Transcriptomic profiling of skin disease using next generation sequencing allows for detailed information on aspects of RNA biology including gene expression, non-coding regulatory elements and gene splicing. The application of RNA sequencing to human skin disease and cancer is often hampered by degraded RNA. Here we describe a protocol that allows for consistently intact RNA to be extracted from snap frozen skin biopsy samples, which has been validated in a clinical trial setting. Human skin tumour punch biopsies (n=28) ranging from 4-6mm in diameter were obtained from 14 patients with an inherited skin tumour syndrome (CYLD cutaneous syndrome) and frozen in liquid nitrogen prior to being stored at -80°C. These samples were then subject to cyrostat sectioning, allowing for histological assessment, and were homogenised using a bead-based lysis platform. RNA extraction was performed using a silica column-based system. RNA concentration was measured using fluorescent quantitation and RNA integrity assessed using microfluidic gel electrophoresis. We also processed normal skin biopsies using the same protocol (n=10). The mean RNA integrity score of the tumour and normal samples was 9.5, and the quantity of RNA obtained from the small amounts of tissue used exceeded requirements for RNA-seq library generation. We propose that the method of RNA extraction suggested here allows for transcriptomic profiling from small pieces of human tissue without the need for PCR amplification during library preparation. This protocol could be utilised in healthy and diseased skin to improve mechanistic understanding in a range of human skin diseases. Skin has evolved as a protective barrier against environmental stresses such as physical damage, ultraviolet radiation exposure and pathogenic infections. It is a complex tissue, consisting of many cell types, including keratinocytes, immune cells, melanocytes, fibroblasts, adipocytes, nerve fibres, smooth muscle and endothelial cells  and the Medicines Health Regulatory authority (EudraCT: 2014-001342-21). Patients provided written, informed consent for biopsy and use of their tissue samples for research. This study used samples collected as part of a clinical trial, registered at ISCRTNTumours scheduled for biopsy as part of the trial protocol were identified using tumour maps and photographs. Tumour identifiers were labelled onto cryovials in advance of the procedure. Skin biopsies were performed under local anaesthetic, and one sample was taken at a time. The biopsy was transferred to a labelled cryovial, with identifiers checked together with a research nurse. The sample was then immediately immersed in liquid nitrogen. All samples were logged in accordance with standard operating procedures and stored in a -80°C freezer within a designated Human Tissue Authority freezer that is subject to temperature monitoring.A full working protocol is detailed inThe protocol supplied with the silica spin column kit  was followed with modifications, as indicated in the working protocol . BrieflySTAR (v.2.5.2b) to generate alignment files (Subread (v.1.28.1)  was obtained at two levels in each biopsyReads derived from next generation sequencing were subject to quality control using FASTQc , and thiDue to its high sensitivity and resolution, RNA sequencing is a powerful tool for investigation of skin disease. It can provide mechanistic insights behind disease pathogenesis, which may offer prognostic information, prediction of response to treatments and the potential for developing novel therapies . To overWe describe a novel method, incorporating cryostat sectioning, resulting in partial disruption of the tissue, whilst simultaneously allowing us to take 8 μm sections for histological assessment. We used bead-based tubes that allowed for up to 24 samples to be processed simultaneously. Importantly, we were able to work with small amounts of skin, which were robustly disrupted using the bead-based lysis system we employed. This prevented carryover of incompletely homogenised material and obstruction of spin columns used for RNA extraction. The bead-based lysis system we employed had a range of disruption settings and after optimisation with a range of bead sizes, we chose a medium bead mix (CK28 Mix), and lysis of 20 seconds at 5500 rpm in lysis buffer as this gave optimal RNA extraction and RINs.After skin homogenisation, there are several established methods for RNA extraction. Phenol/chloroform extraction, one of the commonly used techniques, has potential for phenol contamination of the samples, which is often reported . TherefoGiven RNA’s sensitivity to temperature, we developed a protocol where the skin sample is kept as cold as possible throughout. The tumour samples were first snap frozen in liquid nitrogen, and then cryosectioned at a temperature of -20°C. During rapid homogenisation in our protocol, the tumour material was exposed to ambient temperatures for only a few minutes. This was immediately followed by RNA extraction in a precooled centrifuge at 4°C. Spin columns were incubated on wet ice between centrifugation steps and eluted RNA was immediately frozen after RNA extraction was complete.In summary, we developed a method for efficient extraction of RNA from small cryosectioned pieces of human skin with histological data from adjacent tissue sections. We validated this method in human skin tumours samples in a clinical trial setting. This protocol could be utilised in healthy and diseased skin to improve mechanistic understanding in a range of human skin disease and cancer.http://doi.org/10.17605/OSF.IO/5YX96 , sample description files and uncropped gel images are available at Open Science Framework:O/5YX96 .Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the The article describes a practical and highly effective means to isolate RNA from fresh clinical skin samples for downstream transcriptome analysis. The major issue that I see with the manuscript is the lack of text describing previous studies using the conventional approach to RNA isolation. In my view, this manuscript describes a methodology to generate extremely high quality RNA isolates. Please describe the RIN from other methods in the published literature so that this can be appreciated. The other major point that I see is that the usual work-flow in dermatology is to paraffin embed samples and then isolate RNA or DNA from sections. The authors should comment on the RNA quality and sequencing considerations from this approach. Minor note: It may be worth mentioning that RPKM based assessments of mRNA abundance have issues with inconsistency and that TPM may be the better model. This is only a minor issue. In summary, this is a well written manuscript that has broad application and immediate relevance. All of my comments can be addressed within the document. No further experiments are needed in my opinion.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The paper "Transcriptomic profiling of human skin biopsies in the clinical trial setting: A protocol for high quality RNA extraction from skin tumours" talks about a novel method to isolate high quality RNA from skin biopsies in a clinical trial setting. The method is well described and provides information on several aspects to consider in the course of respective sample preparation.Authors state that this is a new method, circumventing several critical aspects of RNA extraction. However, we feel that the novelty is not well enough discussed. A comparison of the key aspects with some aspects of other standard procedures would facilitate a better understanding of the advantages. Optimally, simultaneous RNA preparation with a commonly used method would have been interesting as a methodical control. The problem statement should be elaborated more in detail in the introduction.A statement on why these skin tumours were chosen for the establishment of the method should be included and how patients could benefit from this method in the clinical trial setting.   However, we would like to address some points we noticed:Some more information on the gene expression signature should be given in the text and what relevance the validation of the expression profile has in a clinical trial setting.Table 3: A short statement on the results shown in the table would be helpful in the table description.Figure 1D is not comprehensively described.Why is the final average RNA concentration 10-fold higher in tumour samples compared to normal skin biopsies? (Tables 1 and 2)Table 1. Subtitle: … is “normal” skin biopsies correct?YRostat; page 5: A false discovery rateS, …)Some typos / spaces in the paper.  is probably not a competitor for this in skin as heating results in lower RNA integrity. Cylindroma and trichoepithlioma are very cellular tumours not hyperkeratotic lesions: would this work in much commoner keratotic tumours? Such a method is of course mainly suitable for research studies and clinical trials not for routine pathology labs where transcriptomic profiling is needed on FFPE sections.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Overall this is an excellent contribution to technical knowledge of handling of skin samples, and a well-presented methods paper in so far as the optimised extraction method goes.You are not comparing extraction techniques here, but sample preparation. In this context, there is inadequate information on the handling of the "control" skin samples - detail is needed on how these were taken, how long they were left (and in what?) before extraction. If an RNA stabilisation solution was not used as a comparator this should be addressed, as really this would be the current gold standard for comparison, not direct extraction from a tissue sample that had been left in the fridge or in culture medium. In addition it is not clear whether the normal skin biopsies were also 4-6mm, or whether these were all 4mm.It is possible to divide even a 4mm skin biopsy for histology and RNA extraction and still get enough for a library prep, so the claim that this method is the only one which would allow histology is not correctIt is possible that tumours and normal skin differ intrinsically in the amount of RNA produced, and that this is responsible for the differences in quantity, particularly as the quality of the RNA is the same from tumour and normal skin. It would have been much better to use half of each tumour biopsy for each preparation method, and if this could be done on 10 samples going forward you would have a much stronger paper. If not possible in the clinical context this point at least needs to be addressed specifically in the discussion. I have a few points to be addressed:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


A simple search for ‘non-coding RNA’ in Pubmed on 10 June 2015 yielded 128,649 articles, half of which were published in the last 10 years [The number of papers dealing with new Non-Coding RNA publishes here its first Journal Club and highlights, in about hundred words, a selection of the most interesting papers published recently. We hope we will tease your curiosity and encourage you to read full papers outside of your research area that you may not have read otherwise.The Scientific board of et al. paper [The Lauressergues l. paper  attracteNon-Coding RNA Scientific Board wishes you a good reading!The Highlight by Joseph H. Taube and Sendurai A. Maniet al. show that circRNAs are not universally-produced (detected from only 22% of transcripts) and that following EMT, circRNAs are more abundant and half of them are undetected prior to EMT [While circular RNAs (circRNAs)—sequential exons spliced to each other in a circular arrangement—have been known since the 1990s, regulators of their expression have remained elusive. Using a model of epithelial-mesenchymal transition (EMT) in mammary epithelial cells, Conn r to EMT . ScreeniHighlight by Gaetano Santulli2+ handling interacting with the SR Ca2+ ATPase (SERCA). Intriguingly, myoregulin displays a robust structural resemblance to phospholamban and sarcolipin, which inhibit SERCA activity in the heart and in slow-type and developing skeletal muscle [Eric N. Olson’s group has recently discovered  a conserl muscle . Specifil muscle . The facl muscle . HoweverHighlight by Gaetano SantulliThe functional link between endoplasmic reticulum (ER) stress and miRNA has been demonstrated as a crucial step in the pathophysiology of diabetic embriopathy . Peixin Highlight by R. Keith Slotkin.et al., recently demonstrated that in contrast to the well-studied C. elegans, most nematode species do not have piRNAs and rely on Dicer and RdRP amplified siRNAs to silence TEs [Transposable element (TE) activity is repressed in eukaryotes by positive feedback loops of small RNA production and activity. Plant small interfering RNAs (siRNAs) are dependent on Dicer and RNA-dependent RNA Polymerase (RdRP) proteins, while in animals, Dicer- and RdRP-independent small RNAs, called piRNAs, repress TEs. Sarkies ence TEs . TherefoHighlight by John S. MattickNature Methods [et al., used a new technique called RNA CaptureSeq, which employs bead arrays to capture transcripts from specific genomic regions—like a molecular microscope—to identify large numbers of previously unknown long noncoding RNAs (lncRNAs), as well as many new isoforms of mRNAs. They also show that the methodology is quantitative and equal or superior to conventional RNAseq across most of the expression range.Transcriptional profiling is bedeviled by the domination of the data by highly expressed RNAs, which means that cell- or stage-specific transcripts are undersampled, especially in complex tissues like the brain. In a recent paper in  Methods , Clark eHighlight by Ulf Andersson Øromet al., present evidence that m6A RNA methylation occurs on pri-miRNA transcripts, in proximity to the stem of the miRNA hairpin, and modulates the processing of pri-miRNAs [In miRNA biogenesis, how the specificity of the recognition by Microprocessor is mediated to avoid processing of non-miRNA hairpins is not fully understood. In their study, Alarcon i-miRNAs . The autHighlight By Jian-Hua Yangin vivo. Sugimoto and colleagues developed hiCLIP  to probe RNA structures interacting with RBPs and revealed a high prevalence of intra-molecular and long-range RNA duplexes recognized by Staufen 1 (STAU1) RBP in human 293 cells [in vivo click selective 2’-hydroxyl acylation and profiling experiment) to decode structural dynamics of RNAs in mouse embryonic stem cells and accurately predicted RBP binding sites and N6-methyladenosine (m6A) modification spots from structural footprints [The structures of RNA molecules, the skeletons bound by trans-acting RNA-Binding proteins (RBPs), are essential for their function and regulation. Two recent studies developed new methods to dissect RNA secondary structures 93 cells . In a seotprints . Future Highlight by Manuela Ferracinet al. reported in Nature recently that some pri-miRNAs contain short open reading frames (ORFs) that can potentially generate peptides [MicroRNAs are generated from longer precursor transcripts called primary miRNAs (pri-miRNAs), either in plants and animals. Pri-miRNAs are thought to be non-coding intermediates in miRNA processing. These transcripts, indeed, are processed rapidly inside the nucleus to generate pre-miRNAs  or miRNA duplexes (in plants). But is this all? Lauressergues peptides . For somHighlight by Florent Hubéet al., points to regulatory peptides encoded by unpredictable short ORFs sheltered in pri-miRNAs [A recent report by Lauressergues i-miRNAs . In a feHighlight by Zofia Szweykowska-KulinskaMIR genes take so long encoding just a small 21 nt long molecule? Now the riddle is partially solved: at least part of the non-coding pri-miRNAs turned to be bifunctional: producing miRNAs and coding small peptide mRNAs.A very exciting discovery was done by the group of Jean-Philippe Combier from the University of Toulouse and CNRS Castanet-Colosan in France . They shHighlight by Daniel GautheretNature Genetics [A group led by Arul Chinnayan from the University of Michigan, Ann Harbor, answers this question in the March issue of Genetics . Their cHighlight by Franck VazquezArabidopis thaliana, an independent endo-siRNA pathway is involved in the establishment of transcriptional gene silencing (TGS) of TAS (trans-acting siRNA) loci and certain transcriptionally active transposable elements (TEs). The group of Keith Slotkin just showed that this so-called RDR6-RdDM pathway depends on direct guiding of AGO6 to TE DNA targets by 21–22-nt [Besides the genetically well-characterized RdDM pathway which implicates Pol IV, Pol V, RDR2 and 23–24-nt long siRNAs in 21–22-nt . This st


Terminologies of ovary development, by somewhat subjective describing and naming main changes of oocytes, have been criticized for confusing and inconsistency of terms and classifications, and the incurred consequences impede communication among researchers. In the present work, we developed regression between ovary development and three ribosome RNA (rRNA) indexes, namely 5S rRNA percent, 18S rRNA percent, and 5S–18S rRNA ratio, using close relationship between volume percent of primary growth stage oocytes or gonadosomatic index and rRNA content, demonstrating species-specific quantification of ovary development can be established in species with either synchronous and asynchronous oogenesis. This approach may be extended to any species with primary growth oocytes, e.g. anurans and reptiles, to predict maturity stages in females. We further confirmed that 5S rRNA percent and 5S/18S rRNA ratio can serve as markers to distinguish sexes unambiguously. A micro-invasive sampling method may be invented for non-lethal prediction of ovary development and sex because only a small amount of ovary sample (<50 mg) is needed for the approach established in the current work. Researchers who work with ovary RNA-seq in these taxa should realize that insufficient depletion of rRNA will probably lead to incorrect quantification of gene expression and inaccurate conclusions. Once primordial germ cells differentiated into either female or male germ cells, they will undergo gametogenesis, reach full maturation, and be ready for fertilizing, along with a series of complex molecular and endocrine changes. In the process of gametogenesis, oocytes and spermatocyte undergo significantly diverse molecular, cellular, and structural changes. Particularly, the oocytes increase size by hundreds of times and accumulate reserve substances, e.g. RNAs, proteins, lipids, carbohydrates, vitamins, and hormones, which are indispensable for proper development of the embryo5. One of these molecules involved considerably in oogenesis is 5S ribosome RNA (5S rRNA). In the oocytes of some amphibian and teleost species, rDNA is amplified ≈1,000-fold and 5S rRNA and transfer RNA (tRNA) constituted more than 90% of the RNA content approximately at the end of primary growth or early cortical alveolus stage9.Sex differentiation, which is triggered by sex-determining factors, is to indicate the physical realization of the events in terms of ovarian or testicular development, including morphological, cellular, and molecular aspects11. However, its precise function remains unknown. Intriguingly, two different types of 5S rRNA genes with several sequence variations have been discovered in Xenopus laevis: one is only expressed in oocytes and the other in both somatic cells and gonads12. A similar pattern with two paralogue genes has been found in several fish species13. Specifically, only oocyte 5S rRNA accumulated in ovaries. The oocyte 5S rRNA undergoes considerable mobilization and decreases to baseline level when oocytes enter vitellogenesis and this massive amount of 5S rRNA is incorporated into the ribosomes in the amplified nucleoli15.The 5S rRNA was discovered as early as in 1963 and has been studied for RNA-protein interactions and used as a phylogenetic tool for several decadesTinca vulgaris and T. tinca7. This interesting feature then has been overlooked. 5S rRNA in fish ovaries has not received attention, until recently, probably due to the booming development of Real-time PCR (RT-PCR) and RNA-seq and their application in biological research in the past decade. The RNA integrity number (RIN), an index for assigning integrity values to RNA measurement combining different features of rRNAs obtained from microcapillary electrophoresis, has served as a “standard” for RNA quality control16 since it has been established. The “abnormal” feature of 5S rRNA in developing ovaries, however, confused many researchers at the beginning because it has been difficult to obtain expected high RIN from developing ovaries, as we obtained from developed ovaries, different stages of testes and somatic tissue samples. The overwhelming accumulation of 5S rRNA in either gonochoristic developing ovaries or hermaphroditic sex-changing gonads in electrophoresis, has been reported or mentioned in several studies regarding RT-PCR and RNA-Seq20, indicating it is a general feature in gonads with primary growth oocytes in fish, reptiles and anurans. In addition, there are indications that ovary 5S rRNA profiling could be applied in the identification of sex, ovarian development, reproductive endocrine disruption19. Changes of 18S rRNA were also observed in fishes and amphibians21.The dynamic feature of oocyte 5S rRNA during oogenesis had been studied in some fish species in the 1970s, e.g. However, how accurate are the rRNA features in sex identification and evaluation of ovary development? Does it work for species displaying asynchronous or synchronous ovaries? With these questions, plus our experience, the objectives of the present study are to: (1) develop an approach for sex identification and oogenesis stage determination in fish with asynchronous or synchronous ovaries using rRNA profiling and evaluate the accuracy; (2) alert researchers who are working on ovary transcriptomics in fish, anurans, and reptiles that incorrect depletion of rRNA, especially the mass accumulated 5S rRNA in developing ovary, may lead to misassembly and incorrect quantification of gene expression.19. Pearson correlation analysis indicates the consistency between the two indexes 5S rRNA/total RNA and 5S/18S rRNA, with correlation coefficient r = 0.947 (P < 0.001), 0.975 (P < 0.001), 0.986 (P < 0.001) in yellow perch, bluegill, and largemouth bass, respectively.RNA integrity index (RIN) ranged between 8.4 to 10 for testis samples, and under detectable level (1) to 10 for ovary samples. No signs of RNA degradation were observed in any of the samples from either regular electrophoresis or chip run in Agilent 2100 Bioanalyzer. A distinctive band with peak size between 130–145 bp were observed without exception, in all samples including gonad, brain, and liver in five studied species, yellow perch, bluegill, largemouth bass, channel catfish, and Nile tilapia. The band, constituted up to 98.77% of the total RNA content in the transcriptome of ovaries as measured in the electropherograms  before spawning season (March for yellow perch), but not at the later month Fig. . The 18SIn species with asynchronous oocyte development, 5S rRNA/total RNA and 5S/18S rRNA indexes can distinguish females from males unambiguously from early gonadal development up to gonadal maturation Fig. . The 18S2 = 0.958), and quadratic regression between Ln(5S/18S rRNA) or 18S rRNA/total RNA % and GSI  in yellow perch females , followed by between 18S rRNA/total RNA % and GSI (R2 = 0.736), and between Ln(5S/18S rRNA) and GSI (R2 = 0.693)  when female and male samples were analyzed separately. We then further examined whether a given rRNA content can be used to estimate the GSI. Results showed a good linear regression between Ln and GSI R = 0.958,The percentage was highest for 5S rRNA (98.77% for yellow perch and 68.58% for bluegill) and lowest for 18S rRNA (0.09% for yellow perch and 5.87% for bluegill) when ovaries are mainly composed of primary growth stage oocytes ; the opposite results were observed when ovaries were close to maturation Fig. . FurtherTotal RNA electropherograms of ovary samples in Channel catfish (6) and Nile tilapia (4), corresponding rRNA profiling and histological images can be found in Supplementary Figure No signs of RNA degradation were observed in any of the samples from either regular electrophoresis or chip run in Agilent 2100 Bioanalyzer. RNA integrity number (RIN) indicates the high quality of RNA from both brain and liver samples , while the 5S–18S rRNA ratio and 18S rRNA percent in brain tissue were significantly higher than liver in both species . Thanks to the technology combination of microfluidic chips, voltage-induced size separation in gel-filled channels, and laser-induced fluorescence detection on a miniaturized scale, Agilent 2100 bioanalyzer is capable of sizing and quantitation of RNA fragments in a high resolution27.The RIN has been considered the standard to evaluate total RNA quality, especially for studies that involve sequencing19. We found that in species with asynchronous oogenesis , either 5S rRNA percent or 5S/18S rRNA can be used to distinguish females from males unambiguously from very early stages of gonad development up to near maturation in bluegill and largemouth bass  and established significant regression with rRNA content , Ln(5S/18S rRNA), or 18SrRNA/total RNA% and ovary development (represented by GSI). The trends of regression were similar between species with synchronous and asynchronous oogenesis for all these three indexes, while the regression estimate was higher for synchronous ovaries than asynchronous ovaries Fig. . While Gent Fig. . It is ring Fig. . The tim34. Researchers criticized that “ovarian terminology is confusing” three decades ago35; nevertheless, the criticism is still relevant34. A major emphasis of a series of workshops on gonadal histology of fishes, including more than 30 papers, has been the development of terminology to describe the reproductive classification of fishes, due to the inconsistency and confusion34. Attempts to build up standardization and consistency into the reproductive classification in the past had not met researchers’ expectations, and the new classification system is still limited to describing and naming major phases during the fish reproductive cycle36. In addition, a diverse group of scientists, including fishery biologists, fishery managers, ecologists, physiologists, toxicologists, environmental biologists, endocrinologists, development biologists, and aquaculturists all work with reproductive biology in both marine and freshwater fishes. Some of them need a unified terminology that addresses morphological and cellular changes, while others may be interested in physiological processes. Therefore, a consistent, simple, and quantitative terminology is urgently needed.Traditionally, identification of the oogenesis stage in fish is dependent on a somewhat subjective description obtained from laborious histology and macroscopic observation23. While in these studies, electrophoresis analysis of RNA quality should be the indispensable procedure. Further, evaluation of fish fecundity, population sex ratio, and/or ovarian development are essential for various studies, such as fisheries, ecology, reproductive biology, sex determination and sex differentiation; traditionally it requires a considerable amount of samples for histological analysis39. Therefore, the approaches established for identification of sexes and quantification of ovary development in the current work, provide a low-cost, simple, and robust solution for these studies.In addition, as the increasing of accessibility of RNA sequencing, a lot of studies are addressing molecular mechanisms involved in gonadal development, markers of egg quality and embryo viability, maternal genes, molecular pathways of sexual size dimorphism, etc. In such studies, obtaining enough RNA samples, identifying sex and/or gonadal development stage from a gonad sample of the same individual are frequently required. However, there is not enough gonad tissue for both RNA isolation and histological analysis, especially during early gonadal developmentXenopus laevis long time ago40, while the other two have never been documented and the basics of dynamic variation of either 5S or 18S rRNA in gonad and somatic tissues are absolutely unknown.In the present work, we found that 18S rRNA percent in ovaries underwent dramatic increase from 0.04% to about 33% (close to the level in somatic tissue) during ovary development. We also discovered that in species with synchronous oogenesis, both 5S and 18S rRNA in testes displayed similar trends of variation as the trends observed in the ovaries Fig. . Interes26, while the precise function of 5S rRNA in protein synthesis is not fully understood41. Two types of 5S rRNA genes, designated as oocyte (or ovary) 5S rRNA and somatic 5S rRNA, have been discovered in many fish and anurans13. Oocyte 5S rRNA is expressed in oocytes while somatic 5S rRNA is found in both somatic cells and gonads. Intriguingly, only oocyte 5S rRNA accumulated and then disappeared in ovaries12. Previous reports suggested that synthesis of 5S rRNA reached a peak at late primary growth or the early cortical alveolus stage (previtellogenesis stage), and was incorporated progressively into ribosomes when the oocytes enter vitellogenesis25. Our data showed that 5S rRNA percent was the highest when ovaries only consist of primary growth stage oocytes in both fish species with synchronous and asynchronous oogenesis  accumulated in excess during the primary growth stage are stored as nucleoprotein particles, and are still present in the fertilized egg and in the developing embryoThe dynamic changes of 5S and 18S rRNA during the development of testis probably associated with protein synthesis in spermatogenesis as in oogenesis. As displayed in Fig. 18S rRNA gene, which has been used as an internal control for gene expression normalization more than a thousand times , should not be used as an internal control for any gene expression studies because of its variation in gonadal and somatic tissues.Changes of rRNA in brain and liver tissue as the gonads develop suggest their participation in oogenesis and spermatogenesis. The 46, if they are not correctly depleted, as we experienced for data generated from developing/resting ovaries. In one of our recent work47, the coverage (expressed as a number of raw reads) for samples from resting ovary (right after spawning) was only about a half for samples from testis or muscle , Ln(5S/18S rRNA), or 18SrRNA/total RNA%] and ovary development stages can be established and utilized as a reference for future prediction of ovary maturity in females. Especially, significant regression between rRNA content and volume percent of PGOO or GSI suggest that ovary development/maturity could be quantified. The confusing and inconsistency of terminology for ovary development may be resolved when this approach is utilized in a considerate amount of species. Meanwhile, laborious histology work can be largely reduced. The regression between testicular development and rRNA content deserves further investigation because the similar regression pattern in testes and ovaries indicate the importance of rRNA accumulation in reproductive success in both females and males. Researchers who work with ovary RNA-seq should realize that insufficient depletion of rRNA will probably lead to incorrect quantification of gene expression and inaccurate conclusions.This study and all experimental procedures involving animals were performed according to the protocol approved by the Ohio State University Institutional Animal Care and Use Committee.Perca flavescens) with synchronous oocyte development, and bluegill (Lepomis macrochirus) with asynchronous oocyte development. Yellow perch, bluegill, and Nile tilapia (Oreochromis niloticus) were obtained from the breeding center of The Ohio State University South Centers. Largemouth bass  and Channel catfish  were purchased from a local commercial fish hatchery. Following anesthetization using MS-222 and measurement of body weight, total length, and gonad weight, gonads of all individuals were dissected and divided into two parts. One part of the gonad was embedded in RNAlater® Stabilization Solution  and kept at 4 °C overnight and then stored at −80 °C until further analysis. The other part was fixed in Prefer  for histological analysis. Goandosomatic index (GSI) is expressed as percent of gonad weight relative to total body weight. Livers and brains were also collected and stored in RNAlater for further analysis.Five major aquaculture species were used for this study, with emphasis on two species: yellow perch  following the manufacturer instructions. Isolated RNA samples were evaluated by Nanodrop 1000  for concentration and by Agilent RNA 6000 Nano Kit Bioanalyzer  for capillary electrophoresis. Generated data were analyzed using Agilent 2100 Expert (free version B.02.08.SI648). The electropherograms generated in Agilent 2100 Bioanalyzer permit fast identification and quantification of 5S, 18S and 28S rRNA, and further comparative analysis. In the current case, as shown in Supplementary Figure 56 with extended time for ovary samples. Tissue slices were examined and photographed under a light microscope with an imaging system . At least four cuttings from two slices were analyzed from each individual. Oocytes were considered as oblate ellipsoid and volumes were calculated using the following formula: Oocyte volume = πab2/8, where a and b are long and short axises of oocytes respectively. Each oocyte which displayed a nucleolus was measured and counted from each cutting. Critical size points (long axis) for primary growth oocytes are 184 and 174 µm for yellow perch and bluegill respectively, according to species-specific measurement in histological images and criteria by McMillan4.Gonad tissues were transferred into 70% ethanol, gradually dehydrated, cleared, embedded in paraffin, cut at 5–7 µm, stained with hematoxylin, counterstained with eosin, and mounted, following routine histological proceduresP < 0.05).Data were analyzed using the SPSS program version 19. The correlation was estimated using two-tailed Pearson correlation. Best regression was obtained through comparing models generated using Curve Estimation. Data sets were checked for variance homogeneity and normality before further analysis. A t-test was applied to analyze the difference of rRNA percent in brain and liver. Significance was indicated at a 5% level (Dataset 1


On the other extreme, RdRPs from negative strand RNA viruses have quite complicated architectures, with influenza virus three-subunit PA-PB1-PB2 replicase complex possessing both polymerase and endonuclease activities and the 250 kD vesicular stomatitis virus (VSV) L protein including at least three enzyme modules. The ≈ 105 kD flavivirus NS5 proteins fall between the two extremes with a ≈ 30 kD methyltransferase (MTase) naturally fused to theN-terminus of the RdRP through a short 10-residue linker, thus being an ideal system to study how the polymerase function is cis-regulated by its fusion partners.Viral RNA-dependent RNA polymerases (RdRPs) are a unique class of enzymes that play the central role in replicating the genome of RNA viruses. The polymerase core of RdRP structurally resembles an encircled human right hand with palm, fingers, and palm domains surrounding the active site, with the unique fingers-thumb interactions to make the encirclement. Seven RdRP catalytic motifs have been identified based on sequence and/or structural homology, and the spatial organization of these motifs around the active site are highly analogous and key catalytic residues within the motifs are highly conserved , 2, makiRecently, we solved the full-length Japanese encephalitis virus (JEV) NS5 crystal structure, providing the first high resolution snapshot of MTase-RdRP interactions in flavivirus NS5 . In suchbona fide elongation process, as the polymerization rate constants we measured were not large enough to match the typical 10-100 nt/sec constants observed in other polymerase elongation complexes (ECs). Our tentative conclusion is that MTase regulates RdRP catalysis mainly during the early stages of synthesis. The regulation differences observed between the unstable complex and relatively stable complex may reflect the change in the equilibrium among conformational states of NS5 (Figure bona fide EC that have established optimum polymerase-nucleic acid interactions for processive RNA synthesis. Further clarification of these uncertain aspects likely requires solving high-resolution structures of full length NS5 catalytic complex, both at the initiation and the elongation stages of RNA synthesis.The assays we utilized in this study likely allow us to probe polymerase initiation and the transition process up to the late stages, but clearly not the


Evidence shows that Luminal A breast cancer is likely to undergo bone metastasis, but the mechanisms involved remain unknown. This study’s aim was to demonstrate a correlation between estrogen receptor (ER) positivity and bone metastasis as the clinically preferred site of metastasis, as well as investigating the role of ERα-Src signaling in MCF-7 cells using Snail over-expression as an in vivo bone metastasis model.ESR1 silence by transfecting shRNA (∆ESR1) into MCF-7 o/e Snail cells in vitro and in vivo.Clinically, the records of breast cancer with distant metastasis were retrospectively reviewed to correlate breast cancer subtypes and preferential metastatic sites. An in vivo bone metastasis model was created by injection of MCF-7 cells with/without Snail over-expression into the tibia of nude mice. The human MCF-7 cells that over-expressed (o/e) Snail were examined and the expression of epithelial–mesenchymal transitions (EMT) markers, ER-Src signaling proteins and p190 RhoGAP analyzed by Western blotting and real-time PCR. The role of ERα was elucidated using ESR1 knock down decreased Src and p190 RhoGAP expression in vitro and also decreased the incidence of bone metastasis in vivo.The clinical results showed that ER ≥1% breast cancers showed a positive correlation with bone metastasis, which was found to be the preferred site of metastasis. An in vivo bone metastasis was successfully established using injection of MCF-7 o/e Snail cells into the tibia of nude mice, but no such metastasis was found using control MCF-7 cells. The proteins expressed in MCF-7 o/e Snail cells showed an EMT pattern, while those of the MCF-7 o/e Snail metastatic tissue showed a mesenchymal–epithelial pattern. There was an increase in cytosolic Src, p190 RhoGAP and nuclear ERα proteins, but not in Snail, in MCF-7 o/e Snail tissue compared to the same cell line in vitro. We conclude that ER-Src signaling plays an important role in ER (+) breast cancer, which shows a high potential for bone metastasis.The online version of this article (doi:10.1186/s12967-017-1192-x) contains supplementary material, which is available to authorized users. Breast cancer is the most common invasive female cancer worldwide and this is also true for Taiwan , 2. RapiThe epithelial–mesenchymal transition (EMT), which is characterized by a loss of cell–cell adhesion, epithelial polarity, and the gain of mesenchymal properties, such as migration and invasiveness, is mechanism well known to be involved in tumor metastasis and invasion , 9. AmonThe Src family is a group of nonreceptor tyrosine kinases. Accumulating evidence has suggested that Src plays an important role in bone resorption and osteoclast activation, which seems to lead to the preferential bone metastasis of prostate cancer and the occurrence of late-onset bone metastasis in breast cancer , 16. RecRhoGTPases and GTPase activating proteins (GAPs) contain a highly conserved GTPase activating domain that targets small GTPases; these latter proteins regulate intracellular cytoskeletal (actomyosin and microtubules) dynamics via their down-stream Rho kinase . Among tSnail, N-cadherin and Vimentin and down-regulated expression of E-cadherin compared to MCF-7 cells, suggesting an EMT process is occurring. In contrast, MCF-7 o/e Snail cells in bone tissue have up-regulated E-cadherin, but down-regulated Snail, N-cadherin and Vimentin. These findings suggest in the latter case a mesenchymal–epithelial transition (MET) process is occurring. Ingenuity pathway analysis (IPA) of the differential gene expression between MCF-7 cells, MCF-7 o/e Snail cells and MCF-7 o/e Snail bone metastasis tissue demonstrates that ERα played a central role in the bone metastasis process  have up-regulated expression of Bases on the above, we propose a hypothesis whereby ERα modulates Snail-regulated bone metastasis via the Src-p190 signaling pathway. The aim of this study was to demonstrate the correlation between ER (+) status and bone metastasis as the preferred site clinically and to investigate the role of ERα-Src signaling in MCF-7 o/e Snail cells using an in vivo bone metastasis model.Between Jan. 2004 and Dec. 2008, 1701 patients with breast cancer were admitted to Taipei Veterans General Hospital. Under the approval of Institutional Review Board (# 2014-11-001AC) of this hospital, their records were retrospectively reviewed in order to investigate the correlation between their sites of distant metastasis and various factors such as the expression of receptors  and their HER2/neu status. The first metastatic site of these patients was defined as metastasis involving a single organ, such as bone, lung, liver, brain and so on. Those who were firstly diagnosed as having multiple organs metastasis (≥2) were excluded. The mean follow up time was >60 months. The positivity of receptor status was defined as positive when ER or PR was ≥10% while, on the other hand, those having ER or PR <10% were classed as negative. The positivity of HER2/neu was defined as positive when staining was +++ by immunohistochemistry or amplified gene expression by FISH examination.l glutamine, 1.5 g/L NaHCO3, 0.1 mM NEAA, 1.0 mM sodium pyruvate, penicillin/streptomycin  and 10% fetal calf serum .The human breast cancer cell line MCF-7  was obtained from the Food Industry Research and Development Institute , while the Snail-over-expressed MCF-7 (MCF-7 o/e Snail) line obtained from Professor Hong SC, Department of Orthopedics, Taipei Veterans General Hospital. Both cell lines were routinely screened to show they were free from mycoplasma contamination. They were maintained in DMEM supplemented with 10% fetal bovine serum (FBS), 2 mM Cultured cells were lysed in a buffer containing 150 mM KCl, 10 mM Tris pH 7.4 and 1% Triton X-100 together with phosphatase inhibitor and protease inhibitors cocktail . The protein concentrations of the cell homogenates were measured using the Bradford method . Thirty Total RNA was isolated using a modified single-step guanidinium thiocyanate method  . Quantification of the RNA transcripts was carried out and the relative mRNA expression levels of the various specific genes were normalized against the expression level of ESR1 gene and the shRNA was obtained from Academia Sinica. One day after the Snail-over-expressing MCF-7 (MCF-7 o/e Snail) cell line was subcultured, the cells (30–40% confluent) were transfected for 24 h with shRNA against ESR1 or a non-silencing control; this was carried using GenePORTER 2 transfection reagent  dissolved in Optimem (Invitrogen) at a final concentration of 80 nM. Next the MCF-7 o/e Snail cells were allowed to recover before further experiments were carried out. After several passages, a ∆ESR1 o/e Snail MCF-7 cell line was established by puromycin selection. The transfection efficiency was validated by Western blot analysis.Short hairpin RNA (shRNA) was used to silence the 7 cells/0.1 mL PBS were used for each mouse, and this gave rise to a noticeable solid tumor around the injection site at day 14. The left site of the same mouse was used as the negative control  and the “Improving bioscience research reporting for animal research”. Immunodeficient NU-Foxn1nu mice were obtained from National Laboratory Animal Center . They were given ad libitum access to food and water and were maintained in a specific pathogen-free environment with 12 h light–dark cycle at 22–24 °C and 50% humidity under the regulations of animal care committee of National Yang-Ming University. The mice were used for the experiments at 8 weeks of age. MCF-7 o/e Snail cells and MCF-7 o/e Snail knocked down ESR1  cells were injected into the right tibia via the knee joint. In total 1 × 10 control , 26. Aftp value of <0.05 is considered statistically significant compared to vehicle or no treatment group.Correlation between ER positivity and metastatic site was analyzed using Fisher’s exact test. Experimental results are expressed as the mean ± SEM. Differences between two groups were analyzed by student t test or Mann–Whitney U test while those between groups at each time point were identified by one-way ANOVA, followed by Dunnett’s post hoc test. A p = 0.002) and PR ≥10% (p = 0.003), either combination with HER2 expression (p = 0.027) and bone as the organ of first distant metastasis , while 120 patients had similar records for the progesterone receptor (PR). There was a positive correlation between ER ≥10%  was established using intratibial injection of MCF-7 o/e Snail; as a control the MCF-7 wild type (MCF-7) was used in a similar manner, but in this no tumors developed, Fig. a, b. ThuWhen comparing the proteins expressed by MCF-7 o/e Snail cells with those expressed by the tumor tissue created by intra-tibia injection of MCF-7 o/e cells, which is denoted as MCF-7 o/e Snail tissue from hereon, it was found that the former showed an EMT expression pattern, namely, increased N-cadherin, Vimentin and Snail and decreased E-cadherin expression, while the latter showed opposite reciprocal changes in these proteins, namely a MET pattern, either at protein level Fig. a, b or aThe expression of Src, p190 RhoGAP and Erα were compared between MCF-7, MCF-7 o/e Snail cells and MCF-7 o/e Snail tissue by Western blotting. In MCF-7 o/e Snail cells compared to MCF-7 wild type cells, there was no significant change in cytosolic Src protein expression, but there was a trend towards increased p190 RhoGAP expression. This contrasted with the situation with MCF-7 o/e Snail tissue, where there was a marked increase in cytosolic Src and p190 RhoGAP expression Fig. a, b. ProTo elucidate the role of ERα on Src and p190 RhoGAP, ERα gene expression was knocked down (MCF-7 o/e Snail ∆ESR1) by transfection of MCF-7 o/e Snail cells with short hairpin RNA. The results showed that MCF-7 o/e Snail ∆ESR1 cells had significantly lower levels of Src and p190 RhoGAP gene expression, both at the protein level Fig. a, b and ESR1 knock down. The results demonstrated that ESR1 knock down of MCF-7 o/e Snail cells decreased the incidence of bone metastasis in vivo  breast cancer and its relationship with bone metastasis, in vivo bone metastases were created by injection of MCF-7 o/e Snail with/without In this study, we demonstrated that there is a correlation between an ER (+) status and bone metastasis as the preferred metastatic site clinically. We then investigated the role of ERα-Src signaling in MCF-7 cells with Snail over-expression using a bone metastasis model in vivo. Although many clinical studies have shown a correlation between breast cancer subtypes and their patterns of distant metastasis , we are There is consensus that an experimental bone metastasis model is not the same situation as that observed in humans. Nonetheless, a bone metastasis-like model involving intratibial injection of cancer cells is generally accepted as a suitable in vivo model when studying the carcinogenesis process related to bone sarcoma , the mecThe epithelial–mesenchymal transition (EMT) and its reverse process  are critical during embryogenesis . Among mThe proto-oncogene c-src, a non-receptor tyrosine kinase, has been demonstrated with its cooperating partners to play an important role in the development of many cancers . Src actRecent evidence has demonstrated that Src plays an important role in the signaling and cross talk between growth-related signaling pathways such as the ER pathways  and thatIn summary, there is a positive correlation between ER (+) and bone metastasis as the preferred site of metastasis in human studies. ERα-Src-p190 RhoGAP signaling, when investigated both in vitro and in vivo, seems to play an important role in explaining the process by which bone metastasis develops from breast cancer.


Individuals make decisions under risk throughout daily life. Standard models of economic decision making typically assume that people evaluate choice options independently. There is, however, substantial evidence showing that this independence assumption is frequently violated in decision making without risk. The present study extends these findings to the domain of decision making under risk. To explain the independence violations, we adapted a sequential sampling model, namely Multialternative Decision Field Theory (MDFT), to decision making under risk and showed how this model can account for the observed preference shifts. MDFT not only better predicts choices compared with the standard Expected Utility Theory, but it also explains individual differences in the size of the observed context effect. Evidence in favor of the chosen option, as predicted by MDFT, was positively correlated with brain activity in the medial orbitofrontal cortex (mOFC) and negatively correlated with brain activity in the anterior insula (aINS). From a neuroscience perspective, the results of the present study show that specific brain regions, such as the mOFC and aINS, not only code the value or risk of a single choice option but also code the evidence in favor of the best option compared with other available choice options. The choice set, i.e., the context in which an option is presented, can influence the evaluation of the option. Such context effects include the attraction effect (AE), the compromise effect (CE), and the similarity effect (SE). All three effects occur when a specific third option is added to a choice set, with a dominated option in case of the AE, an intermediate option in case of the CE, and a similar option for the SE. All three effects lead to a change of the relative preference for two choice options resulting from the addition of a third option. Previous studies examining context effects primarily focused on multi-attribute decision making without risk, such as consumer behavior. Only very few studies have so far investigated context effects in decision making under risk. These studies provide evidence that the AE can also occur for risky decisions for decisions alternatives with both discrete4 and continuous5 outcome distributions3.The assumption that decision makers evaluate choice options independently of each other is a cornerstone of standard economic choice theories. Standard theories postulate that the subjective value (or the expected utility) of an option does not change when another option is added to the choice set. Imagine a choice between two investments for retirement savings, an investment that offers a high return but also has a relatively high risk of low performance and another investment that offers a low return but is almost riskless. If you evaluate each of the two investments independently of each other, the addition of a third investment  should not influence your relative preference between the original two investments. There is, however, a substantial amount of evidence showing that this independence assumption is frequently violated6, cannot explain context effects. The results of previous studies have led to the development of new choice theories explaining some of the violations7. However, new models that can, in principle, simultaneously explain all three context effects have also been developed, including Multialternative Decision Field Theory8 (MDFT), the Leaky, Competing Accumulator Model9 (LCA), the Associative Accumulation Model (AAM), and the Multi-Attribute Linear Ballistic Accumulator Model10. Notably, all four models belong to the class of sequential sampling models, which assume that a preference state develops over time by accumulating evidence for the different choice options11. All models further assume that the relationship of the choice options to each other within the multi-attribute space affects the subjective value of the options and is essential for explaining the context effects. MDFT, for instance, computes the psychological distance between options measured in two dimensions, the indifference and the dominance dimension. The indifference dimension defines a dimension within the multi-attribute space along which all options are equally attractive to the decision maker. The dominance dimension, in contrast, defines a dimension along which options dominate other options and therefore have a strict preference ordering. The overall distance is determined by an additive combination of the two distances where the importance weight given to each dimension strongly influences the effect on how options influence each other. The process in MDFT resulting in the AE is based on distance-based inhibition. The closer two options are with respect to each other in the space of relevant dimensions, the more these options are assumed to inhibit each other. If one of the two options dominates the other option, then the inhibition process results in a boost for the dominating option, thereby inverting the inhibition. This boost increases with decreasing distance. While distance-based inhibition is crucial in MDFT to account for the AE, the other three models mentioned above propose different processes. Whereas according to LCA, loss aversion in the value function leads to the AE, within the framework of the AAM a saliency-driven increase in the weight of the target’s strongest attribute is responsible for the AE. Finally, in MLBA alternatives that are more difficult to discriminate receive more attention, thereby increasing the choice probability of a dominant choice option in case of the AE. While all four models have been specified for multi-attribute decision making, it remains unclear how they should be applied to potential context effects in the domain of decision making under risk.The change of relative preferences represents a violation of essential choice principles, i.e., the independence from irrelevant alternatives and the regularity principle, principles on which standard economic choice theories were built. Consequently, standard economic theories, such as the Logit or Probit model12. MDFT  is a generalization of DFT that can also be applied to choices between more than two alternatives. Furthermore, it includes mechanisms that allow MDFT to predict context effects. Thus, DFT cannot be applied to the three alternative choice tasks we study and we need to rely on MDFT. However, to allow MDFT to predict context effects for decision between risky alternatives, we represent probabilities and outcomes as two attributes . This procedure has already been successfully proposed by Wedell4. Taking the logarithm of both probability and outcome translates the two into additive attributes on which MDFT relies. However, this procedure nevertheless guarantees that the expected value model represents a special case of MDFT.In the following we will focus on MDFT as a prominent representative of the sequential sampling models described. MDFT is an extension of decision field theory (DFT) that was explicitly developed to account for risky choices14. The authors identified the AE in choices between flats and gambles. On the neural level, the authors of these studies observed higher brain activity in the dorsolateral prefrontal cortex (DLPFC), the anterior insula (aINS), and the dorsomedial prefrontal cortex (DMPFC) when a third asymmetrically dominated decoy was present, whereas brain activity in the amygdala and the ventromedial prefrontal cortex (VMPFC) was reduced in this situation. Both studies, however, did not provide a quantitative model to explain the effects and did not account for possible confounds of the number of presented alternatives . Only recently have researchers started to explicitly model the data of context effects using quantitative models, such as MDFT15.Only two studies so far have investigated the neural underpinnings of a context effectIn the present study, we go beyond these past approaches by (a) providing further evidence that decisions under risk can be influenced by context effects, such as the AE, (b) examining whether these effects can be described by evidence accumulation models like MDFT, and (c) determining how the neurobiological underpinnings of the AE are related to the decision process assumed by MDFT.MDFT assumes that similar choice options compete with each other and have a negative effect on each other. However, in the case of an unattractive dominated option with a negative overall evaluation, the competition leads to a positive “boosting” effect for the dominant option, as illustrated with the AE. How similar a dominated option is perceived according to MDFT depends on how much weight is given to the distances along the indifference compared with the dominance dimension. When giving little weight to the dominance dimension, the dominated option will be perceived as very similar to the dominant option, producing a large AE. Here, we specifically test how the overweighting parameter that determines the size of the AE is associated with changes in brain activity. In our study, we use fMRI to explicitly investigate how individual differences in the overweighting parameter are related to individual differences in decision-related brain activity.In our experiment, the participants n = 18) made repeated choices between two or three monetary gambles during fMRI  = xα). The choice probability was specified using a softmax choice rule, including one sensitivity parameter. The baseline chance model assumed that all available choice options are chosen with equal probabilities. EU and MDFT must outperform the baseline model to represent plausible choice theories. The results showed that MDFT outperformed the other two models on an aggregated level  or a third symmetrically dominated option  was added to the choice set see Fig. . There wsee Fig. . On the r = 0.65; p = 0.004), with an average observed AE of 5.7% and a predicted effect of 3.8%. Thus, MDFT not only describes the choice data well but also accurately explains the AE on an individual level.We also quantified the AE on an individual level by determining the difference in the choice frequency of the target option in the decoy condition compared with the average choice frequency of the target in the other two conditions, showing that the larger this difference, the larger the AE. To examine whether MDFT is able to predict the observed AE effect, we also computed the predicted AE effect by taking the difference between the average predicted choice probability of the target in the decoy condition compared with the average predicted choice probability of the target in the other two conditions. Figure 13, who observed higher brain activity in the DLPFC, aINS and DMPFC when an asymmetrically dominated decoy was present, whereas brain activity in the amygdala and the ventromedial prefrontal cortex (VMPFC) was reduced in this situation. We therefore contrasted constant brain activity during the decoy condition with constant brain activity during the baseline condition. Most of the observed differences indeed replicated the findings of Hedgcock and Rao13  and the posterior cingulate cortex (PCC) see Fig. . We furt19 could be related to brain activity. As described above, the similarity between the options defined by the psychological distance between options is essential within MDFT for predicting the AE. According to MDFT, the subjective distance comprises the distance in the indifference dimension and the distance in the dominance dimension  whether an individual’s decisions between risky options are influenced by the composition of the choice set, (b) whether these influences can be described using a sequential sampling model, such as MDFT, and (c) how the neurobiological underpinning of the AE could be related to the specific mechanisms assumed by MDFT to explain the AE.3. Although the AE has previously been demonstrated in the literature21, these studies have primarily focused on consumer choices. More importantly, the effects have often been illustrated using between-subjects designs, leaving open whether the effects can also occur for the same person when making repeated decisions. To answer this question, we have used a within-subject design  the authors showed that VMPFC and amygdala were less active when a decoy was present, whereas DLPFC, DMPFC, and aINS were more active. These authors attributed this finding to a general trade-off aversion causing the AE. Notably, however, the contrast between two alternatives with choices between three alternatives (including a decoy) is confounded with the mere amount of processed information. A comparison of all three conditions in the present study revealed that indeed most of the results of Hedgcock and Rao13 could be explained by comparing a choice between three options with a choice between two options, independent of the presence of a decoy and the AE. Importantly, in our experiment, only a small activation in the aINS remained when comparing the decoy condition with the filler condition and while holding the number of available options constant. As inferring cognitive processes from brain activity is in general problematic24, brain activity in the aINS is not selective, and the observed activation is only significant for very liberal cluster thresholds, we can only speculate on the functional role of the insula-activation for the occurrence of the AE. Changes in brain activity in the aINS have previously been attributed to saliency . Importantly, this interpretation is also consistent with the observations of previous studies investigating the value and/or risk of single choice options, as value and risk were fully confounded with evidence in favor of the chosen option in most tasks.As we were interested in how the neurobiological underpinnings of the AE could be related to the decision processes assumed by MDFT, we correlated choice probabilities predicted by MDFT with changes in brain activity during the decision. The predicted choice probability of the chosen option can be seen as a measure of accumulated evidence. A higher choice probability of the chosen option implies greater evidence in favor of the chosen option compared with the other options. We observed that evidence in favor of the chosen option is positively correlated with brain activity in mOFC/VMPFC and PCC and negatively correlated with brain activity in bilateral aINS, DLPFC, DMPFC, thalamus, and parietal cortex. Both the mOFC/VMPFC and the PCC have frequently been observed in the context of value processing31 found that PCC reflects the subjective value of a choice alternative, even if the subjective value is irrelevant for choices . As we interpret this activation in the context of multi-alternative choices as related to evidence, one potential interpretation of this finding would be that the PCC, in contrast to the mOFC/VMPFC, reflects unbiased evidence in favor of one option. This interpretation is in line with the findings by Grueschow and colleagues31. If the presence of a dominated option biases evidence in favor of one option, then this change results in a lower relation between brain activity in the PCC and evidence. The mOFC/VMPFC, in contrast, seems to be sensitive to the biasing effects of dominated options.To investigate a possible connection between the choice process assumed by MDFT and brain activity during choices, we tested whether the relative distance weighting parameter of MDFT that is essential to predict the AE is associated with the neural processing of evidence. The process in MDFT resulting in the AE is based on distance-based inhibition. The closer two alternatives are with respect to each other in the space of relevant dimensions (in our case magnitude and probability of possible outcomes) the more these alternatives are assumed to inhibit each other. If one of the two options dominates the other option, then the inhibition process results in a boost for the dominating option, thereby inverting the inhibition. This boost is much stronger when the distance is smaller. An increase of the relative distance weighting parameter thus leads to a reduction of the boost and a decrease in the size of the AE. For high parameter values, the AE completely disappears, and the choices are similar to choices in choice sets without a decoy . We observed that individual differences in neural representations of evidence in the PCC are related to individual differences in the relative distance weighting parameter. Interestingly, the lower the relative distance weighting parameter  the lower the relation between evidence and brain activity in the PCC became. The link between evidence  and brain activity in the PCC is thus greatest when there is no AE present. Recent work by Grueschow and colleaguesIn summary, the present results show that in decision making under risk, the set of choice options influences the evaluation of single options, illustrated by the AE. MDFT provides a theoretical account that not only explains the AE in general but also explains individual differences in its size and brain activity related to the AE. From a neuroscience perspective, these results show that specific brain regions, such as mOFC/VMPFC, PCC or aINS, not only code value or risk of a single choice option but also provide evidence in favor of the best option when individuals must choose between several different options. The PCC thereby seems to code evidence in an unbiased way, not reflecting changes in evidence caused by the presence of a decoy option, whereas the mOFC/VMPFC fully reflect these changes.Moreover, these results illustrate that the basic assumption of the independent evaluation of choice options embedded in standard economic choice theories can be substantially violated by human decision making. The results show that the value of choice options is affected by the context in which these options are presented, thus apparently, people do not simply reveal their preferences when making choices; on the contrary, they construct their preferences when making decisions in a specific situation. This idea suggests that, contrary to economic theory, the market price of an investment product might not only reveal the isolated preferences of the investors but also the impact of the context in which the investment product is presented.Eighteen young volunteers  participated in this study. All participants were native German speakers, right-handed, had no history of neurological or psychiatric diseases. All participants were paid for their participation and gave written informed consent. The study was approved by the local ethics committee of the Charité University Medicine, Berlin. All methods were performed in accordance with the relevant guidelines and regulations.Participants performed a series of choices between gambles in three different conditions, (I) the Basic Condition, (II) the Decoy Condition, and (III) the Filler Condition. In (I) the Basic Condition participants made choices between two gambles, one with a high probability to win a small amount of money , and one with a low probability to win a high amount of money . Based on a pre-study preference task, these gambles were individually tailored in a way that the participants should be almost indifferent between the two options, ensuring that their choice behavior does not differ substantially from a 50%/50%-distribution. Specifically, we estimated two indifferences points that served as basis for stimulus generation. Participants initially made repeated choices between a choice option with 20% chance to win 80$ and an option with x% chance to win 20€. The x was varied parametrically in an increasing order. The x at which participants switched from choosing the first option to choosing the second option determines the indifference point. This procedure was repeated for choices between an option offering a chance of 25% to win 64$ and an option with a varying chance of y% to win 25€. The two indifference points were used to generate choice options for the main experiment. Here, we parametrically varied probability and magnitude in steps of 1%/1€ around the indifferences points.In the Decoy Condition (II) one option was added to the choice set compared to the Basic Condition. This additional option, the decoy, was similar to the alternative with a high probability to win a small amount of money, but the probability was exactly 10 percentage points lower to win the same small amount of money . In the Filler Condition (III) also one option was added to the choice sets of the Basic Condition. The option offered the same small win that was offered in one option but only with the low probability of the other option . Participants made 72 choices in each condition. Additionally, we added 36 choices between a sure amount and a risky lottery to prevent participants from recognizing the aim of the experiment.Participants received a flat payment of 10 Euro for their participation in the experiment. After the experiment, one of the 252 choices was randomly chosen to determine decision dependent payments. The option that was actually chosen on the randomly selected trial was played out, so that participants either won an additional amount of money in addition to their initial payment or just received no additional amount to their initial payment. The task was thus fully incentive compatible.16 which penalizes additional parameters but is less conservative compared to the Bayesian Information Criterion (BIC). The Chance Model assumes equal choice probabilities for all available alternatives, that is ½ for two and 1/3 for three alternatives. For EU theory we assumed a simple power utility function of the form u(x) = xq, where u defines the utility of outcome x and q the exponent is a free parameter that represents the risk attitude of the decision maker, with q = 1 representing risk neutrality, q < 1 risk aversion, and q > 1 risk seeking attitudes. Expected Utility (EU) was defined as the sum of the utilities of the outcome x multiplied with the probabilities p that the different outcomes i of an option occurred.We modeled behavioral data with three different models, namely a Baseline Chance Model, EU theory, and MDFT, and compared them in a rigorous model comparison. For the Model Comparison we used the AICTo translate expected utilities into choice probabilities, we used a simple softmax choice rule:N denotes the number of available options of the choice set.where ϑ is a free sensitivity parameter and P. P integrates all previous preference states P and the current evaluation V of the options according to the following updating process. The updating of P over time is assumed to continue until a specific threshold is reached, the decision time is over, or the preference states have stabilized. Here, we focused on the latter possibility. The preference state P is defined as:S describes a feedback matrix reflecting how strongly previous preference states are memorized  and how the options influence each other, depending on their relative distance (D) in the attribute space and V representing the valence of the different options. The feedback matrix S is given by2 a decay parameter that determines the diagonal elements of S, and φ1 a sensitivity parameter that determines the similarity as a function of the distance D between the options in the attribute space. The distance function D describes the subjective distance between two options in the attribute space. It is based on a decomposition of distance in the indifference direction and in the dominance direction. Importantly, a parameter wd allows for an overweighting of distance in the dominance direction and therefore an increase in subjective distance, if options are dominated.In MDFT, the evidence for each option at any point in time is captured by a preference vector Vt, which can be decomposed into three matrices and an error component (ε):C denotes a contrast matrix to compute the relative advantage (disadvantage) of each option to the other options. M is a value matrix that contains the attribute values of each option and Wt, represents a attention weight vector describing the relative importance of each attribute over time.MDFT assumes that options are evaluated relative to each other. This leads to dependencies in the evaluations of choice options that are reflected in the valence vector t to a very high level (t = 100015). In the current form MDFT thus contains five free parameters, the weighting parameter w, the relative distance weighting parameter wd, the sensitivity parameter φ1, the decay parameter φ2, and a variance parameter v of the normally distributed error term ε. The probability and outcomes of a gamble are usually connected in a multiplicative way to determine, for instance, the expected value or the expected utility of an option. We therefore logarithmized the probabilities and outcomes in MDFT, so that it became sensible to use them as additive attributes and thereby expected value maximization becomes a special case of MDFT. We further follow the approach of Berkowitsch et al.15 to normalize attributes. Parameter estimation was performed in MATLAB R2015a (Mathworks), using the function fminsearch. We used the quantiles Q0.25, Q0.5, and Q0.75 of the estimated parameter distribution of Berkowitsch et al.15 as starting values within our optimization procedure. The combination of estimated parameter values resulting in the lowest AIC was used to determine the choice probabilities for every trial of the experiment to inform the fMRI analysis.To estimate choice probabilities predicted by MDFT we assumed that individuals make choices after the preference states have stabilized and therefore set Whole-brain fMRI data were collected on a 3 T Siemens Magnetom Trio scanner using a 12-channel phased-array head coil. Echoplanar images (EPI) were acquired from 36 axial slices of 68 × 68 voxels with 3-mm in-plane resolution, 3.45-mm slice thickness, field of view (FOV) of 204 mm, 80° flip angle, 28-ms echo time (TE), and 2-s repetition time (TR). For each participant two runs of 670 volumes each were acquired. Initial scout scans were performed to localize slice planes parallel to the anterior commissure – posterior commissure line. In addition, high-resolution T1-weighted structural scans were collected for each participant for registration purposes using an MPRAGE sequence .32, part of FSL 4.0 . Pre-processing included slice-timing correction, motion correction, and spatial smoothing using an 8 mm Gaussian kernel. Additionally, pre-whitening was used and high-pass temporal filtering (90 s) was applied to the data. A double-gamma function was used to model the hemodynamic response.MRI data were analyzed using a mixed effects approach within the framework of the general linear model as implemented in the FMRI Expert Analysis Tool  procedure to test whether regression parameters were significantly different zero, or significantly different from each other. For the a priori regions of interest we report those activations as significant that exceed an uncorrected threshold of z-score > 3.1 and a cluster size > 50. A priori regions of interest were regions that have been shown to be implicated in preferential decision making36. These regions include the prefrontal cortex, the cingulate cortex, the striatum, the aINS, the amygdala, the hippocampus, and the parietal cortex.Event-related fMRI data were analyzed using General Linear Models (GLM) with task-related regressors. Images of individual level regression parameters (contrast images) were normalized into a standard stereotaxic space , Montreal, Quebec, Canada) and included a random-effects group analysis. Here we used the Bayesian modeling approach13. We therefore defined three regressors of interest and one regressor of no interest. The regressors of interest modeled constant brain activity during the decision phase (7 s) of the three conditions of our experiment . Additionally, we included one regressor of no interest that modeled the decision phase (7 s) of missed trials as well as choices between a sure amount and a risky option that were included to prevent subjects from recognizing the aim of the experiment (see above). Each of the three regressors of interest was contrasted against the other two.We analyzed two different models. The purpose of GLM1 was to replicate the findings of Hedgcock and RaoThe purpose of GLM2 was to examine the relationship between evidence in favor of the chosen option and brain activity. Here, we included two regressors of interest and one regressor of no interest. One regressor of interest (R1) modeled brain activity during the decision phase (7 s) independent of the condition. The second regressor of interest (R2) modeled a parametric modulation of the decision phase (7 s) with the choice probability of chosen option that was predicted by MDFT. The regressor of no interest modeled the decision phase (7 s) of missed choices (if there were any). Importantly, R2 was orthogonalized with respect to R1. Here, we tested in which brain regions regression parameters of R2 are significantly different from zero (positive or negative). We further analyzed in which brain regions individual differences in the relative distance weighting parameter of MDFT are related to individual differences in regression parameters obtained from R2.The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.Supplementary Information


The presence and relative stability of extracellular RNAs (exRNAs) in biofluids has led to an emerging recognition of their promise as ‘liquid biopsies’ for diseases. Most prior studies on discovery of exRNAs as disease-specific biomarkers have focused on microRNAs (miRNAs) using technologies such as qRT-PCR and microarrays. The recent application of next-generation sequencing to discovery of exRNA biomarkers has revealed the presence of potential novel miRNAs as well as other RNA species such as tRNAs, snoRNAs, piRNAs and lncRNAs in biofluids. At the same time, the use of RNA sequencing for biofluids poses unique challenges, including low amounts of input RNAs, the presence of exRNAs in different compartments with varying degrees of vulnerability to isolation techniques, and the high abundance of specific RNA species (thereby limiting the sensitivity of detection of less abundant species). Moreover, discovery in human diseases often relies on archival biospecimens of varying age and limiting amounts of samples. In this study, we have tested RNA isolation methods to optimize profiling exRNAs by RNA sequencing in individuals without any known diseases. Our findings are consistent with other recent studies that detect microRNAs and ribosomal RNAs as the major exRNA species in plasma. Similar to other recent studies, we found that the landscape of biofluid microRNA transcriptome is dominated by several abundant microRNAs that appear to comprise conserved extracellular miRNAs. There is reasonable correlation of sets of conserved miRNAs across biological replicates, and even across other data sets obtained at different investigative sites. Conversely, the detection of less abundant miRNAs is far more dependent on the exact methodology of RNA isolation and profiling. This study highlights the challenges in detecting and quantifying less abundant plasma miRNAs in health and disease using RNA sequencing platforms. More recently, the application of next generation sequencing to exRNA discovery has led to the recognition that various other species of RNAs are also present in biofluids [Extracellular RNAs (exRNA) have recently been identified as novel biomarkers and potential cell-cell communicators in plasma and other body fluids. To date, the most abundantly studied class of these exRNAs are microRNAs (miRNA), which have been implicated in a wide range of diseases including heart failure , 2, canciofluids , 11. Whiiofluids , and as While exRNA offers an exciting new branch of functional biomarker research, the field is still in its infancy and faces numerous technical challenges. RNA concentrations in biofluids are significantly lower than in tissue and standard methodologies developed for tissue RNA extraction and quantification are not always appropriate. Secondly, the RNAs may be present in different compartments, each of which may have different susceptibilities and vulnerabilities to RNA isolation techniques, thereby creating an unintended bias in RNA profiling based on the exact isolation method used , 14. FurCurrent technologies available for the quantification of exRNA include qRT-PCR, RNA microarray, and RNA sequencing (RNAseq). While qRT-PCR and RNA microarrays utilize specific primers or probes, and as such can only detect known RNA sequences, high through-put RNAseq has the ability to detect novel transcripts across a broad dynamic range and offers a potentially sensitive means to characterize and quantify exRNA. However, in addition to the biases introduced by the technique , 15, theAll experiments were conducted with approval of the Institutional Review Board of Beth Israel Deaconess Medical Center and written consent of the subjects. Blood was obtained from 4 healthy adult volunteers  between the ages of 20–40 for all experiments except the long archived ribodepleted vs non-ribodepleted samples. These samples were obtained from a registry of post-myocardial infarction patients with collected plasma for assay of novel biomarkers (PROSPECT-CMR). These patients  were between the ages of 49–70, where Pt 1 and Pt 3 are females and Pt 2 is male.2EDTA-containing tubes. Blood was immediately processed by centrifugation at 1,000 x g for 10 min at room temperature to separate plasma. The isolated plasma was immediately frozen at -80°C in 1 mL aliquots until use. Samples were frozen for approximately 1 week prior to extraction, except where noted. Plasma was thawed at room temperature and centrifuged at 2,000 x g prior to RNA extraction to remove any remaining platelet or debris contamination.40 mL of blood was obtained by venipuncture using a 21G needle and collected in KRNA was extracted from 1 mL of plasma using the miRCURY RNA Isolation Kit for Biofluids (Exiqon) according to manufacturer’s protocol except for noted modifications. 1 μL of 20 mg/mL glycogen (Roche) was added to plasma prior to RNA extraction. All samples were treated with T4 polynucleotide kinase (New England Biolabs) to facilitate 5’ hydroxyl terminus phosphate labelling and allow greater binding of adaptors during library preparation, and RNA sample volume was reduced to ≤10 μL in a standard lyophilizer at room temperature. RNA concentration was determined by Quant-iT RiboGreen RNA Assay Kit.Prior to RNA extraction, a subset of plasma samples were treated with Proteinase K  for 30 min at 50°C. Samples were treated in one of three groups: 1) no PK treatment; 2) PK treatment prior to addition of guanidinium thiocyanate (GITC) containing lysis buffer (from miRCURY RNA Isolation Kit); 3) PK treatment following the addition of GITC-containing lysis buffer. Where indicated, ribodepletion was performed using the Ribo-Zero Magnetic Gold kit (Epicentre) according to manufacturer’s protocol. A schematic summarizing plasma sample treatments is shown in For samples that were analyzed by digital droplet PCR (ddPCR), ethanol precipitation was performed to remove any excess SDS solution that could inhibit the PCR reaction. Briefly, 0.3 M NaCl, and 3x volumes of 100% ethanol were added to the solution and the RNA precipitated at -80°C overnight. Samples were then pelleted by centrifugation, washed with 80% ethanol, and re-suspended in 10 μL RNAse-free water.Each sequencing library was constructed from 2 ng of isolated and treated plasma RNA. All libraries were uniquely bar-coded with index primer for multiplexing into sequencing lanes. The small RNA libraries were prepared and amplified using the NEBNext small RNA Library Prep Set  following manufacturer instruction. The amplified libraries were resolved on a 10% Novex TBE gel (Life technologies) for size selection and the 140 to 160 nucleotide bands that correspond to adapter-ligated constructs derived from the 21 to 40 nucleotide RNA fragments were excise and recovered in DNA elution buffer. The average size distribution of each library was determined using Agilent Bioanalyzer with High Sensitivity Chip Kit  and quantified on ABI 7900HT Fast RT-PCR instrument using the KAPA Library Quantification kit according to the manufacture’s protocol . Each library was adjusted to final concentration of 2 nM, pooled, and sequenced on an Illumina HiSeq 2000 or MiSeq sequencer for single read 50 cycles at the Center for Cancer Computational Biology at Dana-Farber Cancer Institute.http://hannonlab.cshl.edu/fastx_toolkit). The processed sequences were filtered for small RNAs greater than 16 nucleotides in length. The sequences were then aligned, quantified and annotated using sRNABench 1.0 pipeline [http://exrna.org/resources/data/) as part of Extracellular RNA Communication Consortium (ERCC).The BCL files were de-multiplexed using CASAVA v1.82, and the adaptor sequences within the read sequences were trimmed by FastX-Toolkit  according to manufacturer’s protocol. cDNA was diluted 1:10 and loaded into PCR reactions with EvaGreen supermix (Bio-Rad) and pre-designed primers for miR-30d, miR-150, and miR-122 (Exiqon) according to manufacturer’s protocol (Exiqon). Droplet formation was carried out using a QX200 droplet generator and droplets were transferred to a 96-well plate for PCR. No template reactions were used as a negative control. Endpoint amplification was conducted with the following steps: 95°C for 5 min, 40 cycles of 95°C for 30 s, 58°C for 30 s, 4°C for 5 min and 90°C for 5 min at a ramp-rate of 2.5°C/s for all steps. Droplets were then read with the QX200 droplet detector (Bio-Rad) and analysis conducted with Quantsoft 1.7. Results are presented as expression relative to the no PK treatment counts.a) no PK, b) PK treatment before GITC (Pre-GITC), and c) PK treatment in GITC (PK in GITC and PK in GITC #2) that was drawn from a single subject. The no PK, PK pre-GITC, and PK in GITC samples were from the same blood draw, whereas the PK in GITC #2 was from a separate draw. Total RNA yield from these samples  was a) 22.1 ± 1.3 ng, b) 23.0 ± 3.2 ng, and c) 67.0 ± 6.0 ng (mean ± SEM). Libraries were constructed using NEBNext small RNA Library Prep Set and run on the Illumina HiSeq2000 platform. The number of initial reads post-filtering varies from 5.5 to 8.4 million for each sample, with more than 50% of the reads being ribosomal RNA (rRNA) (To determine the impact of proteinase K (PK) treatment at different stages of RNA extraction affecting miRNA detection, we extracted RNA from plasma treated with A (rRNA) . The rRNDespite the large variation in the total number of miRNA reads, all three samples yielded more than 346 mature miRNA with PK-in-GITC sample yielding the most diverse result (n = 428). Overall we identified reads mapping to 593 mature miRNA across samples with detectable counts count>0, , but onlLastly we checked if commonly observed miRNA species were expressed at higher levels and thus more resistant to technical variation. miRNA that were observed in all samples showed significantly higher expression compared to those observed in two or less samples . Similarly, miRNAs uniquely observed in only one sample generally have lower expression, particularly among those uniquely identified from the PK in GITC sample. We repeated PK-in-GITC treatment on a different plasma sample (PK-in-GITC #2) derived from the same subject, and observed similar level of percentage mapped reads 31.3%, . Taken tTo further validate these findings we performed ddPCR on repeated samples of no PK vs PK in GITC from the original single healthy donor. From the RNAseq results, we selected a high abundant (miR-122), mid-abundant (miR-30d), and low abundant miRNA (miR-150) species to measure . InteresDue to the high percentage of rRNA reads observed, the effect of ribodepletion on sample reads was tested. Plasma samples from the same healthy individual, frozen for either 1 week (fresh) or 1 year (archived) were extracted with PK pre-treatment and ribodepletion was performed. As expected, ribodepleted fresh samples showed decreases in the percentage of rRNA reads, ranging from 15 to 25%, while the percentages of miRNA reads and detectable mature miRNA species both showed increases  when comTo further validate this observation, we sequenced both ribodepleted and non-treated archived plasma samples derived from three cardiovascular patients (subjects Pt1 to Pt3) that were archived for 3–4 years. The result concurred with previous observation. Ribodepleted archived samples consistently yield dramatically lower percentage of miRNA mapping reads and lower number of miRNA species compare to non-treated samples . ParadoxTo assess technical reproducibility of miRNA profiling, we examined duplicated miRNA profiles derived from the plasma of three healthy subjects between the ages of 20 to 40 . Plasma samples collected from umbilical cords showed slightly weaker correlation. On the other hand, correlations between placenta and plasma data were generally weaker  despite showing similar high miRNA expression level among the conserved miRNAs as other sample types. This data indicates that there may exist conserved miRNA expression profiles that are specific to plasma.Next we evaluated if these conserved miRNA expression patterns were consistent between the plasma samples from the two datasets. Correlations across datasets for plasma samples were strong  for plasWe examined the number of reads that were mapped to other class of small RNAs in addition to rRNA and miRNA. Overall, among the total 558,000 reads of small RNA sequences generated from the three healthy subjects, there were 67% miRNA, 23% tRNA, 5% small nucleolar RNA (snoRNA), and 5% piwi-interacting RNA (piRNA) reads mapped . DespiteWe began this study with the intention to establish a robust small RNA extraction procedure for peripheral blood plasma miRNA quantification that can be reliably utilized for biomarker discovery from clinical samples. While settling on a single kit based on other studies, our goal was to examine the effects of modifications of RNA extraction procedures for assessing plasma miRNA expression profiles using NGS as our quantification assay. The experiment was designed to select the optimal RNA extraction procedure on plasma samples derived from a single healthy individual, taking consideration of sample storage duration, and then extended to multiple individuals as well as published data to evaluate consistency.We first evaluated the impact of PK treatment, which has been reported to increase RNA yield , on extrOne of the most surprising outcomes from our sequencing data was the consistently high percentage of ribosomal RNA reads that occupies large proportion of sequence reads. This made it more challenging to achieve a high depth of coverage for profiling miRNA expression. We tested the ability of ribodepletion, which is standardly performed for RNAseq of tissues samples, to remove rRNA from extracted plasma RNA derived from fresh peripheral blood plasma. The result was initially highly encouraging with both substantial decrease in the percentage of rRNA and increase in the percentage of miRNA reads. However, the same plasma sample that were frozen for one year yielded poor sequencing results after ribodepletion, with no reduction in the percentage of rRNA and substantial decrease in the percentage of miRNA reads. As most clinical samples would have been archived for extended period of time, it is important to note that fresh and archived samples should be handled differently. The use of ribodepletion, while potentially beneficial in fresh samples, may not be suitable for the study of archived samples. Other techniques, such as blocking of highly abundant miRNAs for plasma RNA sequencing, have been described but not adapted to multiplexing and could offer another avenue of increasing the detection of less abundant species of exRNAs.Our comparisons of miRNA expression profiles showed strong correlation of miRNA species for both sample replicates and between samples from different healthy donors, supporting the reproducibility of NGS as a methodology for plasma miRNA quantification. In particular, despite the low RNA concentration within peripheral blood plasma that may introduce strong sampling bias, large numbers of abundantly expressed miRNA species were observed across samples that we examined in this study with similar profiles. We further investigated if these conserved plasma miRNA that we observed exhibit similar patterns from healthy individuals in published study and found that these profiles remain strongly correlated with our results despite differences in sample acquisition, RNA extraction, and sequence library preparation protocols. While this may suggest the presence of a stable landscape of extracellular miRNA in healthy plasma, further studies with larger cohorts are required to confirm this observation.As the scope of this study is limited to optimize only small aspects of exRNA extraction process from plasma for small RNAseq quantification, there remain arrays of challenges in generating reproducible and reliable data. For instance, T4 polynucleotide kinase treatment during sequencing library generation likely facilitated extracellular DNA nucleotide for sequencing  to resulS1 TablePlasma was treated with a) no proteinase K(No_PK); b) PK treatment before GITC (PK_pre_GITC); c) PK treatment in GITC (PK_in_GITC); d) Repeated PK treatment in GITC (PK_in_GITC_2)(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.