Application Of Proteomics Research Articles

Flow Cytometric Analysis and Sorting of Plant Chromosomes.

Flow cytometry offers a unique way of analyzing and manipulating plant chromosomes. During a rapid movement in a liquid stream, large populations can be classified in a short time according to their fluorescence and light scatter properties. Chromosomes whose optical properties differ from other chromosomes in a karyotype can be purified by flow sorting and used in a range of applications in cytogenetics, molecular biology, genomics, and proteomics. As the samples for flow cytometry must be liquid suspensions of single particles, intact chromosomes must be released from mitotic cells. This protocol describes a procedure for preparation of suspensions of mitotic metaphase chromosomes from meristem root tips and their flow cytometric analysis and sorting for various downstream applications.

Application of functional proteomics in understanding RNA virus-mediated infection.

Together with the expansion of genome sequencing research, the number of protein sequences whose function is yet unknown is increasing dramatically. The primary goals of functional proteomics, a developing area of study in the realm of proteomic science, are the elucidation of the biological function of unidentified proteins and the molecular description of cellular systems at the molecular level. RNA viruses have emerged as the cause of several human infectious diseases with large morbidity and fatality rates. The introduction of high-throughput sequencing tools and genetic-based screening approaches over the last few decades has enabled researchers to find previously unknown and perplexing elements of RNA virus replication and pathogenesis on a scale never feasible before. Viruses, on the other hand, frequently disrupt cellular proteostasis, macromolecular complex architecture or stoichiometry, and post-translational changes to take over essential host activities. Because of these consequences, structural and global protein and proteoform monitoring is highly necessiated. Mass spectrometry (MS) has the potential to elucidate key details of virus-host interactions and speed up the identification of antiviral targets, giving precise data on the stoichiometry of cellular and viral protein complexes as well as mechanistic insights, has lately emerged as a key part of the RNA virus biology toolbox as a functional proteomics approach. Affinity-based techniques are primarily employed to identify interacting proteins in stable complexes in living organisms. A protein's biological role is strongly suggested by its relationship with other members of a certain protein complex that is involved in a particular process. With a particular emphasis on the most recent advancements in defining host responses and their translational implications to uncover novel tractable antiviral targets, this chapter provides insight on several functional proteomics techniques in RNA virus biology.

Recent Developments in High Spatial-Temporal Image-Based Tracking of Proteins in Subcellular Spatial Proteomics Applications

Recent Developments in High Spatial-Temporal Image-Based Tracking of Proteins in Subcellular Spatial Proteomics Applications

Application of Proteomics in Diagnosis of Kratom Addiction

Kratom (Mitragyna speciosa) is a medicinal plant grown wildly in Southeast Asian. It has been used traditionally for its analgesic and relaxing effects after hard day of labors. The main alkaloid of kratom, mitragynine binds to opioid receptors to give opioid-like effects, therefore it has been widely utilized as opiate substitute.

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research.

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative "omics" approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis.

Proximity-dependent biotin labeling in testicular germ cells identified TESMIN-associated proteins

Characterization of protein–protein interactions (PPI) is a key to understanding the functions of proteins of interest. Recently developed proximity-dependent biotin identification (BioID) has been actively investigated as an alternative PPI mapping method because of its usefulness in uncovering transient PPI. Here, as an example of proximity labeling proteomics application in the testis, we generated two transgenic mouse lines expressing two biotin ligases (BioID2 or TurboID) fused with TESMIN, which translocates from the cytosol to the nucleus during meiotic progression and is required for reproduction. The BioID2 transgene, albeit not the TurboID transgene, rescued fertility defects of the Tesmin KO male mice, indicating that the TESMIN-BioID2 fusion can physiologically replace TESMIN. Furthermore, biotinylated protein pull-down and affinity-purification followed by mass spectrometry using the TESMIN-BioID2 transgenic mice captured components of the MYBL1–MuvB complex that regulate cell-cycle gene expression. Thus, our study shows that proximity labeling proteomics can be applied in male germ cells, although the choice of biotin ligase needs to be carefully tested.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification.

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Labeling of Phospho-Specific Antibodies with oYo-Link® Epitope Tags for Multiplex Immunostaining.

Spatial proteomics has recently garnered significant interest, as it offers to provide unprecedented insight into biological processes in both health and disease, by connecting protein expression patterns from the subcellular level to the tissue or even organism level. These high-content approaches generally rely on a high degree of multiplexing, whereby multiple proteins can be detected simultaneously. The most versatile multiplexing approaches utilize antibodies to confer specificity for various intracellular proteins of interest. Therefore, these methods must be able to differentiate many antibodies at once. In this chapter, we describe a simple and rapid approach to labeling antibodies with distinct epitope tags in a site-specific manner. This allows multiple antibodies, even from the same host species, to be uniquely identified and detected and offers a simple approach for spatial proteomic applications.

A Scalable CMOS Molecular Electronics Chip for Single-Molecule Biosensing.

This work reports the first CMOS molecular electronics chip. It is configured as a biosensor, where the primary sensing element is a single molecule "molecular wire" consisting of a ∼100 GΩ, 25 nm long alpha-helical peptide integrated into a current monitoring circuit. The engineered peptide contains a central conjugation site for attachment of various probe molecules, such as DNA, proteins, enzymes, or antibodies, which program the biosensor to detect interactions with a specific target molecule. The current through the molecular wire under a dc applied voltage is monitored with millisecond temporal resolution. The detected signals are millisecond-scale, picoampere current pulses generated by each transient probe-target molecular interaction. Implemented in a 0.18 μm CMOS technology, 16k sensors are arrayed with a 20 μm pitch and read out at a 1 kHz frame rate. The resulting biosensor chip provides direct, real-time observation of the single-molecule interaction kinetics, unlike classical biosensors that measure ensemble averages of such events. This molecular electronics chip provides a platform for putting molecular biosensing "on-chip" to bring the power of semiconductor chips to diverse applications in biological research, diagnostics, sequencing, proteomics, drug discovery, and environmental monitoring.

Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRASG12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRASG12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

The utility of proteases in proteomics, from sequence profiling to structure and function analysis.

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

Electrostatic Filtering of Polypeptides Through Membrane Protein Pores.

Naturally-occurring membrane proteins have been engineered as nanopore sensors for the single-molecule detection of various biochemical molecules. Here, we present a natural bacterial porin, CymA containing a dynamic component and densely packed charged residues in the pore, shaping a unique structural conformation and charge feature. Using single-channel recordings, we investigated the translocation of charged polypeptides through native CymA and truncated CymA lacking the dynamic element. Cationic polypeptides bind to the pore with high affinity, specifically at low salt conditions indicating an electrostatic charge and voltage-dependent translocation. Anionic peptides did not bind to the pore, confirming the selective binding of polypeptides with the pore due to their specific charge distribution. Further, the distinct peptide translocation kinetics between native and truncated indicated the role of the dynamic segment in molecular transport. We suggest that these natural membrane pores that permit the selective translocation of cationic polypeptides are advantageous for nanopore proteomics applications.

Emerging insights and challenges for understanding T cell function through the proteome.

T cells rapidly transition from a quiescent state into active proliferation and effector function upon exposure to cognate antigen. These processes are tightly controlled by signal transduction pathways that influence changes in chromatin remodeling, gene transcription, and metabolism, all of which collectively drive specific T cell memory or effector cell development. Dysregulation of any of these events can mediate disease and the past several years has shown unprecedented novel approaches to understand these events, down to the single-cell level. The massive explosion of sequencing approaches to assess the genome and transcriptome at the single cell level has transformed our understanding of T cell activation, developmental potential, and effector function under normal and various disease states. Despite these advances, there remains a significant dearth of information regarding how these events are translated to the protein level. For example, resolution of protein isoforms and/or specific post-translational modifications mediating T cell function remains obscure. The application of proteomics can change that, enabling significant insights into molecular mechanisms that regulate T cell function. However, unlike genomic approaches that have enabled exquisite visualization of T cell dynamics at the mRNA and chromatin level, proteomic approaches, including those at the single-cell level, has significantly lagged. In this review, we describe recent studies that have enabled a better understanding of how protein synthesis and degradation change during T cell activation and acquisition of effector function. We also highlight technical advances and how these could be applied to T cell biology. Finally, we discuss future needs to expand upon our current knowledge of T cell proteomes and disease.

Elucidating Cellular Metabolism and Protein Difference Data from DIGE Proteomics Experiments Using Enzyme Assays.

Assays for measuring enzyme activity can be useful tools for proteomics applications. Enzyme testing can be performed to validate an experimental system prior to a difference gel electrophoresis (DIGE) proteomic experiment and can also be utilized as an integral part of multifaceted experiment in conjunction with DIGE. Data from enzyme tests can be used to corroborate results of DIGE proteomic experiments where an enzyme or enzymes are demonstrated by DIGE to be differentially expressed. Enzyme testing can also be utilized to support data from DIGE experiments that demonstrate metabolic changes in a biological system. The different types of enzyme assays that can be performed in conjunction with DIGE experiments are reviewed alongside a discussion of experimental approaches for designing enzyme assays.

Application of Machine Learning in Spatial Proteomics.

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Application of Proteomics to the Study of the Therapeutics and Pathogenicity of Giardia duodenalis.

Giardia duodenalis remains a neglected tropical disease. A key feature of the sustained transmission of Giardia is the ability to form environmentally resistant cysts. For the last 38 years, proteomics has been utilised to study various aspects of the parasite across different life cycle stages. Thirty-one articles have been published in PubMed from 2012 to 2022 related to the proteomics of G. duodenalis. Currently, mass spectrometry with LC-MS/MS and MALDI-TOF/TOF has been commonly utilised in proteomic analyses of Giardia, which enables researchers to determine potential candidates for diagnostic biomarkers as well as vaccine and drug targets, in addition to allowing them to investigate the virulence of giardiasis, the pathogenicity mechanisms of G. duodenalis, and the post-translational modifications of Giardia proteins throughout encystation. Over the last decade, valuable information from proteomics analyses of G. duodenalis has been discovered in terms of the pathogenesis and virulence of Giardia, which may provide guidance for the development of better means with which to prevent and reduce the impacts of giardiasis. Nonetheless, there is room for improving proteomics analyses of G. duodenalis, since genomic sequences for additional assemblages of Giardia have uncovered previously unknown proteins associated with the Giardia proteome. Therefore, this paper aims to review the applications of proteomics for the characterisation of G. duodenalis pathogenicity and the discovery of novel vaccine as well as drug targets, in addition to proposing some general directions for future Giardia proteomic research.

The application of high-throughput proteomics in cytopathology.

High-throughput genomics and transcriptomics are often applied in routine pathology practice to facilitate cancer diagnosis, assess prognosis, and predict response to therapy. However, the proteins rather than nucleic acids are the functional molecules defining the cellular phenotype in health and disease, whereas genomic profiling cannot evaluate processes such as the RNA splicing or posttranslational modifications and gene expression does not necessarily correlate with protein expression. Proteomic applications have recently advanced, overcoming the issue of low depth, inconsistency, and suboptimal accuracy, also enabling the use of minimal patient-derived specimens. This review aims to present the recent evidence regarding the use of high-throughput proteomics in both exfoliative and fine-needle aspiration cytology. Most studies used mass spectrometry, as this is associated with high depth, sensitivity, and specificity, and aimed to complement the traditional cytomorphologic diagnosis, in addition to identify novel cancer biomarkers. Examples of diagnostic dilemmas subjected to proteomic analysis included the evaluation of indeterminate thyroid nodules or prediction of lymph node metastasis from thyroid cancer, also the differentiation between benign and malignant serous effusions, pancreatic cancer from autoimmune pancreatitis, non-neoplastic from malignant biliary strictures, and benign from malignant salivary gland tumors. A few cancer biomarkers-related to diverse cancers involving the breast, thyroid, bladder, lung, serous cavities, salivary glands, and bone marrow-were also discovered. Notably, residual liquid-based cytology samples were suitable for satisfactory and reproducible proteomic analysis. Proteomics could become another routine pathology platform in the near future, potentially by using validated multi-omics protocols.

Proteomics and Genomics as Identification Procedures in Human Anthropology

Context: Since most scientists tend to investigate live biological samples, there is inadequate data on efficient molecular techniques for the anthropological sciences. In this short review, multiple methods were mentioned and compared to provide a brief insight into the application of genomics and proteomics on the post-mortal specimen. Evidence Acquisition: Through the use of proper keywords, the PubMed and Elsevier databases were selected for acquiring relevant articles. Results: During cell death, DNA and proteins degenerate, hence, it is difficult to perform molecular assessments efficiently. Fortunately, with the aid of novel techniques, including uracil-N-glycosylase (UNG) and N-phenacylthiazolium bromide (PTB), we could partly recover the damaged DNA, and by applying PTB-DTT and Qiagen kit, we could analyze the remaining DNA with high efficiency. Nevertheless, there are countless gene sites for molecular studies, and the hypervariable region I (HVRI) of the D-loop in mitochondrial DNA (mtDNA) and Y chromosome microsatellites (Y-STRs) are two potential sites for anthropological studies. Finally, we could utilize proteomics in the remaining mineralization samples of a corpse to study protein variation and different phenotypes in human beings. Conclusions: Genomics and proteomics are two domains of molecular studies in which we can gather useful information about the events which occur in a cell over time. These domains give us data about the Archeological and Anthropological sciences.

High-Field Asymmetric Waveform Ion Mobility Spectrometry Interface Enhances Parallel Reaction Monitoring on an Orbitrap Mass Spectrometer.

High-field asymmetric waveform ion mobility spectrometry (FAIMS) enables gas-phase separations on a chromatographic time scale and has become a useful tool for proteomic applications. Despite its emerging utility, however, the molecular determinants underlying peptide separation by FAIMS have not been systematically investigated. Here, we characterize peptide transmission in a FAIMS device across a broad range of compensation voltages (CVs) and used machine learning to identify charge state and three-dimensional (3D) electrostatic peptide potential as major contributors to peptide intensity at a given CV. We also demonstrate that the machine learning model can be used to predict optimized CV values for peptides, which significantly improves parallel reaction monitoring workflows. Together, these data provide insight into peptide separation by FAIMS and highlight its utility in targeted proteomic applications.

Pitfalls in Proteomics: Avoiding Problems That Can Occur Before Data Acquisition Begins

The analysis of peptides using liquid chromatography–mass spectrometry (LC–MS) for proteomics applications is powerful and can yield tremendously rich data sets. However, the exquisite sensitivity and global applicability of MS detection also makes it prone to contaminants that can easily and seriously compromise the quality of a data set. A short list of tips and tricks can increase the likelihood of consistently producing good LC–MS data in this context and streamline the troubleshooting process when problems do eventually occur.