IUBMB
Life, 64(7): 612–616, July 2012
Research Communication A Simple and Fast Method for Profiling MicroRNA Expression from Low-input Total RNA by Microarray Botao Zhao1*, Li Jin2*, Jiali Wei1, Zhongliang Ma1, Weijun Jiang2, Lijun Ma2, and Youxin Jin1,3 1
School of Life Sciences, Shanghai University, Shanghai 200444, China Department of Oncology, St Luke’s Hospital, Shanghai 200050, China 3 State Key Laboratory of Molecular Biology, Institute of Biochemistry and Cell Biology, Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 2
Summary Analysis of mature microRNA (miRNA) expression is important to understand its physiological functions and pathological implications. Microarray is a powerful technology to profile global miRNA expression. In this study, we developed a rapid miRNA labeling method by which miRNA was directly labeled in total RNA for microarray detection. This method consists of RNA tailing by poly(A) polymerase, reverse transcription, and template-switching by moloney murine leukemia virus (MMLV) reverse transcriptase. After these reactions, the small RNA cDNA was ready for labeling for microarray detection. The whole process of prearray sample preparation was dramatically shortened to 2 h. Furthermore, this method allows very limited starting total RNA (100 ng) for microarray analysis. Our data showed that the results from our method were highly consistent with that of real-time polymerase chain reaction (PCR). Ó 2012 IUBMB IUBMB
Life, 64(7): 612–616, 2012
Keyword eukaryotic gene expression.
INTRODUCTION MicroRNAs (miRNAs) are phylogenetically conserved, functional small noncoding RNAs found in animals, plants, and viruses (1). Evidence shows that miRNAs exhibit a variety of crucial regulatory functions related to many fundamental aspects of life (2–5). In recent years, deregulated expression of miRNA Additional Supporting Information may be found in the online version of this article. Received 11 January 2012; accepted 16 February 2012 *B.Z. and L.J. contributed equally to this work. Address correspondence to: Botao Zhao, School of Life Sciences, Shanghai University, 333 Nanchen Road, Shanghai 200444, China. E-mail: zhaobotao@ shu.edu.cn or Lijun Ma, Department of Oncology, St Luke’s Hospital, 786 Yuyuan Road, Shanghai 200050, China. E-mail:
[email protected] ISSN 1521-6543 print/ISSN 1521-6551 online DOI: 10.1002/iub.1026
has also been found to be associated with various human diseases (6–8). Microarray is an ideal method for high-throughput miRNA gene expression analysis. Owing to the small size, miRNAs are more difficult to analyze than mRNAs. Furthermore, the direct use of total RNA to profile miRNA expression can limit sensitivity because the relative abundance of small RNAs in a total RNA sample is 0.01%. Finally, pre-miRNA and pri-miRNA precursor have potential interference on profiling of mature miRNA. Several labeling technologies were developed to address miRNA microarray analysis (9–11). Most of them applied a small RNA fraction step before miRNA labeling to avoid miRNA precursor contamination. Shingara et al. (12) developed a tailing/labeling procedure on fractioned small RNA using poly(A) polymerase. The 30 tail is a mixture of standard and amine-modified nucleotides, and tailed miRNAs can subsequently be labeled by fluorescence. It has been observed that moloney murine leukemia virus (MMLV) reverse transcriptase (MMLV RT) is able to add a few nontemplate nucleotides (mostly C) to the 30 end of a newly synthesized cDNA strand on reaching the 50 end of the RNA template (13–15). When an oligonucleotide having oligo(G) sequence on its 30 end [template-switch (TS) oligo] is present in the RT reaction, it base-pairs with the attached deoxycytidine stretch. RT then switches templates and continues replicating to the end of the oligo. Thus, the complementary TSoligo sequence becomes attached to the 30 terminus of the cDNA. The template-switching mechanism offers a fast and straightforward approach for generating cDNA for transcriptome analysis and gene discovery. In this study, we combined the poly(A) tailing and templateswitching to develop a fast and simple method for miRNA cloning, labeling, and microarray analysis. Our method dramatically shortens the sample preparation time for miRNA microarray analysis. Our data also showed that this method could start from as little as 100 ng total RNA directly.
A SIMPLE AND FAST METHOD FOR PROFILING MICRORNA EXPRESSION
613
EXPERIMENTAL PROCEDURES Clinical Cancer Samples Human stomach cancer samples were obtained from Department of Oncology, St Luke’s Hospital, Shanghai, China under ethical assessment. Total RNA Isolation and Small RNA Fraction Total RNAs were extracted from mouse tissues and human stomach cancer samples using Trizol regent (Invitrogen, Carlsbad, CA) with the following modification: total RNA was precipitated by ethanol to ensure small RNA recovery. Ten micrograms total RNA was size fractioned on a 15% denaturing polyacrylamide gel. Small RNA of 18–26 nt was excised and eluted in elution buffer (0.3 M NaOAc, 5 mM EDTA) overnight. Eluted small RNA was precipitated and resuspended in 5 lL RNase-free water. Small RNA Tailing and Reverse Transcription by Template-switching Synthetic RNA, small RNA fraction, and total RNA were tailed by yeast poly(A) polymerase (USB, Cleveland, OH) in 10 lL reaction mixture as user’s manual. The tailing reaction mixture was heated at 65 8C for 5 min after adding 1 lL 20 lM universal oligo dT primer and then put on ice. A total of 10 lL standard reverse transcription reaction mixture plus 2 mM MnCl2 and 1 lM TS oligo was added to the tailed RNA mixture and incubated for 37 8C for 10 or 30 min. cDNA was amplified by polymerase chain reaction (PCR) with 50 PCR primer, the 30 universal PCR primer and control primer (Supporting Information Table S1). Microarray Method Microarray used to detect miRNAs was fabricated as described (16). The probe sequences are listed in Supporting Information Table S2. cDNAs from small RNA were labeled by PCR using the 50 Cy3 and Cy5 labeled PCR primers and the 30 universal PCR primer. Fluorescence targets from PCR reaction were denatured and mixed with 23 hybridization buffer (83 SSC/0.2% SDS, pH 7.0). After overnight hybridization at 48 8C, slides were scanned with ScanArray 5000 (PerkimElmer). Images were quantified by QuantArray (PerkimElmer). The raw data was listed in Supporting Information Table S3. All hybridizations were normalized by total intensity. miRNAs (signal value [ 1,000 at least at one sample) that changed more than two-fold with significant P-value (0.001) was considered as differential expressed miRNAs. Real-time PCR for miRNA Quantification Two hundred nanograms total RNA was tailed and reverse transcribed by NCode miRNA First-Strand cDNA Synthesis Kit (Invitrogen, Carlsbad, CA) according to the user’s manual. The miRNA specific primers were listed in Supporting Information
Figure 1. A demo reaction using our method on synthetic small RNA. (A) A diagram showed the expectant length of PCR product. (B) The PCR product from the demo reaction was viewed on agarose gel. The smallest two bands of DNA marker are 50 and 100 bp. Table S1. For estimating the different expression of miRNAs between stomach cancer and normal stomach tissue, the Ct values were normalized to 18S rRNA. The relative miRNA expression in stomach cancer and normal stomach tissue were calculated by using the 22DDCt method.
RESULTS We combined the RNA poly(A) tailing and template-switching during reverse transcription for small RNA cloning and detection by microarray. A flow chart describing our method was detailed in Supporting Information Fig. S1. To test its feasibility, we performed a demo reaction on a synthetic small RNA. After tailing and reverse transcription, product was confirmed by PCR using universal PCR primer along with control primer or 50 PCR primer (Fig. 1A). PCR product showed the exact length as expected (Fig. 1B). This result indicated that our method was potentially feasible for small RNA cloning and detection by microarray. The terminal transferase activity of RT for template-switching is stimulated by manganese ion (Mn21) (14). However, Mn21 also inhibits RT activity (17). To balance its effects and to access higher end-point productivity, we tested three conditions as in Fig. 2A. Consistent with other studies, Mn21 showed indispensible effect for successful template-switching. Condition A was superior to condition B (Fig. 2B). To choose a RT showing better performance for this approach, three MMLV RTs
614
ZHAO ET AL.
Figure 2. The effect of Mn21 on the reverse transcription and template-switching reaction on a poly(A) tailed synthetic small RNA. (A) A diagram showed three reaction conditions. Manganese ion (2 mM) was omitted or added at indicated time points. (B) The amplification plot of real-time PCR used to check the product of the demo reaction with 50 PCR primer and 30 universal primer was shown.
We further determined whether it is possible for our method to directly start from total RNA if reaction condition was carefully controlled. First, the reaction temperature in our approach was 37 8C, lower than standard RT reaction temperature. In such a condition, RT reaction was usually obstructed by RNA secondary structure. Therefore, RT and template-switching reaction were completed dominantly on small RNAs with no complicated secondary structure. A large amount of other RNAs (rRNA, mRNA, tRNA, and etc.) were more difficult to be fully reverse transcribed to its 50 end. Second, shorter reaction time will make the cDNA product more bias to small RNA contents. Actually, we limited the total reverse transcription and template-switching reaction time to only 10 min when started from total RNAs. Finally, extend time of PCR reaction was also set as short as possible to ensure amplification preferentially on small cDNAs. We routinely set 10 sec for PCR extension because less extension time such as in two-step PCR, which omits extension step, showed significant less PCR product in our condition (data not shown). In such controlled reaction, it was surprising that small RNA fraction of expected size from mouse total RNA was successfully converted to cDNA and amplified as a dominant band on agarose gel, although there was a little smear background (Fig. 4A). The expectant product
were tested. Takara MMLV RNase H- (Takara, Japan) showed best performance in our condition (data not shown). We then applied our method on real small RNA samples. Small RNA fractions of 18–26 nt were isolated from total RNA of various mouse tissues. The final PCR product length was in line with our expectation (Fig. 3). It is also consilient that the resolving bands representing product from small RNA of 18–26 nt were slightly less sharper than that from synthetic RNA (Fig. 3). These PCR products were cloned to T-vector. Sequencing of several clones confirmed the small RNA contents from mouse small RNAs and other RNA fragments (data not shown).
Figure 3. Small RNA fractions isolated from total RNA of various mouse tissues were tailed by poly(A) polymerase and were subjected to reverse transcription and template-switching reaction. The end-point product was checked with 50 PCR primer and 30 universal primer.
Figure 4. Our method was directly applied to total RNA. (A) Various amount of starting total RNAs were tailed and reverse transcribed with template-switching mechanism. The expectant product was confirmed by PCR with 50 PCR primer and 30 universal primer. (B). The reaction in (A) was performed at high (0.5 mM) and low ATP ( 0.1 nM) concentration. The expectant product was confirmed by PCR.
A SIMPLE AND FAST METHOD FOR PROFILING MICRORNA EXPRESSION
could be gotten from as little as 0.1 ng starting total RNA (Fig. 4A). The cloned RNA contents were also confirmed to be real small RNAs or RNA fragments from mouse by sequencing (data not shown). We routinely performed small RNA tailing at an adenosine triphosphate (ATP) concentration of 0.5 mM. In invitrogen’s NCode miRNA First-Strand cDNA Synthesis Kit, ATP concentration was diluted by a fold of 5,000/v (v ng of total RNA) from 10 mM ATP stock and then 1 lL diluted ATP was used in a 25 lL tailing reaction. Thus, for 1 ng starting total RNA, only 0.1 nM ATP was used. To check which ATP concentration is the best for our method, the two ATP concentrations were compared. Our result showed that higher ATP concentration was important for tailing reaction, which was the precondition for subsequent reverse transcription (Fig. 4B). However, lower ATP concentration also seems enough for reverse transcription and real-time PCR in invitrogen’s Kit. We routinely used yeast poly(A) polymerase, while Escherichia coli poly(A) polymerase was used in their kit. The different activity and character of the two poly(A) polymerases may be the cause of this inconsistency. At last, we labeled small RNA starting from 100 ng total RNA from a pair of human stomach cancerous and precancerous tissue. The example of scanned images having good quality was shown in Supporting Information Fig. S2. However, lower starting amount of total RNA (10 ng or less) showed much less ‘‘light spots’’ on the array (data not shown). It indicated that the diversity of small RNA content had lost although the expectant band was shown on agarose gel. Upregulated miRNAs in stomach cancer were ranged by the miRNA signal intensity from cancerous sample; similarly, downregulated miRNAs were ranged by the miRNA signal intensity from precancerous sample. We then validated the top 10 of differential expressed miRNAs in each group by real-time PCR. As we show in Table 1, microarray and real-time PCR showed good consistency for most selected miRNAs. Among these miRNAs, miR-19b, miR21, miR-24, miR-25, and miR-130b were also identified as upregulated miRNAs in gastric cancers, while miR-29, miR-30, and miR-31 were shown as downregulated miRNAs in gastric cancers (18). miR-221, which is downregulated in our study, was previously identified as a upregulated miRNA in gastric cancer (18). This inconsistent may be due to the individual variation. Overall, these results indicated that our method was feasible for miRNA expression analysis directly from low amount of total RNA by microarray.
DISCUSSION In this study, the whole process for RNA tailing and reverse transcription with template-switching mechanism only needs about 1 h. The product of reverse transcription was ready for labeling by PCR reaction, which took about 40 min. Counting the operation time, the prearray sample preparation only took about 2 h. Thus, our method is extremely time-saving comparing with other existing methods.
615
Table 1 Comparison of microarray and real-time data for top differential expression miRNAs in stomach cancer and normal tissues Fold (cancer/normal tissue) Range
miRNA
Upregulated miRNAs (top 10) 1 hsa-miR-24 2 hsa-miR-19b 3 hsa-miR-25 4 hsa-miR-130b 5 hsa-let-7g 6 hsa-miR-194 7 hsa-miR-21 8 hsa-miR-142–3p 9 hsa-miR-145 10 hsa-miR-126* Downregulated miRNAs (top 10) 1 hsa-miR-31 2 hsa-miR-221 3 hsa-miR-498 4 hsa-miR-30a-3p 5 hsa-miR-296 6 hsa-miR-520d 7 hsa-miR-422b 8 hsa-miR-29a 9 hsa-miR-188 10 hsa-miR-302c
Microarray
Real-time PCR
2.29 2.30 2.49 9.01 3.43 13.86 15.66 2.00 6.19 8.82
3.28 3.91 2.87 7.32 2.33 9.78 18.30 1.33 4.54 6.98
0.25 0.33 0.32 0.08 0.41 0.35 0.08 0.10 0.37 0.37
0.19 0.55 0.46 0.14 0.73 0.42 0.05 0.14 0.31 0.26
To avoid pri-miRNA and pre-miRNA contamination for microarray analysis, small RNA was usually isolated by size fraction before labeling and detection. Small RNA isolation was time-consuming, and the recovery efficiency was often low. To get enough signal intensity for microarray detection, one needed large amount of starting total RNA. Our method does not need to isolate small RNA and could start from total RNA directly. As little as 100 ng, starting total RNA could be used. For diagnosis purpose before treatment, it is impossible to get large amount of samples from patients. Our method makes it possible to profile miRNA expression from tiny samples such as puncture biopsies. Recently, existence of miRNA in serum suggested that serum miRNAs may serve as potential convenient biomarkers for the detection and screen of various cancers and other diseases (19, 20). The serum RNA yield is 1–2 lg/10 mL according a previous study (21). By using our method, only 1 mL serum is needed for one donor. Therefore, our method has great potential practical value for these applications.
616
ZHAO ET AL.
ACKNOWLEDGEMENTS This work was supported by the grants from The National Key Research and Development Program of China (2011CB811304), the National Basic Research Program of China (2011CBA01105), the National Natural Science Funds for Distinguished Young Scholar (31100570), Shanghai Science and Technology Committee (11DZ2272100), Innovation Program of Shanghai Municipal Education Commission (12YZ032), and Innovation Funding of Shanghai University. This work was also supported by Shanghai Key Laboratory of Bio-Energy Crops.
REFERENCES 1. Bartel, D. P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297. 2. Anglicheau, D., Muthukumar, T., and Suthanthiran, M. (2010) MicroRNAs: small RNAs with big effects. Transplantation 90, 105–112. 3. Guarnieri, D. J. and DiLeone, R. J. (2008) MicroRNAs: a new class of gene regulators. Ann. Med. 40, 197–208. 4. Kato, M. and Slack, F. J. (2008) MicroRNAs: small molecules with big roles—C. elegans to human cancer. Biol. Cell 100, 71–81. 5. Schickel, R., Boyerinas, B., Park, S. M., and Peter, M. E. (2008) MicroRNAs: key players in the immune system, differentiation, tumorigenesis and cell death. Oncogene 27, 5959–5974. 6. Bandiera, S., Hatem, E., Lyonnet, S., and Henrion-Caude, A. (2010) MicroRNAs in diseases: from candidate to modifier genes. Clin. Genet. 77, 306–313. 7. Di Leva, G. and Croce, C. M. (2010) Roles of small RNAs in tumor formation. Trends Mol. Med. 16, 257–267. 8. Tsai, L. M. and Yu, D. (2009) MicroRNAs in common diseases and potential therapeutic applications. Clin. Exp. Pharmacol. Physiol. 37, 102–107.
9. Li, W. and Ruan, K. (2009) MicroRNA detection by microarray. Anal. Bioanal. Chem. 394, 1117–1124. 10. Yin, J. Q., Zhao, R. C., and Morris, K. V. (2008) Profiling microRNA expression with microarrays. Trends Biotechnol. 26, 70–76. 11. Qavi, A. J., Kindt, J. T., and Bailey, R. C. (2010) Sizing up the future of microRNA analysis. Anal. Bioanal. Chem. 398, 2535–2549. 12. Shingara, J., Keiger, K., Shelton, J., Laosinchai-Wolf, W., Powers, P., et al. (2005) An optimized isolation and labeling platform for accurate microRNA expression profiling. RNA 11, 1461–1470. 13. Matz, M., Shagin, D., Bogdanova, E., Britanova, O., Lukyanov, S., et al. (1999) Amplification of cDNA ends based on template-switching effect and step-out PCR. Nucleic Acids Res. 27, 1558–1560. 14. Schmidt, W. M. and Mueller, M. W. (1999) CapSelect: a highly sensitive method for 50 CAP-dependent enrichment of full-length cDNA in PCR-mediated analysis of mRNAs. Nucleic Acids Res. 27, e31. 15. Zhu, Y. Y., Machleder, E. M., Chenchik, A., Li, R., and Siebert, P. D. (2001) Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30, 892–897. 16. Zhao, B., Liang, R., Ge, L., Li, W., Xiao, H., et al. (2007) Identification of drought-induced microRNAs in rice. Biochem. Biophys. Res. Commun. 354, 585–590. 17. Bolton, E. C., Mildvan, A. S., and Boeke, J. D. (2002) Inhibition of reverse transcription in vivo by elevated manganese ion concentration. Mol. Cell 9, 879–889. 18. Wu, W. K., Lee, C. W., Cho, C. H., Fan, D., Wu, K., et al. (2010) MicroRNA dysregulation in gastric cancer: a new player enters the game. Oncogene 29, 5761–5771. 19. Wittmann, J. and Jack, H. M. (2010) Serum microRNAs as powerful cancer biomarkers. Biochim. Biophys. Acta 1806, 200–207. 20. Cortez, M. A., Bueso-Ramos, C., Ferdin, J., Lopez-Berestein, G., Sood, A. K., et al. (2011) MicroRNAs in body fluids—the mix of hormones and biomarkers. Nat. Rev. Clin. Oncol. 8, 467–477. 21. Chen, X., Ba, Y., Ma, L., Cai, X., Yin, Y., et al. (2008) Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18, 997–1006.
