Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Liquid chromatography mass spectrometry-based proteomics of Escherichia coli single colony.

ABSTRACT: The Escherichia coli proteome is the most extensively characterized and studied of all prokaryotic proteomes. Despite this, large scale bacterial proteomics experiments performed on E. coli cells grown in liquid cultures have failed to identify key virulence factors thought to be important determinants in establishing bacterial infections. It seems likely that many important determinants associated with virulence and host cell adhesion are exclusively expressed during growth in biofilms, which can be crudely mimicked on solid media. This method describes a simple workflow to characterize the unique proteome signature of individual, isolated single colonies, using E. coli K12 strain grown on solid media as a model system. The workflow thus provides a means to explore the proteomes of minimally passaged clinical isolates of bacteria grown on primary culture plates and to identify both unique and differentially expressed proteins contained therein. Value of the method: - Simple mass spectrometry-based proteomics workflow to characterise the proteome of single colony forming units - Enables exploration of the proteomes of minimally passaged clinical isolates from primary culture plates - Identification of virulence factors expressed in true or mimicked biofilms that may be missed in liquid cultures Method name: E. coli single colony proteome analysis.

SUBMITTER: Fortuin S

PROVIDER: S-EPMC8374269 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses.

Project description:A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quantitative assessment of system performance and evaluation of technical variability. Here we describe 46 system performance metrics for monitoring chromatographic performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlaboratory studies conducted under a common standard operating procedure identified outlier data and provided clues to specific causes. Moreover, interlaboratory variation reflected by the metrics indicates which system components vary the most between laboratories. Application of these metrics enables rational, quantitative quality assessment for proteomics and other LC-MS/MS analytical applications.

| S-EPMC2830836 | biostudies-other

Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical Considerations.

Project description:IntroductionThe quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS.MethodsWe studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability.Results and conclusionWe found that the FFPE TSM is better than the FR TSM. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.

| S-EPMC7059694 | biostudies-literature

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell Proteomics.

Project description:Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. We have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ∼77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

| S-EPMC7793572 | biostudies-literature

Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Proteomics of Drug-Metabolizing Enzymes and Transporters.

Project description:Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics is a powerful tool for identifying and quantifying proteins in biological samples, outperforming conventional antibody-based methods in many aspects. LC-MS/MS-based proteomics studies have revealed the protein abundances of many drug-metabolizing enzymes and transporters (DMETs) in tissues relevant to drug metabolism and disposition. Previous studies have consistently demonstrated marked interindividual variability in DMET protein expression, suggesting that varied DMET function is an important contributing factor for interindividual variability in pharmacokinetics (PK) and pharmacodynamics (PD) of medications. Moreover, differential DMET expression profiles were observed across different species and in vitro models. Therefore, caution must be exercised when extrapolating animal and in vitro DMET proteomics findings to humans. In recent years, DMET proteomics has been increasingly utilized for the development of physiologically based pharmacokinetic models, and DMET proteins have also been proposed as biomarkers for prediction of the PK and PD of the corresponding substrate drugs. In sum, despite the existence of many challenges in the analytical technology and data analysis methods of LC-MS/MS-based proteomics, DMET proteomics holds great potential to advance our understanding of PK behavior at the individual level and to optimize treatment regimens via the DMET protein biomarker-guided precision pharmacotherapy.

| S-EPMC7321193 | biostudies-literature

Proteomics of human glomerulonephritis by laser microdissection and liquid chromatography-tandem mass spectrometry.

Project description:AIM:Laser microdissection (LMD) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) enable clinicians to analyse proteins from tissue sections. In nephrology, these methods are used to diagnose diseases of abnormal protein deposition, such as amyloidosis, but they are seldom applied to the diagnosis and pathophysiological understanding of human glomerular diseases. METHODS:Renal biopsy specimens were obtained from five patients with IgA nephropathy (IgAN), five patients with membranous nephropathy (MN) and five kidney transplant donors (as controls). From 10-?m-thick sections of formalin-fixed, paraffin-embedded specimens, 0.3-mm2 samples of glomerular tissue were subjected to LMD. The samples were analysed by LC-MS/MS and investigated clinically and histologically. RESULTS:From the control glomeruli, we identified more than 300 types of proteins. In patients with IgAN, we detected significant increases not only in IgA1 and in C3, but also in the factors related to oxidative stress and cell proliferation in comparison to the controls. In patients with MN, levels of IgG1, IgG4, C3, C4a and phospholipase-A2-receptor were significantly elevated in comparison to the controls, as were the aforementioned factors related to oxidative stress and cell proliferations detected in IgAN. CONCLUSION:Application of LMD and LC-MS/MS to renal biopsy specimens enabled us to identify not only pathognomonic proteins for the diagnosis, but also several factors possibly involved in the pathogenesis of human glomerular diseases.

| S-EPMC7064884 | biostudies-literature

Interactive Web Tool for Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry Data.

Project description:IntroductionThe proteomics experiments involve several steps and there are many choices available for each step in the workflow. Therefore, standardization of proteomics workflow is an essential task for design of proteomics experiments. However, there are challenges associated with the quantitative measurements based on liquid chromatography-mass spectrometry such as heterogeneity due to technical variability and missing values.MethodsWe introduce a web application, Proteomics Workflow Standardization Tool (PWST) to standardize the proteomics workflow. The tool will be helpful in deciding the most suitable choice for each step of the experimentation. This is based on identifying steps/choices with least variability such as comparing Coefficient of Variation (CV). We demonstrate the tool on data with categorical and continuous variables. We have used the special cases of general linear model, analysis of covariance and analysis of variance with fixed effects to study the effects due to various sources of variability. We have provided various options that will aid in finding the contribution of sum of squares for each variable and the CV. The user can analyze the data variability at protein and peptide level even in the presence of missing values.Availability and implementationThe source code for "PWST" is written in R and implemented as shiny web application that can be accessed freely from https://ulbbf.shinyapps.io/pwst/.

| S-EPMC7059686 | biostudies-literature

Data preprocessing method for liquid chromatography-mass spectrometry based metabolomics.

Project description:A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC-MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS(2), for peak picking, peak list alignment, and quantification.

| S-EPMC3471364 | biostudies-literature

Correlating Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry for Tissue-Based Pharmacokinetic Studies.

Project description:Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistribution within the tissue is impossible. In the study presented here we used a sample embedding protocol optimized for mass spectrometry imaging (MSI) to prepare up to 15 rat intestine specimens at once. Desorption electrospray ionization (DESI) and matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) were employed to determine the distributions and relative abundances of four benchmarking compounds in the intestinal segments. High resolution MALDI-MSI experiments performed at 10 µm spatial resolution allowed to determine the drug distribution in the different intestinal histological compartments to determine the absorbed and tissue bound fractions of the drugs. The low tissue bound drug fractions, which were determined to account for 56-66% of the total drug, highlight the importance to understand the spatial distribution of drugs within the histological compartments of a given tissue to rationalize concentration differences found in PK studies. The mean drug abundances of four benchmark compounds determined by MSI were correlated with the absolute drug concentrations. Linear regression resulted in coefficients of determination (R2) ranging from 0.532 to 0.926 for MALDI-MSI and R2 values ranging from 0.585 to 0.945 for DESI-MSI, validating a quantitative relation of the imaging data. The good correlation of the absolute tissue concentrations of the benchmark compounds and the MSI data provides a bases for relative quantification of compounds within and between tissues, without normalization to an isotopically labelled standard, provided that the compared tissues have inherently similar ion suppression effects.

| S-EPMC8954739 | biostudies-literature

Urine proteomics for profiling of mouse toxoplasmosis using liquid chromatography tandem mass spectrometry analysis.

Project description:BackgroundToxoplasma gondii is an obligate intracellular parasite that causes toxoplasmosis. Urine is an easily obtained clinical sample that has been widely applied for diagnostic purposes. However, changes in the urinary proteome during T. gondii infection have never been investigated.MethodsTwenty four-hour urine samples were obtained from BALB/c mice with acute infection [11 days post infection (DPI)], mice with chronic infection (35 DPI) and healthy controls, and were analyzed using a label-free liquid chromatography tandem mass spectrometry analysis.ResultsWe identified a total of 13,414 peptides on 1802 proteins, of which 169 and 47 proteins were significantly differentially expressed at acute and chronic infection phases, respectively. Clustering analysis revealed obvious differences in proteome profiles among all groups. Gene ontology analysis showed that a large number of differentially expressed proteins (DEPs) detected in acute infection were associated with biological binding activity and single-organism processes. KEGG pathway enrichment analysis showed that the majority of these DEPs were involved in disease-related and metabolic pathways.ConclusionsOur findings revealed global reprogramming of the urine proteome following T. gondii infection, and data obtained in this study will enhance our understanding of the host responses to T. gondii infection and lead to the identification of new diagnostic biomarkers.

| S-EPMC8056516 | biostudies-literature

Comparative Proteomics Analysis of Pig Muscle Exudate through Label-Free Liquid Chromatography-Mass Spectrometry.

Project description:Capital-driven animal husbandry systems undertaken in the last century led to the abandoning of many pig breeds that were not profitable. These local pig breeds and their respective production systems have great potential as they are able to respond to the high criteria and needs of modern society concerning some environmental aspects, animal-welfare, healthiness, etc. This is the case of the black pigs of Italy. The Apulo-Calabrese is a breed of black pig, known by many other names such as Nero d'Abruzzo. In order to further understand the biological differences between different types of porcine genetics (Nero d'Abruzzo and commercial-hybrid) we used a label-free LC-MS strategy and Western-blot to characterize the proteomes of muscle-exudate collected from these pigs. This proteomics approach identified 1669 proteins of which 100 changed significantly in abundance between breeds. Bioinformatics functional analysis indicated that differentially expressed proteins were involved in several biological processes related to energy-metabolism and response to oxidative stress, suggesting that these functions might distinguish between these pigs. Fatty-acid synthase, catalase and glutathione-peroxidase, involved in enzymatic activity were found to be more represented in samples obtained from the Nero d'Abruzzo. This biological information can potentially provide new biological factors that could determine the different production performances of these pigs, distinguished by their different genetic backgrounds.

| S-EPMC10177093 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data