Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins
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.
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).Attached Files
Submitted - 315333.full.pdf
Supplemental Material - 41587_2019_372_MOESM1_ESM.pdf
Supplemental Material - 41587_2019_372_MOESM2_ESM.pdf
Supplemental Material - 41587_2019_372_MOESM3_ESM.xlsx
Supplemental Material - 41587_2019_372_MOESM4_ESM.xlsx
Files
| Name | Size | Download all |
|---|---|---|
|
md5:f21146ca8ffea3acdfabcebad849e290
|
2.4 MB | Preview Download |
|
md5:8fcf48064daf6934b10a09ffa4acec5e
|
19.3 MB | Preview Download |
|
md5:eceda39ac85c662ab113e1f3078574ad
|
30.0 kB | Download |
|
md5:28ba9d580d950398bec436b449252154
|
9.1 MB | Preview Download |
|
md5:9aed0d7b5d69a53debd39502bc180aea
|
54.5 kB | Download |
Additional details
- Alternative title
- Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces
- Eprint ID
- 90524
- Resolver ID
- CaltechAUTHORS:20181030-145533155
- Caltech Beckman Institute
- Created
-
2018-10-30Created from EPrint's datestamp field
- Updated
-
2023-06-01Created from EPrint's last_modified field