SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics

Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies

Developing mass spectrometry-based proteomics and immunopeptidomics platforms to analyze stability profiles and PTMs in peptides bound to HLA class I molecules.

A comprehensive snapshot of immune specificity can be achieved by studying the interaction between peptide-MHC class I (pMHCI) and CD8+ T cells. Since the stability of pMHCI complexes has been postulated to influence the immunogenicity of virus-derived epitopes and cancer neoepitopes, we sought to further establish the correlation between thermostability and immunogenicity. A panel of more than 100 Vaccinia virus (VACV)-derived pMHCI with known CD8+ T cell response profiles were then used to investigate the extent to which their thermostability profiles correlated with their immunogenicity. We successfully developed two machine learning-based models to predict VACV peptide immunogenicity

FTICR mass spectrometry-based multivariate analysis to explore distinctive metabolites and metabolic pathways:A comprehensive bioanalytical strategy toward time-course metabolic profiling of Thymus vulgaris plants responding to drought stress

In this research, metabolic profiling/pathways of Thymus vulgaris (thyme) plant were assessed during a water deficit stress using an FTICR mass spectrometry-based metabolomics strategy incorporating multivariate data analysis and bioinformatics techniques. Herein, differences of MS signals in specific time courses after water deficit stress and control cases without any timing period were distinguished significantly by common pattern recognition techniques, i.e., PCA, HCA-Heatmap, and PLS-DA. Subsequently, the results were compared with supervised Kohonen neural network (SKN) ones as a non-linear data visualization and capable mapping tool. The classification models showed excellent performance to predict the level of drought stress. By assessing variances contribution on the PCA-loadings of the MS data, the discriminant variables related to the most critical metabolites were identified and then confirmed by ANOVA. Indeed, FTICR MS-based multivariate analysis strategy could explore distinctive metabolites and metabolic pathways/profiles, grouped into three metabolism categories including amino acids, carbohydrates (i.e., galactose, glucose, fructose, sucrose, and mannose), and other metabolites (rosmarinic acid and citrate), to indicate biological mechanisms in response to drought stress for thyme. It was achieved and approved through the MS signals, genomics databases, and transcriptomics factors to interpret and predict the plant metabolic behavior. Eventually, a comprehensive pathway analysis was used to provide a pathway enrichment analysis and explore topological pathway characteristics dealing with the remarkable metabolites to demonstrate that galactose metabolism is the most significant pathway in the biological system of thym

Multivariate spectrochemical analysis of interactions of three common Isatin derivatives to calf thymus DNA <i>in vitro</i>

<p>Interactions of Isatin and its derivatives, Isatin-3-isonicotinylhydrazone (IINH) and Isatin-β-thiosemicarbazone (IBT), with calf thymus DNA (ctDNA) have been investigated to delineate pharmaceutical-physicochemical properties using UV–Vis/fluorescence/circular dichroism (CD) spectroscopy, viscosity measurements, and multivariate chemometrics. IINH and IBT molecules intercalate between base pairs of DNA, hypochromism in UV absorptions, increase in the CD positive band, sharp increase in specific viscosity, and the displacement of the methylene blue and Neutral Red dye in complexes with ctDNA, by the IINH and IBT molecules, respectively. The observed intrinsic binding constants (<i>K</i>_{<i>b</i>[IBT–ctDNA]}= 1.03 × 10⁵ and <i>K</i>_{<i>b</i>[IINH–ctDNA]}= 1.09 × 10⁵ L mol⁻¹) were roughly comparable to other intercalators. In contrast, Isatin binds with ctDNA via groove mode (<i>K</i>_{<i>b</i>[Isatin–ctDNA]}= 7.32 × 10⁴ L mol⁻¹) without any significant enhancement in ctDNA viscosity. The fluorescence quenching of Isatin by ctDNA was observed as static. CD spectra indicated that Isatin effectively absorbs into grooves of ctDNA, leading to transition from <i>B</i> to <i>C</i> form. Thermodynamic parameters like enthalpy changes (∆<i>H</i> < 0) and entropy changes (∆<i>S</i> > 0) were calculated according to Van’t Hoff’s equation, indicating the spontaneous interactions. The common soft/hard chemometric methods were used not only to resolve pure concentration and spectral profiles of components using the acquired spectra but also to calculate Stern–Volmer quenching constants, binding stoichiometry, apparent binding constants (<i>K</i>_<i>a</i>), binding constants (<i>K</i>_<i>b</i>), and thermodynamic parameters. The <i>K</i>_<i>b</i> values for Isatin, IINH, and IBT were calculated as 9.18 × 10³, 1.53 × 10⁵, and 2.45 × 10⁴ L mol⁻¹, respectively. The results obtained from experimental-spectroscopic analyses showed acceptable agreement with chemometric outlines.</p

Immunolyser: A web-based computational pipeline for analysing and mining immunopeptidomic data

Immunopeptidomics has made tremendous contributions to our understanding of antigen processing and presentation, by identifying and quantifying antigenic peptides presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. Large and complex immunopeptidomics datasets can now be routinely generated using Liquid Chromatography-Mass Spectrometry techniques. The analysis of this data – often consisting of multiple replicates/conditions – rarely follows a standard data processing pipeline, hindering the reproducibility and depth of analysis of immunopeptidomic data. Here, we present Immunolyser, an automated pipeline designed to facilitate computational analysis of immunopeptidomic data with a minimal initial setup. Immunolyser brings together routine analyses, including peptide length distribution, peptide motif analysis, sequence clustering, peptide-MHC binding affinity prediction, and source protein analysis. Immunolyser provides a user-friendly and interactive interface via its webserver and is freely available for academic purposes at https://immunolyser.erc.monash.edu/. The open-access source code can be downloaded at our GitHub repository: https://github.com/prmunday/Immunolyser. We anticipate that Immunolyser will serve as a prominent computational pipeline to facilitate effortless and reproducible analysis of immunopeptidomic data