Dataset Information

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes.

ABSTRACT: Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression profile matrices of 8 mesenchymal cell subtypes obtained from bleomycin-treated mouse lungs, we identify several mesenchymal cell subtype changes. These include increases in Col13a1 matrix fibroblasts, mesenchymal progenitors and mesothelial cell populations and decreases in lipofibroblasts, endothelial and Pdgfrb high cell populations. Our data suggest that repetitive HBO exposure may affect biological processes in the lungs such as response to wounding, extracellular matrix, vasculature development and immune response.

SUBMITTER: Yuan Y

PROVIDER: S-EPMC7039706 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes.

Yuan Yuan Y Zhou Yilu Y Li Yali Y Hill Charlotte C Ewing Rob M RM Jones Mark G MG Davies Donna E DE Jiang Zhenglin Z Wang Yihua Y

International journal of molecular sciences 20200218 4

Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression p ...[more]

PMID: 32085618

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Project description:BackgroundCell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.MethodsWe developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.ResultsHASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients' overall survival.ConclusionHASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD .

Project description:RNA-seq, a next-generation sequencing based method for transcriptome analysis, is rapidly emerging as the method of choice for comprehensive transcript abundance estimation. The accuracy of RNA-seq can be highly impacted by the purity of samples. A prominent, outstanding problem in RNA-seq is how to estimate transcript abundances in heterogeneous tissues, where a sample is composed of more than one cell type and the inhomogeneity can substantially confound the transcript abundance estimation of each individual cell type. Although experimental methods have been proposed to dissect multiple distinct cell types, computationally "deconvoluting" heterogeneous tissues provides an attractive alternative, since it keeps the tissue sample as well as the subsequent molecular content yield intact.Here we propose a probabilistic model-based approach, Transcript Estimation from Mixed Tissue samples (TEMT), to estimate the transcript abundances of each cell type of interest from RNA-seq data of heterogeneous tissue samples. TEMT incorporates positional and sequence-specific biases, and its online EM algorithm only requires a runtime proportional to the data size and a small constant memory. We test the proposed method on both simulation data and recently released ENCODE data, and show that TEMT significantly outperforms current state-of-the-art methods that do not take tissue heterogeneity into account. Currently, TEMT only resolves the tissue heterogeneity resulting from two cell types, but it can be extended to handle tissue heterogeneity resulting from multi cell types. TEMT is written in python, and is freely available at https://github.com/uci-cbcl/TEMT.The probabilistic model-based approach proposed here provides a new method for analyzing RNA-seq data from heterogeneous tissue samples. By applying the method to both simulation data and ENCODE data, we show that explicitly accounting for tissue heterogeneity can significantly improve the accuracy of transcript abundance estimation.

Dataset Information

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes.

Publications

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes.

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