Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

By Marlon Stoeckius, Shiwei Zheng, B. Houck-Loomis, S. Hao, Bertrand Z. Yeung, William M. Mauck, Peter Smibert, Rahul Satija. Genome Biology, 2018

899 citations recorded by the source corpus.

Despite rapid developments in single cell sequencing, sample-specific batch effects, detection of cell multiplets, and experimental costs remain outstanding challenges. Here, we introduce Cell Hashing, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its original sample, robustly identify cross-sample multiplets, and “super-load” commercial droplet-based systems for significant cost reduction. We validate our approach using a complementary genetic approach and demonstrate how hashing can generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

Subfields: Biomedical Engineering, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6bee3a039f18e56c10883bb2cda1204e2588daa0

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Citations in the corpus

Integrated analysis of multimodal single-cell data (2020, positive): “In addition, we have recently demonstrated that the signal-to-noise for each antibody can be optimized for any individual marker as a function of antibody concentration, and we have shown that CITE-seq data quality does not deteriorate with greater amounts of total antibody (Stoeckius et al., 2018).”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, neutral): “Recent advances in single-cell and multiplexed spatial analysis of tissue allow us to interrogate this complexity at unprecedented resolution (Angelo et al., 2014; Buenrostro et al., 2015; Chen et al., 2016a; Chen et al., 2016b; Du et al., 2019; Gaublomme et al., 2019; Gierahn et al., 2017; Giesen e”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “Spleen and lymph node cell suspensions were stained with different hashtag antibodies [22].”
Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, positive): “taken and processed separately as reported previously (Stoeckius et al., 2018) to generate a small-length HTO library for sequencing using the online step-by-step protocol with 10 cycles of PCR library amplification (Stoeckius, 2015).”
Comprehensive integration of single-cell data (2018, positive): “Cells were resuspended in CITE-seq [Stoeckius et al., 2017] staining buffer (2%BSA/0.01%Tween in PBS) and incubated with FcX blocking reagent for 10 min (BioLegend, cat #: 422302) to block nonspeciﬁc antibody binding.”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “Samples were tagged or "cell hashed" (Stoeckius et al., 2018) with distinct BD sample tags (BD Bioscience, cat#PN 633780) to enable demultiplexing of cells, and then pooled for scRNA-seq.”