Nuclei multiplexing with barcoded antibodies for single-nucleus genomics

Jellert T. Gaublomme (), Bo Li, Cristin McCabe, Abigail Knecht, Yiming Yang, Eugene Drokhlyansky, Nicholas Wittenberghe, Julia Waldman, Danielle Dionne, Lan Nguyen, Philip L. De Jager, Bertrand Yeung, Xinfang Zhao, Naomi Habib, Orit Rozenblatt-Rosen () and Aviv Regev ()
Additional contact information
Jellert T. Gaublomme: Broad Institute of Harvard and MIT
Bo Li: Broad Institute of Harvard and MIT
Cristin McCabe: Broad Institute of Harvard and MIT
Abigail Knecht: Broad Institute of Harvard and MIT
Yiming Yang: Allergy, and Immunology Massachusetts General Hospital and Harvard Medical School
Eugene Drokhlyansky: Broad Institute of Harvard and MIT
Nicholas Wittenberghe: Broad Institute of Harvard and MIT
Julia Waldman: Broad Institute of Harvard and MIT
Danielle Dionne: Broad Institute of Harvard and MIT
Lan Nguyen: Broad Institute of Harvard and MIT
Philip L. De Jager: Columbia University Medical Center
Bertrand Yeung: BioLegend Inc.
Xinfang Zhao: BioLegend Inc.
Naomi Habib: Broad Institute of Harvard and MIT
Orit Rozenblatt-Rosen: Broad Institute of Harvard and MIT
Aviv Regev: Broad Institute of Harvard and MIT

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: Abstract Single-nucleus RNA-seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate or are frozen, and opens the way to human genetics studies, clinical trials, and precise cell atlases of large organs. However, such applications are currently limited by batch effects, processing, and costs. Here, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter recovered profiles. We develop DemuxEM, a computational tool that detects inter-sample multiplets and assigns singlets to their sample of origin, and validate its accuracy using sex-specific gene expression, species-mixing and natural genetic variation. Our approach will facilitate tissue atlases of isogenic model organisms or from multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-10756-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10756-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-10756-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().