ImmuSort, a database on gene plasticity and electronic sorting for immune cells

Abstract

Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://202.85.212.211/Account/ImmuSort.html.

Electronic Sorting of Immune Cell Subpopulations Based on Highly Plastic Genes

“…More than 35,000 potential protein-coding genes exist in the human genome (12), and .3,700 genes potentially encode cell surface transmembrane proteins (13). Because gene expression is highly plastic, and because each gene has its characteristic gene expression profile under various experiments, treatments, or pathophysiological conditions (14), we can expect that growing numbers of subpopulations will be discovered. One important question is whether these potential immune cell subpopulations and their functions can be predicted.…”

“…These big data imply that a great deal of knowledge will be discovered through data mining. Previously, we used immunologically relevant transcriptome data from microarrays to analyze gene coexpression (15), and we evaluated marker molecules based on gene plasticity (GPL) (14). GPL is the degree of change in the expression of a gene that happens in response to various environmental or genetic influences.…”

“…GPL is the degree of change in the expression of a gene that happens in response to various environmental or genetic influences. The GPL score is a quantitative measurement that can be used for the evaluation of biomarkers (14). Highly plastic genes often have large GPL scores; thus, they are not suitable as lineage-specific biomarkers of immune cells.…”

“…We expanded the meaning of electronic sorting, which previously described the retrieval of cell states or experimental conditions based on gene expression intensity from miscellaneous immune cell samples (14). As a method to "virtually sort big data samples to do analysis," we could also call this process "virtual sorting" or "in silico sorting," because no actual immune cells were sorted as they are in the FACS process.…”

“…All microarray data and immune cell groups were described in a previous study (14). The 619 samples (Dataset 1) of human CD4 + T cells in the ImmuSort database (http://immusort.bjmu.edu.cn or http://immu.tipsci.…”