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Distribution of Flavan-3-ol Species in Ripe Strawberry Fruit Revealed by Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging
摘要： Flavan-3-ols, which comprise proanthocyanidins and their monomers, are major ?avonoids in strawberries, and they have a wide range of biological activities and health bene?ts. However, their spatial distribution in strawberry fruit remains poorly understood. Therefore, we performed matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI), to visualize ? ?avan-3-ols in ripe strawberry fruit. Peaks matching the m/z values of ?avan-3-ols [M ? H] ions were detected in the negative ion mode using 1,5-diaminonaphthalene as matrix. Catechin and/or epicatechin, three B-type procyanidins, and two B-type propelargonidins were identi?ed by MALDI-tandem MS. These ?avan-3-ols were mainly distributed in the calyx, in and around the vascular bundles, and in the skin. In-source fragmentation of proanthocyanidins was determined using their standards, suggesting their distribution was mixed ion images of themselves, and fragment ions generated from those had a higher degree of polymerization. B-type procyanidins were predominantly distributed in the vascular bundles than in the skin, whereas B-type propelargonidins were almost equally distributed between the vascular bundles and skin, suggesting that their distribution patterns are di?erent from the type of their ?avan-3-ol monomers. Flavan-3-ols, especially B-type procyanidins, may help prevent pathogen infection not only in the skin but also in and around the vascular bundles.
关键词： mass spectrometry imaging (MSI),propelargonidins,procyanidins,proanthocyanidins,?avan-3-ols,strawberry,matrix-assisted laser desorption/ionization (MALDI)
更新于2025-09-16 10:30:52
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Egg white as a quality control in matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI).
摘要： The strength of MALDI-MSI is to analyse and visualize spatial intensities of molecular features from an intact tissue. The distribution of the intensities can then be visualized within a single tissue section or compared in between sections, acquired consecutively. This method can be reliably used to reveal physiological structures and has the potential to identify molecular details, which correlate with biological outcomes. MALDI-MSI implementation in clinical laboratories requires the ability to ensure method quality and validation to meet diagnostic expectations. To be able to get consistent qualitative and quantitative results, standardized sample preparation and data acquisition are of highest priority. We have previously shown that the deposition of internal standards onto the tissue section during sample preparation can be used to improve mass accuracy of monitored m/z features across the sample. Here, we present the use of external and internal controls for the quality check of sample preparation and data acquisition, which is particularly relevant when either many spectra are acquired during a single MALDI-MSI experiment or data from independent experiments are processed together. To monitor detector performance and sample preparation, we use egg white as an external control for peptide and N-glycan MALDI-MSI throughout the experiment. We have also identified endogenous peptides from cytoskeletal proteins, which can be reliably monitored in gynecological tissue samples. Lastly, we summarize our standard quality control workflow designed to produce reliable and comparable MALDI-MSI data from single sections and tissue microarrays (TMAs).
关键词： egg white,MALDI-MSI,quality control,N-glycan,peptide,tissue microarrays,FFPE tissue
更新于2025-09-11 14:15:04
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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States
摘要： Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.
关键词： Sensor integration,ETM+,Sentinel-2,MSI,OLI,Time series,Change detection,Landsat
更新于2025-09-09 09:28:46
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Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer
摘要： Purpose: To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. Experimental design: Given a list of putative breast and ovarian cancer biomarker proteins, we extracted a set of related m/z values from heterogeneous MSI datasets derived from formalin-fixed paraffin-embedded tissue material. Based on these features, a linear discriminant analysis classification model was trained to discriminate the two tumor types. Results: We show that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). Conclusions and clinical relevance: We obtained robust classification models not confounded by technical variation between MSI measurements. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.
关键词： feature extraction,tumor typing,MALDI MSI,tissue classification,biomarker proteins
更新于2025-09-09 09:28:46
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Using new remote sensing satellites for assessing water quality in a reservoir
摘要： Water quality monitoring could benefit from information derived from the newest generation of medium resolution Earth observation satellites. The main objective of our study was to assess the suitability of both Landsat 8 and Sentinel-2A satellites for estimating and mapping Secchi disk transparency (SDT), a common measurement of water clarity, in Cassaffousth Reservoir (Córdoba, Argentina). Ground observations and a dataset of four Landsat 8 and four Sentinel-2A images were used to create and validate models to estimate SDT in the reservoir. The selected algorithms were used to obtain graphic representations of water clarity. Slight differences were found between Landsat 8 and Sentinel-2 estimations. Thus, we demonstrated the suitability of both satellites for estimating and mapping water quality. This study highlights the importance of free and readily-available satellite datasets in monitoring water quality especially in countries where conventional monitoring programs are either lacking or unsatisfactory.
关键词： water clarity,Secchi disk depth,monitoring,Sentinel-2 MSI,remote sensing,Landsat 8 OLI
更新于2025-09-09 09:28:46
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Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis
摘要： The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models have not yet been adapted to the inherent complexity of remotely sensed data, which eventually may limit their resulting performance. In this scenario, this paper presents a new topic-based image fusion framework, specially designed to fuse synthetic aperture radar (SAR) and multispectral imaging (MSI) data for unsupervised land cover categorization tasks. Specifically, we initially propose a hierarchical multi-modal probabilistic latent semantic analysis (HMpLSA) model that takes advantage of two different vocabulary modalities, as well as two different levels of topics, in order to effectively uncover intersensor semantic patterns. Then, we define an SAR and MSI data fusion framework based on HMpLSA in order to perform unsupervised land cover categorization. Our experiments, conducted using three different SAR and MSI data sets, reveal that the proposed approach is able to provide competitive advantages with respect to standard clustering methods and topic models, as well as several multimodal topic model variants available in the literature.
关键词： Image fusion,probabilistic latent semantic analysis (pLSA),synthetic aperture radar (SAR),multispectral imaging (MSI)
更新于2025-09-04 15:30:14