Modern machine-learning applications in ambient ionization mass spectrometry.

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Mass spectrometry for neurosurgery: Intraoperative support in decision-making.

Ambient ionization mass spectrometry was proved to be a powerful tool for oncological surgery. Still, it remains a translational technique on the way from laboratory to clinic. Brain surgery is the most sensitive to resection accuracy field since the balance between completeness of resection and minimization of nerve fiber damage determines patient outcome and quality of life. In this review, we summarize efforts made to develop various intraoperative support techniques for oncological neurosurgery and discuss difficulties arising on the way to clinical implementation of mass spectrometry-guided brain surgery.

Computational Pipeline for Analysis of Biomedical Networks with BioNAR.

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

BioNAR: an integrated biological network analysis package in bioconductor.

Motivation: Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Results: Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. Availability and implementation: The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.

Genomic analysis and assessment of pathogenic (toxicogenic) potential of Staphylococcus haemolyticus and Bacillus paranthracis consortia isolated from bovine mastitis in Russia.

Three stable microbial consortia, each composed of Bacillus paranthracis and Staphylococcus haemolyticus strains, were isolated from milk of cows diagnosed with mastitis in three geographically remote regions of Russia. The composition of these consortia remained stable following multiple passages on culture media. Apparently, this stability is due to the structure of the microbial biofilms formed by the communities. The virulence of the consortia depended on the B. paranthracis strains. It seems plausible that the ability of the consortia to cause mastitis in cattle was affected by mutations of the cytK gene of B. paranthracis.

FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models.

BACKGROUND: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. METHODS: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. RESULTS: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. CONCLUSIONS: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.

Incorporation of a Disposable ESI Emitter into Inline Cartridge Extraction Mass Spectrometry Improves Throughput and Spectra Stability.

Rapid and reliable methods for detecting tumor margins are crucial for neuro-oncology. Several mass spectrometry-based methods have been recently proposed to address this problem. Inline Cartridge Extraction (ICE) demonstrates the potential for clinical application, based on ex-vivo analysis of dissected tissues, but requires time-consuming steps to avoid cross-contamination. In this work, a method of incorporating a disposable electrospray emitter into the ICE cartridge by PEEK sleeves melting is developed. It reduces total analysis time and improves throughput. The proposed setup also improves the robustness of the ICE molecular profiling as demonstrated with human glial tumor samples in that stability and reproducibility of the spectra were increased.

Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling.

Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.

Synaptome.db: a bioconductor package for synaptic proteomics data.

Summary: The neuronal synapse is underpinned by a large and diverse proteome but the molecular evidence is spread across many primary datasets. These data were recently curated into a single dataset describing a landscape of ∼8000 proteins found in studies of mammalian synapses. Here, we describe programmatic access to the dataset via the R/Bioconductor package Synaptome.db, which enables convenient and in-depth data analysis from within the Bioconductor environment. Synaptome.db allows users to obtain the respective gene information, e.g. subcellular localization, brain region, gene ontology, disease association and construct custom protein-protein interaction network models for gene sets and entire subcellular compartments. Availability and implementation: The package Synaptome.db is part of Bioconductor since release 3.14, https://bioconductor.org/packages/release/data/annotation/html/synaptome.db.html, it is open source and available under the Artistic license 2.0. The development version is maintained on GitHub (https://github.com/lptolik/synaptome.db). Full documentation including examples is provided in the form of vignettes on the package webpage. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Comparative Analysis of Milk Microbiomes and Their Association with Bovine Mastitis in Two Farms in Central Russia.

Bovine mastitis is a widespread infectious disease. In addition to the economic damages associated with reduced milk yield due to mastitis, the problem of food contamination by microorganism metabolites, in particular toxins, is also a concern. Horizontal transfer of microorganisms from animal populations to humans can also be complicated by antibiotic resistance. Therefore, bovine mastitis is relevant to the study of microbiology and veterinary medicine. In this study, we investigated the microbiome of milk samples from healthy cows and cows with different forms of mastitis from individual quarters of the udder of cows during first and second lactation. Total DNA was extracted from milk samples. The V3-V4 regions of the bacterial 16S rRNA genes from each sample were amplified to generate a library via high-throughput sequencing. We revealed significant dominance of several operational taxonomic units (OTUs) corresponding mostly to groups of Staphylococcus aureus, Aerococcus spp., and Streptococcus spp. In addition, we unexpectedly identified Streptococcus thermophilus in samples with high SCC quantities. We found some infectious agents that characterized summer mastitis. We demonstrated that in Central Russia, mastitis is associated with a wide variety of causal organisms. We observed some differences in the diversity of the two investigated farms. However, we did not find any significant difference among healthy, mastitis and subclinical samples according to their SCC status from either farms by principal component analysis. Linear discriminant analysis effect size (LEfSe) confirmed the presence of several indicator genera in farms from Moscow and the Tula Region. These results confirm the complex bacterial etiology of bovine mastitis.

Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues.

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry.

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues. In connection with this, here we demonstrate the possibility of using non-imaging ambient mass spectrometry to evaluate TCP in glial tumor tissues with a high degree of confidence. Molecular profiles of histologically annotated human glioblastoma tissue samples were obtained using the inline cartridge extraction ambient mass spectrometry approach. XGBoost regressors were trained to evaluate tumor cell percentage. Using cross-validation, it was estimated that the TCP was determined by the regressors with a precision of approximately 90% using only low-resolution data. This result demonstrates that ambient mass spectrometry provides an accurate method todetermine TCP in dissected tissues even without implementing mass spectrometry imaging. The application of such techniques offers the possibility to automate routine tissue screening and TCP evaluation to boost the throughput of pathology laboratories. Rapid estimation of tumor cell percentage during neurosurgery.

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging.

In this work, we demonstrate a new approach for interactively assessing hyperspectral data spatial structures for heterogeneity using mass spectrometry imaging. This approach is based on the visualization of the cosine distance as the similarity levels between mass spectra of a chosen region and the rest of the image (sample). The applicability of the method is demonstrated on a set of mass spectrometry images of frontal mouse brain slices. Selection of the reference pixel of the mass spectrometric image and a further view of the corresponding cosine distance map helps to prepare supporting vectors for further analysis, select features, and carry out biological interpretation of different tissues in the mass spectrometry context with or without histological annotation. Visual inspection of the similarity maps reveals the spatial distribution of features in tissue samples, which can serve as the molecular histological annotation of a slide.

Assessment of variation of inline cartridge extraction mass spectra.

Recently, mass-spectrometry methods show its utility in tumor boundary location. The effect of differences between research and clinical protocols such as low- and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful.

DNA sequence, physics, and promoter function: Analysis of high-throughput data On T7 promoter variants activity.

RNA polymerase/promoter recognition represents a basic problem of molecular biology. Decades-long efforts were made in the area, and yet certain challenges persist. The usage of certain most suitable model subjects is pivotal for the research. System of T7 bacteriophage RNA-polymerase/T7 native promoter represents an exceptional example for the purpose. Moreover, it has been studied the most and successfully applied to aims of biotechnology and bioengineering. Both structural simplicity and high specificity of this molecular duo are the reason for this. Despite highly similar sequences of distinct T7 native promoters, the T7 RNA-polymerase enzyme is capable of binding respective promoter in a highly specific and adjustable manner. One explanation here is that the process relies primarily on DNA physical properties rather than nucleotide sequence. Here, we address the issue by analyzing massive data recently published by Komura and colleagues. This initial study employed Next Generation Sequencing (NGS) in order to quantify activity of promoter variants including ones with multiple substitutions. As a result of our work substantial bias in simultaneous occurrence of single-nucleotide sequence alterations was found: the highest rate of co-occurrence was evidenced within specificity loop of binding region while the lowest - in initiation region of promoter. If both location and a kind of nucleotides involved in replacement (both initial and resulting) are taken into consideration, one can easily note that N to A substitutions are most preferred ones across the whole 19 b.p.-long sequence. At the same time, N to C are tolerated only at crucial position in recognition loop of binding region, and N to G are uniformly least tolerable. Later in this work the complete set of variants was split into groups with mutations (1) exclusively in binding region; (2) exclusively in melting region; (3) in both regions. Among these three groups second comprises extremely few variants (at triple-digit rate lesser than in two other groups, 46 versus over one and six thousand). Yet these are all promoter with substantial to high activity. This group two appeared heterogenous by primary sequence; indeed, upon further subdivision into above versus below average activity subgroups first one was found to comprise promoters with negligible conservation at -2 position of melting region; the second was hardly conserved in this region at all. This draws our attention to perfect consensus sequence of class III T7 promoter with -2 nucleotide randomized (all four are present by one to several copies in the previously published source dataset), the picture becomes even more pronounced. We therefore suggest that mutations at the position therefore do not cause significant changes in terms of promoter activity. At the same time, such modifications dramatically change DNA physical properties which were calculated in our study (namely electrostatic potential and propensity to bend). One possible suggestion here is that -2 nucleotide might function as a generic switch; if so, substitution -2A to -2T has important regulatory consequences. The fact that that -2 b.p. is the most evidently different nucleotide between class II versus class III promoters of T7 genome and that it also distinguishes the class III promoter in T7 genome versus promoters of its relative but reproductively isolated bacteriophage T3. In other words, it appears feasible that mutation at -2 nucleotide does not impede promoter activity yet alter its physical properties thus affecting differential RNA polymerase/promoter interaction.

Virulence Factors and Phylogeny of Staphylococcus aureus Associated With Bovine Mastitis in Russia Based on Genome Sequences.

Staphylococcus aureus is a causative agent of different infectious processes, food poisoning, and autoimmune disorders. The horizontal transfer of pathogenic strains can occur from animal to human under both house and farm conditions, and the spread of strains with antibiotic resistance is an existing problem. In addition to the spread of antibiotic-resistant strains in clinics, this problem also exists in veterinary medicine. It is especially important to monitor antibiotic resistance on farms where antibiotics are the standard treatment of animals, which may trigger the spread of antibiotic-resistant strains among animals and to the human population, and these strains can also be distributed in milk products produced by these farms (milk, cheese, and butter). In this work, we investigated 21 S. aureus isolates using whole-genome sequence analysis and tried to establish a relationship between these isolates with the development of bovine mastitis in seven regions of Western Russia. An S. aureus virulence profile was identified. We identified two groups of S. aureus associated with subclinical mastitis, namely, the enterotoxin-positive and enterotoxin-negative groups. The most prevalent factor associated with bovine mastitis in Russia was cytotoxins, including hemolysins and leukocidins. Multidrug resistance strains were investigated, and antibiotic resistance genes were identified. We identified S. aureus ST 97 type as the most common type in the regions in Western Russia. To the best of our knowledge, this is the first in-depth study of a range S. aureus isolates originating from cattle infections in Russia.

Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling.

The development of perspective diagnostic techniques in medicine requires efficient high-throughput biological sample analysis methods. Here, we present an inline cartridge extraction that facilitates the screening rate of mass spectrometry shotgun lipidomic analysis of tissue samples. We illustrate the method by its application to tumor tissue identification in neurosurgery. In perspective, this high-performance method provides new possibilities for the investigation of cancer pathogenesis and metabolic disorders.

Untangling the Metabolic Reprogramming in Brain Cancer: Discovering Key Molecular Players Using Mass Spectrometry.

Cells metabolism alteration is the new hallmark of cancer, as well as an important method for carcinogenesis investigation. It is well known that the malignant cells switch to aerobic glycolysis pathway occurring also in healthy proliferating cells. Recently, it was shown that in malignant cells de novo synthesis of the intracellular fatty acid replaces dietary fatty acids which change the lipid composition of cancer cells noticeably. These alterations in energy metabolism and structural lipid production explain the high proliferation rate of malignant tissues. However, metabolic reprogramming affects not only lipid metabolism but many of the metabolic pathways in the cell. 2-hydroxyglutarate was considered as cancer cell biomarker and its presence is associated with oxidative stress influencing the mitochondria functions. Among the variety of metabolite detection methods, mass spectrometry stands out as the most effective method for simultaneous identification and quantification of the metabolites. As the metabolic reprogramming is tightly connected with epigenetics and signaling modifications, the evaluation of metabolite alterations in cells is a promising approach to investigate the carcinogenesis which is necessary for improving current diagnostic capabilities and therapeutic capabilities. In this paper, we overview recent studies on metabolic alteration and oncometabolites, especially concerning brain cancer and mass spectrometry approaches which are now in use for the investigation of the metabolic pathway.

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0.

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).