Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Area of Science:

• Single cell biology
• Genomics and multi-omics
• Computational biology

Background:

• Single cell sequencing technologies have advanced rapidly, enabling detailed analysis of individual cells.
• These methods now allow simultaneous profiling of multiple cellular features, including gene expression, chromatin accessibility, and protein levels.
• Spatial context is increasingly integrated through *in situ* multi-omic approaches.

Purpose of the Study:

• To summarize current experimental and computational methods for generating and integrating single cell multi-omic datasets.
• To highlight the potential of single cell multi-omics in advancing therapeutic development for kidney disease.
• To explore applications in biomarker identification, disease stratification, and target discovery.

Main Methods:

• Review of single cell sequencing techniques for multi-omic data generation.
• Summary of computational tools for integrating diverse single cell data types.
• Application of machine learning approaches for data analysis and insight extraction.

Main Results:

• Current single cell multi-omic methods enable simultaneous measurement of various molecular features.
• Computational tools facilitate the integration of complex multi-omic datasets.
• Machine learning enhances the extraction of biological insights from these data.

Conclusions:

• Single cell multi-omics offers unprecedented resolution for understanding cellular complexity.
• This technology holds significant promise for revolutionizing kidney disease therapeutic development.
• Key applications include identifying novel biomarkers, stratifying patients, and discovering therapeutic targets.