Specific Lipid and Metabolic Profiles of R-CHOP-Resistant Diffuse Large B-Cell Lymphoma Elucidated by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging and in Vivo Imaging

Low serum apolipoprotein A1 level predicts poor prognosis of patients with diffuse large B-cell lymphoma in the real world: a retrospective study

BACKGROUND

Apolipoprotein A1 (ApoA1) is a member of the apolipoprotein family with diverse functions. It is associated with the pathogenesis and prognosis of several types of tumors. However, the role of serum apolipoprotein A1 (ApoA1) in the prognosis of patients with diffuse large B-cell lymphoma (DLBCL) remains unclear. This study aimed to elucidate its influence on clinical outcomes in patients with DLBCL.

METHODS

We retrospectively analyzed a cohort of 1583 consecutive DLBCL patients admitted to the Fujian Medical University Union Hospital between January 2011 and December 2021. 949 newly diagnosed DLBCL patients who met the inclusion criteria were enrolled for statistical analysis. Receiver operating characteristic curve analysis was performed to determine the optimal cut-off value for serum ApoA1 levels for prognostic prediction among patients with DLBCL. The correlations between serum ApoA1 levels and clinical and laboratory parameters were analyzed. Prognostic significance was analyzed using univariate and multivariate Cox proportional hazards models.

RESULTS

Newly diagnosed patients with DLBCL demonstrated low serum ApoA1 levels (< 0.925 g/L), had more B symptoms, higher levels of serum lactate dehydrogenase (LDH) (>upper limit of normal), poorer performance status (Eastern Cooperative Oncology Group score of 2-4), higher percentage of advanced stage and non-germinal center B-cell (non-GCB) subtype, more cases of > 1 extranodal site, higher International Prognostic Index (IPI) score (3-5), and higher incidence of relapse or refractory diseases compared with those with high serum ApoA1 levels (≥ 0.925 g/L). Low serum ApoA1 levels were an independent adverse prognostic factor for overall survival (OS) but not progression-free survival (PFS).

CONCLUSIONS

Low serum ApoA1 levels were associated with poor treatment response and inferior survival in newly diagnosed patients with DLBCL.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans' algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

Matrix-assisted laser desorption/ionization mass spectrometry imaging sample preparation using wet-interface matrix deposition for lipid analysis

RATIONALE

Sample preparation is one of the most crucial steps for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Scientists beginning their study with this technique may be overwhelmed by the variety of matrices, solvents, and concentrations; the methods of their applications; and the lack of widely available knowledge of the effect of these parameters on the results. Here we present in depth the aspects of matrix deposition, which will be helpful for the scientific community.

METHODS

In this study, we tested several MALDI matrices, such as 2,5-dihydroxybenzoic acid (DHB), norharmane, N-(1-naphthyl)ethylenediamine dihydrochloride (NEDC), and 9-aminoacridine (9AA), using the SunCollect system: wet-interface matrix deposition in the context of lipid analysis. We optimized the number of matrix layers and nozzle settings in terms of spectral intensity and the overall quality of the obtained ion maps.

RESULTS

Our research presents the effect of the number of matrix layers and nozzle settings on the results and allows for choosing the optimal parameters for the analyses. In positive ionization mode, DHB matrix could be chosen first. In the negative ionization mode, 1,5-diaminonaphthalene matrix produces a higher peak intensity in a lower mass range and seems to provide more information than 9AA. We recommend NEDC for particular processes such as glucose analysis. Compared to the remaining matrices, norharmane shows significant changes in the obtained ion maps.

CONCLUSIONS

Such a large amount of data allow us to observe an interesting conclusion: the obtained ion image for a particular ion could differ dramatically with a change in the matrix, the solvent composition, or even the number of matrix layers. This must be considered when interpreting the result, impelling us to compare the results obtained with different matrices with caution.

The influence of matrix concentration and solvent composition on the results of MALDI MSI, with the aid of wet-interface matrix deposition

Imaging mass spectrometry is a powerful technique for the molecular analysis of tissue sections. As in many analytical methods, sample preparation is one of the main and most important steps to obtain results of good quality. Usually, the matrix concentration and solvent composition in different studies are taken for granted without any further consideration. In our studies, we aimed to find how matrix concentration and a type of solvent influence the signal. Moreover, we also aimed to find the relationship between these parameters, how they influence the spectra, and how they influence obtained ion maps. In our experiments, we used SunCollect®, which is a commercially available wet-interface system for matrix deposition. We decided to choose two matrix concentrations (2,5-dihydroxybenzoic acid [DHB]: 15 and 25 mg/mL; 9-aminoacridine [9AA]: 7 and 5 mg/mL) and two different water solutions of solvents in two different percentages for the matrices (DHB: 50% and 70% of methanol [MeOH] and acetonitrile [ACN]; 9AA 70% and 50% of ethanol [EtOH] and MeOH). In the end, the influence of these parameters on obtained spectra and ion maps was assessed.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Prognostic and therapeutic value of serum lipids and a new IPI score system based on apolipoprotein A-I in diffuse large B-cell lymphoma

Lipid metabolism is associated with lymphomagenesis and functions as a new therapeutic target in patients with lymphoma. Several serum lipids and lipoproteins have prognostic value in solid tumors; however, their value in diffuse large B-cell lymphoma (DLBCL) has been poorly described. We retrospectively analyzed and compared pre-treatment serum lipid and lipoprotein levels, including triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (ApoA-I), and apolipoprotein B (ApoB) between 105 DLBCL and 105 controls (no DLBCL). The prognostic significance of serum lipid and lipoprotein levels was determined using univariate and multivariate Cox proportional hazards models. The primary outcomes, overall survival (OS) and progression-free survival (PFS), were assessed by the Kaplan-Meier method. We combined the International Prognostic Index (IPI) with ApoA-I to build a nomogram model (IPI-A) to predict the OS and PFS of DLBCL. Serum TG, LDL-C, HDL-C, ApoA-I, and ApoB levels were significantly lower in the DLBCL patients than in controls and significantly increased after chemotherapy. Multivariate analyses showed that the ApoA-I level was an independent predictor of OS and PFS. In addition, our findings indicated that the prognostic index IPI-A significantly improves risk prediction over the traditional IPI score system. ApoA-I is an independent prognostic factor associated with poor OS and PFS in DLBCL patients. Our findings suggested that IPI-A is a prognostic index accurately used for risk assessment in patients with DLBCL.

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.

Sphingolipids and Lymphomas: A Double-Edged Sword

Lymphomas are a highly heterogeneous group of hematological neoplasms. Given their ethiopathogenic complexity, their classification and management can become difficult tasks; therefore, new approaches are continuously being sought. Metabolic reprogramming at the lipid level is a hot topic in cancer research, and sphingolipidomics has gained particular focus in this area due to the bioactive nature of molecules such as sphingoid bases, sphingosine-1-phosphate, ceramides, sphingomyelin, cerebrosides, globosides, and gangliosides. Sphingolipid metabolism has become especially exciting because they are involved in virtually every cellular process through an extremely intricate metabolic web; in fact, no two sphingolipids share the same fate. Unsurprisingly, a disruption at this level is a recurrent mechanism in lymphomagenesis, dissemination, and chemoresistance, which means potential biomarkers and therapeutical targets might be hiding within these pathways. Many comprehensive reviews describing their role in cancer exist, but because most research has been conducted in solid malignancies, evidence in lymphomagenesis is somewhat limited. In this review, we summarize key aspects of sphingolipid biochemistry and discuss their known impact in cancer biology, with a particular focus on lymphomas and possible therapeutical strategies against them.

Performing 2D-1D-2D Mass Spectrometry Imaging Using Strings

The mass spectrometry imaging (MSI) technique is widely used in several fields due to its ability to provide spatial information of samples. However, for existing MSI methods, the sample is typically placed on a two-dimensional (2D) platform and is scanned back and forth. As a result, the platform size limits the imaging size. This paper proposes a new MSI method that involves the initial imprinting of chemicals on a two-dimensional string plane area. The string plane was then unraveled to a one-dimensional (1D) string, and the chemicals imprinted on it were ionized using a lab-made ion source. Finally, a 2D MSI image was reconstructed through data processing (2D-1D-2D mass imaging). Compared with traditional MSI methods, the imaging size is no longer limited by the platform size, making it possible to perform the MSI of large samples. As proof of concept, this method was used to image an intact seedling of Broussonetia papyrifera. As a result, clear and overall MS images were obtained, demonstrating the ability of this method to analyze large samples.

Prognostic significance of pretreatment serum free fatty acid in patients with diffuse large B-cell lymphoma in the rituximab era: a retrospective analysis

Background

Fatty acid metabolism is reportedly associated with various cancers. However, the role of pretreatment serum free fatty acid (FFA) levels in diffuse large B-cell lymphoma (DLBCL) prognosis is still unclear, and our study aimed to better elucidate its influence on clinical outcomes.

Methods

The medical records of 221 newly diagnosed DLBCL patients admitted to Fujian Medical University Union Hospital from January 2011 to December 2016 were analysed retrospectively. Receiver operating characteristic curve analysis was used to determine a cut-off value for pretreatment serum FFA levels for prognostic prediction in DLBCL patients. The relationship between pretreatment serum FFA levels and clinical and laboratory parameters was analysed. Univariate and multivariate analyses were used to assess prognostic factors for overall survival (OS) and progression-free survival (PFS).

Results

Newly diagnosed DLBCL patients with high pretreatment serum FFA levels (≥0.495 mmol/l) had more B symptoms, higher serum lactate dehydrogenase levels (> upper limit of normal), >1 extranodal site, and higher International Prognostic Index score (3–5) compared to those with low pretreatment serum FFA levels (<0.495 mmol/l). Higher serum FFA levels were independent prognostic factors for poor OS, but not PFS.

Conclusions

High pretreatment serum FFA levels are associated with lower survival in untreated DLBCL patients.

Treatment resistance in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous disease and represents the most common subtype of lymphoma. Although 60-70% of all patients can be cured by the current standard of care in the frontline setting, the majority of the remaining patients will experience treatment resistance and have a poor clinical outcome. Numerous efforts have been made to improve the efficacy of the standard regimen by, for example, dose intensification or adding novel agents. However, these results generally failed to demonstrate significant clinical benefits. Hence, understanding treatment resistance is a pressing need to optimize the outcome of those patients. In this Review, we first describe the conceptual sources of treatment resistance in DLBCL and then provide detailed and up-to-date molecular insight into the mechanisms of resistance to the current treatment options in DLBCL. We lastly highlight the potential strategies for rationally managing treatment resistance from both the preventive and interventional perspectives.

Heterogeneity of Lipid and Protein Cartilage Profiles Associated with Human Osteoarthritis with or without Type 2 Diabetes Mellitus

Osteoarthritis (OA) is a multifactorial pathology and comprises a wide range of distinct phenotypes. In this context, the characterization of the different molecular profiles associated with each phenotype can improve the classification of OA. In particular, OA can coexist with type 2 diabetes mellitus (T2DM). This study investigates lipidomic and proteomic differences between human OA/T2DM- and OA/T2DM+ cartilage through a multimodal mass spectrometry approach. Human cartilage samples were obtained after total knee replacement from OA/T2DM- and OA/T2DM+ patients. Label-free proteomics was employed to study differences in protein abundance and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for spatially resolved-lipid analysis. Label-free proteomic analysis showed differences between OA/T2DM- and OA/T2DM+ phenotypes in several metabolic pathways such as lipid regulation. Interestingly, phospholipase A2 protein was found increased within the OA/T2DM+ cohort. In addition, MALDI-MSI experiments revealed that phosphatidylcholine and sphingomyelin species were characteristic of the OA/T2DM- group, whereas lysolipids were more characteristic of the OA/T2DM+ phenotype. The data also pointed out differences in phospholipid content between superficial and deep layers of the cartilage. Our study shows distinctively different lipid and protein profiles between OA/T2DM- and OA/T2DM+ human cartilage, demonstrating the importance of subclassification of the OA disease for better personalized treatments.

Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI

Background

Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.

Methods

Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.

Results

Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.

Conclusions

This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00242-z.

Recent developments of novel matrices and on-tissue chemical derivatization reagents for MALDI-MSI

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a fast-growing technique for visualization of the spatial distribution of the small molecular and macromolecular biomolecules in tissue sections. Challenges in MALDI-MSI, such as poor sensitivity for some classes of molecules or limited specificity, for instance resulting from the presence of isobaric molecules or limited resolving power of the instrument, have encouraged the MSI scientific community to improve MALDI-MSI sample preparation workflows with innovations in chemistry. Recent developments of novel small organic MALDI matrices play a part in the improvement of image quality and the expansion of the application areas of MALDI-MSI. This includes rationally designed/synthesized as well as commercially available small organic molecules whose superior matrix properties in comparison with common matrices have only recently been discovered. Furthermore, on-tissue chemical derivatization (OTCD) processes get more focused attention, because of their advantages for localization of poorly ionizable metabolites and their‚ in several cases‚ more specific imaging of metabolites in tissue sections. This review will provide an overview about the latest developments of novel small organic matrices and on-tissue chemical derivatization reagents for MALDI-MSI.

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

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

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

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.

Spatially resolved endogenous improved metabolite detection in human osteoarthritis cartilage by matrix assisted laser desorption ionization mass spectrometry imaging

Osteoarthritis (OA) is one of the most common musculoskeletal diseases, characterized by the progressive deterioration of articular cartilage. Although the disease has been well studied in the past few years, the endogenous metabolic composition and more importantly the spatial information of these molecules in cartilage is still poorly understood. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has been previously used for the investigation of the bimolecular distribution of proteins and lipids through the in situ analysis of cartilage tissue sections. MALDI-MSI as a tool to detect metabolites remains challenging, as these species have low abundance and degrade rapidly. In this work, we present a complete methodology, from sample preparation to data analysis for the detection of endogenous metabolites on cartilage by MSI. Our results demonstrate for the first time the ability to detect small molecules in fragile, challenging tissues through an optimized protocol, and render MSI as a tool towards a better understanding of OA.

Improvement of Sensitivity and Reproducibility for Imaging of Endogenous Metabolites by Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry

Matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) is a powerful technique to visualize the distributions of biomolecules without any labeling. In MALDI-MSI experiments, the choice of matrix deposition method is important for acquiring favorable MSI data with high sensitivity and high reproducibility. Generally, manual or automated spray-coating and automated sublimation methods are used, but these methods have some drawbacks with respect to detection sensitivity, spatial resolution, and data reproducibility. Herein, we present an optimized matrix deposition method of sublimation coupled with recrystallization using 9-aminoacridine (9-AA) as a matrix capable of ionizing endogenous metabolites. The matrix recrystallization process after sublimation was optimized for the solvent concentration and reaction temperature for matrix-metabolite co-crystallization. This optimized method showed excellent reproducibility and spatial resolution compared to the automatic spray-coating method. Furthermore, the recrystallization step after sublimation remarkably improved the detectability of metabolites, including amino acids, nucleotide derivatives, and lipids, compared with the conventional sublimation method. To date, there have been no other reports of 9-AA-based sublimation combined with recrystallization. The present method provides an easy, sensitive, and reproducible matrix deposition method for MALDI-MSI of endogenous metabolites. Graphical Abstract.

norHarmane containing ionic liquid matrices for low molecular weight MALDI-MS carbohydrate analysis: The perfect couple with α-cyano-4-hydroxycinnamic acid

Cinnamic acid derivatives, particularly α-cyano-4-hydroxycinnamic acid (E-α-cyano-4-hydroxycinnamic acid or (E)-2-cyano-3-(4-hydroxyphenyl)prop-2-enoate; CHCA), have been extensively used especially for protein and peptide analysis. Together with the introduction of ionic liquid MALDI matrix (ILM) started the study of applications of IL prepared with CHCA and a counter organic base (ie, aliphatic amines) in which CHCA moiety is the chromophore responsible of UV-laser absorption. Despite the extensive studies of norharmane (9H-pyrido[3,4-b]indole; nHo) applications as matrix and its peculiar basic properties in the ground and electronic excited state, nHo containing ILM was never tested in MALDI-MS experiments. This pyrido-indole compound was introduced as MALDI matrix 22 years ago for different applications including low molecular weight (LMW) carbohydrates (neutral, acidic, and basic carbohydrates). These facts encouraged us to use it as a base, for the first time, for ILM preparation. As a rational design of new IL MALDI matrices, E-α-cyanocinnamic acid.nHo and E-cinnamic acid.nHo were prepared and their properties as matrices studied. Their performance was compared with that of (a) the corresponding IL prepared with butylamine as basic component, (b) the corresponding crystalline E-α-cyanocinnamic and E-cinnamic acid, and (c) the classical crystalline matrices (2,5-dihydroxybenzoic acid, DHB; nHo) used in the analysis of neutral/sulfated carbohydrates. The IL DHB.nHo was tested, too. Herein, we demonstrate the outstanding performance for the IL CHCA.nHo for LMW carbohydrate in positive and negative ion mode (linear and reflectron modes). Sulfated oligosaccharides were detected in negative ion mode, and although the dissociation of sulfate groups was not completely suppressed the relative intensity (RI) of [M - Na]^- peak was quite high. Additionally, to better understand the quite different performance of each IL tested as matrix, the physical and morphological properties in solid state were studied (optical image; MS image).

"Redox lipidomics technology: Looking for a needle in a haystack"

Aerobic life is based on numerous metabolic oxidation reactions as well as biosynthesis of oxygenated signaling compounds. Among the latter are the myriads of oxygenated lipids including a well-studied group of polyunsaturated fatty acids (PUFA) - octadecanoids, eicosanoids, and docosanoids. During the last two decades, remarkable progress in liquid-chromatography-mass spectrometry has led to significant progress in the characterization of oxygenated PUFA-containing phospholipids, thus designating the emergence of a new field of lipidomics, redox lipidomics. Although non-enzymatic free radical reactions of lipid peroxidation have been mostly associated with the aberrant metabolism typical of acute injury or chronic degenerative processes, newly accumulated evidence suggests that enzymatically catalyzed (phospho)lipid oxygenation reactions are essential mechanisms of many physiological pathways. In this review, we discuss a variety of contemporary protocols applicable for identification and quantitative characterization of different classes of peroxidized (phospho)lipids. We describe applications of different types of LCMS for analysis of peroxidized (phospho)lipids, particularly cardiolipins and phosphatidylethanolalmines, in two important types of programmed cell death - apoptosis and ferroptosis. We discuss the role of peroxidized phosphatidylserines in phagocytotic signaling. We exemplify the participation of peroxidized neutral lipids, particularly tri-acylglycerides, in immuno-suppressive signaling in cancer. We also consider new approaches to exploring the spatial distribution of phospholipids in the context of their oxidizability by MS imaging, including the latest achievements in high resolution imaging techniques. We present innovative approaches to the interpretation of LC-MS data, including audio-representation analysis. Overall, we emphasize the role of redox lipidomics as a communication language, unprecedented in diversity and richness, through the analysis of peroxidized (phospho)lipids.

Polydopamine-capped AgNPs as a novel matrix overcoming the ion suppression of phosphatidylcholine for MALDI MS comprehensive imaging of glycerophospholipids and sphingolipids in impact-induced injured brain

In this study, AgNPs@PDA was synthesized as a matrix for the analysis of lipids in both positive and negative ion modes.