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Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins

Gehring, Jase and Park, Jong Hwee and Chen, Sisi and Thomson, Matthew and Pachter, Lior (2020) Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nature Biotechnology, 38 (1). pp. 35-38. ISSN 1087-0156. https://resolver.caltech.edu/CaltechAUTHORS:20181030-145533155

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Abstract

We describe a universal sample multiplexing method for single-cell RNA sequencing in which fixed cells are chemically labeled by attaching identifying DNA oligonucleotides to cellular proteins. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing an efficient method for surveying cell populations from large experiments or clinical samples with the depth and resolution of single-cell RNA sequencing.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41587-019-0372-zDOIArticle
https://rdcu.be/bZAv3PublisherFree ReadCube access
https://doi.org/10.1101/315333DOIDiscussion Paper
https://doi.org/10.22002/D1.1311DOIData
https://www.kallistobus.tools/kite_tutorial.htmlRelated ItemCode and tutorials
https://github.com/pachterlab/GPCTP_2019Related ItemPython notebooks
ORCID:
AuthorORCID
Gehring, Jase0000-0002-3894-9495
Chen, Sisi0000-0001-9448-9713
Pachter, Lior0000-0002-9164-6231
Alternate Title:Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces
Additional Information:© 2019 Nature Publishing Group. Received 07 August 2018; Accepted 27 November 2019; Published 23 December 2019. We thank Z. Gartner and C. McGinnis for helpful feedback regarding the ClickTag protocol and V. Svensson for suggestions regarding analysis of multiplexed datasets. Thanks to P. Melsted and S. Booeshaghi for developing the ‘kallisto | bustools’ functions used in the preprocessing workflow and to P. Rivaud for assistance with 10x data processing. Additional support was provided by the the Caltech Bioinformatics Resource Center and the Single Cell Profiling and Engineering Center (SPEC) in the Beckman Institute at Caltech. Data availability: Sequencing data from these experiments can be obtained from CaltechDATA at https://doi.org/10.22002/D1.1311. Code availability: Code and tutorials for the kITE demultiplexing workflow can be found at https://www.kallistobus.tools/kite_tutorial.html. Python notebooks used to process data and generate figures are available on GitHub at https://github.com/pachterlab/GPCTP_2019. The same GitHub repository also contains a fully reproducible reanalysis using ‘kallisto | bustools’ transcript alignments and a Google Colab notebook. Author Contributions: J.G. conceived and developed the ClickTag multiplexing strategy. J.G., J.H.P. and S.C. designed the scRNA-seq experiments and J.G. and J.H.P. performed the experiments. J.H.P. performed all tissue culture operations and J.G. developed the kITE demultiplexing workflow and analyzed the scRNA-seq data. J.G., J.H.P., S.C, M.T. and L.P. contributed to the interpretation of the results and writing of the manuscript. Competing interests: J.G., L.P., S.C. and J.H.P. are listed as co-inventors on a patent application related to this work (US patent application 16/296,075).
Funders:
Funding AgencyGrant Number
Caltech Beckman InstituteUNSPECIFIED
Subject Keywords:DNA; Molecular biology; Sequencing
Issue or Number:1
Record Number:CaltechAUTHORS:20181030-145533155
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20181030-145533155
Official Citation:Gehring, J., Hwee Park, J., Chen, S. et al. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat Biotechnol 38, 35–38 (2020) doi:10.1038/s41587-019-0372-z
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90524
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:30 Oct 2018 22:35
Last Modified:18 Nov 2020 18:16

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