Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq.
CITE-seq
Hashing
MULTI-seq
Sample multiplexing
scRNA-seq
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
16 02 2022
16 02 2022
Historique:
received:
24
12
2020
accepted:
08
02
2022
entrez:
17
2
2022
pubmed:
18
2
2022
medline:
1
3
2022
Statut:
epublish
Résumé
Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing." Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.
Sections du résumé
BACKGROUND
Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing."
RESULTS
Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.
CONCLUSIONS
Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.
Identifiants
pubmed: 35172874
doi: 10.1186/s13059-022-02628-8
pii: 10.1186/s13059-022-02628-8
pmc: PMC8851857
doi:
Substances chimiques
Antibodies
0
Lipids
0
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
55Informations de copyright
© 2022. The Author(s).
Références
Nat Commun. 2019 Jul 2;10(1):2907
pubmed: 31266958
Genome Biol. 2020 Jul 30;21(1):188
pubmed: 32731885
Nat Biotechnol. 2017 Oct;35(10):936-939
pubmed: 28854175
Science. 2016 Jun 24;352(6293):1586-90
pubmed: 27339989
Proc Natl Acad Sci U S A. 2013 Dec 3;110(49):19802-7
pubmed: 24248345
Science. 2016 Aug 26;353(6302):925-8
pubmed: 27471252
Nat Biotechnol. 2018 Jun;36(5):411-420
pubmed: 29608179
Cell Syst. 2019 Apr 24;8(4):329-337.e4
pubmed: 30954475
Nature. 2015 Oct 1;526(7571):68-74
pubmed: 26432245
Annu Rev Immunol. 2003;21:107-37
pubmed: 12414720
Nature. 2018 Oct;562(7727):367-372
pubmed: 30283141
Nat Methods. 2017 Sep;14(9):865-868
pubmed: 28759029
Sci Rep. 2017 Mar 14;7:44447
pubmed: 28290550
Elife. 2021 Apr 16;10:
pubmed: 33861199
Cell Rep. 2019 Nov 5;29(6):1718-1727.e8
pubmed: 31693907
Genome Biol. 2019 May 9;20(1):90
pubmed: 31072405
Nat Methods. 2019 Jul;16(7):619-626
pubmed: 31209384
Science. 2020 Feb 21;367(6480):
pubmed: 32079746
Nat Methods. 2017 Oct;14(10):955-958
pubmed: 28846088
Cell Syst. 2019 Apr 24;8(4):281-291.e9
pubmed: 30954476
Cell. 2018 Aug 9;174(4):982-998.e20
pubmed: 29909982
Nat Biotechnol. 2018 Jan;36(1):89-94
pubmed: 29227470
Cell. 2018 Feb 22;172(5):1091-1107.e17
pubmed: 29474909
Cell. 2019 Jun 13;177(7):1888-1902.e21
pubmed: 31178118
Curr Protoc Immunol. 2018 Apr;121(1):e45
pubmed: 30040218
Genome Biol. 2018 Dec 19;19(1):224
pubmed: 30567574
Nature. 2019 Aug;572(7768):199-204
pubmed: 31292543
Nat Neurosci. 2019 Jun;22(6):1021-1035
pubmed: 31061494