Spectral Library Research Articles

Proteome profiling in patients with peripartum cardiomyopathy according to ethnicity a biomarker study on the EORP cohort

Abstract Background Peripartum cardiomyopathy (PPCM) is a pregnancy-associated form of heart failure, which occurs in previously healthy women towards the end of pregnancy and in the first months postpartum. Previous reports showed a varying prevalence of PPCM across different regions and potentially a higher incidence in patients with black African ethnicity. The correct and timely diagnosis remains challenging and there is a need for novel disease-specific biomarkers. Accuracy in diagnosing PPCM can be improved by using serum-protein biomarkers such as QSOX1, adiponectin and ITIH3 in addition to NT-proBNP. However, it still needs to be ascertained whether there are ethnic differences in proteomic profiles amongst patients with PPCM with different ethnicities. Aims To delineate possible differences in serum protein biomarker profiles across different ethnic groups within the EURObservation Research Programme (EORP) multi-national cohort of women with PPCM. Methods Demographic and clinical data, as well as serum samples were collected from 84 patients with PPCM from seven EORP participating countries. Serum proteomic profiling was conducted using DIA-based label-free quantitative (LFQ) LC-MS at the time of diagnosis from depleted serum samples. Mass spectrometry data were analyzed by Spectronaut v15 using a study-specific spectral library. From the 84 patients in the EORP PPCM registry on whom we performed proteomic analyses, 82 patients self-declared their ethnicity. These 82 patients were assessed for any ethnical differences in their proteomic expression profile using unsupervised principal component analysis (PCA). Results In this cohort of 82 women with PPCM, the mean age was 30.5±6.7 years (Table 1). Three quarters of this cohort had no hypertension during pregnancy and only 13.8% had a prior diagnosis of PPCM. The mean left ventricular ejection fraction (LVEF) was 35% at time of first diagnosis, with no difference between the ethnic groups. However, women of Middle Eastern decent had more severe LV dilatation (LVEDD: 64.0 (62.0-65.0) mm). Three hundred and twenty-nine (329) proteins were identified in the serum samples. As depicted in Figure 1, PCA did not yield a distinct separation between the different ethnic groups. Principal component 1 (PC1) accounted for 15.2% of the total variability in protein expression among ethnic groups, PC2 explained a comparatively lower percentage at 12%. Conclusion The protein biomarkers identified in PPCM patients showed no discernible racial differences, demonstrating consistent performance across all ethnic groups.

A complementary approach for detecting biological signals through a semi-automated feature selection tool

IntroductionUntargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches.MethodsMetabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter.Results and DiscussionRegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user’s interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Predicting USCS soil texture classes utilizing soil spectra and deep learning

Abstract Purpose Soil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general. Methods Two large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes. Results The results indicate that the Swin transformer model performed the best with an accuracy of 67% for six USCS soil texture class predictions and 81% for three USCS soil texture group predictions. Cohen’s kappa value implies a moderate agreement (0.55) for soil texture class predictions and a substantial agreement (0.64) for soil texture group predictions. Conclusion The proposed methodology offers a novel approach for USCS soil texture class predictions utilizing SSLs and deep learning techniques.

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models.

Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability

Abstract. Painted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the field of heritage conservation. In this study, hyperspectral remote sensing was used to invert the abundance of mixed pigments by spectral unmixing. Spectral variability effects are often present in hyperspectral images. Hyperspectral images of cultural artefacts also suffer from spectral variability due to factors such as particle size and impurities within the pigment, and acquisition conditions. Therefore, spectral variability was incorporated into the Linear Mixed Model of the pigment spectral unmixing. The unmixing methods are classified into two categories based on whether or not spectral libraries are used. In the experiment, computer synthesized data and laboratory-made sample blocks of mixed pigments, which mixed with different ratios of Azurite and Malachite, were selected as validation data. Five commonly used algorithms for solving the spectral variability problem, namely MESMA, Fractional Sparse SU, ELMM, RUSAL, and BCM, are used for pigment unmixing and compared with FCLS, which does not consider spectral variability. The results show that ELMM has the highest unmixing accuracy of aRMSE and xRMSE compared to other methods. At the same time, it can be seen from the metric of SAM that ELMM is able to extract reliable endmember variability spectral, which is more suitable for solving the problem of spectral variability in pigment unmixing. Finally, we apply ELMM to real artefacts Yungang Grottoes mural paintings, and obtain a lower xRMSE than FCLS, which improves the unmixing accuracy.

Study on radiation characteristics of multi-phase plumes containing ice crystals in orbit-control engines

In order to obtain the radiation characteristics of the multiphase plume containing ice crystals in the high-altitude orbit control engine and analyze its influence on the telemetry effect, this paper deduces the radiation characteristics parameters of gases based on Einstein radiation theory and radiation transfer equation. Based on the latest HITRAN2020 spectral line library solving gas radiation and the Mie theory of particle scattering solving particle radiation, the radiation characteristic parameters of the multi-phase plume containing ice crystals in the high-altitude orbital control engine and the radiation intensity distribution in the near-infrared and mid-long wave infrared bands are simulated, and the main influencing factors of the radiation are analyzed. The results show that, in general, the main radiation of the engine plume includes the scattered light in the near infrared band and the thermal radiation in the middle long wave infrared band. In the near-infrared band, considering solar background radiation, the total radiation amount in the calculation domain is about 10 −6 W/sr, and the thermal radiation is dominant within the axial distance of 0.5 m, while the ice crystal scattering is dominant beyond the axial distance of 0.5 m . In the mid-long wave infrared band, the total radiation amount in the calculation domain is about 10 W/sr, and the thermal radiation of gas dominates within 1 m axial distance, while the thermal radiation of ice crystal dominates beyond 1 m axial distance.

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Discriminant analysis using feature extraction from spectral domain responses to achieve accurate delineation for robust evaluation or classification of soil properties

ABSTRACT Soil spectroscopy offers a method for quantitatively analysing soil chromophores and employing a screening approach. However, utilizing general models with this data at once doesn’t consistently yield accurate results in determining soil parameters. Ambiguities in response within certain domains of the reflectance spectrum are observed. To address this issue, a method is proposed where quantities associated with spectral response are outputted within well-defined domains corresponding to specific parameters. This article introduces a novel approach centred on identifying features from reflectance spectra to assess soil properties and mitigate issues arising from a heterogeneous soil dataset. The focus is on swiftly automating the classification of soil spectral data by extracting features directly related to the varied composition of chromophores in the samples. These features are categorized for each chromophore parameter and analysed using linear discriminant analysis (LDA). The study employs the ‘309 soil samples’ from the GEO-CRADLE open spectral soil library in Greece. Specifically, relevant data from six spectral responses are extracted to separate and quantify certain soil parameters such as clay content, NO3− levels, and SOM (Soil Organic Matter), despite their interrelations. We developed a method to visually represent the determination outcomes quantitatively by establishing three threshold parameter values: 1.6% for SOM, 19% for clay, and 13.6 ppm for NO3−. These determinations will span the explored ranges of ’0.3 to 4.18%’ for SOM, “3 to 48%” for clay, and ’0 to 661.2 ppm’ for NO3−. Utilizing supervised LDA proves notable classification rates of 93.3% for the SOM task, 90% for the Clay task, and 86.6% for NO3− with the specific cut-off value parameter. The final result is presented as a map showing the positions of various prediction samples, highlighting the percentages of clay and organic matter relative to 19% and 1.6%, respectively, as well as the nitrate concentration in these samples relative to 13.6 ppm.

Marine Bacillus pumilus substances exhibit antimicrobial effect on multidrug-resistant Staphylococcus aureus.

This study aimed to assess the antimicrobial potential of Bp1-AdE, produced by Bacillus pumilus 64-1, and to investigate its mode of action against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA). Bp-1AdE, derived from sponge-associated B. pumilus, exhibited bactericidal activity at 1,550 µg ml-1 against S. aureus ATCC29213 and MRSA strains. Light and fluorescence microscopy revealed drastic cell lysis of S. aureus treated with Bp-1AdE. Scanning and transmission electron microscopy suggested that Bp-1AdE disrupts the cytoplasmic membrane. Toxicity assays showed that Bp-1AdE was non-toxic to Tenebrio molitor larvae. Liquid chromatography-mass spectrometry (LC-MS) and Global Natural Product Social (GNPS) spectral libraries identified four substances within Bp-1AdE, including aliphatic alcohols [3,4-dipentylhexane-2,5-diol and 1,1'-(4,5-dibutyl-3,6-dimethylcyclohexane-1,2-diyl)bis(ethan-1-one)] and terpenoids (cholic acid and canrenone). Bp-1AdE demonstrated selective toxicity and bactericidal activity, highlighting its potential for controlling infections caused by multidrug-resistant S. aureus strains.

An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.

In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Deep Spectral Library of Mice Retina for Myopia Research: Proteomics Dataset generated by SWATH and DIA-NN

The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that the use of a predicted retinal-specific spectral library combined with the robust quantification achieved within this dataset will contribute to a better understanding of the proteome complexity. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.

Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints

Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite. It decomposes the hyperspectral data to ascertain the abundance corresponding to each endmember in the spectral library. Currently, the majority of sparse unmixing methods are inadequate in high-noise environments due to their failure to comprehensively account for hyperspectral characteristics. Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints (SWSU-ATVLR). Initially, the sparse unmixing algorithm is introduced in detail. Subsequently, we present the our method. This method seamlessly integrates the low-rank, adaptive TV and spectral weighting characteristics of hyperspectral data. While preserving the low-rank attributes and sparsity of abundance, the adaptively adjusted abundance matrix exhibits a regularized horizontal and vertical difference ratio across various structures and fully utilizes spectral information, enhancing denoising efficiency. Subsequently, the ADMM algorithm is employed to solve the new model. To validate our proposed algorithm, SWSU-ATVLR method is compared and analysed in detailed experiments with several current state-of-the-art methods through simulated and real data experiments. Experimental results prove that our proposed method is superior to these state-of-the-art methods.

Deep Proteome Analysis of Cerebrospinal Fluid from Pediatric Patients with Central Nervous System Cancer.

The cerebrospinal fluid (CSF) is a key matrix for discovery of biomarkers relevant for prognosis and the development of therapeutic targets in pediatric central nervous system malignancies. However, the wide range of protein concentrations and age-related differences in children makes such discoveries challenging. In addition, pediatric CSF samples are often sparse and first prioritized for clinical purposes. The present work focused on optimizing each step of the proteome analysis workflow to extract the most detailed proteome information possible from the limited CSF resources available for research purposes. The strategy included applying sequential ultracentrifugation to enrich for extracellular vesicles (EV) in addition to analysis of a small volume of raw CSF, which allowed quantification of 1351 proteins (+55% relative to raw CSF) from 400 μL CSF. When including a spectral library, a total of 2103 proteins (+240%) could be quantified. The workflow was optimized for CSF input volume, tryptic digestion method, gradient length, mass spectrometry data acquisition method and database search strategy to quantify as many proteins a possible. The fully optimized workflow included protein aggregation capture (PAC) digestion, paired with data-independent acquisition (DIA, 21 min gradient) and allowed 2989 unique proteins to be quantified from only 400 μL CSF, which is a 340% increase in proteins compared to analysis of a tryptic digest of raw CSF.

A dataset for developing proteomic tools for pathogen detection via differential cell lysis of whole blood samples

This data descriptor presents a curated dataset for pathogen detection and identification (Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans) directly from whole-blood samples. The dataset was created using differential cell lysis combined with rapid extraction, digestion, and mass spectrometry-based proteomics. Our method offers a rapid diagnostic alternative to traditional culture, enabling timely disease management, such as sepsis. Highlighting our dataset’s uniqueness, it features a three-tier structure: Spectral Libraries of Pathogens for identifying peptide peaks for putative biomarkers; Spiked pathogen in blood MS data for biomarker panel optimization through varied concentration samples; and Parallel Reaction Monitoring (PRM) data from sepsis patients for validating our biomarker panel, achieving 83.3% sensitivity within seven hours without microbial enrichment culture. This dataset serves as a comprehensive reference for bioinformatic tool development and biomarker panel proposals, advancing microbial detection, antimicrobial resistance, and epidemiological studies.

Comprehensive Data Evaluation Methods Used in Developing the SWGDRUG Mass Spectral Reference Library for Seized Drug Identification.

The mass spectral library of the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) is the most comprehensive free reference database of its kind in the world. It provides reliable mass spectra for identification of seized drugs, their metabolites, and related forensic compounds when using gas chromatography/mass spectrometry (GC/MS). The SWGDRUG library (version 3.13) contains spectra for 3598 compounds. All spectra are evaluated by the Mass Spectrometry Data Center (MSDC) at the National Institute of Standards and Technology (NIST). Over the past few years, new evaluation methods aided by improved software have been developed. First, all chemical information, such as chemical structure and name, is confirmed. Second, the product ions in each spectrum are verified to match the compound structure using the NIST MS Interpreter software tool. Subsequently, the mass spectra are compared to the same or similar compounds across six different mass spectral reference libraries using three distinct library search methods. Additionally, the NIST Artificial Intelligence Retention Indices (AIRI) software is used to help confirm the corresponding compounds of spectra, especially for those without molecular ions. Low-quality and incorrect spectra are rejected for inclusion in the library.

Fourier analysis for detecting vegetation in hyperspectral images

Introduction: hyperspectral images, unlike conventional images, are composed of numerous channels that provide detailed information about the spectral signatures of objects. This allows for the identification of the materials that make them up, and given their potential for detecting environmental changes, identifying vegetation in urban settings using effective computational methods becomes relevant.Objective: the objective of this research is to propose a computational method based on Fourier analysis for detecting vegetation in hyperspectral images.Methods: the research was developed in four methodological phases: selection of technologies, acquisition of the characteristic vegetation pixel, determination of phase similarity between the characteristic pixel and vegetation and non-vegetation pixels, validation of the method on a test hyperspectral image. A method was implemented using the spectral and numpy libraries in Python.Results: the Fourier analysis yielded an average phase similarity of 89.89% and a minimum similarity of 64.54% between the characteristic vegetation pixel and 100 training vegetation pixels. For non-vegetation pixels, the average phase similarity was 42.19%, with a maximum similarity of 63.98%. These results indicate that the proposed method successfully differentiates between vegetation and non-vegetation pixels.Conclusion: the results demonstrate that the Fourier-based method can accurately identify vegetation areas in hyperspectral images, showing non-overlapping phase similarities between vegetation and non-vegetation. This validates the effectiveness of the proposed approach in detecting vegetation in urban environments.

Matrix-assisted laser desorption/ionization mass spectrometry imaging as a new tool for molecular histopathology in epilepsy surgery.

Epilepsy surgery is a treatment option for patients with seizures that do not respond to pharmacotherapy. The histopathological characterization of the resected tissue has an important prognostic value to define postoperative seizure outcome in these patients. However, the diagnostic classification process based on microscopic assessment remains challenging, particularly in the case of focal cortical dysplasia (FCD). Imaging mass spectrometry is a spatial omics technique that could improve tissue phenotyping and patient stratification by investigating hundreds of biomolecules within a single tissue sample, without the need for target-specific reagents. An insitu proteomic technique called matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is here investigated as a potential new tool to expand conventional diagnosis on standard paraffin brain tissue sections. Unsupervised and region of interest-based MALDI-MSI analyses of sections from 10 FCD type IIb (FCDIIb) cases were performed, and the results were validated by immunohistochemistry. MALDI-MSI identified distinct histopathological features and the boundaries of the dysplastic lesion. The capability to visualize the spatial distribution of well-known diagnostic markers enabling multiplex measurements on single tissue sections was demonstrated. Finally, a fingerprint list of potential discriminant peptides that distinguish FCD core from peri-FCD tissue was generated. This is the first study that explores the potential application of MALDI-MSI in epilepsy postsurgery fixed tissue, by utilizing the well-characterized FCDIIb features as a model. Extending these preliminary analyses to a larger cohort of patients will generate spectral libraries of molecular signatures that discriminate tissue features and will contribute to patient phenotyping.

Identification of Potential Growth-Related Proteins in Chick Vitreous during Emmetropization Using SWATH-MS and Targeted-Based Proteomics (MRMHR).

The vitreous humor (VH) is a transparent gelatin-like substance that occupies two-thirds of the eyeball and undergoes the most significant changes during eye elongation. Quantitative proteomics on the normal growth period in the VH could provide new insights into understanding its progression mechanism in the early stages of myopia. In this study, a data-independent acquisition (SWATH-MS) was combined with targeted LC-ESI-MS/MS to identify and quantify the relative protein changes in the vitreous during the normal growth period (4, 7, 14, 21 and 28 days old) in the chick model. Chicks were raised under normal growing conditions (12/12 h Dark/light cycle) for 28 days, where ocular measurements, including refractive and biometric measurements, were performed on days 4 (baseline), 7, 14, 21 and 28 (n = 6 chicks at each time point). Extracted vitreous proteins from individual animals were digested and pooled into a left eye pool and a right pool at each time point for protein analysis. The vitreous proteome for chicks was generated using an information-dependent acquisition (IDA) method by combining injections from individual time points. Using individual pool samples, SWATH-MS was employed to quantify proteins between each time point. DEPs were subsequently confirmed in separate batches of animals individually on random eyes (n = 4) using MRMHR between day 7 and day 14. Refraction and vitreous chamber depth (VCD) were found to be significantly changed (p < 0.05, n = 6 at each time point) during the period. A comprehensive vitreous protein ion library was built with 1576 non-redundant proteins (22987 distinct peptides) identified at a 1% false discovery rate (FDR). A total of 12 up-regulated and 26 down-regulated proteins were found across all time points compared to day 7 using SWATH-MS. Several DEPs, such as alpha-fetoprotein, the cadherin family group, neurocan, and reelin, involved in structural and growth-related pathways, were validated for the first time using MRMHR under this experimental condition. This study provided the first comprehensive spectral library of the vitreous for chicks during normal growth as well as a list of potential growth-related protein biomarker candidates using SWATH-MS and MRMHR during the emmetropization period.