SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis

PLoS Computational Biology

Volumn 11, Issue 11, 2015, Pages

  Guo, Minzhe ^a,b   Wang, Hui ^a   Potter, S Steven ^a   Whitsett, Jeffrey A ^a   Xu, Yan ^a  

^a CINCINNATI CHILDREN'S HOSPITAL MEDICAL CENTER   (United States)

^b University of Cincinnati   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ORGANS; CELL CULTURE; DATA HANDLING; ENDOTHELIAL CELLS; GENES; MAMMALS; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; POPULATION STATISTICS; RNA;

CELL TYPES; CELLULAR PROCESS; DEVELOPMENTAL BIOLOGY; GENE SIGNATURES; GENETIC PROCESS; MOUSE LUNG; PROCESS PROJECTS; RNA-SEQ DATUM; SINGLE CELLS; TRANSCRIPTOME ANALYSIS;

PIPELINES;

ANIMAL CELL; ANIMAL EXPERIMENT; BIOPROCESS; EMBRYO; FETUS; FIBROBLAST; FIBROBLAST CULTURE; LICENCE; LUNG; MOUSE; NONHUMAN; ORGAN; ORGAN CULTURE; PERICYTE; PIPELINE; SMOOTH MUSCLE; ANIMAL; BIOLOGY; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; METABOLISM; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE;

RNA;

ANIMALS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; MICE; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 84949293695     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004575     Document Type: Article

References (89)

1. Huang DW, Sherman BT, Lempicki RA, (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57. doi: 10.1038/nprot.2008.211 19131956
2. Li L, Clevers H, (2010) Coexistence of quiescent and active adult stem cells in mammals. Science 327: 542–545. doi: 10.1126/science.1180794 20110496
3. Pujadas E, Feinberg AP, (2012) Regulated noise in the epigenetic landscape of development and disease. Cell 148: 1123–1131. doi: 10.1016/j.cell.2012.02.045 22424224
4. Neildez-Nguyen TM, Parisot A, Vignal C, Rameau P, Stockholm D, Picot J, Allo V, Le Bec C, Laplace C, Paldi A, (2008) Epigenetic gene expression noise and phenotypic diversification of clonal cell populations. Differentiation 76: 33–40. 17825084
5. Raj A, van Oudenaarden A, (2008) Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135: 216–226. doi: 10.1016/j.cell.2008.09.050 18957198
6. Johnston RJ, Jr.Desplan C, (2010) Stochastic mechanisms of cell fate specification that yield random or robust outcomes. Annu Rev Cell Dev Biol 26: 689–719. doi: 10.1146/annurev-cellbio-100109-104113 20590453
7. Yin H, Marshall D, (2012) Microfluidics for single cell analysis. Curr Opin Biotechnol 23: 110–119. doi: 10.1016/j.copbio.2011.11.002 22133547
8. Shapiro E, Biezuner T, Linnarsson S, (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14: 618–630. doi: 10.1038/nrg3542 23897237
9. Saliba AE, Westermann AJ, Gorski SA, Vogel J, (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res 42: 8845–8860. doi: 10.1093/nar/gku555 25053837
10. Guo G, Huss M, Tong GQ, Wang C, Li Sun L, Clarke ND, Robson P, (2010) Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell 18: 675–685. doi: 10.1016/j.devcel.2010.02.012 20412781
11. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA, (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6: 377–382. doi: 10.1038/nmeth.1315 19349980
12. Tang F, Barbacioru C, Bao S, Lee C, Nordman E, Wang X, Lao K, Surani MA, (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6: 468–478. doi: 10.1016/j.stem.2010.03.015 20452321
13. Qiu S, Luo S, Evgrafov O, Li R, Schroth GP, Levitt P, Knowles JA, Wang K, (2012) Single-neuron RNA-Seq: technical feasibility and reproducibility. Front Genet 3: 124. doi: 10.3389/fgene.2012.00124 22934102
14. Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R, (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30: 777–782. 22820318
15. Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S, (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21: 1160–1167. doi: 10.1101/gr.110882.110 21543516
16. Narsinh KH, Sun N, Sanchez-Freire V, Lee AS, Almeida P, Hu S, Jan T, Wilson KD, Leong D, Rosenberg J, Yao M, Robbins RC, Wu JC, (2011) Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells. J Clin Invest 121: 1217–1221. doi: 10.1172/JCI44635 21317531
17. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, Schwartz S, Yosef N, Malboeuf C, Lu D, Trombetta JJ, Gennert D, Gnirke A, Goren A, Hacohen N, Levin JZ, Park H, Regev A, (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498: 236–240. doi: 10.1038/nature12172 23685454
18. Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C, (2013) Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol 31: 748–752. doi: 10.1038/nbt.2642 23873083
19. Satija R, Farrell JA, Gennert D, Schier AF, Regev A, (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33: 495–502. doi: 10.1038/nbt.3192 25867923
20. Pettit JB, Tomer R, Achim K, Richardson S, Azizi L, Marioni J, (2014) Identifying cell types from spatially referenced single-cell expression datasets. PLoS Comput Biol 10: e1003824. doi: 10.1371/journal.pcbi.1003824 25254363
21. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR, (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509: 371–375. doi: 10.1038/nature13173 24739965
22. Vaughan AE, Brumwell AN, Xi Y, Gotts JE, Brownfield DG, Treutlein B, Tan K, Tan V, Liu FC, Looney MR, Matthay MA, Rock JR, Chapman HA, (2015) Lineage-negative progenitors mobilize to regenerate lung epithelium after major injury. Nature 517: 621–625. doi: 10.1038/nature14112 25533958
23. Kim JK, Marioni JC, (2013) Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol 14: R7. doi: 10.1186/gb-2013-14-1-r7 23360624
24. Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG, (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 10: 1093–1095. doi: 10.1038/nmeth.2645 24056876
25. Kharchenko PV, Silberstein L, Scadden DT, (2014) Bayesian approach to single-cell differential expression analysis. Nat Methods 11: 740–742. doi: 10.1038/nmeth.2967 24836921
26. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL, (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32: 381–386. doi: 10.1038/nbt.2859 24658644
27. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, 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. doi: 10.1038/nbt.3102 25599176
28. Yan L, Yang M, Guo H, Yang L, Wu J, Li R, Liu P, Lian Y, Zheng X, Yan J, Huang J, Li M, Wu X, Wen L, Lao K, Qiao J, Tang F, (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 20: 1131–1139. doi: 10.1038/nsmb.2660 23934149
29. Xue Z, Huang K, Cai C, Cai L, Jiang CY, Feng Y, Liu Z, Zeng Q, Cheng L, Sun YE, Liu JY, Horvath S, Fan G, (2013) Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500: 593–597. doi: 10.1038/nature12364 23892778
30. Usoskin D, Furlan A, Islam S, Abdo H, Lonnerberg P, Lou D, Hjerling-Leffler J, Haeggstrom J, Kharchenko O, Kharchenko PV, Linnarsson S, Ernfors P, (2015) Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat Neurosci 18: 145–153. doi: 10.1038/nn.3881 25420068
31. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, Hayden Gephart MG, Barres BA, Quake SR, (2015) A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A 112: 7285–7290. doi: 10.1073/pnas.1507125112 26060301
32. Xu C, Su Z, (2015) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31: 1974–1980. doi: 10.1093/bioinformatics/btv088 25805722
33. Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He L, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-Leffler J, Linnarsson S, (2015) Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347: 1138–1142. doi: 10.1126/science.aaa1934 25700174
34. Katayama S, Tohonen V, Linnarsson S, Kere J, (2013) SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization. Bioinformatics 29: 2943–2945. doi: 10.1093/bioinformatics/btt511 23995393
35. Li J, Tibshirani R, (2013) Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res 22: 519–536. doi: 10.1177/0962280211428386 22127579
36. Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC, (2014) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A 111: E5643–5650. doi: 10.1073/pnas.1408993111 25512504
37. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L, (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28: 511–515. doi: 10.1038/nbt.1621 20436464
38. Li B, Dewey CN, (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12: 323. doi: 10.1186/1471-2105-12-323 21816040
39. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL, (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14: R36. doi: 10.1186/gb-2013-14-4-r36 23618408
40. Trapnell C, Pachter L, Salzberg SL, (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25: 1105–1111. doi: 10.1093/bioinformatics/btp120 19289445
41. Li H, Durbin R, (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168
42. Yanai I, Benjamin H, Shmoish M, Chalifa-Caspi V, Shklar M, Ophir R, Bar-Even A, Horn-Saban S, Safran M, Domany E, Lancet D, Shmueli O, (2005) Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21: 650–659. 15388519
43. Dudoit S, Yang YH, Callow MJ, Speed TP, (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica. 111–140 p.
44. Wilk MB, Gnanadesikan R, (1968) Probability plotting methods for the analysis of data. Biometrika 55: 1–17. 5661047
45. Monti S, Tamayo P, Mesirov JP, Golub TR, (2003) Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning 52: 91–118.
46. Wilkerson MD, Hayes DN, (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26: 1572–1573. doi: 10.1093/bioinformatics/btq170 20427518
47. Tseng GC, Wong WH, (2005) Tight clustering: a resampling-based approach for identifying stable and tight patterns in data. Biometrics 61: 10–16. 15737073
48. Ward JH, Jr (1963) Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association 58: 236–244.
49. Welch BL, (1947) The generalisation of student's problems when several different population variances are involved. Biometrika 34: 28–35. 20287819
50. Macklin MT, Mann HB, (1947) Fallacies inherent in the proband method of analysis of human pedigrees for inheritance of recessive traits; two methods of correction of the formula. Am J Dis Child 74: 456–467. 18896867
51. Benjamini Y, Hochberg Y, (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289–300.
52. Chen J, Bardes EE, Aronow BJ, Jegga AG, (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37: W305–311. doi: 10.1093/nar/gkp427 19465376
53. Huang DW, Sherman BT, Lempicki RA, (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1–13. doi: 10.1093/nar/gkn923 19033363
54. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP, (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550. 16199517
55. Kolde R, Laur S, Adler P, Vilo J, (2012) Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28: 573–580. doi: 10.1093/bioinformatics/btr709 22247279
56. Lebre S, (2009) Inferring dynamic genetic networks with low order independencies. Stat Appl Genet Mol Biol 8: Article 9.
57. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G, (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9: 796–804. doi: 10.1038/nmeth.2016 22796662
58. Yu J, Smith VA, Wang PP, Hartemink EJ, Jarvis ED (2002) Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. International Conference on Systems Biology.
59. Leclerc RD, (2008) Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol 4: 213. doi: 10.1038/msb.2008.52 18682703
60. Wille A, Buhlmann P, (2006) Low-order conditional independence graphs for inferring genetic networks. Stat Appl Genet Mol Biol 5: Article1.
61. Borgatti SP, Mehra A, Brass DJ, Labianca G, (2009) Network Analysis in the Social Sciences. Science 323: 892–895. doi: 10.1126/science.1165821 19213908
62. Hahn MW, Kern AD, (2005) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 22: 803–806. 15616139
63. Borgatti SP, Breiger R, Carley K, Pattison P, (2003) The Key Player Problem. In: Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers: National Academy of Sciences Press. pp. 241–252.
64. Borgatti SP, (2006) Identifying sets of key players in a social network. Computational & Mathematical Organization Theory 12: 21–34.
65. Schwartz D, Rouselle T, (2009) Using social network analysis to target criminal networks. Trends in Organized Crime 12: 188–207.
66. Jordán F, Liu W-c, Davis AJ, (2006) Topological keystone species: measures of positional importance in food webs. Oikos 112: 535–546.
67. Sing T, Sander O, Beerenwinkel N, Lengauer T, (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940–3941. 16096348
68. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J, (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80. 15461798
69. Bohinski RJ, Di Lauro R, Whitsett JA, (1994) The lung-specific surfactant protein B gene promoter is a target for thyroid transcription factor 1 and hepatocyte nuclear factor 3, indicating common factors for organ-specific gene expression along the foregut axis. Mol Cell Biol 14: 5671–5681. 8065304
70. DeFelice M, Silberschmidt D, DiLauro R, Xu Y, Wert SE, Weaver TE, Bachurski CJ, Clark JC, Whitsett JA, (2003) TTF-1 phosphorylation is required for peripheral lung morphogenesis, perinatal survival, and tissue-specific gene expression. J Biol Chem 278: 35574–35583. 12829717
71. Kimura S, Hara Y, Pineau T, Fernandez-Salguero P, Fox CH, Ward JM, Gonzalez FJ, (1996) The T/ebp null mouse: thyroid-specific enhancer-binding protein is essential for the organogenesis of the thyroid, lung, ventral forebrain, and pituitary. Genes Dev 10: 60–69. 8557195
72. Xu Y, Wang Y, Besnard V, Ikegami M, Wert SE, Heffner C, Murray SA, Donahue LR, Whitsett JA, (2012) Transcriptional programs controlling perinatal lung maturation. PLoS One 7: e37046. doi: 10.1371/journal.pone.0037046 22916088
73. Yin Z, Gonzales L, Kolla V, Rath N, Zhang Y, Lu MM, Kimura S, Ballard PL, Beers MF, Epstein JA, Morrisey EE, (2006) Hop functions downstream of Nkx2.1 and GATA6 to mediate HDAC-dependent negative regulation of pulmonary gene expression. Am J Physiol Lung Cell Mol Physiol 291: L191–199. 16510470
74. Dahlin K, Mager EM, Allen L, Tigue Z, Goodglick L, Wadehra M, Dobbs L, (2004) Identification of genes differentially expressed in rat alveolar type I cells. Am J Respir Cell Mol Biol 31: 309–316. 15205179
75. Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE, Mouse Genome Database G (2014) The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res 42: D810–817. doi: 10.1093/nar/gkt1225 24285300
76. Nazri A, Lio P, (2012) Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context. PLoS One 7: e28713. doi: 10.1371/journal.pone.0028713 22253694
77. Lo K, Raftery AE, Dombek KM, Zhu J, Schadt EE, Bumgarner RE, Yeung KY, (2012) Integrating external biological knowledge in the construction of regulatory networks from time-series expression data. BMC Syst Biol 6: 101. doi: 10.1186/1752-0509-6-101 22898396
78. Gao J, Liang F, Fan W, Sun Y, Han J, (2013) A Graph-Based Consensus Maximization Approach for Combining Multiple Supervised and Unsupervised Models. IEEE Transactions on Knowledge and Data Engineering 25: 15–28.
79. Maeda Y, Tsuchiya T, Hao H, Tompkins DH, Xu Y, Mucenski ML, Du L, Keiser AR, Fukazawa T, Naomoto Y, Nagayasu T, Whitsett JA, (2012) Kras(G12D) and Nkx2-1 haploinsufficiency induce mucinous adenocarcinoma of the lung. J Clin Invest 122: 4388–4400. doi: 10.1172/JCI64048 23143308
80. Niimi T, Nagashima K, Ward JM, Minoo P, Zimonjic DB, Popescu NC, Kimura S, (2001) claudin-18, a novel downstream target gene for the T/EBP/NKX2.1 homeodomain transcription factor, encodes lung- and stomach-specific isoforms through alternative splicing. Mol Cell Biol 21: 7380–7390. 11585919
81. Bondjers C, He L, Takemoto M, Norlin J, Asker N, Hellstrom M, Lindahl P, Betsholtz C, (2006) Microarray analysis of blood microvessels from PDGF-B and PDGF-Rbeta mutant mice identifies novel markers for brain pericytes. FASEB J 20: 1703–1705. 16807374
82. Li C, Zhu NL, Tan RC, Ballard PL, Derynck R, Minoo P, (2002) Transforming growth factor-beta inhibits pulmonary surfactant protein B gene transcription through SMAD3 interactions with NKX2.1 and HNF-3 transcription factors. J Biol Chem 277: 38399–38408. 12161428
83. Margana RK, Boggaram V, (1997) Functional analysis of surfactant protein B (SP-B) promoter. Sp1, Sp3, TTF-1, and HNF-3alpha transcription factors are necessary for lung cell-specific activation of SP-B gene transcription. J Biol Chem 272: 3083–3090. 9006959
84. Wert SE, Dey CR, Blair PA, Kimura S, Whitsett JA, (2002) Increased expression of thyroid transcription factor-1 (TTF-1) in respiratory epithelial cells inhibits alveolarization and causes pulmonary inflammation. Dev Biol 242: 75–87. 11820807
85. Yan C, Naltner A, Conkright J, Ghaffari M, (2001) Protein-protein interaction of retinoic acid receptor alpha and thyroid transcription factor-1 in respiratory epithelial cells. J Biol Chem 276: 21686–21691. 11274148
86. Yang MC, Guo Y, Liu CC, Weissler JC, Yang YS, (2006) The TTF-1/TAP26 complex differentially modulates surfactant protein-B (SP-B) and -C (SP-C) promoters in lung cells. Biochem Biophys Res Commun 344: 484–490. 16630564
87. Yang YS, Yang MC, Wang B, Weissler JC, (2001) BR22, a novel protein, interacts with thyroid transcription factor-1 and activates the human surfactant protein B promoter. Am J Respir Cell Mol Biol 24: 30–37. 11152647
88. Kelly SE, Bachurski CJ, Burhans MS, Glasser SW, (1996) Transcription of the lung-specific surfactant protein C gene is mediated by thyroid transcription factor 1. J Biol Chem 271: 6881–6888. 8636114
89. Minoo P, Hu L, Xing Y, Zhu NL, Chen H, Li M, Borok Z, Li C, (2007) Physical and functional interactions between homeodomain NKX2.1 and winged helix/forkhead FOXA1 in lung epithelial cells. Mol Cell Biol 27: 2155–2165. 17220277