Investigation of the lipidomic changes in differentiated glioblastoma cells after drug treatment using MALDI-MS

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

UHPLC-HRMS-based metabolomic and lipidomic characterization of glioma cells in response to anlotinib

Anlotinib, as a promising oral small-molecule antitumor drug, its role in glioma has been only reported in a small number of case reports. Therefore, anlotinib has been considered as a promising candidate in glioma. The aim of this study was to investigate the metabolic network of C6 cells after exposure to anlotinib and to identify anti-glioma mechanism from the perspective of metabolic reprogramming. Firstly, CCK8 method was used to evaluate the effects of anlotinib on cell proliferation and apoptosis. Secondly, ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomic and lipidomic were developed to characterize the metabolite and lipid changes in cell and cell culture medium (CCM) caused by anlotinib in the treatment of glioma. As a result, anlotinib had concentration-dependent inhibitory effect with the concentration range. In total, twenty-four and twenty-three disturbed metabolites in cell and CCM responsible for the intervention effect of anlotinib were screened and annotated using UHPLC-HRMS. Altogether, seventeen differential lipids in cell were identified between anlotinib exposure and untreated groups. Metabolic pathways, including amino acid metabolism, energy metabolism, ceramide metabolism, and glycerophospholipid metabolism, were modulated by anlotinib in glioma cell. Overall, anlotinib has an effective treatment against the development and progression of glioma, and these remarkable pathways can generate the key molecular events in cells treated with anlotinib. Future research into the mechanisms underlying the metabolic changes is expected to provide new strategies for treating glioma.

MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective

Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.

A simple preparation step to remove excess liquid lipids in white adipose tissue enabling improved detection of metabolites via MALDI-FTICR imaging MS

Matrix-assisted laser desorption ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) imaging mass spectrometry (MS) is a powerful technology used to analyze metabolites in various tissues. However, it faces significant challenges in studying adipose tissues. Poor matrix distribution and crystallization caused by excess liquid lipids on the surface of tissue sections hamper m/z species detection, an adverse effect that particularly presents in lipid-rich white adipose tissue (WAT). In this study, we integrated a simple and low-cost preparation step into the existing MALDI-FTICR imaging MS pipeline. The new method—referred to as filter paper application—is characterized by an easy sample handling and high reproducibility. The aforementioned filter paper is placed onto the tissue prior to matrix application in order to remove the layer of excess liquid lipids. Consequently, MALDI-FTICR imaging MS detection was significantly improved, resulting in a higher number of detected m/z species and higher ion intensities. After analyzing various durations of filter paper application, 30 s was found to be optimal, resulting in the detection of more than 3700 m/z species. Apart from the most common lipids found in WAT, other molecules involved in various metabolic pathways were detected, including nucleotides, carbohydrates, and amino acids. Our study is the first to propose a solution to a specific limitation of MALDI-FTICR imaging MS in investigating lipid-rich WAT. The filter paper approach can be performed quickly and is particularly effective for achieving uniform matrix distribution on fresh frozen WAT while maintaining tissue integrity. It thus helps to gain insight into the metabolism in WAT.

Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas

Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in glioma cells through single-cell transcriptomics analysis. Based on the DRGs, we separated glioma patients into three clusters with distinct clinicopathological features in combination with bulk RNA-seq data. Weighted correlation network analysis, univariate cox regression analysis and least absolute shrinkage and selection operator analysis were involved in the construction of the prognostic model based on DRGs. Distinct clinicopathological characteristics, TIME, immunogenomic patterns and immunotherapy responses were identified across three clusters. A DRG signature composing of 12 genes were identified for predicting the survival of glioma patients and nomogram model integrating the risk score and multi-clinicopathological factors were constructed for clinical practice. Patients in high-risk group tended to get shorter overall survival and better response to immune checkpoint blockage therapy. We obtained 9 candidate drugs through comprehensive analysis of the differentially expressed genes between the low and high-risk groups in the model. Our findings indicated that the risk score may not only contribute to the determination of prognosis but also facilitate in the prediction of immunotherapy response in glioma patients.

Nanopipette-Based Nanosensor for Label-Free Electrochemical Monitoring of Cell Membrane Rupture under H2O2 Treatment

H₂O₂ is an essential signaling molecule in living cells that can cause direct damage to lipids, proteins, and DNA, resulting in cell membrane rupture. However, current studies mostly focus on probe-based sensing of intracellular H₂O₂, and these methods usually require sophisticated probe synthesis and instruments. In particular, local H₂O₂ treatment induces cell membrane rupture, but the level of cell membrane destruction is unknown because the mechanical properties of the cell membrane are difficult to accurately determine. Therefore, highly sensitive and label-free methods are required to measure and reflect mechanical changes in the cell membrane. Here, using an ultrasmall quartz nanopipette with a tip diameter less than 90 nm as a nanosensor, label-free and noninvasive electrochemical single-cell measurement is achieved for real-time monitoring of cell membrane rupture under H₂O₂ treatment. By spatially controlling the nanopipette tip to precisely approach a specific location on the membrane of a single living cell, stable cyclic membrane oscillations are observed under a constant direct current voltage. Specifically, upon nanopipette advancement, the mechanical status of the cell membrane can be sensibly displayed by continuous current versus time traces. The electrical signals are collected and processed, ultimately revealing the mechanical properties of the cell membrane and the degree of cell apoptosis. This nanopipette-based nanosensor paves the way for developing a facile, label-free, and noninvasive strategy to assay the mechanical properties of the cell membrane during external stimulation at the single-cell level.