Recent advances in mass-spectrometry based proteomics software, tools and databases

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

A proteogenomic pipeline for the analysis of protein biosynthesis errors in the human pathogen Candida albicans

Candida albicans is a diploid pathogen known for its ability to live as a commensal fungus in healthy individuals, but causing both superficial infections and disseminated candidiasis in immunocompromised patients where it is associated with high morbidity and mortality. Its success in colonizing the human host is attributed to a wide range of virulence traits that modulate interactions between the host and the pathogen, such as optimal growth rate at 37 ºC, the ability to switch between yeast and hyphal forms and a remarkable genomic and phenotypic plasticity. A fascinating aspect of its biology is a prominent heterogeneous proteome that arises from frequent genomic rearrangements, high allelic variation, and high levels of amino acid misincorporations in proteins. This leads to increased morphological and physiological phenotypic diversity of high adaptive potential, but the scope of such protein mistranslation is poorly understood due to technical difficulties in detecting and quantifying amino acid misincorporation events in complex protein samples. We have developed and optimized mass spectrometry and bioinformatics pipelines capable of identifying rare amino acid misincorporation events at the proteome level. We have also analysed the proteomic profile of an engineered C. albicans strain that exhibits high level of leucine misincorporation at protein CUG sites and employed an in vivo quantitative gain-of-function fluorescence reporter system to validate our LC-MS/MS data. C. albicans misincorporates amino acids above the background level at protein sites of diverse codons, particularly at CUG, confirming our previous data on the quantification of leucine incorporation at single CUG sites of recombinant reporter proteins, but increasing misincorporation of Leucine at these sites does not alter the translational fidelity of the other codons. These findings indicate that the C. albicans statistical proteome exceeds prior estimates, suggesting that its highly plastic phenome may also be modulated by environmental factors due to translational ambiguity.

Derivation of novel metabolic pathway score identifies alanine metabolism as a targetable influencer of TNF-alpha signaling

Background

Better understanding of the interaction between metabolism and immune response will be key to understanding physiology and disease. Tumor Necrosis Factor-alpha (TNFα) has been studied widely. However, despite the extensive knowledge about TNFα, the cytokine appears to induce not only variable, but often contradictory, effects on inflammation and cell proliferation. Despite advancements in the metabolomics field, it is still difficult to analyze the types of multi-dose, multi-time point studies needed for elucidating the varied immunologic responses induced by TNFα.

Results

We studied the dose and time course effects of TNFα on murine fibroblast cultures and further elucidated these connections using selective blockade of the TNF receptors (TNFR1 and TNFR2). To streamline analysis, we developed a method to collate the metabolic pathway output from MetaboAnalyst into a single value for the Index of pathway significance (IPS). Using this metric, we tested dose-, time-, and receptor-dependent effects of TNFα signaling on cell metabolism. Guided by these results, we then demonstrate that alanine supplementation enriched TNFR1-related responses in both cell and mouse models.

Conclusions

Our results suggest that TNFα, particularly when signaling through TNFR1, may preferentially use alanine metabolism for energy. These results are limited in by cell type used and immune outputs measured. However, we anticipate that our novel method may assist other researchers in identifying metabolic targets that influence their disease or model of interest through simplifying the analysis of multi-condition experiments. Furthermore, our results endorse the consideration of follow up studies in immunometabolism to improve outcomes in TNF-mediated diseases.

Recent advances in mass spectrometry for the detection of doping

INTRODUCTION

The analysis of doping control samples is preferably performed by mass spectrometry, because obtained results meet the highest analytical standards and ensure an impressive degree of reliability. The advancement in mass spectrometry and all its associated technologies thus allow for continuous improvements in doping control analysis.

AREAS COVERED

Modern mass spectrometric systems have reached a status of increased sensitivity, robustness, and specificity within the last decade. The improved sensitivity in particular has, on the other hand, also led to the detection of drug residues that were attributable to scenarios where the prohibited substances were not administered consciously but rather by the unconscious ingestion of or exposure to contaminated products. These scenarios and their doubtless clarification represent a great challenge. Here, too, modern MS systems and their applications can provide good insights in the interpretation of dose-related metabolism of prohibited substances. In addition to the development of new instruments itself, software-assisted analysis of the sometimes highly complex data is playing an increasingly important role and facilitating the work of doping control laboratories.

EXPERT OPINION

The sensitive analysis and evaluation of a higher number of samples in a shorter time is made possible by the ongoing developments in mass spectrometry.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BACKGROUND

Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.

METHODS

A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.

RESULTS

A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.

CONCLUSIONS

The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Modern tools applied to classic structures: Approaches for mammalian male germ cell RNA granule research

First reported in the 1800s, germ cell granules are small nonmembrane bound RNA-rich regions of the cytoplasm. These sites of critical RNA processing and storage in the male germ cell are essential for proper differentiation and development and are present in a wide range of species from Caenorhabditis elegans through mammals. Initially characterized by light and electron microscopy, more modern techniques such as immunofluorescence and genetic models have played a major role in expanding our understanding of the composition of these structures. While these methods have given light to potential granule functions, much work remains to be done. The current expansion of imaging technologies and omics-scale analyses to germ cell granule research will drive the field forward considerably. Many of these methods, both current and upcoming, have considerable caveats and limitations that necessitate a holistic approach to the study of germ granules. By combining and balancing different techniques, the field is poised to elucidate the nature of these critical structures.

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

Challenges and opportunities for single cell computational proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Clinical Proteomics: Liquid Chromatography-Mass Spectrometry (LC-MS) Purification Systems

Liquid chromatography/mass spectrometry (LC/MS) has become a routine powerful technology in clinical proteomic studies for protein identification, protein characterization, and the discovery of biomarkers. In this chapter, we describe two protocol methods to analyze clinical patient samples using a resin-based depletion column followed by either protein In-Gel enzymatic digestion or protein In-Solution enzymatic digestion using a simple kit-based approach (i.e., using the PreOmics iST sample preparation kit), followed by analysis using one-dimensional reverse-phase chromatography (RPC) or high pH reversed-phase peptide fractionation.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

The Omnitrap Platform: A Versatile Segmented Linear Ion Trap for Multidimensional Multiple-Stage Tandem Mass Spectrometry

Multidimensional multiple-stage tandem processing of ions is demonstrated successfully in a novel segmented linear ion trap. The enhanced performance is enabled by incorporating the entire range of ion activation methods into a single platform in a highly dynamic fashion. The ion activation network comprises external injection of reagent ions, radical neutral species, photons, electrons, and collisions with neutrals. Axial segmentation of the two-dimensional trapping field provides access to a unique functionality landscape through a system of purpose-designed regions for processing ions with maximum flexibility. Design aspects of the segmented linear ion trap, termed the Omnitrap platform, are highlighted, and motion of ions trapped by rectangular waveforms is investigated experimentally by mapping the stability diagram, tracing secular frequencies, and exploring different isolation techniques. All fragmentation methods incorporated in the Omnitrap platform involving radical chemistry are shown to provide complete sequence coverage for partially unfolded ubiquitin. Three-stage (MS3) tandem mass spectrometry experiments combining collision-induced dissociation of radical ions produced by electron meta-ionization and further involving two intermediate steps of ion isolation and accumulation are performed with high efficiency, producing information rich spectra with signal-to-noise levels comparable to those obtained in a two-stage (MS2) experiment. The advanced capabilities of the Omnitrap platform to provide in-depth top-down MSn characterization of proteins are portrayed. Performance is further enhanced by connecting the Omnitrap platform to an Orbitrap mass analyzer, while successful integration with time-of-flight analyzers has already been demonstrated.

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.