SoupX Removes Ambient RNA Contamination from Droplet-based Single-cell RNA Sequencing Data

Overview

Journal Gigascience

Publisher Oxford University Press

Specialties Biology
Genetics

Date 2020 Dec 28

PMID 33367645

Citations 513

Authors

Matthew D Young

Sam Behjati

Affiliations

Soon will be listed here.

Abstract

Background: Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data.

Results: We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics.

Conclusions: We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

Citing Articles

Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer's disease.

van Olst L, Simonton B, Edwards A, Forsyth A, Boles J, Jamshidi P Nat Med. 2025; .

PMID: 40050704 DOI: 10.1038/s41591-025-03574-1.

IFN-α Induces Heterogenous ROS Production in Human β-Cells.

Wagner L, Melnyk O, Turner A, Duffett B, Muralidharan C, Martinez-Irizarry M bioRxiv. 2025; .

PMID: 40027743 PMC: 11870469. DOI: 10.1101/2025.02.19.639120.

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression.

Melton R, Jimenez S, Elison W, Tucciarone L, Howell A, Wang G bioRxiv. 2025; .

PMID: 40027657 PMC: 11870426. DOI: 10.1101/2025.02.13.637721.

Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points.

Soelter T, Howton T, Wilk E, Whitlock J, Clark A, Birnbaum A J Cell Commun Signal. 2025; 19(1):e70006.

PMID: 40026671 PMC: 11870853. DOI: 10.1002/ccs3.70006.

A single-cell atlas to map sex-specific gene-expression changes in blood upon neurodegeneration.

Grandke F, Fehlmann T, Kern F, Gate D, Wolff T, Leventhal O Nat Commun. 2025; 16(1):1965.

PMID: 40000636 PMC: 11862118. DOI: 10.1038/s41467-025-56833-7.

References

Popescu D, Botting R, Stephenson E, Green K, Webb S, Jardine L . Decoding human fetal liver haematopoiesis. Nature. 2019; 574(7778):365-371. PMC: 6861135. DOI: 10.1038/s41586-019-1652-y. View

Regev A, Teichmann S, Lander E, Amit I, Benoist C, Birney E . The Human Cell Atlas. Elife. 2017; 6. PMC: 5762154. DOI: 10.7554/eLife.27041. View

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

Bach K, Pensa S, Grzelak M, Hadfield J, Adams D, Marioni J . Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing. Nat Commun. 2017; 8(1):2128. PMC: 5723634. DOI: 10.1038/s41467-017-02001-5. View

Hashimoto S, Tabuchi Y, Yurino H, Hirohashi Y, Deshimaru S, Asano T . Comprehensive single-cell transcriptome analysis reveals heterogeneity in endometrioid adenocarcinoma tissues. Sci Rep. 2017; 7(1):14225. PMC: 5660171. DOI: 10.1038/s41598-017-14676-3. View

Chen Y, Friedman B, Ha C, Durinck S, Liu J, Rubenstein J . Single-cell RNA sequencing identifies distinct mouse medial ganglionic eminence cell types. Sci Rep. 2017; 7:45656. PMC: 5374502. DOI: 10.1038/srep45656. View

Zilionis R, Nainys J, Veres A, Savova V, Zemmour D, Klein A . Single-cell barcoding and sequencing using droplet microfluidics. Nat Protoc. 2016; 12(1):44-73. DOI: 10.1038/nprot.2016.154. View

Young M, Behjati S . SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. 2020; 9(12). PMC: 7763177. DOI: 10.1093/gigascience/giaa151. View

Ilicic T, Kim J, Kolodziejczyk A, Bagger F, McCarthy D, Marioni J . Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016; 17:29. PMC: 4758103. DOI: 10.1186/s13059-016-0888-1. View

10.

Heaton H, Talman A, Knights A, Imaz M, Gaffney D, Durbin R . Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat Methods. 2020; 17(6):615-620. PMC: 7617080. DOI: 10.1038/s41592-020-0820-1. View

11.

Alberti-Servera L, von Muenchow L, Tsapogas P, Capoferri G, Eschbach K, Beisel C . Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors. EMBO J. 2017; 36(24):3619-3633. PMC: 5730887. DOI: 10.15252/embj.201797105. View

12.

Zheng G, Terry J, Belgrader P, Ryvkin P, Bent Z, Wilson R . Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017; 8:14049. PMC: 5241818. DOI: 10.1038/ncomms14049. View

13.

Satija R, Farrell J, Gennert D, Schier A, Regev A . Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015; 33(5):495-502. PMC: 4430369. DOI: 10.1038/nbt.3192. View

14.

Butler A, Hoffman P, Smibert P, Papalexi E, Satija R . Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018; 36(5):411-420. PMC: 6700744. DOI: 10.1038/nbt.4096. View

15.

Daniszewski M, Senabouth A, Nguyen Q, Crombie D, Lukowski S, Kulkarni T . Single cell RNA sequencing of stem cell-derived retinal ganglion cells. Sci Data. 2018; 5:180013. PMC: 5810423. DOI: 10.1038/sdata.2018.13. View

16.

Haghverdi L, Lun A, Morgan M, Marioni J . Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018; 36(5):421-427. PMC: 6152897. DOI: 10.1038/nbt.4091. View

17.

Wolock S, Lopez R, Klein A . Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst. 2019; 8(4):281-291.e9. PMC: 6625319. DOI: 10.1016/j.cels.2018.11.005. View

18.

Svensson V, Natarajan K, Ly L, Miragaia R, Labalette C, Macaulay I . Power analysis of single-cell RNA-sequencing experiments. Nat Methods. 2017; 14(4):381-387. PMC: 5376499. DOI: 10.1038/nmeth.4220. View

19.

Yang S, Corbett S, Koga Y, Wang Z, Johnson W, Yajima M . Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 2020; 21(1):57. PMC: 7059395. DOI: 10.1186/s13059-020-1950-6. View

20.

Stephenson W, Donlin L, Butler A, Rozo C, Bracken B, Rashidfarrokhi A . Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation. Nat Commun. 2018; 9(1):791. PMC: 5824814. DOI: 10.1038/s41467-017-02659-x. View