The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

Nature Biotechnology

Volumn 32, Issue 4, 2014, Pages 381-386

  Trapnell, Cole ^a,b   Cacchiarelli, Davide ^a,b,c   Grimsby, Jonna ^b   Pokharel, Prapti ^b   Li, Shuqiang ^d   Morse, Michael ^a,b   Lennon, Niall J ^b   Livak, Kenneth J ^d   Mikkelsen, Tarjei S ^a,b,c   Rinn, John L ^a,b,e  

^a Harvard University   (United States)

^b BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^c HARVARD STEM CELL INSTITUTE   (United States)

^d FLUIDIGM CORPORATION   (United States)

^e BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CYTOLOGY; DYNAMICS; GENE EXPRESSION; GENE EXPRESSION REGULATION; TIME SERIES ANALYSIS;

CELL DIFFERENTIATION; CELL FATE DECISION; GENE EXPRESSION ANALYSIS; ONCOGENIC TRANSFORMATION; PRIORI KNOWLEDGE; REGULATORY FACTORS; TEMPORAL RESOLUTION; UNSUPERVISED ALGORITHMS;

CELLS;

HISTONE H3; TRANSCRIPTOME; TRANSCRIPTION FACTOR;

ALGORITHM; ARTICLE; CELL ASSAY; CELL DIFFERENTIATION; CELL FATE; CELL PROLIFERATION; CONTROLLED STUDY; GENE CONTROL; GENE EXPRESSION; HUMAN; HUMAN CELL; IMMUNOFLUORESCENCE; INDEPENDENT COMPONENT ANALYSIS; MOLECULAR LIBRARY; MONOCLE; MUSCLE DEVELOPMENT; MYOBLAST; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; PROTEOMICS; REGULATORY MECHANISM; RNA SEQUENCE; SINGLE CELL ANALYSIS; TIME SERIES ANALYSIS; CELL CULTURE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; GENOMICS; METABOLISM; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; CELL DIFFERENTIATION; CELLS, CULTURED; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENOMICS; HUMANS; MUSCLE DEVELOPMENT; MYOBLASTS; REPRODUCIBILITY OF RESULTS; TRANSCRIPTION FACTORS; TRANSCRIPTOME;

EID: 84900873950     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.2859     Document Type: Article

References (42)

1. Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687-696 (2011).
2. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality inexpression and splicing in immune cells. Nature 498, 236-240 (2013).
3. Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675-685 (2010).
4. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Reports 2, 666-673 (2012).
5. Buganim, Y. et al. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150, 1209-1222 (2012).
6. Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468-478 (2010).
7. Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777-782 (2012).
8. Simpson, E.H. The interpretation of interaction in contingency tables. J. R. Stat. Soc. Series B Stat. Methodol. 13, 238-241 (1951).
9. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618-630 (2013).
10. Magwene, P.M., Lizardi, P. & Kim, J. Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19, 842-850 (2003).
11. Gupta, A. & Bar-Joseph, Z. Extracting dynamics from static cancer expression data. IEEE/ACM Trans. Comput. Biol. and Bioinf. 5, 172-182 (2008).
12. Qiu, P., Gentles, A.J. & Plevritis, S.K. Discovering biological progression underlying microarray samples. PLOS Comput. Biol. 7, e1001123 (2011).
13. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886-891 (2011).
14. Abmayr, S.M. & Pavlath, G.K. Myoblast fusion: lessons from flies and mice. Development 139, 641-656 (2012).
15. Tapscott, S.J. The circuitry of a master switch: Myod and the regulation of skeletal muscle gene transcription. Development 132, 2685-2695 (2005).
16. Tomczak, K.K. Expression profiling and identification of novel genes involved in myogenic differentiation. FASEB J. doi:10.1096/fj.03-0568fje (2004).
17. Hyvrinen, A. & Oja, E. Independent component analysis: algorithms and applications. Neural Netw. 411-430 (2000).
18. Amir, E.-A.D. et al. visNe enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545-552 (2013).
19. Joe, A.W.B. et al. Muscle injury activates resident fibro/adipogenic progenitors that facilitate myogenesis. Nat. Cell Biol. 12, 153-163 (2010).
20. Blais, A. An initial blueprint for myogenic differentiation. Genes Dev. 19, 553-569 (2005).
21. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83-90 (2012).
22. Ernst, J. & Kellis, M. ChromHMM: automating chromatin state discovery and characterization. Nat. Methods 9, 215-216 (2012).
23. Lun, Y., Sawadogo, M. & Perry, M. Autoactivation of Xenopus MyoD transcription and its inhibition by USF. Cell Growth Differ. 8, 275-282 (1997).
24. Sansregret, L. & Nepveu, A. The multiple roles of CUX1: insights from mouse models and cell-based assays. Gene 412, 84-94 (2008).
25. Acosta-Alvear, D. et al. XBP1 controls diverse cell type- and condition-specific transcriptional regulatory networks. Mol. Cell 27, 53-66 (2007).
26. De Figueiredo, L.H. & de Miranda Gomes, J. Computational morphology of curves. Vis. Comput. 11, 105-112 (1994).
27. Giesen, J. Curve reconstruction in arbitrary dimension and the traveling salesman problem. Proc. 8th Discrete Geometry and Computational Imagery Conference(DGCI) 164-176 (1999).
28. Giesen, J. Curve reconstruction, the traveling salesman problem, and Mengers theorem on length. Discrete Comput. Geom. 24, 577-603 (2000).
29. Booth, K.S. & Lueker, G.S. Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms. J. Comput. Syst. Sci. 13, 335-379 (1976).
30. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562-578 (2012).
31. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and genefusions. Genome Biol. 14, R36 (2013).
32. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).
33. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7 (suppl. 1), S4.1-S4.9 (2006).
34. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46-53 (2013).
35. Tobin, J. Estimation of relationships for limited dependent variables. Econometrica 26, 24-36 (1958).
36. Hastie, T. and Tibshirani, R. Generalized additive models, Stat. Sci. 1, 297-318 (1986).
37. Yee, T.W. & Wild, C.J. Generalized Additive Models. J. R. Stat. Soc. Series B Stat. Methodol. 58, 481-493 (1996).
38. Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283-1298 (2006).
39. Cacchiarelli, D. et al. miR-31 modulates dystrophin expression: new implications for Duchenne muscular dystrophy therapy. EMBO Rep. 12, 136-141 (2011).
40. Zhao, S. & Fernald, R.D. Comprehensive algorithm for quantitative real-time polymerase chain reaction. J. Comput. Biol. 12, 1047-1064 (2005).
41. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842 (2010).
42. Wu, D. & Smyth, G.K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).