's interaction with the target antigen as well as visualization of its cellular and intercellular behavior are still under development. Recent progress in detecting therapeutic antibodies, in particular, the development of methods suitable for illustrating the molecular dynamics, is described here.

Characterization of an Aggregated Three-Dimensional Cell Culture Model by Multimodal Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is an established analytical tool capable of defining and understanding complex tissues by determining the spatial distribution of biological molecules. Three-dimensional (3D) cell culture models mimic the pathophysiological environment of in vivo tumors and are rapidly emerging as a valuable research tool. Here, multimodal MSI techniques were employed to characterize a novel aggregated 3D lung adenocarcinoma model, developed by the group to mimic the in vivo tissue. Regions of tumor heterogeneity and the hypoxic microenvironment were observed based on the spatial distribution of a variety of endogenous molecules. Desorption electrospray ionization (DESI)-MSI defined regions of a hypoxic core and a proliferative outer layer from metabolite distribution. Targeted metabolites (e.g., lactate, glutamine, and citrate) were mapped to pathways of glycolysis and the TCA cycle demonstrating tumor metabolic behavior. The first application of imaging mass cytometry (IMC) with 3D cell culture enabled single-cell phenotyping at 1 μm spatial resolution. Protein markers of proliferation (K_i-67) and hypoxia (glucose transporter 1) defined metabolic signaling in the aggregoid model, which complemented the metabolite data. Laser ablation inductively coupled plasma (LA-ICP)-MSI analysis localized endogenous elements including magnesium and copper, further differentiating the hypoxia gradient and validating the protein expression. Obtaining a large amount of molecular information on a complementary nature enabled an in-depth understanding of the biological processes within the novel tumor model. Combining powerful imaging techniques to characterize the aggregated 3D culture highlighted a future methodology with potential applications in cancer research and drug development.

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab

As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for example. Classification, as a general problem, is often best accomplished by supervised machine learning strategies; unfortunately, conducting supervised machine learning on MS imaging files is not typically done by mass spectrometrists because a high degree of specialized knowledge is needed. To address this problem, we developed a fully open-source approach that facilitates supervised machine learning on MS imaging files, and we demonstrated its implementation on sets of cancer spheroids that either had or had not undergone chemotherapy treatment. These supervised machine learning studies demonstrated that metabolic changes induced by the monoclonal antibody, Cetuximab, are detectable but modest at 24 h, and by 72 h, the drug induces a larger and more diverse metabolic response.

Development of a Mass Spectrometry Imaging Method for Detecting and Mapping Graphene Oxide Nanoparticles in Rodent Tissues

Graphene-based nanoparticles are continuously being developed for biomedical applications, and their use raises concerns about their environmental and biological impact. In the literature, some imaging techniques based on fluorescence and radioimaging have been used to explore their fate in vivo. Here, we report on the use of label-free mass spectrometry and mass spectrometry imaging (MSI) for graphene oxide (GO) and reduced graphene oxide (rGO) analyses in rodent tissues. Thereby, we extend previous work by focusing on practical questions to obtain reliable and meaningful images. Specific radical anionic carbon clusters ranging from C₂^-• to C₉^-• were observed for both GO and rGO species, with a base peak at m/z 72 under negative laser desorption ionization mass spectrometry (LDI-MS) conditions. Extension to an LDI-MSI method was then performed, thus enabling the efficient detection of GO nanoparticles in lung tissue sections of previously exposed mice. The possibility of quantifying those nanoparticles on tissue sections has also been investigated. Two different ways of building calibration curves (i.e., GO suspensions spotted on tissue sections, or added to lung tissue homogenates) were evaluated and returned similar results, with linear dynamic concentration ranges over at least 2 orders of magnitude. Moreover, intra- and inter-day precision studies have been assessed, with relative standard deviation below 25% for each concentration point of a calibration curve. In conclusion, our study confirms that LDI-MSI is a relevant approach for biodistribution studies of carbon-based nanoparticles, as quantification can be achieved, provided that nanoparticle suspension and manufacturing are carefully controlled.

Identification of lipid biomarkers of metastatic potential and gene expression (HER2/p53) in human breast cancer cell cultures using ambient mass spectrometry

In breast cancer, overexpression of human epidermal growth factor receptor 2 (HER2) correlates with overactivation of lipogenesis, mutation of tumor suppressor p53, and increased metastatic potential. The mechanisms through which lipids mediate p53, HER2, and metastatic potential are largely unknown. We have developed a desorption electrospray ionization mass spectrometry (DESI-MS) method to identify lipid biomarkers of HER2/p53 expression, metastatic potential, and disease state (viz. cancer vs. non-cancerous) in monolayer and suspension breast cancer cell cultures (metastatic potential: MCF-7, T-47D, MDA-MB-231; HER2/p53: HCC2218 (HER2+++/p53+), HCC1599 (HER2-/p53-), HCC202 (HER2++/p53-), HCC1419 (HER2+++/p53-) HCC70 (HER2-/p53+++); non-cancerous: MCF-10A). Unsupervised principal component analysis (PCA) of DESI-MS spectra enabled identification of twelve lipid biomarkers of metastatic potential and disease state, as well as ten lipids that distinguish cell lines based on HER2/p53 expression levels (> 200 lipids were identified per cell line). In addition, we developed a DESI-MS imaging (DESI-MSI) method for mapping the spatial distribution of lipids in metastatic spheroids (MDA-MB-231). Of the twelve lipids that correlate with changes in the metastatic potential of monolayer cell cultures, three were localized to the necrotic core of spheroids, indicating a potential role in promoting cancer cell survival in nutrient-deficient environments. One lipid species, which was not detected in monolayer MDA-MB-231 cultures, was spatially localized to the periphery of the spheroid, suggesting a potential role in invasion and/or proliferation. These results demonstrate that combining DESI-MS/PCA of monolayer and suspension cell cultures with DESI-MSI of spheroids is a promising approach for identifying lipid biomarkers of specific genotypes and phenotypes, as well as elucidating the potential function of these biomarkers in breast cancer. Graphical Absract.

Therapeutic potential of polypeptide-based conjugates: Rational design and analytical tools that can boost clinical translation

The clinical success of polypeptides as polymeric drugs, covered by the umbrella term "polymer therapeutics," combined with related scientific and technological breakthroughs, explain their exponential growth in the development of polypeptide-drug conjugates as therapeutic agents. A deeper understanding of the biology at relevant pathological sites and the critical biological barriers faced, combined with advances regarding controlled polymerization techniques, material bioresponsiveness, analytical methods, and scale up-manufacture processes, have fostered the development of these nature-mimicking entities. Now, engineered polypeptides have the potential to combat current challenges in the advanced drug delivery field. In this review, we will discuss examples of polypeptide-drug conjugates as single or combination therapies in both preclinical and clinical studies as therapeutics and molecular imaging tools. Importantly, we will critically discuss relevant examples to highlight those parameters relevant to their rational design, such as linking chemistry, the analytical strategies employed, and their physicochemical and biological characterization, that will foster their rapid clinical translation.

Characterization of an Amphiphilic Phosphonated Calixarene Carrier Loaded With Carboplatin and Paclitaxel: A Preliminary Study to Treat Colon Cancer in vitro and in vivo

The inadequacy of available detection methods and a naturally aggressive progression have made colon cancer the third most common type of cancer, accounting for ~10% of all cancer cases. The heterogeneity and genomic instability of colon cancer tumors make current treatments unsatisfactory. This study evaluated a novel nanoscale delivery platform comprising phosphonated calixarenes (P4C6) co-loaded with paclitaxel (PTX) and carboplatin (CPT). The nanoparticles showed average hydrodynamic sizes of 84 ± 8 nm for empty P4C6 nanoparticle and 119 ± 13 nm for PTX-CPT-P4C6. The corresponding zeta potentials were −40.8 ± 8.8 and −35.4 ± 4.2 mV. The optimal CPT:PTX ratio was 5.22:1, and PTX-CPT-P4C6 with this ratio was more cytotoxic against HT-29 cells than against Caco-2 cells (IC₅₀, 0.4 ± 0.02 vs. 2.1 ± 0.3 μM), and it induced higher apoptosis in HT-29 cells (56.6 ± 4.5 vs. 44.9 ± 3.44%). PTX-CPT-P4C6 inhibited the invasion and migration of HT-29 cells more strongly than the free drugs. It also inhibited the growth of HT-29 tumors in mice to the greatest extent of all formulations, with negligible side effects. This research demonstrates the potential of P4C6 to deliver two chemotherapeutic agents to colon cancer tumors to provide synergistic efficacy than single drug administration.

Addressing Patient Specificity in the Engineering of Tumor Models

Cancer treatment is challenged by the heterogeneous nature of cancer, where prognosis depends on tumor type and disease stage, as well as previous treatments. Optimal patient stratification is critical for the development and validation of effective treatments, yet pre-clinical model systems are lacking in the delivery of effective individualized platforms that reflect distinct patient-specific clinical situations. Advances in cancer cell biology, biofabrication, and microengineering technologies have led to the development of more complex in vitro three-dimensional (3D) models to act as drug testing platforms and to elucidate novel cancer mechanisms. Mostly, these strategies have enabled researchers to account for the tumor microenvironment context including tumor-stroma interactions, a key factor of heterogeneity that affects both progression and therapeutic resistance. This is aided by state-of-the-art biomaterials and tissue engineering technologies, coupled with reproducible and high-throughput platforms that enable modeling of relevant physical and chemical factors. Yet, the translation of these models and technologies has been impaired by neglecting to incorporate patient-derived cells or tissues, and largely focusing on immortalized cell lines instead, contributing to drug failure rates. While this is a necessary step to establish and validate new models, a paradigm shift is needed to enable the systematic inclusion of patient-derived materials in the design and use of such models. In this review, we first present an overview of the components responsible for heterogeneity in different tumor microenvironments. Next, we introduce the state-of-the-art of current in vitro 3D cancer models employing patient-derived materials in traditional scaffold-free approaches, followed by novel bioengineered scaffold-based approaches, and further supported by dynamic systems such as bioreactors, microfluidics, and tumor-on-a-chip devices. We critically discuss the challenges and clinical prospects of models that have succeeded in providing clinical relevance and impact, and present emerging concepts of novel cancer model systems that are addressing patient specificity, the next frontier to be tackled by the field.