Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology.

Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.

Next-generation pathology practices with mass spectrometry imaging.

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Metabolomics Characterization of Scleractinia Corals with Different Life-History Strategies: A Case Study about Pocillopora meandrina and Seriatopora hystrix in the South China Sea.

Life-history strategies play a critical role in susceptibility to environmental stresses for Scleractinia coral. Metabolomics, which is capable of determining the metabolic responses of biological systems to genetic and environmental changes, is competent for the characterization of species' biological traits. In this study, two coral species (Pocillopora meandrina and Seriatopora hystrix in the South China Sea) with different life-history strategies ("competitive" and "weedy") were targeted, and untargeted mass spectrometry metabolomics combined with molecular networking was applied to characterize their differential metabolic pathways. The results show that lyso-platelet activating factors (lyso-PAFs), diacylglyceryl carboxyhydroxymethylcholine (DGCC), aromatic amino acids, and sulfhydryl compounds were more enriched in P. meandrina, whereas new phospholipids, dehydrated phosphoglycerol dihydroceramide (de-PG DHC), monoacylglycerol (MAG), fatty acids (FA) (C < 18), short peptides, and guanidine compounds were more enriched in S. hystrix. The metabolic pathways involved immune response, energy metabolism, cellular membrane structure regulation, oxidative stress system, secondary metabolite synthesis, etc. While the immune system (lysoPAF) and secondary metabolite synthesis (aromatic amino acids and sulfhydryl compounds) facilitates fast growth and resistance to environmental stressors of P. meandrina, the cell membrane structure (structural lipids), energy storage (storage lipids), oxidative stress system (short peptides), and secondary metabolite synthesis (guanidine compounds) are beneficial to the survival of S. hystrix in harsh conditions. This study contributes to the understanding of the potential molecular traits underlying life-history strategies of different coral species.

Mass spectrometry-based metabolomic signatures of coral bleaching under thermal stress.

Coral bleaching caused by climate change has resulted in large-scale coral reef decline worldwide. However, the knowledge of physiological response mechanisms of scleractinian corals under high-temperature stress is still challenging. Here, untargeted mass spectrometry-based metabolomics combining with Global Natural Product Social Molecular Networking (GNPS) was utilized to investigate the physiological response of the coral species Pavona decussata under thermal stress. A wide variety of metabolites (including lipids, fatty acids, amino acids, peptides, osmolytes) were identified as the potential biomarkers and subjected to metabolic pathway enrichment analysis. We discovered that, in the thermal-stressed P. decussata coral holobiont, (1) numerous metabolites in classes of lipids and amino acids significantly decreased, indicating an enhanced lipid hydrolysis and aminolysis that contributed to up-regulation in gluconeogenesis to meet energy demand for basic survival; (2) pantothenate and panthenol, two essential intermediates in tricarboxylic acid (TCA) cycle, were up-regulated, implying enhanced efficiency in energy production; (3) small peptides (e.g., Glu-Leu and Glu-Glu-Glu-Glu) and lyso-platelet-activating factor (lysoPAF) possibly implicated a strengthened coral immune response; (4) the down-regulation of betaine and trimethylamine N-oxide (TMAO), known as osmolyte compounds for maintaining holobiont homeostasis, might be the result of disruption of coral holobiont.

Reaction Tracking and High-Throughput Screening of Active Compounds in Combinatorial Chemistry by Tandem Mass Spectrometry Molecular Networking.

Combinatorial synthesis has been widely used as an efficient strategy to screen for active compounds. Mass spectrometry is the method of choice in the identification of hits resulting from high-throughput screenings due to its high sensitivity, specificity, and speed. However, manual data processing of mass spectrometry data, especially for structurally diverse products in combinatorial chemistry, is extremely time-consuming and one of the bottlenecks in this process. In this study, we demonstrated the effectiveness of a tandem mass spectrometry molecular networking-based strategy for product identification, reaction dynamics monitoring, and active compound targeting in combinatorial synthesis. Molecular networking connects compounds with similar tandem mass spectra into a cluster and has been widely used in natural products analysis. We show that both the expected and side products can be readily characterized using molecular networking based on their mass spectrometry fragmentation patterns. Additionally, time-dependent molecular networking was integrated to track reaction dynamics to determine the optimal reaction time to maximize target product yields. We also present a proof-of-concept experiment that successfully identified and isolated active molecules from a dynamic combinatorial library. These results demonstrated the potential of using molecular networking for identifying, tracking, and high-throughput screening of active compounds in combinatorial synthesis.

Remodeling nanoDESI Platform with Ion Mobility Spectrometry to Expand Protein Coverage in Cancerous Tissue.

Nanospray desorption electrospray ionization mass spectrometry is an ambient ionization technique that is capable of mapping proteins in tissue sections. However, high-abundant molecules or isobaric interference in biological samples hampers its broad applications in probing low-abundant proteins. To address this challenge, herein we demonstrated an integrated module that coupled pneumatic-assisted nanospray desorption electrospray ionization mass spectrometry with high-field asymmetric ion mobility spectrometry. Using this module to analyze mouse brain sections, the protein coverage was significantly increased. This improvement allowed the mapping of low-abundant proteins in tissue sections with a 5 µm spatial resolution enabled by computationally assisted fusion with optical microscopic images. Moreover, the module was successfully applied to characterize melanoma in skin tissues based on the enhanced protein profiles. The results suggested that this integrating module will be potentially applied to discover novel proteins in cancers.

Precision biomarker discovery powered by microscopy image fusion-assisted high spatial resolution ambient ionization mass spectrometry imaging.

Mass spectrometry imaging (MSI) using the ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry were employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion were verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-µm spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI was ∼100 µm. In this regard, sharpened molecular histology of tissue sections was achieved, providing complementary references to the pathology. Such a multi-modal integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen potential biomarkers on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section were identified.

Molecular networking as a dereplication strategy for monitoring metabolites of natural product treated cancer cells.

RATIONALE: Natural products have been great sources for drug discovery. However, the structures of natural products are diverse and difficult to elucidate. Cordyceps militaris is a parasitic fungus which usually grows on host insects. The metabolites of C. militaris have been reported to act as chemotherapeutic agents. In this study, we aimed for the structural elucidation of specialized metabolites derived from C. militaris, and the metabolic impact in leukemia cells. METHODS: We describe a liquid chromatography data-dependent mass spectrometric platform combining tandem mass analysis and molecular networking. Leukemia cells treated with C. militaris extract and control groups were visualized in terms of their metabolic profiles using Global Natural Product Social (GNPS) molecular networking. By this method, we were able to elucidate the structures of metabolites from medicinal fungus extracts and cancer cells and then to recognize their changes in a semi-quantitative manner. RESULTS: Using C. militaris and leukemia cells as examples, we found that approximately 100 new ion species were present in the treated leukemia cells, suggesting a highly altered metabolic profile. Specifically, based on the tandem mass spectral similarity, we proposed that cordycepin, a key fungus-derived therapeutic agent known for its antitumor activity, was transformed into its methylthio form in leukemia cells. CONCLUSIONS: The platform described provides an ability to investigate complex molecular interactions of natural products in mammalian cells. By incorporating tandem mass spectrometry and molecular networking, we were able to reveal the chemical modification of crude bioactive compounds, for example potential bioactive compounds which might be modified from cordycepin. We envision that such a mass spectrometry (MS)-based workflow, combined with other metabolomics platforms, would enable much wider applicability to cell biology and be of great potential to pharmacological study as well as drug discovery.

Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Typings of Edible Oils through Spectral Networking of Triacylglycerol Fingerprints.

Adulteration of edible oils by the manufacturers has been found frequently in modern societies. Due to the complexity of the chemical contents in edible oils, it is challenging to quantitatively determine the extent of adulteration and prove the authenticity of edible oils. In this study, a robust and simple MALDI-TOF-MS platform for rapid fingerprinting of triacylglycerols (TAGs) in edible oils was developed, where spectral similarity analysis was performed to quantitatively reveal correlations among edible oils in the chemical level. Specifically, we proposed oil networking, a spectral similarity-based illustration, which enabled reliable classifications of tens of commercial edible oils from vegetable and animal origins. The strategy was superior to traditional multivariate statistics due to its high sensitivity in probing subtle changes in TAG profiles, as further demonstrated by the success in determination of the adulterated lard in a food fraud in Taiwan. Finally, we showed that the platform allowed quantitative assessment of the binary mixture of olive oil and canola oil, which is a common type of olive oil adulteration in the market. Overall, these results suggested a novel strategy for chemical fingerprint-based quality control and authentication of oils in the food industry.

Deep Lipidomics and Molecular Imaging of Unsaturated Lipid Isomers: A Universal Strategy Initiated by mCPBA Epoxidation.

Cellular lipidome is highly regulated through lipogenesis, rendering diverse double-bond positional isomers (C═C isomer) of a given unsaturated lipid species. In recent years, there are increasing reports indicating the physiological roles of C═C isomer compositions associated with diseases, while the biochemistry has not been broadly investigated due to the challenge in characterizing lipid isomers inherent to conventional mass spectrometry-based lipidomics. To address this challenge, we reported a universal, user-friendly, derivatization-based strategy, MELDI (mCPBA Epoxidation for Lipid Double-bond Identification), which enables both large-scale identification and spatial mapping of biological C═C isomers using commercial mass spectrometers without any instrument modification. With the developed liquid-chromatography mass spectrometry (LC-MS) lipidomics workflow, we elucidated more than 100 isomers among monounsaturated and polyunsaturated fatty acids and glycerophospholipids in human serum, where uncommon isomers of low abundance were quantified for the first time. The capability of MELDI-LC-MS in lipidome analysis was further demonstrated using the differentiated 3T3-L1 adipocytes, providing an insight into the cellular lipid reprogramming upon stearoyl-coenzyme A desaturase 1 (SCD1) inhibition. Finally, we highlighted the versatility of MELDI coupled with ambient mass spectrometry imaging to spatially resolve cancer-associated alteration of lipid isomers in a metastatic mouse tissue section. Our results suggested that MELDI will contribute to current lipidomics pipelines with a deeper level of structural information, allowing us to investigate the underlying lipid biochemistry.