A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S³ platform.

Area of Science:

• Biotechnology
• Genomics
• Cell Biology

Background:

• Clinical samples offer deep insights into health, disease, and treatment responses.
• Paucicellular specimens require specialized workflows to maximize information yield.
• Existing methods for single-cell multiomics can be costly and technically demanding.

Purpose of the Study:

• To provide a detailed protocol for generating and analyzing single-cell multiomic data from low-input samples.
• To introduce a sample hashing pipeline to reduce costs and technical variation.
• To enable high-quality multiomic insights from challenging clinical specimens.

Main Methods:

• Utilizing the Seq-Well S³ platform for single-cell library generation.
• Incorporating optional antibody-oligonucleotide staining for cell surface protein quantification and multiplexing.
• Employing optional bulk RNA sequencing (SMART-seq2) for genetic demultiplexing.
• Implementing a computational pipeline for data analysis.

Main Results:

• A streamlined and efficient methodology for single-cell multiomic data generation.
• A cost-effective sample hashing strategy that reduces technical variation and increases throughput.
• High-quality multiomic data obtainable from low-input samples.

Conclusions:

• The presented protocol significantly lowers barriers to entry for researchers.
• This approach enables rapid generation of high-quality multiomic insights from clinical samples.
• The methodology is adaptable and leverages standard laboratory equipment and reagents.