SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools

<div><p>SWATH-MS is an acquisition and analysis technique of targeted proteomics that enables measuring several thousand proteins with high reproducibility and accuracy across many samples. OpenSWATH is popular open-source software for peptide identification and quantification from SWATH-MS data. For downstream statistical and quantitative analysis there exist different tools such as MSstats, mapDIA and aLFQ. However, the transfer of data from OpenSWATH to the downstream statistical tools is currently technically challenging. Here we introduce the R/Bioconductor package SWATH2stats, which allows convenient processing of the data into a format directly readable by the downstream analysis tools. In addition, SWATH2stats allows annotation, analyzing the variation and the reproducibility of the measurements, FDR estimation, and advanced filtering before submitting the processed data to downstream tools. These functionalities are important to quickly analyze the quality of the SWATH-MS data. Hence, SWATH2stats is a new open-source tool that summarizes several practical functionalities for analyzing, processing, and converting SWATH-MS data and thus facilitates the efficient analysis of large-scale SWATH/DIA datasets.</p></div

Functions included in the SWATH2stats package.

<p>Functions included in the SWATH2stats package.</p

RNAi–Based Functional Profiling of Loci from Blood Lipid Genome-Wide Association Studies Identifies Genes with Cholesterol-Regulatory Function

<div><p>Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease. However, GWAS only rarely reveal information on the exact genetic elements and pathogenic events underlying an association. In order to extract functional information from genomic data, strategies for systematic follow-up studies on a phenotypic level are required. Here we address these limitations by applying RNA interference (RNAi) to analyze 133 candidate genes within 56 loci identified by GWAS as associated with blood lipid levels, coronary artery disease, and/or myocardial infarction for a function in regulating cholesterol levels in cells. Knockdown of a surprisingly high number (41%) of trait-associated genes affected low-density lipoprotein (LDL) internalization and/or cellular levels of free cholesterol. Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions. Using a set of secondary assays we demonstrate for a number of genes without previously known lipid-regulatory roles (e.g. CXCL12, FAM174A, PAFAH1B1, SEZ6L, TBL2, WDR12) that knockdown correlates with altered LDL–receptor levels and/or that overexpression as GFP–tagged fusion proteins inversely modifies cellular cholesterol levels. By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.</p> </div

Multiparametric analysis and clustering of functional effector genes.

<p>(A) Functional consequences upon knockdown of each candidate gene (using 3–5 different siRNAs/gene) were quantified from microscopic images with regard to seven phenotypic parameters: total cellular LDL-signal; LDL concentration and number of cellular structures; total free cholesterol (FC) signal; and FC concentration, area and number of cellular structures. Shown are heatmaps for 37 out of 55 most pronounced functional effector genes that according to parameter “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene were clustered into five distinct functional groups (B–F) (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s002" target="_blank">Figure S2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s011" target="_blank">Table S4</a> for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> are framed in orange.</p

Comparison of multiparametric datasets for neighboring genes within lipid-trait-associated loci.

<p>Shown are parameters “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene and relative genomic position of lead SNPs (arrowheads) for seven (A–G) selected lipid-trait/CAD/MI loci in which multiple neighboring candidate genes (±50 kB up-/downstream of lead SNP) were functionally analyzed (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s002" target="_blank">Figure S2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s011" target="_blank">Table S4</a> for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> are framed in orange.</p

Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.

<p>(A) Workflow of this study. (B,C) Profiling of lipid-trait associated genes for a cholesterol-regulating function in cells was performed by monitoring LDL-uptake (upper panels) and free perinuclear cholesterol (FC; lower panels) in siRNA-knockdown cells (for details, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338-Bartz1" target="_blank">[30]</a>). Shown are automatically acquired images of Hela-Kyoto cells cultured and reverse siRNA transfected on cell microarrays for 48 h with control siRNAs (B) or indicated siRNAs targeting selected candidate genes increasing (red) or decreasing (blue) typical cellular phenotypes (C; see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen-1003338-g002" target="_blank">Figure 2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.</p

IAV infected and uninfected mDCs have similar CMV antigen load.

<p>(<b>A</b>) mDCs were exposed to infectious IAV, HI IAV or untreated for 4 hr, washed and exposed to increasing doses of whole, γ-irradiated CMV for an additional 3 hr. DCs were then washed, adhered to coverslips and fixed with PFA. After permeabilization, samples were stained for CMV pp65 (green), IAV (red) and HLA-DR (blue). The entire volume of each cell was analyzed using confocal microscopy (100× 1.47NA oil objective, 7× digital zoom) and one single optical slice is shown through the center of the cell with arrowheads pointing to CMV pp65+ structures. Scale bar 5 µm. (<b>B</b>) The frequency of CMV pp65+ uninfected mDCs (blue), IAV infected mDCs (black) or HI IAV stimulated mDCs (white) after 3 hr of CMV exposure was determined by analyzing entire z-stacks of DCs and counting the number of cells that had virus associated with them. The graph shows average frequency of CMV positive DCs ± SD, with 100 cells analyzed per donor and condition (n = 3). (<b>C</b>) The average number of CMV pp65+ puncta per cell after 3 hr of CMV exposure was determined by analyzing entire z-stacks of DCs and counting the number of green (CMV) puncta per nucleated cell, assessed by DAPI staining. The graph shows average frequency of CMV positive DCs ± SD, with 100 cells analyzed per donor and condition (n = 3).</p

DCs exposed to infectious IAV and HI IAV are similar in MHCII restricted antigen-presentation.

<p>pDCs (red) and mDCs (blue) were exposed to infectious IAV, HI IAV or left untreated for 4 hours, washed and co-cultured with autologous CD4 T cells at a 1∶30 DC∶T cell ratio. After 1 hr of co-culture brefeldin A was added. After another 16 hr of co-culture, cells were harvested and stained with antibodies to detect cytokine-producing CD4 T cells and analyzed by flow cytometry. Graph shows mean±SD percent of live, IFNα, TNFα, IL-2+ CD4 T cells (n = 3).</p

Both infectious IAV and HI IAV induce maturation and cytokine secretion in mDCs and pDCs in a pH-dependent manner.

<p>mDCs (<b>A</b>) or pDCs (<b>B</b>) were exposed to infectious IAV or HI IAV in the absence or presence of NH₄Cl for 24 hr. The frequency of IAV+ DCs and their CD86 expression was determined. Dot plots show live DCs and numbers in each quadrant depict the frequency of positive DCs. One representative donor of eight is shown. (<b>C</b>) The surface expression of CD86, CD40, MHCI (HLA-ABC) and MHCII (HLA-DR) on mDCs (blue) and pDCs (red) was determined after 24 hr of exposure to infectious IAV, HI IAV or TLR7/8L in the absence or presence of NH₄Cl. Bar graphs show MFI±SD (n = 3). The levels of secreted IFNα from supernatants of pDCs (<b>D</b>) or mDCs (<b>E</b>) after 24 hr were determined by ELISA. The graphs show mean±SD (n = 9).</p

mDCs but not pDCs are susceptible to IAV infection.

<p>(<b>A</b>) pDCs (red) and mDCs (blue) were continuously exposed to IAV for 1, 6 or 24 hr and the frequency of IAV+ DCs was measured by flow cytometry using a rabbit polyclonal antibody raised against IAV/X31. Graph shows mean±SD percent of IAV+ CD123+ CD14− pDCs and CD11c+ CD14− mDCs (n = 7). Differences between IAV infection of mDCs and pDCs were assessed using paired t test: n.s. no significant difference, ** p<0.01, *** p<0.001. (<b>B</b>) DCs were treated with NH₄Cl, then exposed to IAV for 24 hr at 4°C or 37°C. The frequency of IAV+ DCs and level of CD86 expression was determined by flow cytometry. One representative experiment of six is shown. Dot plots show live DCs and numbers in each quadrant depict the frequency of positive DCs. (<b>C</b>) DCs were exposed to IAV for 1 hr, washed 3 times to remove free virus and allowed to adhere to coverslips. Surface HLA-DR (red) was labeled before fixation to visualize the plasma membrane. After permeabilization, virus was stained using an anti-IAV antibody (green) and the nucleus was stained using DAPI (blue). The entire volume of each cell was analyzed using confocal microscopy (100× 1.47NA oil objective, 6× digital zoom) and one single optical slice through the center of the cell is shown with arrowheads pointing to virus structures. Scale bar 10 µm. (<b>D</b>) The frequency of IAV+ DCs after 1 hr of virus exposure was determined by analyzing entire z-stacks of DCs and counting the number of cells that had virus associated with them, either on the membrane (white) or intracellularly (black). The graph shows average frequency of IAV+ DCs±SD, with 100–300 DCs analyzed per donor and condition (n = 4). (<b>E</b>) DCs were treated with LPS, TLR7/8L, IFNα or nothing for 24 hr and the MxA expression was determined by flow cytometry using intracellular staining with a monoclonal anti-MxA antibody. Graph shows MFI±SD of MxA with isotype control subtracted (n = 3).</p