A high-performance bio-tissue imaging method using air flow-assisted desorption electrospray ionization coupled with a high-resolution mass spectrometer

Targeting tumour metabolism in melanoma to enhance response to immune checkpoint inhibition: A balancing act

Immune checkpoint inhibition has transformed the treatment landscape of advanced melanoma and long-term survival of patients is now possible. However, at least half of the patients do not benefit sufficiently. Metabolic reprogramming is a hallmark of cancer cells and may contribute to both tumour growth and immune evasion by the tumour. Preclinical studies have indeed demonstrated that modulating tumour metabolism can reduce tumour growth while improving the functionality of immune cells. Since metabolic pathways are commonly shared between immune and tumour cells, it is essential to understand how modulating tumour metabolism in patients influences the intricate balance of pro-and anti-tumour immune effects in the tumour microenvironment. The key question is whether modulating tumour metabolism can inhibit tumour cell growth as well as facilitate an anti-tumour immune response. Here, we review current knowledge on the effect of tumour metabolism on the immune response in melanoma. We summarise metabolic pathways in melanoma and non-cancerous cells in the tumour microenvironment and discuss models and techniques available to study the metabolic-immune interaction. Finally, we discuss clinical use of these techniques to improve our understanding of how metabolic interventions can tip the balance towards a favourable, immune permissive microenvironment in melanoma patients.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Correlative radioimaging and mass spectrometry imaging: a powerful combination to study 14C-graphene oxide in vivo biodistribution

Research on graphene based nanomaterials has flourished in the last decade due their unique properties and emerging socio-economic impact. In the context of their potential exploitation for biomedical applications, there is a growing need for the development of more efficient imaging techniques to track the fate of these materials. Herein we propose the first correlative imaging approach based on the combination of radioimaging and mass spectrometry imaging for the detection of Graphene Oxide (GO) labelled with carbon-14 in mice. In this study, ¹⁴C-graphene oxide nanoribbons were produced from the oxidative opening of ¹⁴C-carbon nanotubes, and were then intensively sonicated to provide nano-size ¹⁴C-GO flakes. After Intravenous administration in mice, ¹⁴C-GO distribution was quantified by radioimaging performed on tissue slices. On the same slices, MS-imaging provided a highly resolved distribution map of the nanomaterial based on the detection of specific radical anionic carbon clusters ranging from C2˙^- to C9˙^- with a base peak at m/z 72 (¹²C) and 74 (¹⁴C) under negative laser desorption ionization mass spectrometry (LDI-MS) conditions. This proof of concept approach synergizes the strength of each technique and could be advantageous in the pre-clinical development of future Graphene-based biomedical applications.

Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is one of the least specimen destructive ambient ionization mass spectrometry tissue imaging methods. It enables rapid simultaneous mapping, measurement, and identification of hundreds of molecules from an unmodified tissue sample. Over the years, since its first introduction as an imaging technique in 2005, DESI-MSI has been extensively developed as a tool for separating tissue regions of various histopathologic classes for diagnostic applications. Recently, DESI-MSI has also emerged as a versatile technique that enables drug discovery and can guide the efficient development of drug delivery systems. For example, it has been increasingly employed for uncovering unique patterns of in vivo drug distribution, the discovery of potentially treatable biochemical pathways, revealing novel druggable targets, predicting therapeutic sensitivity of diseased tissues, and identifying early tissue response to pharmacological treatment. These and other recent advances in implementing DESI-MSI as the tool for the development of novel therapies are highlighted in this review.

Comparison of Local Metabolic Changes in Diabetic Rodent Kidneys Using Mass Spectrometry Imaging

Understanding the renal region-specific metabolic alteration in different animal models of diabetic nephropathy (DN) is critical for uncovering the underlying mechanisms and for developing effective treatments. In the present study, spatially resolved metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to compare the local metabolic changes in the kidneys of HFD/STZ-induced diabetic rats and db/db mice. As a result, a total of 67 and 59 discriminating metabolites were identified and visualized in the kidneys of the HFD/STZ-induced diabetic rats and db/db mice, respectively. The result showed that there were significant region-specific changes in the glycolysis, TCA cycle, lipid metabolism, carnitine metabolism, choline metabolism, and purine metabolism in both DN models. However, the regional levels of the ten metabolites, including glucose, AMP, eicosenoic acid, eicosapentaenoic acid, Phosphatidylserine (36:1), Phosphatidylserine (36:4), Phosphatidylethanolamine (34:1), Phosphatidylethanolamine (36:4), Phosphatidylcholine (34:2), Phosphatidylinositol (38:5) were changed in reversed directions, indicating significant differences in the local metabolic phenotypes of these two commonly used DN animal models. This study provides comprehensive and in-depth analysis of the differences in the tissue and molecular pathological features in diabetic kidney injury in HFD/STZ-induced diabetic rats and db/db mice.

LA-ICP-MS bioimaging demonstrated disturbance of metal ions in the brain of Parkinson's disease model mouse undergoing manganese-enhanced MRI

Manganese-enhanced MRI (MEMRI) is a powerful tool to study neuronal activity and microarchitecture in vivo. Yet the influence of exogenous manganese on the brain of the Parkinson's disease (PD) model mouse is poorly understood. Laser ablation connected to inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging for tissue section is an ideal tool to simultaneously analyze the metabolism of endogenous metal ions. In this study, DJ-1 knockout PD model mice were subjected to an MnCl₂ saline treatment and the distribution of Mn and several other endogenous metal ions in brain regions was assessed by MEMRI and LA-ICP-MS imaging. The results demonstrated that Mn mainly deposited in subcortical regions, such as ventricles, hippocampus (HC), medial preoptic nucleus (MPO), lateral septal nucleus (LS), and ventromedial hypothalamic nucleus (VMH). The enhanced signal-to-noise ratio (S/N) determined by MEMRI for Mn is closely related to the signal in LA-ICP-MS imaging. Significantly, the treatment of MnCl₂ disturbs the homeostasis of iron, zinc, copper, and calcium in the DJ-1 mouse, which could result in more severe symptoms of PD. Therefore, the application of MEMRI in the study of neurological disease must be made with caution.

Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics

The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.