CLARA: A web portal for interactive exploration of the cardiovascular cellular landscape in health and disease

Mammalian cardiovascular tissues are comprised of complex and diverse collections of cells. Recent advances in single-cell profiling technologies have accelerated our understanding of tissue cellularity and the molecular networks that orchestrate cardiovascular development, maintain homeostasis, and are disrupted in pathological states. Despite the rapid development and application of these technologies, many cardiac single-cell functional genomics datasets remain inaccessible for most cardiovascular biologists. Access to custom visual representations of the data, including querying changes in cellular phenotypes and interactions in diverse contexts, remains unavailable in publicly accessible data portals. Visualizing data is also challenging for scientists without expertise in processing single-cell genomic data. Here we present CLARA—CardiovascuLAR Atlas—a web portal facilitating exploration of the cardiovascular cellular landscape. Using mouse and human single-cell transcriptomic datasets, CLARA enables scientists unfamiliar with single-cell-omic data analysis approaches to examine gene expression patterns and the cell population dynamics of cardiac cells in a range of contexts. The web-application also enables investigation of intercellular interactions that form the cardiac cellular niche. CLARA is designed for ease-of-use and we anticipate that the portal will aid deeper exploration of cardiovascular cellular landscapes in the context of development, homeostasis and disease. CLARA is freely available at https://clara.baker.edu.au.

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Using web2py Python framework for creating data-driven web applications in the academic library

Library Hi Tech ◽

10.1108/lht-08-2015-0082 ◽

2016 ◽

Vol 34 (1) ◽

pp. 164-171 ◽

Cited By ~ 1

Author(s):

Mathew Miles

Keyword(s):

Open Source ◽

Web Application ◽

Web Applications ◽

Rapid Development ◽

Academic Library ◽

Ease Of Use ◽

Data Driven ◽

Application Framework ◽

Content Type ◽

Open Source Framework

Purpose – Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training – which includes knowledge of SQL, a web application language like PHP, JavaScript, CSS, and jQuery. The web2py framework greatly reduces the learning curve for creating data-driven websites by focussing on three main goals: ease of use; rapid development; and security. web2py follows a strict MVC framework where the controls and web templates are all written in pure Python. No additional templating language is required. The paper aims to discuss these issues. Design/methodology/approach – There are many frameworks available for creating database-driven web applications. The author had used ColdFusion for many years but wanted to move to a more complete web framework which was also open source. Findings – After evaluating a number of Python frameworks, web2py was found to provide the best combination of functionality and ease of use. This paper focusses on the strengths of web2py and not the specifics of evaluating the different frameworks. Practical implications – Librarians who feel that they do not have the skills to create data-driven websites in other frameworks might find that they can develop them in web2py. It is a good web application framework to start with, which might also provide a gateway to other frameworks. Originality/value – web2py is an open source framework that could have great benefit for those who may have struggled to create database-driven websites in other frameworks or languages.

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Giotto, a toolbox for integrative analysis and visualization of spatial expression data

10.1101/701680 ◽

2019 ◽

Cited By ~ 17

Author(s):

Ruben Dries ◽

Qian Zhu ◽

Rui Dong ◽

Chee-Huat Linus Eng ◽

Huipeng Li ◽

...

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Single Cell ◽

Rapid Development ◽

Expression Patterns ◽

Imaging Features ◽

Expression Data ◽

General Applicability ◽

Proteomic Data ◽

Wide Range

AbstractThe rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

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The Application of Single-Cell Technologies in Cardiovascular Research

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.751371 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yinan Chen ◽

Yang Liu ◽

Xiang Gao

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Rapid Development ◽

Cardiovascular Development ◽

Clinical Value ◽

Cardiovascular Research ◽

Tissue Microenvironment ◽

Cause Of Deaths ◽

Cardiovascular Biology ◽

Shed Light

Cardiovascular diseases (CVDs) are the leading cause of deaths in the world. The intricacies of the cellular composition and tissue microenvironment in heart and vasculature complicate the dissection of molecular mechanisms of CVDs. Over the past decade, the rapid development of single-cell omics technologies generated vast quantities of information at various biological levels, which have shed light on the cellular and molecular dynamics in cardiovascular development, homeostasis and diseases. Here, we summarize the latest single-cell omics techniques, and show how they have facilitated our understanding of cardiovascular biology. We also briefly discuss the clinical value and future outlook of single-cell applications in the field.

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽

10.3390/genes12020311 ◽

2021 ◽

Vol 12 (2) ◽

pp. 311

Author(s):

Zhenqiu Liu

Keyword(s):

Single Cell ◽

Free Parameter ◽

Graphical Model ◽

Expression Patterns ◽

Information Criterion ◽

Log P ◽

Rna Seq ◽

Clustering Methods ◽

Wide Range ◽

Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

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ATRT-13. DIFFERENT CELLS OF ORIGIN PAVE THE WAY FOR MOLECULAR HETEROGENEITY IN RHABDOID TUMORS

Neuro-Oncology ◽

10.1093/neuonc/noaa222.012 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii278-iii278

Author(s):

Monika Graf ◽

Marta Interlandi ◽

Natalia Moreno ◽

Dörthe Holdhof ◽

Viktoria Melcher ◽

...

Keyword(s):

Single Cell ◽

Expression Patterns ◽

Time Frame ◽

Precursor Cells ◽

Genetically Engineered ◽

Molecular Heterogeneity ◽

Loss Of Function ◽

Cellular Origin ◽

Cells Of Origin ◽

Rhabdoid Tumors

Abstract Rhabdoid tumors (RT) are rare but highly aggressive pediatric neoplasms. These tumors carry homozygous loss-of-function alterations of SMARCB1 in almost all cases with an otherwise low mutational load. RT arise at different intracranial (ATRT) as well as extracranial (MRT) anatomical sites. Three main molecular subgroups (ATRT-SHH, ATRT-TYR, ATRT-MYC) have been characterized for ATRT which are epigenetically and clinically diverse, while MRT show remarkable similarities with ATRT-MYC distinct from ATRT-SHH and ATRT-TYR. Even though there are hypotheses about various cells of origin among RT subgroups, precursor cells of RT have not yet been identified. Previous studies on the temporal control of SMARCB1 knockout in genetically engineered mouse models have unveiled a tight vulnerable time frame during embryogenesis with regard to the susceptibility of precursor cells to result in RT. In this study, we employed single-cell RNA sequencing to describe the intra- and intertumoral heterogeneity of murine ATRT-SHH and -MYC as well as extracranial MYC tumor cells. We defined subgroup-specific tumor markers for all RT classes but also observed a notable overlap of gene expression patterns in all MYC subgroups. By comparing these single-cell transcriptomes with available single-cell maps of early embryogenesis, we gained first insights into the cellular origin of RT. Finally, unsupervised clustering of published human RT methylation data and healthy control tissues confirmed the existence of different cells of origin for intracranial SHH tumors and MYC tumors independent of their anatomical localizations.

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Recent Advances in Single-Cell Profiling and Multispecific Therapeutics: Paving the Way for a New Era of Precision Medicine Targeting Cardiac Fibroblasts

Current Cardiology Reports ◽

10.1007/s11886-021-01517-z ◽

2021 ◽

Vol 23 (7) ◽

Author(s):

Sally Yu Shi ◽

Xin Luo ◽

Tracy M. Yamawaki ◽

Chi-Ming Li ◽

Brandon Ason ◽

...

Keyword(s):

Heart Failure ◽

Precision Medicine ◽

Single Cell ◽

Cardiac Fibroblasts ◽

Rapid Development ◽

Cardiac Fibroblast ◽

New Era ◽

Fibroblast Activation ◽

Cell Gene Expression ◽

The Way

Abstract Purpose of Review Cardiac fibroblast activation contributes to fibrosis, maladaptive remodeling and heart failure progression. This review summarizes the latest findings on cardiac fibroblast activation dynamics derived from single-cell transcriptomic analyses and discusses how this information may aid the development of new multispecific medicines. Recent Findings Advances in single-cell gene expression technologies have led to the discovery of distinct fibroblast subsets, some of which are more prevalent in diseased tissue and exhibit temporal changes in response to injury. In parallel to the rapid development of single-cell platforms, the advent of multispecific therapeutics is beginning to transform the biopharmaceutical landscape, paving the way for the selective targeting of diseased fibroblast subpopulations. Summary Insights gained from single-cell technologies reveal critical cardiac fibroblast subsets that play a pathogenic role in the progression of heart failure. Combined with the development of multispecific therapeutic agents that have enabled access to previously “undruggable” targets, we are entering a new era of precision medicine.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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