Metabolic Enzyme Alterations and Astrocyte Dysfunction in a Murine Model of Alexander Disease With Severe Reactive Gliosis

Alexander disease (AxD) is a rare and fatal neurodegenerative disorder caused by mutations in the gene encoding glial fibrillary acidic protein (GFAP). In this report, a mouse model of AxD (GFAP^Tg;Gfap^+/R236H) was analyzed that contains a heterozygous R236H point mutation in murine Gfap as well as a transgene with a GFAP promoter to overexpress human GFAP. Using label-free quantitative proteomic comparisons of brain tissue from GFAP^Tg;Gfap^+/R236H versus wild-type mice confirmed upregulation of the glutathione metabolism pathway and indicated proteins were elevated in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, which had not been reported previously in AxD. Relative protein-level differences were confirmed by a targeted proteomics assay, including proteins related to astrocytes and oligodendrocytes. Of particular interest was the decreased level of the oligodendrocyte protein, 2-hydroxyacylsphingosine 1-beta-galactosyltransferase (Ugt8), since Ugt8-deficient mice exhibit a phenotype similar to GFAP^Tg;Gfap^+/R236H mice (e.g., tremors, ataxia, hind-limb paralysis). In addition, decreased levels of myelin-associated proteins were found in the GFAP^Tg;Gfap^+/R236H mice, consistent with the role of Ugt8 in myelin synthesis. Fabp7 upregulation in GFAP^Tg;Gfap^+/R236H mice was also selected for further investigation due to its uncharacterized association to AxD, critical function in astrocyte proliferation, and functional ability to inhibit the anti-inflammatory PPAR signaling pathway in models of amyotrophic lateral sclerosis (ALS). Within Gfap⁺ astrocytes, Fabp7 was markedly increased in the hippocampus, a brain region subjected to extensive pathology and chronic reactive gliosis in GFAP^Tg;Gfap^+/R236H mice. Last, to determine whether the findings in GFAP^Tg;Gfap^+/R236H mice are present in the human condition, AxD patient and control samples were analyzed by Western blot, which indicated that Type I AxD patients have a significant fourfold upregulation of FABP7. However, immunohistochemistry analysis showed that UGT8 accumulates in AxD patient subpial brain regions where abundant amounts of Rosenthal fibers are located, which was not observed in the GFAP^Tg;Gfap^+/R236H mice.     

DeviaTE: Assembly‐free analysis and visualization of mobile genetic element composition

Transposable elements (TEs) are selfish DNA sequences that multiply within host genomes. They are present in most species investigated so far at varying degrees of abundance and sequence diversity. The TE composition may not only vary between but also within species and could have important biological implications. Variation in prevalence among populations may for example indicate a recent TE invasion, whereas sequence variation could indicate the presence of hyperactive or inactive forms. Gaining unbiased estimates of TE composition is thus vital for understanding the evolutionary dynamics of transposons. To this end, we developed DeviaTE, a tool to analyse and visualize TE abundance using Illumina or Sanger sequencing reads. Our tool requires sequencing reads of one or more samples (tissue, individual or population) and consensus sequences of TEs. It generates a table and a visual representation of TE composition. This allows for an intuitive assessment of coverage, sequence divergence, segregating SNPs and indels, as well as the presence of internal and terminal deletions. By contrasting the coverage between TEs and single copy genes, DeviaTE derives unbiased estimates of TE abundance. We show that naive approaches, which do not consider regions spanned by internal deletions, may substantially underestimate TE abundance. Using published data we demonstrate that DeviaTE can be used to study the TE composition within samples, identify clinal variation in TEs, compare TE diversity among species, and monitor TE invasions. Finally we present careful validations with publicly available and simulated data. DeviaTE is implemented in Python and distributed under the GPLv3 ( https://github.com/W-L/deviaTE ).     

Interactive Visualization and Industrial Ecology: Applications,Challenges, and Opportunities

The emergence of increasingly complex data in industrial ecology (IE) has caused scholarly interest in interactive visualization (IV). IV allows users to interact with data, aiding in processing and interpreting complex datasets, processes, and simulations. Consequently, IV can help IE practitioners communicate the complexities of their methods and results, shed light on the underlying research assumptions, and enable more transparent monitoring of data quality and error. This can significantly increase the reach and impact of research, promote transparency, reproducibility, and open science, as well as improve the clarity and presentation of IE research. A review of current IV applications reveals that, while data exploration has received some attention among IE practitioners, IV applications in scientific communication are clearly lacking. With the help of a working example, we explore the value of IV, discuss its operationalization, and highlight challenges that the IE community must face during IV uptake. Such challenges include technical and knowledge limitations, limits on user interaction, and implementation strategies. With these challenges in mind, we outline key aspects needed to lift the IE field to the forefront of scientific communication in the coming years. Among these, we draft the basic principles of a “Hub for Interactive Visualization in Industrial Ecology” (HIVE), a point of encounter where IE practitioners could find an array of data visualization tools that are geared toward IE datasets. IV is here to stay, and its inceptive stage presents many opportunities to IE practitioners to shape its operationalization and benefit from early adoption.     

CellDepot: A Unified Repository for scRNA-seq Data and Visual Exploration

CellDepot containing over 270 datasets from 8 species and many tissues serves as an integrated web application to empower scientists in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical capabilities. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the graphical multi-logic, multi-condition query builder and convenient filtering tool backed by MySQL database system, allows users to quickly find the datasets of interest and compare the expression of gene(s) across these. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research. In summary, the web portal available at http://celldepot.bxgenomics.com, prompts large scale single cell data sharing, facilitates meta-analysis and visualization, and encourages scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software under MIT license to motivate crowd contribution, broad adoption, and local deployment for private datasets.     

A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub ( https://github.com/MannLabs/ProteomicsVisualization ).     

Bottom up proteomics data analysis strategies to explore protein modifications and genomic variants

The quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitude. This means that significant technical and data‐analytic innovations will be needed for the full understanding of biology. Current state of art MS is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analysis strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice, and genomic variants of biological relevance. We demonstrate the application of these data analysis strategies using a bottom‐up proteomics dataset obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.     

国际生物信息学研究的可视化分析

利用当前主流的信息可视化分析软件Cite Space对2005~2014年间SCI收录的生物信息学的5种高影响力外文期刊所刊载论文的题录数据进行统计和可视化分析,绘制该领域的关键词共现、膨胀词共现、经典文献共现、高被引文献共现和关键节点文献共现的网络可视化图谱,试图揭示生物信息学领域的研究热点、研究前沿以及知识基础,以期帮助研究人员了解该领域在国际范围内的研究态势。     

LogViewer: A Software Tool to Visualize Quality Control Parameters to Optimize Proteomics Experiments using Orbitrap and LTQ-FT Mass Spectrometers

Visualization tools that allow both optimization of instrument''s parameters for data acquisition and specific quality control (QC) for a given sample prior to time-consuming database searches have been scarce until recently and are currently still not freely available. To address this need, we have developed the visualization tool LogViewer, which uses diagnostic data from the RAW files of the Thermo Orbitrap and linear trap quadrupole-Fourier transform (LTQ-FT) mass spectrometers to monitor relevant metrics. To summarize and visualize the performance on our test samples, log files from RawXtract are imported and displayed. LogViewer is a visualization tool that allows a specific and fast QC for a given sample without time-consuming database searches. QC metrics displayed include: mass spectrometry (MS) ion-injection time histograms, MS ion-injection time versus retention time, MS² ion-injection time histograms, MS² ion-injection time versus retention time, dependent scan histograms, charge-state histograms, mass-to-charge ratio (M/Z) distributions, M/Z histograms, mass histograms, mass distribution, summary, repeat analyses, Raw MS, and Raw MS². Systematically optimizing all metrics allowed us to increase our protein identification rates from 600 proteins to routinely determine up to 1400 proteins in any 160-min analysis of a complex mixture (e.g., yeast lysate) at a false discovery rate of <1%. Visualization tools, such as LogViewer, make QC of complex liquid chromotography (LC)-MS and LC-MS/MS data and optimization of the instrument''s parameters accessible to users.