Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex ...biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of ...statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin‐secreting beta cells in the pancreas. This process results in life‐long dependence on exogenous insulin for ...survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre‐clinical through to clinically apparent disease.
The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to ...address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR .
Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to ...leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ–dependent genes following infection with robust respiratory viruses including influenza viruses A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04) and coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV). Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV–mediated antagonism of antigen presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.
Quantification of LC‐MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run‐to‐run of the instrument due to both technical and ...biological variation. Thus, normalization of peak intensities across an LC‐MS proteomics dataset is a fundamental step in pre‐processing. However, the downstream analysis of LC‐MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC‐MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between‐group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.
In this review, we apply selected imputation strategies to label-free liquid chromatography–mass spectrometry (LC–MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance ...and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC–MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.
Chlamydia trachomatis is the most common cause of bacterial sexually transmitted infection, responsible for millions of infections each year. Despite this high prevalence, the elucidation of the ...molecular mechanisms of Chlamydia pathogenesis has been difficult due to limitations in genetic tools and its intracellular developmental cycle. Within a host epithelial cell, chlamydiae replicate within a vacuole called the inclusion. Many Chlamydia-host interactions are thought to be mediated by the Inc family of type III secreted proteins that are anchored in the inclusion membrane, but their array of host targets are largely unknown. To investigate how the inclusion membrane proteome changes over the course of an infected cell, we have adapted the APEX2 system of proximity-dependent biotinylation. APEX2 is capable of specifically labeling proteins within a 20 nm radius in living cells. We transformed C. trachomatis to express the enzyme APEX2 fused to known inclusion membrane proteins, allowing biotinylation and purification of inclusion-associated proteins. Using quantitative mass spectrometry against APEX2 labeled samples, we identified over 400 proteins associated with the inclusion membrane at early, middle, and late stages of epithelial cell infection. This system was sensitive enough to detect inclusion interacting proteins early in the developmental cycle, at 8 hours post infection, a previously intractable time point. Mass spectrometry analysis revealed a novel, early association between C. trachomatis inclusions and endoplasmic reticulum exit sites (ERES), functional regions of the ER where COPII-coated vesicles originate. Pharmacological and genetic disruption of ERES function severely restricted early chlamydial growth and the development of infectious progeny. APEX2 is therefore a powerful in situ approach for identifying critical protein interactions on the membranes of pathogen-containing vacuoles. Furthermore, the data derived from proteomic mapping of Chlamydia inclusions has illuminated an important functional role for ERES in promoting chlamydial developmental growth.
The early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. Here we show that exposure to ...proinflammatory cytokines reveals a marked plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and three-dimensional chromatin structure. Our data indicate that the β-cell response to cytokines is mediated by the induction of new regulatory regions as well as the activation of primed regulatory elements prebound by islet-specific transcription factors. We find that T1D-associated loci are enriched with newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human β cells. Our study illustrates how β cells respond to a proinflammatory environment and implicate a role for stimulus response islet enhancers in T1D.
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