α-Cyano-4-hydroxycinnamic Acid and Tri-Potassium Citrate Salt Pre-Coated Silicon Nanopost Array Provides Enhanced Lipid Detection for High Spatial Resolution MALDI Imaging Mass Spectrometry

Isoporous Membrane Mediated Imprinting Mass Spectrometry Imaging for Spatially-Resolved Metabolomics and Rapid Histopathological Diagnosis

Surface-assisted laser desorption/ionization (SALDI) mass spectrometry imaging (MSI) holds great value in spatial metabolomics and tumor diagnosis. Tissue imprinting on the SALDI target can avoid laser-induced tissue ablation and simplifies the sample preparation. However, the tissue imprinting process always causes lateral diffusion of biomolecules, thereby losing the fidelity of metabolite distribution on tissue. Herein, a membrane-mediated imprinting mass spectrometry imaging (MMI-MSI) strategy is proposed using isoporous nuclepore track-etched membrane as a mediating imprinting layer to selectively transport metabolites through uniform and vertical pores onto silicon nanowires (SiNWs) array. Compared with conventional direct imprinting technique, MMI-MSI can not only exclude the adsorption of large biomolecules but also avoid the lateral diffusion of metabolites. The whole time for MMI-based sample preparation can be reduced to 2 min, and the lipid peak number can increase from 46 to 113 in kidney tissue detection. Meanwhile, higher resolution of MSI can be achieved due to the confinement effect of the pore channel in the diffusion of metabolites. Based on MMI-MSI, the tumor margins of liver cancer can be clearly discriminated and their different subtypes can be precisely classified. This work demonstrates MMI-MSI is a rapid, highly sensitive, robust and high-resolution technique for spatially-resolved metabolomics and pathological diagnosis.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Lewis Acidic Metal-Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue

The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids. Here, a metal-organic framework (MOF) Zr6O4(OH)4(1,3,5-Tris(4-carboxyphenyl) benzene)2(triflate)6(Zr6OTf-BTB) was prepared and used for selective on-tissue adsorption of phospholipids to reduce ion suppression from them to poorly ionizable lipids. The results show that Zr6OTf-BTB with strong Lewis acidic sites and a large specific surface area (647.9 m2·g-1) could selectively adsorb phospholipids under 1% FA-MeOH. Adsorption efficiencies of phospholipids are 88.4-144.9 times higher than those of other neutral lipids. Moreover, the adsorption capacity and the adsorption kinetic rate constant of the new material to phospholipids are higher than those of Zr6-BTB (242.72 vs 73.96 mg·g-1, 0.0442 vs 0.0220 g·mg-1·min-1). A Zr6OTf-BTB sheet was prepared by a lamination technique for on-tissue phospholipid adsorption from brain tissue. Then, the tissue section on the Zr6OTf-BTB sheet was directly imaged via ambient liquid extraction-MSI with 1% FA-MeOH as the sampling solvent. The results showed that phospholipids could be 100% removed directly on tissue, and the detection coverage of the Zr6OTf-BTB-enhanced MSI method to ceramides (Cers) and hexosylceramides (HexCers) was increased by 5-26 times compared with direct tissue MSI (26 vs 1 and 17 vs 3). The new method provides an efficient and convenient way to eliminate the ion suppression from phospholipids in MSI, largely improving the detection coverage of low-abundance and poorly ionizable lipids.

Prospective on Imaging Mass Spectrometry in Clinical Diagnostics

Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.

Perfluoroalkyl Group-Covered Organosilica Films for the Sensitive Detection of Sulfonylurea Herbicides in Laser Desorption/Ionization Mass Spectrometry

Simple and rapid screening of agrochemicals greatly contributes to food and environmental safety. Matrix-free laser desorption/ionization mass spectrometry (LDI-MS) is an effective tool for high-throughput analysis of low-molecular-weight compounds. In this study, we report a UV-laser-absorbing organosilica film for the sensitive detection of various sulfonylurea herbicides using LDI-MS. Organosilica films with fluoroalkyl groups on the organic part are fabricated, followed by additional modification of the silica moiety with a fluoroalkyl coupling agent to cover the film surface with hydrophobic fluoroalkyl groups. Nanoimprinting is conducted to impart nanostructures on the film surface to enhance the LDI performance. The fabricated nanostructured organosilica films accomplish sensitive detection of cyclosulfamuron and azimsulfuron at concentrations as low as 1 fmol μL^-1. The applicability of the nanostructured organosilica films is confirmed by the recovery of cyclosulfamuron and ethametsulfuron-methyl from pea sprouts (Pisum sativum) hydroponically grown in herbicide-spiked water at concentrations of 0.5 ppm.

A Five-Year Update on Matrix Compounds for MALDI-MS Analysis of Lipids

Matrix-assisted laser desorption and ionization (MALDI) is a widely used soft-ionization technique of modern mass spectrometry (MS). MALDI enables the analysis of nearly all chemical compounds—including polar and apolar (phospho)lipids—with a minimum extent of fragmentation. MALDI has some particular advantages (such as the possibility to acquire spatially-resolved spectra) and is competitive with the simultaneously developed ESI (electrospray ionization) MS. Although there are still some methodological aspects that need to be elucidated in more detail, it is obvious that the careful selection of an appropriate matrix plays the most important role in (lipid) analysis. Some lipid classes can be detected exclusively if the optimum matrix is used, and the matrix determines the sensitivity by which a particular lipid is detected within a mixture. Since the matrix is, thus, crucial for optimum results, we provide here an update on the progress in the field since our original review in this journal in 2018. Thus, only the development during the last five years is considered, and lipids are sorted according to increasing complexity, starting with free fatty acids and ending with cardiolipins and phosphoinositides.

Dual-Mechanism-Driven Strategy for High-Coverage Detection of Serum Lipids on a Novel SALDI-MS Target

Serum lipid metabolites have been emerging as ideal biomarkers for disease diagnosis and prediction. In the current stage, nontargeted or targeted lipidomic research mainly relies on a liquid chromatography-mass spectrometry (LC-MS) platform, but future clinical applications need more robust and high-speed platforms. Surface-assisted laser desorption ionization mass spectrometry (SALDI-MS) has shown excellent advantages in the high-speed analysis of lipid metabolites. However, the platform in the positive ion mode is more inclined to target a certain class of lipids, leading to the low coverage of lipid detection and limiting its practical translation to clinical applications. Herein, we proposed a dual-mechanism-driven strategy for high-coverage detection of serum lipids on a novel SALDI-MS target, which is a composite nanostructure comprising vertical silicon nanowires (VSiNWs) decorated with AuNPs and polydopamine (VSiNW-Au-PDA). The performance of laser desorption and ionization on the target can be enhanced by charge-driven desorption coupled with thermal-driven desorption. Simultaneous detection of 236 serum lipids (S/N ≥ 5) including neutral and polar lipids can be achieved in the positive ion mode. Among these, 107 lipid peaks were successfully identified. When combined with VSiNW-Au-PDA and VSiNW chips, 479 lipid peaks can be detected in serum samples in positive and negative ion modes, respectively. Based on the platform, serum samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy controls were analyzed. After data mining, 14 lipids containing different lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers. With the assistance of an artificial neural network, a diagnostic model with a sensitivity of 92.7% and a specificity of 96% was constructed for HCC diagnosis.

Tissue Imprinting on 2D Nanoflakes-Capped Silicon Nanowires for Lipidomic Mass Spectrometry Imaging and Cancer Diagnosis

Spatially resolved tissue lipidomics is essential for accurate intraoperative and postoperative cancer diagnosis by revealing molecular information in the tumor microenvironment. Matrix-free laser desorption ionization mass spectrometry imaging (LDI-MSI) is an emerging attractive technology for label-free visualization of metabolites distributions in biological specimens. However, the development of LDI-MSI technology that could conveniently and authentically reveal molecular distribution on tissue samples is still a challenge. Herein, we present a tissue imprinting technology by retaining tissue lipids on 2D nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of molybdenum disulfide (MoS₂) with nitrogen-doped graphene quantum dots (NGQDs), which serve as both intercalation agent and dispersant. The obtained NGQD@MoS₂ nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid NGQD@MoS₂/SiNWs nanostructure, which display excellent lipid extraction ability, enhanced LDI efficiency and molecule imaging capability. The peak number and total ion intensity of different lipids species on animal lung tissues obtained by tissue imprinting LDI-MSI on NGQD@MoS₂/SiNWs were ∼4-5 times greater than those on SiNWs substrate. As a proof-of-concept demonstration, the NGQD@MoS₂/SiNWs nanostructure was further applied to visualize phospholipids on sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal tissue. On the basis of selected feature lipids and machine learning algorithm, a prediction model was constructed to discriminate NSCLC tissues from the adjacent normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for the independent validation cohort.