Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning

Integrated spatial metabolomics and transcriptomics decipher the hepatoprotection mechanisms of wedelolactone and demethylwedelolactone on non-alcoholic fatty liver disease

Eclipta prostrata L. has been used in traditional medicine and known for its liver-protective properties for centuries. Wedelolactone (WEL) and demethylwedelolactone (DWEL) are the major coumarins found in E. prostrata L. However, the comprehensive characterization of these two compounds on non-alcoholic fatty liver disease (NAFLD) still remains to be explored. Utilizing a well-established zebrafish model of thioacetamide (TAA)-induced liver injury, the present study sought to investigate the impacts and mechanisms of WEL and DWEL on NAFLD through integrative spatial metabolomics with liver-specific transcriptomics analysis. Our results showed that WEL and DWEL significantly improved liver function and reduced the accumulation of fat in the liver. The biodistributions and metabolism of these two compounds in whole-body zebrafish were successfully mapped, and the discriminatory endogenous metabolites reversely regulated by WEL and DWEL treatments were also characterized. Based on spatial metabolomics and transcriptomics, we identified that steroid biosynthesis and fatty acid metabolism are mainly involved in the hepatoprotective effects of WEL instead of DWEL. Our study unveils the distinct mechanism of WEL and DWEL in ameliorating NAFLD, and presents a "multi-omics" platform of spatial metabolomics and liver-specific transcriptomics to develop highly effective compounds for further improved therapy.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.

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.

Balance between the cell viability and death in 3D

Cell death is a phenomenon, frequently perceived as an absolute event for cell, tissue and the organ. However, the rising popularity and complexity of such 3D multicellular 'tissue building blocks' as heterocellular spheroids, organoids, and 'assembloids' prompts to revise the definition and quantification of cell viability and death. It raises several questions on the overall viability of all the cells within 3D volume and on choosing the appropriate, continuous, and non-destructive viability assay enabling for a single-cell analysis. In this review, we look at cell viability and cell death modalities with attention to the intrinsic features of such 3D models as spheroids, organoids, and bioprints. Furthermore, we look at emerging and promising methodologies, which can help define and understand the balance between cell viability and death in dynamic and complex 3D environments. We conclude that the recent innovations in biofabrication, biosensor probe development, and fluorescence microscopy can help answer these questions.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease

Cellular heterogeneity is generally overlooked in infectious diseases. In this study, we investigated host cell heterogeneity during infection with Trypanosoma cruzi (T. cruzi) parasites, causative agents of Chagas disease (CD). In chronic-stage CD, only a few host cells are infected with a large load of parasites and symptoms may appear at sites distal to parasite colonization. Furthermore, recent work has revealed T. cruzi heterogeneity with regard to replication rates and drug susceptibility. However, the role of cellular-level metabolic heterogeneity in these processes has yet to be assessed. To fill this knowledge gap, we developed a Single-probe SCMS (single-cell mass spectrometry) method compatible with biosafety protocols, to acquire metabolomics data from individual cells during T. cruzi infection. This study revealed heterogeneity in the metabolic response of the host cells to T. cruzi infection in vitro. Our results showed that parasite-infected cells possessed divergent metabolism compared to control cells. Strikingly, some uninfected cells adjacent to infected cells showed metabolic impacts as well. Specific metabolic changes include increases in glycerophospholipids with infection. These results provide novel insight into the pathogenesis of CD. Furthermore, they represent the first application of bioanalytical SCMS to the study of mammalian-infectious agents, with the potential for broad applications to study infectious diseases.

Metabolomics studies of cell-cell interactions using single cell mass spectrometry combined with fluorescence microscopy

Cell-cell interactions are critical for transmitting signals among cells and maintaining their normal functions from the single-cell level to tissues. In cancer studies, interactions between drug-resistant and drug-sensitive cells play an important role in the development of chemotherapy resistance of tumors. As metabolites directly reflect the cell status, metabolomics studies provide insight into cell-cell communication. Mass spectrometry (MS) is a powerful tool for metabolomics studies, and single cell MS (SCMS) analysis can provide unique information for understanding interactions among heterogeneous cells. In the current study, we utilized a direct co-culture system (with cell-cell contact) to study metabolomics of single cells affected by cell-cell interactions in their living status. A fluorescence microscope was utilized to distinguish these two types of cells for SCMS metabolomics studies using the Single-probe SCMS technique under ambient conditions. Our results show that through interactions with drug-resistant cells, drug-sensitive cancer cells acquired significantly increased drug resistance and exhibited drastically altered metabolites. Further investigation found that the increased drug resistance was associated with multiple metabolism regulations in drug-sensitive cells through co-culture such as the upregulation of sphingomyelins lipids and lactic acid and the downregulation of TCA cycle intermediates. The method allows for direct MS metabolomics studies of individual cells labeled with fluorescent proteins or dyes among heterogeneous populations.

Single cell mass spectrometry studies reveal metabolomic features and potential mechanisms of drug-resistant cancer cell lines

Irinotecan (Iri) is a key drug to treat metastatic colorectal cancer, but its clinical activity is often limited by de novo and acquired drug resistance. Studying the underlying mechanisms of drug resistance is necessary for developing novel therapeutic strategies. In this study, we used both regular and irinotecan-resistant (Iri-resistant) colorectal cell lines as models, and performed single cell mass spectrometry (SCMS) metabolomics studies combined with analyses from cytotoxicity assay, western blot, flow cytometry, quantitative real-time polymerase chain reaction (qPCR), and reactive oxygen species (ROS). Our SCMS results indicate that Iri-resistant cancer cells possess higher levels of unsaturated lipids compared with the regular cancer cells. In addition, multiple protein biomarkers and their corresponding mRNAs of colon cancer stem cells are overexpressed in Iri-resistance cells. Particularly, stearoyl-CoA desaturase 1 (SCD1) is upregulated with the development of drug resistance in Iri-resistant cells, whereas inhibiting the activity of SCD1 efficiently increase their sensitivity to Iri treatment. In addition, we demonstrated that SCD1 directly regulates the expression of ALDH1A1, which contributes to the cancer stemness and ROS level in Iri-resistant cell lines.

Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis

Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.

Mass spectrometry imaging revealed alterations of lipid metabolites in multicellular tumor spheroids in response to hydroxychloroquine

Three-dimensional (3D) multicellular tumor spheroids (MCTS) that mimic the complex tumor microenvironment provide a good platform for in vitro study of drug and endogenous metabolites. Hydroxychloroquine (HCQ) has shown anti-tumor activity in a variety of tumor models. However, the effect of the drug on the alteration of lipid metabolism spatial composition and distribution in the MCTS model is not clear. Herein, we utilized matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) in the analysis of A549 lung cancer multicellular spheroids to investigate the in situ spatial distribution of HCQ and its effect on lipid metabolism. We have successfully observed the spatial variations of HCQ in the inner region of the spheroid at different drug-treated time points. The MSI results also demonstrated that HCQ treatment altered the spatial composition of lipids in the inner and outer regions of treated spheroids. Furthermore, the lipidomic results showed that the identified phosphatidylcholines (PC), lysophosphatidylcholines (LPC), phosphatidylethanolamines (PE), lysophosphatidylethanolamines (LPE), phosphatidylinositols (PI), ceramides (Cer), glucosylceramides (CerG), and diglycerides (DG) were significantly up-regulated, and phosphatidylglycerol (PG) and triglycerides (TG) were remarkable down-regulated. MSI method combined with LC-MS/MS profiling of endogenous metabolites can obtain more detailed information about how spheroids respond to drug and spatial distribution information, thus fostering a better understanding of the relationship between drug-altered lipid metabolism and cancer microenvironment.

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.

THE SPACE DIMENSION AT THE MICRO LEVEL: MASS SPECTROMETRY IMAGING OF DRUGS IN TISSUES

Mass spectrometry imaging (MSI) has seen remarkable development in recent years. The possibility of getting quantitative or semiquantitative data, while maintaining the spatial component in the tissues has opened up unique study possibilities. Now with a spatial window of few tens of microns, we can characterize the events occurring in tissue subcompartments in physiological and pathological conditions. For example, in oncology-especially in preclinical models-we can quantitatively measure drug distribution within tumors, correlating it with pharmacological treatments intended to modify it. We can also study the local effects of the drug in the tissue, and their effects in relation to histology. This review focuses on the main results in the field of drug MSI in clinical pharmacology, looking at the literature on the distribution of drugs in human tissues, and also the first preclinical evidence of drug intratissue effects. The main instrumental techniques are discussed, looking at the different instrumentation, sample preparation protocols, and raw data management employed to obtain the sensitivity required for these studies. Finally, we review the applications that describe in situ metabolic events and pathways induced by the drug, in animal models, showing that MSI makes it possible to study effects that go beyond the simple concentration of the drug, maintaining the space dimension. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

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.

Applications of Omics Technologies for Three-Dimensional In Vitro Disease Models

Omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, multiomics, and integrated modalities, have greatly contributed to our understanding of various diseases by enabling researchers to probe the molecular wiring of cellular systems in a high-throughput and precise manner. With the development of tissue-engineered three-dimensional (3D) in vitro disease models, such as organoids and spheroids, there is potential of integrating omics technologies with 3D disease models to elucidate the complex links between genotype and phenotype. These 3D disease models have been used to model cancer, infectious disease, toxicity, neurological disorders, and others. In this review, we provide an overview of omics technologies, highlight current and emerging studies, discuss the associated experimental design considerations, barriers and challenges of omics technologies, and provide an outlook on the future applications of omics technologies with 3D models. Overall, this review aims to provide a valuable resource for tissue engineers seeking to leverage omics technologies for diving deeper into biological discovery. Impact statement With the emergence of three-dimensional (3D) in vitro disease models, tissue engineers are increasingly interested to investigate these systems to address biological questions related to disease mechanism, drug target discovery, therapy resistance, and more. Omics technologies are a powerful and high-throughput approach, but their application for 3D disease models is not maximally utilized. This review illustrates the achievements and potential of using omics technologies to leverage the full potential of 3D in vitro disease models. This will improve the quality of such models, advance our understanding of disease, and contribute to therapy 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.

Three-Dimensional Imaging with Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry

Mass spectrometry imaging as a field has pushed its frontiers to three dimensions. Most three-dimensional mass spectrometry imaging (3D MSI) approaches require serial sectioning that results in a loss of biological information between analyzed slices and difficulty in reconstruction of 3D images. In this contribution, infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) was demonstrated to be applicable for 3D MSI that does not require sectioning because IR laser ablates material on a micrometer scale. A commercially available over-the-counter pharmaceutical was used as a model to demonstrate the feasibility of IR-MALDESI for 3D MSI. Depth resolution (i.e., z-resolution) as a function of laser energy levels and density of ablated material was investigated. The best achievable depth resolution from a pill was 2.3 μm at 0.3 mJ/pulse. 2D and 3D MSI were performed on the tablet to show the distribution of pill-specific molecules. A 3D MSI analysis on a region of interest of 15 × 15 voxels across 50 layers was performed. Our results demonstrate that IR-MALDESI is feasible with 3D MSI on a pill, and future work will be focused on analyses of biological tissues.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

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.

Redesigning the T-probe for mass spectrometry analysis of online lysis of non-adherent single cells

Single cell mass spectrometry (SCMS) allows for molecular analysis of individual cells while avoiding the inevitable drawbacks of using cell lysate prepared from populations of cells. Based on our previous design of the T-probe, a microscale sampling and ionization device for SCMS analysis, we further developed the device to perform online, and real time lysis of non-adherent live single cells for mass spectrometry (MS) analysis at ambient conditions. This redesigned T-probe includes three parts: a sampling probe with a small tip to withdraw a whole cell, a solvent-providing capillary to deliver lysis solution (i.e., acetonitrile), and a nano-ESI emitter in which rapid cell lysis and ionization occur followed by MS analysis. These three components are embedded between two polycarbonate slides and are jointed through a T-junction to form an integrated device. Colon cancer cells (HCT-116) under control and treatment (using anticancer drug irinotecan) conditions were analyzed. We detected a variety of intracellular species, and structural identification of selected ions was conducted using tandem MS (MS2). We further conducted statistical analysis (e.g., PLS-DA and t-test) to gain biological insights of cellular metabolism. Our results indicate that the influence of anticancer drugs on cellular metabolism of live non-adherent cells can be obtained using the SCMS experiments combined with statistical data analysis.

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aβ) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 μm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 μm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.