Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Multiplexing in single-cell RNA sequencing (scRNA-seq) reduces costs and batch effects.
• Hashing techniques using oligo-conjugated antibodies or lipids barcode samples for multiplexing.

Purpose of the Study:

• To compare the hashing performance of antibody-based and lipid-based methods.
• To evaluate hashing efficiency across different sample types (cells, nuclei) and species (human, mouse).
• To identify optimal hashing strategies for scRNA-seq and single-nucleus RNA-seq (snRNA-seq).

Main Methods:

• Comparison of TotalSeq-A/C antibodies, custom lipids, and MULTI-seq lipid hashes.
• Evaluation on cell lines, human PBMCs, and primary mouse tissues for scRNA-seq and snRNA-seq.
• Assessment of hashing efficiency using intrinsic genetic variation and clinical samples.

Main Results:

• Both antibody and lipid hashing enable accurate demultiplexing of human cells and nuclei.
• Antibody hashing is most efficient for cells, while lipid hashing performs best on nuclei and mouse brain.
• Antibody hashing shows better performance on mouse spleen and lung tissues.

Conclusions:

• Antibody and lipid hashing are effective for scRNA-seq and snRNA-seq multiplexing.
• The optimal hashing strategy depends on the sample type and tissue.
• Hashing provides a cost-effective approach for large-scale clinical sequencing studies.