Skip to main content
SpringerLink
Log in

Transcriptomics and proteomics in stem cell research

  • Review
  • Published:
Frontiers of Medicine Aims and scope Submit manuscript

Abstract

Stem cells are capable of self-renewal and differentiation, and the processes regulating these events are among the most comprehensively investigated topics in life sciences. In particular, the molecular mechanisms of the self-renewal, proliferation, and differentiation of stem cells have been extensively examined. Multi-omics integrative analysis, such as transcriptomics combined with proteomics, is one of the most promising approaches to the systemic investigation of stem cell biology.We reviewed the available information on stem cells by examining published results using transcriptomic and proteomic characterization of the different stem cell processes. Comprehensive understanding of these important processes can only be achieved using a systemic methodology, and employing such method will strengthen the study on stem cell biology and promote the clinical applications of stem cells.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahn SM, Simpson R, Lee B. Genomics and proteomics in stem cell research: the road ahead. Anat Cell Biol 2010;43(1): 1–14

    PubMed Central  PubMed  Google Scholar 

  2. Gangaraju VK, Lin H. MicroRNAs: key regulators of stem cells. Nat Rev Mol Cell Biol 2009;10(2): 116–125

    CAS  PubMed Central  PubMed  Google Scholar 

  3. Wobus AM, Boheler KR. Embryonic stem cells: prospects for developmental biology and cell therapy. Physiol Rev 2005;85(2): 635–678

    CAS  PubMed  Google Scholar 

  4. Callinan PA, Feinberg AP. The emerging science of epigenomics. Hum Mol Genet 2006;15(Spec No 1): R95–R101

    CAS  PubMed  Google Scholar 

  5. Schneider MV, Orchard S. Omics technologies, data and bioinformatics principles. Methods Mol Biol 2011;719: 3–30

    CAS  PubMed  Google Scholar 

  6. Stanton LW, Bakre MM. Genomic and proteomic characterization of embryonic stem cells. Curr Opin Chem Biol 2007;11(4): 399–404

    CAS  PubMed  Google Scholar 

  7. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10(1): 57–63

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Efroni S, Duttagupta R, Cheng J, Dehghani H, Hoeppner DJ, Dash C, Bazett-Jones DP, Le Grice S, McKay RDG, Buetow KH, Gingeras TR, Misteli T, Meshorer E. Global transcription in pluripotent embryonic stem cells. Cell Stem Cell 2008;2(5): 437–447

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Chin MH, Mason MJ, Xie W, Volinia S, Singer M, Peterson C, Ambartsumyan G, Aimiuwu O, Richter L, Zhang J, Khvorostov I, Ott V, Grunstein M, Lavon N, Benvenisty N, Croce CM, Clark AT, Baxter T, Pyle AD, Teitell MA, Pelegrini M, Plath K, Lowry WE. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 2009;5(1): 111–123

    CAS  PubMed Central  PubMed  Google Scholar 

  10. Ginis I, Luo Y, Miura T, Thies S, Brandenberger R, Gerecht-Nir S, Amit M, Hoke A, Carpenter MK, Itskovitz-Eldor J, Rao MS. Differences between human and mouse embryonic stem cells. Dev Biol 2004;269(2): 360–380

    CAS  PubMed  Google Scholar 

  11. Bhattacharya B, Miura T, Brandenberger R, Mejido J, Luo Y, Yang AX, Joshi BH, Ginis I, Thies RS, Amit M, Lyons I, Condie BG, Itskovitz-Eldor J, Rao MS, Puri RK. Gene expression in human embryonic stem cell lines: unique molecular signature. Blood 2004;103(8): 2956–2964

    CAS  PubMed  Google Scholar 

  12. Brandenberger R, Khrebtukova I, Thies RS, Miura T, Jingli C, Puri R, Vasicek T, Lebkowski J, Rao M. MPSS profiling of human embryonic stem cells. BMC Dev Biol 2004;4(1): 10

    PubMed Central  PubMed  Google Scholar 

  13. Zhan M. Genomic studies to explore self-renewal and differentiation properties of embryonic stem cells. Front Biosci 2008;13(13): 276–283

    CAS  PubMed  Google Scholar 

  14. Djouad F, Bony C, Canovas F, Fromigué O, Rème T, Jorgensen C, Noël D. Transcriptomic analysis identifies Foxo3A as a novel transcription factor regulating mesenchymal stem cell chrondrogenic differentiation. Cloning Stem Cells 2009;11(3): 407–416

    CAS  PubMed  Google Scholar 

  15. Ivanova NB, Dimos JT, Schaniel C, Hackney JA, Moore KA, Lemischka IR. A stem cell molecular signature. Science 2002;298(5593): 601–604

    CAS  PubMed  Google Scholar 

  16. Ramalho-Santos M, Yoon S, Matsuzaki Y, Mulligan RC, Melton DA. “Stemness”: transcriptional profiling of embryonic and adult stem cells. Science 2002;298(5593): 597–600

    CAS  PubMed  Google Scholar 

  17. Suárez-Fariñas M, Noggle S, Heke M, Hemmati-Brivanlou A, Magnasco MO. Comparing independent microarray studies: the case of human embryonic stem cells. BMC Genomics 2005; 6(1): 99

    PubMed Central  PubMed  Google Scholar 

  18. Yang Y, Wang H, Chang KH, Qu H, Zhang Z, Xiong Q, Qi H, Cui P, Lin Q, Ruan X, Yang Y, Li Y, Shu C, Li Q, Wakeland EK, Yan J, Hu S, Fang X. Transcriptome dynamics during human erythroid differentiation and development. Genomics 2013;102(5–6): 431–441

    CAS  PubMed Central  PubMed  Google Scholar 

  19. Sigova AA, Mullen AC, Molinie B, Gupta S, Orlando DA, Guenther MG, Almada AE, Lin C, Sharp PA, Giallourakis CC, Young RA. Divergent transcription of long noncoding RNA/mRNA gene pairs in embryonic stem cells. Proc Natl Acad Sci USA 2013;110(8): 2876–2881

    CAS  PubMed Central  PubMed  Google Scholar 

  20. 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, Li R, Qiao J, Tang F. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 2013;20(9): 1131–1139

    CAS  PubMed  Google Scholar 

  21. MacRae T, Sargeant T, Lemieux S, Hébert J, Deneault E, Sauvageau G. RNA-Seq reveals spliceosome and proteasome genes as most consistent transcripts in human cancer cells. PLoS ONE 2013;8(9): e72884

    CAS  PubMed Central  PubMed  Google Scholar 

  22. Jääger K, Islam S, Zajac P, Linnarsson S, Neuman T. RNA-seq analysis reveals different dynamics of differentiation of human dermis- and adipose-derived stromal stem cells. PLoS ONE 2012;7(6): e38833

    PubMed Central  PubMed  Google Scholar 

  23. Gargiulo G, Cesaroni M, Serresi M, de Vries N, Hulsman D, Bruggeman SW, Lancini C, van Lohuizen M. In vivo RNAi screen for BMI1 targets identifies TGF-β/BMP-ER stress pathways as key regulators of neural- and malignant glioma-stem cell homeostasis. Cancer Cell 2013;23(5): 660–676

    CAS  PubMed  Google Scholar 

  24. Salomonis N, Schlieve CR, Pereira L, Wahlquist C, Colas A, Zambon AC, Vranizan K, Spindler MJ, Pico AR, Cline MS, Clark TA, Williams A, Blume JE, Samal E, Mercola M, Merrill BJ, Conklin BR. Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation. Proc Natl Acad Sci USA 2010;107(23): 10514–10519

    CAS  PubMed Central  PubMed  Google Scholar 

  25. Wu JQ, Habegger L, Noisa P, Szekely A, Qiu C, Hutchison S, Raha D, Egholm M, Lin H, Weissman S, Cui W, Gerstein M, Snyder M. Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and pairedend sequencing. Proc Natl Acad Sci USA 2010;107(11): 5254–5259

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Brandenberger R, Wei H, Zhang S, Lei S, Murage J, Fisk GJ, Li Y, Xu C, Fang R, Guegler K, Rao MS, Mandalam R, Lebkowski J, Stanton LW. Transcriptome characterization elucidates signaling networks that control human ES cell growth and differentiation. Nat Biotechnol 2004;22(6): 707–716

    PubMed  Google Scholar 

  27. Anisimov SV, Tarasov KV, Tweedie D, Stern MD, Wobus AM, Boheler KR. SAGE identification of gene transcripts with profiles unique to pluripotent mouse R1 embryonic stem cells. Genomics 2002;79(2): 169–176

    CAS  PubMed  Google Scholar 

  28. He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 2004;5(7): 522–531

    CAS  PubMed  Google Scholar 

  29. Suh MR, Lee Y, Kim JY, Kim SK, Moon SH, Lee JY, Cha KY, Chung HM, Yoon HS, Moon SY, Kim VN, Kim KS. Human embryonic stem cells express a unique set of microRNAs. Dev Biol 2004;270(2): 488–498

    CAS  PubMed  Google Scholar 

  30. Jouneau A, Ciaudo C, Sismeiro O, Brochard V, Jouneau L, Vandormael-Pournin S, Coppée JY, Zhou Q, Heard E, Antoniewski C, Cohen-Tannoudji M. Naive and primed murine pluripotent stem cells have distinct miRNA expression profiles. RNA 2012;18(2): 253–264

    CAS  PubMed Central  PubMed  Google Scholar 

  31. Kirigin FF, Lindstedt K, Sellars M, Ciofani M, Low SL, Jones L, Bell F, Pauli F, Bonneau R, Myers RM, Littman DR, Chong MMW. Dynamic microRNA gene transcription and processing during T cell development. J Immunol 2012;188(7): 3257–3267

    CAS  PubMed Central  PubMed  Google Scholar 

  32. Marson A, Levine SS, Cole MF, Frampton GM, Brambrink T, Johnstone S, Guenther MG, Johnston WK, Wernig M, Newman J, Calabrese JM, Dennis LM, Volkert TL, Gupta S, Love J, Hannett N, Sharp PA, Bartel DP, Jaenisch R, Young RA. Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 2008;134(3): 521–533

    CAS  PubMed Central  PubMed  Google Scholar 

  33. Mattick JS. A new paradigm for developmental biology. J Exp Biol 2007;210(Pt 9): 1526–1547

    PubMed  Google Scholar 

  34. Sheik Mohamed J, Gaughwin PM, Lim B, Robson P, Lipovich L. Conserved long noncoding RNAs transcriptionally regulated by Oct4 and Nanog modulate pluripotency in mouse embryonic stem cells. RNA 2010;16(2): 324–337

    PubMed Central  PubMed  Google Scholar 

  35. Dinger ME, Amaral PP, Mercer TR, Pang KC, Bruce SJ, Gardiner BB, Askarian-Amiri ME, Ru K, Soldà G, Simons C, Sunkin SM, Crowe ML, Grimmond SM, Perkins AC, Mattick JS. Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. Genome Res 2008;18(9): 1433–1445

    CAS  PubMed Central  PubMed  Google Scholar 

  36. Ramos AD, Diaz A, Nellore A, Delgado RN, Park KY, Gonzales-Roybal G, Oldham MC, Song JS, Lim DA. Integration of genomewide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo. Cell Stem Cell 2013;12(5): 616–628

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Unwin RD, Gaskell SJ, Evans CA, Whetton AD. The potential for proteomic definition of stem cell populations. Exp Hematol 2003;31(12): 1147–1159

    CAS  PubMed  Google Scholar 

  38. Baharvand H, Fathi A, van Hoof D, Salekdeh GH. Concise review: trends in stem cell proteomics. Stem Cells 2007;25(8): 1888–1903

    CAS  PubMed  Google Scholar 

  39. Nagano K, Taoka M, Yamauchi Y, Itagaki C, Shinkawa T, Nunomura K, Okamura N, Takahashi N, Izumi T, Isobe T. Largescale identification of proteins expressed in mouse embryonic stem cells. Proteomics 2005;5(5): 1346–1361

    CAS  PubMed  Google Scholar 

  40. Nasrabadi D, Rezaei Larijani M, Pirhaji L, Gourabi H, Shahverdi A, Baharvand H, Salekdeh GH. Proteomic analysis of monkey embryonic stem cell during differentiation. J Proteome Res 2009;8(3): 1527–1539

    CAS  PubMed  Google Scholar 

  41. Böser A, Drexler HCA, Reuter H, Schmitz H, Wu G, Schöler HR, Gentile L, Bartscherer K. SILAC proteomics of planarians identifies Ncoa5 as a conserved component of pluripotent stem cells. Cell Reports 2013;5(4): 1142–1155

    PubMed  Google Scholar 

  42. Sun Y, Yang Y, Zeng S, Tan Y, Lu G, Lin G. Identification of proteins related to epigenetic regulation in the malignant transformation of aberrant karyotypic human embryonic stem cells by quantitative proteomics. PLoS ONE 2014;9(1): e85823

    PubMed Central  PubMed  Google Scholar 

  43. D’Aguanno S, Barcaroli D, Rossi C, Zucchelli M, Ciavardelli D, Cortese C, De Cola A, Volpe S, D’Agostino D, Todaro M, Stassi G, Di Ilio C, Urbani A, De Laurenzi V. p63 Isoforms Regulate Metabolism of Cancer Stem Cells. J Proteome Res 2014;13(4): 2120–2136

    PubMed  Google Scholar 

  44. Lin H, Lee E, Hestir K, Leo C, Huang M, Bosch E, Halenbeck R, Wu G, Zhou A, Behrens D, Hollenbaugh D, Linnemann T, Qin M, Wong J, Chu K, Doberstein SK, Williams LT. Discovery of a cytokine and its receptor by functional screening of the extracellular proteome. Science 2008;320(5877): 807–811

    CAS  PubMed  Google Scholar 

  45. Gonzalez R, Jennings LL, Knuth M, Orth AP, Klock HE, Ou W, Feuerhelm J, Hull MV, Koesema E, Wang Y, Zhang J, Wu C, Cho CY, Su AI, Batalov S, Chen H, Johnson K, Laffitte B, Nguyen DG, Snyder EY, Schultz PG, Harris JL, Lesley SA. Screening the mammalian extracellular proteome for regulators of embryonic human stem cell pluripotency. Proc Natl Acad Sci USA 2010;107(8): 3552–3557

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Gemei M, Corbo C, D’Alessio F, Di Noto R, Vento R, Del Vecchio L. Surface proteomic analysis of differentiated versus stem-like osteosarcoma human cells. Proteomics 2013;13(22): 3293–3297

    CAS  PubMed  Google Scholar 

  47. Van Hoof D, Muñoz J, Braam SR, Pinkse MWH, Linding R, Heck AJR, Mummery CL, Krijgsveld J. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 2009;5(2): 214–226

    PubMed  Google Scholar 

  48. Brill LM, Xiong W, Lee KB, Ficarro SB, Crain A, Xu Y, Terskikh A, Snyder EY, Ding S. Phosphoproteomic analysis of human embryonic stem cells. Cell Stem Cell 2009;5(2): 204–213

    CAS  PubMed Central  PubMed  Google Scholar 

  49. Swaney DL, Wenger CD, Thomson JA, Coon JJ. Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry. Proc Natl Acad Sci USA 2009;106(4): 995–1000

    CAS  PubMed Central  PubMed  Google Scholar 

  50. Rigbolt KT, Prokhorova TA, Akimov V, Henningsen J, Johansen PT, Kratchmarova I, Kassem M, Mann M, Olsen JV, Blagoev B. System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation. Sci Signal 2011;4(164): rs3

    PubMed  Google Scholar 

  51. Xu H, Baroukh C, Dannenfelser R, Chen EY, Tan CM, Kou Y, Kim YE, Lemischka IR, Ma’ayan A. ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database (Oxford) 2013;2013: bat045

    Google Scholar 

  52. Chin MH, Mason MJ, Xie W, Volinia S, Singer M, Peterson C, Ambartsumyan G, Aimiuwu O, Richter L, Zhang J, Khvorostov I, Ott V, Grunstein M, Lavon N, Benvenisty N, Croce CM, Clark AT, Baxter T, Pyle AD, Teitell MA, Pelegrini M, Plath K, Lowry WE. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 2009;5(1): 111–123

    CAS  PubMed Central  PubMed  Google Scholar 

  53. Yu J, Hu K, Smuga-Otto K, Tian S, Stewart R, Slukvin II, Thomson JA. Human induced pluripotent stem cells free of vector and transgene sequences. Science 2009;324(5928): 797–801

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Van Hoof D, Muñoz J, Braam SR, Pinkse MWH, Linding R, Heck AJR, Mummery CL, Krijgsveld J. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 2009;5(2): 214–226

    PubMed  Google Scholar 

  55. Munoz J, Low TY, Kok YJ, Chin A, Frese CK, Ding V, Choo A, Heck AJR. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol Syst Biol 2011;7: 550

    PubMed Central  PubMed  Google Scholar 

  56. Sridharan R, Gonzales-Cope M, Chronis C, Bonora G, McKee R, Huang C, Patel S, Lopez D, Mishra N, Pellegrini M, Carey M, Garcia BA, Plath K. Proteomic and genomic approaches reveal critical functions of H3K9 methylation and heterochromatin protein-1γ in reprogramming to pluripotency. Nat Cell Biol 2013;15(7): 872–882

    CAS  PubMed Central  PubMed  Google Scholar 

  57. Phanstiel DH, Brumbaugh J, Wenger CD, Tian S, Probasco MD, Bailey DJ, Swaney DL, Tervo MA, Bolin JM, Ruotti V, Stewart R, Thomson JA, Coon JJ. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat Methods 2011;8(10): 821–827

    CAS  PubMed Central  PubMed  Google Scholar 

  58. Perez-Iratxeta C, Palidwor G, Porter CJ, Sanche NA, Huska MR, Suomela BP, Muro EM, Krzyzanowski PM, Hughes E, Campbell PA, Rudnicki MA, Andrade MA. Study of stem cell function using microarray experiments. FEBS Lett 2005;579(8): 1795–1801

    CAS  PubMed  Google Scholar 

  59. Sansone SA, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O, Fang H, Neumann S, Tong W, Amaral-Zettler L, Begley K, Booth T, Bougueleret L, Burns G, Chapman B, Clark T, Coleman LA, Copeland J, Das S, de Daruvar A, de Matos P, Dix I, Edmunds S, Evelo CT, Forster MJ, Gaudet P, Gilbert J, Goble C, Griffin JL, Jacob D, Kleinjans J, Harland L, Haug K, Hermjakob H, Ho Sui SJ, Laederach A, Liang S, Marshall S, McGrath A, Merrill E, Reilly D, Roux M, Shamu CE, Shang CA, Steinbeck C, Trefethen A, Williams-Jones B, Wolstencroft K, Xenarios I, Hide W. Toward interoperable bioscience data. Nat Genet 2012;44(2): 121–126

    CAS  PubMed Central  PubMed  Google Scholar 

  60. Ho Sui SJ, Begley K, Reilly D, Chapman B, McGovern R, Rocca-Sera P, Maguire E, Altschuler GM, Hansen TAA, Sompallae R, Krivtsov A, Shivdasani RA, Armstrong SA, Culhane AC, Correll M, Sansone SA, Hofmann O, Hide W. The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons. Nucleic Acids Res 2012;40(Database issue): D984–D991

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Jung M, Peterson H, Chavez L, Kahlem P, Lehrach H, Vilo J, Adjaye J. A data integration approach to mapping OCT4 gene regulatory networks operative in embryonic stem cells and embryonal carcinoma cells. PLoS ONE 2010;5(5): e10709

    PubMed Central  PubMed  Google Scholar 

  62. Mallon BS, Chenoweth JG, Johnson KR, Hamilton RS, Tesar PJ, Yavatkar AS, Tyson LJ, Park K, Chen KG, Fann YC, McKay RDG. StemCellDB: the human pluripotent stem cell database at the National Institutes of Health. Stem Cell Res (Amst) 2013;10(1): 57–66

    CAS  Google Scholar 

  63. Costa V, Angelini C, De Feis I, Ciccodicola A. Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010;2010: 853916.

    PubMed Central  PubMed  Google Scholar 

  64. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science 1995;270(5235): 484–487

    CAS  PubMed  Google Scholar 

  65. Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker’s guide to expressed sequence tag (EST) analysis. Brief Bioinform 2007;8(1): 6–21

    CAS  PubMed  Google Scholar 

  66. Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet 2008;24(3): 133–141

    CAS  PubMed  Google Scholar 

  67. Uchida S, Gellert P, Braun T. Deeply dissecting stemness: making sense to non-coding RNAs in stem cells. Stem Cell Rev 2012;8(1): 78–86

    CAS  PubMed  Google Scholar 

  68. Asmann YW, Wallace MB, Thompson EA. Transcriptome profiling using next-generation sequencing. Gastroenterology 2008;135(5): 1466–1468

    CAS  PubMed  Google Scholar 

  69. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009;10(3): R25

    PubMed Central  PubMed  Google Scholar 

  70. Jean G, Kahles A, Sreedharan VT, De Bona F, Ratsch G. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics 2010; Chapter 11: Unit 11 6

    Google Scholar 

  71. Jiang H, Wong WH. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 2008;24(20): 2395–2396

    CAS  PubMed Central  PubMed  Google Scholar 

  72. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM, MacLeod JN, Chiang DY, Prins JF, Liu J. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 2010;38(18): e178

    PubMed Central  PubMed  Google Scholar 

  73. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25(14): 1754–1760

    CAS  PubMed Central  PubMed  Google Scholar 

  74. Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C, Rinn JL, Lander ES, Regev A. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 2010;28(5): 503–510

    CAS  PubMed Central  PubMed  Google Scholar 

  75. Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res 2010;38(14): 4570–4578

    CAS  PubMed Central  PubMed  Google Scholar 

  76. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol 2011;12(3): R22

    CAS  PubMed Central  PubMed  Google Scholar 

  77. Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008;26(4): 407–415

    PubMed  Google Scholar 

  78. Ronen R, Gan I, Modai S, Sukacheov A, Dror G, Halperin E, Shomron N. miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 2010;26(20): 2615–2616

    CAS  PubMed  Google Scholar 

  79. Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM. miRanalyzer: an update on the detection and analysis of microRNAs in highthroughput sequencing experiments. Nucleic Acids Res 2011;39(Web Server issue): W132–W138

    CAS  PubMed Central  PubMed  Google Scholar 

  80. Huang PJ, Liu YC, Lee CC, Lin WC, Gan RRC, Lyu PC, Tang P. DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res 2010;38(Web Server issue): W385–391

    CAS  PubMed Central  PubMed  Google Scholar 

  81. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005;120(1): 15–20

    CAS  PubMed  Google Scholar 

  82. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N. Combinatorial microRNA target predictions. Nat Genet 2005;37(5): 495–500

    CAS  PubMed  Google Scholar 

  83. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res 2008;36(Database issue): D149–D153

    CAS  PubMed Central  PubMed  Google Scholar 

  84. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 2009;37(Web Server issue): W273–276

    CAS  PubMed Central  PubMed  Google Scholar 

  85. Rabilloud T, Chevallet M, Luche S, Lelong C. Two-dimensional gel electrophoresis in proteomics: Past, present and future. J Proteomics 2010;73(11): 2064–2077

    CAS  PubMed  Google Scholar 

  86. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 2003;422(6928): 198–207

    CAS  PubMed  Google Scholar 

  87. Domon B, Aebersold R. Mass spectrometry and protein analysis. Science 2006;312(5771): 212–217

    CAS  PubMed  Google Scholar 

  88. Stoevesandt O, Taussig MJ, He M. Protein microarrays: highthroughput tools for proteomics. Expert Rev Proteomics 2009;6(2): 145–157

    CAS  PubMed  Google Scholar 

  89. Novak A, Amit M, Ziv T, Segev H, Fishman B, Admon A, Itskovitz-Eldor J. Proteomics profiling of human embryonic stem cells in the early differentiation stage. Stem Cell Rev 2012;8(1): 137–149

    CAS  PubMed  Google Scholar 

  90. Gouw JW, Krijgsveld J. MSQuant: a platform for stable isotopebased quantitative proteomics. Methods Mol Biol 2012;893: 511–522

    CAS  PubMed  Google Scholar 

  91. Pedrioli PG, Eng JK, Hubley R, Vogelzang M, Deutsch EW, Raught B, Pratt B, Nilsson E, Angeletti RH, Apweiler R, Cheung K, Costello CE, Hermjakob H, Huang S, Julian RK, Kapp E, McComb ME, Oliver SG, Omenn G, Paton NW, Simpson R, Smith R, Taylor CF, Zhu W, Aebersold R. A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 2004;22(11): 1459–1466

    CAS  PubMed  Google Scholar 

  92. Mueller LN, Brusniak MY, Mani DR, Aebersold R. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res 2008;7(1): 51–61

    CAS  PubMed  Google Scholar 

  93. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteomewide protein quantification. Nat Biotechnol 2008;26(12): 1367–1372

    CAS  PubMed  Google Scholar 

  94. Khan Z, Bloom JS, Garcia BA, Singh M, Kruglyak L. Protein quantification across hundreds of experimental conditions. Proc Natl Acad Sci USA 2009;106(37): 15544–15548

    CAS  PubMed Central  PubMed  Google Scholar 

  95. Lin WT, Hung WN, Yian YH, Wu KP, Han CL, Chen YR, Chen YJ, Sung TY, Hsu WL. Multi-Q: a fully automated tool for multiplexed protein quantitation. J Proteome Res 2006;5(9): 2328–2338

    CAS  PubMed  Google Scholar 

  96. Shadforth IP, Dunkley TPJ, Lilley KS, Bessant C. i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 2005;6(1): 145

    PubMed Central  PubMed  Google Scholar 

  97. Arntzen MO, Koehler CJ, Barsnes H, Berven FS, Treumann A, Thiede B. IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res 2011;10(2): 913–920

    CAS  PubMed  Google Scholar 

  98. Keller A, Eng J, Zhang N, Li XJ, Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 2005; 1: 2005.0017

    PubMed Central  PubMed  Google Scholar 

  99. Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics 2008;9(1): 542

    PubMed Central  PubMed  Google Scholar 

  100. Tsou CC, Tsai CF, Tsui YH, Sudhir PR, Wang YT, Chen YJ, Chen JY, Sung TY, Hsu WL. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol Cell Proteomics 2010;9(1): 131–144

    CAS  PubMed Central  PubMed  Google Scholar 

  101. Mortensen P, Gouw JW, Olsen JV, Ong SE, Rigbolt KTG, Bunkenborg J, Cox J, Foster LJ, Heck AJR, Blagoev B, Andersen JS, Mann M. MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 2010;9(1): 393–403

    CAS  PubMed  Google Scholar 

  102. Hokke CH, Fitzpatrick JM, Hoffmann KF. Integrating transcriptome, proteome and glycome analyses of Schistosoma biology. Trends Parasitol 2007;23(4): 165–174

    CAS  PubMed  Google Scholar 

  103. Nielsen JA, Lau P, Maric D, Barker JL, Hudson LD. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis. BMC Neurosci 2009;10(1): 98

    PubMed Central  PubMed  Google Scholar 

  104. Liu F, Lu J, Hu W, Wang SY, Cui SJ, Chi M, Yan Q, Wang XR, Song HD, Xu XN, Wang JJ, Zhang XL, Zhang X, Wang ZQ, Xue CL, Brindley PJ, McManus DP, Yang PY, Feng Z, Chen Z, Han ZG. New perspectives on host-parasite interplay by comparative transcriptomic and proteomic analyses of Schistosoma japonicum. PLoS Pathog 2006;2(4): e29

    PubMed Central  PubMed  Google Scholar 

  105. Tarun AS, Peng X, Dumpit RF, Ogata Y, Silva-Rivera H, Camargo N, Daly TM, Bergman LW, Kappe SHI. A combined transcriptome and proteome survey of malaria parasite liver stages. Proc Natl Acad Sci USA 2008;105(1): 305–310

    CAS  PubMed Central  PubMed  Google Scholar 

  106. Unwin RD, Whetton AD. Systematic proteome and transcriptome analysis of stem cell populations. Cell Cycle 2006;5(15): 1587–1591

    CAS  PubMed  Google Scholar 

  107. Xu H, Lemischka IR, Ma’ayan A. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst Biol 2010;4(1): 173

    PubMed Central  PubMed  Google Scholar 

  108. Ho Sui SJ, Begley K, Reilly D, Chapman B, McGovern R, Rocca-Sera P, Maguire E, Altschuler GM, Hansen TA, Sompallae R, Krivtsov A, Shivdasani RA, Armstrong SA, Culhane AC, Correll M, Sansone SA, Hofmann O, Hide W. The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons. Nucleic Acids Res 2012;40(Database issue): D984–D991

    CAS  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China

    Hai Wang, Qian Zhang & Xiangdong Fang

Authors
  1. Hai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangdong Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangdong Fang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Zhang, Q. & Fang, X. Transcriptomics and proteomics in stem cell research. Front. Med. 8, 433–444 (2014). https://doi.org/10.1007/s11684-014-0336-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11684-014-0336-0

Keywords

Search

Navigation