Post-transcriptional regulation of messenger RNA (mRNA) metabolism and subcellular localization is of the utmost importance both during development and in cell differentiation. Besides carrying genetic information, mRNAs contain cis-acting signals (zip codes), usually present in their 5′- and 3′-untranslated regions (UTRs). By binding to these signals, trans-acting factors, such as RNA-binding proteins (RBPs), and/or non-coding RNAs (ncRNAs), control mRNA localization, translation and stability. RBPs can also form complexes with non-coding RNAs of different sizes. The release of extracellular vesicles (EVs) is a conserved process that allows both normal and cancer cells to horizontally transfer molecules, and hence properties, to neighboring cells. By interacting with proteins that are specifically sorted to EVs, mRNAs as well as ncRNAs can be transferred from cell to cell. In this review, we discuss the mechanisms underlying the sorting to EVs of different classes of molecules, as well as the role of extracellular RNAs and the associated proteins in altering gene expression in the recipient cells. Importantly, if, on the one hand, RBPs play a critical role in transferring RNAs through EVs, RNA itself could, on the other hand, function as a carrier to transfer proteins  that, once transferred, can alter the cell’s epigenome. The release of extracellular vesicles (EVs) by a producing cell and their fusion with surrounding, recipient cells is probably an ancient process. It is, indeed, highly conserved in evolution from bacteria ,2 to humCentral protagonists of EV-mediated exchanges are RNAs or, more precisely, RNA–protein complexes. Similar to viruses that bud from infected cells, ready to infect other cells, extracellular RNA–protein complexes can reach cells other than the producers and, once inside, can modify gene expression in many ways. In the last decade, much attention has focused on the RNA component of the complexes. Thus, we now know that EVs can transfer various classes of RNAs, from messenger RNAs (mRNAs), encoding for specific proteins , to non-In this review, we will discuss these hypotheses in the light of the transformative activity that EVs seem to have in cancer , and of In eukaryotes, gene expression is realized by a series of sequential events during which mRNA molecules are produced, processed, transported to the cytoplasm, and translated into proteins. The control of the whole process is ensured by a cohort of proteins, directly or indirectly interacting with mRNAs and with each other, which also determine mRNA stability, localization, and translational levels. In other words, mRNA is not a naked molecule wandering in the cell, but a component of changing complexes, which include RNA binding proteins (RBPs) and other regulatory RNAs. It is generally thought that, during the first steps of the mRNA life, a core group of ”general” proteins is put in place, guided by common features such as the 5′-cap, splicing sites, and poly(A)-tail; thereafter, more “specific” proteins should be positioned on RNA, determining its frequency of transport to the cytoplasm, its trafficking to the final destination, and, finally, its translational level. This view is, however, an oversimplification, because RBPs display a wide range of binding specificity . In addiThe number of identified RBPs has rapidly increased in the last 10 years and the family now includes more than 1000 members. By recognizing and binding specific sequences or stem-loop structures, often located in the 3′-UTR, RBPs control all steps of the mRNA metabolism, from transcription to translation ,19,20,21RBPs also interact with non-coding RNAs, and it is likely that competition for RBP binding among RNA molecules of different classes has a role in controlling gene expression; moreover, at least in some cases, ncRNAs and mRNAs might be part of ternary ncRNA–mRNA–protein complexes. In addition, RBPs can participate in the maturation of miRNAs , and in Actually, RBP functions are even more complex than thought before. For example, they are probably even involved in maintenance of genome integrity , and oveIn general terms, RBPs are very often multifunctional proteins, acting at different stages of gene expression. For example, Wilms’ tumor 1 (WT1) factor has been described as a transcriptional regulator , but it One category of RBPs that is attracting much interest is represented by enzymes already known for their involvement in biochemical pathways, and for which participation in some step of the RNA metabolism has been more recently demonstrated . For exaAmong RBP’s multiple functions, one that is of particular interest is their involvement in RNA localization. In the past, RNA localization was considered an unusual mechanism, operating in a few specific cell types, such as developing embryos and polarized cells , while, nowadays, an increasing number of studies indicate that it is a quite common, evolutionarily conserved process ,32. The The term ‘exosome’ was first used to describe vesicles expulsed by exocytosis from a multivesicular body (MVB), during reticulocyte maturation into erythrocytes . These fExtracellular vesicles have been now recognized as a universal intercellular communication route, both in prokaryotes and eukaryotes. Thanks to their ability to transfer proteins, lipids and nucleic acids, EVs can affect cell functions in both physiological and pathological conditions . They haEVs are particles of spheroidal morphology, surrounded by a membrane consisting of a lipid bilayer, in which proteins are also present, and differ in size, biogenesis, and release mechanisms . EVs cang as “small EVs” (sEVs) rather than exosomes, (ii) those pelleting at an intermediate speed  as “medium EVs” , and (iii) those pelleting at low speed  [The term ‘extracellular vesicles’ has often been used in the scientific literature in a imprecise way . During  bodies) .Obviously, if found, specific EV components could identify different EV species. Many researchers are thus actively investigating the composition of the vesicles, looking in particular for molecular markers that should differentiate classes of EVs, also allowing identification of the cell types from which EVs originate. To identify the EV producers would have important outcomes, especially when we consider that many pathological conditions  associate with a higher production of circulating vesicles and their associated contents, which might thus constitute a new kind of biomarker ,47,48,49Exocarta 2012 , a databThe fast-increasing number of cited molecules clearly shows that the scientific community is making an effort to develop a deeper knowledge of the structure, role, and mechanism of production of EVs. In general terms, the notion that the EV content is specific is now generally accepted, implying a specialized function for them, and the probable existence of tissue- and cell-specific mechanisms controlling their production (see below).A further debated point concerns the possible mechanisms through which EVs, produced by a given cell, communicate with surrounding cells. These mechanisms are probably also diverse. Proteins on the vesicle membrane can interact with receptors present on the target cell, and activate them; alternatively, vesicle membrane proteins could be cut off by proteases and the resulting fragments may act as ligands for cell surface receptors on the target cell. Vesicles may also merge with their targets, with direct transfer of their contents into these cells, or they may be internalized by phagocytosis .In addition, it is also to be remembered that part of the vesicles probably breaks into the extracellular environment, releasing various factors that might themselves act as ligands for nearby cells or alternatively modify the extracellular matrix, thus allowing modification of the adhesion and migration properties of the surrounding cells ,56,57.In any case, it is clear that EVs can have deep effects on recipient cells, especially in pathological conditions ,62,63,64Given the importance of establishing the fate of EVs once released from producing cells, many approaches have been developed for tracking and imaging them, and trying to follow their distribution, targeting, and kinetics in vivo .As mentioned above, a variety of proteins with different functions have been identified in vesicles, especially through proteomic analysis ,64,66. MIn general, EVs must contain different classes of molecules to be formed, to be secreted, and to reach their targets, so, among the many proteins enriched in them, it is possible to find chaperones  and protGiven this ability to package multiple classes of molecules, inside their lumen or on their membrane, EVs have been hypothesized to represent a “cellular strategy evolved to deliver specific combinations of signals to specific target cells” across cell boundaries . From thEVs can also contain growth factors. For example, vesicles purified from primary cultures of rat cortical neurons and astrocytes contain both vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF)-2, which could promote angiogenesis by stimulating brain capillary endothelial cells, also inducing and modulating formation/maintenance of the blood-brain barrier ,78. SimiThese observations suggest that pathogens can spread throughout the brain via EVs ,86; on tStudies on the lipid content of EVs are still much scarcer than those on proteins and nucleic acids. Many authors evidenced a remarkable enrichment in glycosphingolipids, sphingomyelin, cholesterol, phosphatidylserine (PS), and the monosialoganglioside GM3, both in exosomes and in MVs ,90,91,92The cell plasma membrane has a known asymmetric structure, in which the two sheets of the membrane have different lipid composition, with, for example, PS mostly found in the inner leaflet. The same asymmetry is found in EV membranes. However, the presence of PS also in the outer leaflet of EVs has sometimes been reported, on the basis of reaction with Annexin V . ActuallInterestingly, enrichment in phospholipids bearing two saturated fatty acyl groups in exosomes, compared to the parent cell membranes, has been noticed, although many monounsaturated acyl groups are also present .It has also been proposed that lipid transport via EVs can be considered a mechanism to transport fatty acids, in addition to serum albumin, lipoproteins, or fatty acid-binding proteins . MoreoveIt is noteworthy that EVs carry bioactive lipids, such as different classes of eicosanoids, as well as intermediates and enzymes required for their synthesis ; the preAs mentioned, vesicles contain different types of ncRNAs, such as miRNAs ,52, lncRKRAS oncogene. This finding suggests an EV-mediated horizontal transfer of genetic material from cancer cells to their neighbors, although it has not yet been demonstrated whether such DNA is functional in the recipient cells, as the cells contain defense mechanisms against the exogenous DNA integration. For example, Lee et al. [HRAS DNA failed to permanently transform immortalized fibroblasts.EVs do not only contain RNA, but also DNA in different forms (double-stranded as well as single-stranded), and of different origins  . The mece et al.  demonstrExtracellular vesicles also contain retrotransposon elements, as well as Long Interspersed Element-1 , ALU repeated sequences, and human endogenous retroviruses (HERV) . Thus, tCulture media and body fluids usually contain secreted RNA, enclosed in EVs or in free RNP complexes . It has The main problem with RNA sorting is understanding the mechanisms regulating it. Subcellular distribution of RNAs, i.e., localization to specific compartments, might influence the inclusion in EVs and, depending on localization, in different types of EVs. In other words, the regulation of localization of RNAs in the cell, and of their subsequent sorting could share some mechanisms, and maybe also some components. It has been demonstrated that exosome biogenesis relies on the endosomal sorting complexes required for transport (ESCRT), essential for many cell membrane-involving processes, such as plasma membrane abscission, viral budding, and MVB formation. ESCRT has been reported to have a role in sorting mRNA and miRNA cargoes to exosomes . The ESCCastello et al.  recentlyBased on all these findings, one may imagine that inclusion of RNAs in vesicles would reflect their concentration in the vicinity of the sites of EV formation, sites at which RNAs could accumulate because of their interaction with RNA-binding domains present in membrane proteins, or even because of their direct or indirect interactions with the membrane lipids themselves.In any case, RNAs included in EVs most probably contain specific sequences, and it has been shown that repeated sequences, among which LINE-1 and long terminal repeat sequences (LTRs), are highly represented. An enrichment has been also observed for sequences mapping to transfer RNA (tRNA) loci, or matching Signal Recognition Particle (SRP)-RNA and vault- and Y-RNAs ,127,128.In T-cells, a group of miRNAs, found to be enriched in exosomes, shares a specific sequence named the EXOmotif (GGAG), which binds hnRNPA2B1 ribonucleoprotein, probably responsible for miRNA sorting. Most important, sumoylation seems necessary for hnRNPA2B1 function, even though it is not clear whether the modification influences hnRNPA2B1 localization or binding activity ,131.Sequences involved in sorting are often present in the 3′-UTR of the mRNAs: one example is mRNA encoding galanin receptor 3 , found in human primary glioblastoma multiforme cells and enriched in the exosomes produced by these cells. A CUGCC motif forms a target sequence for miR-1289 and seems, at the same time, to be responsible for GalR3 mRNA sorting . It is tOne RBP secreted in vesicles, most likely through the endolysosomal compartment, is Y-box protein (YB)-1, a factor involved in proliferation and in cell migration , and proOther proteins involved in sorting to exosomes are the endonucleases of the Ago2 family. These proteins control the secretion of some miRNAs, and their activity is inhibited by phosphorylation. In turn, phosphorylation of Ago2 depends on the activation of KRAS and on downstream signaling by MEK-ERK kinase . The rolSorting of miRNAs, and in particular of miR-122, may also involve HuR, which binds RNA by its RRMIII domain. HuR function seems to be regulated by ubiquitination . SantangBy a combination of silencing and immunoprecipitation, it has been also demonstrated a role of Annexin A2 in selecting RNA for sorting in EVs, even though it is not clear whether this protein interacts directly with miRNA molecules or indirectly, through other RBPs .As discussed, cells can sort to vesicles different kinds of RNA, mostly bound to proteins. However, what is the fate of these RNAs when they reach the extracellular environment? Are they active in the recipient cells, or are EVs simply a way to eliminate waste RNAs? Here we will try to give at least partial answers to these open questions.EVs transfer different kinds of ncRNAs . The mosMore recently, cancer-associated adipocytes (CAAs) and fibroblasts (CAFs) were found to secrete via exosomes very high amounts of miR-21 and its iso-miRNAs (variants of the main species). By functional studies, the authors demonstrated that miR-21 is transferred from CAAs or CAFs to ovarian cancer cells, where it suppresses apoptosis while conferring chemoresistance. The authors also showed that miRNA-21 acts by targeting the expression of the gene encoding apoptotic peptidase activating factor 1 (APAF1) . Very siIn spite of these and other examples suggesting that miRNAs can be secreted via EVs and then captured by other cells in which they target specific mRNAs, definitive acceptance of this process is still lacking; first of all, it is possible that miRNAs are secreted and reach surrounding cells through other carriers, such as lipoproteins . Second,Actually, miRNAs are not the only small RNAs in EVs. By high-throughput small RNA sequencing, a highly abundant small RNA derived from the 28S ribosomal (rRNA) has been identified , and, asBesides small RNAs, EVs also contain lncRNAs . lncRNAsNow, lncRNAs are enriched in EVs, and specifically sorted to them . Ahadi eBy labeling RNAs produced by HeLa cells in culture and exposing C33A cells to EVs produced by labeled HeLa cells, Hewson et al.  found thIn 2007 it was shown that, after treating human cells in culture with mouse exosomes, mouse proteins could be found in the recipient cells, indicating that mouse mRNAs, transferred by EVs, could be translated . The nexAll these observations suggest that, at least in some cases, messages enriched in EVs can be actively translated in recipient cells, thus ensuring the horizontal transfer of properties from one cell to another. Based on what we have learned about the different classes of ncRNAs, it is also possible to hypothesize that the specific combination of mRNAs and ncRNAs present in EVs has an additive effect on the overall response of the recipient cells. Moreover, it has to be considered that coding mRNAs can also be involved in a number of non-coding functions. Thus, it is not sufficient to know that a given mRNA is transferred from one cell to another if we do not know which other species of interacting RNAs and proteins are also included into EVs.In 1976, it was reported that viroids, uncoated infectious RNAs that are pathogenic for certain plants, are covalently closed circular RNA molecules . In the circRNAs are mainly produced by a particular type of alternative splicing called ‘back-splicing’ ; one of circRNAs have been classified in different ways, the most useful probably being their subdivision into exonic, intronic, antisense, sense overlapping, and intergenic circRNAs, depending on their genomic origin . The exiInterestingly, a correlation has been suggested between the global abundance of circRNAs and the rate of proliferation of tissues, with a higher amount of circRNAs in low-proliferating cells. As a matter of fact, circRNAs are highly represented in the brain , while, Interestingly, some circRNAs do not have any miRNA binding sequence, and regulate gene expression by binding and modulating RNA polymerase II , perhapsOn the other hand, RBPs themselves can be involved in circRNA formation. For example, in epithelial cells undergoing epithelial–mesenchymal transition, Quaking RBP is activated in response to TGF-β and acts, in turn, as a stimulator of circRNA biogenesis .It is also important to remember that circRNAs might in principle be closed mRNAs, which could be translated if they contain an internal ribosome entry site (IRES) , even whComing back to EVs, circRNAs, which are normally cytosolic molecules, have been found in extracellular vesicles, even enriched with respect to the producing cells , as wellGiven the already mentioned cell specificity of circRNAs, and the observation that several circRNAs are modified in tumorigenesis, the stable, EV-associated RNA molecules in the blood of patients with different kind of cancers are now considered as promising biomarkers ,204,205.In In this section, we will propose a different point of view, in which RNA behaves as a carrier, and the transferred proteins are the real protagonists of the epigenetic effects on recipient cells.Our hypothesis starts from the observation that many RBPs can bind both RNA and DNA. Among these proteins, just to give a few examples, we can consider: (i) the histones themselves; for example, a role in RNA processing has been recently suggested for the mouse ortholog of human H2A.B histone ; (ii) thThe presence in EVs of a number of transcriptional regulators has been reported by Ung et al. , based oThus, we asked whether some DNA/chromatin-binding proteins present in EVs might also behave as RBPs.Among the proteins able to bind both DNA and RNA, and certainly present in EVs, one is, as mentioned, YB-1, which is actively secreted through a non-classical pathway, and is protected by microvesicles from protease degradation. In this form, it exerts a potent proliferative effect and promotes cell migration; both actions seem to require its cold shock domain . AnotherAmong the proteins involved in transcription and found in EVs by Ung et al. , FUBP1 iOf course, the two possibilities (RNA–protein complexes as RNA carriers or as protein carriers) are not mutually exclusive, and the main problem, still to be solved, is how cells choose what is “to be sent” outside and what is “to be used” out of the received packages.EVs represent an astonishing tool to transfer biochemical properties from cell to cell, and perhaps an ancient way to level individual potentials within a population, in spite of genetic variability. Central protagonists of EV-mediated exchanges are RNA–protein complexes, formed by a variety of RNA species and by multifunctional RNA-binding proteins. Proteins and RNAs are fundamental for complex formation and both components have a function in driving themselves out of the producer cells and then inside the recipient ones. However, many points remain to be clarified. First, how are proteins and RNAs taken apart in order to start functioning, once in the recipient cell? RNA–protein complexes travelling inside the cell, for example along an axon or a dendrite in a neuron, are not translationally active during the trip, but they can be activated locally by specific signals. Is the entrance into a new cellular environment such a signal for the complexes present in EVs? May the complexes remain silent even inside the new environment unless further signals are delivered? If the RNA-bound proteins can act like chromatin modifiers and/or transcription factors, when and how may they start doing so? The same questions, of course, pertain to RNAs of all classes.In conclusion, in recent decades we have learned a lot about extracellular vesicles and their cargoes. We also know that EVs and their contents can probably be used as useful biomarkers in pathologies such as cancer and neurodegeneration. Much work is still necessary, however, in order to understand the biochemical details of EV formation and delivery, as well as of cargo activation, utilization, and, perhaps, destruction.


Unique molecular identifiers (MIDs) have been demonstrated to effectively improve immune repertoire sequencing (IR-seq) accuracy, especially to identify somatic hypermutations in antibody repertoire sequencing. However, evaluating the sensitivity to detect rare T cells and the degree of clonal expansion in IR-seq has been difficult due to the lack of knowledge of T cell receptor (TCR) RNA molecule copy number and a generalized approach to estimate T cell clone size from TCR RNA molecule quantification. This limited the application of TCR repertoire sequencing (TCR-seq) in clinical settings, such as detecting minimal residual disease in lymphoid malignancies after treatment, evaluating effectiveness of vaccination and assessing degree of infection. Here, we describe using an MID Clustering-based IR-Seq (MIDCIRS) method to quantitatively study TCR RNA molecule copy number and clonality in T cells. First, we demonstrated the necessity of performing MID sub-clustering to eliminate erroneous sequences. Further, we showed that MIDCIRS enables a sensitive detection of a single cell in as many as one million naïve T cells and an accurate estimation of the degree of T cell clonal expression. The demonstrated accuracy, sensitivity, and wide dynamic range of MIDCIRS TCR-seq provide foundations for future applications in both basic research and clinical settings. Immune repertoire sequencing (IR-seq) has become a useful tool to quantify the composition of B or T cell antigen receptor repertoires in basic research, such as vaccination –3, immunThese challenges become roadblocks to accurately quantify T cell receptor (TCR) or BCR RNA molecule copy number, which is important in estimating clonal expansion and identifying rare clones. Robins et al. developed QuanTILfy to attempt to address this problem by counting TILs and assessing T cell clonality in tissue samples through droplet digital PCR (dPCR) of rearranged TCRβ loci . However+ T cells as a test bed to build a model to count TCR RNA molecule copy number based on input cell numbers, percentage of RNA input, and sequencing depth. We also demonstrated a significant improvement in detection sensitivity. A previous study using a different repertoire sequencing methodology reported the capacity to resolve one in 10,000 cells  to study age-related antibody repertoire development and diversification during acute malaria . In this00 cells . With MI+ T cell enrichment was done following the protocol described previously (+ T Cell Enrichment Cocktail (STEMCELL) together with Ficoll-Paque . Then, RBCs were lysed using ACK Lysing Buffer (Lonza). After washing in phosphate-buffered saline with fetal bovine serum, the cell mixture was passed through a cell strainer (Corning) and ready for use. Naïve CD8+ T cells were FACS-sorted into RLT Plus buffer (Qiagen) supplemented with 1% β-mercaptoethanol (Sigma) based on the phenotype of CD8+CD4-CCR7+CD45RA+ using BD FACSAria II cell sorter.Human leukocyte reduction system chambers were obtained from de-identified donors at We Are Blood  with strict adherence to guidelines from the Institutional Review Board of the University of Texas at Austin. CD8eviously  using Ro−Streptmer+CD45RA+CCR7−CD27−IL7Rlo live T cells were sorted into RLT Plus buffer supplemented with 1% β-mercaptoethanol using BD FACSAria II cell sorter.CMVpp65:482-490 (NLVPMVATV) was used to prepare streptamers as previously described . MiltenyTotal RNA was purified using All Prep DNA/RNA kit (Qiagen) following the manufacturer’s protocol. Library preparation and QC were similar to protocols described previously  using TC+ T cells and cultured CMV-specific CD8+ T cell lines were reverse transcribed with polyT primers  using Superscript III in 20 µl reaction following the manufacturer’s protocol. 2 µl of cDNA was subsequently used on QuantStudio 3D dPCR system following manufacturer’s protocol.Total RNA purified from sorted CD8We followed the similar procedure as described previously to generate consensus sequences . First, https://github.com/utjianglab/MIDCIRS.For each MID group, a quality threshold clustering was used to group reads derived from a common ancestor RNA molecule and separate reads derived from distinct RNAs as previously described . BrieflyM and the number of target molecules being N, the probability that a unique MID will occur k time(s) is:We modeled the process of MID labeling as a Poisson distribution. Given the total number of MIDs being P0 and P1 are the probability that a MID will be tagged 0 and 1 time, respectively, and the percentage of MIDs that need sub-clustering, F(k > 1), is given by:Thus, N is less than 5,000,000, Eq. With over 16 million MID combinations from 12 random nucleotides, when the number of target molecules, The estimation of diversity will be affected by the initial RNA input . We used a statistical model to estimate the diversity coverage for the naïve T cells we sorted based on RNA sampling depth.N observed RNA molecules, there are K different RNA clones. The RNA molecule copy number of each clone is mi ), whose sum equals N. After fitting the data, mi follows a power law distribution :m is the RNA molecule copy number per cell, which is a constant across all T cells :mi=m×xi with Eq. m” is a constant , can be calculated as the following:When we sample xi can be sampled from the fitted power law distribution.And P(D), can be estimated as:Then, the percentage of the RNA diversity coverage, Dobs. We then used Eq. m:We scaled the diversity coverage of unique CDR3s to the estimated diversity coverage with 90% RNA input, U test was used to calculate the significance of copy number difference between pairs in naïve, effector, effector memory, and central memory CD8+ T cells and p values was adjusted with Benjamini–Hochberg procedure. Adjusted p-value that was less than 0.05 was considered significant.Mann–Whitney Molecular identifiers have been adopted in IR-seq and DNA/RNA sequencing to reduce error rate. However, during reverse transcription, multiple transcripts could stochastically be tagged with same MID. Previous strategies relied on increasing the length of MID to reduce the probability of non-unique MID tagging when the total RNA molecule copy number was either unknown or very large . However+ T cells  with three different RNA inputs  . We have previously used control template sequences and evaluated the clustering threshold that would separate TCR RNA molecules accidentally tagged with the same MID, which is 15% of the sequence length . After performing sub-clustering, over 97% of sub-clusters have a uniform length , demonstrating the accuracy of sub-clustering step in MIDCIRS.To evaluate the accuracy of the sub-clustering step by an alternative means, we examined the TCR sequence lengths within MIDs that contain sub-clusters. We reasoned that if indeed each TCR RNA molecule was tagged with a unique MID, then the lengths of CDR3 for all reads would be identical under each MID. However, we showed that of the 8.5% of MIDs that contain sub-clusters, about 87% of MIDs contain TCR sequencing reads of different CDR3 lengths while only 13% have the same length for one million naïve CD8More importantly, to our surprise, we found that, without performing sub-clustering, the number of unique consensus sequences (unique CDR3 sequences) was overestimated, especially in samples with one million cells Figure , MIDCIRSBesides correcting PCR and sequencing errors, MIDs have also been used for absolute quantification of RNA molecule copy number in single-cell studies to improve precision –33. HereWe found that a shallower sequencing depth is required to saturate unique CDR3s than RNA molecules Figure B. In addAfter MID correction, with optimal sequencing depth, we stably detected TCR clones with a single TCR RNA molecule . The number of single-copy clones saturates with adequate sequencing depth . Results showed that CDR3 diversity detected by MIDCIRS is very reproducible among the four aliquots and is also proportional to the cell input numbers. In addition, we bioinformatically combined the aliquots into pseudo-40, -60, and -80% of RNA inputs and fitted the diversity coverage using the probability model described in the Section “Since the observed diversity is dependent on total TCR RNA molecules in a sample, which is a function of TCR RNA molecule copy number per cell and RNA input percentage, we next sought to use a probability model to predict TCR RNA molecule copy number per cell using the observed diversity coverage of unique CDR3s as a function of RNA input percentage see . We usedHowever, in order to apply this TCR RNA molecule copy number in estimating T cell clone size, we need to validate it using a different method and also test to see if different phenotypes of T cells might have different TCR RNA molecule copy numbers, which would be similar to the differences seeing in naïve B cells and plasmablasts . Next, w+ T cells. 3, 13, and 3 copies of three spike-ins were reliably detected, respectively . In all 1,000,000 cells we sequenced, we were capable of detecting Jurkat TCR sequences . CMV pp65 peptide has been shown to be the immunodominant target of CD8+ T cell response . These polyclonal CMV-specific effector CD8+ T cells represent about 2.6% of total CD8+ T cells. In addition, our previous study showed that tetramer positive polyclonal CMV precursor cells existed at a frequency of 1 in 100,000 CD8+ T cells in CMV seronegative individuals  the sensitivity of detecting a single cell in as many as one million naïve T cells; and (4) the ability to quantify T cell clonal expansion due to infection in CMV-seropositive patients.In this study, we applied the MIDCIRS, recently developed by our group , in T cep < 0.001, effect size r = 0.62). We demonstrated that using MID-based sub-clustering approach, MIDCIRS could identify new diversities, prevent chimera sequences from being built, and digitally count RNA molecules . With MID length being 12, when there are 200,000 identical RNA molecules, the percentage of identical RNA molecules tagged with same MID is only 1%. While long MID decreases the percentage of identical RNA molecules tagged with same MID, it also decreases efficiency of reverse transcription. Our analysis revealed that MID with 12 nucleotides is appropriate. Therefore, MIDCIRS provides the foundation of accurate assessment of clone size and clonal expansion in infection and vaccination, which would be a useful technology to provide a comprehensive quantification of the T cell repertoire in various basic studies and clinical settings.Last, we applied MIDCIRS to evaluate T cell clonal expansion in CMV-infected patients. Through accurate digital counting of TCR RNA molecules and in combination of precursor T cell frequency, we showed that CMV-specific effector CD8The protocol of using de-identified blood donors’ sample was approved by the IRB board of University of Texas at Austin.All sequencing data are under SRA accession SRP128082.K-YM performed all library preparation, data analysis, and wrote the manuscript; CH developed MIDCIRS-TCR analysis pipeline and RNA copy number simulation model; BW helped with naïve T cell sorting and manuscript editing; CW helped with CMV-specific T cell sorting and CMV-specific T cell line culture; JX helped to optimize MIDCIRS pipeline. HY helped with sequencing. NJ conceived the idea, designed the study, directed data analysis, and revised the manuscript with contributions from all coauthors.The protocol of using de-identified blood donors’ sample was approved by the IRB board of University of Texas at Austin.NJ is a scientific advisor of ImmuDX, LLC. A provisional patent application has been filed by the University of Texas at Austin on the method described here.


Non-coding ribonucleic acid (RNA) has without a doubt captured the interest of biomedical researchers. The ability to screen the entire human genome with high-throughput sequencing technology has greatly enhanced the identification, annotation and prediction of the functionality of non-coding RNAs. In this review, we discuss the current landscape of non-coding RNA research and quantitative analysis. Non-coding RNA will be categorized into two major groups by size: long non-coding RNAs and small RNAs. In long non-coding RNA, we discuss regular long non-coding RNA, pseudogenes and circular RNA. In small RNA, we discuss miRNA, transfer RNA, piwi-interacting RNA, small nucleolar RNA, small nuclear RNA, Y RNA, single recognition particle RNA, and 7SK RNA. We elaborate on the origin, detection method, and potential association with disease, putative functional mechanisms, and public resources for these non-coding RNAs. We aim to provide readers with a complete overview of non-coding RNAs and incite additional interest in non-coding RNA research. It is commonly believed that protein-coding messenger ribonucleic acids (mRNAs) are the primary controller of cells, carrying out the necessary functions for life. Our understanding of how a gene functions has been significantly challenged due to the advances in high-throughput sequencing (HTS) technology, which has provided researchers with an expanded view of the complexity of the human genome, allowing for the identification of diverse types of RNAs. Mounting evidence from various studies has shown that the non-coding portion of the genome plays a more significant role in human biology than previously thought. Depending on the version of the annotation, the protein-coding genome regions represent around 1–3% of the human genome. However, the rest of the genome plays an important role in controlling the expression of the coding deoxyribonucleic acid (DNA), and in the spatial organization of the genome. One of the most critical corroborations of this is the distribution of results of genome-wide association studies (GWAS). Based on the latest GWAS Catalog June 2017), only 3.56% of disease-associated single-nucleotide polymorphisms (SNPs) reside in the protein-coding region and 96.44% lie in non-coding regions equally proportioned between the intergenic regions and intron regions [017, onlyThe Encyclopedia of DNA Elements (ENCODE)  is a larNon-coding RNA (ncRNA) is an RNA molecule that is transcribed from DNA but not translated to a protein. The last decade has witnessed a sharp rise of interest in non-coding RNA research . There aLncRNAs are a class of long transcribed but not translated RNAs that are longer than 200 nucleotides ,9. lncRNThe interest in lncRNA has grown considerably as evidence of lncRNA’s role in various biological contexts has accumulated in recent years . lncRNAdThe identification of lncRNA requires the detection of transcription from unannotated genomic regions. This can be done by a number of techniques, including tiling array, serial analysis of gene expression (SAGE) , cap anaTraditional RNAseq libraries were built based on the presence of a poly(A) tail, which is present in all mRNA except for histone encoded mRNAs . It is eThe number of definable lncRNAs varies by study. In 2005, a study claimed to have identified over 35,000 lncRNAs . AnotherAccording to the latest gene annotation (GRCh38) gene transfer format file (GTF) (Homo_sapiens.GRCh38.89.gtf) from Ensemble, the total length of coding RNA is 97,662,789 nucleotides compared to 9,699,539 nucleotides for lincRNA. How lncRNA exerts its influence over disease is not well-known. The lncRNA-disease association may be activated through the regulation of protein-coding gene expression by a trans-expression quantitative trait locus (eQTL) and these trans-eQTL SNPs could be a surrogate SNP for SNPs residing in protein-coding RNA within the same high-linkage disequilibrium haplotype block. Cabili et al. found that many tissue-specific cis-eQTL are SNPs with known diseases or trait associations . Hon et Even though the majority of lncRNAs are believed to be non-coding, some of them may potentially harbor novel unannotated proteins. In recent years, there have been increasing efforts to predict the functional potential of lncRNA. The first tool created for such a purpose was the Coding Potential Calculator (CPC) . CPC assCircRNA is a type of evolutionarily conserved RNA that forms a covalently closed continuous RNA loop. There are two scenarios of the formation of circRNAs: direct ligation of 5’ and 3’ ends of linear RNA, and backsplicing, wherein a downstream 3’ splice site joins to an upstream 5’ splice site . It has CircRNA was originally identified through the analysis of scrambled exons over two decades ago using older, non-high throughput technology . CircRNANovel bioinformatic tools have contributed greatly to the identification of the new circRNAs from RNA-seq data. Notable circRNA identification tools are find_circ , MapSpliPseudogenes are a category of ncRNA that resembles mRNA, but they are not transcribed into proteins. Currently there are two major mechanisms for the formation of pseudogenes. The first one suggests that pseudogenes are the products of the process of genomic duplication . Genes iThe detection of pseudogenes usually relies on the careful analysis of sequence alignment. A common approach is to determine the homologous sequences to protein coding genes using tools such as FASTA or BLAST. Several more complicated computational approaches have been developed over the years to detect pseudogenes ,94,95. HsRNAs are non-coding RNAs with a length of less than 200 nucleotides. The discovery of sRNAs has substantially enriched our understanding of the diverse world of RNAs. There are many species of sRNA. Here we will discuss eight species that have been proven to be detectable from HTS data; miRNA, transfer RNA (tRNA), piwi-interacting RNA (piRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), Y RNA (yRNA), single-recognition particle RNA (7SL RNA), and 7SK RNA.The most studied smallest RNAs are miRNAs with more than 10,000 published manuscripts on PubMed . Origina2 or nuclease S1 [HTS technology has also facilitated the discovery and identification of a wide range of sRNA species. At the infancy of HTS technology, small RNA-sequencing was often referred to as miRNA-sequencing as the goal was mostly to study miRNA. Through meticulous examination of HTS data, researchers became aware that miRNAs are only a fraction of the sRNA-sequencing data. The sequencing libraries for sRNA are constructed with size-selected gel electrophoresis, which is agnostic to sRNA categories. All RNA of less than 50 nucleotides in size are selected into the library. The most, or second most, abundant sRNA species is often tRNA A. tRNAs lease S1 . The 5’ lease S1 ,121. Thelease S1 ,122. Thelease S1 ,125,126.lease S1 . These 5lease S1 .Other noticeable species of sRNA that can be detected through HTS are piRNA, yRNA, snRNA, snoRNA, 7SL, and 7SK. piRNA is a small RNA with a length of 24 to 32 nucleotides, and is considered by many to be the most abundant species of sRNA . PiRNAs Many miRNA processing pipelines have been established for utilizing HTS data. The major pipelines include: Oasis , ChimiraThe biology of the human body is a vast and complex system and we are just beginning to understand the role of non-coding RNA in regulating that system. The advancement of biotechnology contributes to every breakthrough in our understanding of human biology. ncRNAs, the supposedly insignificant portion of the RNA universe, have exploded with an array of studies centered around the potential functions and disease associations related to ncRNAs. The sudden interest in ncRNA is due to the maturity of HTS technology and the development of bioinformatics allowing the interpretation of HTS data.The interest in ncRNA is reflected by the increasing number of manuscripts published pertaining to ncRNA and the initiation of large consortium projects focused on ncRNA, such as ENCODE. The controversy surrounding whether ncRNAs are functional really comes down to the definition of “functional.” In the 2012 ENCODE publication , all traThe common consensus of the functions of ncRNA is that they regulate gene expression at both transcriptional and epigenetic levels. The exact mechanism of this regulation varies by ncRNA categories; and some may be yet to be discovered. As HTS technology and sequencing library construction methods advance, we come closer to elucidating the entire human RNA spectrum and uncovering the secrets of ncRNAs.


The high-resolution structures of KH3-KH4 with their respective target sequences show that KH4 recognizes a non-canonical GGA sequence via an enlarged and dynamic hydrophobic groove, whereas KH3 binding to a core CA sequence occurs with low specificity. A data-informed kinetic simulation of the two-step binding reaction reveals that the overall reaction is driven by the second binding event and that the moderate affinities of the individual interactions favor RNA looping. Furthermore, the concentration of ZBP1, but not of the target RNA, modulates the interaction, which explains the functional significance of enhanced ZBP1 expression during embryonic development.Zipcode binding protein 1 (ZBP1) is an oncofetal RNA-binding protein that mediates the transport and local translation of   •The dynamic groove of ZBP1’s KH4 domain allows recognition of a G-rich RNA sequence•ZBP1’s KH3 and KH4 domains bind their target RNA sequences with similar affinities•β-actin interactionRNA looping drives the ZBP1-•β-actin mRNA bindingThe protein, rather than the RNA, concentration regulates ZBP1- β-actin mRNA with the RNA-binding protein ZBP1 regulates mRNA transport and translation and is necessary for neuronal development. Using NMR and biophysics, Nicastro et al. have shown that the interaction is driven by RNA looping and regulated by the protein, rather than the RNA, concentration.The interaction of  Zipcode binding protein 1/IGF2 mRNA binding protein 1 (ZBP1/IGF2BP1/IMP1) is an oncofetal protein expressed at high levels in the embryo that is important for the development of the nervous system. A faulty protein or reduced ZBP1 gene expression results in impaired embryonic development  and a smDrosophila (ZBP1 contains six putative RNA-binding domains (two RNA recognition motifs [RRMs] and four hnRNP K-homology [KH] domains) organized in three two-domain units, and is an RNA-binding protein . ZBP1’s osophila  , we knocked out the ability of each individual domain to bind its RNA target and studied RNA binding by the other domain. The Y396F mutation leads to constitutive RNA binding by removing the effect of Src phosphorylation . The mut binding . In our  binding   RNA bound to the UCGGACU RNA  spectroscopy , we solvn motifs  are undeormation , the strβ-actin Zipcode KH3 RNA target was shown to lead to only a few-fold change in affinity in the same study. We find that in the KH3-RNA complex, the two central nucleobases (C4 and A5) are recognized via multiple H-bonds and hydrophobic contacts (d), but the energy penalty for binding a different nucleobase in this position is much lower than that reported for Nova-1 KH3 and other KH domains  are inserted in a non-canonical hydrophobic groove that is unusually large and open, and strong nucleobase discrimination is mediated by a combination of hydrophobic interactions and H-bonds . By contd ∼1.5 μM, KH3 Kd ∼2 μM, Importantly, the high-sequence specificity of KH4 with respect to KH3 and the similar binding affinities of the two domains  as well as kinetic parameters (kon and koff) for the interaction. The equilibrium dissociation constants for the Zipcode RNA-KH4 KO and KH3 KO complexes are 1.5 μM and 0.9 μM, respectively. This is comparable with the Kds we measured for the same two protein constructs in complex with the short RNA target sequences using ITC, which confirms that these interactions recapitulate those with the full-length Zipcode (on: 1.4 × 105 M−1s−1 versus 3.0 × 104 M−1s−1). Conversely, the dissociation rate of the KH3-RNA complex is three times slower than that for KH4 (koff: 0.13 s−1 versus 0.046 s−1).We recorded biolayer interferometry (BLI) experiments on an immobilized 28-nucleotide Zipcode RNA exposed to different concentrations of ZBP1 KH3DD-KH4 (KH3 KO), KH3-KH4DD (KH4 KO), or KH3-KH4 , and obt Zipcode  and S4. With the exceptions of the flexible amino and carboxy-terminal regions flanking the di-domain, the motions are generally limited in the structure . An exced of 20 nM, indicating that the coupling of KH3 and KH4 binding increases the affinity of the individual interactions by a factor of ∼50. Although the 20 nM Kd is higher than that previously reported based on EMSA assays (∼4 nM)  , the dif (∼4 nM)  confirme5 M−1s−1 versus 1.4 × 105 M−1s−1). In contrast, the dissociation rate constant for KH3-KH4 (0.0033 s−1) is between one and two orders of magnitude lower than that of either mutant, indicating that the higher affinity of the KH3-KH4 interaction with Zipcode RNA results almost entirely from the lower dissociation rate. The relatively weak interaction of the individual domains with RNA (∼1 μM) and the weak coupling of KH3 and KH4 binding (one to two orders of magnitude) is similar to that observed for a number of other multi-domain RNA-binding proteins and has been proposed to respond more readily to regulation than a single high-affinity interaction  and kC4 (∼9.4 s−1) were derived from the measured Kd of the KH3-KH4-RNA complex and the on- and off-rate constants of the individual binding events, as detailed in the −1, kC4 = 9.333 s−1, [KH3-KH4] = 0.2 μM, and [RNA] = 0.4 nM until equilibrium was reached, and the Kd was calculated from the concentration of the appropriate species (d (20.6 nM) was in close agreement with the experimentally measured value (20 nM), as was the dissociation rate , but with no closed complex formation, reached equilibrium with 38 pM 1:1 complex with KH3 bound, 63 pM 1:1 complex with KH4 bound, and only 8.2 pM double complex.This analysis also shows that a first slower binding event that depends on protein concentration is followed by the fast concentration-independent binding of the second domain in the two-domain unit that drives the overall reaction toward formation of the closed complex. The alternative binding pathway, i.e., binding of a second protein to the same RNA, would require a significantly higher affinity for the two interactions because the cellular concentrations of protein and RNA are low compared with the Kd values, fractions of RNA bound at equilibrium, and dissociation rates (d and a corresponding decrease in the percentage of bound RNA (from ∼91% to ∼57% with kC3 reduced by an order of magnitude to 0.2 s−1). The simulations also show that although a smaller closing rate would lead to an increased Kd and a correspondingly lower fraction of bound RNA, the fraction of RNA present as 2:1 protein to RNA complex remains negligible, i.e., most of the mRNAs would still be looped and bound by both domains.Although this simulation provides us with an assessment of the reaction time course, it is important to understand how variation in the kCs  would impact this time course. That is, these simulations are important to establish to which extent a larger or smaller kC3 or kC4  would affect the overall interaction. First, we explored the effect of varying the kC value on the behavior of the system by calculating Kon rates . The resβ-actin mRNA. Indeed, our calculations show that were the cellular concentrations of the β-actin mRNA to be several fold higher or lower than our estimate, this would have no significant impact on what fraction of the β-actin mRNA is predicted to be bound by ZBP1. In contrast, the fraction of β-actin mRNA bound by ZBP1 would readily respond to changes in the concentration of the protein, indicating that the ZBP1 concentration controls the interaction. At a KH3-KH4 concentration of ∼5 nM, the fraction of bound RNA is 0.19, whereas at ∼50 nM, it is 0.71, and at ∼200 nM, it is 0.91  in the cell, and the fraction of ZBP1-Zipcode RNA bound is largely independent of the concentration of the  is 0.91 . Additio is 0.91 C, resultin cell quantitative data on protein and RNA concentrations and binding affinities are not copious. However, recent microscopy data obtained on the ZBP1-β-actin mRNA interaction have provided an estimate of both the protein concentration and, importantly, the average number of protein molecules bound to each RNA molecule  in fibrmolecule ). This ais bound . It is n modeled . This smulations  encompasβ-actin mRNA is the best studied function of this protein and an important example of how RNA-binding proteins regulate local protein translation in neuronal development  and has a solvent-exposed side chain that faces the RNA  assays  contain Crucially, KH3 and KH4 bind in a coupled fashion. The two individual domains each have moderate RNA-binding affinities, and coupling of the two interactions is necessary for the interaction in vivo . Howeverd in the low μM range; RNA looping has been reported for other RNA-binding proteins, including the alternative splicing regulator PTB , which iβ-actin-containing ribonucleoprotein particle, and the overall time of assembly is likely to be significantly slower. As more kinetic data become available on inter-coupling in protein-RNA interactions, we expect it will be possible to explore to what extent the mechanistic insight we have derived from the ZBP1 KH3-KH4 model is applicable to other RNA-binding di-domains , in which the Kd of individual domains for the RNA targets is also in the low μM range and RNA looping has been proposed to be an important component of the recognition mechanism.In addition to providing information on the forces driving RNA looping, our model gives unique insight into the timescale of RNA looping, which, as far as we are aware, has not been experimentally characterized in ZBP1, PTB, or any other structurally equivalent systems. The simulations show that the looping associated with the binding of the second domain to the RNA takes place in less than a second after the formation of the first concentration-dependent complex. This includes both the time the RNA must spend exploring the conformational space around the di-domain to reach the proximity of the second hydrophobic groove to be bound and the time required for a productive interaction when the protein groove and the RNA cognate sequence are in physical proximity. It is worth considering that several assembly and RNA remodeling steps are likely to be required to build a ZBP1-β-actin interaction is regulated by the cellular concentration of protein and RNA, and to extend this initial model to interpret data on other ZBP1 targets that share a similar KH3-KH4-based interaction mode. The concentration of ZBP1 in the cell is sub-micromolar, which is nearly three orders of magnitude higher than the β-actin RNA concentration. Our model indicates that at these protein and RNA concentrations, binding is dependent on the concentration of the protein, but not on the concentration of the RNA. This conclusion would hold even if the RNA concentration was 50-fold higher, for example, because of higher local concentration, or 50-fold lower than the one used here. β-actin is a housekeeping gene, and cellular levels of β-actin mRNA are high and maintained in tissues and cancers (https://genevisible.com/cancers/HS/UniProt/P60709). ZBP1, on the other hand, is expressed at high levels at a defined stage in neuronal development, but is low in many adult tissues, and we propose that this mechanism allows the protein-RNA interaction to be regulated effectively by varying the protein concentration within a defined time window. Interestingly, many ZBP1 targets are not housekeeping genes, and their concentrations show a many-fold variation during development and cell cycle  (This study provides a mechanistic framework for quantitatively interpreting a diverse range of in-cell and genomic observations on the ZBP1-er cell) , suggestG. gallus Zipcode binding protein 1 (accession number AF026527) and its GxxG-GDDG mutants were cloned, expressed, and purified as previously described (E. coli BLI21 (DE3) cells (Invitrogen) and purified using an IMAC column. The affinity tag was then cleaved off, and the wanted protein construct was further purified using a MonoQ 5/50 GL column. Protein purity (>95%) and integrity were confirmed using SDS-PAGE and electrospray mass spectrometry, and the protein was then stored at −20°C in 20 mM NaPi, pH 6.5, 20 mM NaCl, 0,05% NaN3, and protease inhibitors (Roche). Concentrations were determined using absorption spectroscopy. Unlabeled samples were obtained from protein expressed in LB media, whereas samples labeled with NMR-active, stable isotopes  were obtained using labeled media as described  of escribed . Brieflyescribed . RNA oli1H frequency. NMR spectra were processed by using the NMRpipe suite of programs  recorded on 1:1 protein (labeled):RNA (unlabeled) samples.NMR experiments were recorded at temperatures between 25°C and 37°C on Bruker Avance and Varian Inova spectrometers operating at a 700-, 600-, and 800-MHz programs  and analprograms  and XEASprograms  programsescribed  and detaescribed . IntermoThe structures of the KH3-GCACACCC and KH4-UCGGACU complexes were calculated using a semi-automated ARIA-2.3-based protocol  and refid) were obtained by fitting the data with a one (for the short RNA sequences) or two non-sequential (for the full-length Zipcode) binding-site model.ITC experiments were recorded on a VP-ITC or MicroCal iTC200 instrument  at 25°C. Protein and RNA samples were dialysed in 20 mM NaPi, pH 6.5, and 100 mM NaCl. For all samples, small aliquots of a 200–300 μM protein solution were injected into a cell containing a 10–15 μM RNA solution, and the heat of reaction was measured. Data were analyzed using Microcal Origin 7.0 software. The independently measured heat of dilution was subtracted, and the dissociation constants  containing 2 mM TCEP, 2 mg/mL of bovine serum albumin (BSA), and 0.005% Tween 20 to reduce non-specific interactions. The assays were carried out at 25°C in a 96-well plate and a sample volume of 200 μL. Streptavidin-coated biosensors were pre-equilibrated, loaded with biotinylated RNAs (ranging from 144 ng/mL to 432 ng/mL in assay buffer), and exposed to protein concentrations ranging from 5 to 160 nM (for KH3-KH4) and from 125 to 8,000 nM (for the two protein mutants). Data were processed, and kinetic parameters were calculated using ForteBio or in-house software . The konKd × kon . The accThe simulation was performed by numerical integration of the system of ordinary differential equations associated with the model presented here. In-house software used the fourth-order Runge-Kutta method as described by Original Clones, D.H.; Samples Preparation, A.M.C., and, in a few cases, by D.H. and G.N.; NMR experiments, G.N., A.M.C., and A.O.; KH3-KH4 Assignment, A.M.C. and G.N.; Structure of the KH3-Bound Complex, A.M.C. and G.N.; Structure of the KH4-Bound Complex, G.N.; ITC, G.N.; BLI, performed by G.N. and analyzed by G.N. and S.R.M.; Biophysical Modeling, S.R.M.; Bioinformatics, M.U. and R.B. The project was designed by A.R.; The paper was written by A.R., S.R.M., and G.N. with M.U. and R.B., and revised by all the authors.


Curation and storage of formalin-fixed, paraffin-embedded (FFPE) samples are standard procedures in hospital pathology laboratories around the world. Many thousands of such samples exist and could be used for next generation sequencing analysis. Retrospective analyses of such samples are important for identifying molecular correlates of carcinogenesis, treatment history and disease outcomes. Two major hurdles in using FFPE material for sequencing are the damaged nature of the nucleic acids and the labor-intensive nature of nucleic acid purification. These limitations and a number of other issues that span multiple steps from nucleic acid purification to library construction are addressed here. We optimized and automated a 96-well magnetic bead-based extraction protocol that can be scaled to large cohorts and is compatible with automation. Using sets of 32 and 91 individual FFPE samples respectively, we generated libraries from 100 ng of total RNA and DNA starting amounts with 95–100% success rate. The use of the resulting RNA in micro-RNA sequencing was also demonstrated. In addition to offering the potential of scalability and rapid throughput, the yield obtained with lower input requirements makes these methods applicable to clinical samples where tissue abundance is limiting. Formalin Fixed Paraffin Embedded (FFPE) tissue is commonly prepared in hospital environments for pathology evaluations. The ability to profile nucleic acids purified from FFPE tissue can lead to important applications of genomic technologies to characterize archival samples and prospective cohorts where collection of fresh frozen samples is impractical.FFPE extraction protocols often involve tedious manual manipulation and hazardous chemicals such as xylene, with the majority being exclusive to RNA or DNA –3. OftenFFPE processing and extraction result in significant fragmentation of DNA and RNA as well as sequencing artifacts resulting from chemical modification of bases . In the RNA sequencing requires the removal of ribosomal RNAs (rRNAs), which would otherwise dominate sequencing reads at >90% frequency –13. TypiIn this study, we have developed protocols that allow the use of up to 34 years old FFPE material for NGS of all three classes of nucleic acids  in large scale studies. These methods involve high throughput and automated end-to-end solutions from the upstream lysis and deparaffinization steps to nucleic acid purifications and library construction.2 (including 2–5 10 μm thick scrolls).The samples used in this study were obtained as part of the lymphoid cancer and personalized oncogenomics projects that were approved by University of British Columbia/BC Cancer Agency Research Ethics Board . Ninety one FFPE samples were chosen for the study, mainly Diffuse Large B Cell Lymphoma (DLBCL) in origin. The sample collection dates vary between 1–34 years and the size of tissue targeted was ~100 mmBesides human FFPE samples, mouse FFPE samples were also used for certain experiments. The C57BL/6 mouse tissues included liver, spleen, stomach, brain, heart, kidney, and lung. The FFPE blocks from these tissues were prepared 3–4 years prior to extraction.Universal Human Reference (UHR) total RNA was purchased from Stratagene . Tumor samples from Fresh frozen and/or fresh tissue embedded in optimal cutting temperature compound (OCT) were obtained as part of the British Columbia Cancer Agency’s personalized oncogenomics (POG) project.°C) was included between proteinase K treatment and bead binding steps.An automated process for sample extraction was developed based on the FormaPure protocol and is described in detail in DNA was quantified using a Qubit dsDNA HS DNA assay  or Quant-iT ™ dsDNA High-Sensitivity Assay Kit . RNA was quantified using an Agilent RNA 6000 Nano Kit  or Caliper HT RNA LabChip kit .et al, submitted].Two other extraction protocols were used for comparisons. The first was the AllPrep DNA/RNA FFPE Kit , performed according to the manufacturer’s instructions. The second one was a protocol developed by The Cancer Genome Atlas (TCGA) project through the Biospecimen Core Resources at Nationwide Children’s Hospital and International Genomics Consortium. The TCGA protocol involves a combination of AllPrep DNA/RNA FFPE and High Pure (Roche) kits. The protocol also includes upstream steps such as heptane-based deparaffinization that are different from those employed in either the Qiagen or Roche protocols. The DNA pellet was processed as per the aforementioned Qiagen’s protocol and the RNA supernatant was purified using the High Pure miRNA Isolation Kit  based on our observations that the former was superior for DNA extraction and the latter was superior for RNA extraction [Zmuda EJ RNA from fresh frozen/OCT-embedded tissue was extracted using the Qiagen’s AllPrep DNA/RNA Mini Kit  according to the manufacturer’s instructions.Q33120) and size profiled using an Agilent DNA 1000 Kit  or Caliper HT DNA High Sensitivity assay . A pool of 32 randomly selected libraries was sequenced using an Illumina HiSeq 2500 lane (paired-end 125 bp).The library construction protocol used for FFPE genomic DNA sequencing is described in detail in st strand cDNA synthesis kit  and the NEBNext directional second strand cDNA module . cDNA was mechanically sheared using Covaris’ sonicator targeting average fragment sizes of 200–300 bp. Fragments were end-repaired using a Paired-End Sample Prep Premix Kit–End Repair  followed by purification using 1.8 (beads): 1(DNA) volume to volume ratio. The rest of library construction steps and quantification of libraries were as described above for DNA libraries. dUTP-marked second-strand cDNA fragments were removed (thereby rendering libraries strand-specific) using the USER enzyme  that was included in the PCR reaction. Libraries were sequenced using Illumina HiSeq 2500 (paired-end 75 bp).The depletion of rRNAs is described in detail in st strand cDNA synthesis using Maxima H Minus reverse transcriptase . Following bead-based size selection, cDNA was amplified using Phusion Hotstart . Amplified libraries were subsequently size-selected using a gel-based system.Total RNA/nucleic acid was used for RNA ligation-based library construction as described in detail in Genome sequencing reads were aligned to the human reference hg19) using BWA version 0.5.7 . RNA seq using BWhttp://picard.sourceforge.net./). Quantification of ribosomal and mitochondrial reads was performed using BioBloom Tools [To exclude total read count as a confounding factor in quality assessment of libraries, libraries were down-sampled and duplicate marked using Picard . Importantly, the automated protocol allowed the processing of 96 samples in a single run of 1–2 days with <1 hr hands-on time.The results described above supported the notion that the FormaPure-based protocol was robust for extraction of DNA/RNA for standard sized FFPE scrolls (~100 mmOur new library construction protocol features an improved workflow  due to  with sev). The relatively high proportion of chimeric reads in FFPE libraries might be associated with a higher proportion of single stranded fragments than in fresh frozen samples, which in turn could be due to the shorter fragmented DNA in the FFPE samples, which may denature during high temperature incubations required for FFPE DNA extraction. The T4 DNA ligase used in NGS library construction is predominantly dependent on double-stranded DNA substrates [One class of artifacts detected in FFPE DNA sequence analysis is chimeric reads apparently originating from segments of DNA from non-contiguous genomic loci. One of the alignment-based sequencing quality metrics we examined was percent of aligned and paired reads. Non-paired reads included chimeric reads. The average percent of paired reads for the 32 libraries was 92% versus >99% for reads from non-FFPE material . The relbstrates . Thus, tbstrates . ConsistTo assess the diversity of the ligation-based genome libraries made from nucleic acid that was extracted using our FormaPure protocol, the percent of duplicate reads was calculated and the average was found to be one percent of aligned reads . At compReproducibility of the library construction protocol was inferred based on the high correlation of library yield obtained for two independent library preparations from all of the 91 FFPE samples . DespiteTaken together, the data discussed above indicate that our automated FormaPure extraction protocol yields DNA that is suitable for genome sequencing.We have used the NEBNext® rRNA Depletion Kit (Human/Mouse/Rat) for high quality library construction from fresh RNA samples, using as little as as 20 ng of total RNA . Here, wAnalysis of reads that mapped to intronic, exonic and intergenic regions indicated that the FFPE libraries had much lower exon content as compared to the UHR libraries . Overall, the quality of the libraries was slightly higher for the FormaPure protocol. For example, adapter dimer content was 10% in the TCGA protocol versus 4% in the FormaPure protocol . miRNA dIn conclusion, this study has established an automated suite of protocols that allow for large-scale transcriptome and genome studies of archived samples. The integrated automated workflow spans all steps, allowing high throughput removal of paraffin, reversal of formaldehyde crosslinks, extraction of RNA and gDNA, and library construction for Illumina sequencing. This set of protocols is developed while ensuring data quality is maintained, and most importantly, meeting input requirements for clinical samples.S1 FigLeft panel: genome; middle panel: RNA-seq; right panel: miRNA.(TIF)Click here for additional data file.S2 FigRNA yield and size profile is using Agilent RNA Nano assay.(TIF)Click here for additional data file.S3 FiggDNA was spike-in with various RNA concentrations and the mixture was measured by Qubit DNA high sensitivity assay.(TIF)Click here for additional data file.S4 FigRNA was assayed on Agilent RNA Nano.(TIF)Click here for additional data file.S5 FigInput amounts are as indicated from three different FFPE sources. % aligned (left panel) and % duplicates (right panel) upon sequencing of the resulting libraries are shown.(TIF)Click here for additional data file.S6 FigStarting gDNA amount was 20 ng from FFPE material.(TIF)Click here for additional data file.S7 FigThe coverage values (Y-axis) were calculated based on 2–2.5 billion reads whereas the duplicate percentages (X-axis) were calculated after down sampling of the same data to 10 million reads.(TIF)Click here for additional data file.S8 FigLibrary yield is shown in (A). duplicate rate of resulting libraries is shown in (B). Besides UHR RNA, RNA from OCT-embeded tumor samples (POGs) were also assessed.(TIF)Click here for additional data file.S9 FigLibrary yield is shown in (A). qRT-PCR measurement of 18s rRNA relative to GAPDH is shown in (B).(TIF)Click here for additional data file.S10 FigqRT-PCR values (Y-axis) were derived from the ratio of 18s rRNA to GAPDH levels. X-axis represents % rRNA as calculated upon sequencing of the same RNA libraries.(TIF)Click here for additional data file.S11 FigLog-log plot of expression correlation between independently generated pairs of libraries from the same sample (for two different sources) are shown.(TIF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(PDF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.


In vitro binding assay with insulin 5’UTR and different HuD isoforms indicate that the ‘N’ terminal region of HuD is important for RNA binding and insulin translation repression. Using reporter assay we showed that specifically full-length HuD A isoform represses translation of reporter containing insulin 5’UTR. We further show that PABP and HuD interact with each other in RNA-dependent manner and this interaction is affected by glucose and PDI (5’UTR associated translation activator). These results suggest that PABP interacts with HuD in basal glucose conditions making translation inhibitory complex, however upon glucose stimulation this association is affected and PABP is acted upon by PDI resulting in stimulation of insulin translation. Together, our findings snapshot the mechanism of post-transcriptional regulation of insulin biosynthesis.Understanding the regulation of insulin biosynthesis is important as it plays a central role in glucose metabolism. The mouse insulin gene2 (Ins2) has two splice variants; long (Ins2L) and short (Ins2S), that differ only in their 5’UTR sequence and Ins2S is the major transcript which translate more efficiently as compared to Ins2L. Here, we show that cellular factors bind preferentially to the Ins2L 5’UTR, and that PABP and HuD can bind to Ins2 splice variants and regulate its translation.  Glucose stimulated insulin translation is a well studied phenomenon. Glucose regulates several steps of insulin biosynthesis starting from transcription, splicing, export, stability, and translation resulting in 20 fold increase in insulin production –3 but thrs/miRNA ,8.cis-acting effector molecule [We have previously identified Protein-Disulfide Isomerase (PDI) as a key factor responsible for glucose stimulated insulin biosynthesis by bringing disulfide bond modification in poly-A-binding protein (PABP) . PABP exmolecule ,11. The molecule .It has also been shown that PABP can bind to ‘A’ rich region in the 5’UTR of its own mRNA and represses the translation . Apart fHuman antigen D (HuD) binds to mouse Ins2 5’UTR and represses insulin translation ,18. Prev2 at 37 °C. For glucose stimulation, cells were treated with serum free DMEM medium containing 20 mM glucose.βTC6 and MIN6 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM-Gibco) in low glucose (1g/l) supplemented with 10% FBS (Gibco), 100 μg/ml penicillin and 100 μg/ml streptomycin, under 5% COBamHI and XhoI (New England Biolabs) respectively.Mouse HuD B isoform (NM_001038698.1) and HuD D isoform (NM_001163397.1) ORFs were amplified from βTC6 cDNA by PCR using gene specific primers  containiHuDA isoform (NM_010488.4) was cloned by replacing the N-terminal of HuD B clone with the N-terminal end of HuDD clone. All three isoforms were also subcloned into pGEX6P3 to make GST-fusion and for mammalian expression vectors into pCDNA3.C3 (Thermo Fisher Scientific) vector having Myc and HA tag at the 5’ end. All clones were confirmed by sequencing.The clones expressing the His-PABP and His-HuD/GST-HuD isoforms were inoculated into LB-kanamycin and LB-ampicillin media respectively. When the OD of both the cultures reached around 0.5–0.6, IPTG (Sigma) was added at the final concentration of 1 mM for PABP and 0.1 mM for HuD. Further, both cultures were incubated at 37°C and 25°C for 3 h and 5 h respectively. The recombinant proteins were purified by following standard protocols of affinity tag purification .RNA-EMSA was performed as described . BrieflyFor ultraviolet (UV) crosslinking, RNA-EMSA reactions were exposed to UV light followed by RNase treatment as described  and anal2, 0.1% NP40) buffer as described earlier and the lysates (about 500 μg/ 800 μl per RIP reaction) were precleared with protein G beads (Upstate) for 1 h and subjected to immunoprecipitation with either control or specific antibody (4 μg of rabbit anti-PABP antibody (Abcam-21060) and anti-HuD antibody (Santa cruz Biotechnology-H300) at 4°C for 10 h. The beads were blocked in a buffer with denatured t-RNA (Sigma) for 90 min on ice with glycogen (Ambion) and salmon sperm DNA (20 μg) in 100 μl before adding the immunoprecipitate. After incubation, beads were extensively washed in NT2 buffer and the RNA associated with the immunoprecipitate was extracted and analyzed by RT-PCR . The 10-fold molar excess of unlabeled long (L) and short (S) RNA were added to RNA bound beads, followed by the addition of the extract (500 μg/mL of reaction) and incubated at 4°C for 2 h. After washing with 1X GSB bound fractions were eluted in 2X-SDS loading dye. The protein complex was resolved on a 10% SDS-PAGE. The gel was transferred on PVDF membrane (Millipore) and probed for PABP (Abcam-21060) and HuD (Santa cruz Biotechnology-H9).Biotinylated long RNA (5 μg) was incubated with 100 μl of streptavidin agarose beads (Invitrogen) for 30 min then washed thrice with 1X GSB  were seeded into 24 well plate and after 16 h media were replaced with DMEM-LG containing 0.1% FBS for 8 h followed by transfection with pcDNA3 L-Luc/ S-Luc construct (450 ng), pcDNA3 renilla luciferase (50 ng) and pcDNA3-HA HuD isoforms such as A, B and D (1 μg each). After 12 h of post transfection cells were recovered for 16 h and then treated with low and high glucose as per standard protocol. The cells were lysed in passive lysis buffer (Promega) after 48 h of post-transfection. For in vitro translation assay, 50 ng of cRNA (non-polyadenylated insulin 5’UTR-luciferase-3’UTR) was translated in rabbit retic lysate (RRL) in presence or absence of recombinant proteins (200 ng) as per manufacturer’s protocol (Promega). Renilla-luciferase (15 ng) was included in all reactions as a translation efficiency control. In decoy experiments, competitors were added up to 250 fold molar excess to the luciferase RNA. The luciferase activity was measured with the Dual-Luciferase Reporter Assay System (Promega Corp.).MIN6 cells (5 x 10HuD isoform (2.5 μg) plasmids were transfected in MIN6 cells and after 24 h of recovery, the cells were treated with low and high glucose media for 30 min. The insulin production was estimated from culture supernatant by ELISA (Millipore EZRMI-13K) following the instruction manual and the intracellular insulin levels were also assessed by probing with anti-insulin antibody (Santa Cruz Biotechnology-9160).The extracts prepared in RIPA buffer were subjected to IP, a portion of extracts were also subjected to RNase treatment (Ambion) at 37°C for 10 min before IP. Rabbit polyclonal anti-PABP antibody (Abcam) or rabbit polyclonal anti-HuD antibody (Santacruz Biotechnology-H300) was added to the extracts along with separate reaction of rabbit IgG control for overnight immunoprecipitation followed by binding to agarose protein G beads (Upstate) for 2 h at 4°C.Ni-NTA and GST-pull down were performed as per standard protocol of affinity tag purification. The (co)-purified proteins were resolved on 10% SDS PAGE and analyzed by western blotting or stained with Coomassie to see efficiency of pull-down.To examine the interaction between purified proteins equal amount of recombinant His-PABP, His-HuD A and His-GFP (2 μg of each) were mixed and subjected to IP. The recombinant His-PDI, or its C-terminal deletion mutant or the long 5’UTR RNA (150 ng) was also added to the protein mixture before IP and further analyzed by western blotting.The Secondary structure of the RNA was predicted using the m-fold algorithm .Quantitative data are represented as the mean ± SEM and compared statistically by Student’s t test, using Sigma plot (10.0). The p-value of < 0.05 was considered statistically significant and indicated in the figures.trans-acting factors to short variant (We have previously shown that long variant of mouse insulin gene 2 (Ins2L) is less efficiently translated as compared with short variant ; we post variant . Further variant . Sequenc variant . The com variant  indicatiPABP and HuD have been shown to bind to insulin 5’UTR leading us to hypothesize that these two could be the two cross linked RNA binding proteins that we have detected in our UV crosslinking experiments. To assess endogenous binding of PABP or HuD to mouse insulin RNA, we performed UV crosslinked RNA Immunoprecipitation (RIP) followed by RT-PCR. A specific amplification of 108 bp corresponding to mouse insulin was observed in input as well as in PABP or HuD immunoprecipitate, but not in IgG control . We furtIn order to define the region in the 5’UTR RNA to which PABP interacts, we performed competitive RNA-EMSA with recombinant PABP , radiolaL2 region is present in both long and short insulin 5’UTR and conserved across species , howeverRecently, it was shown that HuD binds to insulin 5’UTR and regulates its translation . We clonTo assess the binding ability of HuD isoforms A/D with respect to splice variants, we performed RNA-EMSA in the presence of unlabeled long and short RNA as competitors. Both HuD-A and D isoforms formed specific complex and was competed out with unlabeled long and short 5’UTR with similar efficiency at lower fold excess competitors, but at higher fold excess there seems to be a differential association suggesting that HuDA and D can bind differentially to the insulin 5’UTR splice isoform at higher concentration of RNA . Also, cin vitro translation assay. We used RNA containing luciferase ORF with long 5’UTR as reporter and performed translation reactions in the presence of recombinant proteins and renilla luciferase as normalizing RNA control. Although, PABP was able to stimulate the general translation of insulin 5’UTR as compared to non specific protein GFP  of Ins2 are translated differentially . In the reported ,21 howevosophila . We beliIt has been reported that HuD also requires a poly-A-tail or cap structure to stimulate translation globally . We propIn summary, we showed that for the first time endogenous PABP and HuD interact with each other in RNA dependent manner and this interaction is diminished when the cells are stimulated with glucose. These data are consistent with the previous reports showing that HuD under high glucose condition is displaced from insulin 5’UTR and localizes to the P-bodies like structures . We alsoS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.