Sulfatide imaging identifies tumor cells in colorectal cancer peritoneal metastases.

Even with systemic chemotherapy, cytoreductive surgery (CRS), and hyperthermic intraperitoneal chemotherapy (HIPEC), peritoneal metastases (PM) remain a common site of disease progression for colorectal cancer (CRC) and are frequently associated with a poor prognosis. The mass spectrometry (MS) method known as Matrix-Assisted Laser Desorption/Ionization - Time of Flight (MALDI-TOF) is frequently used in medicine to identify structural compounds and biomarkers. It has been demonstrated that lipids are crucial in mediating the aggressive growth of tumors. In order to investigate the lipid profiles, particularly with regard to histological distribution, we used MALDI-TOF MS (MALDI-MS) and MALDI-TOF imaging MS (MALDI-IMS) on patient-derived tumor organoids (PDOs) and PM clinical samples. According to the MALDI-IMS research shown here, the predominant lipid signature of PDOs in PM tissues, glycosphingolipid (GSL) sulfates or sulfatides, or STs, is unique to the areas containing tumor cells and absent from the surrounding stromal compartments. Bioactive lipids are derived from arachidonic acid (AA), and AA-containing phosphatidylinositol (PI), or PI (18:0-20:4), is shown to be highly expressed in the stromal components. On the other hand, the tumor components contained a higher abundance of PI species with shorter and more saturated acyl chains (C34 and C36 carbons). The cellular subversion of PI and ST species may alter in ways that promote the growth, aggressiveness, and metastasis of tumor cells. Together, these findings suggest that the GSL/ST metabolic programming of PM may contain novel therapeutic targets to impede or halt PM progression.

Novel pyrrole based CB2 agonists: New insights on CB2 receptor role in regulating neurotransmitters' tone.

The cannabinoid system is one of the most investigated neuromodulatory systems because of its involvement in multiple pathologies such as cancer, inflammation, and psychiatric diseases. Recently, the CB2 receptor has gained increased attention considering its crucial role in modulating neuroinflammation in several pathological conditions like neurodegenerative diseases. Here we describe the rational design of pyrrole-based analogues, which led to a potent and pharmacokinetically suitable CB2 full agonist particularly effective in improving cognitive functions in a scopolamine-induced amnesia murine model. Therefore, we extended our study by investigating the interconnection between CB2 activation and neurotransmission in this experimental paradigm. To this purpose, we performed a MALDI imaging analysis on mice brains, observing that the administration of our lead compound was able to revert the effect of scopolamine on different neurotransmitter tones, such as acetylcholine, serotonin, and GABA, shedding light on important networks not fully explored, so far.

Spatial MS multiomics on clinical prostate cancer tissues.

Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.

Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow.

The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

Spatial Proteomics for the Molecular Characterization of Breast Cancer.

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

MALDI Imaging, a Powerful Multiplex Approach to Decipher Intratumoral Heterogeneity: Combined Hepato-Cholangiocarcinomas as Proof of Concept.

Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking advantage of in situ non-targeted molecular mapping provided by MALDI (Matrix Assisted Laser Desorption Ionization) imaging, we sought to develop a multiscale and multiparametric morphological approach, integrating molecular and conventional pathological analysis. MALDI imaging was applied to five representative cases of resected cHCC-CCA. Principal component analysis and segmentations with MALDI imaging techniques identified areas related to either iCCA or HCC and also hidden tumor areas not visible microscopically. In addition, the overlap between MALDI segmentation and immunostaining provided a comprehensive description of cHCC-CCA tumor heterogeneity by identifying transitional and micro-metastatic areas. Moreover, a list of peptides derived from in silico digestion was obtained for each immunohistochemical marker and was matched within the peptide peak list acquired by MALDI. Comparison of immunostaining images with ions from in silico digestion revealed an accurate identification of iCCA and HCC areas. Our study provides further evidence on the performance of MALDI imaging in exploring intratumor heterogeneity and offering virtual multiplex immunostaining through a single acquisition.

A Review on Data Fusion of Multidimensional Medical and Biomedical Data.

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.

In situ mass spectrometry imaging reveals heterogeneous glycogen stores in human normal and cancerous tissues.

Glycogen dysregulation is a hallmark of aging, and aberrant glycogen drives metabolic reprogramming and pathogenesis in multiple diseases. However, glycogen heterogeneity in healthy and diseased tissues remains largely unknown. Herein, we describe a method to define spatial glycogen architecture in mouse and human tissues using matrix-assisted laser desorption/ionization mass spectrometry imaging. This assay provides robust and sensitive spatial glycogen quantification and architecture characterization in the brain, liver, kidney, testis, lung, bladder, and even the bone. Armed with this tool, we interrogated glycogen spatial distribution and architecture in different types of human cancers. We demonstrate that glycogen stores and architecture are heterogeneous among diseases. Additionally, we observe unique hyperphosphorylated glycogen accumulation in Ewing sarcoma, a pediatric bone cancer. Using preclinical models, we correct glycogen hyperphosphorylation in Ewing sarcoma through genetic and pharmacological interventions that ablate in vivo tumor growth, demonstrating the clinical therapeutic potential of targeting glycogen in Ewing sarcoma.

Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning.

Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.

Glycosphingolipids are mediators of cancer plasticity through independent signaling pathways.

The molecular repertoire promoting cancer cell plasticity is not fully elucidated. Here, we propose that glycosphingolipids (GSLs), specifically the globo and ganglio series, correlate and promote the transition between epithelial and mesenchymal cells. The epithelial character of ovarian cancer remains stable throughout disease progression, and spatial glycosphingolipidomics reveals elevated globosides in the tumor compartment compared with the ganglioside-rich stroma. CRISPR-Cas9 knockin mediated truncation of endogenous E-cadherin induces epithelial-to-mesenchymal transition (EMT) and decreases globosides. The transcriptomics analysis identifies the ganglioside-synthesizing enzyme ST8SIA1 to be consistently elevated in mesenchymal-like samples, predicting poor outcome. Subsequent deletion of ST8SIA1 induces epithelial cell features through mTORS2448 phosphorylation, whereas loss of globosides in ΔA4GALT cells, resulting in EMT, is accompanied by increased ERKY202/T204 and AKTS124. The GSL composition dynamics corroborate cancer cell plasticity, and further evidence suggests that mesenchymal cells are maintained through ganglioside-dependent, calcium-mediated mechanisms.

Novel Combined Enzymatic Approach to Analyze Nonsialylated N-Linked Glycans through MALDI Imaging Mass Spectrometry.

Alterations to N-glycan expression are relevant to the progression of various diseases, particularly cancer. In many cases, specific N-glycan structural features such as sialylation, fucosylation, and branching are of specific interest. A novel MALDI imaging mass spectrometry workflow has been recently developed to analyze these features of N-glycosylation through the utilization of endoglycosidase enzymes to cleave N-glycans from associated glycoproteins. Enzymes that have previously been utilized to cleave N-glycans include peptide-N-glycosidase F (PNGase F) to target N-glycans indiscriminately and endoglycosidase F3 (Endo F3) to target core fucosylated N-glycans. In addition to these endoglycosidases, additional N-glycan cleaving enzymes could be used to target specific structural features. Sialidases, also termed neuraminidases, are a family of enzymes that remove terminal sialic acids from glycoconjugates. This work aims to utilize sialidase, in conjunction with PNGase F/Endo F3, to enzymatically remove sialic acids from N-glycans in an effort to increase sensitivity for nonsialylated N-glycan MALDI-IMS peaks. Improving detection of nonsialylated N-glycans allows for a more thorough analysis of specific structural features such as fucosylation or branching, particularly of low abundant structures. Sialidase utilization in MALDI-IMS dramatically increases sensitivity and increases on-tissue endoglycosidase efficiency, making it a very useful companion technique to specifically detect nonsialylated N-glycans.

High-dimensionality reduction clustering of complex carbohydrates to study lung cancer metabolic heterogeneity.

The tumor microenvironment contains a heterogeneous population of stromal and cancer cells that engage in metabolic crosstalk to ultimately promote tumor growth and contribute to progression. Due to heterogeneity within solid tumors, pooled mass spectrometry workflows are less sensitive at delineating unique metabolic perturbations between stromal and immune cell populations. Two critical, but understudied, facets of glucose metabolism are anabolic pathways for glycogen and N-linked glycan biosynthesis. Together, these complex carbohydrates modulate bioenergetics and protein-structure function, and create functional microanatomy in distinct cell populations within the tumor heterogeneity. Herein, we combine high-dimensionality reduction and clustering (HDRC) analysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and demonstrate its ability for the comprehensive assessment of tissue histopathology and metabolic heterogeneity in human FFPE sections. In human lung adenocarcinoma (LUAD) tumor tissues, HDRC accurately clusters distinct regions and cell populations within the tumor microenvironment, including tumor cells, tumor-infiltrating lymphocytes, cancer-associated fibroblasts, and necrotic regions. In-depth pathway enrichment analyses revealed unique metabolic pathways are associated with each distinct pathological region. Further, we highlight the potential of HDRC analysis to study complex carbohydrate metabolism in a case study of lung cancer disparity. Collectively, our results demonstrate the promising potentials of HDRC of pixel-based carbohydrate analysis to study cell-type and regional-specific stromal signaling within the tumor microenvironment.

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework.

BACKGROUND: Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. METHODS: Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. RESULTS: Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links . CONCLUSION: Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

Dual-polarity MALDI mass spectrometry and imaging of oil binders and fatty acids in artworks using cyanographene as a single matrix.

Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and imaging mass spectrometry (IMS) are being increasingly recognized for the detection and visualization of various organic species including lipids and fatty acids. Nevertheless, most MALDI matrices perform optimally in one ionization mode. This study investigates the performance of cyano derivative of graphene (G-CN) as a matrix in two polarities of MALDI MS and IMS for the detection of oil binders and fatty acids in artworks, and compares it with classical MALDI matrices (2,5-dihydroxybenzoic acid, 9-aminoacridine). Results revealed the ability of G-CN to provide high quality positive and negative mass spectra of oils and fatty acids, respectively, with lowest matrix-induced interferences among tested matrices and minimal effects of the presence of inorganic pigments. The newly developed approach makes both oil and fatty acid identifiable in a single spot simply by covering the sample surface with one matrix and switching the polarity in MALDI without any sample manipulation. G-CN offers effective matrix to analyte energy transfer, ability to detect components in less than 100 ng of oil at a MALDI spot and lesser analyte fragmentation than the compared conventional matrices. Furthermore, it enables direct mapping of specific m/z features corresponding to triacylglycerol (TAG), products of TAG oxidation and deprotonated acids using one nanoparticle matrix in MALDI IMS. This research shows potential for technical innovations in the study of art micro-environments and degradation phenomena of historical artworks.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging.

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Visualization of calcium channel blockers in human adrenal tissues and their possible effects on steroidogenesis in the patients with primary aldosteronism (PA).

Voltage-gated L-type calcium channel (CaV) isoforms are well known to play pivotal tissue-specific roles not only in vasoconstriction but also in adrenocortical steroidogenesis including aldosterone biosynthesis. Alpha-1C subunit calcium channel (CC) (CaV1.2) is the specific target of anti-hypertensive CC blockers (CCBs) and its Alpha-1D subunit (CaV1.3) regulates depolarization of cell membrane in aldosterone-producing cells. Direct effects of CCBs on aldosterone biosynthesis were previously postulated but their intra-adrenal distribution and effects on steroid production in primary aldosteronism (PA) patients have remained virtually unknown. In this study, frozen tissue specimens constituting tumor, adjacent adrenal gland and peri-adrenal adipose tissues of nine aldosterone-producing adenoma (APA) cases were examined for visualization of amlodipine and aldosterone themselves using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Liquid chromatography-mass spectrometry (LC-MS) analysis was also performed to quantify amlodipine and 17 adrenal steroids in those cases above and compared the findings with immunohistochemical analysis of steroidogenic enzymes and calcium channels (CaV1.2 and CaV1.3). Effects of amlodipine on mRNA level of aldosterone biosynthetic enzymes were also explored using human adrenocortical carcinoma cell line (H295R). Amlodipine-specific peak (m/z 407.1 > 318.1) was detected only in amlodipine treated cases. Accumulation of amlodipine was marked in adrenal cortex compared to peri-adrenal adipose tissues but not significantly different between APA tumors and adjacent adrenal glands, which was subsequently confirmed by LC-MS quantification. Intra-adrenal distribution of amlodipine was generally consistent with that of CCs. In addition, quantitative steroid profiles using LC-MS and in vitro study demonstrated the lower HSD3B activities in amlodipine treated cases. Immunoreactivity of CaV1.2 and HSD3B2 were also correlated. We report the first demonstration of specific visualization of amlodipine in human adrenal tissues by MALDI-MSI. Marked amlodipine accumulation in the adrenal glands suggested its direct effects on steroidogenesis in PA patients, possibly targeting on CaV1.2 and suppressing HSD3B activity.

Proteomic Distributions in CD34+ Microvascular Niche Patterns of Glioblastoma.

The poor clinical prognosis and microvascular patterns of glioblastoma (GBM) are of serious concern to many clinicians and researchers. However, very few studies have examined the correlation between microvascular niche patterns (MVNPs) and proteomic distribution. In this study, CD34 immunofluorescence staining and matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-IMS) technology were used to investigate the protein distributions in MVNPs. CD34+ microvascular phenotype could be divided into four types: microvascular sprouting (MS), vascular cluster (VC), vascular garland (VG), and glomeruloid vascular proliferation (GVP). Based on such characteristics, MVNPs were divided into two types by cluster analysis, namely, type I, comprising primarily MS and VC, and type II, comprising many VGs and GVPs. Survival analysis indicated the type of MVNPs to be an independent prognostic factor for progression-free and overall survival in GBM. MALDI-IMS results showed the peaks at m/z 1037 and 8960 to exhibit stronger ion signals in type II, while those at m/z 3240 and 3265 exhibited stronger ion signals in type I. The findings may assist future research on therapy and help predict prognosis in GBM. However, due to the limited number of studies, more well-designed studies are warranted to further verify our results.

Proteomic Analysis Identifies FNDC1, A1BG, and Antigen Processing Proteins Associated with Tumor Heterogeneity and Malignancy in a Canine Model of Breast Cancer.

New insights into the underlying biological processes of breast cancer are needed for the development of improved markers and treatments. The complex nature of mammary cancer in dogs makes it a great model to study cancer biology since they present a high degree of tumor heterogeneity. In search of disease-state biomarkers candidates, we applied proteomic mass spectrometry imaging in order to simultaneously detect histopathological and molecular alterations whilst preserving morphological integrity, comparing peptide expression between intratumor populations in distinct levels of differentiation. Peptides assigned to FNDC1, A1BG, and double-matching keratins 18 and 19 presented a higher intensity in poorly differentiated regions. In contrast, we observed a lower intensity of peptides matching calnexin, PDIA3, and HSPA5 in poorly differentiated cells, which enriched for protein folding in the endoplasmic reticulum and antigen processing, assembly, and loading of class I MHC. Over-representation of collagen metabolism, coagulation cascade, extracellular matrix components, cadherin-binding and cell adhesion pathways also distinguished cell populations. Finally, an independent validation showed FNDC1, A1BG, PDIA3, HSPA5, and calnexin as significant prognostic markers for human breast cancer patients. Thus, through a spatially correlated characterization of spontaneous carcinomas, we described key proteins which can be further validated as potential prognostic biomarkers.

Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research.

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.

Absolute quantification of 2-hydroxyglutarate on tissue by matrix-assisted laser desorption/ionization mass spectrometry imaging for rapid and precise identification of isocitrate dehydrogenase mutations in human glioma.

Isocitrate dehydrogenase (IDH) gene mutations are important predictive molecular markers to guide surgical strategy in brain cancer therapy. Herein, we presented a method using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) for absolute quantification of 2-hydroxyglutarate (2-HG) on tissues to identify IDH mutations and evaluate tumor residue. This analytical method was tested among 34 glioma patients and validated with gold standard clinical technologies. The cut-off value of 2-HG was set as 0.81 pmol/µg to identify IDH mutant (IDHmt) gliomas with 100% specificity and sensitivity. In addition, 2-HG levels and tumor cell density (TCD) showed positive correlation in IDHmt gliomas by this spatial method. This MALDI MSI-based absolute quantification method has great potentiality for incorporating into surgical workflow in the future.