Nature CommunicationsGenomicsPeer-reviewed journal article
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics
Jellert T. GaublommeiD, Bo LiiD, Cristin AubiniD, Abigail KnechtiD, Yiming YangiD, Eugene DrokhlyanskyiD, Nicholas Van WittenbergheiD, Julia WaldmaniD, Danielle Ashley DionneiD, Lan NguyễniD, Philip L. De JageriD, Bertrand Z. YeungiD, Xinfang Zhao, Naomi HabibiD, Orit Rozenblatt–RoseniD, Aviv RegeviD
Abstract
Single-nucleus RNA-Seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate, including frozen clinical samples. This opens the way, in principle, to large studies, such as those required for human genetics, clinical trials, or precise cell atlases of large organs. However, such applications are currently limited by batch effects, sequential processing, and costs. To address these challenges, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies against the nuclear pore complex to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled in multiplex with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter the recovered transcriptome profiles. We further developed demuxEM, a novel computational tool that robustly detects inter-sample nucleus multiplets and assigns singlets to their samples of origin by antibody barcodes, and validated its accuracy using gender-specific gene expression, species-mixing and natural genetic variation. Nucleus hashing significantly reduces cost per nucleus, recovering up to about 5 times as many single nuclei per microfluidc channel. Our approach provides a robust technique for diverse studies including tissue atlases of isogenic model organisms or from a single larger human organ, multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.
- organism: human
- assay: snRNA-seq
- research focus: batch correction / integration
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: experimental assay
- Citations
- 189
- FWCI
- 6.62
- Field percentile
- top 2%
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