Robust detection of alternative splicing in a population of single cells

Nucleic Acids Research

Volumn 44, Issue 8, 2016, Pages e73-

  Welch, Joshua D ^a   Hu, Yin ^b   Prins, Jan F ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b SAGE BIONETWORKS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATIVE RNA SPLICING; ANIMAL CELL; ANIMAL TISSUE; ARTICLE; CELL CYCLE PHASE; CELL HETEROGENEITY; CELL POPULATION; CELL SIZE; CONTROLLED STUDY; EMBRYO; GENE EXPRESSION; GENETIC IDENTIFICATION; GENETIC VARIABILITY; MATHEMATICAL MODEL; MOUSE; NOISE; NONHUMAN; PRIORITY JOURNAL; RNA SEQUENCE; RNA TRANSCRIPTION; SENSITIVITY AND SPECIFICITY; SINGLE CELL; SPIKE WAVE; STATISTICAL MODEL; ANIMAL; BIOLOGY; CELL CYCLE; CYTOLOGY; EMBRYONIC STEM CELL; GENE EXPRESSION PROFILING; GENETICS; NUCLEOTIDE SEQUENCE; PROCEDURES; SEQUENCE ANALYSIS;

ISOPROTEIN; RNA;

ALTERNATIVE SPLICING; ANIMALS; BASE SEQUENCE; CELL CYCLE; COMPUTATIONAL BIOLOGY; EMBRYONIC STEM CELLS; GENE EXPRESSION PROFILING; MICE; MODELS, STATISTICAL; PROTEIN ISOFORMS; RNA; SEQUENCE ANALYSIS, RNA;

EID: 84962684884     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkv1525     Document Type: Article

References (33)

1. Nilsen,T.W. and Graveley,B.R. (2010) Expansion of the eukaryotic proteome by alternative splicing. Nature, 463, 457-463.
2. Wang,Z., Gerstein,M. and Snyder,M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57-63.
3. Sandberg,R. (2013) Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods, 11, 22-24.
4. Shapiro,E., Biezuner,T. and Linnarsson,S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet., 14, 618-630.
5. Saliba,A.-E., Westermann,A.J., Gorski,S.A. and Vogel,J. (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res., 42, 8845-8860.
6. Stegle,O., Teichmann,S.A. and Marioni,J.C. (2015) Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet., 16, 133-145.
7. Brennecke,P., Anders,S., Kim,J.K., Ko-lodziejczyk,A.A., Zhang,X., Proserpio,V., Baying,B., Benes,V., Teichmann,S.A., Marioni,J.C. et al. (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods, 10, 1093-1095.
8. Streets,A.M. and Huang,Y. (2014) How deep is enough in single-cell RNA-seq? Nat. Biotechnol., 32, 1005-1006.
9. Buettner,F., Natarajan,K.N., Casale,F.P., Proserpio,V., Scialdone,A., Theis,F.J., Teichmann,S.A., Marioni,J.C. and Stegle,O. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol., 33, 155-160.
10. Grün,D., Kester,L. and van Oudenaarden,A. (2014) Validation of noise models for single-cell transcriptomics. Nat. Methods, 11, 637-640.
11. Kharchenko,P.V., Silberstein,L. and Scadden,D.T. (2014) Bayesian approach to single-cell differential expression analysis. Nat. Methods, 11, 740-742.
12. Wu,A.R., Neff,N.F., Kalisky,T., Dalerba,P., Treutlein,B., Rothenberg,M.E., Mburu,F.M., Mantalas,G.L., Sim,S., Clarke,M.F. et al. (2014) Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods, 11, 41-46.
13. Treutlein,B., Brownfield,D.G., Wu,A.R., Neff,N.F., Mantalas,G.L., Espinoza,F.H., Desai,T.J., Krasnow,M.A. and Quake,S.R. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature, 509, 371-375.
14. Macosko,E.Z., Basu,A., Satija,R., Nemesh,J., Shekhar,K., Goldman,M., Tirosh,I., Bialas,A.R., Kamitaki,N., Martersteck,E.M. et al. (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161, 1202-1214.
15. Fan,H.C., Fu,G.K. and Fodor,S.P.A. (2015) Combinatorial labeling of single cells for gene expression cytometry. Science 347.
16. Hu,Y., Huang,Y., Du,Y., Orellana,C.F., Singh,D., Johnson,A.R., Monroy,A., Kuan,P.-F., Hammond,S.M., Makowski,L. et al. (2013) DiffSplice: the genome-wide detection of differential splicing events with RNA-seq. Nucleic Acids Res., 41, e39.
17. Singh,D., Orellana,C.F., Hu,Y., Jones,C.D., Liu,Y., Chiang,D.Y., Liu,J. and Prins,J.F. (2011) FDM: a graph-based statistical method to detect differential transcription using RNA-seq data. Bioinformatics, 27, 2633-2640.
18. Pertea,M., Pertea,G.M., Antonescu,C.M., Chang,T.-C., Mendell,J.T. and Salzberg,S.L. (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol., 33, 290-295.
19. Katz,Y., Wang,E.T., Airoldi,E.M. and Burge,C.B. (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods, 7, 1009-1015.
20. Jiang,L., Schlesinger,F., Davis,C.A., Zhang,Y., Li,R., Salit,M., Gingeras,T.R. and Oliver,B. (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res., 21, 1543-1551.
21. König,J., Zarnack,K., Rot,G., Curk,T., Kayikci,M., Zupan,B., Turner,D.J., Luscombe,N.M. and Ule,J. (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol., 17, 909-915.
22. Yang,F., Yi,F., Han,X., Du,Q. and Liang,Z. (2013) MALAT-1 interacts with hnRNP C in cell cycle regulation. FEBS Lett., 587, 3175-3181.
23. Kim,J.H., Paek,K.Y., Choi,K., Kim,T.-D., Hahm,B., Kim,K.-T. and Jang,S.K. (2003) Heterogeneous nuclear ribonucleoprotein C modulates translation of c-myc mRNA in a cell cycle phase-dependent manner. Mol. Cell. Biol., 23, 708-720.
24. Piñol-Roma,S. and Dreyfuss,G. (1993) Cell cycle-regulated phosphorylation of the pre-mRNA-binding (heterogeneous nuclear ribonucleoprotein) C proteins. Mol. Cell. Biol., 13, 5762-5770.
25. Zhou,A., Ou,A.C., Cho,A., Benz,E.J. and Huang,S.-C. (2008) Novel splicing factor RBM25 modulates Bcl-x pre-mRNA 5′ splice site selection. Mol. Cell. Biol., 28, 5924-5936.
26. Shalek,A.K., Satija,R., Adiconis,X., Gertner,R.S., Gaublomme,J.T., Raychowdhury,R., Schwartz,S., Yosef,N., Malboeuf,C., Lu,D. et al. (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 498, 236-240.
27. Trapnell,C., Cacchiarelli,D., Grimsby,J., Pokharel,P., Li,S., Morse,M., Lennon,N.J., Livak,K.J., Mikkelsen,T.S. and Rinn,J.L. (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 381-386.
28. Shalek,A.K., Satija,R., Shuga,J., Trombetta,J.J., Gennert,D., Lu,D., Chen,P., Gertner,R.S., Gaublomme,J.T., Yosef,N. et al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 509, 363-369.
29. Patel,A.P., Tirosh,I., Trombetta,J.J., Shalek,A.K., Gillespie,S.M., Wakimoto,H., Cahill,D.P., Nahed,B.V., Curry,W.T., Martuza,R.L. et al. (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344, 1396-1401.
30. Dey,S.S., Kester,L., Spanjaard,B., Bienko,M. and van Oudenaarden,A. (2015) Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol., 33, 285-289.
31. Nagano,T., Lubling,Y., Stevens,T.J., Schoenfelder,S., Yaffe,E., Dean,W., Laue,E.D., Tanay,A. and Fraser,P. (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature, 502, 59-64.
32. Buenrostro,J.D., Wu,B., Litzenburger,U.M., Ruff,D., Gonzales,M.L., Snyder,M.P., Chang,H.Y. and Greenleaf,W.J. (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature, 523, 486-490.
33. Rotem,A., Ram,O., Shoresh,N., Sperling,R.A., Goren,A., Weitz,D.A. and Bernstein,B.E. (2015) Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol., 33, 1165-1172.