Integrative analyses of single-cell transcriptome and regulome using MAESTRO

Area of Science:

• Computational Biology
• Genomics
• Single-cell Multi-omics Analysis

Background:

• Single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) provide valuable insights into cellular heterogeneity.
• Integrating these datasets is crucial for understanding gene regulation but presents computational challenges.
• Existing methods often struggle with accurate cell-type identification and regulatory element discovery across multiple platforms.

Purpose of the Study:

• To develop a comprehensive computational workflow, MAESTRO (Model-based AnalysEs of Transcriptome and RegulOme), for the integrated analysis of scRNA-seq and scATAC-seq data.
• To improve the accuracy of cell clustering and annotation by modeling gene regulatory potential.
• To provide a user-friendly, open-source tool for multi-platform single-cell multi-omics data integration.

Main Methods:

• Development of MAESTRO, an open-source computational workflow.
• Implementation of functions for data pre-processing, alignment, quality control, and quantification.
• Modeling of gene regulatory potential using single-cell chromatin accessibility data.
• Integration of scRNA-seq and scATAC-seq data for clustering and differential analysis.
• Incorporation of automatic cell-type annotation and driver regulator identification.

Main Results:

• MAESTRO effectively integrates scRNA-seq and scATAC-seq data from multiple platforms.
• The workflow outperforms existing methods in integrating cell clusters between the two data types.
• MAESTRO enables accurate cell-type annotation using marker genes.
• The tool successfully identifies driver regulators from differential gene expression and chromatin accessibility data.

Conclusions:

• MAESTRO offers a robust and comprehensive solution for integrated single-cell multi-omics analysis.
• The workflow enhances the understanding of gene regulation and cell-type heterogeneity.
• MAESTRO facilitates advanced biological discovery through its integrative and analytical capabilities.