A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell...

Area of Science:

• Immunology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is vital for studying immune cell diversity.
• Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) integrates RNA and protein analysis but demands high sequencing depth.
• Existing tools for visualizing combined transcript-protein data are limited.

Purpose of the Study:

• To develop a cost-effective and sensitive method for simultaneous RNA and protein analysis in single cells.
• To address the limitations of high sequencing requirements in current multi-omic single-cell studies.
• To create effective visualization tools for integrated transcript-protein datasets.

Main Methods:

• A targeted transcriptomics approach analyzing over 400 genes and 40 proteins across 2x10^4 cells.
• Reduced sequencing read depth requirement to approximately one-tenth of whole-transcriptome methods.
• Adaptation of one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE) for data visualization.

Main Results:

• The targeted approach maintains high sensitivity for low-abundance transcripts.
• Significant reduction in sequencing depth requirements compared to whole-transcriptome CITE-seq.
• Successful visualization of protein-transcript relationships at the single-cell level using adapted One-SENSE.

Conclusions:

• Targeted transcriptomics offers a more efficient and sensitive alternative for multi-omic single-cell analysis.
• The developed method and visualization tool facilitate deeper understanding of immune cell heterogeneity.
• This approach lowers the barrier for comprehensive single-cell multi-omic studies.