Discovery of lipid biomarkers correlated with disease progression in clear cell renal cell carcinoma using desorption electrospray ionization imaging mass spectrometry

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies

Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Medium-chain and long-chain fatty acids are associated with diarrheal predominant irritable bowel syndrome revealed by DESI-MSI

BACKGROUND

Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders, but its pathogenesis remains unknown. Its development may be linked to intestinal dysmetabolism, directly and indirectly. The present study aimed to screen the differentially expressed small molecular substances in the mucosa of the colon between IBS with diarrhea (IBS-D) patients and healthy subjects and explore the pathogenesis of IBS-D.

METHODS

In this pilot study, the metabolites of colonic mucosa in ten patients with IBS-D and six healthy controls (HC) were analyzed by DESI-MSI. We also mapped the spatial distribution of the screened differential metabolites from samples of the IBS-D group and HC group.

RESULTS

The results showed that 20 metabolites in the colonic mucosa of IBS-D were significantly more abundant, while the other 77 substances were significantly reduced. Enrichment analysis of 97 differential metabolites and KEGG pathway analysis revealed that six medium-chain and long-chain fatty acids were determined to be most overrepresented in the IBS-D group compared to the HC group. Four of these six fatty acids are all PUFAs. The DESI-MSI results suggested that these fatty acids were localized in the colonic mucosa and confirmed the differences in these fatty acids between IBS-D and HC.

CONCLUSIONS

Medium-chain and long-chain fatty acids localized in the colonic mucosa are likely to be potential indicators for the differentiation of IBS-D from healthy subjects which may have implications in the mechanisms and possible preventive measures against IBS.

CLINICAL TRIAL REGISTRY REGISTRATION NUMBER

ChiCTR2200060224.

Mass Spectrometry Analysis for Clinical Applications: A Review

Mass spectrometry (MS) has become an attractive analytical method in clinical analysis due to its comprehensive advantages of high sensitivity, high specificity and high throughput. Separation techniques coupled MS detection (e.g., LC-MS/MS) have shown unique advantages over immunoassay and have developed as golden criterion for many clinical applications. This review summarizes the characteristics and applications of MS, and emphasizes the high efficiency of MS in clinical research. In addition, this review also put forward further prospects for the future of mass spectrometry technology, including the introduction of miniature MS instruments, point-of-care detection and high-throughput analysis, to achieve better development of MS technology in various fields of clinical application. Moreover, as ambient ionization mass spectrometry (AIMS) requires little or no sample pretreatment and improves the flux of MS, this review also summarizes its potential applications in clinic.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

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.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview

Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.

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.

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.

Lipid biomarkers that reflect postoperative recurrence risk in lung cancer patients who smoke: a case-control study

BACKGROUND

The risk of postoperative recurrence is higher in lung cancer patients who smoke than non-smokers. However, objective evaluation of the postoperative recurrence risk is difficult using conventional pathological prognostic factors because of their lack of reproducibility. Consequently, novel objective biomarkers that reflect postoperative risk in lung cancer patients who smoke must be identified. Because cigarette smoking and oncogenesis alter lipid metabolism in lung tissue, we hypothesized that the lipid profiles in lung cancer tissues are influenced by cigarette smoking and can reflect the postoperative recurrence risk in smoking lung cancer patients. This study aimed to identify lipid biomarkers that reflect the smoking status and the postoperative recurrence risk.

METHODS

Primary tumor tissues of lung adenocarcinoma (ADC) (n = 26) and squamous cell carcinoma (SQCC) (n = 18) obtained from surgery were assigned to subgroups according to the patient's smoking status. The ADC cohort was divided into never smoker and smoker groups, while the SQCC cohort was divided into moderate smoker and heavy smoker groups. Extracted lipids from the tumor tissues were subjected to liquid chromatography-tandem mass spectrometry analysis. Lipids that were influenced by smoking status and reflected postoperative recurrence and pathological prognostic factors were screened.

RESULTS

Two and 12 lipid peaks in the ADC and SQCC cohorts showed a significant positive correlation with the Brinkman index, respectively. Among them, in the ADC cohort, a higher lipid level consisted of three phosphatidylcholine (PC) isomers, PC (14:0_18:2), PC (16:1_16:1), and PC (16:0_16:2), was associated with a shorter recurrence free period (RFP) and a greater likelihoods of progressed T-factor (≥ pT2) and pleural invasion. In the SQCC cohort, a lower m/z 736.5276 level was associated with shorter RFP and greater likelihood of recurrence.

CONCLUSIONS

From our data, we propose three PC isomers, PC (14:0_18:2), PC (16:1_16:1), and PC (16:0_16:2), and a lipid peak of m/z 736.5276 as novel candidate biomarkers for postoperative recurrence risk in lung ADC and SQCC patients who are smokers.

Using the Synergy between HPLC-MS and MALDI-MS Imaging to Explore the Lipidomics of Clear Cell Renal Cell Carcinoma

Lipid imaging mass spectrometry (LIMS) has been tested in several pathological contexts, demonstrating its ability to segregate and isolate lipid signatures in complex tissues, thanks to the technique's spatial resolution. However, it cannot yet compete with the superior identification power of high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS), and therefore, very often, the latter is used to refine the assignment of the species detected by LIMS. Also, it is not clear if the differences in sensitivity and spatial resolution between the two techniques lead to a similar panel of biomarkers for a given disease. Here, we explore the capabilities of LIMS and HPLC-MS to produce a panel of lipid biomarkers to screen nephrectomy samples from 40 clear cell renal cell carcinoma patients. The same set of samples was explored by both techniques, and despite the important differences between them in terms of the number of detected and identified species (148 by LIMS and 344 by HPLC-MS in negative-ion mode) and the presence/absence of image capabilities, similar conclusions were reached: using the lipid fingerprint, it is possible to set up classifiers that correctly identify the samples as either healthy or tumor samples. The spatial resolution of LIMS enables extraction of additional information, such as the existence of necrotic areas or the existence of different tumor cell populations, but such information does not seem determinant for the correct classification of the samples, or it may be somehow compensated by the higher analytical power of HPLC-MS. Similar conclusions were reached with two very different techniques, validating their use for the discovery of lipid biomarkers.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

Update DESI Mass Spectrometry Imaging (MSI)

Desorption electrospray ionization (DESI) is an ambient technique that allows chemical information to be obtained directly from a wide range of surfaces, without pretreatment. Here we describe the improvements that have been developed to be able to achieve low tens of microns pixel size MSI experiments with high sensitivity for metabolites and lipids from biological tissue sections.In the last decade, DESI mass spectrometry has undergone developmental improvements, with regard to the method of desorption and ionization as well as the mass spectrometer to which the DESI source has been coupled to. DESI is becoming a mass spectrometry imaging technique, which can match and complement the currently most widely adopted ionization technique, the matrix-assisted laser desorption/ionization (MALDI).

Fatty acid metabolism reprogramming in ccRCC: mechanisms and potential targets

Lipid droplet formation is a defining histological feature in clear-cell renal cell carcinoma (ccRCC) but the underlying mechanisms and importance of this biological behaviour have remained enigmatic. De novo fatty acid (FA) synthesis, uptake and suppression of FA oxidation have all been shown to contribute to lipid storage, which is a necessary tumour adaptation rather than a bystander effect. Clinical studies and mechanistic investigations into the roles of different enzymes in FA metabolism pathways have revealed new metabolic vulnerabilities that hold promise for clinical effect. Several metabolic alterations are associated with worse clinical outcomes in patients with ccRCC, as lipogenic genes drive tumorigenesis. Enzymes involved in the intrinsic FA metabolism pathway include FA synthase, acetyl-CoA carboxylase, ATP citrate lyase, stearoyl-CoA desaturase 1, cluster of differentiation 36, carnitine palmitoyltransferase 1A and the perilipin family, and each might be potential therapeutic targets in ccRCC owing to the link between lipid deposition and ccRCC risk. Adipokines and lipid species are potential biomarkers for diagnosis and treatment monitoring in patients with ccRCC. FA metabolism could potentially be targeted for therapeutic intervention in ccRCC as small-molecule inhibitors targeting the pathway have shown promising results in preclinical models.

Distinct lipid profiles of radiation-induced carotid plaques from atherosclerotic carotid plaques revealed by UPLC-QTOF-MS and DESI-MSI

BACKGROUND AND PURPOSE

Radiotherapy is a standard treatment for head and neck tumors that significantly increases patients' long-term survival rates. However, late cerebrovascular complications, especially carotid artery stenosis (CAS), have gained increasing attention. Investigation of biomarkers of radiation-induced CAS may help to elucidate the mechanism by which radiation induces damage to blood vessels and identify possible preventive measures against such damage.

MATERIALS AND METHODS

In this study, we used lipidomics strategy to characterize the lipids present in 8 radiation-induced carotid plaques (RICPs) and 12 atherosclerotic carotid plaques (ASCPs). We also used desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) to map the spatial distribution of the screened lipids from 2 RICPs samples and 2 ASCPs samples.

RESULTS

The results showed that 31 metabolites in RICPs were significantly higher than that in ASCPs, 24 of which were triglycerides (TGs). We used four machine learning models to select potential indicators from the 31 metabolites. Six TGs [TG(17:2/17:2/18:0), TG(17:1/17:2/18:0), TG(17:0/17:2/18:0), TG(17:2/17:2/20:0), TG(17:1/17:2/20:0), TG(15:0/22:0/22:2)] were found to be the potential markers for distinguishing RICPs and ASCPs (AUC = 0.83). The DESI-MSI results suggested that the 6 TGs were localized in the collagen fiber regions and confirmed the differences of these TGs between the two kinds of plaques.

CONCLUSIONS

The 6 TGs primarily localized in the collagen fiber regions of plaques are likely to be potential indicators for the differentiation of RICPs from ASCPs which may have implications in the mechanisms and possible preventive measures against RICPs.

Decreased sphingomyelin (t34:1) is a candidate predictor for lung squamous cell carcinoma recurrence after radical surgery: a case-control study

Background

To reduce disease recurrence after radical surgery for lung squamous cell carcinomas (SQCCs), accurate prediction of recurrent high-risk patients is required for efficient patient selection for adjuvant chemotherapy. Because treatment modalities for recurrent lung SQCCs are scarce compared to lung adenocarcinomas (ADCs), accurately selecting lung SQCC patients for adjuvant chemotherapy after radical surgery is highly important. Predicting lung cancer recurrence with high objectivity is difficult with conventional histopathological prognostic factors; therefore, identification of a novel predictor is expected to be highly beneficial. Lipid metabolism alterations in cancers are known to contribute to cancer progression. Previously, we found that increased sphingomyelin (SM)(d35:1) in lung ADCs is a candidate for an objective recurrence predictor. However, no lipid predictors for lung SQCC recurrence have been identified to date. This study aims to identify candidate lipid predictors for lung SQCC recurrence after radical surgery.

Methods

Recurrent (n = 5) and non-recurrent (n = 6) cases of lung SQCC patients who underwent radical surgery were assigned to recurrent and non-recurrent groups, respectively. Extracted lipids from frozen tissue samples of primary lung SQCC were analyzed by liquid chromatography-tandem mass spectrometry. Candidate lipid predictors were screened by comparing the relative expression levels between the recurrent and non-recurrent groups. To compare lipidomic characteristics associated with recurrent SQCCs and ADCs, a meta-analysis combining SQCC (n = 11) and ADC (n = 20) cohorts was conducted.

Results

Among 1745 screened lipid species, five species were decreased (≤ 0.5 fold change; P < 0.05) and one was increased (≥ 2 fold change; P < 0.05) in the recurrent group. Among the six candidates, the top three final candidates (selected by AUC assessment) were all decreased SM(t34:1) species, showing strong performance in recurrence prediction that is equivalent to that of histopathological prognostic factors. Meta-analysis indicated that decreases in a limited number of SM species were observed in the SQCC cohort as a lipidomic characteristic associated with recurrence, in contrast, significant increases in a broad range of lipids (including SM species) were observed in the ADC cohort.

Conclusion

We identified decreased SM(t34:1) as a novel candidate predictor for lung SQCC recurrence. Lung SQCCs and ADCs have opposite lipidomic characteristics concerning for recurrence risk.

Trial registration

This retrospective study was registered at the UMIN Clinical Trial Registry (UMIN000039202) on January 21, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08948-5.

The Hypoxic Microenvironment Induces Stearoyl-CoA Desaturase-1 Overexpression and Lipidomic Profile Changes in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear cell renal cell carcinoma (ccRCC) is characterized by a high rate of cell proliferation and an extensive accumulation of lipids. Uncontrolled cell growth usually generates areas of intratumoral hypoxia that define the tumor phenotype. In this work, we show that, under these microenvironmental conditions, stearoyl-CoA desaturase-1 is overexpressed. This enzyme induces changes in the cellular lipidomic profile, increasing the oleic acid levels, a metabolite that is essential for cell proliferation. This work supports the idea of considering stearoyl-CoA desaturase-1 as an exploitable therapeutic target in ccRCC.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). It is characterized by a high cell proliferation and the ability to store lipids. Previous studies have demonstrated the overexpression of enzymes associated with lipid metabolism, including stearoyl-CoA desaturase-1 (SCD-1), which increases the concentration of unsaturated fatty acids in tumor cells. In this work, we studied the expression of SCD-1 in primary ccRCC tumors, as well as in cell lines, to determine its influence on the tumor lipid composition and its role in cell proliferation. The lipidomic analyses of patient tumors showed that oleic acid (18:1n-9) is one of the major fatty acids, and it is particularly abundant in the neutral lipid fraction of the tumor core. Using a ccRCC cell line model and in vitro-generated chemical hypoxia, we show that SCD-1 is highly upregulated (up to 200-fold), and this causes an increase in the cellular level of 18:1n-9, which, in turn, accumulates in the neutral lipid fraction. The pharmacological inhibition of SCD-1 blocks 18:1n-9 synthesis and compromises the proliferation. The addition of exogenous 18:1n-9 to the cells reverses the effects of SCD-1 inhibition on cell proliferation. These data reinforce the role of SCD-1 as a possible therapeutic target.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.

Chemical Composition of Tomato Seed Flours, and Their Radical Scavenging, Anti-Inflammatory and Gut Microbiota Modulating Properties

In the current study, the chemical composition and total phenolic content of tomato seed flours, along with potential health beneficial properties, including free radical scavenging capacities, anti-inflammatory capacities, and gut microbiota profile modulation, were examined using two different batches. Eight compounds were identified in the tomato seed flour, including malic acid, 2-hydroxyadipic acid, salicylic acid, naringin, N-acetyl-tryptophan, quercetin-di-O-hexoside, kaempferol-di-O-hexoside, and azelaic acid. The total phenolic contents of tomato seed flour were 1.97-2.00 mg gallic acid equivalents/g. Oxygen radical absorbing capacities (ORAC), 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacities (DPPH), and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) cation radical scavenging capacities (ABTS) were 86.32-88.57, 3.57-3.81, and 3.39-3.58 µmoles Trolox equivalents/g, respectively, on a per flour dry weight basis. The mRNA expression of the pro-inflammatory markers, interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), were dose-dependently suppressed by tomato seed flour extracts. The extracts altered five of the eight bacterial phyla and genera evaluated. The results may provide some scientific support for the use of tomato seed flour as value-added food ingredients.

Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N6-Methyladenosine RNA Methylation Regulators

As the most prevalent internal eukaryotic modification, N6-methyladenosine (m6A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m6A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m6A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m6A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature's predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different (p = 7.47e - 12). The ROC curve also indicated that the risk signature had a decent predictive significance (AUC = 0.72). These results imply that the risk signature has a potential value for ccRCC treatment.

Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N6-Methyladenosine RNA Methylation Regulators

As the most prevalent internal eukaryotic modification, N6-methyladenosine (m6A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m6A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m6A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m6A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature's predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different (p = 7.47e - 12). The ROC curve also indicated that the risk signature had a decent predictive significance (AUC = 0.72). These results imply that the risk signature has a potential value for ccRCC treatment.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Laser Ablation Remote-Electrospray Ionisation Mass Spectrometry (LARESI MSI) Imaging-New Method for Detection and Spatial Localization of Metabolites and Mycotoxins Produced by Moulds

To date, no method has been developed to assess the distribution of mycotoxins on the surface of grains, or other plant material, and the depth of their penetration into the interior. The Infrared (IR) Laser Ablation-Remote-Electrospray Ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS), measuring in selected reaction monitoring (SRM) mode, was employed for the targeted imaging of selected metabolites of Aspergillus fumigatus, including mycotoxins in biological objects for the first time. This methodology allowed for the localisation of grain metabolites and fungal metabolites of grain infected by this mould. The distribution of metabolites in spelt grain was differentiated: fumigaclavine C, fumitremorgin C, and fumiquinazoline D were located mainly in the embryo, brevianamide F in the seed coat, and fumagillin in the endosperm. The LARESI mass spectrometry imaging method can be used in the future for the metabolomic analysis of mould metabolites in various plants and agricultural products.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Sphingomyelin(d35:1) as a novel predictor for lung adenocarcinoma recurrence after a radical surgery: a case-control study

Background

To improve the postoperative prognosis of patients with lung cancer, predicting the recurrence high-risk patients is needed for the efficient application of adjuvant chemotherapy. However, predicting lung cancer recurrence after a radical surgery is difficult even with conventional histopathological prognostic factors, thereby a novel predictor should be identified. As lipid metabolism alterations are known to contribute to cancer progression, we hypothesized that lung adenocarcinomas with high recurrence risk contain candidate lipid predictors. This study aimed to identify candidate lipid predictors for the recurrence of lung adenocarcinoma after a radical surgery.

Methods

Frozen tissue samples of primary lung adenocarcinoma obtained from patients who underwent a radical surgery were retrospectively reviewed. Recurrent and non-recurrent cases were assigned to recurrent (n = 10) and non-recurrent (n = 10) groups, respectively. Extracted lipids from frozen tissue samples were subjected to liquid chromatography-tandem mass spectrometry analysis. The average total lipid levels of the non-recurrent and recurrent groups were compared. Candidate predictors were screened by comparing the folding change and P-value of t-test in each lipid species between the recurrent and non-recurrent groups.

Results

The average total lipid level of the recurrent group was 1.65 times higher than that of the non-recurrent group (P < 0.05). A total of 203 lipid species were increased (folding change, ≥2; P < 0.05) and 4 lipid species were decreased (folding change, ≤0.5; P < 0.05) in the recurrent group. Among these candidates, increased sphingomyelin (SM)(d35:1) in the recurrent group was the most prominent candidate predictor, showing high performance of recurrence prediction (AUC, 9.1; sensitivity, 1.0; specificity, 0.8; accuracy, 0.9).

Conclusion

We propose SM(d35:1) as a novel candidate predictor for lung adenocarcinoma recurrence. Our finding can contribute to precise recurrence prediction and qualified postoperative therapeutic strategy for lung adenocarcinomas.

Trial registration

This retrospective study was registered at the UMIN Clinical Trial Registry (UMIN000039202) on 21st January 2020.

Recent advances of ambient ionization mass spectrometry imaging in clinical research

The structural information and spatial distribution of molecules in biological tissues are closely related to the potential molecular mechanisms of disease origin, transfer, and classification. Ambient ionization mass spectrometry imaging is an effective tool that provides molecular images while describing in situ information of biomolecules in complex samples, in which ionization occurs at atmospheric pressure with the samples being analyzed in the native state. Ambient ionization mass spectrometry imaging can directly analyze tissue samples at a fairly high resolution to obtain molecules in situ information on the tissue surface to identify pathological features associated with a disease, resulting in the wide applications in pharmacy, food science, botanical research, and especially clinical research. Herein, novel ambient ionization techniques, such as techniques based on spray and solid-liquid extraction, techniques based on plasma desorption, techniques based on laser desorption ablation, and techniques based on acoustic desorption were introduced, and the data processing of ambient ionization mass spectrometry imaging was briefly reviewed. Besides, we also highlight recent applications of this imaging technology in clinical researches and discuss the challenges in this imaging technology and the perspectives on the future of the clinical research.

Higher Accumulation of Docosahexaenoic Acid in the Vermilion of the Human Lip than in the Skin

The vermilion of the human lip is a unique facial area because of certain distinguishing features from the adjacent tissues such as the white lip (skin) and oral mucosa. However, the distinction in terms of molecular distribution between the vermilion and skin has remained unexplored. Therefore, we aimed to map the human lip by mass spectrometry imaging to gain understanding of the free fatty acid distribution in the vermilion. The lip specimens trimmed off during cheiloplasty were analyzed using desorption electrospray ionization-mass spectrometry imaging. Distributions of two monounsaturated fatty acids and three polyunsaturated fatty acids were observed in the human lip tissue: palmitoleic acid (POA) and oleic acid (OA) and linoleic acid (LA), arachidonic acid (AA), and docosahexaenoic acid (DHA), respectively. Although POA, OA, LA, and AA were differentially distributed across the vermilion and skin, DHA showed a higher accumulation in the epithelium of the vermilion compared to that in the skin. Our results clearly demonstrated the difference in fatty acid distributions between the vermilion and skin. The highly abundant DHA in the epithelium of the vermilion may have an antioxidant role and may thus protect the lip from aging. Our findings can provide a novel strategy for treating lip disorders.

Green Nut Oil or DHA Supplementation Restored Decreased Distribution Levels of DHA Containing Phosphatidylcholines in the Brain of a Mouse Model of Dementia

Dementia is a major public health concern nowadays. Reduced levels of brain docosahexaenoic acid (DHA) and DHA-phosphatidylcholines (DHA-PCs) in dementia patients were reported previously. Recently, we have reported that supplementation of green nut oil (GNO) or DHA improves memory function and distribution levels of brain DHA in senescence accelerated mice P8 (SAMP8). GNO is extracted from Plukenetia volubilis seeds, and SAMP8 is a well-known model mouse of dementia. In this current study, we examined the results of GNO or DHA supplementation in the distribution levels of brain DHA-PCs in same model mouse of dementia using desorption electrospray ionization (DESI) mass spectrometry imaging (MSI). We observed significantly decreased distribution of brain DHA-PCs, PC (16:0_22:6), and PC (18:0_22:6) in SAMP8 mice compared to wild type mice, and GNO or DHA treatment restored the decreased distribution levels of PC (16:0_22:6) and PC (18:0_22:6) in the brain of SAMP8 mice. These results indicate that GNO or DHA supplementation can ameliorate the decreased distribution of brain DHA-PCs in dementia, and could be potentially used for the prevention and treatment of dementia.

Identification of novel biomarkers of hepatocellular carcinoma by high-definition mass spectrometry: Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry and desorption electrospray ionization mass spectrometry imaging

RATIONALE

Hepatocellular carcinoma (HCC) is a highly malignant disease for which the development of prospective or prognostic biomarkers is urgently required. Although metabolomics is widely used for biomarker discovery, there are some bottlenecks regarding the comprehensiveness of detected features, reproducibility of methods, and identification of metabolites. In addition, information on localization of metabolites in tumor tissue is needed for functional analysis. Here, we developed a wide-polarity global metabolomics (G-Met) method, identified HCC biomarkers in human liver samples by high-definition mass spectrometry (HDMS), and demonstrated localization in cryosections using desorption electrospray ionization MS imaging (DESI-MSI) analysis.

METHODS

Metabolic profiling of tumor (n = 38) and nontumor (n = 72) regions in human livers of HCC was performed by an ultrahigh-performance liquid chromatography quadrupole time-of-flight MS (UHPLC/QTOFMS) instrument equipped with a mixed-mode column. The HCC biomarker candidates were extracted by multivariate analyses and identified by matching values of the collision cross section and their fragment ions on the mass spectra obtained by HDMS. Cryosections of HCC livers, which included both tumor and nontumor regions, were analyzed by DESI-MSI.

RESULTS

From the multivariate analysis, m/z 904.83 and m/z 874.79 were significantly high and low, respectively, in tumor samples and were identified as triglyceride (TG) 16:0/18:1(9Z)/20:1(11Z) and TG 16:0/18:1(9Z)/18:2(9Z,12Z) using the synthetic compounds. The TGs were clearly localized in the tumor or nontumor areas of the cryosection.

CONCLUSIONS

Novel biomarkers for HCC were identified by a comprehensive and reproducible G-Met method with HDMS using a mixed-mode column. The combination analysis of UHPLC/QTOFMS and DESI-MSI revealed that the different molecular species of TGs were associated with tumor distribution and were useful for characterizing the progression of tumor cells and discovering prospective biomarkers.

Localization of Metabolites of Human Kidney Tissue with Infrared Laser-Based Selected Reaction Monitoring Mass Spectrometry Imaging and Silver-109 Nanoparticle-Based Surface Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Infrared (IR) laser ablation-remote-electrospray ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS) operated in selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) modes was developed and employed for imaging of target metabolites in human kidney cancer tissue. SRM or MRM modes were employed to avoid artifacts that are present in full scan MS mode. Four tissue samples containing both cancerous and noncancerous regions, obtained from three patients with renal cell carcinoma (RCC), were imaged. Sixteen endogenous metabolites that were reported in the literature as varying in abundance between cancerous and noncancerous areas in various human tissues were selected for analysis. Target metabolites comprised ten amino acids, four nucleosides and nucleobases, lactate, and vitamin E. For comparison purposes, images of the same metabolites were obtained with ultraviolet (UV) desorption/ionization mass spectrometry imaging (UV-LDI-MSI) using monoisotopic silver-109 nanoparticle-enhanced target (¹⁰⁹AgNPET) in full-scan MS mode. The acquired MS images revealed differences in abundances of selected metabolites between cancerous and noncancerous regions of the kidney tissue. Importantly, the two imaging methods offered similar results. This study demonstrates the applicability of the novel ambient LARESI SRM/MRM MSI method to both investigating and discovering cancer biomarkers in human tissue.

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Dietary Intake of Green Nut Oil or DHA Ameliorates DHA Distribution in the Brain of a Mouse Model of Dementia Accompanied by Memory Recovery

Docosahexaenoic acid (DHA), an omega-3 polyunsaturated fatty acid, has significant healthbenefits. Previous studies reported decreased levels of DHA and DHA-containing phosphatidylcholines inthe brain of animals suffering from Alzheimer's disease, the most common type of dementia; furthermore,DHA supplementation has been found to improve brain DHA levels and memory efficiency in dementia. Oilextracted from the seeds of Plukenetia volubilis (green nut oil; GNO) is also expected to have DHA like effectsas it contains approximately 50% α-linolenic acid, a precursor of DHA. Despite this, changes in the spatialdistribution of DHA in the brain of animals with dementia following GNO or DHA supplementation remainunexplored. In this study, desorption electrospray ionization imaging mass spectrometry (DESI-IMS) wasapplied to observe the effects of GNO or DHA supplementation upon the distribution of DHA in the brain ofmale senescence-accelerated mouse-prone 8 (SAMP8) mice, a mouse model of dementia. DESI-IMS revealedthat brain DHA distribution increased 1.85-fold and 3.67-fold in GNO-fed and DHA-fed SAMP8 mice,respectively, compared to corn oil-fed SAMP8 mice. Memory efficiency in SAMP8 mice was also improvedby GNO or DHA supplementation. In summary, this study suggests the possibility of GNO or DHAsupplementation for the prevention of dementia.

Comparative analysis of the serum proteome profiles of thyroid cancer: An initial focus on the lipid profile

The serum lipid profile and clinical outcomes of cancer patients are commonly correlated in a wide range of carcinomas. However, few studies have investigated the serum lipid profile of patients with thyroid cancer (TC). The present study therefore aimed to analyze the lipid profiles of patients with TC. The serum proteomes of 31 participants with stage I-IV TC were screened using Orbitrap Q Exactive Plus. Analytical data collected between November 1, 2013 and November 11, 2018 from the laboratory information system included the total cholesterol (CHO), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), lipoprotein (a) and apolipoprotein B (ApoB) levels that were used to validate the screening results. A total of 3875 outpatients were enrolled in this study. A number of 17 differentially expressed proteins were identified. An Ingenuity pathway analysis identified activation of the liver X receptor/retinoid X receptor (LXR/RXR) activation, which is a crucial pathway involved in lipid metabolism. The results demonstrated that the total CHO levels were significantly different between patients with TC and control groups, both in men and women. In women, the levels of TG, HDL-C, Apo A1 and LDL-C/HDL-C were significantly different between patients with TC and control groups (all P<0.05). Higher concentrations of TG and LDL-C/HDL-C were observed in the cancer group compared with the control group. However, lower levels of Apo A1 and HDL-C were observed in women from the cancer group compared with the control group. The results from the present study revealed the presence of a disordered lipid profile in patients with TC. The molecular mechanism underlying the association between lipid metabolism and cancer requires further investigation and may be used to develop novel diagnostic biomarkers and therapeutic targets in human cancers.