Abstract
BackgroundMultiplexing 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.”ResultsHere, we compare 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. We also compare 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 we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.ConclusionsBenchmarking 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.
Highlights
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
The hashing accuracy or Overall Classification Accuracy (OCA) was calculated as the overlap between cell line annotation using Seurat (MULTISeqDemux and HTODemux functions) or GMM-Demux [21], and freemuxlet as a reference annotation as follows: the OCA equals the number of all matching singlets, plus a number of matching non-singlets,divided by the number of all cells (Fig. 1)
One of the first characteristics evaluated for all types of hashing techniques in our study was the level of detection of each hashtag oligo (HTO)
Summary
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. Published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.”. In standard single-cell workflows, individual samples need to be processed in parallel, which limits the throughput, increases reagent costs and has the potential to introduce batch effects. Multiplexing samples by labeling cells or nuclei with sample-specific barcodes before pooling and single-cell compartmentalization, a technique called “hashing,” allows for accurate detection of two (doublets) or more (multiplets) cells originating from different samples but captured in the same compartment, which inevitably occurs in standard single-cell workflows. Implementing a barcoding multiplexing paradigm allows users to drastically increase the number of cells or nuclei loaded per reaction, which decreases percell library preparation cost
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