Xiaodong Wang

Amino acids are essential protein components and key bioactive molecules governing metabolism and signal transduction, playing vital roles in plant growth, physiological homeostasis, immunity, and stress adaptation. Negative-ion matrices for amino acid detection in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) remain underdeveloped, largely due to low ionization efficiency and strong background interference. To address this limitation, we developed a novel negative-ion MALDI matrix, Michler’s ketone (MK), for efficient amino acid detection and imaging. MK exhibits several superior properties as a powerful MALDI matrix, including strong UV–vis absorption at 355 nm, excellent vacuum stability, low matrix-related ion interference, and high detection efficiency for amino acids. Using MK, all 20 proteinogenic amino acids were successfully detected in the mixed standard, whereas conventional matrices, namely, 2-mercaptobenzothiazole (2-MBT) and α-cyano-4-hydroxycinnamic acid (CHCA), detected only 10 and 8 amino acids, respectively. Furthermore, MK enabled high-resolution MALDI-MSI of amino acids in Actinidia arguta tissues, allowing clear visualization of tissue-specific spatial distribution patterns. Overall, MK represents a high-performance negative-ion MALDI matrix that significantly enhances amino acid detection and imaging.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful tool for the in situ mapping and spatial characterization of endogenous saccharides in biological tissues. However, current MALDI-MSI methods cannot simultaneously discriminate and precisely visualize mono- and oligosaccharide isomers in native tissues, creating a key analytical bottleneck. To overcome this limitation, we developed a rationally optimized 6-aminoquinoline/2,5-dihydroxybenzoic acid (6-AQ/DHB) binary matrix for high-sensitivity, isomer-selective saccharide imaging. The 6-AQ/DHB matrix produces homogeneous nanoscale crystalline deposits, ensures exceptional vacuum stability, enables isomer discrimination, and provides a wide linear dynamic response. This matrix enables qualitative and quantitative saccharide analysis of serum samples from diabetic patients and healthy individuals. Furthermore, we realized in situ spatial discrimination and visualization of various saccharide isomers in tissue sections of Actinidia arguta, germinating Taxus chinensis var. mairei seed and rat brain. This advancement establishes a robust analytical platform for in situ saccharide imaging, offering good potential for studies in spatial glycomics, analytical chemistry, and plant metabolism.

Summary statement: MALDI‐MSI overcomes the homogenization bottleneck of conventional omics by enabling the in situ visualization of metabolites while preserving native tissue architecture. This spatial perspective reveals metabolic heterogeneity, offering key insights into plant environment adaptation mechanisms.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and its imaging variant (MALDI-MSI) are pivotal analytical tools for biological sample analysis, allowing detection and visualization of diverse biomolecules. At the heart of MALDI-MS/MSI performance lies the crucial yet often empirically determined choice of matrix. A suitable matrix not only boosts analyte ionization efficiency but also enhances sensitivity, salt tolerance, and overall applicability, which are key for specific compound detection. To date, a cumulative total of 467 matrices have been successfully discovered and developed. However, merely a handful of them have achieved widespread utilization, which strongly suggests that there remains a substantial reservoir of untapped potential within this domain. This review offers a systematic and comprehensive overview of MALDI matrices over four decades (1985-present). It starts by outlining MALDI-MS analysis principles and procedures, providing a basis for understanding matrix functions. Then, matrices are systematically classified according to their features. We also spotlight recent matrix applications in MALDI-MS detection and imaging in proteomics, lipidomics, metabolomics, glycomics, nucleic acid analysis, and quantitative analysis. Finally, we chart future research directions, aiming to unlock the full potential of matrices in this dynamic field.

Background: Matrine, a bioactive isoquinoline alkaloid, exhibits antitumor efficacy by modulating multiple signaling pathways to suppress cancer cell proliferation, migration and invasion. However, its metabolic regulatory mechanisms in prostate cancer intervention require systematic characterization. Methods: We implemented matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for spatial metabolomic profiling of prostate tissues, integrated with multivariate analytical approaches including principal component analysis, Pearson correlation-based clustering heatmap, partial least squares-discriminant analysis, and hierarchical clustering heatmap analysis. This multimodal strategy enabled comparative evaluation of low-molecular-weight metabolite distributions across normal control, prostate cancer, and matrine-treated prostate cancer cohorts. Results: Multi-omics integration identified 19 discriminant metabolites (VIP >1.0) spanning lipid signaling mediators (choline, glycerophosphoglycerol, sphinganine, glycerophosphoinositol, linoleic acid, oleic acid, N,N-Dimethylsphingosine), amino acid network regulators (cysteic acid, 5-Hydroxylysine, glutamine-glutamate axis components), nucleotide biosynthesis (adenine, Ribose 1,5-bisphosphate, uracil, dihydrouracil, deoxyinosine, adenosine), markers of oxidative damage (8-Hydroxyguanine) and cofactor of nitric oxide synthases and aromatic amino acid hydroxylases (tetrahydropteridine). Linoleic acid, oleic acid, and N,N-Dimethylsphingosine exhibited the highest levels in the NC group; these metabolites were significantly downregulated in the PCa group and partially restored in the PCa+MAT group. In addition, the results revealed a progressive depletion of tetrahydropteridine across experimental groups, with the PCa+MAT group exhibiting significantly lower tetrahydropteridine levels compared to both NC and PCa groups (PCa+MAT < NC < PCa). Notably, the expression levels of other compounds were the lowest in the NC group, while they were significantly upregulated in the PCa group and with intermediate levels observed in the PCa+MAT group. Spatial metabolomics delineated dynamic metabolic reprogramming during prostate cancer progression, with matrine treatment demonstrating partial reversal of cancer-associated metabolic shifts, particularly in lipid pathways, underscoring its potential as a modulator of oncogenic metabolism. Conclusion: This study establishes MALDI-MSI as a powerful platform for pharmacometabolomic evaluation, while elucidating matrine’s therapeutic potential through coordinated regulation of lipid metabolic remodeling, amino acid/nucleotide biosynthesis pathways and oxidative stress responses. Our findings provide mechanistic insights into matrine’s anticancer action and validate metabolomic approaches for natural product evaluation.

The simultaneous achievement of matrix nano‐coating and efficient in situ enrichment of analytes is currently an important aspect affecting the ability of matrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) to efficiently explore endogenous compounds in situ with high‐resolution. Herein, a novel matrix coating approach called alternating electric field‐assisted matrix nano‐coating (AEFAMnC) is reported, which is designed to amplify the capabilities of high‐resolution MALDI imaging. The use of AEFAMnC achieves matrix nano‐coating (the smallest crystal size, ca. 70 nm), resulting in superior uniform matrix deposition. Interestingly, AEFAMnC leverages asymmetric alternating electric fields to initiate a repetitive forced micro‐extraction process, which significantly enhance the in situ enrichment of compounds on the sample surface for more adequate co‐crystallization with the matrix. These features finally improve in situ ionization efficiency and high‐resolution imaging performance of endogenous compounds across various biological samples (i.e ., rat brain, germinating Chinese yew seeds, and single cells) at 5‐ µ m pixel size. Overall, for the first time, AEFAMnC simultaneously achieves both matrix nano‐coating and in situ repetitive forced micro‐extraction of analytes, significantly improving the performance of high‐spatial‐resolution MALDI‐MSI. AEFAMnC has far‐reaching implications in high‐resolution MALDI imaging, particularly in MS‐based single‐cell spatial omics studies.

Immuno-oncology is increasingly becoming the standard of care for cancers, with the identification of biomarkers that reliably classify immune checkpoint inhibitor response, resistance, and toxicity becoming the next frontier toward improvements in immunomodulatory treatment regimens. Recent advances in multiparametric, multiomics, and computational data platforms generating an unprecedented depth of data may assist in the discovery of increasingly robust biomarkers for enhanced patient selection and more personalized or longitudinal treatment approaches. Which emerging technologies to implement in future research and clinical settings, used alone or in combination, relies on weighing the pros and cons that aid in maximizing data outputs while minimizing patient sampling, with high reproducibility and representativeness, and minimal turnaround time and data fragmentation toward later private and public dataset harmonization strategies. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and highlight advances in biomarker discovery using liquid biopsy and in vivo imaging technologies. We address advances in liquid biopsy technologies monitoring cells, proteins, nucleic acids, antibodies, and drugs or analytes and radiomics technologies monitoring whole host-level imaging methods, including immuno-PET and MRI technologies, which are able to couple biomarkers with physical location. We include a summary of key metrics obtained by these technologies and their ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons. By highlighting some of the most interesting recent examples contributed by these technologies and providing examples of improved outputs, we hope to guide correlative research directions and assist in their becoming clinically useful in immuno-oncology.

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry–based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.