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In omics sciences, many compounds are measured simultaneously in a sample in a single run. Such analytical performance opens up prospects for improving cellular cancer vaccines and other cell-based immunotherapeutics. This article provides an overview of proteomics technology, known as cell proteomic footprinting. The molecular phenotype of cells is highly variable, and their antigenic profile is affected by many factors, including cell isolation from the tissue, cell cultivation conditions, and storage procedures. This makes the therapeutic properties of cells, including those used in vaccines, unpredictable. Cell proteomic footprinting makes it possible to obtain controlled cell products. Namely, this technology facilitates the cell authentication and quality control of cells regarding their molecular phenotype, which is directly connected with the antigenic properties of cell products. Protocols for cell proteomic footprinting with their crucial moments, footprint processing, and recommendations for the implementation of this technology are described in this paper. The provided footprints in this paper and program source code for their processing contribute to the fast implementation of this technology in the development and manufacturing of cell-based immunotherapeutics. [ABSTRACT FROM AUTHOR]

In metabolomics, many metabolites are measured simultaneously in a single run. Such analytical performance opens up prospects for clinical laboratory diagnostics. In this work, a mass spectrometric metabogram was developed as a simplified and clinically applicable way of measuring the blood plasma metabolome. To develop the metabogram, blood plasma samples from healthy male volunteers (n = 48) of approximately the same age, direct infusion mass spectrometry (DIMS) of the low molecular fraction of samples, and principal component analysis (PCA) of the mass spectra were used. The seven components of the metabogram defined by PCA, which cover ~70% of blood plasma metabolome variability, were characterized using a metabolite set enrichment analysis (MSEA) and clinical test results of participating volunteers. It has been established that the components of the metabogram are functionally related groups of the blood metabolome associated with regulation, lipid–carbohydrate, and lipid–amine blood components, eicosanoids, lipid intake into the organism, and liver function thereby providing a lot of clinically relevant information. Therefore, metabogram provides the possibility to apply the metabolomics performance in the clinic. The features of the metabogram are also discussed in comparison with the thin-layer chromatography and with the analysis of blood metabolome by liquid chromatography combined with mass spectrometry. [ABSTRACT FROM AUTHOR]

Simple Summary: Low-molecular-weight substances are participants in all biochemical processes occurring in the body. Therefore, by measuring them we can obtain new knowledge about aging mechanisms. At the same time, various animals, which are distinguished by different life expectancies, are excellent objects for such studies, and modern science, known as metabolomics, offers efficient methods to measure them, taking into account their huge diversity. This review describes the aging data accumulated today, obtained by such methods in various animal models and humans. Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics—untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development. [ABSTRACT FROM AUTHOR]

Scientists currently use only a small portion of the information contained in the blood metabolome. The identification of metabolites is a huge challenge because only highly abundant and well-separated compounds can be easily identified in complex samples. However, new approaches that enhance the identification of compounds have emerged; among them, the identification of compounds based on their involvement in a particular biological context is a recent development. In this work, this approach was first applied to identify metabolites in complex samples and, together with metabolite set enrichment analysis, was used for the evaluation of blood plasma from obese patients. The proposed approach was found to provide a statistically sound overview of the biochemical pathways, thus presenting additional information on obesity. Obesity progression was demonstrated to be accompanied by marked alterations in steroidogenesis, androstenedione metabolism, and androgen and estrogen metabolism. The findings of this study suggest that the workflow used for blood analysis is sufficient to demonstrate obesity at the biochemical pathway level as well as to monitor the response to treatment. This workflow is also expected to be suitable for studying other metabolic diseases. [ABSTRACT FROM AUTHOR]

In metabolomics, a large number of small molecules can be detected in a single run. However, metabolomic data do not include the absolute concentrations of each metabolite. Generally, mass spectrometry analyses provide metabolite concentrations that are derived from mass peak intensities, and the peak intensities are strictly dependent on the type of mass spectrometer used, as well as the technical characteristics, options and protocols applied. To convert mass peak intensities to actual concentrations, calibration curves have to be generated for each metabolite, and this represents a significant challenge depending on the number of metabolites that are detected and involved in metabolome-based diagnostics. To overcome this limitation, and to facilitate the development of diagnostic tests based on metabolomics, mass peak intensities may be expressed in quintiles. The present study demonstrates the advantage of this approach. The examples of diagnostic signatures, which were designed in accordance to this approach, are provided for lung and prostate cancer (leading causes of mortality due to cancer in developed countries) and impaired glucose tolerance (which precedes type 2 diabetes, the most common endocrinology disease worldwide). [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) is one of the specific complications of diabetes mellitus and one of the leading kidney-related disorders, often requiring renal replacement therapy. Currently, the tests commonly used for the diagnosis of DN, albuminuria (AU) and glomerular filtration rate (GFR), have limited sensitivity and specificity and can usually be noted when typical morphological changes in the kidney have already been manifested. That is why the extreme urgency of the problem of early diagnosis of this disease exists. The untargeted metabolomics analysis of blood plasma samples from 80 patients with type 1 diabetes and early and late stages of DN according to GFR was performed using direct injection mass spectrometry and bioinformatics analysis for diagnosing signatures construction. Among the dysregulated metabolites, combinations of 15 compounds, including amino acids and derivatives, monosaccharides, organic acids, and uremic toxins were selected for signatures for DN diagnosis. The selected metabolite combinations have shown high performance for diagnosing of DN, especially for the late stage (up to 99%). Despite the metabolite signature determined for the early stage of DN being characterized by a diagnostic performance of 81%, these metabolites as potential biomarkers might be useful in the evaluation of treatment of the disease, especially at early stages that may reduce the risk of kidney failure development. [ABSTRACT FROM AUTHOR]

Simple Summary: Early cancer vaccines include whole-cell formulations, which operate on the principle that you should vaccinate with what you want to develop protection against. Such vaccines have been widely tested in various cancers and their advantages described but have not yet managed to pass clinical trials. Antigenic essence technology offers the possibility to revitalize the field of whole-cell-based vaccination, as the essence comprises the entire diversity of native cellular antigens. At the same time, the technology allows for precise control and purposeful change of essence composition as well as purification of essence from ballast cellular substances and also addresses issues of major histocompatibility complex restriction. Antigenic essence technology makes it possible to update many cellular vaccines that have already been developed, as well as to develop new ones, therefore introducing a new direction for anticancer vaccination research. The development of anticancer immunotherapy is characterized by several approaches, the most recognized of which include cellular vaccines, tumor-associated antigens (TAAs), neoantigens, and chimeric antigen receptor T cells (CAR-T). This paper presents antigenic essence technology as an effective means for the production of new antigen compositions for anticancer vaccination. This technology is developed via proteomics, cell culture technology, and immunological assays. In terms of vaccine development, it does not fit into any of the above-noted approaches and can be considered a new direction. Here we review the development of this technology, its main characteristics, comparison with existing approaches, and the features that distinguish it as a novel approach to anticancer vaccination. This review will also highlight the benefits of this technology over other approaches, such as the ability to control composition, optimize immunogenicity and similarity to target cells, and evade major histocompatibility complex restriction. The first antigenic essence products, presented under the SANTAVAC brand, are also described. [ABSTRACT FROM AUTHOR]

The creation of cancer vaccines is a constant priority for research and biotechnology. Therefore, the emergence of any new technology in this field is a significant event, especially because previous technologies have not yielded results. Recently, the development of a cancer vaccine has been complemented by a new proteomics technology platform that allows the creation of antigen compositions known as antigenic essences. Antigenic essence comprises a target fraction of cellular antigens, the composition of which is precisely controlled by peptide mass spectrometry and compared to the proteomic footprint of the target cells to ensure similarity. This proteomics platform offers potential for a massive upgrade of conventional cellular cancer vaccines. Antigenic essences have the same mechanism of action, but without the disadvantages, and with notable advantages such as precise targeting of the immune response, safety, controlled composition, improved immunogenicity, addressed MHC restriction, and extended range of vaccination doses. The present paper calls attention to this novel platform, stimulates discussion of the role of antigenic essence in vaccine development, and consolidates academic science with biotech capabilities. A brief description of the platform, list of cellular cancer vaccines suitable for the upgrade, main recommendations, limitations, and legal and ethical aspects of vaccine upgrade are reported here. [ABSTRACT FROM AUTHOR]

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided. [ABSTRACT FROM AUTHOR]

Recently it was demonstrated that tumors induce specific changes to the surface of human endothelial cells thereby providing the basis for designing endothelial cell-based vaccines that directly target antigens expressed by the tumor endothelium. The present report extends these studies in vitro by investigating the efficacy of allogeneic antigens with regard to their ability to target immune responses against the tumor vasculature since alloantigens simplify vaccine development and implementation in clinical practice. We demonstrated that allogeneic SANTAVAC (Set of All Natural Target Antigens for Vaccination Against Cancer), which presents a specifically prepared composition of cell surface antigens from tumor-stimulated endothelial cells, allows targeting of the tumor vasculature with efficacy of 17, where efficacy represents the killing rate of target cells before normal cells are adversely affected, and efficacy of 60, where efficacy represents the fold decrease in the number of target cells and directly relates to tumor growth arrest. These data suggest that allogeneic SANTAVAC may be considered an antigenic composition that following administration in the presence of respective adjuvants may be clinically tested as a therapeutic or prophylactic universal cancer vaccine without adverse side effects to the normal vasculature. [ABSTRACT FROM AUTHOR]

Simple Summary: Renal cell carcinoma (RCC) is one of the most common cancer types. However, the lack of clinical symptoms and validated biomarkers for early stage RCC prevent timely disease diagnosis. The study was focused on revealing potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from non-cancer volunteers (control) and RCC patients (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC), and advanced stages of ccRCC) was performed. A set of metabolites with diagnostic power for the early stages of ccRCC was detected. Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis. [ABSTRACT FROM AUTHOR]

Parkinson's disease is the second most frequent neurodegenerative disease, representing a significant medical and socio-economic problem. Modern medicine still has no answer to the question of why Parkinson's disease develops and whether it is possible to develop an effective system of prevention. Therefore, active work is currently underway to find ways to assess the risks of the disease, as well as a means to extend the life of patients and improve its quality. Modern studies aim to create a method of assessing the risk of occurrence of Parkinson's disease (PD), to search for the specific ways of correction of biochemical disorders occurring in the prodromal stage of Parkinson's disease, and to personalize approaches to antiparkinsonian pharmacotherapy. In this review, we summarized all available clinically approved tests and techniques for PD diagnostics. Then, we reviewed major improvements and recent advancements in genomics, transcriptomics, and proteomics studies and application of metabolomics in PD research, and discussed the major metabolomics findings for diagnostics and therapy of the disease. [ABSTRACT FROM AUTHOR]

A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(−)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities. [ABSTRACT FROM AUTHOR]

Background: In metabolomics, thousands of substances can be detected in a single assay. This capacity motivates the development of metabolomics testing, which is currently a very promising option for improving laboratory diagnostics. However, the simultaneous measurement of an enormous number of substances leads to metabolomics data often representing concentrations only in conditional units, while laboratory diagnostics generally require actual concentrations. To convert metabolomics data to actual concentrations, calibration curves need to be generated for each substance, and this process represents a significant challenge due to the number of substances that are present in the metabolomics data. To overcome this limitation, a label-free standardization algorithm for metabolomics data is required. Results: It was discovered that blood plasma has a set of stable internal standards. The appropriate usage of these newly discovered internal standards provides a background for the label-free standardization of metabolomics data that underlies the SantaOmics (Standardization algorithm for nonlinearly transformed arrays in Omics) algorithm. In this study, using the knee point, it was shown that the metabolomics data can be converted by SantaOmics into a standardized scale that can substitute actual concentration measurements, thus making the metabolomics data directly comparable with each other as well as with reference data presented in the same scale. Conclusion: The developed algorithm sufficiently facilitates the usage of metabolomics data in laboratory diagnostics. [ABSTRACT FROM AUTHOR]

Recent decades have been marked by advances in omics sciences based on high-throughput technologies, which have enabled the measurement of enormous numbers of molecules in biosamples. This study investigated the capacity of blood plasma metabolome to be used for diagnostics. Blood plasma samples ( n = 120) were methanol-treated to remove proteins. The remaining metabolite fractions were directly analyzed by mass spectrometry. Various diagnostic substances with different molecular weights, chemical classes, and plasma abundances were directly estimated from mass spectra or predicted from mass spectra by neural networking. Predicted levels of these substances were correlated ( r = 0.51–0.78) with their actual values. Blood plasma metabolomics may be considered as a diagnostic tool capable of substituting for several clinical laboratory tests. [ABSTRACT FROM AUTHOR]

The goal of this study was to evaluate the capacity for mass spectrometry of blood plasma to diagnose impaired glucose tolerance (IGT). For this study, blood plasma samples from control subjects (n = 30) and patients with IGT (n = 20) were treated with methanol and low molecular weight fraction were then analyzed by direct infusion mass spectrometry. A total of 51 metabolite ions strongly associated with IGT were detected. The area under a receiver operating characteristic (ROC) curve (AUC) for diagnosing IGT that was based on an analysis of all these metabolites was 0.93 (accuracy 90%, specificity 90%, and sensitivity 90%). The associated reproducibility was 85%. The metabolites identified were also consistent with risk factors previously associated with the development of diabetes. Thus, direct infusion mass spectrometry of blood plasma metabolites represents a rapid, single-step, and reproducible method for the analysis of metabolites. Moreover, this method has the potential to serve as a prototype for clinical analyses that could replace the currently used glucose tolerance test with a more patient-friendly assay. [ABSTRACT FROM AUTHOR]

The increase in life expectancy, leading to a rise in the proportion of older people, is accompanied by a prevalence of age-related disorders among the world population, the fight against which today is one of the leading biomedical challenges. Exploring the biological insights concerning the lifespan is one of the ways to provide a background for designing an effective treatment for the increase in healthy years of life. Untargeted direct injection mass spectrometry-based metabolite profiling of 12 species of Drosophila with significant variations in natural lifespans was conducted in this research. A cross-comparison study of metabolomic profiles revealed lifespan signatures of flies. These signatures indicate that lifespan extension is associated with the upregulation of amino acids, phospholipids, and carbohydrate metabolism. Such information provides a metabolome-level view on longevity and may provide a molecular measure of organism age in age-related studies. [ABSTRACT FROM AUTHOR]

In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine. [ABSTRACT FROM AUTHOR]

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is designed, manufactured and used in the same laboratory (i.e., an in-house test). In this study, a metabolomics-based LDT was developed. This test involves a blood plasma preparation, direct-infusion mass spectrometry analysis with a high-resolution mass spectrometer, alignment and normalization of mass peaks using original algorithms, metabolite annotation by a biochemical context-driven algorithm, detection of overrepresented metabolic pathways and results in a visualization in the form of a pathway names cloud. The LDT was applied to detect early stage Parkinson's disease (PD)—the diagnosis of which currently requires great effort due to the lack of available laboratory tests. In a case–control study (n = 56), the LDT revealed a statistically sound pattern in the PD-relevant pathways. Usage of the LDT for individuals confirmed its ability to reveal this pattern and thus diagnose PD at the early-stage (1–2.5 stages, according to Hoehn and Yahr scale). The detection of this pattern by LDT could diagnose PD with a specificity of 64%, sensitivity of 86% and an accuracy of 75%. Thus, this LDT can be used for further widespread testing. [ABSTRACT FROM AUTHOR]