Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell...

Area of Science:

• Single-cell biology
• Genomics
• Molecular profiling

Background:

• Multiomic profiling of single cells is crucial for understanding cellular diversity.
• Current droplet-based methods suffer from cell-free droplets, while microplate methods are labor-intensive.
• There is a need for high-throughput, efficient, and versatile single-cell multiomic profiling techniques.

Purpose of the Study:

• To introduce Overloading And unpacKing (OAK), a novel method for combinatorial indexing in single-cell multiomic profiling.
• To demonstrate the versatility and efficiency of OAK across different single-cell sequencing applications.
• To showcase OAK's capability in analyzing complex biological samples and identifying rare cell populations.

Main Methods:

• OAK employs a two-round strategy: initial droplet-based barcoding followed by aliquoting for combinatorial indexing.
• The method was validated using single-cell RNA sequencing and paired single-nucleus RNA sequencing with accessible chromatin profiling.
• OAK was applied to complex samples like bronchial epithelial cells and retinal tissue.

Main Results:

• OAK demonstrated broad compatibility, high sensitivity, and ultra-high throughput.
• The method successfully profiled over 400,000 melanoma cells treated with a RAF inhibitor.
• OAK identified a rare resistant melanoma cell population at 0.12% frequency.

Conclusions:

• OAK offers a simplified and powerful approach for large-scale single-cell molecular analysis.
• The technique's high throughput and sensitivity are advantageous for discovering rare cell types and responses.
• OAK represents a significant advancement in single-cell multiomics, facilitating deeper biological insights.