Characterization of human plasma-derived exosomal RNAs by deep sequencing
© Huang et al.; licensee BioMed Central Ltd. 2013
Received: 17 January 2013
Accepted: 2 May 2013
Published: 10 May 2013
Exosomes, endosome-derived membrane microvesicles, contain specific RNA transcripts that are thought to be involved in cell-cell communication. These RNA transcripts have great potential as disease biomarkers. To characterize exosomal RNA profiles systemically, we performed RNA sequencing analysis using three human plasma samples and evaluated the efficacies of small RNA library preparation protocols from three manufacturers. In all we evaluated 14 libraries (7 replicates).
From the 14 size-selected sequencing libraries, we obtained a total of 101.8 million raw single-end reads, an average of about 7.27 million reads per library. Sequence analysis showed that there was a diverse collection of the exosomal RNA species among which microRNAs (miRNAs) were the most abundant, making up over 42.32% of all raw reads and 76.20% of all mappable reads. At the current read depth, 593 miRNAs were detectable. The five most common miRNAs (miR-99a-5p, miR-128, miR-124-3p, miR-22-3p, and miR-99b-5p) collectively accounted for 48.99% of all mappable miRNA sequences. MiRNA target gene enrichment analysis suggested that the highly abundant miRNAs may play an important role in biological functions such as protein phosphorylation, RNA splicing, chromosomal abnormality, and angiogenesis. From the unknown RNA sequences, we predicted 185 potential miRNA candidates. Furthermore, we detected significant fractions of other RNA species including ribosomal RNA (9.16% of all mappable counts), long non-coding RNA (3.36%), piwi-interacting RNA (1.31%), transfer RNA (1.24%), small nuclear RNA (0.18%), and small nucleolar RNA (0.01%); fragments of coding sequence (1.36%), 5′ untranslated region (0.21%), and 3′ untranslated region (0.54%) were also present. In addition to the RNA composition of the libraries, we found that the three tested commercial kits generated a sufficient number of DNA fragments for sequencing but each had significant bias toward capturing specific RNAs.
This study demonstrated that a wide variety of RNA species are embedded in the circulating vesicles. To our knowledge, this is the first report that applied deep sequencing to discover and characterize profiles of plasma-derived exosomal RNAs. Further characterization of these extracellular RNAs in diverse human populations will provide reference profiles and open new doors for the development of blood-based biomarkers for human diseases.
KeywordsExosome microRNA Next generation sequencing Plasma Biomarker
Many cells produce exosomes [1–3], small (30–100 nm) membrane vesicles that are released into the extracellular environment by fusing with the plasma membrane . Although previously considered to be cellular waste products, emerging evidence indicates that exosomes can mediate diverse biological functions including angiogenesis, cell proliferation, tumor cell invasion and metastasis, immune response, and antigen presentation by the transfer of proteins, mRNAs and non-coding RNAs to neighboring or distant cells [3, 5].
The existence of exosomes has been known for many years; however, it is only recently that these lipid-rich vesicles have been reported to contain an abundance of nucleic acids, in particular small non-coding RNAs . Studies have now shown that the packaging of RNAs into exosomes is selective because the RNA profiles in exosomes do not fully reflect the RNA profiles observed in the parental cells [6–10]. When released from their cells of origin, exosomes may enter blood or other bodily fluids. To date, the microvesicles have been detected in blood (plasma and serum), bronchoalveolar lavage, urine, bile, ascites, breast milk, and cerebrospinal fluid [8, 11–26]. These circulating vesicles can be taken up by recipient cells, allowing for cell-cell communication regardless of the distance between the cells. Exosome-mediated RNA transfer is believed to be an effective method for cell signaling and the exosomal RNA will certainly impact biological processes in the recipient cells [8, 27–29].
Exosomal RNAs have been implicated in many exosome-mediated biological functions . For example, RNAs delivered by exosomes prepared from X-ray treated cells were implicated in disseminating a bystander effect to target cells . MicroRNAs (miRNAs) transferred by tumor-derived exosomes were reported to down-regulate the TAK1 pathway in hepatocarcinogenesis  and were pivotal in promoting tumor metastasis via a proinflammatory cytokine-driven expansion of myeloid-derived suppressor cells . The let-7 miRNA family was selectively packaged into exosomes from a metastatic gastric cancer cell line and may have a role in the delivery of oncogenic signals to promote metastasis . Exosomes derived from human (HMC-1) and mouse (MC/9) mast cell lines transported RNA to neighboring mast cells, impacting the function of the recipient mast cells [6, 35]. The miRNAs transferred by the immune synapse were found to alter gene expression in the recipient antigen presenting cells . These findings support the existence of a novel exosome-mediated mechanism by which one cell can regulate the activity or differentiation of other cells.
While exosomes have been shown to play functional roles in recipient cells, the RNA content of the exosomes may provide unique molecular signatures for disease diagnosis and prognosis [9, 36–39]. It has been reported that exosomes from diseased individuals contained RNAs that were not found in healthy subjects [7, 9]. These exosomes may carry RNA signatures that are characteristic of the parental cells, for example, tumor cells. So far, tumor-derived exosomes have been identified in the plasma of patients with lung adenocarcinoma, glioblastoma multiforme, malignant glioma, prostate cancer, and ovarian carcinoma ascites [9, 10, 30, 40, 41]. The association of exosomal miR-141 and miR-375 with metastatic prostate cancer has been confirmed in a cohort of patients with recurrent or non-recurrent cancer following radical prostatectomy . These results suggest that circulating exosomes may provide a powerful tool for the non-invasive diagnosis and prognosis of human diseases.
Most of the current studies have used microarray or real time quantitative PCR (qPCR) assays to examine exosomal RNAs, with a focus on miRNAs. Because of the inherent limitations of these technologies, unknown miRNAs or other RNA species are often undetectable. Importantly, no systemic analysis of exosomal RNAs in peripheral blood has been reported until now. Blood is an important medium that allows exosomes to circulate and deliver cell signaling molecules to any part of the body. In this study, we performed a sequencing-based RNA profiling analysis using the blood from three blood donors. We evaluated three small RNA library preparation protocols and systemically characterized the extracellular RNA species. This study will provide a general guideline for blood-based exosomal RNA sequencing analysis and contribute to an understanding of exosome-mediated biological functions and mechanisms.
Exosome size and RNA stability
Comparison of three small RNA library preparation protocols
Data processing and genome mapping
Sequence read counts from RNA sequencing for the 14 libraries
Exosomal miRNA content
The twenty most abundant miRNAs among the plasma exosomal RNAs (normalized read counts per million mappable miRNAs)
Variability of miRNAs between technical replicates, samples, and preparation protocols
For sample-sample (biological) variations, we compared pooled samples (samples A, B and C) prepared using the Bioo Scientific and NEB kits. Overall, we found that there was significant correlation between samples B and C (r = 0.986) (Figure 3B), followed by between samples A and B (r = 0.983), and then between samples A and C (r = 0.981). However, when comparing the variations among the different library preparation protocols, we found striking differences although the average correlation coefficient r value was close to 0.884. The correlation r values were 0.898 between NEB and Bioo Scientific (Figure 3C), 0.889 between Bioo Scientific and Illumima, and 0.866 between NEB and Illumima.
To better demonstrate the technical, biological, and methodological variations, we performed an unsupervised hierarchical clustering analysis using the log2-transformed sequence counts of the 100 most abundant miRNA transcripts. As expected, the heat map showed that there was a clear separation between groups composed of replicates, samples and library preparation kits (Figure 3D). Nearly all of the 100 miRNAs showed similar expression patterns between technical replicates; however, some of them showed significant variations among different samples and most showed differences among different preparation kits. For example, the NEB kit detected over 21 fold more miR-129-5p sequences than either the Illumina or the Bioo Scientific kits. The Illumina kit generated over 50 fold more miR-486-5p sequences in sample A than either the Bioo Scientific or the NEB kits. The Bioo Scientific kit produced over 31 fold more miR-124-3p sequences than either the Illumina or the NEB kits. The methodological variations were also evident for the top 20 most abundant miRNAs (Table 2).
Sharing of detectable miRNAs
Additionally, we examined different samples for miRNA that were shared. Samples A, B and C each had 379, 343 and 356 miRNAs with >5 reads per million, respectively and 328 of them were shared among the three samples. Samples A, B and C also had 29, 3, and 10 unique miRNAs, respectively (Figure 4B). However, similar to the findings for the methodological differences, most of the sample-specific miRNAs were low in abundance.
Other RNA species
The most abundant of the gene fragments that contained the CDS, 5′UTR or 3′UTR sequences that were found in the small RNA libraries were involved in fundamental metabolic processes [see Additional file 3]. For example, the most common CDS sequences mapped to NYNRIN [GenBank:NM_025081] and LARS2 [GenBank:NM_015340], both of which encode proteins that participate in tRNA or rRNA metabolism. The most frequent 5′UTR sequence mapped to PVRL2 [GenBank:NM_001042724], which encodes a protein that is involved in the cell to cell spreading of herpes simplex virus and pseudorabies viruses . The second most common 5′UTR sequence mapped to ENTPD4 [GenBank: NM_004901], which encodes an endo-apyrase that is capable of cleaving nucleoside tri- and/or di-phosphates . The most frequent 3′UTR sequence mapped to PAQR5 [GenBank:NM_017705], which encodes progestin and adipoQ receptor family member V, which functions as a membrane progesterone receptor .
Predicted novel miRNAs
To identify novel miRNAs in the 14 libraries, all the raw data were processed independently using miRDeep2 . The miRDeep2 software detected 185 distinct novel miRNAs in the 14 libraries and 15, 88 and 111 novel miRNAs in the individual libraries generated by the Illumina, Bioo Scientific and NEB kits, respectively [see Additional file 4]. Among the putative miRNAs, two were common to libraries prepared with the Illumina and NEB kits, six were common to the Illumina and Bioo Scientific libraries, and 22 were common to the NEB and Bioo Scientific libraries. Of the 15 putative miRNAs in the Illumina libraries, four (26.7%) were found in technical replicates. For the Bioo Scientific-derived library, 16 of the 88 novel miRNAs (18.2%) were found in at least two replications and 19 (21.6%) were common to at least two samples. For the NEB-derived libraries, 33 of the 111 novel miRNAs (29.7%) were present in technical replicates and 38 (34.2%) were common to at least two samples. A representative readout of the predicted miRNAs from an NEB library is shown in Figure 5B. Multiple reads of both the mature and star miRNA sequences (typical components of a miRNA) were found in this library. All of the predicted miRNAs had the typical miRNA features at genomic DNA level.
Potential regulatory roles of exosomal miRNAs
miRNA target enrichment analysis
Bonferroni p value
nucleotide phosphate-binding region: ATP
neurotrophin signaling pathway
Exosomes circulating in the blood carry regulatory RNA molecules, thereby allowing for long distance cell-cell communication. Because diseased cells, including tumor cells, actively release exosomes into the blood stream, the circulating exosomes may provide a stable source of RNAs for disease diagnosis, prognosis and treatment management [2, 6, 39, 48]. In this study, we developed a protocol for isolating exosomal small RNA from a very low volume of plasma. We performed deep sequencing analysis of the exosomal RNAs, and generated expression profiles of the important extracellular RNAs. Our findings will not only help characterize the RNA content of exosomes but will also contribute to understanding exosome function and biology.
Exosomal RNA profiling analysis is not possible without high quality RNA. Compared to cellular RNAs, exosomal RNAs are more stable , and are reportedly resistant to physical degradation such as prolonged storage and freeze/thaw cycles . The circulating exosomal RNAs have been found to be resistant to biochemical degradation by ribonuclease in serum as well as by RNase A under an in vitro condition. This stability makes reproducible and consistent evaluation of blood-based non-coding RNA possible . Indeed, our study strongly supports the protective role of the microvesicles or other proteins in the stability of the circulating plasma RNA. Recently, Argonaute 2 was reported to bind and protect miRNAs from degradation in the circulation . It appears that Argonaute 2-protected miRNAs contribute to a significant proportion of the RNA circulating in the blood. Therefore, RNAs (at least miRNAs) in the blood stream are protected by multiple mechanisms and may be more stable than previously believed .
The dominant size of the exosomal RNA that was detected in this study was 18–28 nt. This size range is apparently smaller than that of the small RNAs derived from culture medium [34, 54, 55], where the sizes were centered at about 70 nt. Different isolation methods may account for the size discrepancies. Ultracentrifugation at 100,000 g seems to be less capable of discriminating exosomes from other microvesicles, especially when the exosomes are large. The mixed sizes of the isolated microvesicles may have caused more heterogeneity of RNA biotypes, which in turn impacted on the size and abundance of the RNAs in the libraries. In addition, the ExoQuick-based assay that we used to precipitate the exosomes may co-precipitate non-exosomal microparticles or RNA-binding proteins. Therefore, technically, the exosomal RNA may account for a fraction of all RNAs isolated by this assay. To obtain reproducible and reliable expression data, further study of the isolation methods is highly recommended.
The highly enriched exosomal miRNAs may have significant impacts on the target cells. For example, miR-99a-5p, the most abundant miRNA in the plasma exosomes, functions in a tissue-dependent manner. In prostate tumor tissue, miR-99a-5p was found to be down-regulated and its overexpression in a prostate cancer cell line was reported to inhibit the growth of the recipient cells and decreased the expression of the prostate-specific antigen . However, overexpression of the miR-99a was also reported to be responsible for increased proliferation, migration and fibronectin levels in a murine epithelial cell line NMUMG, possibly via modulating the TGF-β pathway . The functional role of miR-124 as a tumor suppressor has been established in glioblastoma, breast cancer, hepatocellular carcinoma, gastric cancer, and prostate cancer [58–62]. Another study demonstrated that miR-124 silencing in neuroblastoma cells led to cell differentiation, cell cycle arrest and apoptosis . In support of the important functions of the highly expressed exosomal miRNAs, our GO-based target prediction showed their potential roles in phosphorylation, RNA splicing, chromosomal abnormality, and angiogenesis; however, these predictions need further functional confirmation. Clearly, once released into target cells, the highly enriched miRNAs may participate directly in the regulation of mRNA translation and influence cell functions.
We also observed low level of “long” RNA fragments such as mRNA and lncRNA in the small RNA sequencing libraries. Our library preparation protocols were designed to capture small non-coding RNAs (~20–40 nt long). Therefore, the mRNAs and lncRNAs that were identified in this study should all be treated as fragmented RNAs. The procedures that were used for RNA extraction and library preparation may have caused partial RNA degradation, enabling the detection of fragments of the long RNAs in the small RNA libraries. Another possible explanation for the presence of long RNA fragments is that the exosomes also function as a “reservoir” to remove degraded mRNA and lncRNA derived from the cytosol. The exact mechanism underlying the presence of fragmented long RNAs in exosomes remains to be unraveled.
The current study demonstrated the reproducibility for each library preparation kit. Both Pearson correlation and hierarchical cluster analysis showed highly correlated RNA profiles between technical replicates, suggesting the consistency of these commercial kits. However, the study also showed significant biases between the library preparation methods. Each kit preferentially captured specific RNA sequences. For high abundant RNAs, this bias does not seem to be problematic because all three kits detected these RNAs. For low abundant RNAs, however, the bias could be an issue because these RNAs may be detected by one kit but not by another. Protocol-based bias may also create problems in data interpretation if different commercial kits are used. We suggest that separate validation using qPCR should be performed for all sequencing-based detections.
The ever growing number of novel sequences in the miRNA database implies that human miRNA annotation is far from complete . To identify novel miRNAs, next generation sequencing is the most powerful and the most popular approach. However, systematic bias during library preparation and the limited power of prediction algorithms means that some of the novel miRNAs may have been falsely predicted. We strongly recommended using other complementary methods such as Northern blot and qPCR for subsequent validation. Additionally, this study used only three plasma samples and, therefore, our findings may not fully represent all exosomal RNAs in human populations. To completely survey the exosomal transcriptome more samples from diverse populations and with different disease status are required.
The plasma exosomes are believed to be derived from a variety of cell populations. Their heterogeneous origin may limit the detection of disease-specific exosomes in peripheral blood samples. Vast numbers of exosomes shed from other cell types may dilute the exosome population derived from tumor cells, significantly reducing the proportion of tumor-derived miRNAs in the sequencing libraries. Because the less common tumor-derived miRNA may be a direct reflection of the disease status and critical for tumor development, the increased read depth of RNA sequencing is required. It is worth mentioning, that although the detection of rare RNA transcripts will increase as sequencing depth increases, the rare sequences still account for a tiny fraction of the exosomal RNAs. Whether or not the rare exosomal miRNAs are functional remains to be determined.
We developed a comprehensive data-generation and data-analysis pipeline that includes exosome isolation, RNA extraction, library preparation, RNA sequencing, and RNA annotation. Our data show that plasma-derived exosomes contain diverse RNA species, in particular, miRNA. The abundance of the exosomal RNAs varies dramatically. Some highly abundant miRNAs may play critical roles after being transferred to target cells. The three commercial small RNA preparation kits that we tested generated sufficient DNA fragments for sequencing but had significant biases towards capturing specific RNAs. The use of large-scale RNA sequencing will ensure the discovery and characterization of the whole transcriptome (known and unknown RNAs) of the blood-derived exosomes, which has not been completely examined so far. A fully characterized transcriptome will help gain a better understanding of exosome-mediated molecular mechanisms and will contribute to biomarker discovery. It is expected that the blood-based sequencing assay described here will find clinical applications as a biomarker discovery tool for disease diagnosis and prognosis.
Study design and participant consent
Human plasma samples were obtained from the Mayo Clinic and stored at -80°C before use. Exosomes were isolated from 250 μL of plasma using the ExoQuick exosome precipitation solution (System Biosciences, Mountain View, CA, USA) according to the manufacturer’s instructions with minor modifications. Briefly, the plasma was incubated with thromboplastin D (Thermo Scientific, Middletown, VA, USA) for 15 min at 37°C. After centrifugation at 10,000 rpm for 5 min, the supernatant was mixed with 75 μL of ExoQuick solution and RNase A (Sigma, St. Louis, MO, USA) to a final concentration of 10 μg/mL. The mixture was kept at 4°C overnight and then further mixed with 150 units/mL of murine RNase inhibitor (NEB) before centrifugation at 1500 g for 30 min. The exosome pellet was dissolved in 25 μL 1 × PBS; 2 μL of the solution was reserved for evaluation of exosome size and concentration using the NanoSight LM10 instrument (Particle Characterization Laboratories, Novato, CA, USA), and RNA was extracted immediately from the remaining solution.
Exosome quantitation and size determination
The concentration and size distribution of the isolated exosomes were measured using NanoSight. Prior to sampling, the sample solutions were homogenized by vortexing, followed by serial dilution to a final dilution of 1:100,000 in 0.2 μm-filtered 1x PBS. The National Institute of Standards and Technology (NIST) traceable 97 nm ± 3 nm polystyrene latex standards were added and analyzed along with the diluted exosome solution to validate the operation of the instrumentation. A blank 0.2 μm-filtered 1x PBS was also run as a negative control. Each sample analysis was conducted for 90 seconds. The Nanosight automatic analysis settings (high sensitivity, blue laser [405 nm, 645 mW]) were used to process the data. All samples were evaluated in triplicate.
Exosomal or HEK293 cellular RNA was prepared using a miRNeasy Micro Kit (QIAGEN, Valencia, CA, USA). Twenty-three μL of exosome suspension or 1 × 106 HEK293 cells were mixed with 700 μL QIAzol lysis buffer, and the mixture was processed according to the manufacturer’s standard protocol. The extracted RNA was eluted with 14 μL of RNase-free water. The quantity and quality of the RNA were determined by Agilent Bioanalyzer 2100 with a Small RNA Chip for exosomal RNA, and a RNA 6000 Pico Kit for cellular RNA (Agilent Technologies, Santa Clara, CA, USA).
Enzyme protection assay
RNA isolated from the plasma exosomes was first incubated at room temperature, either with 30 units/μL of DNase I (QIAGEN) for 10 min or with 10 μg/mL RNase A for 30 min. The RNase A digestion was terminated by adding 150 units/mL of murine RNase inhibitor. The resultant RNA samples were processed with the Agilent Bioanalyzer. In another enzyme protection assay, before the addition of murine RNase inhibitor, plasma samples were incubated with 10 μg/mL RNase A under various conditions, namely, at 37°C for 15 min, at room temperature for 30 min, or at 4°C overnight, followed by exosome isolation and RNA extraction. The same procedure was carried out using commercially available small RNA, which acted as a control for this assay. The RNA eluents along with the naked small RNase A-treated RNA were then evaluated with the Agilent Bioanalyzer.
RNA library preparation
For each library, 2 ng of small RNA was used in all the experimental procedures. Each library was prepared with a unique indexed primer so that the libraries could all be pooled into one sequencing lane. The 14 RNA libraries were prepared and amplified following the instruction of each manufacturer. The amplified libraries were resolved on a native 5% acrylamide gel. DNA fragments from 140–160 bp (the length of miRNA inserts plus the 3′ and 5′ adaptors) were recovered in 12 μL elution buffer (QIAGEN). The indexed libraries were quantified on the Bio-Rad 1000 qPCR instrument using the KAPA Library Quantification Kit in triplicates, according to the manufacture’s protocol (Kapa Biosystems, Woburn, MA, USA). Ten μL of the pooled library at a final concentration of 2 nM were then sent to the Core Facility at Medical College of Wisconsin for sequencing using Illumina HiSeq2000 DNA sequence analyzer.
Sequencing data analysis
Perl scripts (available upon request) were developed to process the data from the RNA sequencing. Raw reads were first extracted from FASTQ files, and trimmed using a sequencing quality control of Q >13 . Then the 3′ adaptor sequences within the read sequences were cleaned up. The prepared sequences were filtered and sequences with lengths ≥16 nt were aligned using Bowtie (version 0.12.8)  against both the human miRNA sequences downloaded from miRBase (Release 19, 2043 entries)  and the human genome reference sequences downloaded from the NCBI ftp site (Release 103). The Bowtie parameters that were used for the alignments were: -m 3 -n 1 -f -a --best --strata. Normalization of the miRNA profiles was based on the following formula:
(read counts of an individual miRNA/sum of read counts of all mappable miRNAs) multiplied by 1 × 106.
The RNA sequencing data are available from the NCBI Gene Expression Omnibus database [GEO: GSE45722].
Quantitative real-time PCR
To validate the RNA sequencing data, we performed a qPCR analysis of miR-92a-3p, miR-191-3p, miR26b-5p, and β-actin. The miRNA-specific miScript Primer Assays and the primer set specific for β-actin were purchased from QIAGEN (MS00006594 for miR-92a-3p, MS00031528 for miR-191-3p, MS00003234 for miR26b-5p, and QT01680476 for β-actin). First, 5 ng exosomal RNA or 20 ng cellular RNA was reverse transcribed by the miScript II RT kit (QIAGEN) at 37°C for 60 min, and then the enzyme was inactivated at 95°C for 5 min. After the activation of the polymerase enzyme at 95°C for 15 min, 40 cycles of 94°C for 15 s, 55°C for 30 s, and 72°C for 30 s were performed on the SteponePlus instrument (ABI). Melting curve analysis was used to confirm the specificity of the amplification reactions.
Prediction of novel miRNA
To find novel miRNAs, we applied miRDeep2 and processed the raw sequencing data independently . Predicted miRNAs with miRDeep2 total scores ≥2 were considered to be significant. If a predicted miRNA sequence resembled a reference rRNA or tRNA sequence, the sequence was discarded in the subsequent analysis regardless of the score.
miRNA target gene enrichment analysis
We downloaded all miRNA target genes from miRDB (http://mirdb.org/miRDB/), an online database for miRNA target prediction and functional annotations. All the targets were predicted using MirTarget2 [68, 69]. DAVID was used for the significant gene enrichment analysis. DAVID (Database for Annotation, Visualization and Integrated Discovery) (http://david.abcc.ncifcrf.gov/) provides a comprehensive set of functional annotation tools to understand biological meaning behind large list of genes . Because each miRNA could target hundreds of genes, we limited the analysis to the top five most abundant exosomal miRNAs.
This study was supported by the Advancing a Healthier Wisconsin fund to LW and the National Institute of Health (NIH), USA (Grant Nos: HL082798 and HL111580 to ML). We thank Kimberly Cook and Ruth Johnson at the Mayo Clinic for preparing the samples. We also thank Sequencing Core at the Medical College of Wisconsin for sequencing consultation and support.
- Mears R, Craven RA, Hanrahan S, Totty N, Upton C, Young SL, Patel P, Selby PJ, Banks RE: Proteomic analysis of melanoma-derived exosomes by two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Proteomics. 2004, 4 (12): 4019-4031. 10.1002/pmic.200400876.View ArticlePubMed
- Vlassov AV, Magdaleno S, Setterquist R, Conrad R: Exosomes: Current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochim Biophys Acta. 2012, 1820: 940-948. 10.1016/j.bbagen.2012.03.017.View ArticlePubMed
- Thery C, Ostrowski M, Segura E: Membrane vesicles as conveyors of immune responses. Nat Rev Immunol. 2009, 9 (8): 581-593. 10.1038/nri2567.View ArticlePubMed
- van Niel G, Porto-Carreiro I, Simoes S, Raposo G: Exosomes: a common pathway for a specialized function. J Biochem. 2006, 140 (1): 13-21. 10.1093/jb/mvj128.View ArticlePubMed
- Pegtel DM, van de Garde MD, Middeldorp JM: Viral miRNAs exploiting the endosomal-exosomal pathway for intercellular cross-talk and immune evasion. Biochim Biophys Acta. 2011, 1809 (11–12): 715-721.View ArticlePubMed
- Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ, Lotvall JO: Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007, 9 (6): 654-U672. 10.1038/ncb1596.View ArticlePubMed
- Taylor DD, Gercel-Taylor C: MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol. 2008, 110 (1): 13-21. 10.1016/j.ygyno.2008.04.033.View ArticlePubMed
- Mittelbrunn M, Gutierrez-Vazquez C, Villarroya-Beltri C, Gonzalez S, Sanchez-Cabo F, Gonzalez MA, Bernad A, Sanchez-Madrid F: Unidirectional transfer of microRNA-loaded exosomes from T cells to antigen-presenting cells. Nat Commun. 2011, 2: 282-PubMed CentralView ArticlePubMed
- Rabinowits G, Gercel-Taylor C, Day JM, Taylor DD, Kloecker GH: Exosomal microRNA: a diagnostic marker for lung cancer. Clin Lung Cancer. 2009, 10 (1): 42-46. 10.3816/CLC.2009.n.006.View ArticlePubMed
- Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT, Carter BS, Krichevsky AM, Breakefield XO: Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008, 10 (12): 1470-1476. 10.1038/ncb1800.PubMed CentralView ArticlePubMed
- Johansson SM, Admyre C, Rahman QK, Filen JJ, Lahesmaa R, Norman M, Neve E, Scheynius A, Gabrielsson S: Exosome-like vesicles in human breast milk. J Immunol. 2006, 176: S184-S184.
- Saman S, Kim W, Raya M, Visnick Y, Miro S, Saman S, Jackson B, McKee AC, Alvarez VE, Lee NCY: Exosome-associated Tau Is Secreted in Tauopathy Models and Is Selectively Phosphorylated in Cerebrospinal Fluid in Early Alzheimer Disease. J Biol Chem. 2012, 287 (6): 3842-3849. 10.1074/jbc.M111.277061.PubMed CentralView ArticlePubMed
- Gatti JL, Metayer S, Belghazi M, Dacheux F, Dacheux JL: Identification, proteomic profiling, and origin of ram epididymal fluid exosome-like vesicles. Biol Reprod. 2005, 72 (6): 1452-1465. 10.1095/biolreprod.104.036426.View ArticlePubMed
- Harada H, Mitsuhashi M: Assessment of Post-Transplant Kidney Function by Measuring Glomerulus- and Tubule-Specific mRNAs in Urine Exosome. Am J Transplant. 2012, 12: 369-370. 10.1111/j.1600-6143.2011.03888.x.View Article
- Ben-Dov IZ, Brown M, Whalen VM, Tuschl T: Profiling Urine Cell and Exosome Microrna Using a Barcoded Small Rna Deep Sequencing Approach. Am J Kidney Dis. 2011, 57 (4): A24-A24.View Article
- Conde-Vancells J, Rodriguez-Suarez E, Gonzalez E, Berisa A, Gil D, Embade N, Valle M, Luka Z, Elortza F, Wagner C: Candidate biomarkers in exosome-like vesicles purified from rat and mouse urine samples. Proteom Clin Appl. 2010, 4 (4): 416-425. 10.1002/prca.200900103.View Article
- Blanc L, De Gassart A, Geminard C, Bette-Bobillo P, Vidal M: Exosome release by reticulocytes - An integral part of the red blood cell differentiation system. Blood Cell Mol Dis. 2005, 35 (1): 21-26. 10.1016/j.bcmd.2005.04.008.View Article
- Jones JC, Knox SJ: Serum Exosome Biomarkers for Immunotherapy and Radiation Responses. Int J Radiat Oncol. 2011, 81 (2): S754-S755.View Article
- Alge JL, Janech M, Schwacke J, Arthur J, Costa LJ: Proteomic Analysis of Plasma Exosome-Associated Proteins Reveals That Differences In Kappa: Lambda Ratios Predict Severe Acute Graft-Versus-Host Disease Early After Allogeneic Hematopoietic Stem Cell Transplantation. Blood. 2010, 116 (21): 547-547.
- Looze C, Yui D, Leung L, Ingham M, Kaler M, Yao XL, Wu WW, Shen RF, Daniels MP, Levine SJ: Proteomic profiling of human plasma exosomes identifies PPAR gamma as an exosome-associated protein. Biochem Biophys Res Commun. 2009, 378 (3): 433-438. 10.1016/j.bbrc.2008.11.050.PubMed CentralView ArticlePubMed
- Zhang J, Hawari FI, Shamburek RD, Adamik B, Kaler M, Islam A, Liao DW, Rouhani FN, Ingham M, Levine SJ: Circulating TNFR1 exosome-like vesicles partition with the LDL fraction of human plasma. Biochem Biophys Res Commun. 2008, 366 (2): 579-584. 10.1016/j.bbrc.2007.12.011.PubMed CentralView ArticlePubMed
- Paredes PT, Esser J, Admyre C, Nord M, Rahman QK, Lukic A, Radmark O, Gronneberg R, Grunewald J, Eklund A: Bronchoalveolar lavage fluid exosomes contribute to cytokine and leukotriene production in allergic asthma. Allergy. 2012, 67 (7): 911-919. 10.1111/j.1398-9995.2012.02835.x.View Article
- Palanisamy V, Sharma S, Deshpande A, Zhou H, Gimzewski J, Wong DT: Nanostructural and Transcriptomic Analyses of Human Saliva Derived Exosome. PLoS One. 2010, 5 (1): e8577-10.1371/journal.pone.0008577.PubMed CentralView ArticlePubMed
- Peng P, Yan Y, Keng S: Exosomes in the ascites of ovarian cancer patients: Origin and effects on anti-tumor immunity. Oncol Rep. 2011, 25 (3): 749-762.PubMed
- Zhong HJ, Yang YS, Ma SL, Xiu FM, Cai ZJ, Zhao HG, Du LB: Induction of a tumour-specific CTL response by exosomes isolated from heat-treated malignant ascites of gastric cancer patients. Int J Hyperther. 2011, 27 (6): 604-611. 10.3109/02656736.2011.564598.View Article
- Street JM, Barran PE, Mackay CL, Weidt S, Balmforth C, Walsh TS, Chalmers RTA, Webb DJ, Dear JW: Identification and proteomic profiling of exosomes in human cerebrospinal fluid. J Transl Med. 2012, 10: 5-10.1186/1479-5876-10-5.PubMed CentralView ArticlePubMed
- Yu SH, Liu CR, Su KH, Wang JH, Liu YL, Zhang LM, Li CY, Cong YZ, Kimberly R, Grizzle WE: Tumor exosomes inhibit differentiation of bone marrow dendritic cells. J Immunol. 2007, 178 (11): 6867-6875.View ArticlePubMed
- Kosaka N, Iguchi H, Yoshioka Y, Takeshita F, Matsuki Y, Ochiya T: Secretory Mechanisms and Intercellular Transfer of MicroRNAs in Living Cells. J Biol Chem. 2010, 285 (23): 17442-17452. 10.1074/jbc.M110.107821.PubMed CentralView ArticlePubMed
- Montecalvo A, Larregina AT, Shufesky WJ, Stolz DB, Sullivan MLG, Karlsson JM, Baty CJ, Gibson GA, Erdos G, Wang ZL: Mechanism of transfer of functional microRNAs between mouse dendritic cells via exosomes. Blood. 2012, 119 (3): 756-766. 10.1182/blood-2011-02-338004.PubMed CentralView ArticlePubMed
- Peinado H, Aleckovic M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, Garcia-Santos G, Ghajar C: Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med. 2012, 18 (6): 883-891. 10.1038/nm.2753.PubMed CentralView ArticlePubMed
- Al-Mayah AH, Irons SL, Pink RC, Carter DR, Kadhim MA: Possible role of exosomes containing RNA in mediating nontargeted effect of ionizing radiation. Radiat Res. 2012, 177 (5): 539-545. 10.1667/RR2868.1.View ArticlePubMed
- Kogure T, Lin WL, Yan IK, Braconi C, Patel T: Intercellular Nanovesicle-Mediated microRNA Transfer: A Mechanism of Environmental Modulation of Hepatocellular Cancer Cell Growth. Hepatology. 2011, 54 (4): 1237-1248. 10.1002/hep.24504.PubMed CentralView ArticlePubMed
- Liu YL, Xiang XY, Zhuang XY, Zhang SY, Liu CR, Cheng ZQ, Michalek S, Grizzle W, Zhang HG: Contribution of MyD88 to the Tumor Exosome-Mediated Induction of Myeloid Derived Suppressor Cells. Am J Pathol. 2010, 176 (5): 2490-2499. 10.2353/ajpath.2010.090777.PubMed CentralView ArticlePubMed
- Ohshima K, Inoue K, Fujiwara A, Hatakeyama K, Kanto K, Watanabe Y, Muramatsu K, Fukuda Y, Ogura S, Yamaguchi K: Let-7 microRNA family is selectively secreted into the extracellular environment via exosomes in a metastatic gastric cancer cell line. PLoS One. 2010, 5 (10): e13247-10.1371/journal.pone.0013247.PubMed CentralView ArticlePubMed
- Schorey JS, Bhatnagar S: Exosome function: From tumor immunology to pathogen biology. Traffic. 2008, 9 (6): 871-881. 10.1111/j.1600-0854.2008.00734.x.PubMed CentralView ArticlePubMed
- Michael A, Bajracharya SD, Yuen PST, Zhou H, Star RA, Illei GG, Alevizos I: Exosomes from human saliva as a source of microRNA biomarkers. Oral Dis. 2010, 16 (1): 34-38. 10.1111/j.1601-0825.2009.01604.x.PubMed CentralView ArticlePubMed
- Ciesla M, Skrzypek K, Kozakowska M, Loboda A, Jozkowicz A, Dulak J: MicroRNAs as biomarkers of disease onset. Anal Bioanal Chem. 2011, 401 (7): 2051-2061. 10.1007/s00216-011-5001-8.View ArticlePubMed
- Wittmann J, Jack HM: Serum microRNAs as powerful cancer biomarkers. Bba-Rev Cancer. 2010, 1806 (2): 200-207.
- Bellingham SA, Coleman BM, Hill AF: Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells. Nucleic Acids Res. 2012, 40 (21): 10937-10949. 10.1093/nar/gks832.PubMed CentralView ArticlePubMed
- Rupp AK, Rupp C, Keller S, Brase JC, Ehehalt R, Fogel M, Moldenhauer G, Marme F, Sultmann H, Altevogt P: Loss of EpCAM expression in breast cancer derived serum exosomes: role of proteolytic cleavage. Gynecol Oncol. 2011, 122 (2): 437-446. 10.1016/j.ygyno.2011.04.035.View ArticlePubMed
- Lance RS, Drake RR, Troyer DA: Multiple recognition assay reveals prostasomes as promising plasma biomarkers for prostate cancer. Expert Rev Anticanc. 2011, 11 (9): 1341-1343. 10.1586/era.11.134.View Article
- Bryant RJ, Pawlowski T, Catto JW, Marsden G, Vessella RL, Rhees B, Kuslich C, Visakorpi T, Hamdy FC: Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer. 2012, 106 (4): 768-774. 10.1038/bjc.2011.595.PubMed CentralView ArticlePubMed
- Zhang Y, Wiggins BE, Lawrence C, Petrick J, Ivashuta S, Heck G: Analysis of plant-derived miRNAs in animal small RNA datasets. BMC Genomics. 2012, 13: 381-10.1186/1471-2164-13-381.PubMed CentralView ArticlePubMed
- Warner MS, Geraghty RJ, Martinez WM, Montgomery RI, Whitbeck JC, Xu R, Eisenberg RJ, Cohen GH, Spear PG: A cell surface protein with herpesvirus entry activity (HveB) confers susceptibility to infection by mutants of herpes simplex virus type 1, herpes simplex virus type 2, and pseudorabies virus. Virology. 1998, 246 (1): 179-189. 10.1006/viro.1998.9218.View ArticlePubMed
- Biederbick A, Rosser R, Storre J, Elsasser HP: The VSFASSQQ motif confers calcium sensitivity to the intracellular apyrase LALP70. BMC Biochem. 2004, 5: 8-10.1186/1471-2091-5-8.PubMed CentralView ArticlePubMed
- Smith JL, Kupchak BR, Garitaonandia I, Hoang LK, Maina AS, Regalla LM, Lyons TJ: Heterologous expression of human mPRalpha, mPRbeta and mPRgamma in yeast confirms their ability to function as membrane progesterone receptors. Steroids. 2008, 73 (11): 1160-1173. 10.1016/j.steroids.2008.05.003.PubMed CentralView ArticlePubMed
- Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012, 40 (1): 37-52. 10.1093/nar/gkr688.PubMed CentralView ArticlePubMed
- Thery C, Zitvogel L, Amigorena S: Exosomes: composition, biogenesis and function. Nat Rev Immunol. 2002, 2 (8): 569-579.PubMed
- Keller S, Ridinger J, Rupp AK, Janssen JWG, Altevogt P: Body fluid derived exosomes as a novel template for clinical diagnostics. J Transl Med. 2011, 9: 86-10.1186/1479-5876-9-86.PubMed CentralView ArticlePubMed
- Reid G, Kirschner MB, van Zandwijk N: Circulating microRNAs: Association with disease and potential use as biomarkers. Crit Rev Oncol Hematol. 2011, 80 (2): 193-208. 10.1016/j.critrevonc.2010.11.004.View ArticlePubMed
- Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X: Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 2008, 18 (10): 997-1006. 10.1038/cr.2008.282.View ArticlePubMed
- Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogosova-Agadjanyan EL, Stirewalt DL: Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci USA. 2011, 108 (12): 5003-5008. 10.1073/pnas.1019055108.PubMed CentralView ArticlePubMed
- Gallo A, Tandon M, Alevizos I, Illei GG: The Majority of MicroRNAs Detectable in Serum and Saliva Is Concentrated in Exosomes. PLoS One. 2012, 7 (3): e30679-10.1371/journal.pone.0030679.PubMed CentralView ArticlePubMed
- Eldh M, Lotvall J, Malmhall C, Ekstrom K: Importance of RNA isolation methods for analysis of exosomal RNA: evaluation of different methods. Mol Immunol. 2012, 50 (4): 278-286. 10.1016/j.molimm.2012.02.001.View ArticlePubMed
- Nolte-'t Hoen EN, Buermans HP, Waasdorp M, Stoorvogel W, Wauben MH, t Hoen PA: Deep sequencing of RNA from immune cell-derived vesicles uncovers the selective incorporation of small non-coding RNA biotypes with potential regulatory functions. Nucleic Acids Res. 2012, 40 (18): 9272-9285. 10.1093/nar/gks658.PubMed CentralView ArticlePubMed
- Sun D, Lee YS, Malhotra A, Kim HK, Matecic M, Evans C, Jensen RV, Moskaluk CA, Dutta A: miR-99 family of MicroRNAs suppresses the expression of prostate-specific antigen and prostate cancer cell proliferation. Cancer Res. 2011, 71 (4): 1313-1324. 10.1158/0008-5472.CAN-10-1031.PubMed CentralView ArticlePubMed
- Turcatel G, Rubin N, El-Hashash A, Warburton D: MIR-99a and MIR-99b modulate TGF-beta induced epithelial to mesenchymal plasticity in normal murine mammary gland cells. PLoS One. 2012, 7 (1): e31032-10.1371/journal.pone.0031032.PubMed CentralView ArticlePubMed
- Shi XB, Xue L, Ma AH, Tepper CG, Gandour-Edwards R, Kung HJ, Devere White RW: Tumor suppressive miR-124 targets androgen receptor and inhibits proliferation of prostate cancer cells. Oncogene. 2012, 10.1038/onc.2012.425.
- Lang Q, Ling C: MiR-124 suppresses cell proliferation in hepatocellular carcinoma by targeting PIK3CA. Biochem Biophys Res Commun. 2012, 426 (2): 247-252. 10.1016/j.bbrc.2012.08.075.View ArticlePubMed
- Xia J, Wu Z, Yu C, He W, Zheng H, He Y, Jian W, Chen L, Zhang L, Li W: miR-124 inhibits cell proliferation in gastric cancer through down-regulation of SPHK1. J Pathol. 2012, 227 (4): 470-480. 10.1002/path.4030.View ArticlePubMed
- Lv XB, Jiao Y, Qing Y, Hu H, Cui X, Lin T, Song E, Yu F: miR-124 suppresses multiple steps of breast cancer metastasis by targeting a cohort of pro-metastatic genes in vitro. Chin J Cancer. 2011, 30 (12): 821-830. 10.5732/cjc.011.10289.PubMed CentralView ArticlePubMed
- Silber J, Lim DA, Petritsch C, Persson AI, Maunakea AK, Yu M, Vandenberg SR, Ginzinger DG, James CD, Costello JF: miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells. BMC Med. 2008, 6: 14-10.1186/1741-7015-6-14.PubMed CentralView ArticlePubMed
- Huang TC, Chang HY, Chen CY, Wu PY, Lee H, Liao YF, Hsu WM, Huang HC, Juan HF: Silencing of miR-124 induces neuroblastoma SK-N-SH cell differentiation, cell cycle arrest and apoptosis through promoting AHR. FEBS Lett. 2011, 585 (22): 3582-3586. 10.1016/j.febslet.2011.10.025.View ArticlePubMed
- Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N: Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008, 26 (4): 407-415. 10.1038/nbt1394.View ArticlePubMed
- Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM: The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010, 38 (6): 1767-1771. 10.1093/nar/gkp1137.PubMed CentralView ArticlePubMed
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10 (3): R25-10.1186/gb-2009-10-3-r25.PubMed CentralView ArticlePubMed
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39 (Database issue): 152-157.View Article
- Wang X: miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA. 2008, 14 (6): 1012-1017. 10.1261/rna.965408.PubMed CentralView ArticlePubMed
- Wang X, El Naqa IM: Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics (Oxford, England). 2008, 24 (3): 325-332. 10.1093/bioinformatics/btm595.View Article
- da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.View ArticlePubMed
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.