Drosophila melanogaster and estimate that in humans, circular RNA may account for 1% as many molecules as poly(A) RNA. Analysis of data from the ENCODE consortium revealed that the repertoire of genes expressing circular RNA, the ratio of circular to linear transcripts for each gene, and even the pattern of splice isoforms of circular RNAs from each gene were cell-type specific. These results suggest that biogenesis of circular RNA is an integral, conserved, and regulated feature of the gene expression program.Thousands of loci in the human and mouse genomes give rise to circular RNA transcripts; at many of these loci, the predominant RNA isoform is a circle. Using an improved computational approach for circular RNA identification, we found widespread circular RNA expression in  Last year, we reported that circular RNA isoforms, previously thought to be very rare, are actually a pervasive feature of eukaryotic gene expression programs; indeed, the major RNA isoform from hundreds of human genes is a circle. Previous novel RNA species that initially appeared to be special cases, of dubious biological significance, have subsequently proved to have critical, conserved biological roles. An almost universal characteristic of regulatory macromolecules is that they are themselves regulated during development and differentiation. Here, we show that the repertoire of genes expressing circular RNA, the relative levels of circular: linear transcripts from each gene, and even the pattern of splice isoforms of circular RNAs from each gene were cell-type specific, including examples of striking regulation. In humans, we estimate that circular RNA may account for about 1% as many molecules as poly(A) RNA. The ubiquity of circular RNA and its specific regulation could significantly alter our perspective on post-transcriptional regulation and the roles that RNA can play in the cell. In many applications, computational challenges associated with mapping and with distinguishing between sequencing errors and sequence homology prevent reliable identification of structural variants, including circular RNAs. Indeed, although de novo splicing detection algorithms have been used in more than a thousand published studies, including studies aimed at identifying gene fusions and internal tandem duplications, most instances of scrambled exons in human RNAs, and thus the circular species that they represent, had eluded detection.A major challenge in bioinformatic and statistical identification of novel RNA isoforms, particularly circular RNA, involves distinguishing bona fide evidence of scrambled exons in RNA from confounding factors such as sequence degeneracy at exon boundaries and sequencing errors. To address these challenges and to identify circular isoforms from public ENCODE RNA-Seq data, we developed a new bioinformatic approach. The main idea behind our computational method is that it refrains from qualitative hard thresholding of read alignment quality, and instead computes statistical averages of alignment quality scores. This approach allows us to distinguish putative novel splice junctions where the majority of reads align to the ‘novel’ junction with high quality alignment scores from those where reads with high alignment scores are rare. The method allows for systematic FDR-based thresholding, rather than qualitative cut-offs, to determine classification as a scrambled junction at a prescribed confidence level.Our method was focused on identifying circular RNA transcribed from genes whose linear isoform exons are annotated: we first built a database of all scrambled junctions between annotated exon boundaries, essentially as previously described The improved sensitivity of this approach compared to previous methods allowed us to identify thousands of previously unreported circular isoforms and some very small circular RNAs, exemplified by a <150 nt circular RNA isoform ABTB1 resulting from the splicing of two short exons. Other small circular isoforms that we identified and confirmed include a 204 nt circular isoform from a single exon of LINC00340 - a long intergenic noncoding RNA, and a two exon circle of 151 nt from the RNA binding motif gene RBM5.Experimental and bioinformatic noise can give rise to spurious evidence of circular transcripts, especially for highly expressed genes. To tackle this problem, we combined the bioinformatic approach above with a statistical strategy to distinguish reads supporting exon scrambling from reads likely to be homology and sequencing artifacts. Briefly, we did not impose any thresholds on alignment quality of either read 1  or read 2 . All read pairs with evidence of scrambled exon splicing at a particular pair of genomic coordinates were aggregated by averaging, measuring the overall quality of reads aligning to the putative circle.We generated an empirical null distribution of average alignment qualities using “decoy” read pairs. These “decoy” reads had the property that read 1 mapped to a scrambled intragenic exon X – exon Y junction and read 2 mapped within the same gene but would be excluded from a circle composed of exons Y, Y+1, … X, see Using this approach, we were able to enhance statistical discrimination between case 1: false positive evidence of circular RNA isoforms in highly expressed genes resulting from reads with sequencing errors observed due to high sampling of these genes, and case 2: bona fide low-level expression of circular isoforms from these highly expressed genes. For example, the vast majority of reads (99.995%) from putative circular isoforms of GAPDH had a FDR significantly surpassing our threshold of .025; these reads would be taken as evidence of circular isoform expression with a naïve approach.We applied our method to a large publicly available data set from the ENCODE consortium see , with thAcross the 15 cell types, at an FDR of .025, we found 46866 distinct intragenic splice junctions at annotated exon boundaries in 8466 genes. Across cell types, we detected the largest number of genes with evidence of circular RNA expression in the leukemia cell line K562 (16559 distinct circle-specific splice junctions); in the fetal lung fibroblast line AG04450, we detected 11590 distinct splicing circle-specific splice junctions and in the human foreskin fibroblast line BJ, we detected 7771 . Our estimate of each circular RNA isoform's abundance from sequencing was the number of read pairs, in poly(A)-minus fractions, in which one read spanned a circular junction . To determine the equivalence factor , we measured the abundance of circular and linear isoforms of FAT1 and HIPK3 across three ENCODE cell lines  by qPCR. This allowed us to relate the abundance of the linear isoforms of FAT1 and HIPK3 as measured in units of RPKM to the abundance of the circular isoforms of these genes as measured in units of junctional read counts. Since the equivalence factor is the same for all genes in the genome, we were then able to compute the relative abundance of circular and linear isoforms for all genes detected in the sequencing data.FAT1 and HIPK3 were chosen because they have large, abundant circular RNA isoforms and high linear RNA isoform expression, thus mitigating potential factors confounding this estimation such as rolling circle amplification of small circular RNA during the RT, and statistical uncertainty introduced by estimating the expression of low abundance circle or linear isoforms.These estimates suggested that there was roughly 1 molecule of circular RNA for every 100 molecules of poly(A) RNA in the cell lines we evaluated: A549, AG04450 and HeLa. For roughly 50 genes in each cell line, circular transcript isoforms were estimated to be more abundant than linear isoforms  depleted fractions of HeLa-S3, the highest read density was in exons 2 and 3 followed by intron 2, with other introns more than 10-fold lower. Of note, our estimate of the ratio of intron 2 to exon 3 expression (based on number of reads with zero mismatches to the genome) was somewhat higher in the nuclear fraction (38%) compared to in the cytosolic (10%) or RNA isolated from total cells (16%). There was also cell type variation of the ratio of intron 2 to exon 3 read density in “cell” fractions across the ENCODE data set, from 16% in HeLa-S3 down to 3.5% in NHEK, suggesting that intron-retention in CAMSAP1 circles may be under regulatory control.We previously reported that genes with circular RNA transcripts tend to have larger introns than genome-wide averages. That analysis showed that even after controlling for the tendency for intron lengths to decrease from 5′ to 3′ along the canonical transcript We ranked introns that flanked spliced exons generating circular RNA isoforms in two ways: 1) weighting the lengths of introns flanking each circular isoform by the abundance of the corresponding circular RNA  gene expression. For a set of relatively highly expressed circular isoforms, we evaluated the fit of a Poisson model in which circular RNA expression increased with linear isoform abundance, controlling for effects of sequencing depth and incorporating experimental variation by treating experimental replicates as distinct. We also included cell-type effects to further account for circular RNA expression.We observed the largest dynamic range in circular RNA production in the gene CYP24A1, a candidate oncogene encoding a component of the vitamin D3 metabolic pathway. Its linear mRNA product and the CYP24A1 protein have been reported to be expressed at elevated levels in many primary lung and other cancers, and in many lung cancer cell lines, including A549; no amplification of the CYP24A1 gene has been reported in A549 Our statistical model also suggested that other highly expressed RNA circles had cell-type specific increases in expression that could not be accounted for by an increase in overall expression of the corresponding linear RNA. One example is the circular isoform of DOCK1, whose linear isoform encodes a “dedicator of cytokinesis”, a RacGEF, and was the most highly expressed circular isoform in MCF-7, a breast cancer cell line. DOCK1 also had the highest estimated ratio of circular: linear RNA expression in MCF-7 among all cell types in the ENCODE panel, including those where the linear isoform of DOCK1 was more highly expressed see .A final highlighted example of cell-type-specific selective increases in the ratio of circular to linear RNA isoforms in In these specific examples, and in general, variation in the abundance of hundreds of circular RNA isoforms appeared to have little or no correlation with variation in the abundance of the cognate linear RNA between the cell lines we compared. In particular, we did not observe a correlation between circle-specific junctional counts and overall abundance of the corresponding RNA as measured by RPKM, even at the lowest levels of gene expression. Further evidence that RNA circles are not just an accidental aberration of normal splicing is provided by the fact that circular RNA isoforms are generated by splicing very specific pairs of exons see  below.When a gene encodes multiple alternatively spliced circular isoforms, what patterns characterize the use of splice acceptor and donor pairs to produce the circle junction? To study this question, we distinguished three broad classes of splice site pairings, which we term stereotyped, proximal and combinatorial pairing, respectively. Examples of each are depicted in A variant of stereotyped splicing was exemplified by the circular RNA isoforms of MBOAT2. These isoforms were expressed at levels similar to MCU, but with a distinctly different pattern of splicing: one particular splice acceptor was highly preferred, but several alternative splice donors were used and each produced similar levels of the corresponding spliced circular RNA isoform. It is noteworthy that none of the exons that participate producing the MBOAT2 circles have been reported to participate in alternative splicing of linear RNA MBOAT2 isoforms.For many transcripts in which multiple splice donor and multiple splice acceptor sites were used in circular splicing, proximal donor-acceptor pairs were strongly preferred. This “proximal” pattern of circular splicing is exemplified by the circular isoforms of ABCC1.The “combinatorial” pattern of circular splicing is exemplified by CAMSAP1 and especially PICALM. Multiple splice donors and multiple splice acceptors participate in production of circular isoforms, with little preference for proximal donor and acceptor sites. In contrast to PICALM, across cell types, CAMSAP1 has a single dominant isoform.Although detection of rare circular RNA isoforms increased with sampling depth of the RNA sequences see , within C, which compares the number of observed circular isoforms to the number of possible pairings of the detected acceptor and donor splice sites (see Methods). In general, regardless of the total expression level of circular RNA isoforms, half or less of the combinatorial space of circular isoforms—conditioned on acceptor donor and acceptor sites used in circular RNA splicing– had detectable expression, and many genes expressed the minimum number of potential circular RNA isoforms represented by the lowest value of C.Considering all genes with circular RNA isoforms, we found that cells generally expressed a small fraction of the number of circular RNA isoforms that could, in principle, be produced by splicing a downstream splice donor to an upstream splice acceptor see . We quanWe used a statistical model to identify genes with regulated use of donor and acceptor sites characterizing the diagnostic non-canonical exon junction. For each gene and each cell type, we estimated the frequency with which each donor and acceptor splice site was used see , S5, andFor hundreds of genes, we found statistical evidence of cell type-specific preferences in patterns of splice donor and acceptor usage in the biogenesis of circular RNA , S5. ThrZFAND6 is a third example of a gene with regulated circular RNA expression. A549 cells expressed a single circular isoform, while the remaining cell types expressed two circular isoforms. These differences cannot be readily explained either by differences in sequencing depth, cell-type-specific differences in linear or circular RNA expression or any cell-type independent differences in the RNA, such as intron lengths or structure see . For exaDrosophila brains muscleblind locus Muscleblind was among the most highly expressed circular isoforms, but our analysis indicated that circular RNAs from 11 other genes in these samples were even more abundant: the most highly expressed putative circular RNAs were from Pka-C3, encoding a cAMP-dependent protein kinase, and scarecrow (scro), encoding an NK-2 homeobox protein. Other highly expressed RNA circles included Caps, ps, mGluRA, caps, snap25, jp, zfh2 and two genes of unknown function, CG40178 and CG17471. Overall, we found evidence for exon scrambling in more than 800 distinct Drosophila splice junctions supported by more than one sequencing read  molecules in the cells we investigated. This estimate is remarkably similar to a report published more than 30 years ago, which found physical evidence of circular RNA by examining cellular RNA by electron microscopy We tested the hypothesis that circular RNAs might be the result of a background “noise” level of dysfunctional splicing. Under this model, we would expect a positive relationship between linear RNA isoform expression from a given gene and the probability of detecting a circular RNA isoform from that gene. We found no evidence of such a relationship, suggesting instead that even low rates of circular RNA production are regulated, or that highly expressed genes have evolved specific mechanisms to prevent splicing errors that could result in RNA circles.Drosophila. It is likely that human cells express even more circular RNAs than we report here: we did not attempt a ‘de novo’ identification of circular RNA, and instead focused on circular RNA produced by splicing at annotated exon boundaries.Our initial report of the ubiquity of circular RNA, based on sequencing an ribosomal-RNA depleted RNA fraction, has since been confirmed in an independent study in which circular RNAs from human and mouse fibroblasts were enriched by treating RNA with RNase R, and in a second genome-wide search for evidence of circular RNA by sequencing ribosomal-RNA-depleted RNA samples Drosophila (spp4), and it would be interesting to study intron retention in these organisms.For example, by analysis of a Northern blot for CAMSAP1 in HeLa cells, and a subsequent limited bioinformatic survey of 6 genes, we found evidence of cell-type specific variation in rates of intron retention as well as evidence that circular, intron-retained transcripts in HeLa-S3 cells may be sequestered in the nucleus and potentially exported to the cytoplasm. CAMSAP1 is a calmodulin regulated gene and has conserved circle expression in mouse and The most highly expressed circular RNA identified in our analysis was from the CYP24A1 gene, in a lung cancer cell line, A549. CYP24A1, which encodes 1,25-dihydroxyvitamin D 3 24-hydroxylase, has been suggested to play a role in the pathogenesis of many carcinomas Recent reports have shown that an antisense circular transcript from the CDR1 locus is enriched for functional microRNA binding sites Our findings that mouse orthologs of human genes with circular RNA products are themselves more likely to encode circular RNAs are consistent with a similar independent analysis of circular RNA conservation and support the hypothesis that circular RNAs have an evolutionarily conserved function http://hgdownload.cse.UCSC.edu/goldenPath/hg19/encodeDCC/wgEncodeCshlLongRnaSeq. Read 1 and Read 2 reflect directionality of original RNA and were not processed symmetrically.Raw fastq files available on Sept. 3, 2012 were downloaded from the ENCODE project website and processed in batch using custom Perl scripts. At that time, 2 replicates from each of 15 cell types were available, with the exceptions that 1 HMEC and 3 NHEK were downloadable. We selected all long poly(A) minus reads banked at We constructed a custom database for sequence alignment as follows: all UCSC annotated exons in scrambled order were identified and for each pair, 66 nt from each side the 3′ and 5′ ends of flanking exon were concatenated. Sequence alignment of a 76 nt read hence required alignment with a minimum of 10 nt overhang. Cases where exons were <66 nt were treated separately, by concatenating exons upstream of the donor or downstream of the acceptor exon in the scrambled pair, or using ‘in silico’ rolling circle concatenation in cases where the total circle size was smaller than 132 nt.In detail, read 1 and read 2 were not treated symmetrically as the input library was a directional RNA-Seq library. Read 1 was aligned to UCSC knowngenes and the human genome under bowtie2 default conditions In conjunction with the alignment to the above database of exon-exon junctions, we modeled the null distribution for rates of mismatch of reads aligning to this database using a method that should be of general interest in discovery of structural variants using high throughput sequencing data. In overview, we considered all reads that aligned with qualities a) and b) below, without imposing hard thresholding on the quality of alignment of either read:Read 1 maps to an annotated hg19-UCSC-knowngene intragenic scrambled junction database. This database contains sequences of length 132 and hence requires a 76 nt read to align with at least a 10 nt overhang.Read 2 maps to the same annotated UCSC gene, either to anywhere within the gene or to the same exon-exon junction as read 1.A read reflecting a circular RNA isoform transcribed from the same strand as the canonical isoform has the property that read 1 maps to the −orientation and read 2 to the +orientation.Alignment scores were calculated using the bowtie2 default which for example, adds a ‘−6’ penalty for a mismatch between the reference and aligned read at a high quality base call. Summing penalties for mismatches produces an overall alignment score per read, one score for the read spanning junction (read 1) and one score for read 2. Three statistics measuring alignment were calculated for each pair of scrambled exons for each UCSC isoform supported by at least one read: the average alignment score of read 1, the average alignment score of read 2, and the average product alignment score of read 1, read 2 although this measure was not ultimately used to calculate the FDR reported.In detail, to compute the FDR, we created a null distribution of the joint alignment statistics for read 1 and read 2 using reads where read 1 mapped to a junction between exon x and exon y (x> = y) and read 2 mapped upstream of exon y or downstream of exon x which is incompatible with it deriving from a circular RNA molecule. We used the pair of  alignment statistics from such reads to generate the FDR per isoform depicted in Subsequently, all reads were filtered to an FDR level of .025 unless otherwise specified. See HeLa total RNA was isolated by TRIZOL lysis followed by PureLink purification of the aqueous phase (Life Technologies). 2 micrograms of total RNA was treated in a 10 microliter reaction with 0 units (mock treatment) or 20 units of RNase R (Epicentre) in 1× RNase R buffer, 1 unit/microliter murine Ribonuclease Inhibitor (New England Biolabs), and incubated at 37DEGC for 1 hr. 1 microliter 1 mM EDTA, 1 microliter 10 mM each dNTP, and 1 microliter 100 microM random hexamer were added and the RNA denatured at 65DEGC for 5 min and placed on ice. 4 microliters 5× buffer , 1 microliter murine Ribonuclease Inhibitor (40 units/microliter), and 1 microliter Superscript III (LIfe Technologies) were added; this cDNA reaction was incubated at 25 deg C 10 min, 50 deg C 50 min, 55 deg C 10 min, 85 deg C 5 min, 4 deg C hold. 0.5 microliter cDNA reaction was used as the template for qPCR and fraction resistant was computed as 2∧(RNase R C_t - Mock C_t).”HeLa-S3, A549 and AG04450 cells were grown in standard media and conditions. RNA was harvested by lysing cells with the TRIZOL reagent and purified on Purelink columns under ethanol concentrations that retain small and large RNAs. Total RNA reaction was reverse transcribed using the SuperScript III First-Strand Synthesis System  with random hexamers according to the manufacturer's instructions. 500 ng/ul of cDNA was then used for each qPCR validation; dilution series were performed at concentrations of .5, 5, 50 and 500 ng/ul. Each qPCR experiment was done in biological duplicate with 3 technical replicates each.http://hgdownload.cse.UCSC.edu/goldenPath/hg19/encodeDCC/wgEncodeCshlLongRnaSeq/wgEncodeCshlLongRnaSeqSknshraCellPapGeneGencV7.gtf.gzFor each cell type, we downloaded gtf files with gene level RPKM (Reads per kilobase mapped) estimates from the Poly(A) fraction; eg. for SK-NS-H_RA, we downloaded the file: RPKMs were summed across all genes to estimate total annotated poly(A) transcript abundance. In parallel, we summed all reads mapping to a circular RNA junction. These two values provide total abundance estimates of poly(A) and circular RNA respectively, up to a normalizing constant. We determined that normalizing constant by performing qPCR with two calibrating genes: FAT1 and HIPK3. These genes were chosen for the reasons described in the main text. Standard curves were computed for each set of primers listed in For each circular isoform represented by at least 50 counts in one sample (and satisfying an FDR cut-off of .025), we fit a Poisson model per gene modeling circle counts by poly(A) gene expression (genexp), celltype and total circles (totcircles) using the glm poisson model in R with the formula:cir∼log(genexp)+celltype+totcircles−1. Coefficients in this model were used to choose genes shown in 10 micrograms total RNA was denatured with glyoxal and run on a 2% agarose gel The following procedure was used to access statistical significance of the use of donor and acceptor sites across cell types. We analyzed donor and acceptor sites separately. Splice sites represented by more than 50 counts (and satisfying an FDR cut-off of .025) in at least one cell type were analyzed using this approach. For each such donor and acceptor site that was supported by more than 5 reads and present in at least two cell types, we computed an exact .999 binomial confidence interval for its probability of use in that cell type. Sites with at least one pair of non-overlapping confidence intervals were identified and used to choose genes depicted in ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/litesra/SRP/SRP007/SRP007416/ was aligned to a custom database of annotated Drosophila exon-exon junctions using Jan. 2012 flyBase exon annotation and previously described methods and filters. A complete list of detected events is listed in Poly(A) depleted RNA isolated from Drosophila brain, available at http://inparanoid.sbc.su.se/download/7.0_current/sqltables/To access evolutionary conservation, orthology of protein products was defined by Inparanoid using the following databases: For statistical assessment of expression of circular isoforms between mouse and human, a list of orthologous genes expressed (measured by detected gene expression from RNA-Seq data sets used to measure circle abundance) was compiled . We then counted the number of genes in this table with more than 1 circle count in the mouse RNA-Seq data and with expression in the top 100 most expressed circular isoforms in one ENCODE (human) experimental replicate. 57 genes matched this criterion (4%). 147 genes on the list of 1402 were in the top 100 most expressed circular isoforms in one experimental replicate; 332 had more than 1 count in the mouse RNA-Seq data. Under an independence model, we expected 35 genes to match the joint criterion (2.5%). The residual from a chi-squared test for the independence model is (O-E)/sqrt(E) = 3.7, which corresponds to a one sided p value of .0001.Read counts were summed across cell types and replicates for each isoform, defined here as a unique combination of gene, circularization splice donor coordinate, and circularization splice acceptor coordinate. Isoforms were filtered by requiring 20 or more read counts in total across ENCODE cell lines. Intron length was computed as described in the text. For each gene, intron lengths were considered either as fractions of the longest intron length within the gene, or as quantile ranks within the gene.Read counts were summed across replicates for each unique combination of cell type, gene, circularization splice donor coordinate, and circularization splice acceptor coordinate.Read counts were summed across cell types and replicates for each isoform, defined here as a unique combination of gene, circularization splice donor coordinate, and circularization splice acceptor coordinate. Genes where some annotated isoforms satisfied splice acceptor<donor and other isoforms satisfied donor<acceptor were removed from consideration; the remaining genes were then oriented such that all isoforms were acceptor upstream of donor.C compares the number of observed circular isoforms to the number of possible pairings of the detected acceptor and donor sites; C = 1 means that all possible pairings were actually detected, whereas C = 0 means that the minimum possible number of pairings was detected (we adopted the convention that C is undefined when max. poss. isoforms = min. poss. isoforms). Precisely, C was defined for each gene as (# of distinct circular isoforms detected – min. poss. isoforms)/(max. poss. isoforms – min. poss. isoforms), where min. poss. isoforms = max, and max. poss. isoforms = # of combinations of 1 detected acceptor and 1 detected donor in which the acceptor is upstream of the donor. C is evaluated in For each gene, the combinatorial index For each cell type and replicate, isoforms were defined here as unique combinations of gene, circularization splice donor coordinate, circularization splice acceptor coordinate, read 1 orientation, and read 2 orientation.Instances in which the junction-defining read (“read 1”) and its mate-pair read (“read 2”) were on the same strand were removed from consideration. For each cell type, replicates were pooled, and isoforms were defined here as unique combinations of gene, circularization splice donor, circularization splice acceptor, and read 1 orientation. The percentage of circular isoforms in the sense orientation (with respect to the linear isoform) is 96% for HMEC and >99% for the other 14 cell types.http://mirbase.org/ftp.shtml annotated as ‘Homo sapiens’) and corresponding 6mer seed match (nt 2–7). For each possible non-canonically ordered exon X, exon Y pair within a transcript in the UCSC knowngene transcript database (enumeration beginning at 0) to 30, we generated a corresponding 132 nt sequence consisting of 66 nt upstream and 66 nt downstream of the exon-exon junction. For each group of exon X -exon Y sequences, we compared the number of microRNA seed matches per offset (from 0 to 126) divided by the total number of junctions evaluated. We compared these statistics for circular junctions expressed at rank <1000 in at least one cell type, ranked based on aligning paired end sequencing reads to a database of all UCSC knowngene exon-exon junctions and all other expressed circular junctions  and B) show the genome-wide distributions of flanking intron length, normalized by their ratio to the maximum intron length within a gene; A) weights each isoform by total reads summed over all replicates and samples; B) counts each isoform once, provided it has at least 20 distinct read counts.(EPS)Click here for additional data file.Figure S2Linear Isoform variation does not predict circular isoform variation: Bars are colored by isoform per gene. Linear isoform-specific expression for LPAR1, RNF19B, ZFAND6 per cell type does not show a relationship to circular isoform variation. Colors of bars represent different GENCODE V7 isoforms and are consistent across cell types within a gene. Linear isoform expression in LPAR1 is similar between NHLF and AG04450 (sharing the same dominant isoform), but different from BJ and HSMM, which have a different dominant isoform. Circular isoform expression is similar between the pairs BJ and AG04450 and different from the similar pair NHLF and HSMM). The same trend holds for RNF19B: HMEC and NHEK have the same dominant linear isoform , whereas NHLF has a distinct circular isoform expression pattern. No alternative linear isoforms are annotated for ZFAND6. Data was taken from analysis available from ENCODE poly(A)+ transcript quantification with a GENCODE V7 annotation.(EPS)Click here for additional data file.Figure S3Number of detected circular isoforms correlates with total circular isoform expression. Across cell types and replicates, total sequencing counts representing all detected circles on the x axis and number of distinct isoforms on the y axis are correlated. However, for a fixed level of circle expression, greater variation in number of distinct isoforms is observed across cell types than replicates.(EPS)Click here for additional data file.Figure S4Combinatorial features of circular RNA isoform expression. A): A genome-wide distribution of circle expression (x axis) vs number of detected circles (y axis) showing that the most highly expressed circles also exhibit the largest number of detectable circular isoforms. B) numbers of splice acceptor and donor sites used in circle splicing are correlated. C) genome-wide combinatorial index is low: most loci only express a small subset of circles compared to all possible splice site pairings. Moreover, increased total expression of circular isoforms does not show a relationship with increased detection of circles involving all potential splice site pairs, measured by the combinatorial index.(TIF)Click here for additional data file.Figure S5http://www.mirbase.org/ftp.shtml) might be enriched in a 66 nt window around these junctions (the junction is at offset position 66). At each offset position, we plot the ratio of microRNA seed matches in highly expressed circles (rank <1000 in at least one cell type) to lowly expressed circles . The average ratio was roughly 1.05 over all offsets, with a maximum of 1.25 at any position.No systematic enrichment for microRNA sites near circle junctions. The only sequences unique to circles (not present in linear counterparts) are in the region of the scrambled exon-exon junction. We test if microRNA seed matches (determined using mature.fa from (TIFF)Click here for additional data file.Table S1Expression of circular and linear isoforms and normalized relative expression for A549. qPCR was performed as described as in the main text to normalize between linear and circular RNA expression measurements. Genes are ordered according to circular: linear ratios which is provided in the field rpkm_div_cir.count. The value at which rpkm_div_cir.count corresponds to equal numbers of linear and circular molecules is 315.05. cir.count is the number of total RNA-Seq reads representing each circular junction.(CSV)Click here for additional data file.Table S2Expression of circular and linear isoforms and normalized relative expression for AG04450. qPCR was performed as described as in the main text to normalize between linear and circular RNA expression measurements. Genes are ordered according to circular: linear ratios and provided in the field rpkm_div_cir.count. The value at which rpkm_div_cir.count corresponds to equal numbers of linear and circular molecules is 523.942. cir.count is the number of total RNA-Seq reads representing each circular junction.(CSV)Click here for additional data file.Table S3Expression of circular and linear isoforms and normalized relative expression for HeLa-S3. qPCR was performed as described as in the main text to normalize between linear and circular RNA expression measurements. Genes are ordered according to circular: linear ratios which is provided in the field rpkm_div_cir.cout. The value at which rpkm_div_cir.cout corresponds to equal numbers of linear and circular molecules is 275.005. cir.count is the number of total RNA-Seq reads representing each circular junction.(CSV)Click here for additional data file.Table S4Estimated .999 CI of rate of donor site used in expressed circular RNA by ENCODE cell type per circular isoform. Fields in table: type, method = exact binomial CI, x = number of circles using donor site per gene per cell type, n = total number of circles in this gene and type, mean = fraction of circles using this donor site, lower = lower .999 CI for p, upper = upper .999 CI for p, donor = donor position, gene.(CSV)Click here for additional data file.Table S5Estimated .999 CI of rate of acceptor site used in expressed circular RNA by ENCODE cell type per circular isoform. Fields in table: type, method = exact binomial CI, x = number of circles using acceptor site per gene per cell type, n = total number of circles in this gene and type, mean = fraction of circles using this acceptor site, lower = lower .999 CI for p, upper = upper .999 CI for p, acceptor = acceptor position, gene.(CSV)Click here for additional data file.Table S6Drosophila data. SRR = data file id; gene = common gene name; flygene_id = flygene identifier; exon1 = acceptor exon–exons enumerated beginning with 0; exon2 = donor exon (exons enumerated beginning with 0); strain = period null or wt; time = time of replicate; count = total circle counts.All circular junctions detected in the (CSV)Click here for additional data file.Table S7All circular junctions detected in the ENCODE data. Fields in table: type = cell line; place = location of RNA isolate given by ENCODE; start = acceptor (donor) site and stop = donor (acceptor) site for detected circular junction for genes transcribed on the “+” (resp. “−”) strand;ave_size = average length of all transcripts in UCSC knowngene annotation between start and stop coordinates; gene; score1info,score2info,score12info = average alignment score for read 1, read 2 and product of read 1 and read 2 (respectively),sum = number of junctional counts; srr = replicate, rank = ranked circle expression (highest = 1).(BZ2)Click here for additional data file.Table S8qPCR primers for linear and circular isoform detection.(DOCX)Click here for additional data file.Table S9Estimated coefficients in Poisson model of linear and circular isoform abundance by gene. Fields in table: reptype = celltype and replicate type, celltype = cell type, isomark = concatenated coordinates of start and stop from (CSV)Click here for additional data file.


Since the Spiegelzymes contain the same building blocks as the Spiegelmers, it can be expected that they will have similar favorable biological characteristics concerning toxicity and immunogenety. In trying to understand the mechanism of action of the Spiegelzymes described in this study, we have initiated for the first time a model building system with L-nucleic acids. The models for L-hammerhead ribozyme and L-DNAzyme interaction with the same L-RNA target will be presented.In this manuscript we describe for the first time mirror image catalytic nucleic acids (Spiegelzymes), which hydrolyze sequence specifically L-ribonucleic acid molecules. The mirror image nucleic acid ribozymes designed are based upon the known hammerhead ribozyme and DNAzyme structures that contain L-ribose or L-deoxyribose instead of the naturally occurring D-ribose or D-deoxyribose, respectively. Both Spiegelzymes show similar hydrolytic activities with the same L-RNA target molecules and they also exhibit extra ordinary stabilities when tested with three different human sera. In this respect they are very similar to Spiegelmers (mirror image aptamers), which we had previously developed and for which it has been shown that they are non-toxic and non-immunogenic. Since we are also able to demonstrate that the hammerhead and DNAzyme Spiegelzymes can also hydrolyze mirror image oligonucleotide sequences, like they occur in Spiegelmers,  In vitro selection techniques were developed to produce high affinity nucleic acids, called aptamers, and also different types of ribozymes and DNAzymes. Thus, it was established that RNA and DNA molecules can be selected to carry out functions that were previously solely attributed to proteins Within the last 30 years it became apparent that natural nucleic acids may possess, besides their traditionally known functions in gene expression, an unforeseen structural and functional repertoire. This could range from enzymatic to high affinity binding functions. Nucleic acid aptamers are single stranded DNA or RNA oligonucleotides, which are capable of adopting structures that bind any target imaginable with very high specificities and affinities. Thus, the binding properties of RNA and DNA aptamers can make them to excellent alternatives to antibodies Staphylococcus enterotoxin Spiegelmers can be generated against a wide range of targets, since their development makes use of the methods of molecular evolution in vitro and in vivo conditions. Thus, the Spiegelzymes fulfill a very essential and basic prerequisite as potential functional antidotes for Spiegelmers.In this communication we describe for the first time mirror image catalytic nucleic acids, i.e., L-RNA hammerhead ribozymes and L-DNAzymes, constructed on the basis of the known D-RNA hammerhead ribozyme and D-DNAzyme 1CAGUCGGUCGCC13-3′. For the L-RNA1 substrate the same nucleotide sequence was used, except that the D-nucleotides were replaced by the corresponding L-nucleotides. Both target forms of RNA1 were labeled with fluorescein at their 5′ ends.All RNA and DNA oligonucleotides were chemically synthesized by ChemGenes (USA) or by IBA (Germany). The D-RNA1 substrate of 13 nucleotide length had the following sequence: 5′-A1GCGACCCUGAUGAGGCCGAAAGGCCGAAACUGU34-3′, while the L-RNA hammerhead ribozyme (Spiegelzyme) designed for the L-RNA1 target had the same sequence with the exception that L-nucleotides, instead of the D-nucleotides, were used in the sequence of the Spiegelzyme.The D-RNA hammerhead ribozyme used for the D-RNA1 target had the following sequence 5′-G1UUCAAGUCCGCCA14-3′. The difference between both targets was in that the D-RNA2 consisted of D-nucleotides and the L-RNA2 target of L-nucleotides. The L-RNA2 target actually corresponds with its sequence to a fragment of the green fluorescent protein (GFP) mRNA (positions 252 through 265). Both target forms of the RNA2 were labeled with fluorescein at their 5′ ends. The hammerhead ribozyme and hammerhead Spiegelzyme (34 nt) specific for RNA2 had the following sequence:The 14 nt sequences of the D-RNA2 and L-RNA2 targets were the 5′-C1GGCGCUGAUGAGGCCGAAAGGCCGAAACUUGA33-3′. The difference being that the hammerhead Spiegelzyme consisted of the L-form of the nucleotides.5′-U1CAGTCGGTCGCC13-3′. Thus, the sequences corresponded in principle to the target RNA1 sequence.The two different DNA1 substrates used were the 5′ fluorescein labeled D- and L- forms with the sequence 5′-A1GCGGAGGCTAGCTACAACGATTGAAG27-3′ in the D- and L-form respectively.The chemically synthesized D-DNAzyme and L-DNAzyme were 27 nucleotides in length with the following sequence 5′-G2, at an ribozyme concentration of 2 µM, and for 2 h unless otherwise indicated in the legends of the figures. Prior to the reaction the target D-RNA1 and D-hammerhead ribozymes were denatured for 2 min at 73°C and cooled to the reaction temperature of 25°C at 1°C per min in a heating block.The D-RNA1 target was hydrolyzed with the D-hammerhead ribozyme under the following conditions: 0.2 µM 5′ fluorescein labeled D-RNA was incubated in 10 µl 50 mM Tris-HCl buffer, pH 7.5, at 37°C in the presence of 1–10 mM MgClThe hydrolysis of the L-RNA1 target with the L-hammerhead ribozyme was carried out under identical conditions as described for the D-RNA1 hydrolysis by the D-hammerhead ribozyme.2 at 37o C for 3 h in the presence of 0.02–10 µM of D-DNAzyme. The enzyme: substrate ratio was 10∶1. Prior to the reaction, the substrate D-RNA2 and D-DNAzyme were denatured for 2 min at 73oC and cooled to the temperature of 25oC (1°C/min) in a heating block.The activity of D-DNAzyme was analyzed in cleavage reactions, in which 0.2 µM of 5′ fluorescein labeled D-RNA2 was incubated in 10 µl of a reaction mixture containing 50 mM Tris-HCl buffer, pH 7.5, 10 mM MgClThe hydrolysis of the L-RNA target with the L-DNAzyme was carried out as described for the D-RNA2 target hydrolysis by the D-DNAzyme, except that the chemically synthesized target and DNAzyme consisted of L-nucleotides.Three different sera preparations where used: two sera were purchased from Sigma in 1996 and 2009 and one serum was derived from whole blood of a healthy blood donor, who signed the “Agreement for blood donation (Einwilligung zur Blutspende)” incl. a detailed questionnaire which German Red Cross Blood Donor Service East uses for interviewing each blood donor before blood collection.Blood was collected without anticoagulant and clotting was allowed over night at room temperature. The serum tested was prepared from whole blood of a healthy blood donor who donated blood at German Red Cross Blood Donor Service East (DRK-Blutspendedienst Ost gemeinnützige GmbH). Serum was prepared by centrifugation for 30 min at 3000 rpm in a Heraeus Megafuge 3 or at 4°C and stored in aliquots at −25°C. Serum was tested negative for HIV and hepatitis B and C by routine serological tests.2. The RNase S1 (Promega) digestion was performed by incubating 5 U of the enzyme for 45 min at 37°C in a reaction buffer consisting of 50 mM sodium acetate (pH 4.5), 28 mM NaCl and 4.5 mM ZnSO4. The RNase T2 (Promega) digestion was performed by incubating 2 U enzyme with the L-RNA2 sequence for 15 min at 37°C. The reaction buffer consisted of 50 mM sodium acetate pH 4.5, 28 mM NaCl and 4.5 mM ZnSO4.The stability of 0.2 µM 5′ labeled fluorescein labeled L-RNA1 substrate was studied with T1, V1, S1, and T2 nucleases. The L-RNA 14-mer was incubated in a 10 µl reaction volume with the ribonucleases: 10 U T1 ribonuclease (Ambion) was incubated with the L-RNA1 substrate in a buffer containing 20 mM sodium citrate (pH 5.0), 7 M urea and 1 mM EDTA, at 55°C for 20 minutes. RNase V1 (Ambion) digestion was carried out by using 10 U of the ribonuclease for 30 min at 25°C in a buffer containing 50 mM Tris-HCl pH 7.2, 100 mM NaCl and 10 mM MgClThe RNA sequence ladders were obtained by alkaline hydrolysis. The incubation was at 95°C for 2 min in 10 µl of the reaction mixture containing 50 mM NaOH, 1 mM EDTA and 4 µg of crude tRNA (Serva) and 4 µg of 5′- fluorescein labeled L-RNA2.Unless otherwise stated the reaction products were separated by 20% polyacrylamide gels containing 7 M urea in 0.09 M Tris-borate buffer, pH 8.3, and analyzed by a Fuji Film FLA 5100 phosphoimager by using the manufacturer’s software. The polyacrylamide gels were run at room temperature for four to six hours using bromophenyl blue as a marker.There were several reasons, which prompted us to try to develop the Spiegelzymes described in this study. After we had originally developed the mirror image aptamers called Spiegelmers In vitro analysis demonstrated that the L-ribozyme can efficiently hydrolyze the fluorescein labeled substrate at the predicted site, producing a 6 nucleotide long fluorescently labeled product . Thus, we conclude that both enzymatic reactions exhibit very similar kinetic properties.The calculated Kin vitro conditions with human sera, i.e., in an commercially purchased human serum (Sigma 2009), and it showed at L-hammerhead to L-RNA1 ratios of 10∶1, 1∶1 and 1∶10 very effective hydrolysis levels of 94, 77 and 18%, respectively  can be constructed on the basis of a hammerhead ribozyme and that these Spiegelzymes are able to efficiently hydrolyze target L-RNA molecules and that this hydrolysis can also take place in human sera. Fulfilling these important prerequisites, it can be foreseen that Spiegelzymes may qualify for Next we were interested to analyze the cleavage properties of another type of nucleic acid ribozyme, i.e. the DNAzyme, which is a catalytically active single-stranded DNA. DNAzymes recognize and hydrolyze RNA targets containing a …NG↓UN… sequence, in which N stands for anyone of the common four bases In the study reported here we designed an L-DNAzyme 10/23 based on L-deoxyribose and we checked its cleavage properties with an L-RNA2 target . The L-R2 and 3 hrs reaction time, a nearly 100% hydrolysis efficiency was reached for both D- and L-homochiral cases at an enzyme to substrate ration of 10∶1 (In the next series of experiments we compared the hydrolytic efficiencies of the D-DNAzyme with the D-RNA2 target  and the  of 10∶1 , indicatOf interest to us was also the question, if the L-DNAzyme would show similar stability characteristics in different human sera, as the L-hammerhead ribozyme. As expected the results summarized in in vitro. These data show also that mirror image nucleic acids enzymes like L-RNA hammerheads and L-DNA DNAzymes are equally active as their corresponding enantiomers in homochiral reactions. Since some slight structural differences have been reported earlier from us on the basis of x-ray analysis of RNA crystals from D- and L-RNA molecules, and since these differences did also involved divalent cation and water interactions The data presented so far in this manuscript demonstrate for the first time, the construction of two mirror image enzymes, called Spiegelzymes, namely the L-hammerhead ribozyme and L-DNAzyme, which can sequence specifically recognize and hydrolyze homochiral target RNA molecules at low magnesium concentrations Nevertheless, since the atomic structures of our L-enzymes with their L-substrates are currently not known, it may be of interest to try to get an insight of the mechanism of action of theses Spiegelzymes by model building. The first attempt in this direction is shown in In From our three-dimensional modeling experiments we can conclude that the sequence specific cleavages of LRNA2 by the L-hammerhead ribozyme and L-DNAzyme are feasible at the cleavage points observed in our experiments.The results obtained in this study demonstrate for the first time that L-hammerhead ribozymes and L-DNAzymes are capable of cleaving L-RNA target sequences specifically and very efficiently. Stability studies performed for the L-hammerhead ribozyme and L-DNAzyme with three different human sera demonstrated that these two L-RNA cleaving enzymes are very stable in different sera even up to the 72 hours tested. This observation is in agreement with our earlier results obtained with Spiegelmers The fact that the L-hammerhead ribozyme could very efficiently cleave its L-RNA target, in a human serum, indicates that the mirror image ribozymes may be suitable for employment in biological environments.Considering the efficient activities of the L-hammerhead ribozyme and L-DNAzyme to hydrolyze sequence specifically L-RNA sequences, they may be well suited as an antidote against several Spiegelmers, which are currently in second medical testing phases by the Noxxon Pharma AG. Not only that, it can also be envisioned that new kinds of bio chips can be developed on the Spiegelmer Spiegelzyme basis for diagnostic purposes and the isolation of specific proteins. Also, the construction of new kind of nanoparticles should be possible, based upon a combination of Spiegelmers and Spiegelzymes for the site directed delivery of drugs.Intriguing will also be to determine the mechanism how these mirror image enzymes are functioning and compare those with the mechanism determined for the natural hammerhead ribozymes. Here it will be especially interesting, if one could compare the L-hammerhead ribozyme mechanism with that determined by Scott and collaborators In summary, with the first description of functionally active L-hammerhead ribozymes and L-DNAzymes, which were shown to specifically hydrolyze L-RNA targets the principles are laid down that these molecules may become important tools for future applications in biotechnology and molecular medicine. These theoretical potentials are further supported by the very high stabilities of these enzymes in human sera, and the recent evidence that mirror image nucleic acids are not toxic and not immunogenic.


Exosomes are small (30–150 nm) vesicles containing unique RNA and protein cargo, secreted by all cell types in culture. They are also found in abundance in body fluids including blood, saliva, and urine. At the moment, the mechanism of exosome formation, the makeup of the cargo, biological pathways, and resulting functions are incompletely understood. One of their most intriguing roles is intercellular communication—exosomes function as the messengers, delivering various effector or signaling macromolecules between specific cells. There is an exponentially growing need to dissect structure and the function of exosomes and utilize them for development of minimally invasive diagnostics and therapeutics. Critical to further our understanding of exosomes is the development of reagents, tools, and protocols for their isolation, characterization, and analysis of their RNA and protein contents. Here we describe a complete exosome workflow solution, starting from fast and efficient extraction of exosomes from cell culture media and serum to isolation of RNA followed by characterization of exosomal RNA content using qRT-PCR and next-generation sequencing techniques. Effectiveness of this workflow is exemplified by analysis of the RNA content of exosomes derived from HeLa cell culture media and human serum, using Ion Torrent PGM as a sequencing platform. Dictyostelium cells by the secretion of chemoattractant signals [Exosomes are a type of vesicle, 30–150 nm in size, that have received increased attention over the past few years . Exosome signals . Valadi  signals  demonstr signals . When ta signals . Exosome signals –17. Inte signals –20. Despite a rapidly growing number of research studies, there is still rather limited and superficial information available regarding RNA content of exosomes . SeveralHere we describe a complete exosome workflow solution, starting from fast and efficient extraction of exosomes from cell culture media and serum to robust isolation of RNA and characterization of exosomal RNA content using qRT-PCR and next-generation sequencing techniques. Effectiveness of this workflow is demonstrated by analysis of the RNA content of exosomes derived from HeLa cells and blood serum, using Ion Torrent PGM as a sequencing platform. This straightforward standardized workflow can be utilized for analysis of the exosomal RNA cargo, shedding some light on the mechanisms of formation, preferential loading, and functions of these fascinating vesicles. In a similar fashion, disease-specific RNA signatures residing within the exosomes, can be uncovered and potentially used for development of minimally invasive or noninvasive diagnostics, providing the possibility of someday being able to analyze RNA “content” of organs and tissues without biopsy. Total exosome isolation (from serum) reagent (Invitrogen), Total exosome isolation (from cell culture media) reagent (Invitrogen), Total exosome RNA and protein isolation kit (Invitrogen), blood serum from two donors, cell culture media from HeLa cells, 10x PBS, nuclease-free water (Ambion), 100% ethanol, nonoptical adhesive covers (Applied Biosystems), optical adhesive covers (Applied Biosystems), 384-well PCR standard plates (Applied Biosystems), 96-well PCR standard plates (Applied Biosystems), universal PCR master mix II (Applied Biosystems), human TaqMan miRNA assays, Veriti 96-well thermocyclers (Applied Biosystems), 7900HT Instrument, SW v2.3, TaqMan microRNA reverse transcription kit (Applied Biosystems), 1000 reactions, and Ion Total RNA-Seq kit v2 (Life Technologies) were utilized.μL–50 mL was used depending on the downstream application. The samples were incubated at 4°C overnight and then centrifuged at 4°C at 10,000 g for 1 h. The supernatant was aspirated and discarded, and the exosome pellet was resuspended in PBS buffer and then stored at 4°C short term (1–7 days) or −20°C long term. Fresh cell media was harvested from HeLa cells, grown in T175 flasks. Initially, the cells were grown in media containing 10% FBS (to ~90% cell density), then washed twice with PBS and grown for the remaining 12 h in 10% exosome-depleted FBS. The cell media samples were then centrifuged at 2,000 g for 30 min to remove cell debris. The supernatant containing the cell-free cell media was transferred to a fresh container and held on ice until use. Next, each sample was combined with 1/2 volume of Total exosome isolation (from cell media) reagent and mixed well by vortexing or pipetting up and down until a homogenous solution was formed. Typical cell media volume utilized was 1 mL; however, the range of 100 μL; however, the range of 50 μL–5 mL was used depending on the downstream application. The samples were incubated at 4°C for 30 min and then centrifuged at room temperature at 10,000 g for 10 min. The supernatant was aspirated and discarded, and the exosome pellet was resuspended in PBS buffer and then stored at 4°C short term (1–7 days) or −20°C for long term. Frozen serum samples were thawed in a water bath at room temperature until samples were completely liquid and then centrifuged at 2,000 g for 30 min to remove any cellular debris. The supernatant containing the cell-free serum was transferred to a fresh container and briefly held on ice until use. Next, each serum sample was combined with 1/5th volume of Total exosome isolation (from serum) reagent and then mixed well by vortexing or pipetting up and down until a homogenous solution was formed. Typical serum volume utilized was 100 8–8 × 108) and then quantified and sized using the Nanosight LM10 instrument , following the manufacturer's protocol. The LM10 uses a laser light source to illuminate nanoscale particles (10–1000 nm) which are seen as individual pointscatters moving under Brownian motion. The paths of the point scatters, or particles, are calculated over time to determine their velocity which can be used to calculate their size independent of density. The image analysis NTA software compiles this information and allows the user to automatically track the size distribution and number of the nanoparticles. Exosomes purified from cell media and blood serum were diluted with PBS buffer  were mixed with 2x nonreducing Tris-glycine SDS sample buffer (Novex) for CD63, and 2X reducing buffer for CD9, then heated at 75°C for 5 min and loaded onto a 1.5 mm × 15 well 4–20% Tris-Glycine gel (Novex). Benchmark prestained protein ladder (Invitrogen) was added to one well as a control to monitor the molecular weight of the protein samples. The gel was run under denaturing conditions at 150 V for 1.5 h and then transferred to a membrane using the iBlot instrument (Life Technologies). After transfer, the membranes were processed on the BenchPro 4100 (Life Technologies) with CD63 or CD9 antibody (Abcam) diluted 100 μg into 20 mL. The WesternBreeze Chemiluminescence kit was utilized on the next step; membranes were exposed to X-ray film for 1–10 min and the film was analyzed.Exosome samples isolated from cell media or blood serum  and cell-free serum. 200 μL of each sample (brought up to volume with PBS if necessary) was combined with 205 μL of 2x denaturing solution, vortexed to lyse, and then incubated on ice for 5 min. After incubation, 410 μL of acid-phenol : chloroform was added to the mixture and vortexed for 30–60 sec to mix. Samples were then centrifuged for 5 min at 10,000 g at room temperature to separate the mixture into aqueous and organic phases. Once centrifugation was complete, the aqueous (upper) phase was carefully removed without disturbing the lower phase or the interphase and transferred to a fresh tube. The Total exosome RNA and protein isolation kit (Invitrogen) was utilized for recovery of RNA from the exosome samples obtained with the reagent and ultracentrifugation protocol and parental samples for each sample type, HeLa cell pellets . After washing, filter was dried by spinning for an additional 1 min at 10,000 g. The filter cartridge was transferred into a fresh collection tube and 50 μL of preheated (95°C) nuclease-free water was applied to the center of the filter. Samples were centrifuged for 30 sec at 10,000 g to recover the RNA, and then a second 50 μL volume of preheated (95°C) nuclease-free water was applied to the center of the filter and centrifuged for 30 sec at 10,000 g. After the second spin, the eluate containing the RNA was collected and stored at −20°C. For cell pellet RNA, a DNase treatment was performed using the DNase-free Kit (Ambion) to remove any contaminating DNA; DNase treatment was not performed on exosome samples as they had a much smaller input. After treatment, each sample was diluted to 2 ng/μL and 1 μL analyzed on the Agilent 2100 Bioanalyzer using the Agilent RNA 6000 Pico Kit (Series II) to determine the mass of RNA going into downstream analysis. 1.25 volume of 100% EtOH was added to the aqueous phase for each sample and then vortexed to mix. 700 μL of the RT master mix was added to corresponding wells in a 96-well plate, and 5 μL of each sample was added to the master mix. Plates were covered with adhesive  cover and spun down to remove air bubbles and then placed into a 9700 thermocycler and incubated as follows: 4°C for 5 min, 16°C for 30 min, 42°C for 30 min, and 85°C for 5 min. Reactions were kept at 4°C until use.Reverse Transcription (RT) Master Mix was prepared for each sample using the TaqMan MicroRNA Reverse Transcription Kit reagents and protocol (Applied Biosystems) with gene specific RT primers for five miRNA targets . Ten μL of AB Universal PCR Master Mix II, 2.5 μL of nuclease-free water, and 0.5 μL of the 20x TaqMan assay. After mixing, 8 μL of each master mix was placed into wells in a 384-well plate (enough for triplicate reactions for each isolation replicate). Two μL of each RT reaction was added in triplicate to the master mix of each target and the plates were sealed with an optical adhesive cover. Plates were spun down to remove air bubbles and then placed into a 7900HT instrument and run using the following thermocycler protocol 95°C for 10 min +  for 40 cycles. Once the run was complete, automatic Ct analysis was performed with SDS v2.3 software, and average and standard deviations were calculated for each set of isolations and qPCR reactions for each target.qPCR master mixes were prepared for each of five microRNAs by combining 5 μL and then combined with the hybridization reagents and incubated at 65°C for 10 min and 16°C for 5 min. Ligation reagents were then added and the samples were incubated at 16°C for 16 h (overnight). After ligation, reverse transcription was performed: RT master mix was added to the samples, tubes were incubated at 70°C for 10 min, samples were snap-cooled on ice, the RT enzyme was added, and the samples were incubated at 42°C for 30 min. cDNA from the RT reaction was purified using the kit's clean-up module containing MagMAX Beads (5 μL per well of a 96 well plate) and eluted in 12 μL of nuclease-free water. Six μL of the purified cDNA was combined with PCR primers and Platinum PCR SuperMix High Fidelity reaction mix was then placed in a thermocycler and amplified using the following protocol: 94°C for 2 min  2 cycles;  16 cycles; 68°C for 5 min. Once protocol was complete, reactions were stored on ice until purification. The amplified DNA  for each sample was purified using the kit's clean-up module containing MagMAX Beads (5 μL per well of a 96-well plate) and eluted in 10 μL of nuclease-free water. Final libraries were stored on ice for the short term and at −20°C for long term. To assess the yield and size distribution, 1 μL of the library was run on an Agilent DNA High Sensitivity chip (Agilent). The molar concentration of the library was determined with the Agilent 2100 Bioanalyzer Instrument Expert software and used to dilute libraries to correct concentration for sequencing. Sequencing was performed for each sample on the Ion Torrent PGM instrument using 318 chips  and the protocol listed in the Total exosome RNA and protein isolation kit (Invitrogen) with 160 flows (40 cycles). Small RNA libraries were prepared using the Ion Total RNA-Seq Kit v2 (Life Technologies) protocol and materials. However, a number of modifications were introduced into the RNA-Seq protocol in order to accommodate the specific nature of the exosome samples: (1) relatively low amount of RNA and (2) majority of the RNA cargo being <200 nt in size. For library construction, the RNA sample was dried down to 3 http://ioncommunity.lifetechnologies.com/docs/DOCS-7189/) performed base calling from raw signals. Reads from polyclonal beads and low quality reads were filtered out, and 3′ adapter sequences were trimmed before the sequence was output into a FASTQ formatted file for analysis (http://en.wikipedia.org/wiki/FASTQ_format/). Contained within this file are the sequences (reads) which correspond to a single bead location on the Ion 318 chip and per base PHRED scaled [http://en.wikipedia.org/wiki/Phred_quality_score/, http://ioncommunity.lifetechnologies.com/docs/DOC-2306/) quality values. Upon completion of each PGM sequencing run, the Torrent Suite software  and Cutadapt [FASTX-Toolkit subprogram fastq_quality_trimmer scans each read and its respective quality values from the 5′ to the 3′ end of the reads and trims bases below a PHRED quality value of 17. If the read length falls below 17 bases in length, it is removed from further processing. This is to reduce the number of ambiguously mapped reads which become more frequent as reads get shorter. The remaining reads are scanned for P1 adapter sequences at the 3′ end using cutadapt. Again, if the read length falls below 17 bases after adapter trimming, it is removed from analysis. With the data in the FASTQ format, it was entered into the exosome-seq mapping pipeline for final analysis see . While tCutadapt  programsThese preprocessing steps are critical since low quality sequence and 3′ adapter sequences can be a source of misalignment (noise) or preclude the read from mapping at all which causes problems when trying to map reads to the locations in the genome from which they are transcribed.http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/. The resulting graphs and statistics were based on quality values, nucleotide composition, sequence length, and most highly represented sequences (k-mers) for each library. This analysis provided a high-level evaluation of sequence quality and potential biases which could be present in a given RNA-Seq sample. If irregularities are observed, such as quality value distributions shifted lower, this may be an indication that a problem has occurred upstream from the analysis pipeline. After preprocessing each read of the FASTQ file was examined based on global quality metrics using fastqc SHRiMP2 [SHRiMP2 aligner was chosen as it has been tested and optimized specifically for mapping miRNA sequences using special parameters provided by the authors and can be implemented to align longer sequences with high sensitivity. The alignment algorithm used attempts to optimally align each read sequence to a reference containing miRNA hairpins (miRBase build 18.0), tRNA, and ribosomal RNAs and then report each alignment in a sequence alignment/map (SAM) (http://samtools.sourceforge.net/SAM1.pdf) formatted file. This file format is the current standard alignment format, and numerous downstream analysis tools have been written to use it for easy parsing and counting of reads to individual transcripts. As mentioned earlier, the goal of the sequencing and analysis is to determine measurable global expression profiles using multiple reference datasets. The alignment steps in this workflow utilize the SHRiMP2  aligner Previously published reports , 39 haveFinally, in order to comprehensively account for additional known RNA species which could be present in exosome fractions, the reads that did not map in the RefSeq step were taken and mapped to the noncoding RNA database Noncode (v2.0) . This stTo determine read counts matching each reference RNA at each step, a simple Perl script was written to scan each of the three SAM files and tally mapped read counts per transcript and then report them in tabular format. Once complete, these count tables were used to compare enrichment of particular transcript across sample types.The original approach for isolation of exosomes, still widely used, is based on ultracentrifugation in combination with sucrose density gradients or sucrose cushions to float the relatively low-density exosomes away from other vesicles and particles . These pWe isolated exosomes from HeLa cell culture media  and blood serum samples  using Total exosome isolation reagents as well as the ultracentrifugation procedure , for com7 cells per T175 flask, in 30 mL cell culture media, in the presence of exosome-depleted FBS. From 1 mL of this cell medium, ~4–8 × 109 exosomes were isolated with the reagent and ~2–4 × 109 with ultracentrifugation. For the reagent protocol, 100 μL−50 mL cell media volume inputs was tested, and exosome recovery was linear in this range. The process is scalable up or down depending on the needs for downstream analysis—for example, for sequencing applications, larger amounts of RNA are required, compared to qRT-PCR, and thus larger scale of exosome isolation is required, from 10–50 mL cell media.Both the reagent and ultracentrifugation procedures allowed recovery of a significant number of nanovesicles: HeLa cells were grown to ~2 × 10μL serum, ~1.5–3 × 1011 exosomes were recovered with the reagent and ~1.5–5 × 1010 with ultracentrifugation. For the reagent protocol, 50 μL−5 mL serum volume inputs was tested, and exosome recovery was linear in this range. For sequencing applications, larger amounts of RNA are required, compared to qRT-PCR, and thus larger scale of exosome isolation is required, from 1–5 mL serum. From 100 Samples were next analyzed by Western blotting with antibodies specific to tetraspanins CD63 and CD9-well characterized exosomal markers , 31. ResOnce the purity of recovered exosomes was confirmed and the amount determined, we proceeded with the isolation and subsequent analysis of the exosomal RNA. Using the Total exosome RNA and protein isolation kit, developed specifically for this purpose, samples were put through an organic extraction followed by immobilization of RNA on glass-fiber filters, to purify the total RNA. We followed this protocol to isolate RNA from exosomes derived from HeLa cell culture media and blood serum samples using both the Total exosome isolation reagents and ultracentrifugation procedure. Subsequent analysis with Nanodrop and Bioanalyzer has shown that for exosomes isolated from 30 mL of HeLa cell culture media using the ultracentrifugation protocol ~3.8 ng RNA was recovered. The reagent method resulted in isolation of somewhat more exosomes from the same volume of HeLa cell culture media, from which ~7.5 ng RNA was extracted. For exosomes isolated from 4 mL of serum using the ultracentrifugation protocol ~0.9 pg RNA was recovered. The reagent method resulted in isolation of somewhat more exosomes from the same volume of serum, from which ~2 ng RNA was extracted.μL serum or 30 μL cell media is sufficient for one qPCR reaction. The reagent method recovered somewhat higher levels to recover of exosomes compared to ultracentrifugation procedure, as indicated by 1–3 Ct shift for different RNAs. From this point, we focused on analysis of the RNA content of exosomes isolated with the reagent.Before proceeding with the sequencing analysis of the RNA, the levels of five microRNAs miR16, miR24, miR26a, miR451, and let7e, earlier reported to be present in exosomes , 21, werExosomal RNA recovered from cell culture media and serum derived samples was analyzed by sequencing and compared to the parental samples, whole cells and cell-free serum, respectively. For the library construction, the primer binding flanking sequences were ligated to the RNA in the samples, followed by reverse transcription and PCR amplification. Certain modifications were introduced to the standard protocol to address the specific nature of the exosome samples: longer ligation and extended amplification were performed, as relatively low amount of RNA is recovered from the exosomes. Once the libraries were constructed, they were sequenced using the Ion Torrent PGM instrument, with 318 chips, following standard protocols.The PGM 318 chips hold over 11 million wells. For the samples derived from cell media as well as serum, 9-10 million wells were loaded (75–90%) and sequenced. After subtracting the polyclonals, low quality sequences, and nontemplated ISPs, 5-6 million final readouts were obtained from each run. Of those reads, 90–98% were mapped. An iterative mapping pipeline was used to map and quantify RNAs sequenced from both exosome and parental samples . Each rea priori exosome RNA content information is known for HeLa cell culture media, no assumptions were made about what databases should be referenced for mapping, quantitation, and comparison. For this reason, we designed an iterative mapping strategy for both sample types that included three mapping references utilized in a specific order. This is a “catch all” approach that takes advantage of published transcriptome annotations and sequences which are publicly available. The implementation of this approach involves a very intuitive workflow. Briefly, reads were first mapped to miRBase [Since no  miRBase  microRNA miRBase  transcriUsing mapped sequence read counts, we quantified the RNA cargo of exosomes extracted from HeLa cell culture media and then compared it to RNA from the “parental” cells. This is, to our knowledge, the first report on RNA content of exosomes secreted by HeLa cells and also comparing exosomes to the parental samples. As illustrated in When summarizing total counts per samples type, we calculated mean raw counts per RNA-Seq library and then ranked them from high to low for miRNA, tRNA, mRNA, and ncRNA categories. The top ten are listed in Several targets from four of the different HeLa major RNA species  showed significant differences in representation between the two sample types . In partIt is also worth noting from Cell culture is a useful model for initial characterization studies; however, blood samples are of higher interest due to their clinical relevance and potential for use in human diagnostics. In the latter case more heterogeneity in RNA composition should be obviously expected versus cell culture since the samples are taken from multiple individuals. We analyzed the RNA cargo of exosomes extracted from the blood serum of healthy human donors, as well as “parental” serum samples. As illustrated in When RNA composition profiles were compared between serum and cell culture samples, general differences were observable. Notably, rRNA read counts compose a smaller percentage of total reads in serum while levels of mRNA/ncRNA make up a higher percentage irrespective of sample replicate or type. In addition, the serum and exosomal libraries showed more variability in terms of RNA species distribution in duplicate libraries made from two blood donors. We expect that donor to donor variability may be a challenge in serum samples moving forward, so there is a need to further explore methods to reduce or account for this variability for future studies. Levels of certain RNA sequences were substantially different between exosomes and parental serum sample as illustrated in The mechanism of sorting particular RNA sequences into exosomes is not well understood at the moment, and is extremely challenging to study. Each cell secretes several types of exosomes and microvesicles, with different functions and containing different cargo. For example some of these vesicles simply allow cells to expel unnecessary RNA and proteins, while others allow cells to send signaling or affector molecules to other cell populations within the body . When anWe have successfully developed a set of reagents and a complete exosome workflow solution, starting from fast and efficient extraction of exosomes from cell culture media and blood serum to robust isolation of RNA and characterization of exosomal RNA content using qRT-PCR and next-generation sequencing methods. The procedure is completed in a fraction of the time, compared to the current standard protocols utilizing ultracentrifugation, and allows recovery of fully intact exosomes in higher yields. Analysis of the sequencing data revealed a very sophisticated RNA content in exosomes, with most of the cellular coding and noncoding RNA present in exosomes but in many cases at significantly different levels and ratios. It will be of paramount importance to gain a better understanding regarding the sorting of particular RNA sequences into exosomes and further into different exosome subpopulations. The work described here is the first step towards developing standardized techniques and protocols for isolation of exosomes and downstream analysis of their constituents. These reagents and workflows will be highly useful for scientists working on both basic research aspects of exosomes and developing next-generation minimally invasive diagnostics.


Lys3 to viral RNA, and elevating the ratio of unspliced to spliced viral RNA. At its amino terminus are two double-stranded RNA binding domains (dsRBDs) that are essential for RHA-viral RNA interaction. Linking the dsRBDs to the core helicase domain is a linker region containing 6 predicted helices. Working in vitro with purified mutant RHAs containing deletions of individual helices reveals that this region may regulate the enzyme's helicase activity, since deletion of helix 2 or 3 reduces the rate of unwinding RNA by RHA. The biological significance of this finding was then examined during HIV-1 production. Deletions in the linker region do not significantly affect either RHA-HIV-1 RNA interaction in vivo or the incorporation of mutant RHAs into progeny virions. While the partial reduction in helicase activity of mutant RHA containing a deletion of helices 2 or 3 does not reduce the ability of RHA to stimulate viral RNA synthesis, the promotion of tRNALys3 annealing to viral RNA is blocked. In contrast, deletion of helices 4 or 5 does not affect the ability of RHA to promote tRNALys3 annealing, but reduces its ability to stimulate viral RNA synthesis. Additionally, RHA stimulation of viral RNA synthesis results in an increased ratio of unspliced to spliced viral RNA, and this increase is not inhibited by deletions in the linker region, nor is the pattern of splicing changed within the ∼ 4.0 kb or ∼ 1.8 kb HIV-1 RNA classes, suggesting that RHA's effect on suppressing splicing is confined mainly to the first 5′-splice donor site. Overall, the differential responses to the mutations in the linker region of RHA reveal that RHA participates in HIV-1 RNA metabolism by multiple distinct mechanisms.RNA helicase A (RHA) promotes multiple steps in HIV-1 production including transcription and translation of viral RNA, annealing of primer tRNA HIV-1, like other retroviruses, does not encode its own RNA helicases, but employs cellular counterparts to facilitate its replication Lys3, the primer for reverse transcription of HIV-1 RHA, a DExH box protein, is a member of the ATP-dependent helicase superfamily 2 (SF2). It can unwind both double-stranded RNA (dsRNA) and double-stranded DNA (dsDNA) by hydrolyzing any of the four ribo- or deoxyribo- nucleotide triphosphates (NTP) RHA consists of 7 domains (depicted in RHA is characterized by two double-stranded RNA binding domains (dsRBD1 and dsRBD2) at the N-terminus in vitro (we were unable to purify mutant RHA containing deletions of either helix 1 or 6). We have also examined the ability of mutant RHAs to promote some steps of HIV-1 RNA metabolism in vivo and have found that while helices 2 and 3 are indispensable for the promotion of tRNALys3 annealing to viral RNA, helices 4 and 5 are more critical for stimulation of viral RNA synthesis, indicating the distinct mechanisms responsible for RHA to participate in these two viral processes. Additionally, both wild-type RHA and RHA containing linker region mutations inhibit the splicing events required for the generation of singly (∼ 4.0 kb) and multiply (∼ 1.8 kb) spliced HIV-1 RNA species, resulting in a greater production of unspliced (∼ 9.2 kb) HIV-1 RNA. However, the splicing patterns within the ∼ 4.0 kb or ∼1.8 kb HIV-1 RNA classes remain unchanged, suggesting that RHA's effect on suppressing splicing is confined mainly to the first 5′-splice donor site, SD1.The linker region of RHA  contains32pCp]-3′-end-labeled synthetic duplex RNA. As shown in 32pCp]-labeled duplex RNA with similar efficiency, as indicated by the shift in probe mobility. The specificity of this interaction was evaluated by replacing the RHAs with GST protein, which does not bind to RNA.In order to determine whether the linker region of RHA influences either RHA binding to RNA or RHA's helicase activity, we made recombinant plasmids encoding His-tagged mutant RHAs containing deletions of either helix 2, 3, 4 or 5 . We did 32pCp  and RNA isolated from the His-RHA precipitates (precipitate RNA) was analyzed by RT-PCR using primer pair P1-F/R in vitro binding of mutant RHAs to synthetic duplex RNA  results in less increase in overall mRNA synthesis, indicating that these two helices are required for the stimulation of HIV-1 mRNA synthesis by RHA. Consistent with a previous report Overexpression of RHA in 293T cells has been shown to stimulate the synthesis of HIV-1 mRNA ant RHAs . Lane 1 ant RHAs , represeΔLinker-Helix2 results in an increase of similar proportions between the different mRNA classes, these proportions are reproducibly diverse for other mutant RHAs , and rescued by either exogenous wild-type RHA (lane 3), or mutant RHAΔLinker-Helix4 or RHAΔLinker-Helix5 (lanes 6 or 7). By contrast, little rescue of annealing was obtained by expression of the mutant RHAΔLinker-Helix2 or RHAΔLinker-Helix3 that contains a deletion of either helix 2 or 3 (lanes 4 or 5). These results indicate that helices 2 and 3 are required for the promotion of tRNALys3 annealing to viral RNA by RHA, but none of the helices in the linker region analyzed in this report are required for the RHA packaging into HIV-1 particles.Next, the one nucleotide extension assay was carried out using total viral RNA isolated from purified HIV-1 particles as the source of primer tRNAin vitro , SS (∼ 4.0 kb), or MS (∼ 1.8 kb) RNAs In conclusion, the differential responses to the mutations in the linker region of RHA in this report reveal that RHA participates in multiple steps of HIV-1 RNA metabolism by multiple distinct mechanisms.The partial sequence of HIV-1 RNA transcripts containing exon 6D was deposited in GenBank .293T, a human embryonic kidney cell line, was maintained in Dulbecco's modified Eagle's medium  supplemented with L-glutamine (Gibco), penicillin-streptomycin (Gibco), and 10% fetal bovine serum. HEK 293E cells are a stably transfected 293 cell line SVC21.BH10 is a simian virus 40-based vector that contains full-length wild-type HIV-1 BH10 proviral DNA 2PO4, pH 7.4, 300 mM NaCl, 10 mM imidazole, 0.5% Triton-X 100, 10% glycerol, and protease inhibitor cocktail tablets (Roche)]. The cell lysates were cleared by centrifugation and then incubated with Ni-nitrilotriacetic acid (NTA) agarose (Qiagen) at 4°C for 2 hours to capture His-tagged proteins. After extensive washing, recombinant proteins were eluted by 250 mM imidazole solution (pH 7.4). N-terminally 6×His-tagged full-length RHA without deletion mutation was referred to as wild-type (WT) in this study, and has been described previously 2, 2 mM dithiothreitol, 10% glycerol] and then stored at –80°C. The purity and the identity of purified protein were determined by Coomassie Brilliant Blue R250 staining and Western blot analysis using anti-His, respectively. Protein concentration was determined using the Bio-Rad protein assay reagent.The N-terminally 6×His-tagged mutant RHAs containing deletions in the linker region were expressed and isolated as described Small interfering RNA oligonucleotides (siRNA) were employed to knockdown the endogenous RHA in 293T cells as previously described in vivo as described The RNA-protein coprecipitation assay was performed to examine the RNA-protein interaction Lys3-viral RNA template was extracted from purified HIV-1 particles as described Lys3 annealed in vivo to viral RNA to be extended by one nucleotide (dCTP) by reverse transcription was assayed using a 20 µl reaction volume containing 50 mM Tris-HCl, pH 7.8, 100 mM KCl, 10 mM MgCl2, 10 mM dithiothreitol, 0.16 µM [α-32P]-dCTP, 50 ng of HIV-1 RT, and RNase inhibitor (Ambion). The reaction was incubated for 15 min at 37°C. The RNAs in the reaction were precipitated with 2-propanol, separated in a denaturing 6% polyacrylamide gel. The relative amounts of extended radioactive tRNALys3 were determined using a PhosphorImager instrument. The relative amount of viral RNA used in the reactions was also determined by measuring the ability of a DNA oligonucleotide (5′-TCTAATTCTCCCCCGCTTAATACTGACGCT-3′) annealed at room temperature to the viral RNA sequence downstream of the tRNALys3 binding site to prime a one base ([α-32P]-dCTP) extension.The tRNAin vitro transcription using T7 RNA polymerase . The DNA template for in vitro transcription was amplified by PCR from a luciferase open reading frame (ORF) in pGL3-basic (Promega) using primer pair LucMb-F /LucMb-R . The sense RNA strand was labeled with [5'-32P]cytidine 3',5'-bis(phosphate) (32pCp) using T4 RNA ligase (Fermentas), and then annealed to cold complementary antisense RNA strand to produce radioactive duplex RNA  at room temperature for 30 min in 20 µl of binding buffer . The RNA-protein mixture was then separated in a 5% native polyacrylamide gel in 0.5×TBE. Helicase activity of RHA was measured by performing electrophoretic helicase assay as described 2, 2 mM dithiothreitol, 2 units of RNasin, 2.5 mM NaH2PO4, 15 mM NaCl, 10 nM gel-purified duplex RNA, and, where indicated, 1 mM ATP, and 150 nM purified proteins. The reaction was incubated at 37°C for indicated time periods and then stopped by adding 5 µl of stop buffer . 10 µl of aliquots for each reaction were separated in a 15% native polyacrylamide gel in 0.5×TBE. Radioactive RNAs were visualized and quantitated using a PhosphorImager instrument.EMSA was performed to examine the ability of wild-type or mutant RHA to bind dsRNA. 100 nM [32P]-labeled DNA probes. The radioactive probes were prepared using random primer DNA labeling system (Invitrogen) and DNA template containing sequence of HIV-1 5′-UTR. These probes can recognize all the HIV-1 transcripts produced during virus replication.RNA isolation and Northern analysis were performed as described previously 5'-TAACTAGAGATCCCTCAGAC-3') are located in the 5′-UTR of HIV-1 upstream of the major splice donor SD1 at nt 289 relative to the +1 of the mRNA start site. Primers KPNA, 3311A, and SJ4.7A were designed by Neumann et al 32P]-labeled oligonucleotide P131. The PCR products were also purified, cloned into pJET1.2 using CloneJET PCR cloning kit (Fermentas), and then sequenced to determine their identities.HIV-1 RNA Splicing was analyzed by performing RT-PCR as in principle described previously t test. P value < 0.05 was considered significant and was indicated with asterisks.Statistical analysis was performed by Student's


Phase-contrast velocity images often contain a background or baseline offset error, which adds an unknown offset to the measured velocities. For accurate flow measurements, this offset must be shown negligible or corrected. Some correction techniques depend on replicating the clinical flow acquisition using a uniform stationary phantom, in order to measure the baseline offset at the region of interest and subtract it from the clinical study. Such techniques assume that the background offset is stable over the time of a patient scan, or even longer if the phantom scans are acquired later, or derived from pre-stored background correction images. There is no published evidence regarding temporal stability of the background offset.This study assessed the temporal stability of the background offset on 3 different manufacturers’ scanners over 8 weeks, using a retrospectively-gated phase-contrast cine acquisition with fixed parameters and at a fixed location, repeated 5 times in rapid succession each week. A significant offset was defined as 0.6 cm/s within 50 mm of isocenter, based upon an accuracy of 10% in a typical cardiac shunt measurement.Over the 5 repeated cine acquisitions, temporal drift in the baseline offset was insignificant on two machines , and marginally insignificant on the third machine (0.5 cm/s) due to an apparent heating effect. Over a longer timescale of 8 weeks, insignificant drift (0.4 cm/s) occurred on one, with larger drifts  on the other machines.During a typical patient study, background drift was insignificant. Extended high gradient power scanning with work requires care to avoid drift on some machines. Over the longer term of 8 weeks, significant drift is likely, preventing accurate correction by delayed phantom corrections or derivation from pre-stored background offset data. Phase-contrast velocity mapping is applied to blood flow and myocThe background offset error may be corrected using the apparent velocity values in stationary tissue close to the vessel of interest. This correction is sufficient in most applications outside the thorax. However, for flow quantification near the heart adjacent stationary tissue is often absent. This can be solved by determining the offset in stationary tissue from the whole FOV and subtracting it using a fitted surface,8, thereA more basic method measures the background offset by repeating the flow study using a uniform stationary phantom after the patient, with identical sequence parameters, and is This work evaluated the temporal stability of phase-contrast background velocity offsets where there appear to be no previous publications.The temporal stability of the background error might be questioned on three timescales. First, is it stable over a long period enabling the use of pre-stored corrections? Second, is the background error stable during the patient session, so that a phantom scan acquired at some time after the patient session is a reliable measurement of the error during a clinical flow study? Third, is the background error stable during all the images of a cine velocity map, even where it was retrospectively gated with continuous gradient waveform activity? The third question was confirmed long ago. In the The specification was to measure apparent velocity in a stationary uniform phantom, up to 50 mm distance from isocenter as measured in the image plane. Each centre used a different test phantom of gadolinium doped gelatine, or water which was allowed to settle for 5 minutes before beginning scanning. In one instance, to confirm the fluid’s stability, repeated phase-contrast scans were acquired during the settling time, showing that fluid settled to < 0.1 cm/s.The study was limited to 1.5T whole body systems as this is currently the most widely-used main field strength for cardiac CMR. Pre-emphasis for reduction of eddy current effects and automatic correction of concomitant gradient terms were empOn six machines, two from each of the 3 manufacturers, a further study examined the effect of a high gradient-power sequence run continuously for at least 5 minutes between two sets of 5 cine studies as acquired above. This was acquired at a later date than the main 8-week data, following initial evaluation of that data, and two machines of each type were used to test the consistency of their behaviour.Similar phase-contrast sequence parameters were used for each of the three scanner types, although we emphasise that the background offset values themselves were not being compared between scanners in this study, rather their drift with time. It was therefore essential that no changes in sequence parameters or slice locations were made during the 8 weeks. All cine phase-contrast acquisitions were retrospectively gated pulse sequences, where the phase-encode group was updated by each simulated ECG R-wave. The continuous gradient activity of this approach, with no silent gap while waiting for the next R-wave, has the advantage of a more stable background offset during the cardiac cycle. Multipli.e. temporal interpolation was applied by reconstruction).GE Signa Excite at VUMC Amsterdam NL, and Addenbrooke’s Hospital Cambridge, UK. The readout ADC bandwidth was 41.67 kHz (pixel bandwidth 326 Hz/pixel). This used symmetric velocity-encoding . The “flow analysis” flag was on, disabling a spatial high-pass filter used for phase-contrast angiography background suppression. The GE “flow optimization” control resulted in longer TE and TR than the other scanners and was therefore not used. Twenty cardiac phases were reconstructed in a 600 ms simulated R-R interval .Philips Intera. Both systems were at Lund University Hospital, Sweden. Pixel bandwidth 355 Hz/pixel (Fat/Water Shift 0.62 pixels). The “Gradient mode” was set to “Default” and “PNS mode” to “Moderate”. The slice-selective RF pulse used an asymmetric design with a late centre. The background phase-offset correction (“LPC filter”) was obviously switched off for this study. Fifteen cardiac phases were reconstructed .All images were analyzed using software written in Matlab . The software reported the velocity offsets measured as the mean within square ROIs (of the same areas as circular ‘great vessel’ regions of 30 mm diameter) centeredTwo timescales of variations in the offsets were considered: among the 8 weekly sessions, and during each session of five scans. For these purposes, the mean over all the frames of cine acquisitions was taken for each ROI and the standard-deviation over these frames was also recorded. The standard-deviation over the cine frames was used only to check whether the background offset variations exceeded the level of random noise and systematic errors in the retro-gated cine reconstruction.The following procedure was adopted for the purpose of determining a significant background drift. The largest peak-to-peak differences in the offsets were measured within each group of 5 scans and also across all 40 scans. This gave the largest error which could have occurred if the stationary phantom correction method had been used at any time during the weekly scans or the full 8 weeks of data collection. The errors were compared against the 0.6 cm/s offset which was suggested as liablThe results for all 4 ROIs placed on the 5 scans of each of weeks 1–8 are plotted for the three scanners as FiguresThe largest peak-to-peak differences within any 5-scan session and across all 40 scans of the 8 weeks are shown in TableThe results before and after 5 minutes of high-power scanning are plotted for all 4 ROIs placed on the 5 scans, for the two systems of each of the three scanner types  and also for measurements derived from multiple flow acquisitions, such as the Qp/Qs ratio. The maxThis study implies that correction using the clinical phase-contrast scan itself is preferable, since this would annul the question of drift during the time between clinical application and a separate correction scan, but this demands further work on the automated background correction or field-camera methods. The resFor this work the relevant value for validation of robustness against noise was the variation in the mean ROI value when making repeated measurements. The spatial standard deviation (Ss) shown in TableThis study was limited to 1.5T systems as most CMR is acquired at this field strength, and there was no reason to expect a major difference in stability at other field strengths using superconducting magnets. The study was also limited to phantom acquisitions, and a more “real-life” study of changes in these after patient scanning might be more relevant. However, phantom acquisitions were essential to establish the underlying stability of the scanners. In vivo, correction algorithms are often applied. However, with the phantoms used in this study the evaluation of such algorithms is not realistic for in-vivo situations, as the most critical issue is not simulated well i.e. the lack of stationary tissue around the vessels of interest.Over the duration and activity of a typical patient study, background drift was insignificant. However, the combination of extended high gradient power scanning and work requiring background correction requires care to avoid drift on some machines. Thus, on most systems, the background offset in the velocity measurement can be corrected by a separate phantom scan within the same imaging session. Over the longer term of 8 weeks, significant drift in offset value is likely, preventing accurate correction by delayed phantom correction scans or derivation from pre-stored background offset images.Peter Gatehouse, David Firmin, Philip Kilner, Marijn Rolf and Mark Hofman work in departments with research collaborations with Siemens. Karin Markenroth Bloch is an employee of Philips. Martin Graves has a research collaboration with GE.PG conceived the study. PG, MBMH, MPR, KMB enrolled and co-ordinated the multiple centers and led the design and the work. Data was acquired by PG, MPR, KMB, MJG. All authors took part in critical review and drafting of the manuscript and have read and approved the final manuscript.