Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq

Nature

Volumn 534, Issue 7607, 2016, Pages 391-395

  Treutlein, Barbara ^a,b   Lee, Qian Yi ^a,c   Camp, J Gray ^c   Mall, Moritz ^c   Koh, Winston ^a   Shariati, Seyed Ali Mohammad ^d   Sim, Sopheak ^c   Neff, Norma F ^a   Skotheim, Jan M ^d   Wernig, Marius ^c   Quake, Stephen R ^a,d,e  

^a Stanford University   (United States)

^b MAX PLANCK INSTITUTE FOR EVOLUTIONARY ANTHROPOLOGY   (Germany)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d STANFORD UNIVERSITY   (United States)

^e HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEMENTARY DNA; MESSENGER RNA; RNA; TRANSCRIPTOME; ASCL1 PROTEIN, MOUSE; BASIC HELIX LOOP HELIX TRANSCRIPTION FACTOR; HOMEODOMAIN PROTEIN; MYT1L PROTEIN, MOUSE; NERVE PROTEIN; TRANSCRIPTION FACTOR; TRANSCRIPTION FACTOR BRN-2; TRANSCRIPTION FACTOR POU;

CELLS AND CELL COMPONENTS; CYTOLOGY; GENE EXPRESSION; HETEROGENEITY; NEUROLOGY; PHYLOGENY; POLYMER; RNA;

ANIMAL CELL; ARTICLE; CELL CYCLE; CELL DIFFERENTIATION; CELL HETEROGENEITY; CELL LINEAGE; CELL MATURATION; CELL PROLIFERATION; CONTROLLED STUDY; EMBRYO; FIBROBLAST; GENE OVEREXPRESSION; MOUSE; MOUSE EMBRYONIC STEM CELL; NERVE CELL; NONHUMAN; NUCLEAR REPROGRAMMING; PRIORITY JOURNAL; RNA SEQUENCE; RNA TRANSCRIPTION; SINGLE CELL ANALYSIS; SINGLE CELL RNA SEQUENCING; TARGET CELL; TRANSCRIPTOMICS; TRANSGENE; ANIMAL; CELL TRANSDIFFERENTIATION; CYTOLOGY; GENE EXPRESSION PROFILING; GENE SILENCING; GENETICS; MAMMALIAN EMBRYO; METABOLISM; SEQUENCE ANALYSIS; TIME FACTOR;

ANIMALS; BASIC HELIX-LOOP-HELIX TRANSCRIPTION FACTORS; CELL CYCLE; CELL LINEAGE; CELL TRANSDIFFERENTIATION; CELLULAR REPROGRAMMING; EMBRYO, MAMMALIAN; FIBROBLASTS; GENE EXPRESSION PROFILING; GENE SILENCING; HOMEODOMAIN PROTEINS; MICE; NERVE TISSUE PROTEINS; NEURONS; POU DOMAIN FACTORS; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TIME FACTORS; TRANSCRIPTION FACTORS; TRANSCRIPTOME; TRANSGENES;

EID: 84975473183     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature18323     Document Type: Article

References (45)

1. Xu, J., Du, Y. & Deng, H. Direct lineage reprogramming: strategies, mechanisms, and applications. Cell Stem Cell 16, 119-134 (2015).
2. Arlotta, P. & Berninger, B. Brains in metamorphosis: reprogramming cell identity within the central nervous system. Curr. Opin. Neurobiol. 27, 208-214 (2014).
3. Graf, T. Historical origins of transdifferentiation and reprogramming. Cell Stem Cell 9, 504-516 (2011).
4. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375 (2014).
5. Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236-240 (2013).
6. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015).
7. Ramsköld, 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. Vierbuchen, T. et al. Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035-1041 (2010).
9. Pfisterer, U. et al. Direct conversion of human fibroblasts to dopaminergic neurons. Proc. Natl Acad. Sci. USA 108, 10343-10348 (2011).
10. Yoo, A. S. et al. MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 476, 228-231 (2011).
11. Ambasudhan, R. et al. Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions. Cell Stem Cell 9, 113-118 (2011).
12. Caiazzo, M. et al. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts. Nature 476, 224-227 (2011).
13. Wapinski, O. L. et al. Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155, 621-635 (2013).
14. Chanda, S. et al. Generation of induced neuronal cells by the single reprogramming factor ASCL1. Stem Cell Rep. 3, 282-296 (2014).
15. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381-386 (2014).
16. Buettner, F. et al. Computational analysis of cell-To-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155-160 (2015).
17. Merkle, F. T. & Eggan, K. Modeling human disease with pluripotent stem cells: from genome association to function. Cell Stem Cell 12, 656-668 (2013).
18. Perrier, A. L. et al. Derivation of midbrain dopamine neurons from human embryonic stem cells. Proc. Natl Acad. Sci. USA 101, 12543-12548 (2004).
19. Li, X. J. et al. Specification of motoneurons from human embryonic stem cells. Nat. Biotechnol. 23, 215-221 (2005).
20. Camp, J. G. et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl Acad. Sci. USA 112, 15672-15677 (2015).
21. Di Stefano, B. et al. C/EBPα poises B cells for rapid reprogramming into induced pluripotent stem cells. Nature 506, 235-239 (2014).
22. Lujan, E. et al. Early reprogramming regulators identified by prospective isolation and mass cytometry. Nature 521, 352-356 (2015).
23. Takahashi, K. et al. Induction of pluripotency in human somatic cells via a transient state resembling primitive streak-like mesendoderm. Nat. Commun. 5, 3678 (2014).
24. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929-11947 (2014).
25. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511-515 (2010).
26. Baker, S. C. et al. The External RNA Controls Consortium: A progress report. Nat. Methods 2, 731-734 (2005).
27. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543-1551 (2011).
28. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
29. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41-46 (2014).
30. Babraham Institute. Babraham Bioinformatics. FASTQC. http://www. bioinformatics.bbsrc.ac.uk/projects/fastqc
31. Martin, M. Cutadapt removes adapter sequences from high-Throughput sequencing reads. EMBnet.journal 17, 10-12 (2011).
32. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863-864 (2011).
33. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memoryefficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
34. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).
35. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105-1111 (2009).
36. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).
37. RStudio. Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/(2015).
38. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
39. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015).
40. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495-502 (2015).
41. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695 (2006).
42. Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44-57 (2009).
43. Zhang, H. M. et al. AnimalTFDB: A comprehensive animal transcription factor database. Nucleic Acids Res. 40, D144-D149 (2012).
44. Kumar, R. M. et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56-61 (2014).
45. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1401 (2014).