An accelerated workflow for untargeted metabolomics using the METLIN database

Role of Kidney Biopsies for Biomarker Discovery in Diabetic Kidney Disease.

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually relies on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are found by combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. To obtain biochemically meaningful results requires an elaborate optimization function.We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: Both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our methods performs significantly better than the previous scoring, as well as other methods for this task. Our method can expedite the workflow for untargeted metabolomics, allowing researchers to investigate unknowns using automated computational methods.KeywordsMolecular FormulaTandem Mass SpectrumFragmentation SpectrumNoise PeakUntargeted MetabolomicsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

ABSTRACTIntroduction: Small molecules play vital roles in all biological systems, including cellular processes, intra-organ and inter-organ communications, and individual–environmental interactions. Their impacts range from the molecular to the population level. Therefore, their comprehensive characterization represents the potential to understand biology and diseases. Metabolomics is an omics technology that allows simultaneous, global, and comprehensive characterization of small molecules in a biological system.Areas covered: We present the different analytical tools and strategies used in metabolomics. Furthermore, we discuss how metabolomics is being used in disease biomarker discovery, diagnosis, and treatment customization in the precision medicine era on the individual level and the population level. Finally, challenges facing clinical translation of metabolomics are discussed. Data for this review were identified using searches of PubMed and references from relevant articles using the search terms ‘metabolomics,’ ‘metabonomics,’ ‘metabolic profiling,’ ‘metabolic phenotyping,’ and ‘precision medicine.’Expert commentary: Metabolomics, with its functional readout capabilities, is reshaping medical diagnostics, biomarker discovery, and drug discovery. However, a major limitation of the current metabolomics capabilities is the need to compromise on either the scope of the experiment or the completeness of the measurements. Furthermore, metabolite identification, standardization, and bioinformatics pipeline improvements are needed for full clinical implementation.

Dataset of why inclusion matters for Alzheimer's disease biomarker discovery in plasma

In search of more accurate, non-invasive biomarkers of bladder cancer (BC), a comprehensive aptamer-based screen of urine proteins was conducted using urine samples from a BC cohort, followed by systems biology analyses. Following an initial 1317-plex screen, an extended 11,000 plex aptamer-based proteomic screen is in progress. Based on the initial screen, 30 urine proteins were next ELISA validated in an independent cohort of 68 subjects, and the results are shown in Table 1. Of 21 urine proteins that discriminated BC from urology clinic controls (UC), urine D-dimer displayed the highest ROC AUC value (0.96) and sensitivity (95%) (Table 1). Furthermore, 9 urine proteins significantly discriminated more advanced BC (T1-T4) from earlier stages (Ta-Tis), with IL-8 and MMP-12 being the best performers. Urine MMP12 exhibited the highest specificity of 80% at fixed sensitivity for identifying advanced BC. Systems biology analysis implicated molecular functions related to extracellular matrix, collagen, integrin, heparin and transmembrane tyrosine kinase signaling in BC susceptibility, with HNF4A and NFKB1 emerging as key molecular regulators. STEM analysis of the dysregulated pathways implicated a functional role for the immune system, complement and interleukins in BC disease progression. Urine C2, D-dimer, and Elastase were additionally ELISA validated in a second BC cohort of Chinese ethnicity. Thus, comprehensive proteomics and independent ELISA validation have uncovered novel urine biomarkers for initial BC diagnosis and subsequent disease progression, that warrant further head to head comparison with current diagnostic tests. Citation Format: Chandra Mohan, Kamala Vanarsa, Jessica Castillo, Long Wang, Yair Lotan. 11,000-plex aptamer based proteomics of bladder cancer urine for the discovery of novel disease biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3762.

Given the morbidity and mortality associated with colorectal cancer, novel biomarkers are clearly warranted, especially for early detection, particularly for advanced adenomas. An earlier 1317-plex aptamer-based proteomic screen of stool proteins revealed 92 proteins to be significantly elevated in CRC stool, with the most discriminatory proteins being MMP9, haptoglobin, myeloperoxidase, fibrinogen, and adiponectin (PMID: 34117903). Results from a recent 2000-plex proteomic screen of CRC stools are detailed below, while a 11,000-plex aptamer based proteomic screen is in progress. Comprehensive proteomic screening revealed 116 proteins to be differentially expressed in CRC stool compared to control stool, with 45 being elevated 2-fold or higher. 37 of these proteins were ELISA validated in 3 independent cohorts. Stool MMP-8, MMP-9, Hemoglobin, PGRP-S, Haptoglobin, Myeloperoxidase and Fibrinogen emerged as being most discriminatory for distinguishing CRC from healthy controls (AUC: 0.91-0.95), while Stool Fibrinogen, MMP-9, Hemoglobin, MMP-8, and PGRP-S best distinguished advanced adenoma from healthy control (HC) stool, with stool Fibrinogen topping the list with an ROC AUC value of 0.86, as detailed in Table 1. Functional pathway analysis revealed a significant over-representation of pathways related to anti-oxidant activity, integrin/receptor binding, cytokines, blood coagulation, and lipoprotein biosynthesis in patients with CRC compared to HC. Nuclear Factor IC (NFIC) and IKZF2 were identified as key regulators of the molecular cascades over-represented in CRC. Stool Fibrinogen, MMP-8, MMP-9, PGRP-S, Haptoglobin, and Myeloperoxidase emerge as promising biomarkers for distinguishing CRC/advanced adenomas/healthy stools, outperforming Hemoglobin. Thus, comprehensive proteomics and independent ELISA validation have uncovered novel stool biomarkers for identifying CRC and advanced adenomas, that warrant further head to head comparison against current diagnostic tests. Citation Format: Chandra Mohan, Kamala Vanarsa, Jessica Castillo, Robert Bresalier, Nicholas Chia. 11,000-plex aptamer based proteomics of colorectal cancer stools for the discovery of novel disease biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1845.

Integrated top-down and bottom-up proteomics mass spectrometry for the characterization of endogenous ribosomal protein heterogeneity

Analytical and Preparative Instrumentation

Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation – namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker’s informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.

Urinary Proteomics Based on Capillary Electrophoresis-Coupled Mass Spectrometry in Kidney Disease: Discovery and Validation of Biomarkers, and Clinical Application

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

Gastric cancer has significant morbidity and mortality worldwide and locally. Good prognosis relies on an early diagnosis. However, this remains a challenge due to the lack of specific and sensitive serum biomarkers for early detection. Hence, there is a constant search for these biomarkers for screening purposes. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. This review presents recent attempts in search of gastric cancer serum biomarker using proteomics. Different methodologies and different types of samples were employed by different groups of researchers. Major difficulties were encountered in the discovery processes, including interference from abundant proteins and continuous changing serum proteomes from different individuals.

Proteome analysis is now emerging as key technology for deciphering biological processes and the discovery of biomarkers for diseases from tissues and body fluids. The complexity and wide dynamic range of protein expression poses a formidable challenge to both peptide separation technologies and mass spectrometry (MS). Here we review the efforts that have been undertaken to date, focussing on capillary electrophoresis coupled to mass spectrometry (CE-MS). We discuss CE-MS from an application point of view evaluating its merits and vices in regard to biomarker discovery and clinical applications. As examples, we present the use of CE-MS for the determination of protein patterns in urine, serum, and other body fluids. Finally, the benefits and limitations of CE-MS for the analysis of proteins in clinical samples are discussed against the background of alternative technologies.

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.