nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows.

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

Analytical Pipeline for Discovery and Verification of Glycoproteins from Plasma-Derived Extracellular Vesicles as Breast Cancer Biomarkers.

Glycoproteins comprise more than half of current FDA-approved protein cancer markers, but the development of new glycoproteins as disease biomarkers has been stagnant. Here we present a pipeline to develop glycoproteins from extracellular vesicles (EVs) through integrating quantitative glycoproteomics with a novel reverse phase glycoprotein array and then apply it to identify novel biomarkers for breast cancer. EV glycoproteomics show promise in circumventing the problems plaguing current serum/plasma glycoproteomics and allowed us to identify hundreds of glycoproteins that have not been identified in blood. We identified 1,453 unique glycopeptides representing 556 glycoproteins in EVs, among which 20 were verified significantly higher in individual breast cancer patients. We further applied a novel glyco-specific reverse phase protein array to quantify a subset of the candidates. Together, this study demonstrates the great potential of this integrated pipeline for biomarker discovery.

TNF is a potential therapeutic target to suppress prostatic inflammation and hyperplasia in autoimmune disease.

Autoimmune (AI) diseases can affect many organs; however, the prostate has not been considered to be a primary target of these systemic inflammatory processes. Here, we utilize medical record data, patient samples, and in vivo models to evaluate the impact of inflammation, as seen in AI diseases, on prostate tissue. Human and mouse tissues are used to examine whether systemic targeting of inflammation limits prostatic inflammation and hyperplasia. Evaluation of 112,152 medical records indicates that benign prostatic hyperplasia (BPH) prevalence is significantly higher among patients with AI diseases. Furthermore, treating these patients with tumor necrosis factor (TNF)-antagonists significantly decreases BPH incidence. Single-cell RNA-seq and in vitro assays suggest that macrophage-derived TNF stimulates BPH-derived fibroblast proliferation. TNF blockade significantly reduces epithelial hyperplasia, NFκB activation, and macrophage-mediated inflammation within prostate tissues. Together, these studies show that patients with AI diseases have a heightened susceptibility to BPH and that reducing inflammation with a therapeutic agent can suppress BPH.

scTree: An R package to generate antibody-compatible classifiers from single-cell sequencing data.

Single-cell RNA sequencing (scRNA-seq) is now a commonly used technique to measure the transcriptome of populations of cells. Clustering heterogeneous cells based on these transcriptomes enables identification of cell populations (Butler, Hoffman, Smibert, Papalexi, & Satija, 2018; Trapnell et al., 2014). There are multiple methods available to identify "marker" genes that differ between these populations (Butler et al., 2018; Love, Huber, & Anders, 2014; Robinson, McCarthy, & Smyth, 2009). However, there are usually too many genes in these lists to directly suggest an experimental follow-up strategy for selecting them from a bulk population (e.g. via FACS (Tung et al., 2007)). Here we present scTree, a tool that aims to provide biologists using the R programming language and scRNA-seq analysis programs a minimal set of genes that can be used in downstream experiments. The package is free, open source and available though GitHub at github.com/jspaezp/sctree.