Dataset Information

Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain.

ABSTRACT: In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

SUBMITTER: Arora S

PROVIDER: S-EPMC10014937 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain.

Arora Sonali S Szulzewsky Frank F Jensen Matt M Nuechterlein Nicholas N Pattwell Siobhan S SS Holland Eric C EC

Scientific reports 20230314 1

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available dataset ...[more]

PMID: 36918656

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Project description:BackgroundsAbnormal vertebral growth and development have been found in adolescent idiopathic scoliosis (AIS) patients, and the proliferation and differentiation of bone development-related cells play important roles in its pathogenesis. However, a comprehensive single-cell-level differentiation roadmap in AIS has not been achieved.MethodsThe present study compared the single-cell level cellular landscapes of spinal cancellous bone tissues between AIS patients and healthy subjects using high throughput single-cell RNA sequencing (scRNA-seq), which covers multiple cellular lineages including osteoblast, chondrocyte, osteoclast and related immunocytes. We constructed the differentiation trajectories of bone development-related cell lineages through pseudotime analysis, and the intercellular-communication networks between bone development-related cells and immunocytes were further developed.ResultsA total of 11 distinct cell clusters were identified according to the genome-wide transcriptome profiles. t-Distributed stochastic neighbor embedding (t-SNE) analysis showed that mesenchymal stem cells (MSC) were classified into three subtypes: MSC-LOXL2, MSC-IGFBP5, and MSC-GJA1. Gene ontology (GO) analysis showed that MSC-GJA1 might possess greater osteoblast differentiation potential than the others. MSC-IGFBP5 was the specific MSC subtype observed only in AIS. There were two distinct gene expression clusters: OB-DPT and OB-OLFML2B, and the counts of osteoblasts derived from AIS was significantly less than that of non-AIS subjects. In AIS patients, MSC-IGFBP5 failed to differentiate into osteoblasts and exhibited negative regulation of cell proliferation and enhanced cell death. CPC-PCNA was found to be the specific chondrocyte progenitor cell (CPC) subtype observed only in AIS patients. The cell counts of OC-BIRC3 in AIS were less than those in controls. Pseudotime analysis suggested two possible distinct osteoclast differentiation patterns in AIS and control subjects. Monocytes in AIS mainly differentiated into OC-CRISP3.ConclusionsOur single-cell analysis first revealed differences existed in the cellular states between AIS patients and healthy subjects and found the differentiation disruption of specific MSC and CPC clusters in AIS. Cell communication analysis provided the possible pathogenesis of osteoblast and chondrocyte differentiation dysfunction in AIS.

Project description:Improvements in next-generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of '-seq'-based methods, of which RNA sequencing (RNA-seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA-seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user-friendly RNA-seq data exploration tool, called the 'eFP-Seq Browser', that shows the read map coverage of a gene of interest in each of the samples along with 'electronic fluorescent pictographic' (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA-seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression-level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA-seq alignments summarized in eFP-Seq Browser as coverage graphs. We present four cases of use of the eFP-Seq Browser for ABI3, SR34, SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. Tool is at https://bar.utoronto.ca/eFP-Seq_Browser/; RNA-seq data at https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/ and https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/Klepikova/. Code is available at https://github.com/BioAnalyticResource/eFP-Seq-Browser.

Dataset Information

Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain.

Publications

Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain.

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