![]() For human samples, the pre-mixed TotalSeq hashtag reagents recognize cell surface markers CD298 and β2-microglobulin. These are designed to work with any sequencing platform that relies on poly-dT oligonucleotides as the mRNA capture method, while TotalSeq-B and TotalSeq-C antibodies contain a capture sequences that are compatible with the 10x Genomics 3’ scRNA-seq (v3 or v3.1) and 5’ scRNA-seq workflows, respectively. There are several types of TotalSeq antibodies to be used for cell labeling, including TotalSeq-A antibodies that contain a poly-A sequence mimicking a natural mRNA. One of the most widely used methods to date for detection of the cell epitome is by using the TotalSeq antibodies from Biolegend in combination with the scRNA-seq technologies from 10x Genomics. The development of oligo-labeled antibodies directed against cell surface proteins for sample multiplexing, is a direct evolution from the Abseq, REAP-seq, and CITE-seq protocols. Therefore, implementing a barcoding multiplexing paradigm allows users to drastically increase the number of cells or nuclei loaded per reaction, which consequently decreases per-cell library preparation cost. 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. Recently, several approaches for multiplexing have been described, including the use of pre-existing genetic diversity or by introducing sample-specific barcodes using oligo-labeled antibodies, oligo-labeled lipid anchors, chemical labeling with oligos, or genetic cell labeling. ![]() 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. Current scRNA-seq experiments make it possible to routinely assay many thousands of cells at once, with recent datasets reporting hundreds of thousands to millions of cells. Recent advances in single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) have had an unprecedented impact on our understanding of heterogenous cell populations. However, antibodies demonstrate better results on tissues like spleen or lung. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. On nuclei datasets, lipid hashing delivers the best results. Conclusionsīenchmarking 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. 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. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Here, 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. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.” Results 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.
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