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. CT coronary angiograms were analysed to categorise patients as having CAD (CAD+) or having normal coronary arteries (CAD-). Results: The discovery cohort included 117 patients (mean age 61 ± 12 years, 49% female); 79 patients (68%) were CAD+. Mass cytometry identified changes in 15 T-cell subsets, with higher numbers of proliferating, highly differentiated and cytotoxic cells and decreases in naïve T cells. Five T-regulatory subsets were related to an age and gender-independent increase in the odds of CAD incidence when expressing CCR2 (OR 1.12), CCR4 (OR 1.08), CD38 and CD45RO (OR 1.13), HLA-DR (OR 1.06) and Ki67 (OR 1.22). Markers of proliferation and differentiation were also increased within B cells, while plasmacytoid dendritic cells were decreased. This combination of changes was assessed using SVM models in discovery and validation cohorts (area under the curve = 0.74 for both), confirming the robust nature of the immune signature detected. Conclusion: We identified differences within immune subpopulations of CAD+ patients which are indicative of a systemic immune response to coronary atherosclerosis. This immune signature needs further study via incorporation into risk scoring tools for the precision diagnosis of CAD.

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. We then detail protein colocalization analysis using heatmap and ranked correlation plot. For complete details on the use and execution of this protocol, please refer to Kim et al. (2022).1.

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. We found the knockdown of LSD1 preferentially affects genes with higher RNAPII pausing. Next, we demonstrate that the co-localization sites of LSD1 and MYC, a factor known to regulate pause-release, are enriched for other RNAPII pausing factors. We show that LSD1 and MYC directly interact and MYC recruitment to genes co-regulated with LSD1 is dependent on LSD1 but not vice versa. The co-regulated gene set is significantly enriched for housekeeping processes and depleted of transcription factors compared to those bound by LSD1 alone. Collectively, our integrative analysis reveals a pleiotropic role of LSD1 in promoting RNAPII pausing.

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. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

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. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).

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. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12-1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02-0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09-2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04-0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23-1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01-3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53-0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26-2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59-3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14-0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.

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. We aimed to identify OA treatment usage subtypes in the first 60 days and the earliest predictors of these usage patterns. METHODS: OSA patients were recruited for a study of OA therapy with an embedded compliance chip (DentiTrac, Braebon, Canada). Fifty-eight participants with 60 days of downloadable treatment usage data (5-minute readings) were analyzed. A hierarchical cluster analysis was used to group participants with similar usage patterns. A random forest classification model was used to identify the minimum number of days to predict usage subtype. RESULTS: Three user groups were identified and named: "Consistent Users" (48.3%), "Inconsistent Users," (32.8%) and "Non-Users" (19.0%). The first 20 days provided optimal data to predict the treatment usage group a patient would belong to at 60 days (90% accuracy). The strongest predictors of user group were downloaded usage data, average wear time, and number of days missed. CONCLUSIONS: Granular analysis of OA usage data suggests the existence of treatment user subtypes (Consistent, Inconsistent, and Non-Users). Our data suggest that 60-day usage patterns can be identified in the first 20 days of treatment using downloaded treatment usage data. Understanding initial treatment usage patterns provide an opportunity for early intervention to improve long-term usage and outcomes. CITATION: Sutherland K, Almeida FR, Kim T, et al. Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis. J Clin Sleep Med. 2021;17(9):1785-1792.

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. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

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.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. RESULTS: Here, we propose a semi-supervised learning framework, named scReClassify, for 'post hoc' cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. CONCLUSIONS: scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify.

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. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification. RESULTS: Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets to generate clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metric used. CONCLUSIONS: Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/scCCESS.

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., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/.

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. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising individual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subsampling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson's correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson's correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson's correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at http://www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.

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. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co-localization at these respective regions. Using this extensive dataset, we discovered an extensive co-localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co-localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co-localization sites. Most notably, our analysis reveals the co-dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome-wide protein binding profiles in our online resource PAD. AVAILABILITY AND IMPLEMENTATION: PAD is freely available at http://pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at https://github.com/VCCRI/PAD/. CONTACT: pengyi.yang@sydney.edu.au or j.ho@victorchang.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.