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Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.
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Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.
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Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.
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Ellagitannins (ETs) are hydrolysable tannins composed of a polyol core, primarily glucose, which is esterified with hexahydroxydiphenic acid (HHDP), and in some cases, gallic acid. ETs are the major phenolic compounds found in strawberries and may contribute to the health-related properties of strawberries, because of their strong antioxidative activity. However, their distribution in the strawberry fruit remains unclear. In this study, matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) was used to visualize ETs in ripe strawberry fruits. Five peaks, corresponding to the m/z values of ET [M-H]- ions detected in the MALDI-MS spectrum of strawberry extracts, were identified as strictinin, pedunculagin, casuarictin, davuriicin M1, and an unknown ET using MALDI-tandem MS (MS/MS). In addition, liquid chromatography-electrospray ionization-MS/MS of the extracts revealed the presence of pedunculagin isomers and the unknown ET. Ion images of these five ETs were reconstructed using MALDI-MSI. Strictinin was widely distributed in and around the achene seed coats, while the other ETs were dispersed in and around the seed coats, and at the bottom of the receptacle; pedunculagin was distributed in the epidermis and pith, whereas casuarictin, the unknown ET, and davuriicin M1 were distributed in the pith. Moreover, MALDI-MSI of a casuarictin standard indicated that in-source fragmentation weakly affected the ion images. The results suggest that the distribution of ETs depends on the presence or absence of their constituents, namely galloyl units, HHDP, and bis-HHDP. To the best of my knowledge, this is the first report on the visualization of ETs in plant tissues using MSI, MALDI-MSI may be a useful tool for analyzing the distribution of ETs in the strawberry fruit.
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Detailed knowledge on tissue-specific metabolic reprogramming in diabetic nephropathy (DN) is vital for more accurate understanding the molecular pathological signature and developing novel therapeutic strategies. In the present study, a spatial-resolved metabolomics approach based on air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) integrated mass spectrometry imaging (MSI) was proposed to investigate tissue-specific metabolic alterations in the kidneys of high-fat diet-fed and streptozotocin (STZ)-treated DN rats and the therapeutic effect of astragaloside IV, a potential anti-diabetic drug, against DN. As a result, a wide range of functional metabolites including sugars, amino acids, nucleotides and their derivatives, fatty acids, phospholipids, sphingolipids, glycerides, carnitine and its derivatives, vitamins, peptides, and metal ions associated with DN were identified and their unique distribution patterns in the rat kidney were visualized with high chemical specificity and high spatial resolution. These region-specific metabolic disturbances were ameliorated by repeated oral administration of astragaloside IV (100 mg/kg) for 12 weeks. This study provided more comprehensive and detailed information about the tissue-specific metabolic reprogramming and molecular pathological signature in the kidney of diabetic rats. These findings highlighted the promising potential of AFADESI and MALDI integrated MSI based metabolomics approach for application in metabolic kidney diseases.
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Understanding of the nephrotoxicity induced by drug candidates is vital to drug discovery and development. Herein, an in situ metabolomics method based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was established for direct analysis of metabolites in renal tissue sections. This method was subsequently applied to investigate spatially resolved metabolic profile changes in rat kidney after the administration of aristolochic acid I, a known nephrotoxic drug, aimed to discover metabolites associated with nephrotoxicity. As a result, 38 metabolites related to the arginine-creatinine metabolic pathway, the urea cycle, the serine synthesis pathway, metabolism of lipids, choline, histamine, lysine, and adenosine triphosphate were significantly changed in the group treated with aristolochic acid I. These metabolites exhibited a unique distribution in rat kidney and a good spatial match with histopathological renal lesions. This study provides new insights into the mechanisms underlying aristolochic acids nephrotoxicity and demonstrates that AFADESI-MSI-based in situ metabolomics is a promising technique for investigation of the molecular mechanism of drug toxicity.
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â¢MALDI-MS is a valuable analytical tool in pathology and laboratory medicine.â¢Advantages of MALDI-MS include ease of sample preparation and analysis.â¢Uses include ID of pathogens, monoclonal proteins, variants and diseased tissue.
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Matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) is a sensitive label-free technique that can be used to study a wide variety of clinical phenotypes. In this context, MSI offers huge diagnostic potential by supporting decision making in the determination of personalized treatment strategies. However, improvements in throughput and robustness are still needed before it finds a place in routine application. While the field has seen tremendous improvements in the throughput of data acquisition, robust and high-throughput sample preparation methods compatible with these acquisition methods need to be developed. To address this challenge, we have developed several methods to reduce the matrix application time to less than 5â¯min, while maintaining sensitivity and reproducibility. Workflows incorporating these methods provide a pipeline analysis time for MSI sample preparation and acquisition of less than 30â¯min. The reduced time for these analyses will contribute towards the integration of MSI into routine molecular pathology for clinical diagnostics.
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Antibody-drug conjugate (ADC), as a next generation of antibody therapeutics, is a combination of an antibody and a drug connected via a specialized linker. ADC has four action steps: systemic circulation, the enhanced permeability and retention (EPR) effect, penetration within the tumor tissue, and action on cells, such as through drug delivery system (DDS) drugs. An antibody with a size of about 10 nm has the same capacity for passive targeting as some DDS carriers, depending on the EPR effect. In addition, some antibodies are capable of active targeting. A linker is stable in the bloodstream but should release drugs efficiently in the tumor cells or their microenvironment. Thus, the linker technology is actually a typical controlled release technology in DDS. Here, we focused on molecular imaging. Fluorescent and positron emission tomography (PET) imaging is useful for the visualization and evaluation of antibody delivery in terms of passive and active targeting in the systemic circulation and in tumors. To evaluate the controlled release of the ADC in the targeted area, a mass spectrometry imaging (MSI) with a mass microscope, to visualize the drug released from ADC, was used. As a result, we succeeded in confirming the significant anti-tumor activity of anti-fibrin, or anti-tissue factor-ADC, in preclinical settings by using DDS and molecular imaging.