Mass cytometry analysis reveals altered immune profiles in patients with coronary artery disease.

Objective: The importance of inflammation in atherosclerosis is well accepted, but the role of the adaptive immune system is not yet fully understood. To further explore this, we assessed the circulating immune cell profile of patients with coronary artery disease (CAD) to identify discriminatory features by mass cytometry.

Methods: Mass cytometry was performed on patient samples from the BioHEART-CT study, gated to detect 82 distinct cell subsets.

PAD2: interactive exploration of transcription factor genomic colocalization using ChIP-seq data.

Characterizing transcription factor (TF) genomic colocalization is essential for identifying cooperative binding of TFs in controlling gene expression. Here, we introduce a protocol for using PAD2, an interactive web application that enables the investigation of colocalization of various TFs and chromatin-regulating proteins from mouse embryonic stem cells at various functional genomic regions. We describe steps for accessing and searching the PAD2 database and selecting and submitting genomic regions.

Integrative analysis reveals histone demethylase LSD1 promotes RNA polymerase II pausing.

Lysine-specific demethylase 1 (LSD1) is well-known for its role in decommissioning enhancers during mouse embryonic stem cell (ESC) differentiation. Its role in gene promoters remains poorly understood despite its widespread presence at these sites. Here, we report that LSD1 promotes RNA polymerase II (RNAPII) pausing, a rate-limiting step in transcription regulation, in ESCs.

A hierarchical approach to removal of unwanted variation for large-scale metabolomics data.

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions.

Protocol for the processing and downstream analysis of phosphoproteomic data with PhosR.

Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction.

Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study.

Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study.

Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis.

Study Objectives: Oral appliance (OA) therapy usage can be objectively measured through temperature-sensing data chips embedded in the appliance. Initial reports of group data for short-term treatment usage suggest good nightly hours of usage. However, individual variability in treatment usage patterns has not been assessed.

PhosR enables processing and functional analysis of phosphoproteomic data.

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways.

scReClassify: post hoc cell type classification of single-cell rNA-seq data.

Background: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.

Background: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells.

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems.

The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g.

Impact of similarity metrics on single-cell RNA-seq data clustering.

Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery.

Integrative analysis identifies co-dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells.

Motivation: DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized.

Results: Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines.