10x chromium system12/5/2023 ![]() Cell hashtags allow for robust sample multiplexing, confident multiplet identification, and discrimination of low-quality cells from ambient RNA. We demonstrate this approach by labeling and pooling eight human PBMC samples and running them simultaneously in a single droplet-based scRNA-seq run. We refer to this approach as Cell Hashing, based on the concept of hash functions in computer science to index datasets with specific features our set of oligo-derived hashtags equally define a “lookup table” to assign each multiplexed cell to its original sample. This enables us to pool these together and use the barcoded antibody signal as a fingerprint for reliable demultiplexing. We reasoned that a defined set of oligo-tagged antibodies against ubiquitous surface proteins could uniquely label different experimental samples. We recently introduced CITE-seq, where oligonucleotide-tagged antibodies are used to convert the detection of cell surface proteins into a sequenceable readout alongside scRNA-seq. ![]() For instance, sample multiplexing is frequently utilized in flow and mass cytometry by labeling distinct samples with antibodies to the same ubiquitously expressed surface protein but conjugated to different fluorophores or isotopes, respectively. While this elegant approach requires pooled samples to originate from previously genotyped individuals, in principle, any approach assigning sample fingerprints that can be measured alongside scRNA-seq would enable a similar strategy. This workflow also enables the detection of multiplets originating from two individuals, reducing non-identifiable multiplets at a rate that is directly proportional to the number of multiplexed samples. Here, the sample-specific genetic polymorphisms serve as a fingerprint for the sample of origin and therefore can be used to assign each cell to an individual after sequencing. For example, the demuxlet algorithm enables the pooling of samples with distinct genotypes together into a single scRNA-seq experiment. Recent developments have poignantly demonstrated how sample multiplexing can simultaneously overcome multiple challenges. Similarly, technical and “batch” effects have been demonstrated to mask biological signal in the integrated analysis of scRNA-seq experiments, necessitating experimental solutions to mitigate these challenges. ![]() While multiplets are expected to generate higher complexity libraries compared to singlets, the strength of this signal is not sufficient for unambiguous identification. In particular, reliably identifying expression profiles corresponding to more than one cell remains an unsolved challenge in single-cell RNA-seq (scRNA-seq) analysis, and a robust solution would simultaneously improve data quality and enable increased experimental throughput. īroadly related challenges also remain, including the robust identification of artifactual signals arising from cell multiplets or technology-dependent batch effects. ![]() While the per-cell cost of library prep has dropped, routine profiling of tens to hundreds of thousands of cells remains costly both for individual labs and for consortia such as the Human Cell Atlas. As studies have progressed to profiling complex human tissues and even entire organisms, there is a growing appreciation of the need for massively parallel technologies and datasets to uncover rare and subtle cell states. Single cell genomics offers enormous promise to transform our understanding of heterogeneous processes and to reconstruct unsupervised taxonomies of cell types. ![]()
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