Mammalian transcriptomes mainly consist of non protein coding RNAs. These ncRNAs play various roles in all cells and are involved in multiple regulation pathways. More recently, ncRNAs have also been described as valuable diagnostic tools. While RNA-seq approaches progressively replace microarray-based technologies for high-throughput expression profiling, they are still not routinely used in diagnostic. Microarrays, on the other hand, are more widely used for diagnostic profiling, especially for very small ncRNA (e.g., miRNAs), employing locked nucleic acid (LNA) arrays. However, LNA microarrays are quite expensive for high-throughput studies targeting longer ncRNAs, while DNA arrays do not provide satisfying results for the analysis of small RNAs. Here, we describe a mixed DNA/LNA microarray platform, where directly labeled small and longer ncRNAs are hybridized on LNA probes or custom DNA probes, respectively, enabling sensitive and specific analysis of a complex RNA population on a unique array in one single experiment. The DNA/LNA system, requiring relatively low amounts of total RNA, which complies with diagnostic references, was successfully applied to the analysis of differential ncRNA expression in mouse embryonic stem cells and adult brain cells. 1. Introduction The high-resolution analysis of 1% of the human genome by the ENCODE project has shown that up to 90% of the genome is being transcribed while only about 1.5% of these transcripts correspond to protein coding exons [1]. Therefore, it was suggested that the majority of the transcripts might serve as a source for regulatory non coding RNAs (ncRNAs) [2, 3], with the predicted number of ncRNAs present in the human genome reaching up to 0.5 million transcripts [4]. However, most of these transcripts still remain of unknown function, and their functionality is even debated [4]. These novel exciting aspects of the cellular transcriptome content thus require novel methods for profiling ncRNAs expression in a high-throughput manner. Lately, the most widely used expression profiling technique has become high-throughput sequencing or RNA-seq [5, 6], with numerous advantages. RNA-seq provides full genome coverage and allows detection of single nucleotide polymorphisms as well as RNA editing events, independently of hybridization artifacts. However, RNA-seq drawbacks and artifacts are not completely absent, generally linked to reverse transcription or library generation protocols [6, 7]. In addition, analysis of sequencing datasets is still rather time consuming and requires a strong bioinformatic
References
[1]
E. Birney, J. A. Stamatoyannopoulos, A. Dutta et al., “Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project,” Nature, vol. 447, no. 7146, pp. 799–816, 2007.
[2]
J. S. Mattick and I. V. Makunin, “Small regulatory RNAs in mammals,” Human Molecular Genetics, vol. 14, no. 1, pp. R121–R132, 2005.
[3]
J. S. Mattick and I. V. Makunin, “Non-coding RNA,” Human Molecular Genetics, vol. 15, pp. R17–R29, 2006.
[4]
A. T. Willingham and T. R. Gingeras, “TUF Love for “Junk” DNA,” Cell, vol. 125, no. 7, pp. 1215–1220, 2006.
[5]
A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008.
[6]
A. Saxena and P. Carninci, “Whole transcriptome analysis: what are we still missing?” Wiley Interdisciplinary Reviews, vol. 3, no. 5, pp. 527–543, 2011.
[7]
C. A. Raabe, C. H. Hoe, G. Randau, J. Brosius, T. H. Tang, and T. S. Rozhdestvensky, “The rocks and shallows of deep RNA sequencing: examples in the Vibrio cholerae RNome,” RNA, vol. 17, no. 7, pp. 1357–1366, 2011.
[8]
M. Castoldi, S. Schmidt, V. Benes, M. W. Hentze, and M. U. Muckenthaler, “miChip: an array-based method for microRNA expression profiling using locked nucleic acid capture probes,” Nature Protocols, vol. 3, no. 2, pp. 321–329, 2008.
[9]
M. Hackl, S. Brunner, K. Fortschegger et al., “miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging,” Aging Cell, vol. 9, no. 2, pp. 291–296, 2010.
[10]
N. Jacobsen, J. Bentzen, M. Meldgaard et al., “LNA-enhanced detection of single nucleotide polymorphisms in the apolipoprotein E.,” Nucleic Acids Research, vol. 30, no. 19, article e100, 2002.
[11]
R. N. Veedu and J. Wengel, “Locked nucleic acids: promising nucleic acid analogs for therapeutic applications,” Chemistry and Biodiversity, vol. 7, no. 3, pp. 536–542, 2010.
[12]
J. Liao, L. Yu, Y. Mei et al., “Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer,” Molecular Cancer, vol. 9, article 198, 2010.
[13]
X. Liu, K. Fortin, and Z. Mourelatos, “MicroRNAs: biogenesis and molecular functions,” Brain Pathology, vol. 18, no. 1, pp. 113–121, 2008.
[14]
N. Rosenfeld, R. Aharonov, E. Meiri et al., “MicroRNAs accurately identify cancer tissue origin,” Nature Biotechnology, vol. 26, no. 4, pp. 462–469, 2008.
[15]
K. Skreka, M. Karbiener, M. Zywicki, H. Alexander, S. Marcel, and R. Mathieu, “Expression profiling of ncRNAs employing RNP libraries and custom LNA/DNA microarray analysis,” Regulatory RNAs: Basics, Methods and Applications, Springer, 2012.
[16]
M. Rederstorff, S. H. Bernhart, A. Tanzer et al., “RNPomics: defining the ncRNA transcriptome by cDNA library generation from ribonucleo-protein particles.,” Nucleic Acids Research, vol. 38, no. 10, article e113, 2010.
[17]
M. W. Pfaffl, “A new mathematical model for relative quantification in real-time RT-PCR,” Nucleic Acids Research, vol. 29, no. 9, article e45, 2001.
[18]
R. Hutzinger, J. Mrázek, S. Vorwerk, and A. Hüttenhofer, “NcRNA-microchip analysis: a novel approach to identify differential expression of non-coding RNAs,” RNA Biology, vol. 7, no. 5, pp. 586–595, 2010.
[19]
H. Fang, X. Fan, L. Guo et al., “Self-self hybridization as an alternative experiment design to dye swap for two-color microarrays,” OMICS A Journal of Integrative Biology, vol. 11, no. 1, pp. 14–24, 2007.
[20]
J. Lingner, I. Radtke, E. Wahle, and W. Keller, “Purification and characterization of poly(A) polymerase from Saccharomyces cerevisiae,” The Journal of Biological Chemistry, vol. 266, no. 14, pp. 8741–8746, 1991.
[21]
W. A. Cantara, P. F. Crain, J. Rozenski et al., “The RNA modification database, RNAMDB: 2011 update,” Nucleic Acids Research, vol. 39, no. 1, pp. D195–D201, 2011.
[22]
I. Behm-Ansmant, M. Helm, and Y. Motorin, “Use of specific chemical reagents for detection of modified nucleotides in RNA,” Journal of Nucleic Acids, vol. 2011, Article ID 408053, 17 pages, 2011.
[23]
R. Ishitani, S. Yokoyama, and O. Nureki, “Structure, dynamics, and function of RNA modification enzymes,” Current Opinion in Structural Biology, vol. 18, no. 3, pp. 330–339, 2008.
[24]
Y. Wang and R. Blelloch, “Cell cycle regulation by microRNAs in stem cells,” Results Probl Cell Differ, vol. 53, pp. 459–472, 2010.
[25]
A. Rybak, H. Fuchs, L. Smirnova et al., “A feedback loop comprising lin-28 and let-7 controls pre-let-7 maturation during neural stem-cell commitment,” Nature Cell Biology, vol. 10, no. 8, pp. 987–993, 2008.
[26]
F. G. Wulczyn, L. Smirnova, A. Rybak et al., “Post-transcriptional regulation of the let-7 microRNA during neural cell specification,” The FASEB Journal, vol. 21, no. 2, pp. 415–426, 2007.
[27]
C. Ender, A. Krek, M. R. Friedl？nder et al., “A Human snoRNA with MicroRNA-Like Functions,” Molecular Cell, vol. 32, no. 4, pp. 519–528, 2008.
[28]
S. Kishore, A. Khanna, Z. Zhang et al., “The snoRNA MBII-52 (SNORD 115) is processed into smaller RNAs and regulates alternative splicing,” Human Molecular Genetics, vol. 19, no. 7, Article ID ddp585, pp. 1153–1164, 2010.
[29]
S. Kishore and S. Stamm, “The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C,” Science, vol. 311, no. 5758, pp. 230–232, 2006.
[30]
A. A. Saraiya and C. C. Wang, “snoRNA, a novel precursor of microRNA in Giardia lamblia,” PLoS Pathogens, vol. 4, no. 11, Article ID e1000224, 2008.
[31]
R. J. Taft, E. A. Glazov, T. Lassmann, Y. Hayashizaki, P. Carninci, and J. S. Mattick, “Small RNAs derived from snoRNAs,” RNA, vol. 15, no. 7, pp. 1233–1240, 2009.
[32]
S. Gilad, E. Meiri, Y. Yogev et al., “Serum microRNAs are promising novel biomarkers,” PLoS One, vol. 3, no. 9, Article ID e3148, 2008.
[33]
J. Lu, G. Getz, E. A. Miska et al., “MicroRNA expression profiles classify human cancers,” Nature, vol. 435, no. 7043, pp. 834–838, 2005.
[34]
R. A. Gupta, N. Shah, K. C. Wang et al., “Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis,” Nature, vol. 464, no. 7291, pp. 1071–1076, 2010.
[35]
M. Huarte and J. L. Rinn, “Large non-coding RNAs: missing links in cancer?” Human Molecular Genetics, vol. 19, no. 2, pp. R152–R161, 2010.
[36]
J. Kocerha, S. Kauppinen, and C. Wahlestedt, “microRNAs in CNS disorders,” Neuromolecular Medicine, vol. 11, no. 3, pp. 162–172, 2009.
[37]
M. Rederstorff and A. Hüttenhofer, “Small non-coding RNAs in disease development and host-pathogen interactions,” Current Opinion in Molecular Therapeutics, vol. 12, no. 6, pp. 684–694, 2010.
