UDA-seq: universal droplet microfluidics-based combinatorial indexing for massive-scale multimodal single-cell...

Area of Science:

• Single-cell genomics
• Biotechnology
• Microfluidics

Background:

• Droplet microfluidics enables scalable single-cell sequencing but often requires modality-specific protocols, hindering automation and clinical use.
• Existing methods lack a universal approach for integrating diverse single-cell omics data.
• Tailored protocols limit the automation potential and clinical applicability of current single-cell multimodal sequencing.

Purpose of the Study:

• To develop a universal workflow, UDA-seq, for adaptable and high-throughput single-cell multimodal sequencing.
• To overcome the limitations of existing methods by integrating a post-indexing step for enhanced compatibility.
• To enable robust and scalable multimodal single-cell analysis from clinical samples.

Main Methods:

• UDA-seq employs a universal workflow with a post-indexing step for droplet-based single-cell multimodal assays.
• The method was benchmarked across various tissue and cell types for RNA, VDJ, chromatin, and CRISPR perturbation co-assays.
• High-throughput generation of single-cell datasets from clinical biopsy specimens using single-channel droplet microfluidics.

Main Results:

• UDA-seq successfully generated over 100,000 high-quality single-cell datasets from clinical biopsies.
• The workflow demonstrated robustness in common multimodal analyses, including RNA-VDJ, RNA-chromatin, and RNA-CRISPR.
• Identified rare cell subpopulations linked to clinical phenotypes and explored cancer cell vulnerabilities.

Conclusions:

• UDA-seq provides a universal, scalable, and robust platform for single-cell multimodal analysis.
• The workflow significantly enhances throughput and clinical applicability of droplet-based sequencing.
• UDA-seq facilitates discovery in clinical research by enabling the analysis of rare cell populations and cancer vulnerabilities.