The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing
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Abstract
Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed ‘single-cell combinatorial fluidic indexing’ (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation. Combining whole-transcriptome preindexing with standard droplet microfluidics, scifi-RNA-seq enables single-cell RNA-seq with massive throughput and built-in sample multiplexing.
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Metadata
- Authors
- Datlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C.
- Venue
- Nature Methods
- Published
- 2021-05-31
- Volume
- 18
- Issue
- 6
- Pages
- 635-642
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3165246254
- Cited by
- 269
- References
- 44
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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- Datlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C. (2021). Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nature Methods. https://doi.org/10.1038/s41592-021-01153-z
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