Mcclellanchase3873

Recent advances in MS/MS technology have made it possible to use proteomic data to predict protein-coding sequences. This approach is called proteogenomics, and it allows to correctly translate start and stop sites and to reveal new open reading frames. Here, we focus on using proteogenomics to improve the annotation of Mycobacteriumtuberculosis strains. We also describe detail procedures of the extraction of proteins and their further preparation for LC-MS/MS analysis and outline the main steps of data analysis.Mass spectrometry-based single-cell proteomic analysis has recently gained momentum and is now an emerging area with established protocols and promising results. Traditional proteomic studies, especially involving bacteria, have been limited to suspension cultures with large protein yields. Such studies, however, remain population centered with the uniqueness of individual responses to environmental challenges becoming diluted. To enable bacterial single-colony proteomics, we describe a quantitative mass spectrometry-based protocol to isolate and analyze the proteome of a single mycobacterial colony from 7H10 media, with growth supplements for optimal growth. Following protein purification and digestion, tryptic peptides are analyzed by UHPLC coupled to a hybrid Q Exactive mass spectrometer. Raw data were analyzed using the MaxQuant Suite, and downstream statistical analysis was performed using Perseus software. A total of 7805 unique peptides and 1387 proteins were identified. Data are available via ProteomeXchange with identifier PXD018168. In this chapter, we identify steps most prone to sample loss and describe measures of alleviation that allows the preservation of protein yield and boosts quantitative power while increasing reproducibility, of "very limiting samples."Metaproteomics of host-microbiome interfaces comprises the analysis of complex mixtures of bacteria, archaea, fungi, and viruses in combination with its host cells. Microbial niches can be found all over the host including the skin, oral cavity, and the intestine and are considered to be essential for the homeostasis. The complex interactions between the host and diverse commensal microbiota are poorly characterized while of great interest as dysbiosis is associated with the development of various inflammatory and metabolic diseases. selleck inhibitor The metaproteomics workflows to study these interfaces are currently being established, and many challenges remain. The major challenge is the large diversity in species composition that make up the microbiota, which results in complex samples that require extended mass spectrometry analysis time. In addition, current database search strategies are not developed to the size of the search space required for unbiased microbial protein identification.Here, we describe a workflow for the proteomics analysis of microbial niches with a focus on intestinal mucus layer. We will cover step-by-step the sample collection, sample preparation, liquid chromatography-mass spectrometry, and data analysis.Proteomic tools are especially useful when it comes to investigating complex samples such as human blood plasma, in which protein quantities can span across up to ten orders of magnitude. Ultra definition mass spectrometry, in combination with two-dimensional liquid chromatography, provides better coverage of complex proteomes and allows for better control of collision energy, keeping the fragmentation benefits of high collision energy associated with drift time measurements from ion mobility separation. Here, we present a protocol to assist in the identification of proteins in human blood plasma and other similar samples with a large dynamic range.The versatility of protein microarrays provides researchers with a wide variety of possibilities to address proteomic studies. Therefore, protein microarrays are becoming very useful tools to identify candidate biomarkers in human body fluids for disease states such as rheumatoid arthritis (RA). In RA serum, there is a high prevalence of rheumatoid factor (RF), which is an antibody with high specificity against Fc portion of IgG. The presence of RF, in particular RF-IgM, has the great potential to interfere with antibody-based immunoassays by nonspecifically binding capture antibodies. Because of this concern, we describe a procedure to reduce the interference of RF-IgM on RA serum protein profiling approaches based on multiplexed antibody suspension bead arrays.Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).During the last decade, we have witnessed outstanding advances in proteomics led mostly by great technological improvements in mass spectrometry field allowing high-throughput production of high-quality data used for massive protein identification and quantification. From a practical viewpoint, these advances have been mainly exploited in research projects involving model organisms with abundant genomic and proteomic information available in public databases. However, there is a growing number of organisms of high interest in different disciplines, such as ecological, biotechnological, and evolutionary research, yet poorly represented in these databases. Important advances in massive parallel sequencing technology and easy accessibility of this technology to many research laboratories have made nowadays possible to produce customized genomic and proteomic databases of any organism. Along this line, the use of proteogenomic approaches by combining in the same analysis the data obtained from different omic levels has emerged as a very useful and powerful strategy to run shotgun proteomic experiments specially focused on non-model organisms. In this chapter, we provide detailed procedures to undertake shotgun quantitative proteomic experiments following either a label-free or an isobaric labeling approach in non-model organisms, emphasizing also a few key aspects related to experimental design and data analysis.Anisakis simplex s.s. is a parasitic nematode that causes anisakiasis in humans. L3 stage larvae, which are present in many fish species and cephalopods all over the globe, might be consumed and develop occasionally into the L4 stage but cannot reproduce. Anisakiasis is an emerging health problem and economic concern. In recent years, proteomic methods have gained greater acceptance among scientists involved in parasitology and food science. According to that, here, we present tandem mass tag (TMT)-based shotgun proteomics to define differences in proteomic composition between L3 and L4 development stages of A. simplex s.s.Proteomics is one of the key approaches to understand plant cell physiology involving the regulation of expression of many genes and metabolite production. Technical advances allowed a deeper characterization of plant proteomes, highlighting the need to study cellular compartments. The apoplast is the cellular compartment external to the plasma membrane including the cell wall, where a broad range of processes take place including intercellular signaling, metabolite transport, and plant-microbe interactions. Due to the fragile nature of leaf tissues, it is a challenge to obtain apoplastic fluids from leaves while maintaining cell integrity, which is particularly true for woody plants. Here, we describe the vacuum infiltration-centrifugation (VIC) method for the extraction of the apoplastic fluid compatible with high-throughput proteomic approaches and biochemical analysis from different woody plants.Laser capture microdissection (LCM) provides a fast, specific, and versatile method to isolate and enrich cells in mixed populations and/or subcellular structures, for further proteomic study. Furthermore, mass spectrometry (MS) can quickly and accurately generate differential protein expression profiles from small amounts of samples. Although cellular protrusions-such as tunneling nanotubes, filopodia, growth cones, invadopodia, etc.-are involved in essential physiological and pathological actions such as phagocytosis or cancer-cell invasion, the study of their protein composition is progressing slowly due to their fragility and transient nature. The method described herein, combining LCM and MS, has been designed to identify the proteome of different cellular protrusions. First, cells are fixed with a novel fixative method to preserve the cellular protrusions, which are isolated by LCM. Next, the extraction of proteins from the enriched sample is optimized to de-crosslink the fixative agent to improve the identification of proteins by MS. The efficient protein recovery and high sample quality of this method enable the protein profiling of these small and diverse subcellular structures.In recent years, technical improvements in proteomics have allowed its rapid application for biomarker discovery, new drug target identification, and the study of disease progression and drug resistance. The clinical potential of circulating extracellular vesicles (EVs) as a source of biomarkers is one of the reasons why several research groups have recently applied proteomics to their study. A large variety of proteomic approaches such as gel-based proteomics and bottom-up and top-down mass spectrometry have been applied to the study of EVs. In this chapter, we will present basic protocols for gel-based and quantitative MS-based approaches applied to the study of EVs.In the present protocol, extracellular vesicles (EVs) released from a primary culture of human umbilical cord mesenchymal stem cells (MSCs) were isolated by ultracentrifugation processes, characterized by transmission electron microscopy (TEM) and measured by nanoparticle tracking analysis (NTA). Protein was extracted from EVs using RIPA buffer and then was assessed for integrity. The proteomic content of the total EV protein samples was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) after labeling by tandem mass tag (TMT). This combined approach allowed the development of an effective strategy to study the protein cargo from MSC-derived EVs.

Commercial myeloid next-generation sequencing (NGS) panels may facilitate uniform generation of raw data between laboratories. However, different strategies for data filtering and variant annotation may contribute to differences in variant detection and reporting. Here, we present how custom data filtering or the use of Oncomine extended data filtering improve detection of clinically relevant mutations with the Oncomine Myeloid Research Assay.

The study included all patient samples (n = 264) analyzed during the first-year, single-site, clinical use of the Ion Torrent Oncomine Myeloid Research Assay. In data analysis, the default analysis filter was supplemented with our own data filtering algorithm in order to detect additional clinically relevant mutations. In addition, we developed a sensitive supplementary test for the ASXL1 c.1934dupG p.Gly646fs mutation by fragment analysis.

Using our custom filter chain, we found 96 different reportable variants that were not detected by the default filter chain. Twenty-six of these were classified as variants of strong or potential clinical significance (tier I/tier II variants), and the custom filtering discovered otherwise undetected tier I/tier II variants in 25 of 132 patients with clinically relevant mutations (19%).