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Pass4sure EX0-113 Practice Test | Pass4sure braindumps of EX0-113 with real questions - alphernet.com.au

EX0-113 | TMap Next Test Engineer

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EX0-113 - TMap Next Test Engineer - braindump

Vendor Exin
Exam Number EX0-113
Exam Name TMap Next Test Engineer
Questions 60 Q & A
Recent Update February 14, 2019
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EX0-113 exam Dumps Source : TMap Next Test Engineer

Test Code : EX0-113
Test Name : TMap Next Test Engineer
Vendor Name : Exin
Q&A : 60 Real Questions

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Exin TMap Next Test Engineer

Sogeti Launches Free iOS and Android utility trying out lifestyles Cycle App: TMap® life Cycle | killexams.com Real Questions and Pass4sure dumps

Free app places greater than 25 years of application trying out and excellent Assurance competencies into the hands of Sogeti shoppers.

DAYTON, Ohio, Jan. 26, 2012 -- Sogeti, a number one issuer of professional know-how capabilities, focusing on utility administration, Infrastructure management, high-Tech Engineering and testing, has launched a free app for iOS and Android: TMap® existence Cycle.

Sogeti's TMap® is the realm-main methodology for structured possibility-primarily based utility testing. a necessary point of TMap® is the lifecycle, covering the key steps in testing strategy and execution, within the conclusion-to-end procedure of reaching strong company-essential applications.

Sogeti's TMap® lifestyles Cycle app provides this mobile-primarily based framework, guiding software testers during the application checking out lifecycle, from planning and infrastructure to manage and all stages of a crucial course. the use of TMap®'s structured framework for conclusion-to-conclusion check procedure, this app helps trying out authorities song the development of their initiatives. It also allows for the prior identification of defects ensuing within the consistent discount of timelines by as a minimum 30%, decreasing universal fees.

the new app is accessible now in the Android market and the App store. it is suitable with Android mobile instruments as well as the Apple iPhone, iPod contact and iPads. It offers users with free downloads to aid the TMap® system, including checklists and templates. The app additionally elements video clips explaining examine design recommendations, product chance evaluation and strategies to determine look at various method, in addition to hyperlinks to utility testing substances equivalent to eBooks and whitepapers.

"TMap® is an advanced, confirmed and depended on software look at various management methodology, relied upon via tens of hundreds of application testers throughout the globe for structured chance-based mostly checking out," pointed out Dan Hannigan, country wide vice chairman of the Managed checking out follow for Sogeti country. "Releasing this free app is simply yet another extension of our basic aim, which is making structured application checking out and quality assurance strategies conveniently accessible to professional testers."

To download this free app, go to the Android industry or App keep, and seek "TMap® lifestyles Cycle."

About TMap®

TMap® (verify administration strategy) is Sogeti's business-driven, risk-based methodology for structured software testing, critical to organizations of all sizes and vertical markets. An adaptive method, suitable for all check situations in construction environments, including new construction, protection, waterfall/iterative/agile building, customized or packaged application, TMap® addresses the key issues of quality, time and cost throughout the construction lifecycle. The app describes distinctive phases of the TMap® lifecycle.TMap®, TMap subsequent®, TPI® and TPI subsequent® are registered trademarks of Sogeti.

About Sogeti usa

Sogeti usa is a premier provider of advice technology capabilities to businesses and public sector corporations. working in 23 U.S. places, Sogeti's company model is built on offering shoppers with local accountability and tremendous beginning abilities. Sogeti is a frontrunner in helping purchasers develop, put in force and control useful IT options to help run their company greater. With over 40 years of adventure, Sogeti offers a finished portfolio of functions together with Advisory services, software building & Integration, business suggestions management, Engineering capabilities, Infrastructure capabilities and checking out features. To learn more, consult with: www.us.sogeti.com

About Sogeti

Sogeti is a number one provider of knowledgeable expertise functions, that specialize in utility management, Infrastructure administration, excessive-Tech Engineering and trying out. Working carefully with its clients, Sogeti enables them to leverage technological innovation and obtain maximum effects. Sogeti brings collectively greater than 20,000 professionals in 15 international locations and is current in over a hundred locations in Europe, the united states and India. Sogeti is a totally-owned subsidiary of Cap Gemini S.A., listed on the Paris inventory exchange. For greater suggestions please discuss with www.sogeti.com.

source Sogeti u . s .

CONTACT: CONTACT: Nicole Scrivner, Sogeti usa advertising supervisor, +1-937-424-9487, nicole.scrivner@us.sogeti.com

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TMap next® testing Clouds: First Cloud e-ebook to be launched by using Sogeti | killexams.com Real Questions and Pass4sure dumps

ti, kesä 21, 2011 08:30 CET

a new ebook in Sogeti’s TMap® series; TMap subsequent® trying out Clouds is launched.

twenty first June, Paris/Helsinki: Sogeti, a number one provider of expert know-how features, has launched a brand new publication in its TMap® series, at IBM Innovate 2011 in Orlando united states. TMap next® checking out Clouds is a comprehensive overview of how testing clouds can carry cost for the early adopter. The creator, Ewald Roodenrijs, describes the cloud business model for testing, the evolution from assistance know-how (IT) to the thought of enterprise technology (BT), and the steps that deserve to be taken in implementing cloud initiatives.

Cloud computing is currently viewed as one of the crucial excellent three IT technologies for the ahead thinking CIO. however the cloud is still at an early stage of building, we're beginning to see powerful boom in cloud-based mostly computing, which is outstripping even essentially the most optimistic predictions. it is more and more clear that the cloud model will complement, if now not entirely exchange, mainframe and customer/server installations within the years to return. here is in keeping with a compelling cost proposition: velocity to market, agility to convey ahead or retire carrier, and the probability to circulation expenditure from Capital Expenditure (CapEx) to Operational Expenditure (OpEx).

As cloud provider adoption turns into ever more extensive-ranging, a new global infrastructure is being created that creates enormous new alternatives for application fine assurance and checking out. This new booklet TMap subsequent® testing Clouds describes in selected two aspects of the cloud: the company model and the cloud platform, each of which ought to be demonstrated. This incorporates a number of aspects; checking out the infrastructure, cloud-enabled applications, and the ability to have instant deployable look at various infrastructure.

Ewald Roodenrijs comments: “This all has an affect on the manner we do testing sooner or later. trying out applications on the cloud is a similar as checking out functions on a traditional infrastructure. most effective what's validated is distinct”.

TMap subsequent® trying out Clouds is a development from its TMap® predecessors. It outlines how structured checking out, the usage of TMap®, will also be leveraged inside a cloud ambiance, it is not a step-with the aid of-step instruction manual, but stories in element the framework and magnitude of testing on the cloud, checking out cloud approach (in, on or in the cloud) and the hazards worried for companies seeing that migrating functions onto the cloud, such as security, data integrity, privacy issues, facts restoration and efficiency.

It can also also be read as a partner or follow-on e-book to seize the Cloud – A manager’s e-book to Success with Cloud Computing[1], exploring in more element the trying out facets of the cloud.

TMap next® trying out Clouds is attainable as an e-ebook in both epub and pdf structure and may be downloaded for free of charge from the TMap trying out web page or the Sogeti.com web page. Sogeti is increasingly producing e-books as an efficient ability of timely distribution in addition to enabling the enterprise to cut back its carbon footprint. a printed version is attainable through the use of Printing on Demand (POD) by way of online bookstores or www.tmapbooks.com.

in line with Nijs Blokland, global trying out community Lead and Head of Sogeti NL R&D; “Most of us are aware of the benefits of cloud computing, however have less understanding of the practicalities of efficiently and securely imposing cloud functions and using the cloud as a cost-positive supply of infrastructure and check tools. This publication TMap subsequent® checking out Clouds provides early insight and counsel on these concerns, and is extra evidence of Sogeti’s imaginitive pondering in bringing the benefits of both cloud and structured testing collectively”.

when you consider that launching their utility checking out as a service (STaaS) globally in 2008, Sogeti has been pioneering using cloud-based mostly options. As an instantaneous development of this, Sogeti Netherlands will be launching their first checking out Cloud solution, a finished portal if you want to provide a full menu of features, direct access to speedy automated pricing and the capacity to add testing artifacts and assets for cloud-primarily based execution.

For the Dutch company’s customers, here is an awful lot greater than a entrance-end website. This portal will provide full clarity of the service, together with a set turnaround time and price for each and every of the 22 functions, including the creation of a grasp verify Plan, mannequin based testing, tool preference, efficiency checking out, test Automation, internet Accessibility and safety testing.

Notes to Editors

in regards to the creator

Ewald Roodenrijs, writer of TMap next trying out Clouds, is the world Lead of the Cloud trying out features inside the Sogeti neighborhood and a member of the checking out R&D group of Sogeti Netherlands. he's also the co-writer of the ebook TMap subsequent® – BDTM, contributor to catch the Cloud, a regular contributor to national and overseas technical/expert publications, a speaker at a lot of overseas conferences, including most lately at IBM Innovate June 2011, a eager blogger on the Cloud and checking out and enthusiastic coach. he is at the moment working at the side of IBM Rational to extra improve this cloud offering and recently received the Capgemini-Sogeti checking out features Innovation Award (individual) 2011 for his work on Cloud testing.

in regards to the publication

The publication will also be ordered at principal online books save, together with Amazon, or directly from the writer by the use of www.tmapbooks.com or by way of www.sogeti.com. a published replica may also be ordered at www.tmapbooks.com.

Introduction, Chapter 1: An introduction to TMap subsequent® trying out Clouds and Chapter 2 Framework and importance of checking out; even in the cloud; of pastime to all readers.

Chapter three: The business in cost of IT; the Cloud and Chapter 4 whatever thing as a provider; cloud-enabled software trying out as a service of pastime to look at various managers, application managers and company managers and IT department managers.

Chapter 5 ‘testing Cloud approach; a move to 3D’’, Chapter 6 ‘checking out the Cloud; in, on or with...’ and Chapter 7 ‘Cloud hazards; worth testing for…’ are peculiarly interesting for the goal businesses of verify managers, verify coordinators, infrastructure testers and testers.

concerning the Contributors

here contributors helped within the introduction of the content of this book from Sogeti, Capgemini and additionally IBM Rational: Andréas Prins, Mark Buenen, Nick Lloyd, Ramanathan Iyer, Alfonso López de Arenosa, Rob Baarda, Richard Ammerlaan, Erik Smit, Flavien Bouche, Kanchan Apt, Michiel Boreel, Pierre Bedard, Michiel Rigterink, Karl Snider, John Bloedjes, Dan Hannigan, Dirkjan Kaper, plus extra assist from Leo van der Aalst, Nicolas Claudon and Clare Argent.

For extra assistance please contact:

Ewald Roodenrijs, global Lead for Cloud trying out, SogetiTel: +31 (0)6 fifty two 32 77 73Email: ewald.roodenrijs@sogeti.nl

Therese Sinter, community company Communications Director, Sogeti Tel: +46 70-361 46 21 email: therese.sinter@sogeti.se

About Sogeti

Sogeti is a number one company of professional know-how functions, that specialize in application management, Infrastructure administration, excessive‐Tech Engineering and trying out. Working carefully with its shoppers, Sogeti allows for them to leverage technological innovation and achieve highest effects. Sogeti brings together more than 20,000 authorities in 15 international locations and is present in over 100 areas in Europe, the united states and India. Sogeti is a unconditionally‐owned subsidiary of Cap Gemini S.A., listed on the Paris stock alternate.

together, Sogeti and Capgemini have developed imaginitive, company-driven fine assurance (QA) and testing features, combining optimal-in-breed testing methodologies (TMap® and TPI®) and the international birth model, Rightshore®, to help groups achieve their trying out and QA desires. Sogeti and Capgemini have created some of the largest committed testing practices on earth, with over eight,200 examine professionals and an extra 12,500 application specialists, particularly through a typical core of excellence with trying out specialists developed in India.

In Finland Sogeti is concentrated fully on featuring trying out functions on the local market.

For extra information please discuss with www.sogeti.com and www.sogeti.fi.

[1] Written by means of Erik van Ommeren, Martin van den Berg, Sogeti and published by way of Sogeti March 2010. capture the Cloud is a e book through the company and commercial enterprise structure facets of Cloud Computing, together with 11 cases by which leading companies share insights received from their adventure with Cloud.

Yritysesittely Sogeti Finland OySogeti on erikoistunut korkealuokkaisten IT-palvelujen toimittamiseen Suomen markkinoille. Keskitämme Suomessa palvelumme ohjelmistojen testaukseen ja laadunvarmistukseen. Sogeti-konsernin pääkonttori on Pariisissa Ranskassa. Konsernilla on palveluksessaan yhteensä noin 20 000 konsulttia 15 maassa: Belgia, Espanja, Hollanti, Intia, Irlanti, Iso-Britannia, Luxemburg, Norja, Ranska, Saksa, Suomi, Sveitsi, Ruotsi, Tanska ja Yhdysvallat. Pohjoismaissa konsultteja on Suomessa, Ruotsissa, Tanskassa ja Norjassa yhteensä yli 1 100. Sogeti-konsernin yhtiöt ovat Pariisin pörssissä listatun Cap Gemini S.A.:n kokonaan omistamia tytäryhtiöitä.

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Characterization of RNA in exosomes secreted by means of human breast cancer telephone traces the usage of next-era sequencing | killexams.com Real Questions and Pass4sure dumps

Introduction

Exosomes are nanosized membrane vesicles secreted by dissimilar cell forms (Raposo & Stoorvogel, 2013). The time period “exosomes” became delivered in 1987 by means of Johnstone et al. (1987) to describe the “trash” vesicles released via differentiating reticulocytes to dispose undesirable membrane proteins that are prevalent to scale back throughout maturation of reticulocytes to erythrocytes. Later exosomes had been implicated in more complicated services. Raposo et al. (1996) have verified that exosomes derived from B lymphocytes prompt T lymphocytes, suggesting a task for exosomes in antigen presentation in vivo. In early stories, secreted microvesicles were named according to their cellular origins, e.g., archaeosomes, argosomes, dexosomes, epididymosomes, prostasomes, oncosomes, etc. (Raposo & Stoorvogel, 2013). extra lately it grew to become apparent that vesicles released with the aid of the equal phone classification are heterogeneous and may be categorized into at the least three courses in keeping with their mode of biogenesis: (1) exosomes (30–a hundred thirty nm in diameter), which originate from multivesicular endosome (MVE); (2) microvesicles (one hundred–one thousand nm in diameter), which might be shed from the plasma membrane; (three) apoptotic our bodies (1–four µm in diameter), which are released from fragmented apoptotic cells all the way through late levels of mobilephone dying (Raposo & Stoorvogel, 2013). quite a few purification procedures including sequential centrifugation protocols have been proposed to separate these vesicles for additional analysis. Biochemical and proteomic analyses showed that exosomes comprise specific protein set reflecting their intracellular web site of formation. Exosomes from diverse mobile forms include endosome-associated proteins, e.g., tetraspanins (CD9, CD63, CD81, CD82), annexins, Rab GTPases and flotillin. Exosomes are additionally enriched in proteins worried in MVE formation (Tsg101 and Alix), chaperones (Hsc73 and Hsc90) and cytoskeletal proteins (Raimondo et al., 2011). in addition, exosomes comprise proteins which are particular to the cells from which they're derived (Choi et al., 2012; Sandvig & Llorente, 2012).

The analysis in the exosome box has exploded rapidly after the discovery that these microvesicles transport a large number of mRNA and miRNA (Valadi et al., 2007) and that exosomal mRNAs may well be translated into proteins through recipient cells (Ratajczak et al., 2006; Valadi et al., 2007) and exosomal miRNAs are able to modulate gene expression in recipient cells (Mittelbrunn et al., 2011). apparently, definite mRNAs and miRNAs were recognized as highly enriched in exosomes in comparison to that of the host cells indicating the existence of selective sorting mechanism controlling incorporation of RNA into exosomes (Skog et al., 2008; Valadi et al., 2007). in the past, we demonstrated that exosomal RNAs share selected sequence motifs that might also potentially characteristic as cis-acting points for concentrated on to exosomes (Batagov, Kuznetsov & Kurochkin, 2011).

Given the proven fact that exosomes lift complicated organic tips along with proteins, lipids and RNAs, it is not outstanding to find that they've been implicated in a number of physiological and pathological circumstances. Skokos et al. (2001) said, for example, that mast cells communicate with different cells of the immune gadget via exosomes promoting mitogenic undertaking in B and T lymphocytes. a number of reviews have established the role of exosomes within the building of the frightened gadget, synaptic activity, neuronal regeneration, neuron-glia communication and coverage in opposition t injury (Lai & Breakefield, 2012). furthermore, exosomes can also be concerned in the pathogenesis of cancer and degenerative diseases. The fact that tumor cells release a huge amount of exosomes became in the beginning proven in ovarian cancer sufferers (Taylor, Homesley & Doellgast, 1983). Exosomes had been shown to be secreted by numerous tumor cells including those derived from breast (King, Michael & Gleadle, 2012), colorectum (Silva et al., 2012), brain (Graner et al., 2009), ovarian (Escrevente et al., 2011), prostate (Mitchell et al., 2009; Nilsson et al., 2009), lung (Rabinowits et al., 2009), and bladder (Welton et al., 2010) melanoma. A significantly bigger amount of exosomes became present in plasma from lung cancer sufferers compared to that of handle people (Rabinowits et al., 2009). In colorectal melanoma sufferers, the volume of plasma circulating exosomes turned into constitutively greater than in regular fit people showing the direct correlation between exosomes volume and malignancy (Silva et al., 2012).

Manipulating tumor cells to decrease the free up of exosomes followed through their injection into immunocompetent mice resulted in a tremendously slower tumor boom compared to that of unperturbed cells (Bobrie et al., 2012). It has been proven that exosomes that are released from tumor cells are capable of switch loads of molecules, together with cancer-certain, to different cells (Al-Nedawi, Meehan & Rak, 2009; Muralidharan-Chari et al., 2009) to control their ambiance, making it more favorable for tumor growth and invasion. Glioblastoma-derived exosomes had been found to be enriched in angiogenic proteins that allowed them to stimulate angiogenesis in endothelial cells (Skog et al., 2008). Melanoma exosomes have been shown to be instrumental in melanoma mobilephone dissemination via modifications within the angiogenic microenvironment. Hood et al. tested that metastatic elements accountable for the recruitment of melanoma cells to sentinel lymph nodes are upregulated by using melanoma exosomes themselves (Muralidharan-Chari et al., 2009). Exosomes shed through human MDA-MB-231 breast carcinoma cells and U87 glioma cells had been capable of conferring the converted traits of melanoma cells onto common fibroblasts and epithelial cells partly because of transferring tissue transglutaminase and fibronectin (Hood, San & Wickline, 2011).

as a result of their small size exosomes have an ability to penetrate intercellular contacts to reach distant ingredients of the physique with the help of the blood movement and other physique fluids. Exosomes had been purified from human plasma, serum, bronchoalveolar fluid, urine, tumoral effusions, epididymal fluid, amniotic fluid and breast milk (Raposo & Stoorvogel, 2013). on the grounds that exosomes possess attribute protein and RNA signatures of their host cells, analysis of exosomes in a variety of physique fluids can be potentially utilized for non-invasive diagnostics of melanoma and different disorders. for instance, aggressive human gliomas frequently express a truncated and oncogenic variety of the epidermal boom ingredient receptor, known as EGFRvIII. The tumour-specific EGFRvIII was detected in serum microvesicles from glioblastoma patients (Skog et al., 2008). excessive steadiness of exosomal RNA (Skog et al., 2008; Valadi et al., 2007) and ease of RNA detection by enormously delicate PCR makes detection of exosomal RNA a beautiful strategy for the invention of biomarkers. certainly, mRNA variants and miRNAs characteristic of gliomas could be detected in serum microvesicles of glioblastoma sufferers (Skog et al., 2008). Expression profiles of serum microvesicle mRNA by microarrays correctly separated individuals with glioblastoma from regular controls (Noerholm et al., 2012). Of all RNA species, secreted miRNAs were most generally utilized toward discovery of physique fluid-primarily based biomarkers possibly because miRNA expression profiles are extra informative than mRNA expression profiles in a few illnesses (Grady & Tewari, 2010). in one study, analysis of plasma- and serum-derived microvesicles published 12 miRNAs in another way expressed in prostate melanoma patients in comparison to that of fit controls and 11 miRNAs upregulated in patients with metastases in comparison to that of patients devoid of metastases (Bryant et al., 2012). interestingly, exosomes released by way of breast cancer cells can be separated into different classes counting on their miRNAs content (Palma et al., 2012). Cells undergoing malignant transformation produced exosomes containing selective miRNAs, whose unencumber is multiplied via malignant transformation, in contrast to cells that don't seem to be littered with malignancy, whose exosomes are packed with neutral miRNAs (Palma et al., 2012). The changes in exosomal miRNA cargo could supply a signature of the presence of malignant cells within the physique.

the majority of stated so far exosomal miRNA and mRNA profiles have been generated the use of microarray approaches that suffer from several boundaries. Microarrays are biased for investigation of already found transcripts. moreover, there is talents for cross-hybridization of RNAs which are incredibly related in sequence. lately developed subsequent generation RNA sequencing expertise (RNA-Seq) permits detection of all RNA subtypes in addition to of unannotated transcripts and has a high sensitivity towards identification of low-abundance RNAs. In case of exosomes, this approach was utilized only for the evaluation of small (20–70 nt) RNAs (Bellingham, Coleman & Hill, 2012; Nolte-’t Hoen et al., 2012).

during this look at, we utilized the RNA-Seq approach to characterize the transcriptomes of exosomes secreted with the aid of two metastatic human breast cancer cellphone strains. We describe optimized computational workflow to analyze records generated by using the Ion Torrent semiconductor chip-primarily based technology. we have identified and profiled RNA species present in exosomes and host cells and focus on the utility of exosomal RNA as competencies breast cancer-specific biomarkers.

materials and methods telephone culture

Human breast cancer cellphone traces MDA-MB-436 (ATCC® HTB-a hundred thirty™) and MDA-MB-231 (ATCC® HTB-26™) were maintained at 37°C in 5% CO2 and cultured in DMEM/F12 supplemented with 10% FBS. 48 h previous to exosome collection, cells have been washed 3 times with PBS and the medium turned into changed to serum-free CCM5 medium (Thermo Scientific).

Exosome extraction

Exosomes have been remoted and purified from the media of MDA-MB-436 and MDA-MB-231 mobile cultures the use of sequential centrifugation protocol. in brief, media was amassed and mobile particles turned into eliminated via centrifugation at three,000×g for 10 min. The supernatant was centrifuged at 17,000×g for 30 min at four°C. The supernatant turned into amassed and centrifuged at 100,000×g for two h to pellet exosomes. Exosomes pellets have been then washed in filtered PBS and re-centrifuged at one hundred,000×g, the supernatant changed into removed and the last exosomal pellet changed into re-suspended in one hundred µl PBS.

Transmission electron microscopy

A 50 µl aliquot of exosomes changed into absorbed onto formvar carbon lined nickel grid for 1 h. The grid become placed with the coating side dealing with the drop containing exosomes. Then the grids were washed through sequentially positioning them on exact of the droplets of 0.1 M sodium cacodylate, pH 7.6 and then fixed in 2% paraformaldehyde and 2.5% glutaraldehyde in 0.1 M sodium cacodylate, pH 7.6 for 10 min. Then grids had been washed again with 0.1 M sodium cacodylate, pH 7.6 and contrasted with 2% uranyl acetate in 0.1 M sodium cacodylate, pH 7.6 for 15 min. After washing, the grids have been incubated on correct of the drop of 0.13% methyl cellulose and negatively stained with 0.4% uranyl acetate for 10 min, air dried for five min and examined with a JEM-2200FS transmission electron microscope operated at a hundred kV.

Nanoparticle monitoring analysis

Supernatants containing vesicles were analyzed using a Nano-Sight LM10 instrument fitted with a 405 nm laser (NanoSight, Amesbury, UK) at 25°C. Particle flow turned into tracked via NTA application (edition 2.2, NanoSight) with low refractive index corresponding to mobile-derived vesicles. every song was then analyzed to get the suggest, mode, and median vesicle measurement together with the vesicle attention (in thousands and thousands) for each and every dimension.

RNA isolation and analysis

total RNA from exosomes (MDA-MB-436 and 231) and cultured cells (MDA-MB-231) had been isolated using the TRIzol reagent (Invitrogen). RNA excellent and awareness were assessed with the Agilent 2100 Bioanalyzer (Thermo Scientific). cellular RNA turned into analyzed using RNA 6000 Nano equipment (Agilent) and exosomal RNA become analyzed the use of RNA 6000 Pico package (Agilent).

RNA-seq with ion torrent personalized genome laptop (PGM)

Two a whole lot ng of exosomal RNA and 3 µg of total cell RNA changed into used because the starting enter for RNA-Seq library instruction. Sequencing was carried out by means of AITbiotech company (Singapore). briefly, total cell RNA changed into handled with RiboMinus Eukaryote kit (lifestyles technologies) to remove rRNA. Then, exosomal and rRNA-depleted cellular RNAs had been fragmented the usage of RNaseIII. entire transcriptome library become constructed the use of the Ion complete-RNA Seq equipment v2 (existence applied sciences). Bar-coded libraries had been quantified with qRT–PCR. each library template changed into clonally amplified on Ion Sphere Particles (existence applied sciences) using Ion One touch 200 Template kit v2 (existence technologies). Preparations containing bar-coded libraries had been loaded into 318 Chips and sequenced on the PGM (existence technologies).

cDNA synthesis and quantitative precise-time PCR (RT-PCR)

Two-step RT-PCR turned into carried out using the QuantiTect Reverse Transcription equipment (QIAGEN GmbH, Hilden, Germany) in line with brand’s protocol. RT-PCR become carried out in a Rotor-Gene (Qiagen) using a SYBR green PCR grasp combine. Primer sequences are provided in desk S3. All reactions with template and devoid of template (bad controls) have been run in duplicate and averaged. GAPDH become used as inside control for mRNA. Ct price become detected for every gene meaning the cycle number at which the volume of amplified gene of activity reaches a set threshold. Relative quantification (fold exchange) changed into decided for the host cells and exosomal genes expression and normalized to an endogenous manage GAPDH relative to a calibrator as 2−ΔΔCt (the place ΔC = (Ct of gene of hobby) – (Ct of endogenous control gene (GAPDH) and ΔΔCt = (ΔCt of samples for gene of interest) – (ΔCt of calibrator for the gene of pastime). Melting curves of each and every amplified items had been analyzed to make certain uniform amplification of the PCR items.

Bioinformatics evaluation uncooked reads filtering

uncooked reads generated by means of sequencing had been subjected to a couple of first-class exams. The low fine reads have been eliminated through study trimming and read filtering. study trimming protected removing of adapter sequences, removal of the three′ ends with low quality ratings and trimming based on excessive-Residual Ionogram Values. Filtering of entire reads included removal of brief reads, adapter dimers, reads missing sequencing key, reads with off-scale sign and polyclonal reads. Subsequent evaluation turned into carried out with excessive best reads which passed through the described above filtering steps.

Reads mapping

Bowtie 2 edition 2.1.0 changed into used to align all notable reads to rRNA sequences including 28S (NR_003287.2), 18S (NR_003286.2), 5S (NR_023379.1), and 5.8S (NR_003285.2) rRNA. Reads mapped to rRNA sequences were filtered out whereas the leisure of the reads were mapped to the human genome. The extraordinary reads have been mapped to hg19 build of the human genome from school of California Santa Cruz (united states of america) genome browser database (Meyer et al., 2013) the usage of TopHat version 2.0.6 with the aligner Bowtie 2.0.5 (Kim et al., 2013; Langmead & Salzberg, 2012) with their default parameters in conclusion-to-end mode (-b2-delicate) and defining splice-junctions in line with typical splice-junctions (-G). to classify the reads into regularly occurring and unknown genes, the BAM file generated with the aid of Tophat2 was intersected to regular gene (RefGene and GENCODE developed V14 from u.s. database) the use of BEDtools (Quinlan & hall, 2010) and changed into used to count number the number of reads by way of SAMtools (Li et al., 2009).

submit-processing of the aligned reads

The mapped reads were additional manipulated via casting off the reads that mapped to multiple locations. In specific, the short aligned reads with the size of <20 nucleotides had been eradicated to steer clear of the alignment mistakes similar to mapping to dissimilar genomic locations. extra filtering included the removal of the low high-quality reads which fall beneath the mapping high-quality score of 10 (-q 10) the usage of SAMtools. For the insurance search, the BAM file generated by way of Tophat2 changed into transformed to mattress format with choice (-break up) the use of BEDtools. The mattress file become transformed once again to BAM layout the use of BEDtools. We then developed python script (using pysam as a part of the scripts) to calculate the number of reads and read insurance in exons and protein-coding sequence (CDS) regions consecutively.

RNA abundance calculation

RNA abundance become estimated with the support of Partek Genomics Suite application (Partek Inc., St. Lous, MO) using Reference Sequence Gene (RefSeq Gene) and GENCODE annotation developed version 14 of these no longer overlapped with RefSeq Gene from america genome browser. The Expectation-Maximization (E/M) Algorithm (Xing et al., 2006) turned into used to estimate the undoubtedly relative expression stages of each gene isoform. Partek’s algorithm become used to quantify the gene isoform expression stage as reads per kilo base per million mapped reads (RPKM).

practical evaluation of genes

Database for Annotation, Visualization, and built-in Discovery (DAVID) (version 6.7) turned into used to determine gene purposeful annotation terms that are tremendously enriched in selected gene lists with the whole Human genes because the history (Huang, Sherman & Lempicki, 2009). a listing of gene symbols become generated for every dataset and became used as input into DAVID. DAVID calculates a modified Fishers accurate p-cost to display Gene ontology (GO) and KEGG molecular pathway enrichment, the place p-values under 0.05 after Benjamini distinct look at various correction are considered to be strongly enriched within the annotation class. We also used a count threshold of 5 and the default value of 0.1 for the ease (enrichment) score settings. We used greater specific GO term categories supplied with the aid of DAVID, referred to as GO fat, to lower the redundancy of frequent GO phrases in the evaluation to enhance the specificity of the phrases.

consequences Characterization of exosomes released by way of breast cancer cells

Exosomes had been isolated from two breast melanoma phone strains, MDA-MB-436 and MDA-MB-231 the usage of classical ultracentrifugation protocol. The size distribution and amount of exosomes had been analyzed using NanoSight LM10 nanoparticle tracking evaluation (NTA). NTA confirmed that MDA-MB-231 cells released 4 × 106 vesicles per cm2 of boom area per forty eight h that had been predominantly a hundred and fifteen nm in size. MDA-MB-436 cells launched 1.04 × 107 vesicles per cm2 of increase area per forty eight h that had been predominantly ninety one nm in size (Fig. 1). The size of exosomes launched from both mobilephone lines ranged from ∼70 nm to ∼300 nm. An examination of the purified vesicles using transmission electron microscopy printed that they had the size (∼50–one hundred nm) and morphology (Fig. 2) general of that of exosomes.

figure 1: analysis of exosomes produced by using breast cancer cell traces, MDA-MB-436 and MDA-MB-231, with Nanosight LM10-HS instrument. Characterization of exosomal RNA

RNA become isolated from exosomes launched via each breast cancer phone lines. total RNA become additionally extracted from MDA-MB-231 phone line as a control host phone line that produced exosomes. Bioanalyzer facts revealed that exosomes include a wide range of RNA sizes (30–500 nt) and have very small amount of intact rRNA (5.2% in MDA-MB-231 exosomes and 5.6% in MDA-MB-436 exosomes) (Fig. 3) in keeping with old stories on exosomal RNA (Rabinowits et al., 2009; Valadi et al., 2007).

figure 2: TEM photograph of the exosomes produces by way of MDA-MB-436 mobile line. Electron microscopy allowed visualizing membrane-bound nanovesicles sized ∼a hundred nm. White arrowheads pointing to the exosomes. Scale bar = one hundred nm. determine three: analysis of RNA from cells and exosomes by using Bioanalyzer. Exosomal and complete mobilephone RNA turned into analyzed with PicoChip and NanoChip, respectively. Exosomal RNA deep sequencing the use of Ion Torrent technology

We utilized the Ion Torrent sequencing expertise to profile exosomal RNA produced with the aid of MDA-MB-436 and MDA-MB-231 phone strains, in addition to RNA bought from host mobilephone line MDA-MB-231. based on RNA great assessment the use of Bioanalyzer profiling (Fig. 3) we performed rRNA depletion for mobile RNA but now not for exosomal RNA.

Identification of acceptable alignment device for productive capture of splice-junction reads produced by way of the Ion Torrent expertise

RNA-Seq computational evaluation workflow is offered in Fig. four. the single-end RNA reads generated from sequencing of the exosomal and host cellphone libraries had been trimmed to eradicate adapter sequences and then filtered. After filtering out low-best reads, the exquisite reads had a various size starting from 6 bp to >300 bp. These preceded tremendous reads had been considered for extra evaluation. the entire quantity of reads for RNA from MDA-MB-231 cells changed into about four.8 M. RNA-Seq resulted in ∼three.5 M and ∼three.2 M reads for RNA from MDA-MB-436 and MDA-MB-231 cells derived exosomes, respectively. in the beginning, the reads had been aligned to rRNA sequences including 28S, 18S, 5S, and 5.8S rRNA (see strategies). 24% of the reads in cellular and greater than 80% of the reads of each exosome samples were mapped to rRNA sequences (Fig. S2). The relaxation of the reads were mapped to the human genome (united states; hg19 developed) with the assist of TMAP aligner application in line with the Ion Torrent counseled pipeline (technologies) (information no longer proven). however, using TMAP resulted in misalignment of the splice-junction reads or reads containing distinctive exons to the genome (Fig. S1). hence, we used alternative aligner Tophat 2.0.6 together with the Bowtie 2.0.5 for the study mapping.The mapped consequences are proven in Fig. S2. In total, this alignment produced more than four.3 M (∼ninety%) reads of MDA-MB-231 mobile RNA that could be mapped to rRNA sequences and the human genome. For the MDA-MB-231 and MDA-MB-436 mobilephone-derived exosomes, these alignments resulted in ∼2.8 M (∼90%) and ∼3.2 M (∼ninety one%) of mapped reads, respectively (Fig. S2). more than eighty% of mapped reads from exosomal samples were found to be mapped to rRNA sequences. We additional investigated the reads of each exosomal RNA samples, which mapped to rRNA sequences. These reads were counted and plotted as examine density over every rRNA sequence (shown in Fig. S3A). The mapped reads covered complete size of all rRNA sequences. The predominant fractions of mapped reads had been 28S and 18S rRNA (Fig. S3B).

figure four: Flowchart of RNA-seq data evaluation. The raw reads are exposed to pre-alignment first-class checks including the removing of adaptor sequences and low best reads. The high first-rate reads are then mapped to rRNA sequences using Bowtie2 version 2.1.0. The non-mapped rRNA reads have been mapped to human genome hg19 construct of the human genome the use of TopHat version 2.0.6 with the aligner Bowtie 2.0.5. After mapping, low mapping high-quality reads less than 10; short reads with size less than 20 base pairs and multi- reads have been eliminated. Estimation of study counts and skim coverage on mapped reads where over 90% of exon (non-coding) and CDs (for coding) in transcript isoforms of RefGene and/ or GENCODE v14 gene fashions. The EM algorithm together with GENCODE v14 annotations changed into used to estimate the examine count number and reads per kilo base per million mapped reads (RPKM) on mapped transcripts.

Reads mapped to distinct places within the genome were eradicated to acquire uniquely mapped reads. Subsequent downstream evaluation become performed with the high best reads which had the examine size greater than 20 bps and mapping nice rating of above 10 (see methods). in consequence, about eighty one, seventy six, and 70% out of all mapped reads had been regarded as excessive satisfactory mapped reads for MDA-MB-231 and MDA-MB-436 exosomal RNA, and MDA-MB- 231 mobile RNA, respectively.

approximately ninety seven% (∼2 M reads) of reads from both exosomal RNA samples were mapped to rRNA sequences (Fig. 5) and ∼2% of the reads were mapped to known genes (RefSeq and/or GENCODE gene fashions). on the equal time, for the MDA-MB-231 cellular RNA, for which rRNA depletion step become carried out, the vast majority of the reads (∼fifty eight%) became mapped to frequent genes (Fig. 5). To raise the number of reads for other RNAs we attempted to burn up rRNA with the RiboMinus™Eukaryote equipment informed via Ion complete RNA-Seq package protocol. As anticipated, this protocol efficaciously pulled down rRNA from cellular RNA (>60%) but had little impact on removal of rRNA from exosomal RNA (Fig. S4). The failure to fritter away exosomal fragmented rRNA can also be defined through the design of the RiboMinus™Probe. It incorporates 2 probes each and every for 5S, 5.8S, 18S and 28S RNA. since the probe size is 22–25 nucleotides, many fragments of rRNA aren't centered.

determine 5: Distribution of uniquely mapped RNA-seq reads amongst transcriptome. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “conventional genes”. Reads that placed outdoor of annotated gene models are termed as “unknown”. Reads which can be mapped to rRNA sequences together with 5S, 5.8S, 18S, and 28S rRNA are named as “rRNA”. content material of cellular and exosomal transcriptomes

In complete, 16,086 transcripts (11,657 genes) have been detected with a normalized RPKM cost of superior than 1.0 at least in a single pattern. We used Integrative Genomics Viewer (IGV) edition 2.1.21 (Thorvaldsdottir, Robinson & Mesirov, 2013) to imagine mapped reads and to investigate the coverage across human transcriptome. We observed commonly misannotated transcripts in exosome samples with high RPKM cost in those circumstances when handiest small elements of the transcripts were lined by means of the reads (Fig. 6). This become the effect of low depth sequencing caused by using the presence of a large amount of reads representing rRNA. therefore, we applied greater stringent standards to gain full-length expressed genes via filtering genes based on the RNA reads insurance. Reads which cover over 90% of protein-coding sequence (CDS) of protein-coding genes and over ninety% of exons in non-coding sequence of non-coding genes had been regarded for further evaluation. To identify protein-coding genes in exosomal samples, the mapped reads of exosomal RNA have been pooled with estimated CDS insurance of >ninety% for both exosomal reads. The pooled mapped reads were used to calculate CDS in each exosomal pattern as >50% of coverage.

determine 6: example of low coverage transcript however very high RPKM in AURKAIP1 and ATPIF1 genes. (A) AURKAIP1 gene from chromosome position chr1:1,309,009–1,310,847 is shown using Integrative Genomic Viewer. among the three variants, the optimum value of protein-coding sequence (CDS) insurance, examine count number and RPKM is proven within the appropriate panel of examine mapping. each the exosomes suggests very low insurance (7–22%) with examine counts of 4, whereas the RPKM value is 65.forty four and 80.seventy eight RPKM for exosomes of MDA-MB-231 and MDA-MB-436, respectively. (B) ATPIF1 gene from chromosome position chr1:28,562,494–28,564,655 is visualized. The MDA-MB-231 exosomes exhibit excessive CDS coverage (ninety one%) with an exon count of 4.

the usage of these standards, we obtained lower number of annotated transcripts (6,187 transcripts or three,437 genes) compared to that after RPKM values had been considered (Fig. S5 and table S1). due to this fact, some transcripts showed high coverage with CDSs standards (as an instance, ATPIF1) in exosomal RNA from MDA-MB-231 cells, even when the variety of reads turned into small (lower than 5) (Fig. 6B). Such genes had been additionally considered in our evaluation. In total, 5821 (3115 genes) and 187 (one hundred fifteen genes) protein-coding transcripts have been detected based on the RNA reads insurance in mobile and exosomal samples, respectively. For non-coding genes, 360 (317 genes) and 131 (131 genes) transcripts were detected in line with the RNA reads insurance for cellular and exosomal samples, respectively. analysis of those transcripts published that they represented 90.8% of protein-coding genes and 9.2% of non-coding genes for host cellphone sample; while exosomal RNA samples represented 50.4% and 49.6% of protein-coding and forty seven.6% and 52.4% of non-coding genes in MDA-MB-231 and MDA-MB-436 cells derived exosomes, respectively (desk 1). We found that 98.three% of protein-coding and 97.7% of non-coding exosomal transcripts have been present in host cells (Fig. 7).

desk 1:

quantity of genes in cellular and exosomal RNA in response to ninety% coverage over protein-coding sequence of genes and exons of non-coding genes.

word the huge proportion of non-coding transcripts in exosomal RNA. Transcript category MDA-MB-231cellulargenes (%) MDA-MB-231exosomalgenes (%) MDA-MB-436exosomalgenes (%) Protein-coding ninety.eight 50.4 forty seven.6 Non-coding 9.2 forty nine.6 fifty two.four determine 7: Venn diagram gifts overlap among protein-coding and non-coding gene symbols in exosomes and cells. very nearly all of the genes in both exosomal RNA are the subset of mobile genes. Exosomes are enriched in mRNAs functioning in protein translation and rRNA processing

We carried out Gene Ontology (GO) enrichment analysis using the DAVID bioinformatics resource, which employs a Fisher’s actual examine with Benjamini–Hochberg correction. a complete of 377 enriched GO categories had been derived using a P-price cut-off of p < 0.05 for 3115 host MDA-MB-231 cellular genes: 286 biological system (BP) classes and 91 Molecular feature (MF) classes (table S2). In complete, 18 GO classes together with 11 BP and seven MF were derived from 115 exosomal genes from each mobile lines. figure eight shows correct 20 BP categories of the host cellular genes which encompass translation method, cell cycle, RNA processing, and many others. (Fig. 8A and desk S2B). at the same time exosomal genes printed biological tactics in translation, ribosome biogenesis, rRNA and ncRNA processing GO classes (Fig. 8B and desk S2C). due to the fact that the major fraction of exosomal samples were rRNA species, tremendously lessen variety of mRNA can be detected in exosomal samples. We hypothesized that the genes detected from exosomal samples may still be totally expressed in the cells. To look at various the hypothesis, we performed GO enrichment evaluation for 115 excellent expressed genes from MDA-MB-231 cellular pattern. The correct 10 GO phrases (Fig. 8C and desk S2D) of these good expressed genes are the same as in exosomal fraction (Fig. 8B and table S2C). We extra created box plot of 115 exosomal genes in MDA-MB-231 cellular pattern the usage of expression values (RPKM) (Fig. 9). These records certainly confirmed that exosomes are enriched in genes which are enormously expressed within the host cells.

figure eight: Gene Ontology (GO) enrichment analysis of genes detected in mobile and exosomal RNA from breast cancer cell strains. The enormous GO terms changed into defined as described in substances and methods. (A) good 20 massive GO phrases present in MDA-MB-231 cellular genes (3115 genes). (B) colossal GO terms found in exosomal genes from both mobile-strains (MDA-MB-231 and MDA-MB-436). (C) good 20 huge GO terms found in the most expressed 115 genes from MDA-MB-231 mobile genes. The asterisks (*) indicate GO phrases that existing in exosomal genes. determine 9: Expressed genes in exosomes found to be enormously expressed within the host cells. The container plot suggests expression stage of all genes in cellular samples as in comparison to that of genes that have been discovered to be express in exosomes. Wilcoxon rank sum look at various represents large difference in expression level of both sets.

Non-coding transcripts could be categorized into 13 categories (see desk 2). both exosomal and cellular samples contained small nucleolar RNA (snoRNA) as main species. The second most abundant class of non-coding transcripts in response to GENCODE annotation was “non-coding RNA” in mobile pattern and small nuclear RNA (snRNA) in exosomal samples. ordinary, the properly 5 RNA classes represented about 90% of all noncoding genes in each exosomal and cellular RNA.

table 2:

amount of non-coding gene image in mobile and exosomal RNA in accordance with ninety% coverage over exons of non-coding transcripts.

In exosomes, the desirable 5 non-coding gene types including small nucleolar RNA, small nuclear RNA, Mt_tRNA, microRNA, and non-coding RNA represents about 90% of non-coding genes in each exosome samples. Gene class MDA-MB-231cellular(gene symbols) MDA-MB-231exosomal(gene symbols) MDA-MB-436 exosomal(gene symbols) small nucleolar RNA 214 83 51 small nuclear RNA 23 11 10 Mt_tRNA 13 7 4 microRNA 34 6 2 non-coding RNA forty two 1 0 e-book RNA 20 0 1 vault RNA three 0 3 rRNA 1 1 2 RNase MRP RNA 1 1 1 RNase P RNA 1 1 1 Mt_rRNA 1 1 0 lincRNA 1 0 0 telomerase RNA 1 0 0 Validation analysis of RNA-seq statistics via qRT-PCR

in response to RNA-Seq statistics we evaluated presence and enrichment of several mRNA transcripts in exosomal RNA - RAB13, RPPH1, EEF1A1, FTH1, FTL and RPL28. qRT-PCR analysis showed presence of all chosen transcripts in exosomal samples (Fig. 10A). figure 10B demonstrates that the fold-alternate of qRT-PCR effects are in step with the fold-trade of RNA-seq facts.

determine 10: Validation of RNA-seq statistics by way of qRT-PCR. (A) Ct values for six mRNA transcripts which were detected in exosomal samples by way of RNA-seq are shown. (B) assessment of different expression values (RPKM; MDA-MB-436/RPKM; MDA-MB-231) detected by using RNA-Seq (dark-grey columns) and qRT-PCR (mild-gray columns) for six in a different way expressed genes. discussion

until lately, the changes in gene expression throughout a considerable number of organic methods had been analyzed the usage of microarray processes that focal point largely on the behavior of protein-coding transcripts. as a result of microarrays are based on hybridization, they have excessive historical past as a result of pass-hybridization, they have got a restrained dynamic latitude of detection and they count upon well-known buildings of genes. construction of RNA-Seq technology authorized complete analysis of whole transcriptomes with the one nucleotide resolution permitting quantification of most RNA molecules expressed within the mobile or tissue (Mortazavi et al., 2008). during this look at, we used the Ion Torrent platform to interrogate transcriptomes of exosomes released from two metastatic breast melanoma telephone lines. on the time of conducting our analysis this technology produced noticeably low variety of reads, yet we selected it as it provided the longest reads than any other sequencing platform. This function of the Ion Torrent expertise became standard as we dealt with RNA isolated from exosomes whose nature and composition are nevertheless no longer smartly centered. RNA-seq facts analysis is complicated with the aid of the intricacy of coping with significant datasets, reads excellent manage, alignment system etc. diverse workflows and a number of algorithms were proposed to map reads to the reference genome and to function records evaluation (Chen, Wang & Shi, 2011; Mortazavi et al., 2008). evaluation of expression tiers across diverse samples and experiments is commonly intricate and requires complex normalization methods and these are nevertheless below lively development. The condition is even more complex in case of exosomal transcriptomes that differ greatly from cellular transcriptomes.To handle this challenge, we developed during this look at customized bioinformatics workflow and tested its utility for analysis of exosomal RNA. since the Ion Torrent platform produces reads with diverse length the committed algorithm for his or her alignment to the genome called TMAP became recommended. We found out, despite the fact, that this device does not permit enough mapping of reads that contain splice-junctions or span introns. therefore, we choose option aligning device TopHat2 (with Bowtie2) which could deal with reads of various length and establish splice-junctions in keeping with commonly used splice-junctions in addition to allowed the invention of latest splice-junctions (Kim et al., 2013; Langmead & Salzberg, 2012).

We accompanied a big proportion of reads mapped to rRNA regions in exosomal samples. This become fantastic given the proven fact that intact 18S and 28S rRNA peaks have been nearly undetectable in exosomal RNA (Fig. three). This commentary counseled that the majority of exosomal rRNA is fragmented. Exosomal rRNA fragments can be mapped over whole length of rRNA (Fig. S3). Fragmented 28S and 18S rRNA have been fundamental rRNA species existing in exosomes. The reads mapped to 28S and 18S rRNA have been distributed pretty much equally in exosomal and cellular RNA samples. what is the feasible reason for era of exosomal rRNA fragments? RNases existing in cellphone way of life conditioned medium are not likely to make contributions to rRNA fragmentation considering that exosomal membranes supply insurance policy in opposition t RNase assault. certainly, medicine of the exosomal preparations with RNase A did not result in large difference between handled and handle samples in RNA size distribution (information now not shown). in the look at of Skog et al. (2008) RNase treatment of the glioblastoma exosomes ended in a very insignificant (lower than 7%) lessen in RNA suggesting that exosomal RNA is inaccessible for RNase from outdoor the vesicles. A possibility exists that the rRNA fragments are generated after secretion by way of RNases originated from the host cells and incorporated into exosome vesicles. alternatively, rRNA fragments may be generated interior cells in advance of their unlock to exosomes. one more classification of RNA, tRNA is represented in exosomes specially through its fragments (Nolte-’t Hoen et al., 2012). the most ample tRNA hits in exosomal RNA are all located at the 5′ end of mature tRNAs (Nolte-’t Hoen et al., 2012).

Regardless the biogenesis of rRNA fragments, it's advisable to perform rRNA depletion step even in the absence of seen rRNA peaks on RNA profiles. This technique would allow obtaining much larger sequencing depth for different RNA species. Our try and deplete fragmented rRNA with the common RiboMinus™Eukaryote kit failed on account of the design of the probes. since the probes size is short, many fragments of rRNA don't seem to be centered. the use of bigger number of longer probes is expected to produce a more productive manner of pulling-down fragmented rRNA. This technical aspect of working with exosomal RNA samples should be certainly considered in the future studies.

as a result of huge rRNA presence in exosomal samples we accompanied handiest 2% of mapped reads to accepted transcripts the usage of RefSeq and GENECODE gene models. in addition, with RPKM price >1 we accompanied a huge quantity of misannotation due to negative insurance of the reads over transcripts. therefore, we cautioned a different strategy, namely filtering genes in keeping with reads insurance over protein coding sequence for mRNA or exons for non-coding RNA. This manner allowed us to obtain greater than 90% coverage for protein-coding and non-coding areas which we regarded as enormously reliable for useful classification. This strategy turned into useful to exhibit particularly expressed genes in exosomes which could be potentially used as noninvasive breast cancer markers.

We file that exosomes are carrying mRNAs which are enormously expressed within the host breast cancer cells (Fig. 9). as a result exosomal transcriptomes are consultant of their cells of origin and may provide a platform for detection of tumor specific markers. GO analysis revealed that main biological and molecular capabilities of each mobile and exosomal transcripts are enriched in proteins concerned in ribosome biogenesis, translational elongation, ribosomal subunit meeting and rRNA processing. What could be the value of these capabilities in exosomal transcriptome? Exosome-linked mRNAs have been proven to be translated into proteins through recipient cells (Ratajczak et al., 2006; Valadi et al., 2007). We hypothesize that upon arrival to the recipient cells exosomal mRNAs are translated into proteins helping ribosomal functions to ensure effective translation of alternative exosomal mRNAs inside a cellular compartment the place exosome content material is launched. Valadi et al. (2007) also described presence of mRNAs for many ribosomal proteins in exosomes secreted by way of mouse mast cellphone line. interestingly, Graner et al. (2009) validated the presence of elongation and translation factors in exosomes derived from brain tumor.

In conclusion, right here we demonstrated for the primary time that fragmented rRNA is a huge species of exosomal RNA. Proposed here customized bioinformatics workflow allowed us to reliably detect different, non-ribosomal RNAs beneath conditions of limited study numbers. Classification and quantification of the RNA-Seq information published that exosomal transcripts are consultant of their cells of foundation and as a consequence could form groundwork for detection of tumor certain markers. This guidance can even be used for getting insights in the molecular underpinnings of organic consequences produced by way of these microvesicles. discovering that exosomes undergo mRNAs encoding the critical accessories to construct-on-website ribosomes provides a advantageous insight into organic feature of those vesicles.

Supplemental information comparison of mapping pleasant between the alignment tools TopHat2.0.6 and TMAP 0.three.7

FTL gene (chromosome place chr19:forty nine,468,467-forty nine,470,296) is chosen for instance of alignment evaluation. TMAP alignment resulted in poor reads mapping and absence of junctions over exon-exon area. as the identical time, TopHat identifies the exon-exon splice junctions and connects the exons through a linker.

Distribution of all mapped and unmapped RNA-seq reads amongst genomic compartments

rRNA defined as 5S, 5.8S, 18S, and 28S rRNA sequences. Reads which overlapped with annotated gene fashions (RefSeq and/or GENCODE) are termed as “usual genes”. Reads that placed outdoor of annotated gene models are termed as “unkown”.

Fragments of rRNA in exosomes signify full-size of rRNA sequence

(A) RNA examine density plot represents RNA fragments which absolutely covers of 5S, 5.8S, 18S, and 28S rRNA sequences from exosomal RNA. (B) 18S and 28S rRNA were main fractions of rRNA species.

analysis of rRNA depletion from MDA-MB-231 cellular and exosomal RNA using RiboMinus™ Eukaryote package for RNASeq

RNA changed into detected with PicoChip using Bioanalyzer. The depletion procedure has been performed in line with the company’s protocol. manage samples (pink) had been handled exactly as experimental samples (blue) except they didn't include RiboMinus™ Probe.

Venn diagram of genes generated through RPKM and read coverage processes

The detection standards is that gene has greater than 1 RPKM in as a minimum one pattern, whereas a further strategy is that gene has more than 90% coverage over protein-coding or non-coding sequence.

Two strategies of gene transcripts selection the usage of RPKM or read coverage

(A) The Partek genomic suite output showing transcripts with RPKM >1 in as a minimum one pattern. The examine counts from the transcript isoforms were estimated the usage of EM algorithm from Partek genomic suite on RefGene and/or GENCODE v14 gene models. (B) Estimation of read coverage (in percent) and skim count number of transcript. The transcripts with >ninety% coverage for protein-coding sequence and exonic sequence (in case of non-coding transcript) of transcript isoforms on RefGene and/or GENCODE v14 gene fashions are shown.

Gene Ontology (GO) enrichment evaluation the use of the DAVID bioinformatics resource of Genes present in cellular and exosomal samples

(A) Gene lists for GO enrichment evaluation. (B) GO enrichment of cellular genes. (C) GO enrichment of exosomal genes. (D) GO enrichment of desirable 115 expressed mobile genes.

list of primers used for qRT-PCR

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Sogeti Launches Free iOS and Android Software Testing Life Cycle App: TMap® Life Cycle | killexams.com real questions and Pass4sure dumps

Free app places more than 25 years of software Testing and Quality Assurance expertise into the hands of Sogeti customers.

DAYTON, Ohio, Jan. 26, 2012 -- Sogeti, a leading provider of professional technology services, specializing in Application Management, Infrastructure Management, High-Tech Engineering and Testing, has launched a free app for iOS and Android: TMap® Life Cycle.

Sogeti's TMap® is the world-leading methodology for structured risk-based software testing. An essential element of TMap® is the lifecycle, covering the key steps in testing strategy and execution, in the end-to-end process of achieving robust business-critical applications.

Sogeti's TMap® Life Cycle app provides this mobile-based framework, guiding software testers through the software testing lifecycle, from planning and infrastructure to control and all stages of a critical path. Using TMap®'s structured framework for end-to-end test process, this app helps testing professionals track the progress of their projects. It also enables the earlier identification of defects resulting in the consistent reduction of timelines by at least 30%, lowering overall costs.

The new app is available now in the Android Marketplace and the App Store. It is compatible with Android mobile devices as well as the Apple iPhone, iPod Touch and iPads. It provides users with free downloads to support the TMap® process, including checklists and templates. The app also features videos explaining test design techniques, product risk analysis and methods to determine test strategy, as well as links to software testing resources such as eBooks and whitepapers.

"TMap® is a sophisticated, proven and trusted software test management methodology, relied upon by tens of thousands of software testers across the globe for structured risk-based testing," said Dan Hannigan, National Vice President of the Managed Testing Practice for Sogeti USA. "Releasing this free app is just another extension of our primary goal, which is making structured software testing and quality assurance methods easily available to professional testers."

To download this free app, go to the Android Marketplace or App Store, and search for "TMap® Life Cycle."

About TMap®

TMap® (Test Management Approach) is Sogeti's business-driven, risk-based methodology for structured software testing, relevant to organizations of all sizes and vertical markets. An adaptive method, suitable for all test situations in development environments, including new development, maintenance, waterfall/iterative/agile development, customized or packaged software, TMap® addresses the key issues of quality, time and cost across the development lifecycle. The app describes different phases of the TMap® lifecycle.TMap®, TMap NEXT®, TPI® and TPI NEXT® are registered trademarks of Sogeti.

About Sogeti USA

Sogeti USA is a premier provider of information technology services to businesses and public sector organizations. Operating in 23 U.S. locations, Sogeti's business model is built on providing customers with local accountability and vast delivery expertise. Sogeti is a leader in helping clients develop, implement and manage practical IT solutions to help run their business better. With over 40 years of experience, Sogeti offers a comprehensive portfolio of services including Advisory Services, Application Development & Integration, Business Information Management, Engineering Services, Infrastructure Services and Testing Services. To learn more, visit: www.us.sogeti.com

About Sogeti

Sogeti is a leading provider of professional technology services, specializing in Application Management, Infrastructure Management, High-Tech Engineering and Testing. Working closely with its clients, Sogeti enables them to leverage technological innovation and achieve maximum results. Sogeti brings together more than 20,000 professionals in 15 countries and is present in over 100 locations in Europe, the US and India. Sogeti is a wholly-owned subsidiary of Cap Gemini S.A., listed on the Paris Stock Exchange. For more information please visit www.sogeti.com.

SOURCE Sogeti USA

CONTACT: CONTACT: Nicole Scrivner, Sogeti USA Marketing Manager, +1-937-424-9487, nicole.scrivner@us.sogeti.com

Web Site: www.us.sogeti.com

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TMap NEXT® Testing Clouds: First Cloud e-book to be launched by Sogeti | killexams.com real questions and Pass4sure dumps

ti, kesä 21, 2011 08:30 CET

A new publication in Sogeti’s TMap® series; TMap NEXT® Testing Clouds is launched.

21st June, Paris/Helsinki: Sogeti, a leading provider of professional technology services, has launched a new publication in its TMap® series, at IBM Innovate 2011 in Orlando USA. TMap NEXT® Testing Clouds is a comprehensive overview of how testing clouds can bring value for the early adopter. The author, Ewald Roodenrijs, describes the cloud business model for testing, the evolution from Information Technology (IT) to the concept of Business Technology (BT), and the steps that need to be taken in implementing cloud projects.

Cloud computing is currently regarded as one of the top three IT technologies for the forward thinking CIO. Although the cloud is still at an early stage of development, we are starting to see strong growth in cloud-based computing, which is outstripping even the most optimistic predictions. It is increasingly clear that the cloud model will supplement, if not entirely replace, mainframe and client/server installations in the years to come. This is based on a compelling value proposition: speed to market, agility to bring forward or retire service, and the chance to move expenditure from Capital Expenditure (CapEx) to Operational Expenditure (OpEx).

As cloud service adoption becomes ever more wide-ranging, a new global infrastructure is being created that creates significant new opportunities for application quality assurance and testing. This new book TMap NEXT® Testing Clouds describes in particular two aspects of the cloud: the business model and the cloud platform, both of which must be tested. This consists of several aspects; testing the infrastructure, cloud-enabled applications, and the ability to have instant deployable test infrastructure.

Ewald Roodenrijs comments: “This all has an impact on the way we do testing in the future. Testing applications on the cloud is the same as testing applications on a traditional infrastructure. Only what is tested is different”.

TMap NEXT® Testing Clouds is a development from its TMap® predecessors. It outlines how structured testing, using TMap®, can be leveraged within a cloud environment, it is not a step-by-step handbook, but reviews in detail the framework and importance of testing on the cloud, testing cloud strategy (in, on or within the cloud) and the risks involved for companies considering migrating applications onto the cloud, such as security, data integrity, privacy issues, data recovery and performance.

It also can also be read as a companion or follow-on publication to Seize the Cloud – A Manager’s Guide to Success with Cloud Computing[1], exploring in more detail the testing aspects of the cloud.

TMap NEXT® Testing Clouds is available as an e-book in both epub and pdf format and can be downloaded for free from the TMap testing website or the Sogeti.com website. Sogeti is increasingly producing e-books as an efficient means of timely distribution as well as enabling the company to reduce its carbon footprint. A printed version is available by using Printing on Demand (POD) via online bookstores or www.tmapbooks.com.

According to Nijs Blokland, Global Testing Community Lead and Head of Sogeti NL R&D; “Most of us are aware of the benefits of cloud computing, but have less understanding of the practicalities of successfully and securely implementing cloud applications and using the cloud as a cost-effective source of infrastructure and test tools. This book TMap NEXT® Testing Clouds provides early insight and guidance on these issues, and is further evidence of Sogeti’s innovative thinking in bringing the advantages of both cloud and structured testing together”.

Since launching their Software Testing as a Service (STaaS) globally in 2008, Sogeti has been pioneering the use of cloud-based solutions. As a direct development of this, Sogeti Netherlands will be launching their first Testing Cloud Solution, a comprehensive portal which will provide a full menu of services, direct access to fast automated pricing and the ability to upload testing artifacts and assets for cloud-based execution.

For the Dutch company’s clients, this is much more than a front-end website. This portal will provide full clarity of the service, together with a fixed turnaround time and price for each of the 22 services, including the creation of a Master Test Plan, Model Based Testing, Tool selection, Performance testing, Test Automation, Web Accessibility and Security Testing.

Notes to Editors

About the Author

Ewald Roodenrijs, author of TMap NEXT Testing Clouds, is the Global Lead of the Cloud Testing services within the Sogeti Group and a member of the testing R&D team of Sogeti Netherlands. He is also the co-author of the book TMap NEXT® – BDTM, contributor to Seize the Cloud, a regular contributor to national and international technical/expert publications, a speaker at numerous international conferences, including most recently at IBM Innovate June 2011, a keen blogger on the Cloud and testing and enthusiastic trainer. He is currently working together with IBM Rational to further develop this cloud offering and recently received the Capgemini-Sogeti Testing Services Innovation Award (Individual) 2011 for his work on Cloud Testing.

About the Book

The book can be ordered at major online books store, including Amazon, or directly from the publisher via www.tmapbooks.com or via www.sogeti.com. A printed copy can be ordered at www.tmapbooks.com.

Introduction, Chapter 1: An introduction to TMap NEXT® Testing Clouds and Chapter 2 Framework and importance of testing; even in the cloud; of interest to all readers.

Chapter 3: The Business in charge of IT; the Cloud and Chapter 4 Whatever as a Service; cloud-enabled Software Testing as a Service of interest to test managers, program managers and business managers and IT department managers.

Chapter 5 ‘Testing Cloud Strategy; a move to 3D’’, Chapter 6 ‘Testing the Cloud; in, on or with...’ and Chapter 7 ‘Cloud risks; worth testing for…’ are mainly interesting for the target groups of test managers, test coordinators, infrastructure testers and testers.

About the Contributors

The following contributors helped in the creation of the content of this book from Sogeti, Capgemini and also IBM Rational: Andréas Prins, Mark Buenen, Nick Lloyd, Ramanathan Iyer, Alfonso López de Arenosa, Rob Baarda, Richard Ammerlaan, Erik Smit, Flavien Bouche, Kanchan Apt, Michiel Boreel, Pierre Bedard, Michiel Rigterink, Karl Snider, John Bloedjes, Dan Hannigan, Dirkjan Kaper, plus additional support from Leo van der Aalst, Nicolas Claudon and Clare Argent.

For more information please contact:

Ewald Roodenrijs, Global Lead for Cloud Testing, SogetiTel: +31 (0)6 52 32 77 73Email: ewald.roodenrijs@sogeti.nl

Therese Sinter, Group Corporate Communications Director, Sogeti Tel: +46 70-361 46 21 Email: therese.sinter@sogeti.se

About Sogeti

Sogeti is a leading provider of professional technology services, specializing in Application Management, Infrastructure Management, High‐Tech Engineering and Testing. Working closely with its clients, Sogeti enables them to leverage technological innovation and achieve maximum results. Sogeti brings together more than 20,000 professionals in 15 countries and is present in over 100 locations in Europe, the US and India. Sogeti is a wholly‐owned subsidiary of Cap Gemini S.A., listed on the Paris Stock Exchange.

Together, Sogeti and Capgemini have developed innovative, business-driven quality assurance (QA) and testing services, combining best-in-breed testing methodologies (TMap® and TPI®) and the global delivery model, Rightshore®, to help organizations achieve their testing and QA goals. Sogeti and Capgemini have created one of the largest dedicated testing practices in the world, with over 8,200 test professionals and a further 12,500 application specialists, notably through a common center of excellence with testing specialists developed in India.

In Finland Sogeti is focused solely on providing testing services on the local market.

For more information please visit www.sogeti.com and www.sogeti.fi.

[1] Written by Erik van Ommeren, Martin van den Berg, Sogeti and published by Sogeti March 2010. Seize the Cloud is a guide through the business and enterprise architecture aspects of Cloud Computing, including 11 cases in which leading organizations share insights gained from their experience with Cloud.

Yritysesittely Sogeti Finland OySogeti on erikoistunut korkealuokkaisten IT-palvelujen toimittamiseen Suomen markkinoille. Keskitämme Suomessa palvelumme ohjelmistojen testaukseen ja laadunvarmistukseen. Sogeti-konsernin pääkonttori on Pariisissa Ranskassa. Konsernilla on palveluksessaan yhteensä noin 20 000 konsulttia 15 maassa: Belgia, Espanja, Hollanti, Intia, Irlanti, Iso-Britannia, Luxemburg, Norja, Ranska, Saksa, Suomi, Sveitsi, Ruotsi, Tanska ja Yhdysvallat. Pohjoismaissa konsultteja on Suomessa, Ruotsissa, Tanskassa ja Norjassa yhteensä yli 1 100. Sogeti-konsernin yhtiöt ovat Pariisin pörssissä listatun Cap Gemini S.A.:n kokonaan omistamia tytäryhtiöitä.

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Characterization of RNA in exosomes secreted by human breast cancer cell lines using next-generation sequencing | killexams.com real questions and Pass4sure dumps

Introduction

Exosomes are nanosized membrane vesicles secreted by multiple cell types (Raposo & Stoorvogel, 2013). The term “exosomes” was introduced in 1987 by Johnstone et al. (1987) to describe the “trash” vesicles released by differentiating reticulocytes to dispose unwanted membrane proteins which are known to diminish during maturation of reticulocytes to erythrocytes. Later exosomes have been implicated in more complex functions. Raposo et al. (1996) have demonstrated that exosomes derived from B lymphocytes activate T lymphocytes, suggesting a role for exosomes in antigen presentation in vivo. In early studies, secreted microvesicles were named based on their cellular origins, e.g., archaeosomes, argosomes, dexosomes, epididymosomes, prostasomes, oncosomes, etc. (Raposo & Stoorvogel, 2013). More lately it became apparent that vesicles released by the same cell type are heterogeneous and can be classified into at least three classes based on their mode of biogenesis: (1) exosomes (30–130 nm in diameter), which originate from multivesicular endosome (MVE); (2) microvesicles (100–1000 nm in diameter), which are shed from the plasma membrane; (3) apoptotic bodies (1–4 µm in diameter), which are released from fragmented apoptotic cells during late stages of cell death (Raposo & Stoorvogel, 2013). Various purification procedures including sequential centrifugation protocols have been proposed to separate these vesicles for further analysis. Biochemical and proteomic analyses showed that exosomes contain specific protein set reflecting their intracellular site of formation. Exosomes from different cell types contain endosome-associated proteins, e.g., tetraspanins (CD9, CD63, CD81, CD82), annexins, Rab GTPases and flotillin. Exosomes are also enriched in proteins involved in MVE formation (Tsg101 and Alix), chaperones (Hsc73 and Hsc90) and cytoskeletal proteins (Raimondo et al., 2011). Furthermore, exosomes contain proteins that are specific to the cells from which they are derived (Choi et al., 2012; Sandvig & Llorente, 2012).

The research in the exosome field has exploded rapidly after the discovery that these microvesicles transport a large number of mRNA and miRNA (Valadi et al., 2007) and that exosomal mRNAs could be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007) and exosomal miRNAs are able to modulate gene expression in recipient cells (Mittelbrunn et al., 2011). Interestingly, certain mRNAs and miRNAs were identified as highly enriched in exosomes compared to that of the host cells indicating the existence of selective sorting mechanism controlling incorporation of RNA into exosomes (Skog et al., 2008; Valadi et al., 2007). Previously, we demonstrated that exosomal RNAs share specific sequence motifs that may potentially function as cis-acting elements for targeting to exosomes (Batagov, Kuznetsov & Kurochkin, 2011).

Given the fact that exosomes carry complex biological information consisting of proteins, lipids and RNAs, it is not surprising to find that they have been implicated in a variety of physiological and pathological conditions. Skokos et al. (2001) reported, for example, that mast cells communicate with other cells of the immune system via exosomes promoting mitogenic activity in B and T lymphocytes. A number of studies have demonstrated the role of exosomes in the development of the nervous system, synaptic activity, neuronal regeneration, neuron-glia communication and protection against injury (Lai & Breakefield, 2012). Furthermore, exosomes can be involved in the pathogenesis of cancer and degenerative diseases. The fact that tumor cells release a large amount of exosomes was initially demonstrated in ovarian cancer patients (Taylor, Homesley & Doellgast, 1983). Exosomes were shown to be secreted by various tumor cells including those derived from breast (King, Michael & Gleadle, 2012), colorectum (Silva et al., 2012), brain (Graner et al., 2009), ovarian (Escrevente et al., 2011), prostate (Mitchell et al., 2009; Nilsson et al., 2009), lung (Rabinowits et al., 2009), and bladder (Welton et al., 2010) cancer. A significantly higher amount of exosomes was found in plasma from lung cancer patients compared to that of control individuals (Rabinowits et al., 2009). In colorectal cancer patients, the amount of plasma circulating exosomes was constitutively higher than in normal healthy individuals showing the direct correlation between exosomes quantity and malignancy (Silva et al., 2012).

Manipulating tumor cells to decrease the release of exosomes followed by their injection into immunocompetent mice led to a significantly slower tumor growth compared to that of unperturbed cells (Bobrie et al., 2012). It has been shown that exosomes which are released from tumor cells are able to transfer a variety of molecules, including cancer-specific, to other cells (Al-Nedawi, Meehan & Rak, 2009; Muralidharan-Chari et al., 2009) to manipulate their environment, making it more favorable for tumor growth and invasion. Glioblastoma-derived exosomes were found to be enriched in angiogenic proteins that allowed them to stimulate angiogenesis in endothelial cells (Skog et al., 2008). Melanoma exosomes were shown to be instrumental in melanoma cell dissemination via alterations in the angiogenic microenvironment. Hood et al. demonstrated that metastatic factors responsible for the recruitment of melanoma cells to sentinel lymph nodes are upregulated by melanoma exosomes themselves (Muralidharan-Chari et al., 2009). Exosomes shed by human MDA-MB-231 breast carcinoma cells and U87 glioma cells were capable of conferring the transformed characteristics of cancer cells onto normal fibroblasts and epithelial cells in part due to transferring tissue transglutaminase and fibronectin (Hood, San & Wickline, 2011).

Because of their small size exosomes have an ability to penetrate intercellular contacts to reach distant parts of the body with the help of the blood stream and other body fluids. Exosomes have been purified from human plasma, serum, bronchoalveolar fluid, urine, tumoral effusions, epididymal fluid, amniotic fluid and breast milk (Raposo & Stoorvogel, 2013). Since exosomes possess characteristic protein and RNA signatures of their host cells, analysis of exosomes in various body fluids can be potentially utilized for non-invasive diagnostics of cancer and other disorders. For example, aggressive human gliomas often express a truncated and oncogenic form of the epidermal growth factor receptor, known as EGFRvIII. The tumour-specific EGFRvIII was detected in serum microvesicles from glioblastoma patients (Skog et al., 2008). High stability of exosomal RNA (Skog et al., 2008; Valadi et al., 2007) and ease of RNA detection by highly sensitive PCR makes detection of exosomal RNA an attractive approach for the discovery of biomarkers. Indeed, mRNA variants and miRNAs characteristic of gliomas could be detected in serum microvesicles of glioblastoma patients (Skog et al., 2008). Expression profiles of serum microvesicle mRNA by microarrays correctly separated individuals with glioblastoma from normal controls (Noerholm et al., 2012). Of all RNA species, secreted miRNAs were most frequently utilized toward discovery of body fluid-based biomarkers perhaps because miRNA expression profiles are more informative than mRNA expression profiles in a number of diseases (Grady & Tewari, 2010). In one study, analysis of plasma- and serum-derived microvesicles revealed 12 miRNAs differently expressed in prostate cancer patients compared to that of healthy controls and 11 miRNAs upregulated in patients with metastases compared to that of patients without metastases (Bryant et al., 2012). Interestingly, exosomes released by breast cancer cells can be separated into different classes depending on their miRNAs content (Palma et al., 2012). Cells undergoing malignant transformation produced exosomes containing selective miRNAs, whose release is increased by malignant transformation, in contrast to cells that are not affected by malignancy, whose exosomes are packed with neutral miRNAs (Palma et al., 2012). The changes in exosomal miRNA cargo could provide a signature of the presence of malignant cells in the body.

The majority of reported to date exosomal miRNA and mRNA profiles have been generated using microarray approaches that suffer from several limitations. Microarrays are biased for investigation of already discovered transcripts. In addition, there is potential for cross-hybridization of RNAs that are highly related in sequence. Recently developed next generation RNA sequencing technology (RNA-Seq) allows detection of all RNA subtypes as well as of unannotated transcripts and has a high sensitivity toward identification of low-abundance RNAs. In case of exosomes, this approach was applied only for the analysis of small (20–70 nt) RNAs (Bellingham, Coleman & Hill, 2012; Nolte-’t Hoen et al., 2012).

In this study, we utilized the RNA-Seq approach to characterize the transcriptomes of exosomes secreted by two metastatic human breast cancer cell lines. We describe optimized computational workflow to analyze data generated by the Ion Torrent semiconductor chip-based technology. We have identified and profiled RNA species present in exosomes and host cells and discuss the utility of exosomal RNA as potential breast cancer-specific biomarkers.

Materials and Methods Cell culture

Human breast cancer cell lines MDA-MB-436 (ATCC® HTB-130™) and MDA-MB-231 (ATCC® HTB-26™) were maintained at 37°C in 5% CO2 and cultured in DMEM/F12 supplemented with 10% FBS. 48 h prior to exosome collection, cells were washed 3 times with PBS and the medium was changed to serum-free CCM5 medium (Thermo Scientific).

Exosome extraction

Exosomes were isolated and purified from the media of MDA-MB-436 and MDA-MB-231 cell cultures using sequential centrifugation protocol. Briefly, media was collected and cellular debris was removed by centrifugation at 3,000×g for 10 min. The supernatant was centrifuged at 17,000×g for 30 min at 4°C. The supernatant was collected and centrifuged at 100,000×g for 2 h to pellet exosomes. Exosomes pellets were then washed in filtered PBS and re-centrifuged at 100,000×g, the supernatant was removed and the final exosomal pellet was re-suspended in 100 µl PBS.

Transmission electron microscopy

A 50 µl aliquot of exosomes was absorbed onto formvar carbon coated nickel grid for 1 h. The grid was positioned with the coating side facing the drop containing exosomes. Then the grids were washed by sequentially positioning them on top of the droplets of 0.1 M sodium cacodylate, pH 7.6 and then fixed in 2% paraformaldehyde and 2.5% glutaraldehyde in 0.1 M sodium cacodylate, pH 7.6 for 10 min. Then grids were washed again with 0.1 M sodium cacodylate, pH 7.6 and contrasted with 2% uranyl acetate in 0.1 M sodium cacodylate, pH 7.6 for 15 min. After washing, the grids were incubated on top of the drop of 0.13% methyl cellulose and negatively stained with 0.4% uranyl acetate for 10 min, air dried for 5 min and examined with a JEM-2200FS transmission electron microscope operated at 100 kV.

Nanoparticle tracking analysis

Supernatants containing vesicles were analyzed using a Nano-Sight LM10 instrument equipped with a 405 nm laser (NanoSight, Amesbury, UK) at 25°C. Particle movement was tracked by NTA software (version 2.2, NanoSight) with low refractive index corresponding to cell-derived vesicles. Each track was then analyzed to get the mean, mode, and median vesicle size together with the vesicle concentration (in millions) for each size.

RNA isolation and analysis

Total RNA from exosomes (MDA-MB-436 and 231) and cultured cells (MDA-MB-231) were isolated using the TRIzol reagent (Invitrogen). RNA quality and concentration were assessed with the Agilent 2100 Bioanalyzer (Thermo Scientific). Cellular RNA was analyzed using RNA 6000 Nano Kit (Agilent) and exosomal RNA was analyzed using RNA 6000 Pico kit (Agilent).

RNA-seq with ion torrent personalized genome machine (PGM)

Two hundreds ng of exosomal RNA and 3 µg of total cell RNA was used as the starting input for RNA-Seq library preparation. Sequencing was performed by AITbiotech company (Singapore). Briefly, total cell RNA was treated with RiboMinus Eukaryote kit (Life Technologies) to remove rRNA. Then, exosomal and rRNA-depleted cellular RNAs were fragmented using RNaseIII. Whole transcriptome library was constructed using the Ion Total-RNA Seq Kit v2 (Life Technologies). Bar-coded libraries were quantified with qRT–PCR. Each library template was clonally amplified on Ion Sphere Particles (Life Technologies) using Ion One Touch 200 Template Kit v2 (Life Technologies). Preparations containing bar-coded libraries were loaded into 318 Chips and sequenced on the PGM (Life Technologies).

cDNA synthesis and quantitative real-time PCR (RT-PCR)

Two-step RT-PCR was performed using the QuantiTect Reverse Transcription Kit (QIAGEN GmbH, Hilden, Germany) according to manufacturer’s protocol. RT-PCR was performed in a Rotor-Gene (Qiagen) using a SYBR Green PCR Master Mix. Primer sequences are provided in Table S3. All reactions with template and without template (negative controls) were run in duplicate and averaged. GAPDH was used as internal control for mRNA. Ct value was detected for each gene meaning the cycle number at which the amount of amplified gene of interest reaches a fixed threshold. Relative quantification (fold change) was determined for the host cells and exosomal genes expression and normalized to an endogenous control GAPDH relative to a calibrator as 2−ΔΔCt (where ΔC = (Ct of gene of interest) – (Ct of endogenous control gene (GAPDH) and ΔΔCt = (ΔCt of samples for gene of interest) – (ΔCt of calibrator for the gene of interest). Melting curves of each amplified products were analyzed to ensure uniform amplification of the PCR products.

Bioinformatics analysis Raw reads filtering

Raw reads generated by sequencing were subjected to several quality checks. The low quality reads were removed by read trimming and read filtering. Read trimming included removal of adapter sequences, removal of the 3′ ends with low quality scores and trimming based on High-Residual Ionogram Values. Filtering of entire reads included removal of short reads, adapter dimers, reads lacking sequencing key, reads with off-scale signal and polyclonal reads. Subsequent analysis was performed with high quality reads which passed through the described above filtering steps.

Reads mapping

Bowtie 2 version 2.1.0 was used to align all high-quality reads to rRNA sequences including 28S (NR_003287.2), 18S (NR_003286.2), 5S (NR_023379.1), and 5.8S (NR_003285.2) rRNA. Reads mapped to rRNA sequences were filtered out while the rest of the reads were mapped to the human genome. The high-quality reads were mapped to hg19 build of the human genome from University of California Santa Cruz (UCSC) genome browser database (Meyer et al., 2013) using TopHat version 2.0.6 with the aligner Bowtie 2.0.5 (Kim et al., 2013; Langmead & Salzberg, 2012) with their default parameters in end-to-end mode (-b2-sensitive) and defining splice-junctions based on known splice-junctions (-G). To classify the reads into known and unknown genes, the BAM file generated by Tophat2 was intersected to known gene (RefGene and GENCODE built V14 from UCSC database) using BEDtools (Quinlan & Hall, 2010) and was used to count the number of reads by SAMtools (Li et al., 2009).

Post-processing of the aligned reads

The mapped reads were further manipulated by removing the reads that mapped to multiple locations. In particular, the short aligned reads with the length of <20 nucleotides were eliminated to avoid the alignment errors such as mapping to multiple genomic locations. Further filtering included the removal of the low quality reads which fall below the mapping quality score of 10 (-q 10) using SAMtools. For the coverage search, the BAM file generated by Tophat2 was converted to BED format with option (-split) using BEDtools. The BED file was converted again to BAM format using BEDtools. We then developed python script (using pysam as part of the scripts) to calculate the number of reads and read coverage in exons and protein-coding sequence (CDS) regions consecutively.

RNA abundance calculation

RNA abundance was estimated with the help of Partek Genomics Suite software (Partek Inc., St. Lous, MO) using Reference Sequence Gene (RefSeq Gene) and GENCODE annotation built version 14 of those not overlapped with RefSeq Gene from UCSC genome browser. The Expectation-Maximization (E/M) Algorithm (Xing et al., 2006) was used to estimate the most likely relative expression levels of each gene isoform. Partek’s algorithm was used to quantify the gene isoform expression level as reads per kilo base per million mapped reads (RPKM).

Functional analysis of genes

Database for Annotation, Visualization, and Integrated Discovery (DAVID) (version 6.7) was used to identify gene functional annotation terms that are significantly enriched in particular gene lists with the whole Human genes as the background (Huang, Sherman & Lempicki, 2009). A list of gene symbols was generated for each dataset and was used as input into DAVID. DAVID calculates a modified Fishers Exact p-value to demonstrate Gene ontology (GO) and KEGG molecular pathway enrichment, where p-values less than 0.05 after Benjamini multiple test correction are considered to be strongly enriched in the annotation category. We also used a count threshold of 5 and the default value of 0.1 for the EASE (enrichment) score settings. We used more specific GO term categories provided by DAVID, called GO FAT, to minimize the redundancy of general GO terms in the analysis to increase the specificity of the terms.

Results Characterization of exosomes released by breast cancer cells

Exosomes were isolated from two breast cancer cell lines, MDA-MB-436 and MDA-MB-231 using classical ultracentrifugation protocol. The size distribution and amount of exosomes were analyzed using NanoSight LM10 nanoparticle tracking analysis (NTA). NTA showed that MDA-MB-231 cells released 4 × 106 vesicles per cm2 of growth area per 48 h that were predominantly 115 nm in size. MDA-MB-436 cells released 1.04 × 107 vesicles per cm2 of growth area per 48 h that were predominantly 91 nm in size (Fig. 1). The size of exosomes released from both cell lines ranged from ∼70 nm to ∼300 nm. An examination of the purified vesicles using transmission electron microscopy revealed that they had the size (∼50–100 nm) and morphology (Fig. 2) typical of that of exosomes.

Figure 1: Analysis of exosomes produced by breast cancer cell lines, MDA-MB-436 and MDA-MB-231, with Nanosight LM10-HS instrument. Characterization of exosomal RNA

RNA was isolated from exosomes released by both breast cancer cell lines. Total RNA was also extracted from MDA-MB-231 cell line as a control host cell line that produced exosomes. Bioanalyzer data revealed that exosomes contain a broad range of RNA sizes (30–500 nt) and have very small amount of intact rRNA (5.2% in MDA-MB-231 exosomes and 5.6% in MDA-MB-436 exosomes) (Fig. 3) consistent with previous reports on exosomal RNA (Rabinowits et al., 2009; Valadi et al., 2007).

Figure 2: TEM image of the exosomes produces by MDA-MB-436 cell line. Electron microscopy allowed visualizing membrane-bound nanovesicles sized ∼100 nm. White arrowheads pointing to the exosomes. Scale bar = 100 nm. Figure 3: Analysis of RNA from cells and exosomes by Bioanalyzer. Exosomal and total cell RNA was analyzed with PicoChip and NanoChip, respectively. Exosomal RNA deep sequencing using Ion Torrent technology

We utilized the Ion Torrent sequencing technology to profile exosomal RNA produced by MDA-MB-436 and MDA-MB-231 cell lines, as well as RNA obtained from host cell line MDA-MB-231. Based on RNA quality assessment using Bioanalyzer profiling (Fig. 3) we performed rRNA depletion for cellular RNA but not for exosomal RNA.

Identification of appropriate alignment tool for efficient capture of splice-junction reads produced by the Ion Torrent technology

RNA-Seq computational analysis workflow is presented in Fig. 4. The single-end RNA reads generated from sequencing of the exosomal and host cell libraries were trimmed to remove adapter sequences and then filtered. After filtering out low-quality reads, the high-quality reads had a varying length ranging from 6 bp to >300 bp. These preceded high-quality reads were considered for further analysis. The total amount of reads for RNA from MDA-MB-231 cells was about 4.8 M. RNA-Seq resulted in ∼3.5 M and ∼3.2 M reads for RNA from MDA-MB-436 and MDA-MB-231 cells derived exosomes, respectively. At first, the reads were aligned to rRNA sequences including 28S, 18S, 5S, and 5.8S rRNA (see Methods). 24% of the reads in cellular and more than 80% of the reads of both exosome samples were mapped to rRNA sequences (Fig. S2). The rest of the reads were mapped to the human genome (UCSC; hg19 built) with the help of TMAP aligner program in accordance with the Ion Torrent recommended pipeline (Technologies) (data not shown). However, the use of TMAP resulted in misalignment of the splice-junction reads or reads containing multiple exons to the genome (Fig. S1). Therefore, we used alternative aligner Tophat 2.0.6 along with the Bowtie 2.0.5 for the read mapping.The mapped results are shown in Fig. S2. In total, this alignment produced more than 4.3 M (∼90%) reads of MDA-MB-231 cellular RNA that could be mapped to rRNA sequences and the human genome. For the MDA-MB-231 and MDA-MB-436 cell-derived exosomes, these alignments resulted in ∼2.8 M (∼90%) and ∼3.2 M (∼91%) of mapped reads, respectively (Fig. S2). More than 80% of mapped reads from exosomal samples were found to be mapped to rRNA sequences. We further investigated the reads of both exosomal RNA samples, which mapped to rRNA sequences. These reads were counted and plotted as read density over each rRNA sequence (shown in Fig. S3A). The mapped reads covered entire length of all rRNA sequences. The major fractions of mapped reads were 28S and 18S rRNA (Fig. S3B).

Figure 4: Flowchart of RNA-seq data analysis. The raw reads are exposed to pre-alignment quality checks including the removal of adaptor sequences and low quality reads. The high quality reads are then mapped to rRNA sequences using Bowtie2 version 2.1.0. The non-mapped rRNA reads were mapped to human genome hg19 build of the human genome using TopHat version 2.0.6 with the aligner Bowtie 2.0.5. After mapping, low mapping quality reads less than 10; short reads with length less than 20 base pairs and multi- reads were removed. Estimation of read counts and read coverage on mapped reads where over 90% of exon (non-coding) and CDs (for coding) in transcript isoforms of RefGene and/ or GENCODE v14 gene models. The EM algorithm along with GENCODE v14 annotations was used to estimate the read count and reads per kilo base per million mapped reads (RPKM) on mapped transcripts.

Reads mapped to multiple locations in the genome have been eliminated to obtain uniquely mapped reads. Subsequent downstream analysis was performed with the high quality reads which had the read length greater than 20 bps and mapping quality score of above 10 (see Methods). As a result, about 81, 76, and 70% out of all mapped reads were considered as high quality mapped reads for MDA-MB-231 and MDA-MB-436 exosomal RNA, and MDA-MB- 231 cellular RNA, respectively.

Approximately 97% (∼2 M reads) of reads from both exosomal RNA samples were mapped to rRNA sequences (Fig. 5) and ∼2% of the reads were mapped to known genes (RefSeq and/or GENCODE gene models). At the same time, for the MDA-MB-231 cellular RNA, for which rRNA depletion step was performed, the majority of the reads (∼58%) was mapped to known genes (Fig. 5). To increase the number of reads for other RNAs we attempted to deplete rRNA with the RiboMinus™Eukaryote Kit recommended by Ion Total RNA-Seq Kit protocol. As expected, this protocol efficiently pulled down rRNA from cellular RNA (>60%) but had little effect on removal of rRNA from exosomal RNA (Fig. S4). The failure to deplete exosomal fragmented rRNA can be explained by the design of the RiboMinus™Probe. It consists of 2 probes each for 5S, 5.8S, 18S and 28S RNA. Because the probe size is 22–25 nucleotides, many fragments of rRNA are not targeted.

Figure 5: Distribution of uniquely mapped RNA-seq reads among transcriptome. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “known genes”. Reads that placed outside of annotated gene models are termed as “unknown”. Reads which are mapped to rRNA sequences including 5S, 5.8S, 18S, and 28S rRNA are named as “rRNA”. Content of cellular and exosomal transcriptomes

In total, 16,086 transcripts (11,657 genes) were detected with a normalized RPKM value of greater than 1.0 at least in one sample. We used Integrative Genomics Viewer (IGV) version 2.1.21 (Thorvaldsdottir, Robinson & Mesirov, 2013) to visualize mapped reads and to check the coverage across human transcriptome. We observed frequently misannotated transcripts in exosome samples with high RPKM value in those cases when only small parts of the transcripts were covered by the reads (Fig. 6). This was the effect of low depth sequencing caused by the presence of a large amount of reads representing rRNA. Therefore, we implemented more stringent criteria to obtain full-length expressed genes by filtering genes based on the RNA reads coverage. Reads which cover over 90% of protein-coding sequence (CDS) of protein-coding genes and over 90% of exons in non-coding sequence of non-coding genes were considered for further analysis. To identify protein-coding genes in exosomal samples, the mapped reads of exosomal RNA were pooled with estimated CDS coverage of >90% for both exosomal reads. The pooled mapped reads were used to calculate CDS in each exosomal sample as >50% of coverage.

Figure 6: Example of low coverage transcript but very high RPKM in AURKAIP1 and ATPIF1 genes. (A) AURKAIP1 gene from chromosome position chr1:1,309,009–1,310,847 is shown using Integrative Genomic Viewer. Among the three variants, the maximum value of protein-coding sequence (CDS) coverage, read count and RPKM is shown in the right panel of read mapping. Both the exosomes shows very low coverage (7–22%) with read counts of 4, whereas the RPKM value is 65.44 and 80.78 RPKM for exosomes of MDA-MB-231 and MDA-MB-436, respectively. (B) ATPIF1 gene from chromosome position chr1:28,562,494–28,564,655 is visualized. The MDA-MB-231 exosomes exhibit high CDS coverage (91%) with an exon count of 4.

Using these criteria, we obtained lower number of annotated transcripts (6,187 transcripts or 3,437 genes) compared to that when RPKM values were considered (Fig. S5 and Table S1). As a result, some transcripts showed high coverage with CDSs criteria (for example, ATPIF1) in exosomal RNA from MDA-MB-231 cells, even when the number of reads was small (less than 5) (Fig. 6B). Such genes were also taken into consideration in our analysis. In total, 5821 (3115 genes) and 187 (115 genes) protein-coding transcripts were detected based on the RNA reads coverage in cellular and exosomal samples, respectively. For non-coding genes, 360 (317 genes) and 131 (131 genes) transcripts were detected based on the RNA reads coverage for cellular and exosomal samples, respectively. Analysis of these transcripts revealed that they represented 90.8% of protein-coding genes and 9.2% of non-coding genes for host cell sample; while exosomal RNA samples represented 50.4% and 49.6% of protein-coding and 47.6% and 52.4% of non-coding genes in MDA-MB-231 and MDA-MB-436 cells derived exosomes, respectively (Table 1). We found that 98.3% of protein-coding and 97.7% of non-coding exosomal transcripts were present in host cells (Fig. 7).

Table 1:

Amount of genes in cellular and exosomal RNA based on 90% coverage over protein-coding sequence of genes and exons of non-coding genes.

Note the large proportion of non-coding transcripts in exosomal RNA. Transcript type MDA-MB-231cellulargenes (%) MDA-MB-231exosomalgenes (%) MDA-MB-436exosomalgenes (%) Protein-coding 90.8 50.4 47.6 Non-coding 9.2 49.6 52.4 Figure 7: Venn diagram presents overlap among protein-coding and non-coding gene symbols in exosomes and cells. Almost all the genes in both exosomal RNA are the subset of cellular genes. Exosomes are enriched in mRNAs functioning in protein translation and rRNA processing

We performed Gene Ontology (GO) enrichment analysis using the DAVID bioinformatics resource, which employs a Fisher’s Exact Test with Benjamini–Hochberg correction. A total of 377 enriched GO categories were derived using a P-value cut-off of p < 0.05 for 3115 host MDA-MB-231 cellular genes: 286 Biological Process (BP) categories and 91 Molecular Function (MF) categories (Table S2). In total, 18 GO categories including 11 BP and 7 MF were derived from 115 exosomal genes from both cell lines. Figure 8 shows top 20 BP categories of the host cellular genes which include translation process, cell cycle, RNA processing, etc. (Fig. 8A and Table S2B). At the same time exosomal genes revealed biological processes in translation, ribosome biogenesis, rRNA and ncRNA processing GO categories (Fig. 8B and Table S2C). Since the major fraction of exosomal samples were rRNA species, significantly lower number of mRNA could be detected in exosomal samples. We hypothesized that the genes detected from exosomal samples should be highly expressed in the cells. To test the hypothesis, we performed GO enrichment analysis for 115 top expressed genes from MDA-MB-231 cellular sample. The top 10 GO terms (Fig. 8C and Table S2D) of these top expressed genes are the same as in exosomal fraction (Fig. 8B and Table S2C). We further created box plot of 115 exosomal genes in MDA-MB-231 cellular sample using expression values (RPKM) (Fig. 9). These data clearly showed that exosomes are enriched in genes which are highly expressed in the host cells.

Figure 8: Gene Ontology (GO) enrichment analysis of genes detected in cellular and exosomal RNA from breast cancer cell lines. The significant GO terms was defined as described in Materials and Methods. (A) Top 20 significant GO terms found in MDA-MB-231 cellular genes (3115 genes). (B) Significant GO terms found in exosomal genes from both cell-lines (MDA-MB-231 and MDA-MB-436). (C) Top 20 significant GO terms found in the most expressed 115 genes from MDA-MB-231 cellular genes. The asterisks (*) indicate GO terms that present in exosomal genes. Figure 9: Expressed genes in exosomes found to be highly expressed in the host cells. The box plot indicates expression level of all genes in cellular samples as compared to that of genes which were found to be express in exosomes. Wilcoxon rank sum test represents significant difference in expression level of the two sets.

Non-coding transcripts could be classified into 13 categories (see Table 2). Both exosomal and cellular samples contained small nucleolar RNA (snoRNA) as major species. The second most abundant class of non-coding transcripts according to GENCODE annotation was “non-coding RNA” in cellular sample and small nuclear RNA (snRNA) in exosomal samples. Overall, the top five RNA categories represented about 90% of all noncoding genes in both exosomal and cellular RNA.

Table 2:

Amount of non-coding gene symbol in cellular and exosomal RNA based on 90% coverage over exons of non-coding transcripts.

In exosomes, the top 5 non-coding gene types including small nucleolar RNA, small nuclear RNA, Mt_tRNA, microRNA, and non-coding RNA represents about 90% of non-coding genes in both exosome samples. Gene type MDA-MB-231cellular(gene symbols) MDA-MB-231exosomal(gene symbols) MDA-MB-436 exosomal(gene symbols) small nucleolar RNA 214 83 51 small nuclear RNA 23 11 10 Mt_tRNA 13 7 4 microRNA 34 6 2 non-coding RNA 42 1 0 guide RNA 20 0 1 vault RNA 3 0 3 rRNA 1 1 2 RNase MRP RNA 1 1 1 RNase P RNA 1 1 1 Mt_rRNA 1 1 0 lincRNA 1 0 0 telomerase RNA 1 0 0 Validation analysis of RNA-seq data by qRT-PCR

Based on RNA-Seq data we evaluated presence and enrichment of several mRNA transcripts in exosomal RNA - RAB13, RPPH1, EEF1A1, FTH1, FTL and RPL28. qRT-PCR analysis showed presence of all selected transcripts in exosomal samples (Fig. 10A). Figure 10B demonstrates that the fold-change of qRT-PCR results are consistent with the fold-change of RNA-seq data.

Figure 10: Validation of RNA-seq data by qRT-PCR. (A) Ct values for six mRNA transcripts which were detected in exosomal samples by RNA-seq are shown. (B) Comparison of different expression values (RPKM; MDA-MB-436/RPKM; MDA-MB-231) detected by RNA-Seq (dark-grey columns) and qRT-PCR (light-grey columns) for six differently expressed genes. Discussion

Until recently, the changes in gene expression during various biological processes have been analyzed using microarray approaches that focus largely on the behavior of protein-coding transcripts. Because microarrays are based on hybridization, they have high background owing to cross-hybridization, they have a limited dynamic range of detection and they rely upon known structures of genes. Development of RNA-Seq technology permitted comprehensive analysis of whole transcriptomes with the single nucleotide resolution allowing quantification of most RNA molecules expressed in the cell or tissue (Mortazavi et al., 2008). In this study, we used the Ion Torrent platform to interrogate transcriptomes of exosomes released from two metastatic breast cancer cell lines. At the time of conducting our analysis this technology produced relatively low number of reads, yet we selected it as it provided the longest reads than any other sequencing platform. This feature of the Ion Torrent technology was essential as we dealt with RNA isolated from exosomes whose nature and composition are still not well established. RNA-seq data analysis is complicated by the intricacy of dealing with large datasets, reads quality control, alignment procedure etc. Different workflows and several algorithms have been proposed to map reads to the reference genome and to perform data analysis (Chen, Wang & Shi, 2011; Mortazavi et al., 2008). Comparison of expression levels across different samples and experiments is often difficult and requires complicated normalization methods and these are still under active development. The situation is even more complex in case of exosomal transcriptomes that differ significantly from cellular transcriptomes.To address this issue, we developed in this study customized bioinformatics workflow and demonstrated its utility for analysis of exosomal RNA. Because the Ion Torrent platform produces reads with different length the dedicated algorithm for their alignment to the genome called TMAP was recommended. We found out, however, that this tool does not allow satisfactory mapping of reads that contain splice-junctions or span introns. Therefore, we choose alternative aligning tool TopHat2 (with Bowtie2) which could handle reads of varying length and identify splice-junctions based on known splice-junctions as well as allowed the discovery of new splice-junctions (Kim et al., 2013; Langmead & Salzberg, 2012).

We observed a large proportion of reads mapped to rRNA regions in exosomal samples. This was surprising given the fact that intact 18S and 28S rRNA peaks were almost undetectable in exosomal RNA (Fig. 3). This observation suggested that the majority of exosomal rRNA is fragmented. Exosomal rRNA fragments could be mapped over entire length of rRNA (Fig. S3). Fragmented 28S and 18S rRNA were major rRNA species present in exosomes. The reads mapped to 28S and 18S rRNA were distributed almost equally in exosomal and cellular RNA samples. What is the possible reason for generation of exosomal rRNA fragments? RNases present in cell culture conditioned medium are unlikely to contribute to rRNA fragmentation since exosomal membranes provide protection against RNase attack. Indeed, treatment of the exosomal preparations with RNase A did not lead to significant difference between treated and control samples in RNA size distribution (data not shown). In the study of Skog et al. (2008) RNase treatment of the glioblastoma exosomes led to a very insignificant (less than 7%) decrease in RNA suggesting that exosomal RNA is inaccessible for RNase from outside the vesicles. A possibility exists that the rRNA fragments are generated after secretion by RNases originated from the host cells and incorporated into exosome vesicles. Alternatively, rRNA fragments could be generated inside cells prior to their release to exosomes. Another class of RNA, tRNA is represented in exosomes mainly by its fragments (Nolte-’t Hoen et al., 2012). The most abundant tRNA hits in exosomal RNA are all located at the 5′ end of mature tRNAs (Nolte-’t Hoen et al., 2012).

Regardless the biogenesis of rRNA fragments, it is advisable to perform rRNA depletion step even in the absence of visible rRNA peaks on RNA profiles. This procedure would allow obtaining much higher sequencing depth for other RNA species. Our attempt to deplete fragmented rRNA with the popular RiboMinus™Eukaryote Kit failed because of the design of the probes. Because the probes size is short, many fragments of rRNA are not targeted. The use of larger number of longer probes is expected to produce a more efficient way of pulling-down fragmented rRNA. This technical aspect of working with exosomal RNA samples should be certainly considered in the future studies.

As a result of large rRNA presence in exosomal samples we observed only 2% of mapped reads to known transcripts using RefSeq and GENECODE gene models. Moreover, with RPKM value >1 we observed a large amount of misannotation due to poor coverage of the reads over transcripts. Therefore, we suggested another approach, namely filtering genes based on reads coverage over protein coding sequence for mRNA or exons for non-coding RNA. This procedure allowed us to achieve more than 90% coverage for protein-coding and non-coding regions which we considered as highly reliable for functional classification. This approach was helpful to reveal highly expressed genes in exosomes which could be potentially used as noninvasive breast cancer markers.

We report that exosomes are carrying mRNAs that are highly expressed in the host breast cancer cells (Fig. 9). Thus exosomal transcriptomes are representative of their cells of origin and should provide a platform for detection of tumor specific markers. GO analysis revealed that main biological and molecular functions of both cellular and exosomal transcripts are enriched in proteins involved in ribosome biogenesis, translational elongation, ribosomal subunit assembly and rRNA processing. What could be the significance of these functions in exosomal transcriptome? Exosome-associated mRNAs were shown to be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007). We hypothesize that upon arrival to the recipient cells exosomal mRNAs are translated into proteins supporting ribosomal functions to ensure efficient translation of other exosomal mRNAs within a cellular compartment where exosome content is released. Valadi et al. (2007) also described presence of mRNAs for many ribosomal proteins in exosomes secreted by mouse mast cell line. Interestingly, Graner et al. (2009) demonstrated the presence of elongation and translation factors in exosomes derived from brain tumor.

In conclusion, here we demonstrated for the first time that fragmented rRNA is a major species of exosomal RNA. Proposed here custom bioinformatics workflow allowed us to reliably detect other, non-ribosomal RNAs under conditions of limited read numbers. Classification and quantification of the RNA-Seq data revealed that exosomal transcripts are representative of their cells of origin and thus could form basis for detection of tumor specific markers. This information can also be used for getting insights in the molecular underpinnings of biological effects produced by these microvesicles. Finding that exosomes bear mRNAs encoding the necessary components to build-on-site ribosomes provides a valuable insight into biological function of these vesicles.

Supplemental Information Comparison of mapping quality between the alignment tools TopHat2.0.6 and TMAP 0.3.7

FTL gene (chromosome position chr19:49,468,467-49,470,296) is selected as an example of alignment comparison. TMAP alignment resulted in poor reads mapping and absence of junctions over exon-exon region. As the same time, TopHat identifies the exon-exon splice junctions and connects the exons through a linker.

Distribution of all mapped and unmapped RNA-seq reads among genomic compartments

rRNA defined as 5S, 5.8S, 18S, and 28S rRNA sequences. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “known genes”. Reads that placed outside of annotated gene models are termed as “unkown”.

Fragments of rRNA in exosomes represent full-length of rRNA sequence

(A) RNA read density plot represents RNA fragments which fully covers of 5S, 5.8S, 18S, and 28S rRNA sequences from exosomal RNA. (B) 18S and 28S rRNA were major fractions of rRNA species.

Analysis of rRNA depletion from MDA-MB-231 cellular and exosomal RNA using RiboMinus™ Eukaryote Kit for RNASeq

RNA was detected with PicoChip using Bioanalyzer. The depletion procedure has been performed according to the manufacturer’s protocol. Control samples (red) were treated exactly as experimental samples (blue) except they did not contain RiboMinus™ Probe.

Venn diagram of genes generated by RPKM and read coverage approaches

The detection criteria is that gene has more than 1 RPKM in at least one sample, while another approach is that gene has more than 90% coverage over protein-coding or non-coding sequence.

Two approaches of gene transcripts selection using RPKM or read coverage

(A) The Partek genomic suite output showing transcripts with RPKM >1 in at least one sample. The read counts from the transcript isoforms were estimated using EM algorithm from Partek genomic suite on RefGene and/or GENCODE v14 gene models. (B) Estimation of read coverage (in percentage) and read count of transcript. The transcripts with >90% coverage for protein-coding sequence and exonic sequence (in case of non-coding transcript) of transcript isoforms on RefGene and/or GENCODE v14 gene models are shown.

Gene Ontology (GO) enrichment analysis using the DAVID bioinformatics resource of Genes found in cellular and exosomal samples

(A) Gene lists for GO enrichment analysis. (B) GO enrichment of cellular genes. (C) GO enrichment of exosomal genes. (D) GO enrichment of top 115 expressed cellular genes.

List of primers used for qRT-PCR


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