Abstract
Mice are some of the widely used experimental animal models for studying human diseases. Defining the compositions of immune cell populations in various tissues from experimental mouse models is critical to understanding the involvement of immune responses in various physiological and patho-physiological conditions. However, non-lymphoid tissues are normally composed of vast and diverse cellular components, which make it difficult to quantify the relative proportions of immune cell types. Here we report the development of a computational algorithm, ImmuCC, to infer the relative compositions of 25 immune cell types in mouse tissues using microarray-based mRNA expression data. The ImmuCC algorithm showed good performance and robustness in many simulated datasets. Remarkable concordances were observed when ImmuCC was used on three public datasets, one including enriched immune cells, one with normal single positive T cells, and one with leukemia cell samples. To validate the performance of ImmuCC objectively, thorough cross-comparison of ImmuCC predicted compositions and flow cytometry results was done with in-house generated datasets collected from four distinct mouse lymphoid tissues and three different types of tumor tissues. The good correlation and biologically meaningful results demonstrate the broad utility of ImmuCC for assessing immune cell composition in diverse mouse tissues under various conditions.
Highlights
Mice are some of the widely used experimental animal models for studying human diseases
The expression profile of a given tissue is considered as a hybrid ensemble of gene expressions across multiple cell subsets
A computational model named ImmuCC was developed to infer the relatively compositions of 25 immune cell types with 511 signature genes via the linear support vector regression (SVR) approach
Summary
Mice are some of the widely used experimental animal models for studying human diseases. Characterizing tissue infiltration of immune cells would be highly useful towards quantifying immune responses inside the affected tissues and for better understanding the immunological mechanisms involved in disease development Based on their cell surface markers, immune cell types could be qualitatively and quantitatively measured via several experimental methods, including flow cytometry[7], affinity purification[8], and immunohistochemistry[9]. To complement the histological and immunological approaches, several computational deconvolution methods have been developed to calculate the tissue immune cell fractions from human transcriptome data[12,13] The principle underlying this computational strategy is that the gene expression profile of heterogeneous tissues is assumed to be a mixture of cell-type specific expression. A nu-support vector regression (SVR) based method, CIBERSORT, showed significant advantages, its tolerance to background noise introduced by other unknown cells[18]
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