scholarly journals SpatialDB: a database for spatially resolved transcriptomes

2019 ◽  
Author(s):  
Zhen Fan ◽  
Runsheng Chen ◽  
Xiaowei Chen
Keyword(s):  
Spatial Organization ◽  
Expression Patterns ◽  
Functional Enrichment ◽  
Web Interface ◽  
Current Version ◽  
Transcriptomic Data ◽  
Spatially Resolved ◽  
User Friendly ◽  
New Strategies

Abstract Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.

scholarly journals miR2Diabetes: A Literature-Curated Database of microRNA Expression Patterns, in Diabetic Microvascular Complications

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 784 ◽  
Author(s):  
Sungjin Park ◽  
SeongRyeol Moon ◽  
Kiyoung Lee ◽  
Ie Byung Park ◽  
Dae Ho Lee ◽  
...  
Keyword(s):  
Association Studies ◽  
Microvascular Complications ◽  
Expression Patterns ◽  
Disease Model ◽  
Model Organisms ◽  
Web Interface ◽  
Diabetic Microvascular Complications ◽  
User Friendly ◽  
Rats And Mice ◽  
Kidney Liver

microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, “miR2Diabetes,” equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases.

scholarly journals Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images

2021 ◽  
Author(s):  
Sungwoo Bae ◽  
Hongyoon Choi ◽  
Dong Soo Lee
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Biological Function ◽  
Expression Patterns ◽  
Expression Data ◽  
Transcriptomic Data ◽  
Spatially Resolved ◽  
Molecular Features ◽  
Image Patches ◽  
Latent Features

Abstract Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.

scholarly journals webMCP-counter: a web interface for transcriptomics-based quantification of immune and stromal cells in heterogeneous human or murine samples

2020 ◽  
Author(s):  
Maxime Meylan ◽  
Etienne Becht ◽  
Catherine Sautès-Fridman ◽  
Aurélien de Reyniès ◽  
Wolf H. Fridman ◽  
...  
Keyword(s):  
Stromal Cells ◽  
R Package ◽  
Web Interface ◽  
Transcriptomic Data ◽  
R Programming Language ◽  
Link Type ◽  
Precise Estimation ◽  
R Packages ◽  
R Programming ◽  
User Friendly

AbstractSummaryWe previously reported MCP-counter and mMCP-counter, methods that allow precise estimation of the immune and stromal composition of human and murine samples from bulk transcriptomic data, but they were only distributed as R packages. Here, we report webMCP-counter, a user-friendly web interface to allow all users to use these methods, regardless of their proficiency in the R programming language.Availability and ImplementationFreely available from http://134.157.229.105:3838/webMCP/. Website developed with the R package shiny. Source code available from GitHub: https://github.com/FPetitprez/webMCP-counter.

scholarly journals Identification of spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics by region-specific denoising

2021 ◽  
Author(s):  
Linhua Wang ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Missing Values ◽  
Spatial Organization ◽  
Expression Profiles ◽  
New Technology ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Tissue Heterogeneity ◽  
Region Detection ◽  
Spatially Resolved

Abstract We are pleased to introduce a first-of-its-kind tool that combines in-silico region detection and missing value estimation for spatially resolved transcriptomics. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for each detected region. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify intra-tissue heterogeneity and recover spatial gene-gene co-expression signals. We therefore strongly encourage using MIST before downstream ST analysis because it provides unbiased region annotations and enables accurately denoised spatial gene-expression profiles.

scholarly journals Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

2020 ◽  
Author(s):  
Jian Hu ◽  
Xiangjie Li ◽  
Kyle Coleman ◽  
Amelia Schroeder ◽  
David J. Irwin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Spatial Location ◽  
Convolutional Network ◽  
Tissue Microenvironment ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Spatial Domains

AbstractRecent advances in spatial transcriptomics technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, making it a desirable tool for spatial transcriptomics studies.

scholarly journals A Systematic Comparison of Differential Analysis Methods for CyTOF Data

2021 ◽  
Author(s):  
Lis Arend ◽  
Judith Bernett ◽  
Quirin Manz ◽  
Melissa Klug ◽  
Olga Lazareva ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differential Analysis ◽  
Web Interface ◽  
Analysis Methods ◽  
Shiny App ◽  
R Shiny ◽  
User Friendly

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data is not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the Earth Mover's Distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS, a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.

scholarly journals Generic and context-dependent gene modulations duringHydrawhole body regeneration

2019 ◽  
Author(s):  
Yvan Wenger ◽  
Wanda Buzgariu ◽  
Chrystelle Perruchoud ◽  
Gregory Loichot ◽  
Brigitte Galliot
Keyword(s):  
Classical Model ◽  
Expression Patterns ◽  
Bmp Signaling ◽  
Whole Body ◽  
Basal Region ◽  
Web Interface ◽  
Regenerative Processes ◽  
Ectopic Activation ◽  
Gene Regulations

AbstractThe cnidarianHydrais a classical model of whole-body regeneration. Historically,Hydraapical regeneration has received more attention than its basal counterpart, most studies considering these two regenerative processes independently. We present here a transcriptome-wide comparative analysis of apical and basal regeneration after decapitation and mid-gastric bisection, augmented with a characterization of positional and cell-type expression patterns in non-regenerating animals. The profiles of 25’637Hydratranscripts are available on HydrATLAS (https://hydratlas.unige.ch), a web interface allowing a convenient access to each transcript profile. These data indicate that generic impulse-type modulations occur during the first four hours post-amputation, consistent with a similar integration of injury-related cues on both sides of the amputation plane. Initial divergences in gene regulations are observed in regenerating tips between four and eight hours post-amputation, followed by a dramatic transcriptomic reprogramming between eight and 16 hours when regulations become sustained. As expected, central components of apical patterning,Wnt3andHyBra1, are among the earliest genes up-regulated during apical regeneration. During early basal regeneration, a BMP signaling ligand (BMP5-8c) and a potential BMP inhibitor (NBL1)are up-regulated, suggesting that BMP signaling is involved in the basal organizer, as supported by higher levels of phosphorylated Smad in the basal region and by the LiCl-induced extension ofNBL1expression. By contrast, upon ectopic activation of Wnt/β-catenin signaling,NBL1is no longer expressed, basal differentiation is not maintained and basal regeneration is abolished. A tight cross-talk between Wnt/β-catenin apically and BMP signaling basally appears necessary for maintaining and regeneratingHydraanatomy.

scholarly journals Inferring spatially transient gene expression pattern from spatial transcriptomic studies

2020 ◽  
Author(s):  
Jan Kueckelhaus ◽  
Jasmin von Ehr ◽  
Vidhya M. Ravi ◽  
Paulina Will ◽  
Kevin Joseph ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Organization ◽  
Transient Gene Expression ◽  
Gene Expression Pattern ◽  
Spatially Resolved ◽  
Regional Adaptation ◽  
Spatial Trajectories ◽  
Comprehensive Characterization ◽  
Transcriptomic Studies

AbstractSpatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization. Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression, regional adaptation of transcriptional programs and transient dynamics along spatial trajectories.

scholarly journals Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

2020 ◽  
Author(s):  
Mingyao Li ◽  
Jian Hu ◽  
Xiangjie Li ◽  
Kyle Coleman ◽  
Amelia Schroeder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Spatial Location ◽  
Convolutional Network ◽  
Tissue Microenvironment ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Spatial Domains

Abstract Recent advances in spatial transcriptomics technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, making it a desirable tool for spatial transcriptomics studies.

A single-cell atlas of mouse lung development

Development ◽  
2021 ◽  
Vol 148 (24) ◽  
Author(s):  
Nicholas M. Negretti ◽  
Erin J. Plosa ◽  
John T. Benjamin ◽  
Bryce A. Schuler ◽  
A. Christian Habermann ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Mesenchymal Cell ◽  
Mouse Lung ◽  
Rna In Situ Hybridization ◽  
And Function ◽  
Parenchymal Cells

ABSTRACT Lung organogenesis requires precise timing and coordination to effect spatial organization and function of the parenchymal cells. To provide a systematic broad-based view of the mechanisms governing the dynamic alterations in parenchymal cells over crucial periods of development, we performed a single-cell RNA-sequencing time-series yielding 102,571 epithelial, endothelial and mesenchymal cells across nine time points from embryonic day 12 to postnatal day 14 in mice. Combining computational fate-likelihood prediction with RNA in situ hybridization and immunofluorescence, we explore lineage relationships during the saccular to alveolar stage transition. The utility of this publicly searchable atlas resource (www.sucrelab.org/lungcells) is exemplified by discoveries of the complexity of type 1 pneumocyte function and characterization of mesenchymal Wnt expression patterns during the saccular and alveolar stages – wherein major expansion of the gas-exchange surface occurs. We provide an integrated view of cellular dynamics in epithelial, endothelial and mesenchymal cell populations during lung organogenesis.

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