scholarly journals spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

2021 ◽  
Author(s):  
Brenda Pardo ◽  
Abby Spangler ◽  
Lukas M. Weber ◽  
Stephanie C. Hicks ◽  
Andrew E. Jaffe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Data ◽  
Web Application ◽  
Bioconductor Package ◽  
Data Types ◽  
Intact Tissue ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Spatial Coordinates ◽  
Visualization Tools

Motivation: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. Availability: spatialLIBD is available at http://bioconductor.org/packages/spatialLIBD.

scholarly journals VistoSeg: a MATLAB pipeline to process, analyze and visualize high resolution histology images for Visium spatial transcriptomics data

2021 ◽  
Author(s):  
Madhavi Tippani ◽  
Heena Rajesh Divecha ◽  
Joseph L. Catallini ◽  
Lukas M Weber ◽  
Abby Spangler ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Programming Language ◽  
Gene Expression Data ◽  
Expression Data ◽  
Intact Tissue ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
High Resolution Images ◽  
Molecular Information

Motivation: Recent advances in spatially-resolved transcriptomics technologies such as the 10x Genomics Visium platform have enabled the generation of transcriptome-wide spatial expression maps within intact tissue. However, steps for processing the high-resolution histology images, extracting relevant features from the images, and integrating them with the gene expression data remain unresolved. Results: We describe VistoSeg, a MATLAB pipeline to process, analyze, and interactively visualize the high-resolution images from the 10x Genomics Visium platform. The output from VistoSeg can then be integrated with the spatial-molecular information in downstream analyses using any programming language, such as R or Python. Availability: VistoSeg is available at https://github.com/LieberInstitute/VistoSeg with a tutorial at http://research.libd.org/VistoSeg

scholarly journals Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

2020 ◽  
Author(s):  
Kristen R. Maynard ◽  
Leonardo Collado-Torres ◽  
Lukas M. Weber ◽  
Cedric Uytingco ◽  
Brianna K. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Web Application ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Sequencing Data ◽  
Dorsolateral Prefrontal ◽  
Transcriptomics Data

AbstractWe used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

scholarly journals Replicability of spatial gene expression atlas data from the adult mouse brain

2020 ◽  
Author(s):  
Shaina Lu ◽  
Cantin Ortiz ◽  
Daniel Fürth ◽  
Stephan Fischer ◽  
Konstantinos Meletis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Spatial Data ◽  
Adult Mouse ◽  
Brain Area ◽  
Physical Distance ◽  
Mammalian Brain ◽  
Data Types ◽  
Adult Mouse Brain ◽  
Whole Brain

AbstractBackgroundSpatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (ABA) and a similar dataset collected using Spatial Transcriptomics (ST). With the advent of tractable spatial techniques, for the first time we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome.ResultsWe use LASSO, linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen reference atlas labels are classifiable using transcription, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset.ConclusionsIn general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability.

scholarly journals SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor

2021 ◽  
Author(s):  
Dario Righelli ◽  
Lukas M. Weber ◽  
Helena L. Crowell ◽  
Brenda Pardo ◽  
Leonardo Collado-Torres ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Data Infrastructure ◽  
R Programming Language ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Visualization Tools ◽  
R Programming ◽  
Cell Data ◽  
Technological Platforms

AbstractMotivationSpatially resolved transcriptomics is a new set of technologies to measure gene expression for up to thousands of genes at near-single-cell, single-cell, or sub-cellular resolution, together with the spatial positions of the measurements. Analyzing combined molecular and spatial information has generated new insights about biological processes that manifest in a spatial manner within tissues. However, to efficiently analyze these data, specialized data infrastructure is required, which facilitates storage, retrieval, subsetting, and interfacing with downstream tools.ResultsHere, we describe SpatialExperiment, a new data infrastructure for storing and accessing spatially resolved transcriptomics data, implemented within the Bioconductor framework in the R programming language. SpatialExperiment extends the existing SingleCellExperiment for single-cell data from the Bioconductor framework, which brings with it advantages of modularity, interoperability, standardized operations, and comprehensive documentation. We demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms. SpatialExperiment is extendable to alternative technological platforms measuring expression and to new types of data modalities, such as spatial immunofluorescence or proteomics, in the future. We also provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData, and ggspavis packages.Availability and ImplementationSpatialExperiment is freely available from Bioconductor at https://bioconductor.org/packages/SpatialExperiment. The STexampleData, TENxVisiumData, and ggspavis packages are available from GitHub and will be submitted to Bioconductor.

scholarly journals Vitessce: a framework for integrative visualization of multi-modal and spatially-resolved single-cell data

2021 ◽  
Author(s):  
Mark S Keller ◽  
Ilan Gold ◽  
Chuck McCallum ◽  
Trevor Manz ◽  
Peter V Kharchenko ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Imaging Data ◽  
Data Types ◽  
Modular Architecture ◽  
Spatially Resolved ◽  
Wide Range ◽  
Cell Assays ◽  
Integrative Visualization ◽  
Cell Data

Vitessce is an open-source interactive visualization framework for exploration of multi-modal and spatially-resolved single-cell data, with a modular architecture compatible with transcriptomic, proteomic, genome-mapped, and imaging data types. Its modular, coordinated multiple view implementation facilitates a wide range of visualization tasks to support all common single-cell assays. Vitessce is a client-side web application designed to be integrated with computational analysis tools and data resources and does not require specialized server infrastructure. The software is available at http://vitessce.io.

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 Bayesian segmentation of spatially resolved transcriptomics data

2020 ◽  
Author(s):  
Viktor Petukhov ◽  
Ruslan A. Soldatov ◽  
Konstantin Khodosevich ◽  
Peter V. Kharchenko
Keyword(s):  
Spatial Data ◽  
Spatial Dimension ◽  
Cell Segmentation ◽  
Tissue Slices ◽  
Accurate Identification ◽  
Fixed Tissue ◽  
Spatially Resolved ◽  
Bayesian Segmentation ◽  
Transcriptomics Data

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for Bayesian Segmentation of Spatial Transcriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.

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.

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