Outer membrane vesicles (OMVs) are nanosized spheres secreted by bacteria that are similar to the vesicles known as exosomes, which are secreted by most mammalian cell types. In contrast to many studies focusing on optimizing methods for enriching exosomes from biological fluid, few studies have been conducted to investigate outer membrane vesicles from fecal samples. Herein, we have developed a pipeline comprised of membrane filtration and multiple cycles of ultracentrifugation (UC) to isolate OMVs from fecal samples for proteomics analysis, where multiple cycles of UC are required for removal of contaminants. By iTRAQ labeling quantitative proteomics analysis, different filter sizes (0.22 μm and 0.45 μm) were compared in terms of their performance in enriching OMVs and eliminating background fecal material. Using the 0.45 μm filter, a slightly higher protein yield was obtained but no additional contaminating proteins from bacteria were identified compared to those from the 0.22 μm filter. The 0.45 μm filter together with the multiple cycles of UC were thus used to isolate OMVs for proteomics analysis. To our knowledge, this is the first study profiling a large number of OMV proteins from fecal samples. Such capabilities may help provide valuable information in understanding the communication between the host and microbiota, which is critical in preventing cancer and disease development.
Herein we introduce the Visual Mass-Spec Share (vMS-Share), a new public mass spectrometric (MS) repository and data mining website/resource freely accessible at https://vmsshare.nist.gov. vMS-Share is a web-based application developed for instant visualization of raw MS data with integrated display of metadata optimized for the sharing of proteomics and metabolomics experimental results. Each MS-based identification is linked to a given experiment and the entire experimental data can then be viewed using the link associated with a given peptide and/or small molecule. Interactive and user-friendly visualizations are provided to the user via variety of easily accessible search filters.
Background: Investigations in human disease pathogenesis have been hampered due to paucity of access to fresh-frozen tissues (FFT) for use in global, data-driven methodologies. As an alternative, formalin-fixed, paraffin-embedded (FFPE) tissues are readily available in pathology banks. However, the use of formalin for fixation can lead to the loss of proteins that appear during inflammation, thus introducing an inherent sample bias. To address this, we compared FF and FFPE tissue proteomics to determine whether FFPE-tissue can be used effectively in inflammatory diseases.
Methods: Adjacent kidney slices from lupus nephritic mice were processed as FFPE or FFTs. Their tissue lysates were run together using proteomics workflow involving filter-aided sample preparation, in-solution dimethyl isotope labeling, StageTip fractionation, and nano-LC MS/MS through an Orbitrap XL MS.
Results: We report a >97% concordance in protein identification between adjacent FFPE and FFTs in murine lupus nephritic kidneys. Specifically, proteins representing pathways, namely, 'systemic lupus erythematosus', 'interferon-α', 'TGF-β', and 'extracellular matrix', were reproducibly quantified between FFPE and FFTs. However, 12%-29% proteins were quantified differently in FFPE compared to FFTs, but the differences were consistent across experiments. In particular, certain proteins represented in pathways, including 'inflammatory response' and 'innate immune system' were quantified less in FFPE than in FFTs. In a pilot study of human FFPE tissues, we identified proteins relevant to pathogenesis in lupus nephritic kidney biopsies compared to control kidneys.
Conclusion: This is the first report of lupus nephritis kidney proteomics using FFPE tissue. We concluded that archived FFPE tissues can be reliably used for proteomic analyses in inflammatory diseases, with a caveat that certain proteins related to immunity and inflammation may be quantified less in FFPE than in FFTs.
Introduction: The 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.
Methods: We 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 implementation: The 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/.