MATCHER: Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  Welch, Joshua D ^a   Hartemink, Alexander J ^b   Prins, Jan F ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b Duke University   (United States)

Author keywords

Manifold alignment; Manifold learning; Single cell epigenomics; Single cell RNA seq

Indexed keywords

DNA; RNA; TRANSCRIPTOME; HISTONE;

ANIMAL CELL; ARTICLE; CONTROLLED STUDY; DNA METHYLATION; EMBRYO; EMBRYONIC STEM CELL; EPIGENETICS; EPIGENOME; GENE EXPRESSION; GENOME; HUMAN; HUMAN CELL; INDUCED PLURIPOTENT STEM CELL; MANIFOLD ALIGNMENT; MOUSE; NONHUMAN; RNA SEQUENCE; SINGLE CELL ANALYSIS; TRANSCRIPTOMICS; ALGORITHM; ANIMAL; CYTOLOGY; GENETIC EPIGENESIS; GENETICS; HUMAN GENOME; METABOLISM; MOUSE EMBRYONIC STEM CELL; PROCEDURES; SEQUENCE ANALYSIS;

ALGORITHMS; ANIMALS; DNA METHYLATION; EPIGENESIS, GENETIC; GENOME, HUMAN; HISTONES; HUMANS; INDUCED PLURIPOTENT STEM CELLS; MICE; MOUSE EMBRYONIC STEM CELLS; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85025599203     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1269-0     Document Type: Article

References (56)

1. Sandberg R. Entering the era of single-cell transcriptomics in biology and medicine. Nat Methods. 2013;11:22-4.
2. Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14:618-30.
3. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381-6.
4. Llorens-Bobadilla E, Zhao S, Baser A, Saiz-Castro G, Zwadlo K, Martin-Villalba A. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell. 2015;17:329-40.
5. Macaulay IC, Svensson V, Labalette C, Ferreira L, Hamey F, Voet T, et al. Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep. 2016;14:966-77.
6. Hanchate NK, Kondoh K, Lu Z, Kuang D, Ye X, Qiu X, et al. Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis. Science. 2015;350:1251-5.
7. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014;509:371-5.
8. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58:610-20.
9. Welch JD, Hartemink AJ, Prins JF. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 2016;17:106.
10. Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502:59-64.
11. Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014;11:817-20.
12. Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol. 2015;33:1165-72.
13. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486-90.
14. Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods. 2016;13:229-32.
15. Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature. 2015;528:142-6.
16. Zhu C, Gao Y, Guo H, Xia B, Song J, Wu X, et al. Single-cell 5-formylcytosine landscapes of mammalian early embryos and ESCs at single-base resolution. Cell Stem Cell. 2017;338:1622-6.
17. Mooijman D, Dey SS, Boisset J-C, Crosetto N, van Oudenaarden A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat Biotechnol. 2016;34:852-6.
18. Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 2015;10:1386-97.
19. Corces MR, Buenrostro JD, Wu B, Greenside PG, Chan SM, Koenig JL, et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat Genet. 2016;48:1193-203.
20. Bock C, Farlik M, Sheffield NC. Multi-omics of single cells: strategies and applications. Trends Biotechnol. 2016;34:605-8.
21. Macaulay IC, Ponting CP, Voet T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 2017;33:155-68.
22. Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol. 2015;33:285-9.
23. Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015;12:519-22.
24. Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 2016;26:304-19.
25. Darmanis S, Gallant CJ, Marinescu VD, Niklasson M, Segerman A, Flamourakis G, et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 2016;14:380-9.
26. Genshaft AS, Li S, Gallant CJ, Darmanis S, Prakadan SM, Ziegler CGK, et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 2016;17:188.
27. Ham J, Lee DD, Saul LK. Semisupervised alignment of manifolds. Proc 10th Intnl Conf on Artificial Intelligence and Statistics. SAIS. 2005;120-27.
28. Wang C, Mahadevan S. A general framework for manifold alignment. Palo Alto, CA: AAAI; 2009.
29. Ham JH, Lee DD, Saul LK. Learning high dimensional correspondences from low dimensional manifolds. Proc 20th Intnl Conf on Machine Learning. AAAI. 2003.
30. Wang C, Mahadevan S. Manifold alignment without correspondence. Proc 21st Intnl Joint Conf on Artificial Intelligence. AAAI. 2009;1273-78.
31. Damianou A, Ek C, Titsias M, Lawrence N. Manifold relevance determination. Proc 29th Intnl Conf on Machine Learning. ACM. 2012;531-538.
32. Eleftheriadis S, Rudovic O, Pantic M. Discriminative shared gaussian processes for multiview and view-invariant facial expression recognition. IEEE Trans Image Process. 2015;24:189-204.
33. Lawrence ND. Gaussian process latent variable models for visualisation of high dimensional data. Adv Neural Inf Process Syst. 2004;16:329-36.
34. Titsias MK, Lawrence ND. Bayesian Gaussian process latent variable model. Proc 13th Intnl Conf on Artifical Intelligence and Statistics. SAIS. 2010;844-51.
35. Damianou AC, Titsias MK, Lawrence ND. Variational inference for latent variables and uncertain inputs in Gaussian processes. J Mach Learn Res. 2016;17:1-62.
36. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015;33:155-60.
37. Reid JE, Wernisch L. Pseudotime estimation: deconfounding single cell time series. Bioinformatics. 2016;32(19):2973-80.
38. Campbell KR, Yau C. Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference. PLOS Comput Biol. 2016;12:e1005212.
39. Cheow LF, Courtois ET, Tan Y, Viswanathan R, Xing Q, Tan RZ, et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods. 2016;13:833-6.
40. Ek CH. Shared Gaussian process latent variables models. Oxford: Oxford Brookes University; 2009.
41. Kolodziejczyk A, Kim JK, Tsang J, Ilicic T, Henriksson J, Natarajan K, et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell. 2015;17:471-85.
42. Singer ZS, Yong J, Tischler J, Hackett JA, Altinok A, Surani MA, et al. Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol Cell. 2014;55:319-31.
43. Jørgensen HF, Terry A, Beretta C, Pereira CF, Leleu M, Chen Z-F, et al. REST selectively represses a subset of RE1-containing neuronal genes in mouse embryonic stem cells. Development. 2009;136:715-21.
44. Dietrich N, Lerdrup M, Landt E, Agrawal-Singh S, Bak M, Tommerup N, et al. REST-mediated recruitment of polycomb repressor complexes in mammalian cells. PLoS Genet. 2012;8:e1002494.
45. Margueron R, Reinberg D. The Polycomb complex PRC2 and its mark in life. Nature. 2011;469:343-9.
46. Basu A, Wilkinson FH, Colavita K, Fennelly C, Atchison ML. YY1 DNA binding and interaction with YAF2 is essential for Polycomb recruitment. Nucleic Acids Res. 2014;42:2208-23.
47. Surface LE, Thornton SR, Boyer LA. Polycomb group proteins set the stage for early lineage commitment. Cell Stem Cell. 2010;7:288-98.
48. Deng C, Li Y, Liang S, Cui K, Salz T, Yang H, et al. USF1 and hSET1A mediated epigenetic modifications regulate lineage differentiation and HoxB4 transcription. PLoS Genet. 2013;9:e1003524.
49. Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006;125:315-26.
50. Whyte WA, Bilodeau S, Orlando DA, Hoke HA, Frampton GM, Foster CT, et al. Enhancer decommissioning by LSD1 during embryonic stem cell differentiation. Nature. 2012;482:221.
51. Sokol SY. Maintaining embryonic stem cell pluripotency with Wnt signaling. Development. 2011;138:4341-50.
52. Polo JM, Anderssen E, Walsh RM, Schwarz BA, Nefzger CM, Lim SM, et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell. 2012;151:1617-32.
53. Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011;27:1017-8.
54. Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2014;42:D142-7.
55. Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Schöler A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480:490-5.
56. Rasmussen CE, Williams CKI, Sutton RS, Barto AG, Spirtes P, Glymour C, et al. Gaussian processes for machine learning. Cambridge, MA: MIT Press; 2006.