Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering
Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering
Single-cell transcriptomics suffer from sensitivity limits that restrict low abundance transcript identification, affects clustering and can hamper downstream analyses. Here, we describe Constellation sequencing (Constellation-Seq), a molecular transcriptome filter that delivers two orders of magnitude sensitivity gains by maximizing read utility while reducing the data sparsity and sequencing costs. The technique reliably measures changes in gene expression and was demonstrated by resolving rare dendritic cell populations from a peripheral blood mononuclear cell sample sample and exploring their biology with extreme resolution. The simple and powerful method is fully compatible with standard scRNA-Seq library preparation protocols and can be used for hypothesis testing, marker validation or investigating pathways.
Biological Sciences, Omics, Transcriptomics
102147
Vallejo, Andres F.
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Davies, James
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Grover, Amit
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Tsai, Ching-hsuan
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Jepras, Robert
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Polak, Marta E.
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West, Jonathan
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19 March 2021
Vallejo, Andres F.
27bc0b94-0c40-4fd1-9533-7e267d588c0a
Davies, James
a93b4fc9-80a2-4620-ada6-c12f05c5ee38
Grover, Amit
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Tsai, Ching-hsuan
bd1cefe6-6509-4836-856a-afce7459a09f
Jepras, Robert
2268ceb7-0686-4af1-b387-a197fb6961cc
Polak, Marta E.
e0ac5e1a-7074-4776-ba23-490bd4da612d
West, Jonathan
f1c2e060-16c3-44c0-af70-242a1c58b968
Vallejo, Andres F., Davies, James, Grover, Amit, Tsai, Ching-hsuan, Jepras, Robert, Polak, Marta E. and West, Jonathan
(2021)
Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering.
iScience, 24 (3), , [102147].
(doi:10.1016/j.isci.2021.102147).
Abstract
Single-cell transcriptomics suffer from sensitivity limits that restrict low abundance transcript identification, affects clustering and can hamper downstream analyses. Here, we describe Constellation sequencing (Constellation-Seq), a molecular transcriptome filter that delivers two orders of magnitude sensitivity gains by maximizing read utility while reducing the data sparsity and sequencing costs. The technique reliably measures changes in gene expression and was demonstrated by resolving rare dendritic cell populations from a peripheral blood mononuclear cell sample sample and exploring their biology with extreme resolution. The simple and powerful method is fully compatible with standard scRNA-Seq library preparation protocols and can be used for hypothesis testing, marker validation or investigating pathways.
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resolving cellular
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More information
Accepted/In Press date: 31 January 2021
e-pub ahead of print date: 5 February 2021
Published date: 19 March 2021
Additional Information:
Funding Information:
We are grateful to the subjects who participated in this study and Elena Vataga (Computational Modeling Group, University of Southampton) for assistance with the High-Performance Computing | HPC Platform. This study was initially funded by an MRC Discovery grant (MC_PC_15078), M.E.P. is funded by Sir Henry Dale Fellowship, Wellcome Trust. (Grant no 109377/Z/15/Z), and A.F.V. is funded by GSK (project ARCP006668). A.F.V. designed, performed, and analyzed experiments, performed bioinformatic analyses and wrote the manuscript. M.E.P. and J.W. conceived the idea, planned the experiments, and contributed to writing. J.D. and A.G. performed the experiments. A.G. C.H.T. and R.J. contributed to the experimental plan and reviewed the manuscript. The authors declare no conflict of interest.
Funding Information:
We are grateful to the subjects who participated in this study and Elena Vataga (Computational Modeling Group, University of Southampton) for assistance with the High-Performance Computing | HPC Platform. This study was initially funded by an MRC Discovery grant ( MC_PC_15078 ), M.E.P. is funded by Sir Henry Dale Fellowship, Wellcome Trust . (Grant no 109377/Z/15/Z ), and A.F.V. is funded by GSK (project ARCP006668 ).
Publisher Copyright:
© 2021 The Author(s)
Keywords:
Biological Sciences, Omics, Transcriptomics
Identifiers
Local EPrints ID: 446773
URI: http://eprints.soton.ac.uk/id/eprint/446773
ISSN: 2589-0042
PURE UUID: 97f0311b-d67d-43bc-8dd5-4a32a9d7678e
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Date deposited: 22 Feb 2021 17:31
Last modified: 17 Mar 2024 03:46
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Contributors
Author:
Andres F. Vallejo
Author:
James Davies
Author:
Amit Grover
Author:
Ching-hsuan Tsai
Author:
Robert Jepras
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