Single-cell atlas of the human brain vasculature across development, adulthood and disease.

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains incompletely understood1. Here we performed single-cell RNA sequencing analysis of 606,380 freshly isolated endothelial cells, perivascular cells and other tissue-derived cells from 117 samples, from 68 human fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We identify extensive molecular heterogeneity of the vasculature of healthy fetal and adult human brains and across five vascular-dependent central nervous system (CNS) pathologies, including brain tumours and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated genes and pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict substantial endothelial-to-perivascular cell ligand-receptor cross-talk, including immune-related and angiogenic pathways, thereby revealing a central role for the endothelium within brain neurovascular unit signalling networks. Our single-cell brain atlas provides insights into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

Tlr4 Deletion Modulates Cytokine and Extracellular Matrix Expression in Chronic Spinal Cord Injury, Leading to Improved Secondary Damage and Functional Recovery.

Toll-like receptors (TLRs) play an important role in the innate immune response after CNS injury. Although TLR4 is one of the best characterized, its role in chronic stages after spinal cord injury (SCI) is not well understood. We examined the role of TLR4 signaling in injury-induced responses at 1 d, 7 d, and 8 weeks after spinal cord contusion injury in adult female TLR4 null and wild-type mice. Analyses include secondary damage, a range of transcriptome and protein analyses of inflammatory, cell death, and extracellular matrix (ECM) molecules, as well as immune cell infiltration and changes in axonal sprouting and locomotor recovery. Lack of TLR4 signaling results in reduced neuronal and myelin loss, reduced activation of NFκB, and decreased expression of inflammatory cytokines and necroptotic cell death pathway at a late time point (8 weeks) after injury. TLR4 null mice also showed reduction of scar-related ECM molecules at 8 weeks after SCI, accompanied by increase in ECM molecules associated with perineuronal nets, increased sprouting of serotonergic fibers, and improved locomotor recovery. These findings reveal novel effects of TLR4 signaling in chronic SCI. We show that TLR4 influences inflammation, cell death, and ECM deposition at late-stage post-injury when secondary injury processes are normally considered to be over. This highlights the potential for late-stage targeting of TLR4 as a potential therapy for chronic SCI.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Mass Spectrometry in Proteomics: Technologies, Methods, and Research Applications for the Life Sciences.

Mass spectrometry is a powerful tool in the hand of life science researchers, who constantly develop and apply new methods for the investigation of biomolecules, such as proteins, peptides, metabolites, lipids, and glycans. In this review, we will discuss the importance of mass spectrometry for the life science sector, with a special focus on the most relevant current applications in the field of proteomics. Moreover, we will comment on the factors that research groups should consider when setting up a mass spectrometry laboratory, and on the fundamental role played by academic core facilities and industrial service providers.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows.

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

Unraveling the proteomic changes involved in the resistance response of Cajanus platycarpus to herbivory by Helicoverpa armigera.

The pigeonpea wild relative Cajanus platycarpus is resistant to Helicoverpa armigera, one of the major pests responsible for yield losses in Cajanus cajan. Deciphering the molecular mechanism underlying host plant resistance is pertinent to identify proteins that aid in the mitigation of the insect pest. The present study adopted comparative proteomics as a tool to interpret the resistance mechanism(s) in C. platycarpus vis-à-vis C. cajan during continued herbivory (up to 96 h). Over-representation analysis of the differentially expressed proteins implicated a multi-dimensional resistance response accomplished by both physical and chemical barriers in C. platycarpus. While the chemical basis for resistance was depicted by the upregulation of proteins playing a rate limiting role in the phenylpropanoid pathway, the physical basis was provided by the regulation of proteins involved in microtubule assembly and synthesis of lignins. Upregulation of proteins in the polyamine pathway indicated the role of metabolite conjugates to be negatively affecting herbivore growth. Reallocation of resources and diversion of metabolic flux to support the production of secondary metabolites could be the probable approach in the wild relative against herbivory. Our study provided deeper insights into the pod borer resistance mechanism in C. platycarpus for utility in crop improvement. KEY POINTS: • Pod borer resistance in Cajanus platycarpus is multi-dimensional. • Pod borer resistance has been arbitrated to cell wall rigidity and secondary metabolites. • Phenylpropanoid pathway derivatives apparently shaped the plant chemical defense against pod borer.

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts.

Pushing the limits of de novo genome assembly for complex prokaryotic genomes harboring very long, near identical repeats.

Generating a complete, de novo genome assembly for prokaryotes is often considered a solved problem. However, we here show that Pseudomonas koreensis P19E3 harbors multiple, near identical repeat pairs up to 70 kilobase pairs in length, which contained several genes that may confer fitness advantages to the strain. Its complex genome, which also included a variable shufflon region, could not be de novo assembled with long reads produced by Pacific Biosciences' technology, but required very long reads from Oxford Nanopore Technologies. Importantly, a repeat analysis, whose results we release for over 9600 prokaryotes, indicated that very complex bacterial genomes represent a general phenomenon beyond Pseudomonas. Roughly 10% of 9331 complete bacterial and a handful of 293 complete archaeal genomes represented this 'dark matter' for de novo genome assembly of prokaryotes. Several of these 'dark matter' genome assemblies contained repeats far beyond the resolution of the sequencing technology employed and likely contain errors, other genomes were closed employing labor-intense steps like cosmid libraries, primer walking or optical mapping. Using very long sequencing reads in combination with assembly algorithms capable of resolving long, near identical repeats will bring most prokaryotic genomes within reach of fast and complete de novo genome assembly.

The SysteMHC Atlas project.

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

Dependence of Fluorescence Quenching of CY3 Oligonucleotide Conjugates on the Oxidation Potential of the Stacking Base Pair.

To understand the complex fluorescence properties of astraphloxin (CY3)-labelled oligonucleotides, it is necessary to take into account the redox properties of the nucleobases. In oligonucleotide hybrids, we observed a dependence of the fluorescence intensity on the oxidation potential of the neighbouring base pair. For the series I < A < G < 8-oxoG, the extent of fluorescence quenching follows the trend of decreasing oxidation potentials. In a series of 7 nt hybrids, stacking interactions of CY3 with perfect match and mismatch base pairs were found to stabilise the hybrid by 7-8 kJ/mol. The fluorescence measurements can be explained by complex formation resulting in fluorescence quenching that prevails over the steric effect of a reduced excited state trans-cis isomerisation, which was expected to increase the fluorescence efficiency of the dye when stacking to a base pair. This can be explained by the fact that, in a double strand, base pairing and stacking cause a dramatic change in the oxidation potential of the nucleobases. In single-molecule fluorescence measurements, the oxidation of G to 8-oxoG was observed as a result of photoinduced electron transfer and subsequent chemical reactions. Our results demonstrate that covalently linked CY3 is a potent oxidant towards dsDNA. Sulfonated derivatives should be used instead.

Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in α-Proteobacteria.

Heritable DNA methylation imprints are ubiquitous and underlie genetic variability from bacteria to humans. In microbial genomes, DNA methylation has been implicated in gene transcription, DNA replication and repair, nucleoid segregation, transposition and virulence of pathogenic strains. Despite the importance of local (hypo)methylation at specific loci, how and when these patterns are established during the cell cycle remains poorly characterized. Taking advantage of the small genomes and the synchronizability of α-proteobacteria, we discovered that conserved determinants of the cell cycle transcriptional circuitry establish specific hypomethylation patterns in the cell cycle model system Caulobacter crescentus. We used genome-wide methyl-N6-adenine (m6A-) analyses by restriction-enzyme-cleavage sequencing (REC-Seq) and single-molecule real-time (SMRT) sequencing to show that MucR, a transcriptional regulator that represses virulence and cell cycle genes in S-phase but no longer in G1-phase, occludes 5'-GANTC-3' sequence motifs that are methylated by the DNA adenine methyltransferase CcrM. Constitutive expression of CcrM or heterologous methylases in at least two different α-proteobacteria homogenizes m6A patterns even when MucR is present and affects promoter activity. Environmental stress (phosphate limitation) can override and reconfigure local hypomethylation patterns imposed by the cell cycle circuitry that dictate when and where local hypomethylation is instated.

rawDiag: An R Package Supporting Rational LC-MS Method Optimization for Bottom-up Proteomics.

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a nontrivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan-level metadata. rawDiag is implemented as an R package and can be executed on the R command line or through a graphical user interface (GUI) for less experienced users. The code runs platform-independent and can process 100 raw files in <3 min on current consumer hardware, as we show in our benchmark. As a demonstration of the functionality of our package we include a real-world example taken from our daily core facility business.

Natural Genetic Variation Differentially Affects the Proteome and Transcriptome in Caenorhabditis elegans.

Natural genetic variation is the raw material of evolution and influences disease development and progression. An important question is how this genetic variation translates into variation in protein abundance. To analyze the effects of the genetic background on gene and protein expression in the nematode Caenorhabditis elegans, we quantitatively compared the two genetically highly divergent wild-type strains N2 and CB4856. Gene expression was analyzed by microarray assays, and proteins were quantified using stable isotope labeling by amino acids in cell culture. Among all transcribed genes, we found 1,532 genes to be differentially transcribed between the two wild types. Of the total 3,238 quantified proteins, 129 proteins were significantly differentially expressed between N2 and CB4856. The differentially expressed proteins were enriched for genes that function in insulin-signaling and stress-response pathways, underlining strong divergence of these pathways in nematodes. The protein abundance of the two wild-type strains correlates more strongly than protein abundance versus transcript abundance within each wild type. Our findings indicate that in C. elegans only a fraction of the changes in protein abundance can be explained by the changes in mRNA abundance. These findings corroborate with the observations made across species.

New insights into the performance of human whole-exome capture platforms.

Whole exome sequencing (WES) is increasingly used in research and diagnostics. WES users expect coverage of the entire coding region of known genes as well as sufficient read depth for the covered regions. It is, however, unknown which recent WES platform is most suitable to meet these expectations. We present insights into the performance of the most recent standard exome enrichment platforms from Agilent, NimbleGen and Illumina applied to six different DNA samples by two sequencing vendors per platform. Our results suggest that both Agilent and NimbleGen overall perform better than Illumina and that the high enrichment performance of Agilent is stable among samples and between vendors, whereas NimbleGen is only able to achieve vendor- and sample-specific best exome coverage. Moreover, the recent Agilent platform overall captures more coding exons with sufficient read depth than NimbleGen and Illumina. Due to considerable gaps in effective exome coverage, however, the three platforms cannot capture all known coding exons alone or in combination, requiring improvement. Our data emphasize the importance of evaluation of updated platform versions and suggest that enrichment-free whole genome sequencing can overcome the limitations of WES in sufficiently covering coding exons, especially GC-rich regions, and in characterizing structural variants.

Shigella reroutes host cell central metabolism to obtain high-flux nutrient supply for vigorous intracellular growth.

Shigella flexneri proliferate in infected human epithelial cells at exceptionally high rates. This vigorous growth has important consequences for rapid progression to life-threatening bloody diarrhea, but the underlying metabolic mechanisms remain poorly understood. Here, we used metabolomics, proteomics, and genetic experiments to determine host and Shigella metabolism during infection in a cell culture model. The data suggest that infected host cells maintain largely normal fluxes through glycolytic pathways, but the entire output of these pathways is captured by Shigella, most likely in the form of pyruvate. This striking strategy provides Shigella with an abundant favorable energy source, while preserving host cell ATP generation, energy charge maintenance, and survival, despite ongoing vigorous exploitation. Shigella uses a simple three-step pathway to metabolize pyruvate at high rates with acetate as an excreted waste product. The crucial role of this pathway for Shigella intracellular growth suggests targets for antimicrobial chemotherapy of this devastating disease.

Targeted proteomics coming of age - SRM, PRM and DIA performance evaluated from a core facility perspective.

Quantitative mass spectrometry is a rapidly evolving methodology applied in a large number of omics-type research projects. During the past years, new designs of mass spectrometers have been developed and launched as commercial systems while in parallel new data acquisition schemes and data analysis paradigms have been introduced. Core facilities provide access to such technologies, but also actively support the researchers in finding and applying the best-suited analytical approach. In order to implement a solid fundament for this decision making process, core facilities need to constantly compare and benchmark the various approaches. In this article we compare the quantitative accuracy and precision of current state of the art targeted proteomics approaches single reaction monitoring (SRM), parallel reaction monitoring (PRM) and data independent acquisition (DIA) across multiple liquid chromatography mass spectrometry (LC-MS) platforms, using a readily available commercial standard sample. All workflows are able to reproducibly generate accurate quantitative data. However, SRM and PRM workflows show higher accuracy and precision compared to DIA approaches, especially when analyzing low concentrated analytes.

SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis.

BACKGROUND: Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility. RESULTS: We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI. CONCLUSION: SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available.

specL--an R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics.

MOTIVATION: Targeted data extraction methods are attractive ways to obtain quantitative peptide information from a proteomics experiment. Sequential Window Acquisition of all Theoretical Spectra (SWATH) and Data Independent Acquisition (DIA) methods increase reproducibility of acquired data because the classical precursor selection is omitted and all present precursors are fragmented. However, especially for targeted data extraction, MS coordinates (retention time information precursor and fragment masses) are required for the particular entities (peptide ions). These coordinates are usually generated in a so-called discovery experiment earlier on in the project if not available in public spectral library repositories. The quality of the assay panel is crucial to ensure appropriate downstream analysis. For that, a method is needed to create spectral libraries and to export customizable assay panels. RESULTS: Here, we present a versatile set of functions to generate assay panels from spectral libraries for use in targeted data extraction methods (SWATH/DIA) in the area of proteomics. AVAILABILITY AND IMPLEMENTATION: specL is implemented in the R language and available under an open-source license (GPL-3) in Bioconductor since BioC 3.0 (R-3.1) http://www.bioconductor.org (Trachsel et al., 2015). A vignette with a complete tutorial describing data import/export and analysis is included in the package and can also be found as supplement material of this article. CONTACT: cp@fgcz.ethz.ch or jg@fgcz.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage.

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.