Dataset Information

Ontology based molecular signatures for immune cell types via gene expression analysis.

ABSTRACT: New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type's identity.We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis - providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.

SUBMITTER: Meehan TF

PROVIDER: S-EPMC3844401 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Ontology based molecular signatures for immune cell types via gene expression analysis.

Meehan Terrence F TF Vasilevsky Nicole A NA Mungall Christopher J CJ Dougall David S DS Haendel Melissa A MA Blake Judith A JA Diehl Alexander D AD

BMC bioinformatics 20130830

<h4>Background</h4>New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell typ ...[more]

PMID: 24004649

Similar Datasets

Project description:Purpose: To identify distinct gene expression and functional profiles for the three main cell types (epithelial, keratocyte and endothelial) of the human cornea. Methods: RNA-sequencing was performed using total RNA isolated from ex vivo corneal epithelial cells (evCEpC), keratocytes (evK) and endothelial cells (evCEnC) obtained from 3 donor corneas obtained from a commercial eye bank. Transcriptomic analysis was performed using Kallisto (alignment (hg38) and quantification) and Sleuth (differential gene expression(DGE)), with transcript abundances calculated as transcripts per kilobase million (TPM). Expression was defined as TPM≥7.5 and significant DGE as a fold-change ≥4 and a false-discovery rate adjusted p-value≤0.05. Cell type specificity was defined as genes adhering to the above expression and DGE criteria and not expressed (i.e., TPM<7.5) in the other two cell types. Gene ontology enrichment analysis was performed on the cell type-specific gene lists using the gene group functional profiling (g:GOSt) tool within the web-based g:Profiler suite. Results: We identified 205 genes specific in evCEpC, and enrichment of epithelial-associated GO terms (e.g., cornified envelope, epidermis development, cell-cell adhesion). We identified 76 genes specific in evK, and fibroblast-associated GO terms (e.g., collagen metabolic process, metallopeptidase activity, extracellular matrix organization). We identified 96 genes specific in evCEnC, and at least one CEnC-associated GO term (e.g., cellular cation homeostasis) but several synapse-associated GO terms (e.g., synapse organization, synapse part). Conclusions: The human cornea is comprised of three main cell types that play important roles in maintaining corneal clarity and vision. Our results demonstrate that a small subset (0.4-1%) of the protein-coding genes confers distinct and classic functional properties associated with each cell type. In addition, we identified a novel association and significant functional overlap between CEnC and synapses. This may lead to insights into the molecular mechanisms of transendothelial transport and secretion, which are integral metabolic features of the corneal endothelium.

Dataset Information

Ontology based molecular signatures for immune cell types via gene expression analysis.

Publications

Ontology based molecular signatures for immune cell types via gene expression analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure