VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data.

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Immune cell differentiation is regulated by complex signaling pathways and transcription factors.
• Traditional methods like flow cytometry have limitations in comprehensively analyzing multiple proteins simultaneously.
• Single-cell RNA sequencing (scRNA-seq) offers a powerful approach for genome-wide gene expression analysis in individual cells.

Purpose of the Study:

• To present VirtualCytometry, a web-based computational pipeline for immune cell differentiation analysis using scRNA-seq data.
• To enable the identification of cellular subsets and functional states based on gene expression patterns.
• To provide a resource for discovering signaling molecules and transcription factors involved in immune cell transitions.

Main Methods:

• Utilized cell-to-cell variation in gene expression from scRNA-seq data.
• Developed a computational pipeline to identify cellular subsets representing different differentiation states.
• Applied marker gene expression analysis to characterize these subsets.

Main Results:

• VirtualCytometry successfully identifies distinct cellular subsets and functional states during immune cell differentiation.
• Case studies demonstrated its utility in uncovering factors related to human T-cell exhaustion and dendritic cell activation.
• The pipeline leverages a large repository of over 226 scRNA-seq datasets for broad applicability.

Conclusions:

• VirtualCytometry provides a versatile computational framework for dissecting immune cell differentiation.
• It enhances the understanding of molecular programs governing immune cell functional states.
• The resource supports research into immune responses in both normal and disease contexts.