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Scientists develop high-resolution method to analyze skin gene expression post burns

Peer-Reviewed Publication

Hefei Institutes of Physical Science, Chinese Academy of Sciences

Scientists Develop High-Resolution Method to Analyze Skin Gene Expression Post Burns


Scientists Develop High-Resolution Method to Analyze Skin Gene Expression Post Burns

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Credit: LI Xueling

Recently, a research group led by Prof. LI Xueling from Hefei Institutes of Physical Science (HFIPS)Chinese Academy of Sciences (CAS) analyzed the skin gene expression post burns and developed a new cell type signature and pipeline based on a high-resolution group mode of cell type deconvolution.

The findings were published in the Journal of burn care & research: official publication of the American Burn Association.

Studying the timeline of gene activity after a burn injury is crucial for deciding optimal time for treatment. For over 30 years, scientists have mainly relied on analyzing overall gene activity in tissues to understand these changes. However, this approach has limitations—it can't distinguish between different cell types or specific gene activity in those cells. It only provides an average of all gene activity across all cell types.

"Genes behave differently in different cell types. This idea forms the basis for creating labels that represent each cell type's unique molecular activity," said Prof. LI, "But for a more detailed breakdown, we need more data from tissue samples than there are different cell types."

In this research, the team developed a high-resolution cell type deconvolution mode method using CIBERSORTx (cell-type and expression identification by estimating relative subsets of RNA transcripts) to analyze pooled skin bulk transcriptome data, which can identify the source of cell fraction and gene expression changes that caused bulk gene perturbations.

This method helped to identify and analyze different types of cells in the skin. They used data from both blood and skin samples to create a reference signature for eight different skin cell types, which they called Sig_Na, which was proved to be more accurate than other existing reference signatures when tested on independent datasets.

To expand the application of their model, researchers also developed a technique to amplify bulk samples by adding white noise. This overcame the challenge of having too few samples for high-resolution analysis of cell types, allowing them to accurately identify seven different cell types in the skin.

Additionally, they attempted to analyze skin adipocytes and identified additional gene expression markers to understand the process of white fat browning after burns.

"The cause of gene expression changes in skin tissue is a combination of changes in cell gene expression and changes in cell type fractions. It is of great significance to accurately distinguish cell fraction and gene expression changes, and to explore cell and gene targets post burn injury, so as to promote clinical wound healing." said Prof. LI Xueling.

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