Study on Liver Sinusoidal Endothelial Cell Fenestrations Based on Cellular Omics-Structure Integration Technology and...

Area of Science:

• Cellular biology
• Molecular biology
• Biotechnology

Background:

• Traditional methods struggle to simultaneously capture gene expression and detailed cellular structures at the single-cell level.
• Integrating transcriptomics with high-resolution imaging is crucial for understanding cellular function and disease mechanisms.

Purpose of the Study:

• To develop a novel technology platform, Cellular Omics-Structural Integration (COSI), for simultaneous single-cell gene expression and super-resolution structural analysis.
• To identify gene sets associated with endothelial cell fenestration and their role in metabolic diseases.

Main Methods:

• Development of a COSI platform integrating single-cell transcriptomics, super-resolution fluorescence microscopy, and electron microscopy with deep learning enhancement.
• Application to primary liver sinusoidal endothelial cells for correlating gene expression with ultrastructural morphology.
• Analysis of gene sets linked to fenestration in non-alcoholic steatohepatitis (NASH) and diabetic mouse models.

Main Results:

• Successful simultaneous acquisition and analysis of gene expression and super-resolution images at the single-cell level.
• Identification of specific gene sets associated with endothelial cell fenestration number and area.
• Validation of identified gene sets as effective indicators of disease status and drug efficacy in metabolic disease models.

Conclusions:

• The COSI platform bridges the gap between omics data and cellular structure, offering unprecedented insights into cellular biology.
• Identified gene sets provide novel molecular markers for early diagnosis and therapeutic targets in chronic metabolic diseases like NASH and diabetes.
• COSI technology holds significant potential for fundamental research and clinical applications in metabolic diseases, especially for structures lacking specific markers.