Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2014, Vol. 8 Issue (4) : 433-444    https://doi.org/10.1007/s11684-014-0336-0
REVIEW
Transcriptomics and proteomics in stem cell research
Hai Wang, Qian Zhang, Xiangdong Fang()
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
 Download: PDF(690 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.

Keywords embryonic stem cells      transcriptomics      proteomics     
Corresponding Author(s): Xiangdong Fang   
Online First Date: 26 June 2014    Issue Date: 18 December 2014
 Cite this article:   
Hai Wang,Qian Zhang,Xiangdong Fang. Transcriptomics and proteomics in stem cell research[J]. Front. Med., 2014, 8(4): 433-444.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-014-0336-0
https://academic.hep.com.cn/fmd/EN/Y2014/V8/I4/433
Fig.1  Unsupervised clustering of genome-wide gene expression is provided for ESCs form different sequencing studies. These ESCs constitute of ESC.1 (H1 cell line), ESC.2 (H1 cell line), ESC.3 (H7 cell line), ESC.4 (human embryonic stem cell line), HESC (human hematopoietic stem cells), 48hrESC (ESC.2 differentiated for 48?h), MSC (mesenchymal stem cells), CSC cells (cancer stem cells). Gene expression tracks use red and green to represent over- and under-expression, respectively.
Repository Data type Species URL Reference
ESCAPE Transcriptomics, proteomics, ??phosphoproteomics Human, mouse http://www.maayanlab.net/ESCAPE/ [51]
Stem Cell Omics Repository Transcriptomics, proteomics, ??phosphoproteomics Human http://scor.chem.wisc.edu/ [49,5257]
Stem Base Transcriptomics Human, mouse, rat http://www.stembase.ca/?path=/ [58]
SCDE Transcriptomics Human, mouse, rat http://discovery.hsci.harvard.edu/ [59,60]
ESCD Transcriptomics Human, mouse http://biit.cs.ut.ee/escd/ [61]
Stem Cell Commons Transcriptomics Human, mouse, rat, zebrafish http://stemcellcommons.org/node/13552 [62]
Tab.1  Database for stem cell omics research
Fig.2  Systemic analysis brings insights into physiological and pathological molecular mechanisms of stem cells and promotes stem cell clinical utilities.
1 SM Ahn, R Simpson, B Lee. Genomics and proteomics in stem cell research: the road ahead. Anat Cell Biol 2010; 43(1): 1–14
https://doi.org/10.5115/acb.2010.43.1.1 pmid: 21190000
2 VK Gangaraju, H Lin. MicroRNAs: key regulators of stem cells. Nat Rev Mol Cell Biol 2009; 10(2): 116–125
https://doi.org/10.1038/nrm2621 pmid: 19165214
3 AM Wobus, KR Boheler. Embryonic stem cells: prospects for developmental biology and cell therapy. Physiol Rev 2005; 85(2): 635–678
https://doi.org/10.1152/physrev.00054.2003 pmid: 15788707
4 PA Callinan, AP Feinberg. The emerging science of epigenomics. Hum Mol Genet 2006; 15(Spec No 1): R95–R101
https://doi.org/10.1093/hmg/ddl095 pmid: 16651376
5 MV Schneider, S Orchard. Omics technologies, data and bioinformatics principles. Methods Mol Biol 2011; 719: 3–30
https://doi.org/10.1007/978-1-61779-027-0_1 pmid: 21370077
6 LW Stanton, MM Bakre. Genomic and proteomic characterization of embryonic stem cells. Curr Opin Chem Biol 2007; 11(4): 399–404
https://doi.org/10.1016/j.cbpa.2007.05.029 pmid: 17646122
7 Z Wang, M Gerstein, M Snyder. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009; 10(1): 57–63
https://doi.org/10.1038/nrg2484 pmid: 19015660
8 S Efroni, R Duttagupta, J Cheng, H Dehghani, DJ Hoeppner, C Dash, DP Bazett-Jones, S Le Grice, RDG McKay, KH Buetow, TR Gingeras, T Misteli, E Meshorer. Global transcription in pluripotent embryonic stem cells. Cell Stem Cell 2008; 2(5): 437–447
https://doi.org/10.1016/j.stem.2008.03.021 pmid: 18462694
9 MH Chin, MJ Mason, W Xie, S Volinia, M Singer, C Peterson, G Ambartsumyan, O Aimiuwu, L Richter, J Zhang, I Khvorostov, V Ott, M Grunstein, N Lavon, N Benvenisty, CM Croce, AT Clark, T Baxter, AD Pyle, MA Teitell, M Pelegrini, K Plath, WE Lowry. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 2009; 5(1): 111–123
https://doi.org/10.1016/j.stem.2009.06.008 pmid: 19570518
10 I Ginis, Y Luo, T Miura, S Thies, R Brandenberger, S Gerecht-Nir, M Amit, A Hoke, MK Carpenter, J Itskovitz-Eldor, MS Rao. Differences between human and mouse embryonic stem cells. Dev Biol 2004; 269(2): 360–380
https://doi.org/10.1016/j.ydbio.2003.12.034 pmid: 15110706
11 B Bhattacharya, T Miura, R Brandenberger, J Mejido, Y Luo, AX Yang, BH Joshi, I Ginis, RS Thies, M Amit, I Lyons, BG Condie, J Itskovitz-Eldor, MS Rao, RK Puri. Gene expression in human embryonic stem cell lines: unique molecular signature. Blood 2004; 103(8): 2956–2964
https://doi.org/10.1182/blood-2003-09-3314 pmid: 15070671
12 R Brandenberger, I Khrebtukova, RS Thies, T Miura, C Jingli, R Puri, T Vasicek, J Lebkowski, M Rao. MPSS profiling of human embryonic stem cells. BMC Dev Biol 2004; 4(1): 10
https://doi.org/10.1186/1471-213X-4-10 pmid: 15304200
13 M Zhan. Genomic studies to explore self-renewal and differentiation properties of embryonic stem cells. Front Biosci 2008; 13(13): 276–283
https://doi.org/10.2741/2678 pmid: 17981546
14 F Djouad, C Bony, F Canovas, O Fromigué, T Rème, C Jorgensen, D Noël. Transcriptomic analysis identifies Foxo3A as a novel transcription factor regulating mesenchymal stem cell chrondrogenic differentiation. Cloning Stem Cells 2009; 11(3): 407–416
https://doi.org/10.1089/clo.2009.0013 pmid: 19751111
15 NB Ivanova, JT Dimos, C Schaniel, JA Hackney, KA Moore, IR Lemischka. A stem cell molecular signature. Science 2002; 298(5593): 601–604
https://doi.org/10.1126/science.1073823 pmid: 12228721
16 M Ramalho-Santos, S Yoon, Y Matsuzaki, RC Mulligan, DA Melton. “Stemness”: transcriptional profiling of embryonic and adult stem cells. Science 2002; 298(5593): 597–600
https://doi.org/10.1126/science.1072530 pmid: 12228720
17 M Suárez-Fariñas, S Noggle, M Heke, A Hemmati-Brivanlou, MO Magnasco. Comparing independent microarray studies: the case of human embryonic stem cells. BMC Genomics 2005; 6(1): 99
https://doi.org/10.1186/1471-2164-6-99 pmid: 16042783
18 Y Yang, H Wang, KH Chang, H Qu, Z Zhang, Q Xiong, H Qi, P Cui, Q Lin, X Ruan, Y Yang, Y Li, C Shu, Q Li, EK Wakeland, J Yan, S Hu, X Fang. Transcriptome dynamics during human erythroid differentiation and development. Genomics 2013; 102(5-6): 431–441
https://doi.org/10.1016/j.ygeno.2013.09.005 pmid: 24121002
19 AA Sigova, AC Mullen, B Molinie, S Gupta, DA Orlando, MG Guenther, AE Almada, C Lin, PA Sharp, CC Giallourakis, RA Young. Divergent transcription of long noncoding RNA/mRNA gene pairs in embryonic stem cells. Proc Natl Acad Sci USA 2013; 110(8): 2876–2881
https://doi.org/10.1073/pnas.1221904110 pmid: 23382218
20 L Yan, M Yang, H Guo, L Yang, J Wu, R Li, P Liu, Y Lian, X Zheng, J Yan, J Huang, M Li, X Wu, L Wen, K Lao, R Li, J Qiao, F Tang. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 2013; 20(9): 1131–1139
https://doi.org/10.1038/nsmb.2660 pmid: 23934149
21 T MacRae, T Sargeant, S Lemieux, J Hébert, E Deneault, G Sauvageau. RNA-Seq reveals spliceosome and proteasome genes as most consistent transcripts in human cancer cells. PLoS ONE 2013; 8(9): e72884
https://doi.org/10.1371/journal.pone.0072884 pmid: 24069164
22 K Jääger, S Islam, P Zajac, S Linnarsson, T Neuman. RNA-seq analysis reveals different dynamics of differentiation of human dermis- and adipose-derived stromal stem cells. PLoS ONE 2012; 7(6): e38833
https://doi.org/10.1371/journal.pone.0038833 pmid: 22723894
23 G Gargiulo, M Cesaroni, M Serresi, N de Vries, D Hulsman, SW Bruggeman, C Lancini, M van Lohuizen.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
https://doi.org/10.1016/j.ccr.2013.03.030 pmid: 23680149
24 N Salomonis, CR Schlieve, L Pereira, C Wahlquist, A Colas, AC Zambon, K Vranizan, MJ Spindler, AR Pico, MS Cline, TA Clark, A Williams, JE Blume, E Samal, M Mercola, BJ Merrill, BR Conklin. Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation. Proc Natl Acad Sci USA 2010; 107(23): 10514–10519
https://doi.org/10.1073/pnas.0912260107 pmid: 20498046
25 JQ Wu, L Habegger, P Noisa, A Szekely, C Qiu, S Hutchison, D Raha, M Egholm, H Lin, S Weissman, W Cui, M Gerstein, M Snyder. Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing. Proc Natl Acad Sci USA 2010; 107(11): 5254–5259
https://doi.org/10.1073/pnas.0914114107 pmid: 20194744
26 R Brandenberger, H Wei, S Zhang, S Lei, J Murage, GJ Fisk, Y Li, C Xu, R Fang, K Guegler, MS Rao, R Mandalam, J Lebkowski, LW Stanton. Transcriptome characterization elucidates signaling networks that control human ES cell growth and differentiation. Nat Biotechnol 2004; 22(6): 707–716
https://doi.org/10.1038/nbt971 pmid: 15146197
27 SV Anisimov, KV Tarasov, D Tweedie, MD Stern, AM Wobus, KR Boheler. SAGE identification of gene transcripts with profiles unique to pluripotent mouse R1 embryonic stem cells. Genomics 2002; 79(2): 169–176
https://doi.org/10.1006/geno.2002.6687 pmid: 11829487
28 L He, GJ Hannon. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 2004; 5(7): 522–531
https://doi.org/10.1038/nrg1379 pmid: 15211354
29 MR Suh, Y Lee, JY Kim, SK Kim, SH Moon, JY Lee, KY Cha, HM Chung, HS Yoon, SY Moon, VN Kim, KS Kim. Human embryonic stem cells express a unique set of microRNAs. Dev Biol 2004; 270(2): 488–498
https://doi.org/10.1016/j.ydbio.2004.02.019 pmid: 15183728
30 A Jouneau, C Ciaudo, O Sismeiro, V Brochard, L Jouneau, S Vandormael-Pournin, JY Coppée, Q Zhou, E Heard, C Antoniewski, M Cohen-Tannoudji. Naive and primed murine pluripotent stem cells have distinct miRNA expression profiles. RNA 2012; 18(2): 253–264
https://doi.org/10.1261/rna.028878.111 pmid: 22201644
31 FF Kirigin, K Lindstedt, M Sellars, M Ciofani, SL Low, L Jones, F Bell, F Pauli, R Bonneau, RM Myers, DR Littman, MMW Chong. Dynamic microRNA gene transcription and processing during T cell development. J Immunol 2012; 188(7): 3257–3267
https://doi.org/10.4049/jimmunol.1103175 pmid: 22379031
32 A Marson, SS Levine, MF Cole, GM Frampton, T Brambrink, S Johnstone, MG Guenther, WK Johnston, M Wernig, J Newman, JM Calabrese, LM Dennis, TL Volkert, S Gupta, J Love, N Hannett, PA Sharp, DP Bartel, R Jaenisch, RA Young. Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 2008; 134(3): 521–533
https://doi.org/10.1016/j.cell.2008.07.020 pmid: 18692474
33 JS Mattick. A new paradigm for developmental biology. J Exp Biol 2007; 210(Pt 9): 1526–1547
https://doi.org/10.1242/jeb.005017 pmid: 17449818
34 J Sheik Mohamed, PM Gaughwin, B Lim, P Robson, L Lipovich. Conserved long noncoding RNAs transcriptionally regulated by Oct4 and Nanog modulate pluripotency in mouse embryonic stem cells. RNA 2010; 16(2): 324–337
https://doi.org/10.1261/rna.1441510 pmid: 20026622
35 ME Dinger, PP Amaral, TR Mercer, KC Pang, SJ Bruce, BB Gardiner, ME Askarian-Amiri, K Ru, G Soldà, C Simons, SM Sunkin, ML Crowe, SM Grimmond, AC Perkins, JS Mattick. Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. Genome Res 2008; 18(9): 1433–1445
https://doi.org/10.1101/gr.078378.108 pmid: 18562676
36 AD Ramos, A Diaz, A Nellore, RN Delgado, KY Park, G Gonzales-Roybal, MC Oldham, JS Song, DA Lim. Integration of genome-wide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo. Cell Stem Cell 2013; 12(5): 616–628
https://doi.org/10.1016/j.stem.2013.03.003 pmid: 23583100
37 RD Unwin, SJ Gaskell, CA Evans, AD Whetton. The potential for proteomic definition of stem cell populations. Exp Hematol 2003; 31(12): 1147–1159
https://doi.org/10.1016/j.exphem.2003.08.012 pmid: 14662320
38 H Baharvand, A Fathi, D van Hoof, GH Salekdeh. Concise review: trends in stem cell proteomics. Stem Cells 2007; 25(8): 1888–1903
https://doi.org/10.1634/stemcells.2007-0107 pmid: 17495109
39 K Nagano, M Taoka, Y Yamauchi, C Itagaki, T Shinkawa, K Nunomura, N Okamura, N Takahashi, T Izumi, T Isobe. Large-scale identification of proteins expressed in mouse embryonic stem cells. Proteomics 2005; 5(5): 1346–1361
https://doi.org/10.1002/pmic.200400990 pmid: 15742316
40 D Nasrabadi, M Rezaei Larijani, L Pirhaji, H Gourabi, A Shahverdi, H Baharvand, GH Salekdeh. Proteomic analysis of monkey embryonic stem cell during differentiation. J Proteome Res 2009; 8(3): 1527–1539
https://doi.org/10.1021/pr800880v pmid: 19226164
41 A Böser, HCA Drexler, H Reuter, H Schmitz, G Wu, HR Schöler, L Gentile, K Bartscherer. SILAC proteomics of planarians identifies Ncoa5 as a conserved component of pluripotent stem cells. Cell Reports 2013; 5(4): 1142–1155
https://doi.org/10.1016/j.celrep.2013.10.035 pmid: 24268775
42 Y Sun, Y Yang, S Zeng, Y Tan, G Lu, G Lin. 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
https://doi.org/10.1371/journal.pone.0085823 pmid: 24465727
43 S D’Aguanno, D Barcaroli, C Rossi, M Zucchelli, D Ciavardelli, C Cortese, A De Cola, S Volpe, D D’Agostino, M Todaro, G Stassi, C Di Ilio, A Urbani, V De Laurenzi. p63 Isoforms Regulate Metabolism of Cancer Stem Cells. J Proteome Res 2014; 13(4): 2120–2136
https://doi.org/10.1021/pr4012574 pmid: 24597989
44 H Lin, E Lee, K Hestir, C Leo, M Huang, E Bosch, R Halenbeck, G Wu, A Zhou, D Behrens, D Hollenbaugh, T Linnemann, M Qin, J Wong, K Chu, SK Doberstein, LT Williams. Discovery of a cytokine and its receptor by functional screening of the extracellular proteome. Science 2008; 320(5877): 807–811
https://doi.org/10.1126/science.1154370 pmid: 18467591
45 R Gonzalez, LL Jennings, M Knuth, AP Orth, HE Klock, W Ou, J Feuerhelm, MV Hull, E Koesema, Y Wang, J Zhang, C Wu, CY Cho, AI Su, S Batalov, H Chen, K Johnson, B Laffitte, DG Nguyen, EY Snyder, PG Schultz, JL Harris, SA Lesley. Screening the mammalian extracellular proteome for regulators of embryonic human stem cell pluripotency. Proc Natl Acad Sci USA 2010; 107(8): 3552–3557
https://doi.org/10.1073/pnas.0914019107 pmid: 20133595
46 M Gemei, C Corbo, F D’Alessio, R Di Noto, R Vento, L Del Vecchio. Surface proteomic analysis of differentiated versus stem-like osteosarcoma human cells. Proteomics 2013; 13(22): 3293–3297
https://doi.org/10.1002/pmic.201300170 pmid: 24106197
47 D Van Hoof, J Muñoz, SR Braam, MWH Pinkse, R Linding, AJR Heck, CL Mummery, J Krijgsveld. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 2009; 5(2): 214–226
https://doi.org/10.1016/j.stem.2009.05.021 pmid: 19664995
48 LM Brill, W Xiong, KB Lee, SB Ficarro, A Crain, Y Xu, A Terskikh, EY Snyder, S Ding. Phosphoproteomic analysis of human embryonic stem cells. Cell Stem Cell 2009; 5(2): 204–213
https://doi.org/10.1016/j.stem.2009.06.002 pmid: 19664994
49 DL Swaney, CD Wenger, JA Thomson, JJ Coon. Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry. Proc Natl Acad Sci USA 2009; 106(4): 995–1000
https://doi.org/10.1073/pnas.0811964106 pmid: 19144917
50 KT Rigbolt, TA Prokhorova, V Akimov, J Henningsen, PT Johansen, I Kratchmarova, M Kassem, M Mann, JV Olsen, B Blagoev. System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation. Sci Signal 2011; 4(164): rs3
https://doi.org/10.1126/scisignal.2001570 pmid: 21406692
51 H Xu, C Baroukh, R Dannenfelser, EY Chen, CM Tan, Y Kou, YE Kim, IR Lemischka, A Ma’ayan. ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database (Oxford) 2013; 2013: bat045
https://doi.org/10.1093/database/bat045 pmid: 23794736
52 MH Chin, MJ Mason, W Xie, S Volinia, M Singer, C Peterson, G Ambartsumyan, O Aimiuwu, L Richter, J Zhang, I Khvorostov, V Ott, M Grunstein, N Lavon, N Benvenisty, CM Croce, AT Clark, T Baxter, AD Pyle, MA Teitell, M Pelegrini, K Plath, WE Lowry. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 2009; 5(1): 111–123
https://doi.org/10.1016/j.stem.2009.06.008 pmid: 19570518
53 J Yu, K Hu, K Smuga-Otto, S Tian, R Stewart, II Slukvin, JA Thomson. Human induced pluripotent stem cells free of vector and transgene sequences. Science 2009; 324(5928): 797–801
https://doi.org/10.1126/science.1172482 pmid: 19325077
54 D Van Hoof, J Muñoz, SR Braam, MWH Pinkse, R Linding, AJR Heck, CL Mummery, J Krijgsveld. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 2009; 5(2): 214–226
https://doi.org/10.1016/j.stem.2009.05.021 pmid: 19664995
55 J Munoz, TY Low, YJ Kok, A Chin, CK Frese, V Ding, A Choo, AJR Heck. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol Syst Biol 2011; 7: 550
https://doi.org/10.1038/msb.2011.84 pmid: 22108792
56 R Sridharan, M Gonzales-Cope, C Chronis, G Bonora, R McKee, C Huang, S Patel, D Lopez, N Mishra, M Pellegrini, M Carey, BA Garcia, K Plath. 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
https://doi.org/10.1038/ncb2768 pmid: 23748610
57 DH Phanstiel, J Brumbaugh, CD Wenger, S Tian, MD Probasco, DJ Bailey, DL Swaney, MA Tervo, JM Bolin, V Ruotti, R Stewart, JA Thomson, JJ Coon. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat Methods 2011; 8(10): 821–827
https://doi.org/10.1038/nmeth.1699 pmid: 21983960
58 C Perez-Iratxeta, G Palidwor, CJ Porter, NA Sanche, MR Huska, BP Suomela, EM Muro, PM Krzyzanowski, E Hughes, PA Campbell, MA Rudnicki, MA Andrade. Study of stem cell function using microarray experiments. FEBS Lett 2005; 579(8): 1795–1801
https://doi.org/10.1016/j.febslet.2005.02.020 pmid: 15763554
59 SA Sansone, P Rocca-Serra, D Field, E Maguire, C Taylor, O Hofmann, H Fang, S Neumann, W Tong, L Amaral-Zettler, K Begley, T Booth, L Bougueleret, G Burns, B Chapman, T Clark, LA Coleman, J Copeland, S Das, A de Daruvar, P de Matos, I Dix, S Edmunds, CT Evelo, MJ Forster, P Gaudet, J Gilbert, C Goble, JL Griffin, D Jacob, J Kleinjans, L Harland, K Haug, H Hermjakob, SJ Ho Sui, A Laederach, S Liang, S Marshall, A McGrath, E Merrill, D Reilly, M Roux, CE Shamu, CA Shang, C Steinbeck, A Trefethen, B Williams-Jones, K Wolstencroft, I Xenarios, W Hide. Toward interoperable bioscience data. Nat Genet 2012; 44(2): 121–126
https://doi.org/10.1038/ng.1054 pmid: 22281772
60 SJ Ho Sui, K Begley, D Reilly, B Chapman, R McGovern, P Rocca-Sera, E Maguire, GM Altschuler, TAA Hansen, R Sompallae, A Krivtsov, RA Shivdasani, SA Armstrong, AC Culhane, M Correll, SA Sansone, O Hofmann, W Hide. 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
https://doi.org/10.1093/nar/gkr1051 pmid: 22121217
61 M Jung, H Peterson, L Chavez, P Kahlem, H Lehrach, J Vilo, J Adjaye. 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
https://doi.org/10.1371/journal.pone.0010709 pmid: 20505756
62 BS Mallon, JG Chenoweth, KR Johnson, RS Hamilton, PJ Tesar, AS Yavatkar, LJ Tyson, K Park, KG Chen, YC Fann, RDG McKay. StemCellDB: the human pluripotent stem cell database at the National Institutes of Health. Stem Cell Res (Amst) 2013; 10(1): 57–66
https://doi.org/10.1016/j.scr.2012.09.002 pmid: 23117585
63 V Costa, C Angelini, I De Feis, A Ciccodicola. Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010; 2010: 853916.
64 VE Velculescu, L Zhang, B Vogelstein, KW Kinzler. Serial analysis of gene expression. Science 1995; 270(5235): 484–487
https://doi.org/10.1126/science.270.5235.484 pmid: 7570003
65 SH Nagaraj, RB Gasser, S Ranganathan. A hitchhiker’s guide to expressed sequence tag (EST) analysis. Brief Bioinform 2007; 8(1): 6–21
https://doi.org/10.1093/bib/bbl015 pmid: 16772268
66 ER Mardis. The impact of next-generation sequencing technology on genetics. Trends Genet 2008; 24(3): 133–141
https://doi.org/10.1016/j.tig.2007.12.007 pmid: 18262675
67 S Uchida, P Gellert, T Braun. Deeply dissecting stemness: making sense to non-coding RNAs in stem cells. Stem Cell Rev 2012; 8(1): 78–86
https://doi.org/10.1007/s12015-011-9294-y pmid: 21706141
68 YW Asmann, MB Wallace, EA Thompson. Transcriptome profiling using next-generation sequencing. Gastroenterology 2008; 135(5): 1466–1468
https://doi.org/10.1053/j.gastro.2008.09.042 pmid: 18848555
69 B Langmead, C Trapnell, M Pop, SL Salzberg. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009; 10(3): R25
https://doi.org/10.1186/gb-2009-10-3-r25 pmid: 19261174
70 G Jean, A Kahles, VT Sreedharan, F De Bona, G Ratsch. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics 2010; Chapter 11: Unit 11 6
71 H Jiang, WH Wong. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 2008; 24(20): 2395–2396
https://doi.org/10.1093/bioinformatics/btn429 pmid: 18697769
72 K Wang, D Singh, Z Zeng, SJ Coleman, Y Huang, GL Savich, X He, P Mieczkowski, SA Grimm, CM Perou, JN MacLeod, DY Chiang, JF Prins, J Liu. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 2010; 38(18): e178
https://doi.org/10.1093/nar/gkq622 pmid: 20802226
73 H Li, R Durbin. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25(14): 1754–1760
https://doi.org/10.1093/bioinformatics/btp324 pmid: 19451168
74 M Guttman, M Garber, JZ Levin, J Donaghey, J Robinson, X Adiconis, L Fan, MJ Koziol, A Gnirke, C Nusbaum, JL Rinn, ES Lander, A Regev. 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
https://doi.org/10.1038/nbt.1633 pmid: 20436462
75 KF Au, H Jiang, L Lin, Y Xing, WH Wong. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res 2010; 38(14): 4570–4578
https://doi.org/10.1093/nar/gkq211 pmid: 20371516
76 A Roberts, C Trapnell, J Donaghey, JL Rinn, L Pachter. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol 2011; 12(3): R22
https://doi.org/10.1186/gb-2011-12-3-r22 pmid: 21410973
77 MR Friedländer, W Chen, C Adamidi, J Maaskola, R Einspanier, S Knespel, N Rajewsky. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008; 26(4): 407–415
https://doi.org/10.1038/nbt1394 pmid: 18392026
78 R Ronen, I Gan, S Modai, A Sukacheov, G Dror, E Halperin, N Shomron. miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 2010; 26(20): 2615–2616
https://doi.org/10.1093/bioinformatics/btq493 pmid: 20801911
79 M Hackenberg, N Rodríguez-Ezpeleta, AM Aransay. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011; 39(Web Server issue): W132–138
pmid: 21515631
80 PJ Huang, YC Liu, CC Lee, WC Lin, RRC Gan, PC Lyu, P Tang. DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res 2010; 38(Web Server issue): W385–391
pmid: 20478825
81 BP Lewis, CB Burge, DP Bartel. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120(1): 15–20
https://doi.org/10.1016/j.cell.2004.12.035 pmid: 15652477
82 A Krek, D Grün, MN Poy, R Wolf, L Rosenberg, EJ Epstein, P MacMenamin, I da Piedade, KC Gunsalus, M Stoffel, N Rajewsky. Combinatorial microRNA target predictions. Nat Genet 2005; 37(5): 495–500
https://doi.org/10.1038/ng1536 pmid: 15806104
83 D Betel, M Wilson, A Gabow, DS Marks, C Sander. The microRNA.org resource: targets and expression. Nucleic Acids Res 2008; 36(Database issue): D149–D153
pmid: 18158296
84 M Maragkakis, M Reczko, VA Simossis, P Alexiou, GL Papadopoulos, T Dalamagas, G Giannopoulos, G Goumas, E Koukis, K Kourtis, T Vergoulis, N Koziris, T Sellis, P Tsanakas, AG Hatzigeorgiou. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 2009; 37(Web Server issue): W273-276
pmid: 19406924
85 T Rabilloud, M Chevallet, S Luche, C Lelong. Two-dimensional gel electrophoresis in proteomics: Past, present and future. J Proteomics 2010; 73(11): 2064–2077
https://doi.org/10.1016/j.jprot.2010.05.016 pmid: 20685252
86 R Aebersold, M Mann. Mass spectrometry-based proteomics. Nature 2003; 422(6928): 198–207
https://doi.org/10.1038/nature01511 pmid: 12634793
87 B Domon, R Aebersold. Mass spectrometry and protein analysis. Science 2006; 312(5771): 212–217
https://doi.org/10.1126/science.1124619 pmid: 16614208
88 O Stoevesandt, MJ Taussig, M He. Protein microarrays: high-throughput tools for proteomics. Expert Rev Proteomics 2009; 6(2): 145–157
https://doi.org/10.1586/epr.09.2 pmid: 19385942
89 A Novak, M Amit, T Ziv, H Segev, B Fishman, A Admon, J Itskovitz-Eldor. Proteomics profiling of human embryonic stem cells in the early differentiation stage. Stem Cell Rev 2012; 8(1): 137–149
https://doi.org/10.1007/s12015-011-9286-y pmid: 21732092
90 JW Gouw, J Krijgsveld. MSQuant: a platform for stable isotope-based quantitative proteomics. Methods Mol Biol 2012; 893: 511–522
https://doi.org/10.1007/978-1-61779-885-6_31 pmid: 22665320
91 PG Pedrioli, JK Eng, R Hubley, M Vogelzang, EW Deutsch, B Raught, B Pratt, E Nilsson, RH Angeletti, R Apweiler, K Cheung, CE Costello, H Hermjakob, S Huang, RK Julian, E Kapp, ME McComb, SG Oliver, G Omenn, NW Paton, R Simpson, R Smith, CF Taylor, W Zhu, R Aebersold. A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 2004; 22(11): 1459–1466
https://doi.org/10.1038/nbt1031 pmid: 15529173
92 LN Mueller, MY Brusniak, DR Mani, R Aebersold. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res 2008; 7(1): 51–61
https://doi.org/10.1021/pr700758r pmid: 18173218
93 J Cox, M Mann. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 2008; 26(12): 1367–1372
https://doi.org/10.1038/nbt.1511 pmid: 19029910
94 Z Khan, JS Bloom, BA Garcia, M Singh, L Kruglyak. Protein quantification across hundreds of experimental conditions. Proc Natl Acad Sci USA 2009; 106(37): 15544–15548
https://doi.org/10.1073/pnas.0904100106 pmid: 19717460
95 WT Lin, WN Hung, YH Yian, KP Wu, CL Han, YR Chen, YJ Chen, TY Sung, WL Hsu. Multi-Q: a fully automated tool for multiplexed protein quantitation. J Proteome Res 2006; 5(9): 2328–2338
https://doi.org/10.1021/pr060132c pmid: 16944945
96 IP Shadforth, TPJ Dunkley, KS Lilley, C Bessant. i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 2005; 6(1): 145
https://doi.org/10.1186/1471-2164-6-145 pmid: 16242023
97 MO Arntzen, CJ Koehler, H Barsnes, FS Berven, A Treumann, B Thiede. IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res 2011; 10(2): 913–920
https://doi.org/10.1021/pr1009977 pmid: 21067241
98 A Keller, J Eng, N Zhang, XJ Li, R Aebersold. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 2005; 1: 2005.0017
pmid: 16729052
99 MY Brusniak, B Bodenmiller, D Campbell, K Cooke, J Eddes, A Garbutt, H Lau, S Letarte, LN Mueller, V Sharma, O Vitek, N Zhang, R Aebersold, JD Watts. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics 2008; 9(1): 542
https://doi.org/10.1186/1471-2105-9-542 pmid: 19087345
100 CC Tsou, CF Tsai, YH Tsui, PR Sudhir, YT Wang, YJ Chen, JY Chen, TY Sung, WL Hsu. 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
https://doi.org/10.1074/mcp.M900177-MCP200 pmid: 19752006
101 P Mortensen, JW Gouw, JV Olsen, SE Ong, KTG Rigbolt, J Bunkenborg, J Cox, LJ Foster, AJR Heck, B Blagoev, JS Andersen, M Mann. MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 2010; 9(1): 393–403
https://doi.org/10.1021/pr900721e pmid: 19888749
102 CH Hokke, JM Fitzpatrick, KF Hoffmann. Integrating transcriptome, proteome and glycome analyses of Schistosoma biology. Trends Parasitol 2007; 23(4): 165–174
https://doi.org/10.1016/j.pt.2007.02.007 pmid: 17336161
103 JA Nielsen, P Lau, D Maric, JL Barker, LD Hudson. 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
https://doi.org/10.1186/1471-2202-10-98 pmid: 19689821
104 F Liu, J Lu, W Hu, SY Wang, SJ Cui, M Chi, Q Yan, XR Wang, HD Song, XN Xu, JJ Wang, XL Zhang, X Zhang, ZQ Wang, CL Xue, PJ Brindley, DP McManus, PY Yang, Z Feng, Z Chen, ZG Han. New perspectives on host-parasite interplay by comparative transcriptomic and proteomic analyses of Schistosoma japonicum. PLoS Pathog 2006; 2(4): e29
https://doi.org/10.1371/journal.ppat.0020029 pmid: 16617374
105 AS Tarun, X Peng, RF Dumpit, Y Ogata, H Silva-Rivera, N Camargo, TM Daly, LW Bergman, SHI Kappe. A combined transcriptome and proteome survey of malaria parasite liver stages. Proc Natl Acad Sci USA 2008; 105(1): 305–310
https://doi.org/10.1073/pnas.0710780104 pmid: 18172196
106 RD Unwin, AD Whetton. Systematic proteome and transcriptome analysis of stem cell populations. Cell Cycle 2006; 5(15): 1587–1591
https://doi.org/10.4161/cc.5.15.3101 pmid: 16861929
107 H Xu, IR Lemischka, A Ma’ayan. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst Biol 2010; 4(1): 173
https://doi.org/10.1186/1752-0509-4-173 pmid: 21176149
108 SJ Ho Sui, K Begley, D Reilly, B Chapman, R McGovern, P Rocca-Sera, E Maguire, GM Altschuler, TA Hansen, R Sompallae, A Krivtsov, RA Shivdasani, SA Armstrong, AC Culhane, M Correll, SA Sansone, O Hofmann, W Hide. 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
https://doi.org/10.1093/nar/gkr1051 pmid: 22121217
[1] Yao Chen, Fahuan Song, Mengjiao Tu, Shuang Wu, Xiao He, Hao Liu, Caiyun Xu, Kai Zhang, Yuankai Zhu, Rui Zhou, Chentao Jin, Ping Wang, Hong Zhang, Mei Tian. Quantitative proteomics revealed extensive microenvironmental changes after stem cell transplantation in ischemic stroke[J]. Front. Med., 2022, 16(3): 429-441.
[2] Jinrong Liu, Rongfang Shen, Lin Feng, Shujun Cheng, Jun Chen, Ting Xiao, Shunying Zhao. Proteomics study of Mycoplasma pneumoniae pneumonia reveals the Fc fragment of the IgG-binding protein as a serum biomarker and implicates potential therapeutic targets[J]. Front. Med., 2022, 16(3): 378-388.
[3] Xiao Liu, Hui Ren, Daizhi Peng. Sepsis biomarkers: an omics perspective[J]. Front Med, 2014, 8(1): 58-67.
[4] Yang Yang, Xiaofei Han, Jingyun Guan, Xiangzhi Li. Regulation and function of histone acetyltransferase MOF[J]. Front Med, 2014, 8(1): 79-83.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed