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

Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.”

Researchers at VIB Belgium compared the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. The researchers also compared TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where the researchers demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

Classification accuracy on human cells and nuclei with an overview of the experimental setup

A Overall classification accuracy (OCA) for all tested conditions and demultiplexed functions was calculated using freemuxlet demultiplexing as ground truth. SD represents variations of OCA across 4 cell lines or 3 individuals for PBMCs. B Four cancer cell lines or PBMCs were used to extract cells or nuclei to process further with the different labeling methods as indicated on the scheme. After pooling, the samples were run on a 10x Genomics Chromium platform and libraries were sequenced. LMO: lipid-modified oligonucleotides; CMO: cholesterol-modified oligonucleotides. “Pre-sort labeling”—labeling with hashing reagents followed by one wash and live/dead sorting with subsequent loading of the cells on a 10x Genomics chip. Also for these 2 PBMC samples cDNA libraries were generated using dual sample indexing (for all other samples single-sample indexing used)

Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.