Integrative Methods and Practical Challenges for Single-Cell Multi-omics

Overview

Journal Trends Biotechnol

Specialty Biochemistry

Date 2020 Aug 21

PMID 32818441

Citations 100

Authors

Anjun Ma

Adam McDermaid

Jennifer Xu

Yuzhou Chang

Qin Ma

Affiliations

Soon will be listed here.

Abstract

Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses.

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References

Duren Z, Chen X, Zamanighomi M, Zeng W, Satpathy A, Chang H . Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A. 2018; 115(30):7723-7728. PMC: 6065048. DOI: 10.1073/pnas.1805681115. View

Gawad C, Koh W, Quake S . Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016; 17(3):175-88. DOI: 10.1038/nrg.2015.16. View

Li L, Guo F, Gao Y, Ren Y, Yuan P, Yan L . Single-cell multi-omics sequencing of human early embryos. Nat Cell Biol. 2018; 20(7):847-858. DOI: 10.1038/s41556-018-0123-2. View

Zhang L, Lv C, Jin Y, Cheng G, Fu Y, Yuan D . Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma. Front Genet. 2018; 9:477. PMC: 6201709. DOI: 10.3389/fgene.2018.00477. View

Liu J, Lin D, Yardimci G, Noble W . Unsupervised embedding of single-cell Hi-C data. Bioinformatics. 2018; 34(13):i96-i104. PMC: 6022597. DOI: 10.1093/bioinformatics/bty285. View

Liu L, Liu C, Quintero A, Wu L, Yuan Y, Wang M . Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Commun. 2019; 10(1):470. PMC: 6349937. DOI: 10.1038/s41467-018-08205-7. View

Arendt D, Musser J, Baker C, Bergman A, Cepko C, Erwin D . The origin and evolution of cell types. Nat Rev Genet. 2016; 17(12):744-757. DOI: 10.1038/nrg.2016.127. View

Macaulay I, Haerty W, Kumar P, Li Y, Hu T, Teng M . G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015; 12(6):519-22. DOI: 10.1038/nmeth.3370. View

Shi Z, Zhang Q, Yan H, Yang Y, Wang P, Zhang Y . More than one antibody of individual B cells revealed by single-cell immune profiling. Cell Discov. 2019; 5:64. PMC: 6901605. DOI: 10.1038/s41421-019-0137-3. View

10.

Perakakis N, Yazdani A, Karniadakis G, Mantzoros C . Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism. 2018; 87:A1-A9. PMC: 6325641. DOI: 10.1016/j.metabol.2018.08.002. View

11.

Keener A . Single-cell sequencing edges into clinical trials. Nat Med. 2019; 25(9):1322-1326. DOI: 10.1038/d41591-019-00017-6. View

12.

Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S . Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. OMICS. 2018; 22(10):630-636. PMC: 6207407. DOI: 10.1089/omi.2018.0097. View

13.

Stahl P, Salmen F, Vickovic S, Lundmark A, Fernandez Navarro J, Magnusson J . Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353(6294):78-82. DOI: 10.1126/science.aaf2403. View

14.

Rodriques S, Stickels R, Goeva A, Martin C, Murray E, Vanderburg C . Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019; 363(6434):1463-1467. PMC: 6927209. DOI: 10.1126/science.aaw1219. View

15.

SANGER F, Coulson A . A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol. 1975; 94(3):441-8. DOI: 10.1016/0022-2836(75)90213-2. View

16.

Lo P, Zhou Q . Emerging techniques in single-cell epigenomics and their applications to cancer research. J Clin Genom. 2018; 1(1). PMC: 6070152. DOI: 10.4172/JCG.1000103. View

17.

Peterson V, Zhang K, Kumar N, Wong J, Li L, Wilson D . Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017; 35(10):936-939. DOI: 10.1038/nbt.3973. View

18.

Welch J, Hartemink A, Prins J . MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 2017; 18(1):138. PMC: 5525279. DOI: 10.1186/s13059-017-1269-0. View

19.

Datlinger P, Rendeiro A, Schmidl C, Krausgruber T, Traxler P, Klughammer J . Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods. 2017; 14(3):297-301. PMC: 5334791. DOI: 10.1038/nmeth.4177. View

20.

Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P . Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 2016; 26(3):304-19. PMC: 4783472. DOI: 10.1038/cr.2016.23. View