Literature DB >> 25977763

miRNA expression atlas in male rat.

Keiichi Minami1, Takeki Uehara2, Yuji Morikawa2, Ko Omura3, Masayuki Kanki3, Akira Horinouchi4, Atsushi Ono5, Hiroshi Yamada6, Yasuo Ohno7, Tetsuro Urushidani8.   

Abstract

MicroRNAs (miRNAs) are small (~22 nucleotide) noncoding RNAs that play pivotal roles in regulation of gene expression. The value of miRNAs as circulating biomarkers is now broadly recognized; such tissue-specific biomarkers can be used to monitor tissue injury and several pathophysiological conditions in organs. In addition, miRNA profiles of normal organs and tissues are important for obtaining a better understanding of the source of modulated miRNAs in blood and how those modulations reflect various physiological and toxicological conditions. This work was aimed at creating an miRNA atlas in rats, as part of a collaborative effort with the Toxicogenomics Informatics Project in Japan (TGP2). We analyzed genome-wide miRNA profiles of 55 different organs and tissues obtained from normal male rats using miRNA arrays. The work presented herein represents a comprehensive dataset derived from normal samples profiled in a single study. Here we present the whole dataset with miRNA profiles of multiple organs, as well as precise information on experimental procedures and organ-specific miRNAs identified in this dataset.

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Year:  2014        PMID: 25977763      PMCID: PMC4322570          DOI: 10.1038/sdata.2014.5

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


Background & Summary

MicroRNAs (miRNAs) are small (~ 22 nucleotide) noncoding RNAs that play pivotal roles in regulation of gene expression; miRNAs bind via complementary base-pairing to target transcripts to repress translation or promote mRNA degradation. Major miRNAs are highly conserved across species, allowing translation of biomarkers to the clinic. The machinery for miRNA-mediated gene regulation is conserved from plants to humans, and miRNAs are encoded by their respective genes. As with other regulatory molecules, miRNAs are frequently subject to changes in expression level due to a large range of physiological processes, such as development, immune responses, metabolism and diseases, as well as toxicological outcomes[1]. Mounting evidence indicates that miRNAs are frequently overexpressed or downregulated as a result of cancer, obesity, diabetes, inflammation, neurological disorders, cardiovascular diseases or autoimmune diseases[2-72-72-72-72-72-7]. Previous studies have shown that distinct miRNA signatures can be assigned to particular organs and tumor types[8]. For example, miR-21 is ubiquitously expressed and upregulated in various types of cancer, including lymphoma, lung, prostate and colorectal cancers among others[9]. Furthermore, tumor cells have been shown to release tumor-specific miRNAs into systemic circulation. Because of the fundamental roles played by miRNAs in cellular functions, the potential for miRNAs as novel therapeutic targets is now widely recognized. Several candidate therapeutic miRNAs have progressed into clinical and preclinical development; for example, antisense miR-122 is being developed as a treatment for hepatitis C virus, miR-208/499 for chronic heart failure, miR-195 for myocardial infarction and miR-34 and let-7 for cancer[10,11]. However, serious obstacles obstructing the development of miRNA-based therapies remain, including lack of tissue specificity and risks of systemic toxicity. Characterizing miRNA expression profiles of normal organs and tissues may improve our understanding of the etiology of diseased organs and of organ- and tissue-specificity of miRNAs, and the success rate of development of new miRNA-based therapeutics. Additionally, miRNAs have the potential to be useful biomarkers for monitoring physiological conditions and tissue injury. Upon tissue injury, miRNAs are released into systemic circulation or other body fluids; upon release, these miRNAs can be detected in small-volume samples via specific and sensitive quantitative real-time PCR. The precise mechanisms underlying the remarkable stability of miRNAs in the RNase-rich environment of blood are not well understood; nevertheless, miRNAs persist and are remarkably stable in blood; and these miRNAs can persist in an encapsulated state in exosomes or in protein complexes with carriers such as Argonaute 2 and Nucleophosmin or the High Density Lipoprotein[12]. Moreover, several studies have successfully identified circulating miRNA-based biomarkers; for example, Mitchell et al.[13] found that miR-141 was highly elevated in serum from patients with prostate cancer and hypothesized that this miRNA may be useful as a diagnostic marker. Similarly, Wang et al.[14] found that miR-122a, which is specifically expressed in the liver, was circulating systemically in mice with acetaminophen- (APAP-) induced hepatotoxicity; the authors reported that miR-122a and miR-192 were detected in plasma as early as the point when elevated alanine aminotransferase activity was found evident. Laterza et al.[15] also reported that plasma miRNA measurements could be useful for monitoring tissue injury in the liver, muscle and brain. Even in clinical research, plasma miR-122a was significantly elevated in APAP-induced hepatitis patients[16]. These reports clearly indicate that circulating organ-specific miRNAs can serve as useful biomarkers for tissue injury and disease status in various organs. The organ-specificity of circulating miRNAs, or in other words, the detailed tissue distribution of miRNAs, is important information to be used in obtaining biomarkers for organ toxicity, although desirable properties of biomarkers vary with intended use. In addition, miRNA profiles of normal organs and tissues are important for gaining a better understanding of the source of modulated miRNAs in blood and in gaining an understanding of how those modulations reflect various physiological and toxicological conditions. To date, organ-specific miRNA profiles have been reported for several animal species, including human, mouse and rat[17-2017-2017-2017-20]. However, the amount of data currently available is still inadequate, since there are only limited datasets with profiles in a limited number of organs. The main objectives of our data analysis were (i) to establish the validity of this dataset and (ii) to demonstrate organ-specific miRNAs identified in this dataset. In this report, we present a large-scale reference dataset constituting genome-wide miRNA profiles for 55 normal rat organs or tissues. The validity of this dataset was confirmed by comprehensive statistical analysis. We ultimately identified several organ-specific miRNAs in rats. These organ-specific miRNAs can potentially be used as biomarkers for identifying the origin of metastatic tumors and for monitoring toxicity in targeted organs. Furthermore, establishing combinations of organ-specific miRNA measurements may be a novel biomarker for monitoring simultaneous impairment in multiple organs. Our data also support the hypothesis that specificity of miRNA expression is conserved among different species, since the majority of organ-specific miRNAs identified in this rat study were also confirmed as organ-specific in humans. The value of miRNAs as novel therapeutic targets and circulating biomarkers to monitor tissue injury and several pathophysiological conditions in organs is now broadly recognized. In the course of development of miRNAs for practical use as targeted therapeutics and biomarkers, there is no doubt about the importance of open access large-scale datasets for a free miRNA atlas for normal organs/tissues and comparative data analysis. The work presented herein represents a comprehensive dataset derived from normal samples profiled in a single study. We believe that our dataset will be of particular value to both basic and translational scientists in biological and biomedical sciences, especially for novel target discovery and biomarker identification.

Methods

Animal experiments

For each miRNA microarray experiment, 9-week-old male Sprague-Dawley rats were obtained from Charles River Japan, Inc. (Kanagawa, Japan). After a 7-day quarantine and acclimatization period, 10-week-old animals were used (N=6). The animals were individually housed in stainless-steel cages in an animal room set to the following conditions: 12 h (7:00–19:00) light phase; ventilation rate, 12/h; temperature, 20 °C–26 °C; and relative humidity, 35–75%. Each animal had free access to water and pellet diet (CRF-1, sterilized by radiation, Oriental Yeast Co., Ltd., Tokyo, Japan). Each animal was anesthetized with ether. The animals were divided into 2 groups to compare the effects of peripheral blood cells in organs; 3 animals were perfused with saline to remove blood from all organs, while the remaining 3 animals that were not perfused were subjected to collection of only the heart (atrium and interventricular septum), kidney, liver and lung. All organs evaluated in this study are listed in Table 1 (available online only). Experimental protocols were reviewed and approved by the Ethics Review Committee for Animal Experimentation of the National Institute of Health Sciences.
Table 1

List of organs/tissues evaluated in this study

Organ ID Organ name Organ ID Organ name
1Liver* 29Seminal vesicle
2Kidney 30Prostate_ventral lobe
3Lung31Prostate_others
4Heart_cardiac atrium32Testis
5Heart_interventricular septum33Epididymis
6Skin34Eyeball
7Lymph node_cervical35Harderian gland
8Lymph node_mesenteric36Optic nerve
9Sublingual gland37Cerebrum_cerebral cortex
10Mandibular gland38Cerebrum_hippocampus
11Parotid gland39Cerebrum_thalamus
12Thymus40Cerebellum
13Trachea41Medulla oblongata
14Thyroid gland42Pituitary gland
15Tongue43Spinal cord_cervical
16Esophagus44Spinal cord_pectoral
17Stomach_anterior45Spinal cord_pars lumbalis
18Stomach_glandular46Femoris muscle
19Duodenum§ 47Gastrocnemial muscle
20Jejunum§ 48Soleus muscle
21Ileum§ 49Sciatic nerve
22Cecum§ 50Interseptum
23Colon§ 51Bone marrow_femur
24Rectum§ 52Aorta_ abdominal
25Pancreas53Vein_ abdominal
26Spleen54White adipose tissue
27Adrenal gland55Brown adipose tissue
28Urinary bladder

*Left lateral lobe.

†Whole kidney containing cortex, medulla, and papilla.

‡Each cerebrum region, including the cortex, hippocampus and thalamus, was macroscopically collected from the sagittal section of the cerebrum.

§Scrapped mucosal epithelial tissue.

RNA extraction and miRNA microarray analysis

RNA was prepared using the miRNeasy kit (QIAGEN, Hilden, Germany), according to the manufacturer’s instructions. RNA was quantified using a DU-7400 spectrophotometer (Beckman Coulter, Brea, CA) and quality was monitored with the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Cyanine-3 (Cy-3) labeled RNA was prepared from 0.1 μg RNA using the miRNA Complete Labeling and Hyb Kit (Agilent Technologies), according to the manufacturer’s instructions. The entire volume of Cy3-labeled RNA was incubated at 100 °C for 5 min in a 45 μl reaction volume containing 1x Agilent blocking agent, Hybridization Spike-In and Hi-RPM Hybridization Buffer. Upon completion of this incubation, samples were hybridized to an 8×15 k customized Agilent Rat miRNA microarray containing both miRBase 15.0 and 16.0 probes for 20 h at 55 °C on a rotating rack in an Agilent hybridization oven. After hybridization, microarrays were washed for 5 min at room temperature with GE Wash Buffer 1 (Agilent Technologies) and then for 5 min at 37 °C with GE Wash buffer 2 (Agilent Technologies); slides were then dried immediately. Immediately after this washing and drying step, slides were scanned using an Agilent DNA Microarray Scanner (G2565AA) at the following settings: one-color scan for 8×16 k array slides, scan area, 61×21.6 mm; scan resolution, 5 μm; dye channel, green; and extended dynamic range scan mode (Hi=100%, Lo=5%).

Microarray data analysis

Expressionist analysis software Ver. 7.6 (Genedata AG, Basel, Switzerland) was used for normalization, principal component analysis (PCA) and hierarchical clustering. First, all signal intensities were scaled to the 75th percentile of the median of the dynamic target value for each array using the central tendency normalization method. Next, the following two statistical parameters were calculated for each probe: (i) P-value of the Shapiro-Wilk test with Bonferroni correction (R software) and (ii) maximal signal intensity. Probes were then filtered by a P-value of <0.05 and a maximal signal intensity of >100. To identify organ-specific miRNAs, expression profiles of filtered probes were further analyzed by model-based clustering using the ‘mclust’ R package (http://cran.r-project.org/web/packages/mclust/index.html). For this analysis, a histogram was created to determine the distribution of expression values among different organs for each gene. For multiple distributions, the presence of an isolated peak was considered attributable to organ-specific expression of miRNAs. Genes with isolated peaks consisting of less than 37 microarrays (20% of all microarrays) in the histogram were selected as candidate miRNAs that were specifically expressed in one or a few organs.

Data Records

Microarray data are available in the NCBI Gene Expression Omnibus (GEO), accession GSE52754 (Data Citation 1). This accession contains matrix files of normalized miRNA expression data used in this report and raw data files generated from Agilent microarray systems as.gz files.

Technical Validation

miRNA microarray experiments

In this study, microarray experiments were performed according to the manufacturer’s instructions, and the quality of each experiment was assessed in QC reports generated from the Agilent microarray systems.

Possible effects of peripheral blood cell-derived miRNAs

To evaluate the possible effects of peripheral blood cell-derived miRNAs on the miRNA expression profiles of organs, two different sampling conditions, with or without perfusion, were compared to assess effects on miRNA expression profiles in four organs (heart, kidney, liver and lung). As shown in Figure 1, blood-cell–specific miRNAs (e.g., miR-451 and miR-144)[21] were clearly evident in nonperfused samples, but not in perfused samples. On the other hand, for each organ, miRNA profiles for perfused samples were highly correlated with those for nonperfused samples (R2>0.995). These data support the hypothesis that there are no distinct differences in miRNA profiles between these major organs and peripheral blood cells with some exceptions, such as miR-451 and miR-144, and that expression profiles for the majority of miRNAs were not markedly affected by remaining peripheral blood. Moreover, our data showed that miR-451 and miR-144 were specifically expressed in hematopoietic tissues e.g., spleen and bone marrow (refer to the original dataset for details). Therefore, microarray data obtained from nonperfused samples were analyzed in combination with those from perfused samples when generating the final comprehensive dataset.
Figure 1

Effect of circulating blood on miRNA expression in organs. Arrows indicate blood specific miRNAs; red arrows: miR-451, blue arrows: miR-144. Horizontal axis: Perfusion (-), vertical axis: Perfusion (+). Correlation coefficient (R ); (a) Liver: 0.9998, (b) Kidney: 0.9960, (c) Lung: 0.9956, (d) Heart, atrium: 0.9988, (e) Heart, interventricular septum: 0.9994.

Principal component analysis (PCA)

PCA was performed to comprehensively compare miRNA expression profiles among all organs in the entire experimental dataset (Figure 2a). Along the principal component (PC) 1 axis, which accounts for 29.1% of all variation, the clusters of signals from nervous system (brain, spinal cord, optic nerve and sciatic nerve; shown in blue colors), pituitary gland and adrenal gland samples were clearly separated from intestine samples (shown in red), whereas along the PC2 axis (11.4%), muscle (shown in green) samples were clearly separated from nervous system and intestine samples. Along the PC3 axis (8.1%), the clusters of signals from lymph and hematopoietic tissue (shown in pink) samples were clearly separated from those of other samples. Similarly, along the PC4 axis (6.7%) and PC5 axis (5.0%), the clusters of signals from pancreas and lung samples, and those from reproductive organ samples, were clearly separated from others. A tendency towards densely clustered signals from triplicate samples of the same organ was observed in PCA results. Additional information and eigenvalues of PC1 to PC5 axes are summarized in Tables 2 and 3 (available online only).
Figure 2

Principal component analysis (PCA). (a) PCA for all organ miRNAs. Spot colors represent broad types of organs; blue: nervous system, red: intestines, green: smooth or skeletal muscles, pink: lymphoid or hematopoietic system, light blue: reproductive system. Eigenvalues for each component: 29.1% (PC1), 11.4% (PC2); 8.1% (PC3), 6.7% (PC4), 5.0% (PC5). (b) PCA for digestive organs, nervous system or muscles. Eigenvalues for each component: (i) 37.2% (PC1), 10.8% (PC2); (ii) 35.7% (PC1), 13.2% (PC2); (iii) 31.0% (PC1), 16.4% (PC2).

Table 2

Individual sample IDs and additional information

Sample ID * Rat number Tissue Perfused? y/n Organ type category
GSM12754201No.1_livernothers
GSM12754211No.1_kidneynothers
GSM12754221No.1_lungnothers
GSM12754231No.1_heart_atriumnsmooth or skeletal muscles
GSM12754241No.1_heart_interventricular septumnsmooth or skeletal muscles
GSM12754252No.2_livernothers
GSM12754262No.2_kidneynothers
GSM12754272No.2_lungnothers
GSM12754282No.2_heart_atriumnsmooth or skeletal muscles
GSM12754292No.2_heart_interventricular septumnsmooth or skeletal muscles
GSM12754303No.3_livernothers
GSM12754313No.3_kidneynothers
GSM12754323No.3_lungnothers
GSM12754333No.3_heart_atriumnsmooth or skeletal muscles
GSM12754343No.3_heart_interventricular septumnsmooth or skeletal muscles
GSM12754354No.4_liveryothers
GSM12754364No.4_kidneyyothers
GSM12754374No.4_lungyothers
GSM12754384No.4_heart_atriumysmooth or skeletal muscles
GSM12754394No.4_heart_interventricular septumysmooth or skeletal muscles
GSM12754404No.4_skinysmooth or skeletal muscles
GSM12754414No.4_lymph node—cervicalylymphoid or hematopoietic system
GSM12754424No.4_lymph node—mesentericylymphoid or hematopoietic system
GSM12754434No.4_sublingual glandygland
GSM12754444No.4_mandibular glandygland
GSM12754454No.4_parotid glandygland
GSM12754464No.4_thymusylymphoid or hematopoietic system
GSM12754474No.4_tracheayothers
GSM12754484No.4_thyroidygland
GSM12754494No.4_tongueysmooth or skeletal muscles
GSM12754504No.4_esophagusysmooth or skeletal muscles
GSM12754514No.4_stomach_anteriorysmooth or skeletal muscles
GSM12754524No.4_stomach_glandularysmooth or skeletal muscles
GSM12754534No.4_duodenumyintestines (mucosal epithelial tissue)
GSM12754544No.4_jejunumyintestines (mucosal epithelial tissue)
GSM12754554No.4_ileumyintestines (mucosal epithelial tissue)
GSM12754564No.4_cecumyintestines (mucosal epithelial tissue)
GSM12754574No.4_colonyintestines (mucosal epithelial tissue)
GSM12754584No.4_rectumyintestines (mucosal epithelial tissue)
GSM12754594No.4_pancreasygland
GSM12754604No.4_spleenylymphoid or hematopoietic system
GSM12754614No.4_adrenal glandygland
GSM12754624No.4_bladderysmooth or skeletal muscles
GSM12754634No.4_seminal vesicleyreproductive system
GSM12754644No.4_prostate_ventralyreproductive system
GSM12754655No.5_liveryothers
GSM12754665No.5_kidneyyothers
GSM12754675No.5_lungyothers
GSM12754685No.5_heart_atriumysmooth or skeletal muscles
GSM12754695No.5_heart_interventricular septumysmooth or skeletal muscles
GSM12754705No.5_skinysmooth or skeletal muscles
GSM12754715No.5_lymph node—cervicalylymphoid or hematopoietic system
GSM12754725No.5_lymph node—mesentericylymphoid or hematopoietic system
GSM12754735No.5_sublingual glandygland
GSM12754745No.5_mandibular glandygland
GSM12754755No.5_parotid glandygland
GSM12754765No.5_thymusylymphoid or hematopoietic system
GSM12754775No.5_tracheayothers
GSM12754785No.5_thyroidygland
GSM12754795No.5_tongueysmooth or skeletal muscles
GSM12754805No.5_esophagusysmooth or skeletal muscles
GSM12754815No.5_stomach_anteriorysmooth or skeletal muscles
GSM12754825No.5_stomach_glandularysmooth or skeletal muscles
GSM12754835No.5_duodenumyintestines (mucosal epithelial tissue)
GSM12754845No.5_jejunumyintestines (mucosal epithelial tissue)
GSM12754855No.5_ileumyintestines (mucosal epithelial tissue)
GSM12754865No.5_cecumyintestines (mucosal epithelial tissue)
GSM12754875No.5_colonyintestines (mucosal epithelial tissue)
GSM12754885No.5_rectumyintestines (mucosal epithelial tissue)
GSM12754895No.5_pancreasygland
GSM12754905No.5_spleenylymphoid or hematopoietic system
GSM12754915No.5_adrenal glandygland
GSM12754925No.5_bladderysmooth or skeletal muscles
GSM12754935No.5_seminal vesicleyreproductive system
GSM12754945No.5_prostate_ventralyreproductive system
GSM12754955No.5_prostate_otheryreproductive system
GSM12754966No.6_liveryothers
GSM12754976No.6_kidneyyothers
GSM12754986No.6_lungyothers
GSM12754996No.6_heart_atriumysmooth or skeletal muscles
GSM12755006No.6_heart_interventricular septumysmooth or skeletal muscles
GSM12755016No.6_skinysmooth or skeletal muscles
GSM12755026No.6_lymph node—cervicalylymphoid or hematopoietic system
GSM12755036No.6_lymph node—mesentericylymphoid or hematopoietic system
GSM12755046No.6_sublingual glandygland
GSM12755056No.6_mandibular glandygland
GSM12755066No.6_parotid glandygland
GSM12755076No.6_thymusylymphoid or hematopoietic system
GSM12755086No.6_tracheayothers
GSM12755096No.6_thyroidygland
GSM12755106No.6_tongueysmooth or skeletal muscles
GSM12755116No.6_esophagusysmooth or skeletal muscles
GSM12755126No.6_stomach_anteriorysmooth or skeletal muscles
GSM12755136No.6_stomach_glandularysmooth or skeletal muscles
GSM12755146No.6_duodenumyintestines (mucosal epithelial tissue)
GSM12755156No.6_jejunumyintestines (mucosal epithelial tissue)
GSM12755166No.6_ileumyintestines (mucosal epithelial tissue)
GSM12755176No.6_cecumyintestines (mucosal epithelial tissue)
GSM12755186No.6_colonyintestines (mucosal epithelial tissue)
GSM12755196No.6_rectumyintestines (mucosal epithelial tissue)
GSM12755206No.6_pancreasygland
GSM12755216No.6_spleenylymphoid or hematopoietic system
GSM12755226No.6_adrenal glandygland
GSM12755236No.6_bladderysmooth or skeletal muscles
GSM12755246No.6_seminal vesicleyreproductive system
GSM12755256No.6_prostate_ventralyreproductive system
GSM12755264No.4_prostate_otheryreproductive system
GSM12755274No.4_testicleyreproductive system
GSM12755284No.4_epididymisyreproductive system
GSM12755294No.4_eyeballynervous system
GSM12755304No.4_Harderian glandygland
GSM12755314No.4_optic nerveynervous system
GSM12755324No.4_cerebrum_cerebral cortexynervous system
GSM12755334No.4_cerebrum_hippocampusynervous system
GSM12755344No.4_cerebrum_Thalamusynervous system
GSM12755354No.4_cerebellumynervous system
GSM12755364No.4_medulla oblongataynervous system
GSM12755374No.4_Pituitary glandygland
GSM12755384No.4_spinal cord_cervicalynervous system
GSM12755394No.4_spinal cord_pectoralynervous system
GSM12755404No.4_spinal cord_pars lumbalisynervous system
GSM12755414No.4_femoris muscleysmooth or skeletal muscles
GSM12755424No.4_gastrocnemial muscleysmooth or skeletal muscles
GSM12755434No.4_musculus soleusysmooth or skeletal muscles
GSM12755444No.4_ischial nerveynervous system
GSM12755454No.4_interseptumysmooth or skeletal muscles
GSM12755464No.4_bone marrowylymphoid or hematopoietic system
GSM12755474No.4_aortaysmooth or skeletal muscles
GSM12755484No.4_veinysmooth or skeletal muscles
GSM12755494No.4_white adipose tissueyothers
GSM12755504No.4_brown adipose tissueyothers
GSM12755515No.5_testicleyreproductive system
GSM12755525No.5_epididymisyreproductive system
GSM12755535No.5_eyeballynervous system
GSM12755545No.5_Harderian glandygland
GSM12755555No.5_optic nerveynervous system
GSM12755565No.5_cerebrum_cerebral cortexynervous system
GSM12755575No.5_cerebrum_hippocampusynervous system
GSM12755585No.5_cerebrum_Thalamusynervous system
GSM12755595No.5_cerebellumynervous system
GSM12755605No.5_medulla oblongataynervous system
GSM12755615No.5_Pituitary glandygland
GSM12755625No.5_spinal cord_cervicalynervous system
GSM12755635No.5_spinal cord_pectoralynervous system
GSM12755645No.5_spinal cord_pars lumbalisynervous system
GSM12755655No.5_femoris muscleysmooth or skeletal muscles
GSM12755665No.5_gastrocnemial muscleysmooth or skeletal muscles
GSM12755675No.5_musculus soleusysmooth or skeletal muscles
GSM12755685No.5_ischial nerveynervous system
GSM12755695No.5_interseptumysmooth or skeletal muscles
GSM12755705No.5_bone marrowylymphoid or hematopoietic system
GSM12755715No.5_aortaysmooth or skeletal muscles
GSM12755725No.5_veinysmooth or skeletal muscles
GSM12755735No.5_white adipose tissueyothers
GSM12755745No.5_brown adipose tissueyothers
GSM12755756No.6_prostate_otheryreproductive system
GSM12755766No.6_testicleyreproductive system
GSM12755776No.6_epididymisyreproductive system
GSM12755786No.6_eyeballynervous system
GSM12755796No.6_Harderian glandygland
GSM12755806No.6_optic nerveynervous system
GSM12755816No.6_cerebrum_cerebral cortexynervous system
GSM12755826No.6_cerebrum_hippocampusynervous system
GSM12755836No.6_cerebrum_Thalamusynervous system
GSM12755846No.6_cerebellumynervous system
GSM12755856No.6_medulla oblongataynervous system
GSM12755866No.6_Pituitary glandygland
GSM12755876No.6_spinal cord_cervicalynervous system
GSM12755886No.6_spinal cord_pectoralynervous system
GSM12755896No.6_spinal cord_pars lumbalisynervous system
GSM12755906No.6_femoris muscleysmooth or skeletal muscles
GSM12755916No.6_gastrocnemial muscleysmooth or skeletal muscles
GSM12755926No.6_musculus soleusysmooth or skeletal muscles
GSM12755936No.6_ischial nerveynervous system
GSM12755946No.6_interseptumysmooth or skeletal muscles
GSM12755956No.6_bone marrowylymphoid or hematopoietic system
GSM12755966No.6_aortaysmooth or skeletal muscles
GSM12755976No.6_veinysmooth or skeletal muscles
GSM12755986No.6_white adipose tissueyothers
GSM12755996No.6_brown adipose tissueyothers

*Individual sample IDs created by GEO.

†Resistered tissue names in GSE52754.

Table 3

Eigen values and vectors of PCAs in Figure 1

Probe ID Eigenvalue 1: 29.1% Eigenvalue 2: 11.4% Eigenvalue 3: 8.1% Eigenvalue 4: 6.7% Eigenvalue 5: 5.0%
rno-let-7a0.001−0.024−0.008−0.0130.003
rno-let-7a-1*0.004−0.015−0.026−0.029−0.035
rno-let-7b−0.002−0.018−0.013−0.0020.004
rno-let-7b*−0.0250.0130.0130.0400.015
rno-let-7c0.002−0.020−0.017−0.0070.009
rno-let-7c-1*0.023−0.008−0.014−0.0060.030
rno-let-7d0.002−0.027−0.005−0.014−0.010
rno-let-7d*0.008−0.0360.001−0.027−0.060
rno-let-7e0.023−0.034−0.020−0.0440.001
rno-let-7f−0.002−0.023−0.005−0.018−0.005
rno-let-7i0.001−0.027−0.008−0.014−0.004
rno-let-7i*−0.015−0.0040.0080.0350.014
rno-miR-10.011−0.204−0.1350.113−0.015
rno-miR-1*−0.0230.0150.0190.0530.002
rno-miR-1000.026−0.056−0.018−0.0180.033
rno-miR-100*−0.012−0.0060.0110.0370.014
rno-miR-101a−0.006−0.016−0.008−0.0030.008
rno-miR-101a*−0.014−0.0060.0070.0370.015
rno-miR-101b−0.007−0.0120.011−0.0250.001
rno-miR-1030.007−0.014−0.003−0.054−0.030
rno-miR-105−0.015−0.0070.0070.0410.013
rno-miR-106b−0.022−0.0110.012−0.030−0.026
rno-miR-106b*−0.026−0.0070.0410.0120.002
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rno-miR-361*0.0060.0040.0350.0250.001
rno-miR-362−0.041−0.008−0.0160.001−0.107
rno-miR-362*−0.022−0.025−0.010−0.018−0.047
rno-miR-363−0.037−0.0120.086−0.0600.002
rno-miR-363_v15.0−0.0430.0020.081−0.088−0.012
rno-miR-365−0.013−0.046−0.0270.013−0.009
rno-miR-369-3p0.0480.0400.0160.0230.007
rno-miR-369-5p0.1050.011−0.0710.058−0.040
rno-miR-3700.0320.0550.0100.116−0.036
rno-miR-370_v15.0−0.0130.0110.0150.0730.033
rno-miR-3740.003−0.024−0.023−0.061−0.032
rno-miR-375−0.0700.162−0.088−0.0020.099
rno-miR-376a0.1080.031−0.0670.005−0.027
rno-miR-376a*0.0440.0380.0140.0250.010
rno-miR-376b-3p0.0900.067−0.0200.0020.006
rno-miR-376b-5p0.0780.021−0.0480.042−0.021
rno-miR-376c0.074−0.006−0.0900.057−0.042
rno-miR-377−0.0020.009−0.0060.0370.014
rno-miR-377_v15.00.0390.035−0.0120.0330.007
rno-miR-378−0.029−0.040−0.0140.044−0.057
rno-miR-378*−0.031−0.072−0.0300.031−0.097
rno-miR-3790.103−0.007−0.1040.039−0.021
rno-miR-379*0.0750.024−0.0220.029−0.021
rno-miR-3800.0050.0060.0150.0350.012
rno-miR-380*0.0820.0530.0270.008−0.005
rno-miR-3810.096−0.003−0.0910.070−0.050
rno-miR-381*0.0290.008−0.0090.052−0.004
rno-miR-381_v15.0−0.0110.0000.0030.0370.018
rno-miR-3820.1020.011−0.0740.041−0.040
rno-miR-382*0.0710.0530.0070.018−0.001
rno-miR-3830.0720.0530.0370.0080.005
rno-miR-383_v15.00.0650.0510.0430.0200.006
rno-miR-384-3p0.0570.0420.0280.0140.014
rno-miR-384-5p0.1120.0650.023−0.0130.009
rno-miR-409-3p−0.012−0.0020.0110.0360.013
rno-miR-409-5p0.0790.032−0.0160.047−0.021
rno-miR-409-5p_v15.00.0650.0400.0050.032−0.004
rno-miR-4100.1110.022−0.0460.057−0.035
rno-miR-4110.0900.005−0.0860.056−0.040
rno-miR-411*0.102−0.009−0.1040.041−0.037
rno-miR-412*−0.0030.0010.0160.0350.016
rno-miR-421*0.0460.015−0.003−0.075−0.004
rno-miR-423−0.017−0.0120.0230.0280.011
rno-miR-423*−0.016−0.0040.0160.009−0.016
rno-miR-425−0.015−0.0030.020−0.049−0.029
rno-miR-425*−0.015−0.0080.0170.0340.011
rno-miR-429−0.1220.146−0.190−0.0410.071
rno-miR-4310.0750.031−0.0440.061−0.019
rno-miR-4330.1010.0490.0130.027−0.016
rno-miR-433*0.0670.0410.0270.020−0.004
rno-miR-4340.121−0.021−0.1050.0540.012
rno-miR-434*0.0870.036−0.0260.037−0.017
rno-miR-4480.0140.0100.0230.0280.010
rno-miR-448*0.0120.0100.0230.0300.011
rno-miR-448_v15.0−0.010−0.0040.0110.0360.015
rno-miR-449a−0.045−0.0030.055−0.0570.073
rno-miR-449c-5p−0.017−0.0100.0050.0170.021
rno-miR-450a0.019−0.103−0.002−0.0360.031
rno-miR-450a*−0.011−0.0070.0090.0370.011
rno-miR-450a_v15.0−0.004−0.119−0.008−0.025−0.001
rno-miR-451−0.034−0.1310.032−0.025−0.055
rno-miR-455−0.017−0.0180.004−0.015−0.003
rno-miR-455*0.021−0.045−0.007−0.068−0.059
rno-miR-463−0.021−0.0070.0300.0290.099
rno-miR-463*−0.015−0.0080.0170.0370.036
rno-miR-465−0.015−0.0050.0110.0380.013
rno-miR-465*−0.016−0.0080.0220.0370.047
rno-miR-466b−0.0330.0180.0240.043−0.001
rno-miR-466b-1*−0.015−0.0050.0110.043−0.011
rno-miR-466b-2*−0.012−0.0100.0750.001−0.031
rno-miR-466c*−0.018−0.0060.0080.0400.001
rno-miR-471−0.023−0.0040.0300.0290.092
rno-miR-471*−0.021−0.0070.0290.0300.094
rno-miR-4830.008−0.0200.0240.0760.003
rno-miR-483*−0.0560.0540.0090.101−0.121
rno-miR-484−0.001−0.015−0.004−0.034−0.077
rno-miR-4850.0730.0510.0140.0190.001
rno-miR-487b0.1070.008−0.0690.054−0.047
rno-miR-487b_v15.00.0700.0560.0130.027−0.024
rno-miR-4880.0500.0370.0340.0150.021
rno-miR-489−0.008−0.0010.0120.0290.018
rno-miR-4900.026−0.0360.0280.0080.033
rno-miR-490*0.030−0.0130.0300.0200.028
rno-miR-493−0.0130.0140.0080.0720.035
rno-miR-493*−0.0100.000−0.0040.0400.016
rno-miR-494−0.0250.0150.0110.0770.022
rno-miR-494_v15.0−0.0280.0160.0210.0810.012
rno-miR-4950.1090.025−0.0540.054−0.033
rno-miR-4960.0490.0320.0280.0210.001
rno-miR-496_v15.00.0770.0480.0150.012−0.007
rno-miR-497−0.005−0.055−0.010−0.007−0.001
rno-miR-4990.017−0.101−0.048−0.0170.010
rno-miR-500−0.014−0.0090.0040.007−0.006
rno-miR-501*−0.015−0.0040.0120.0450.019
rno-miR-503−0.003−0.117−0.011−0.002−0.019
rno-miR-5050.002−0.0150.013−0.051−0.036
rno-miR-505*0.0290.0100.027−0.052−0.009
rno-miR-511*−0.040−0.1080.0100.011−0.004
rno-miR-532-3p−0.005−0.027−0.008−0.023−0.040
rno-miR-532-5p−0.014−0.0300.000−0.013−0.051
rno-miR-5390.0820.009−0.0540.060−0.024
rno-miR-540−0.0110.0000.0050.0370.019
rno-miR-540*−0.012−0.0020.0070.0370.018
rno-miR-540_v15.0−0.0050.0080.0010.0360.022
rno-miR-5410.0750.031−0.0230.042−0.018
rno-miR-541*−0.0060.005−0.0080.0400.014
rno-miR-542-3p0.027−0.1140.017−0.0370.010
rno-miR-542-5p0.040−0.119−0.002−0.0260.015
rno-miR-543*0.0920.0520.0060.019−0.007
rno-miR-543_v15.00.0330.0270.0200.0250.009
rno-miR-547−0.034−0.0330.037−0.0070.121
rno-miR-547*−0.016−0.0080.0250.0390.052
rno-miR-547_v15.0−0.018−0.0090.0240.0330.074
rno-miR-551b0.0910.0610.037−0.0420.023
rno-miR-5820.0100.075−0.050−0.067−0.048
rno-miR-582*0.0070.0600.0270.048−0.014
rno-miR-5920.0380.0310.0250.0220.014
rno-miR-598-3p0.0820.039−0.022−0.0800.004
rno-miR-652−0.009−0.0090.005−0.0230.008
rno-miR-652*−0.0340.0050.0090.0530.004
rno-miR-653−0.0070.0010.0120.0240.018
rno-miR-6640.000−0.0080.0070.0100.007
rno-miR-664-1*−0.0410.0360.0120.037−0.053
rno-miR-664-2*0.0160.0120.0180.0350.008
rno-miR-6650.0190.0230.0130.0290.016
rno-miR-665_v15.0−0.0040.011−0.0010.0390.016
rno-miR-666−0.0170.0070.0230.0800.036
rno-miR-666_v15.0−0.0180.0050.0200.0690.026
rno-miR-6670.0520.0600.0140.031−0.056
rno-miR-667*−0.020−0.0010.0050.046−0.003
rno-miR-667_v15.00.0290.0230.0290.0240.011
rno-miR-668−0.015−0.0070.0140.0360.013
rno-miR-671−0.015−0.0070.0140.0360.013
rno-miR-6720.0260.071−0.038−0.0560.178
rno-miR-674-3p0.013−0.023−0.006−0.035−0.059
rno-miR-674-5p0.019−0.0040.032−0.033−0.010
rno-miR-675*−0.006−0.063−0.0550.111−0.029
rno-miR-678−0.0240.0110.0160.0800.011
rno-miR-702-3p−0.014−0.0060.0110.0360.013
rno-miR-708−0.014−0.0070.0080.0400.014
rno-miR-711−0.017−0.0030.0110.0380.008
rno-miR-741-3p−0.026−0.0020.0330.0290.118
rno-miR-742−0.026−0.0010.0240.0330.130
rno-miR-742*−0.023−0.0020.0250.0340.112
rno-miR-743a−0.025−0.0010.0230.0330.124
rno-miR-743a*−0.019−0.0070.0230.0340.087
rno-miR-743b−0.027−0.0010.0260.0320.147
rno-miR-743b*−0.021−0.0050.0240.0360.102
rno-miR-7580.0700.0500.0330.0140.004
rno-miR-760-3p0.0000.0180.0720.035−0.017
rno-miR-760-5p−0.0210.0100.0170.0740.023
rno-miR-764*0.0010.0040.0150.0360.012
rno-miR-764_v15.00.0160.0120.0250.0280.010
rno-miR-770*0.0500.0540.0380.0610.022
rno-miR-7a0.0070.0510.038−0.017−0.006
rno-miR-7a-1*0.0000.014−0.001−0.032−0.016
rno-miR-7a-2*0.0240.0320.0140.0300.026
rno-miR-7b0.0470.1000.068−0.032−0.033
rno-miR-802−0.0300.0190.0260.055−0.018
rno-miR-802_v15.0−0.0380.0410.0300.059−0.037
rno-miR-871*−0.021−0.0070.0290.0300.096
rno-miR-871_v15.0−0.017−0.0080.0210.0330.066
rno-miR-872−0.009−0.0110.006−0.041−0.013
rno-miR-872*−0.011−0.0010.025−0.025−0.015
rno-miR-8730.0430.0340.0450.0100.005
rno-miR-8740.0170.0320.0590.0080.026
rno-miR-877−0.0420.0230.0560.044−0.031
rno-miR-878−0.015−0.0080.0160.0370.036
rno-miR-880−0.022−0.0070.0280.0290.097
rno-miR-881−0.015−0.0080.0220.0390.039
rno-miR-881*−0.018−0.0090.0240.0330.075
rno-miR-881_v15.0−0.020−0.0100.0290.0300.098
rno-miR-883−0.026−0.0010.0250.0340.133
rno-miR-883*−0.0230.0030.0340.0600.123
rno-miR-90.1090.0260.025−0.0530.091
rno-miR-9*0.1170.0440.028−0.0500.101
rno-miR-92a−0.029−0.0110.036−0.003−0.015
rno-miR-92a-2*−0.019−0.0090.0330.0290.003
rno-miR-92b0.0250.0350.0350.0260.006
rno-miR-93−0.018−0.0140.010−0.040−0.033
rno-miR-96−0.0600.132−0.147−0.0790.024
rno-miR-96*−0.012−0.0040.0030.0350.019
rno-miR-980.007−0.015−0.007−0.024−0.014
rno-miR-99a0.019−0.051−0.0210.0050.055
rno-miR-99a*0.033−0.071−0.050−0.0100.053
rno-miR-99b0.024−0.044−0.022−0.0560.004
rno-miR-99b*0.0160.010−0.0020.0210.006
Furthermore, miRNA microarray data obtained from digestive organs, the nervous system and skeletal/smooth muscles were extracted from the whole dataset and subjected to system-specific PCA (Figure 2b). In the intestinal-specific subanalysis, clusters of signals from upper digestive organ (esophagus and stomach) samples were clearly separated from those of lower digestive organs (small and large intestine) along PC1 (37.2%), whereas along the PC2 axis (10.8%), those from small and large intestine samples were further divided into each organ by functional anatomy of the digestive system. In the nervous system subanalysis, a clear separation of clusters of signals were observed along both PC1 (35.7%) and PC2 (13.2%); those from cerebrum samples were clearly separated from ischial nerve samples along PC1, and those from the spinal cord were separated from optic nerve samples along PC2 (13.2%). Skeletal/smooth muscles were separated into four clusters: skeletal muscles, smooth muscles (blood vessel), smooth muscles (bladder and stomach) and others (esophagus, tongue and skin) (31.0% for PC1 and 16.4% for PC2). Overall, PCA successfully showed the similarity in gene expression profiles among functionally similar organs. These results provided evidence that the microarray data were of high enough quality to assess similarities and differences among miRNA profiles from different organs and tissues. In addition, the global miRNA expression profiles for these 55 normal organs included high-quality, reliable data that reflected the biological function of each organ.

Organ specific miRNA selection

To identify organ-specific miRNAs, all probes with any detectable signal were further filtered by a statistical analysis with criteria described in the Methods section; 296 probes met these criteria and were further analyzed using model-based clustering. In the model-based clustering, 128 probes were identified as miRNAs that were specifically expressed in one or more organs; further details on the statistical parameters calculated are available in Table 4 (available online only). Figure 3 shows a 2-dimensional hierarchical clustering diagram of groups of organs; the clustering was based on the expression profiles of the 128 filtered probes. Triplicate samples of the same organ were densely clustered; moreover, functionally similar organs were closely clustered to each other. Among these clusters, several miRNAs that were specifically expressed in specific organ(s) were successfully identified. Expression profiles of several representative organ-specific miRNAs are summarized (Figure 4 and Table 5 (available online only)). As shown in Table 5, more than half of these miRNAs were expressed in the corresponding human organs/tissues[22-3122-3122-3122-3122-3122-3122-3122-3122-3122-31].
Table 4

Summary of statistical parameters of miRNAs calculated in this study

Probe ID Min 25% Med 75% Max P value of Shapiro-Wilk test with Bonferroni correction Minimum cluster size by model-based clustering
rno-let-7a1046.426845.0010232.2713716.0231554.598.52E-03NaN
rno-let-7a-1*0.176.199.3412.5032.731.08E-13NaN
rno-let-7b1989.444032.615945.588531.7319369.521NaN
rno-let-7b*0.053.499.4318.50110.781.00E-08NaN
rno-let-7c1934.185813.998433.2211790.2228792.331.00E+00NaN
rno-let-7c-1*0.040.150.272.3514.761.02E-07NaN
rno-let-7d218.802090.723021.184325.459045.133.86E-04NaN
rno-let-7d*0.156.6311.0217.1951.651.71E-10NaN
rno-let-7e1.05538.171224.571897.355184.524.55E-09NaN
rno-let-7e*0.040.100.150.251.051.00E+00NaN
rno-let-7f590.946021.569451.4212156.4722912.601.21E-07NaN
rno-let-7f-1*0.040.100.150.251.051.00E+00NaN
rno-let-7f-2*0.040.100.150.251.051.00E+00NaN
rno-let-7i432.571570.243054.184569.448432.511.98E-03NaN
rno-let-7i*0.040.100.150.259.355.45E-04NaN
rno-miR-10.040.5928.121022.22158531.733.63E-04NaN
rno-miR-1*0.040.130.273.7684.422.15E-07NaN
rno-miR-1000.32147.83330.78609.362208.208.61E-08NaN
rno-miR-100*0.040.100.150.252.491.00E+00NaN
rno-miR-101a92.73216.13364.40521.121626.911.00E+00NaN
rno-miR-101a*0.040.100.150.251.051.00E+00NaN
rno-miR-101b0.69132.64170.04238.463695.661.03E-146
rno-miR-101b*0.040.100.150.251.051.00E+00NaN
rno-miR-1030.66592.23856.151066.141943.702.45E-20NaN
rno-miR-103-1*0.040.100.150.251.051.00E+00NaN
rno-miR-103-2*0.040.100.150.251.051NaN
rno-miR-1050.040.100.150.256.541.05E-02NaN
rno-miR-106b1.05189.31292.88433.682364.884.44E-07NaN
rno-miR-106b*0.040.110.170.3028.204.10E-10NaN
rno-miR-1070.69758.821049.451375.376350.002.12E-18NaN
rno-miR-107*0.040.100.150.251.051.00E+00NaN
rno-miR-10a-3p0.040.140.263.2831.834.68E-09NaN
rno-miR-10a-5p0.08107.73813.281755.775844.591.32E-07NaN
rno-miR-10b0.0467.78405.061718.675033.551.89E-10NaN
rno-miR-10b*0.040.140.263.9733.391.66E-08NaN
rno-miR-1188-3p0.056.2114.5525.71119.771.56E-11NaN
rno-miR-1188-5p0.040.110.160.26201.283.29E-158
rno-miR-1193-3p0.040.100.150.251.051.00E+00NaN
rno-miR-1193-5p0.040.100.150.251.051NaN
rno-miR-1220.040.100.170.27237948.691.83E-196
rno-miR-122*0.040.100.150.256544.332.58E-206
rno-miR-122475.01218.56418.63792.43752897.904.84E-126
rno-miR-1240.040.140.240.6621435.001.80E-1530
rno-miR-124*0.040.130.200.3235.597.23E-12NaN
rno-miR-12490.040.120.2514.28413.905.82E-10NaN
rno-miR-125a-3p0.103.587.9813.733683.656.08E-075
rno-miR-125a-5p0.66150.66369.09587.121078.251.32E-11NaN
rno-miR-125b*0.060.160.283.4519.281.69E-08NaN
rno-miR-125b-3p0.040.100.150.251.051.00E+00NaN
rno-miR-125b-5p80.472268.484729.357736.2733595.294.84E-06NaN
rno-miR-12695.16533.361111.753040.9410867.251.00E+00NaN
rno-miR-126*0.1230.7564.82185.69729.144.18E-10NaN
rno-miR-1270.060.3335.30270.182092.461.01E-07NaN
rno-miR-127*0.060.150.279.2274.431.27E-11NaN
rno-miR-1280.6661.76110.78493.0410166.812.70E-0612
rno-miR-128-1*0.040.100.150.251.051.00E+00NaN
rno-miR-128-2*0.040.110.170.279.241.31E-09NaN
rno-miR-1290.040.140.230.64794.151.55E-1433
rno-miR-129-1*0.067.8313.3731.372391.571.34E-05NaN
rno-miR-129-2*0.060.180.3326.502838.375.43E-11NaN
rno-miR-130a1.05422.65754.011435.883143.451.50E-08NaN
rno-miR-130a*0.040.100.150.251.051.00E+00NaN
rno-miR-130b0.040.160.3116.331214.232.34E-09NaN
rno-miR-130b*0.040.110.190.3941.611.20E-10NaN
rno-miR-1320.060.170.278.361255.415.05E-1230
rno-miR-132*0.040.110.160.266.345.66E-06NaN
rno-miR-133a0.040.164.0359.245011.823.26E-07NaN
rno-miR-133a*0.040.120.2337.465935.051.58E-11NaN
rno-miR-133b0.1833.24188.852606.57191928.270.023667643NaN
rno-miR-133b*0.040.100.150.251.051.00E+00NaN
rno-miR-1340.040.120.200.34478.026.77E-1331
rno-miR-134*0.040.120.190.3011.723.23E-11NaN
rno-miR-135a0.060.160.2929.68189.612.69E-11NaN
rno-miR-135a*0.040.120.180.2931.846.93E-10NaN
rno-miR-135b0.060.150.2721.71277.533.49E-12NaN
rno-miR-135b*0.040.100.150.251.051NaN
rno-miR-1360.060.2521.91121.71650.143.47E-08NaN
rno-miR-136*0.060.180.3950.11296.842.19E-10NaN
rno-miR-1370.060.140.230.61627.294.13E-1427
rno-miR-137*0.040.120.170.278.604.73E-10NaN
rno-miR-1380.060.160.279.34302.152.48E-1227
rno-miR-138-1*0.040.100.150.251.611NaN
rno-miR-138-2*0.040.130.200.3318.759.68E-13NaN
rno-miR-139-3p0.0810.9219.6528.995126.955.10E-108
rno-miR-139-5p0.046.0315.0728.62311.085.65E-09NaN
rno-miR-1400.66204.35269.11353.1810030.812.24E-148
rno-miR-140*0.66140.24207.53291.2211001.852.46E-128
rno-miR-1410.040.2060.09901.048798.658.83E-10NaN
rno-miR-141*0.040.100.160.255.622.71E-05NaN
rno-miR-142-3p0.69138.15336.86746.2753948.661.09E-0515
rno-miR-142-5p0.040.2520.1970.805412.801.47E-05NaN
rno-miR-14384.821078.623009.636557.5583261.411NaN
rno-miR-143*0.040.130.220.6497.332.40E-11NaN
rno-miR-1440.040.120.200.643290.751.84E-126
rno-miR-144*0.040.120.170.31186.039.82E-1422
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rno-miR-8770.040.110.190.34187.381.22E-1230
rno-miR-8780.040.100.150.2511.701.63E-07NaN
rno-miR-8790.040.100.150.251.051.00E+00NaN
rno-miR-879*0.040.100.150.251.051.00E+00NaN
rno-miR-8800.040.100.160.27520.571.34E-177
rno-miR-880*0.040.100.150.251.051NaN
rno-miR-8810.040.100.150.2519.006.71E-10NaN
rno-miR-881*0.040.100.160.2759.572.69E-14NaN
rno-miR-881_v15.00.040.100.160.27480.611.21E-176
rno-miR-8830.040.100.160.271019.836.28E-1811
rno-miR-883*0.040.100.160.27344.364.11E-1712
rno-miR-90.060.190.6745.695173.331.90E-08NaN
rno-miR-9*0.060.180.4155.458290.035.73E-10NaN
rno-miR-92a30.51270.04447.36805.316072.391NaN
rno-miR-92a-1*0.040.100.150.251.051NaN
rno-miR-92a-2*0.040.100.160.2719.819.73E-12NaN
rno-miR-92b0.040.150.283.7062.603.00E-07NaN
rno-miR-92b*0.040.100.150.251.051NaN
rno-miR-930.6676.99122.35185.601312.714.93E-116
rno-miR-93*0.040.100.150.251.051NaN
rno-miR-9350.040.100.150.251.051NaN
rno-miR-960.040.28111.69447.519114.449.25E-09NaN
rno-miR-96*0.040.100.160.257.753.77E-07NaN
rno-miR-981.05251.33342.31409.05750.083.82E-17NaN
rno-miR-98*0.040.100.150.251.051NaN
rno-miR-99a15.96919.181485.492515.585746.901.94E-10NaN
rno-miR-99a*0.077.0316.1827.4652.841.18E-12NaN
rno-miR-99b0.1454.23118.53208.20823.344.90E-11NaN
rno-miR-99b*0.050.170.272.31181.481.44E-083
rno-miR-218aNaNNaNNaNNaNNaNNaNNaN
rno-miR-218a-1*NaNNaNNaNNaNNaNNaNNaN
rno-miR-218a-2*NaNNaNNaNNaNNaNNaNNaN
rno-miR-218bNaNNaNNaNNaNNaNNaNNaN
Figure 3

Two-dimensional hierarchical clustering analysis based on the expression profiles of the 128 filtered probes. Horizontal: individual organs, vertical: miRNAs. In this analysis, expression profiles for the 128 probes filtered by model-based clustering were used for clustering.

Figure 4

Expression profiles of organ-specific miRNAs identified in this study. Horizontal: individual organs, vertical: normalized signal intensity. Each bar represents mean±S.D. (Organ ID 1–5: N=6, organ ID 6–55: N=3).

Table 5

Expression profiles of organ-specific miRNAs

miRNAs Signal intensity across all organs
Highly-expressed organ(s)/tissue(s) Human expression report
Min. 25% Med. 75% Max.
rno-miR-7a17.6940.3973.38254.2121,016Pituitary gland, cerebrum_thalamus, adrenal gland, lymph node_cervical, lymph node_mesentericBottoni, A. et al. ([22])
rno-miR-34b0.090.309.5225.9712,120Testis, trachea, lung, epididymisLiang, Y. ([23])
rno-miR-1220.040.100.170.27237,949LiverStarkey Lewis, P. J. et al. ([16])
rno-miR-1240.040.140.240.6621,435Cerebellum, eyeball, cerebrum_cerebral cortex, cerebrum_hippocampus, medulla oblongataSmirnova, L. et al. ([24])
rno-miR-1280.6661.76110.78493.0410,167Cerebrum_cerebral cortex, cerebellum, cerebrum_hippocampus, thymus, medulla oblongataSmirnova, L. et al. ([24])
rno-miR-142-3p0.69138.15336.86746.2753,949Lymph node_cervical, thymus, lymph node_mesenteric, spleen, bone marrowNo report
rno-miR-1440.040.120.200.643,291Bone marrow, spleenNo report
rno-miR-1500.66105.59216.92582.1214,087Lymph node_mesenteric, lymph node_cervical, spleen, thymus, brown adipose tissueNo report
rno-miR-181b0.3349.4893.90144.482,640Thymus, optic nerve, bone marrow, medulla oblongata, spinal cord_cervicalNo report
rno-miR-1840.040.110.170.2814,536EyeballTian, L. et al. ([25])
rno-miR-1920.2216.6531.0953.9438,628Intenstines (duodenum, jejunum, ileum, cecum, colon, rectum), stomach_glandular, liver, kidneyJenkins, R. H. et al. ([26])
rno-miR-2060.040.120.238.4928,471Skeletal muscles (musculus soleus, femoris muscle, gastrocnemial muscle), interseptum, esophagus, tongueNielsen, S. et al. ([27])
rno-miR-208_v15.00.040.100.150.27939Heart_interventricular septum, heart_atriumvan Rooij, E. et al. ([28])
rno-miR-2150.040.100.190.3250,159Duodenum, jejunum, ileum, rectum, colonSharbati, S. et al. ([29])
rno-miR-216a0.040.110.160.262,713PancreasSzafranska, A. E. et al. ([30])
rno-miR-219-2-3p0.040.130.200.341,915Optic nerve, spinal cord_cervical, spinal cord_pectoral, medulla oblongata, spinal cord_pars lumbalisNo report
rno-miR-4990.118.5717.7043.212,233Heart_interventricular septum, heart_atrium, interseptum, musculus soleusSluijter, J. P. et al. ([31])
rno-miR-542-3p0.050.329.1529.89439Adrenal glandNo report
rno-miR-551b0.040.150.240.721,607Pituitary gland, cerebrum, medulla oblongata, spinal cordNo report
rno-miR-652120.14223.45322.41550.648,315Harderian gland, bone marrow, adrenal gland, rectum, lungNo report

Technical validity and limitation of microarray analysis

Microarray technology has become a crucial tool for large-scale and high-throughput measurement. Non-specific hybridization or cross-hybridization is a common concern when interpreting microarrays, particularly with closely related gene family members having highly similar sequences. However, in such cases cross-hybridization is unavoidable since designing appropriate, highly specific, probes is not easy. Advances in microarray technology have successfully reduced, but not completely eliminated, cross-hybridization between the same subfamilies of miRNAs[32]. Therefore, attention should be paid to interpreting data from genes with highly similar sequences particularly in a microarray dataset similar to the present data. Recently, RNA-sequencing (RNA-seq) technology has emerged as a powerful tool for transcriptomics. This new technology is expected to be a useful tool for miRNA expression analysis.

Usage Notes

Since a large amount of numerical data is produced with microarray analysis, sophisticated bioinformatics approaches are required to analyze and interpret the results. A great number of statistical algorithms for filtering differentially expressed genes are available through the BioConductor project website. In addition, there are several software packages available that combine visualization with statistical analysis, such as Genedata Expressionist (Genedata), GeneSpring (Agilent Technology), Spotfire DecisionSite (Spotfire), TIBCO Spotfire (TIBCO Software Inc.) and ArrayTrack (Tong et al.,[33]).

Additional information

Tables 1, 2, 3, 4, 5 are only available in the online version of this paper. How to cite this article: Minami, K. et al. miRNA expression atlas in male rat. Sci. Data 1:140005 doi: 10.1038/sdata.2014.5 (2014).
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