Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics

Spatial metabolic modulation in vascular dementia by Erigeron breviscapus injection using ambient mass spectrometry imaging

BACKGROUND

Vascular dementia (VaD), a significant cognitive disorder, is caused by reduced cerebral blood flow. Unraveling the metabolic heterogeneity and reprogramming in VaD is essential for understanding its molecular pathology and developing targeted therapies. However, the in situ metabolic regulation within the specific brain regions affected by VaD has not been thoroughly investigated, and the therapeutic mechanisms of Erigeron breviscapus injection (EBI), a traditional Chinese medicine, require further elucidation.

PURPOSE

To investigate the region-specific metabolic alterations in a VaD rat model, explore the therapeutic effects of EBI at a microregional level, identify the key metabolic pathways and metabolites involved in VaD, and elucidate how EBI modulates these pathways to exert its therapeutic effects.

METHODS

Air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), a novel technique, was employed to investigate the metabolic changes in the brain microregions. We used a bilateral common carotid artery occlusion model to induce VaD in rats. Network analysis and network pharmacology were used to assess the local metabolic effects of the EBI treatment (3.6 mL/kg/day for 2 weeks).

RESULTS

The EBI treatment significantly ameliorated the neurological deficits in VaD rats. AFADESI-MSI revealed 31 key metabolites with significant alterations in the VaD model, particularly within the pathways related to neurotransmitter metabolism, redox homeostasis, and osmoregulation. The metabolic disturbances were primarily observed in the striatum (ST), pyriform cortex (PCT), hippocampus (HP), and other critical brain regions. The EBI treatment effectively reversed these metabolic imbalances, especially in neurotransmitter metabolism, suggesting its potential in mitigating VaD-related cognitive decline.

CONCLUSION

Our findings not only shed light on the molecular underpinnings of VaD but also highlight the potential of EBI as a therapeutic agent in neurodegenerative disorders. Moreover, this study demonstrates the power of advanced mass spectrometry imaging techniques in phytomedicine, offering new insights into the spatial metabolic changes induced by botanical treatments.

New perspective on central nervous system disorders: focus on mass spectrometry imaging

An abnormally organized brain spatial network is linked to the development of various central nervous system (CNS) disorders, including neurodegenerative diseases and neuropsychiatric disorders. However, the complicated molecular mechanisms of these diseases remain unresolved, making the development of treatment strategies difficult. A novel molecular imaging technique, called mass spectrometry imaging (MSI), captures molecular information on the surface of samples in situ. With MSI, multiple compounds can be simultaneously visualized in a single experiment. The high spatial resolution enables the simultaneous visualization of the spatial distribution and relative content of various compounds. The wide application of MSI in biomedicine has facilitated extensive studies on CNS disorders in recent years. This review provides a concise overview of the processes, applications, advantages, and disadvantages, as well as mechanisms of the main types of MSI. Meanwhile, this review summarizes the main applications of MSI in studying CNS diseases, including Alzheimer's disease (AD), CNS tumors, stroke, depression, Huntington's disease (HD), and Parkinson's disease (PD). Finally, this review comprehensively discusses the synergistic application of MSI with other advanced imaging modalities, its utilization in organoid models, its integration with spatial omics techniques, and provides an outlook on its future potential in single-cell analysis.

Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI

The study aims to evaluate multiparametric magnetic resonance imaging (MRI) for differentiating Follicular thyroid neoplasm (FTN) from non-FTN and malignant FTN (MFTN) from benign FTN (BFTN). We retrospectively analyzed 702 postoperatively confirmed thyroid nodules, and divided them into training (n = 482) and validation (n = 220) cohorts. The 133 FTNs were further split into BFTN (n = 116) and MFTN (n = 17) groups. Employing univariate and multivariate logistic regression, we identified independent predictors of FTN and MFTN, and subsequently develop a nomogram for FTN and a risk score system (RSS) for MFTN prediction. We assessed performance of nomogram through its discrimination, calibration, and clinical utility. The diagnostic performance of the RSS for MFTN was further compared with the performance of the Thyroid Imaging Reporting and Data System (TIRADS). The nomogram, integrating independent predictors, demonstrated robust discrimination and calibration in differentiating FTN from non-FTN in both training cohort (AUC = 0.947, Hosmer-Lemeshow P = 0.698) and validation cohort (AUC = 0.927, Hosmer-Lemeshow P = 0.088). Key risk factors for differentiating MFTN from BFTN included tumor size, restricted diffusion, and cystic degeneration. The AUC of the RSS for MFTN prediction was 0.902 (95% CI 0.798-0.971), outperforming five TIRADS with a sensitivity of 73.3%, specificity of 95.1%, accuracy of 92.4%, and positive and negative predictive values of 68.8% and 96.1%, respectively, at the optimal cutoff. MRI-based models demonstrate excellent diagnostic performance for preoperative predicting of FTN and MFTN, potentially guiding clinicians in optimizing therapeutic decision-making.

Non-coding RNAs in oral cancer: Emerging biomarkers and therapeutic frontier

Around the world, oral cancer (OC) is a major public health problem, resulting in a significant number of deaths each year. Early detection and treatment are crucial for improving patient outcomes. Recent progress in DNA sequencing and transcriptome profiling has revealed extensive non-coding RNAs (ncRNAs) transcription, underscoring their regulatory importance. NcRNAs influence genomic transcription and translation and molecular signaling pathways, making them valuable for various clinical applications. Combining spatial transcriptomics (ST) and spatial metabolomics (SM) with single-cell RNA sequencing provides deeper insights into tumor microenvironments, enhancing diagnostic and therapeutic precision for OC. Additionally, the exploration of salivary biomarkers offers a non-invasive diagnostic avenue. This article explores the potential of ncRNAs as diagnostic and therapeutic tools for OC.

Spatially resolved metabolomics: From metabolite mapping to function visualising

Mass spectrometry imaging (MSI)-based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography-tandem mass spectrometry (LC-MS)-based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI-driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation. HIGHLIGHTS: MSI-driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis

BACKGROUND

Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C).

METHODS

Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics.

RESULTS

Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls.

CONCLUSIONS

The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

Spatial metabolomics highlights metabolic reprogramming in acute myeloid leukemia mice through creatine pathway

Acute myeloid leukemia (AML) is recognized as an aggressive cancer that is characterized by significant metabolic reprogramming. Here, we applied spatial metabolomics to achieve high-throughput, in situ identification of metabolites within the liver metastases of AML mice. Alterations at metabolite and protein levels were further mapped out and validated by integrating untargeted metabolomics and proteomics. This study showed a downregulation in arginine's contribution to polyamine biosynthesis and urea cycle, coupled with an upregulation of the creatine metabolism. The upregulation of creatine synthetases Gatm and Gamt, as well as the creatine transporter Slc6a8, resulted in a marked accumulation of creatine within tumor foci. This process further enhances oxidative phosphorylation and glycolysis of leukemia cells, thereby boosting ATP production to foster proliferation and infiltration. Importantly, we discovered that inhibiting Slc6a8 can counter these detrimental effects, offering a new strategy for treating AML by targeting metabolic pathways.

Chrysomycin A Reshapes Metabolism and Increases Oxidative Stress to Hinder Glioblastoma Progression

Glioblastoma represents the predominant and a highly aggressive primary neoplasm of the central nervous system that has an abnormal metabolism. Our previous study showed that chrysomycin A (Chr-A) curbed glioblastoma progression in vitro and in vivo. However, whether Chr-A could inhibit orthotopic glioblastoma and how it reshapes metabolism are still unclear. In this study, Chr-A markedly suppressed the development of intracranial U87 gliomas. The results from airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) indicated that Chr-A improved the abnormal metabolism of mice with glioblastoma. Key enzymes including glutaminase (GLS), glutamate dehydrogenases 1 (GDH1), hexokinase 2 (HK2) and glucose-6-phosphate dehydrogenase (G6PD) were regulated by Chr-A. Chr-A further altered the level of nicotinamide adenine dinucleotide phosphate (NADPH), thus causing oxidative stress with the downregulation of Nrf-2 to inhibit glioblastoma. Our study offers a novel perspective for comprehending the anti-glioma mechanism of Chr-A, highlighting its potential as a promising chemotherapeutic agent for glioblastoma.

Advanced progress of spatial metabolomics in head and neck cancer research

Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Spatial metabolomics in head and neck tumors: a review

The joint analysis of single-cell transcriptomics, proteomics, lipidomics, metabolomics and spatial metabolomics is continually transforming our understanding of the mechanisms of metabolic reprogramming in tumor cells. Since head and neck tumor is the sixth most common tumor in the world, the study of the metabolic mechanism of its occurrence, development and prognosis is still undeveloped. In the past decade, this field has witnessed tremendous technological revolutions and considerable development that enables major breakthroughs to be made in the study of human tumor metabolism. In this review, a comprehensive comparison of traditional metabolomics and spatial metabolomics has been concluded, and the recent progress and challenges of the application of spatial metabolomics combined multi-omics in the research of metabolic reprogramming in tumors are reviewed. Furthermore, we also highlight the advances of spatial metabolomics in the study of metabolic mechanisms of head and neck tumors, and provide an outlook of its application prospects.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Direct mass spectrometry analysis of biological tissue for diagnosis of thyroid cancer using wooden-tip electrospray ionization

Direct mass spectrometry (MS) analysis of human tissue at the molecular level could gain insight into biomarker discovery and disease diagnosis. Detecting metabolite profiles of tissue sample play an important role in understanding the pathological properties of disease development. Because the complex matrices in tissue samples, complicated and time-consuming sample preparation processes are usually required by conventional biological and clinical MS methods. Direct MS with ambient ionization technique is a new analytical strategy for direct sample analysis with little sample preparation, and has been proven to be a simple, rapid, and effective analytical tools for direct analysis of biological tissues. In this work, we applied a simple, low-cost, disposable wooden tip (WT) for loading tiny thyroid tissue, and then loading organic solvents to extract biomarkers under electrospray ionization (ESI) condition. Under such WT-ESI, the extract of thyroid was directly sprayed out from wooden tip to MS inlet. In this work, thyroid tissue from normal and cancer parts were analyzed by the established WT-ESI-MS, showing lipids were mainly detectable compounds in thyroid tissue. The MS data of lipids obtained from thyroid tissues were further analyzed with MS/MS experiment and multivariate variable analysis, and the biomarkers of thyroid cancer were also investigated.