This article presents an in vivo imaging technique based on nuclear fragmentation of carbon ions in irradiated tissues for potential real-time monitoring of carbon-ion radiation therapy (CIRT) treatment delivery and quality assurance purposes in clinical settings.2 pixelated charged-particle detector stack positioned downstream from a target phantom and list-mode data acquisition. The abundance and production origins, that is, vertices, of the detected fragments were studied. Fragment trajectories were approximated by straight lines and a beam back-projection algorithm was built to reconstruct the vertices. The spatial distribution of the vertices was then used to determine plan relevant markers.A proof-of-concept imaging and monitoring system (IMS) was devised to implement the technique. Monte Carlo simulations were performed for a prospective pencil-beam scanning CIRT nozzle. The development IMS benchmark considered a 5×5-cmThe IMS technique was applied for a simulated CIRT case, a primary brain tumor. Four treatment plan monitoring markers were conclusively recovered: a depth dose distribution correlated profile, ion beam range, treatment target boundaries, and the beam spot position. Promising millimeter-scale  beam range and submillimeter (≤0.6-mm precision for shifts <3 cm) beam spot position verification accuracies were obtained for typical therapeutic energies between 150 and 290 MeV/u.This work demonstrated a viable online monitoring technique for CIRT treatment delivery. The method's strong advantage is that it requires few signal inputs (position and timing), which can be simultaneously acquired with readily available technology. Future investigations will probe the technique's applicability to motion-sensitive organ sites and patient tissue heterogeneities. In-beam measurements with candidate detector-acquisition systems are ultimately essential to validate the IMS benchmark performance and subsequent deployment in the clinic. Owing to a denser ionization track (high linear energy transfer [LET]) and enhanced relative biological effectiveness (RBE) , carbon-Despite the biological and physical advantages of carbon ions, the typical dose gradients delivered in CIRT pose distinct challenges for treatment delivery accuracy and quality assurance. One significant source of uncertainty stems from determining carbon ions' stopping powers in heterogeneous patient anatomy and water , which iReal-time monitoring of CIRT is therefore highly recommended (1) to ensure accurate  dos12C projectile with traversed tissues produce a mixed secondary radiation field formed by neutrons, prompt γ, e−/e+, and energetic charged fragments. The secondary charged radiation field is dominated by projectilelike fragments: H, He, Li, Be, B isotopes, in order of abundance. These fragments carry much of the 12C projectile's momentum, range longer in tissues (Z < 6), form forward peaked secondary beams, and may escape the CTV. The lightest charged fragments, hydrogen and 4He, account for the bulk of the dose tail beyond the CIRT BP as shown in Figure 1 .In CIRT, beam energies on the order of 90 to 290 MeV/u are required to penetrate a water equivalent thickness (WET) of 2.1 to 16.4 cm . The abl10,11C and 15O β+ emitters are also produced in more peripheral 12C-tissue  interactions, which spurred interest in in vivo position emission tomography (PET) monitoring of CIRT. In-room PET imaging solutions were developed and reported by several working groups [Trace activities of the sue 12C, O interacg groups . While CCharged fragment–based real-time monitoring overcomes these limitations with appreciably higher production cross sections and detection efficiency. As noted earlier, the secondary charged fragments produced in CIRT may escape tissues, preserving information about their parentage. In previous conceptual studies , 21, theWe investigated the performance of an IVI monitoring system (IMS) for a prospective active spot scanning CIRT (AS-CIRT) delivery nozzle at our institution. Special focus was placed on identifying persistent dosimetry and monitoring markers for online, in vivo plan delivery verification.In the clinical workflow, planning computed tomography scans are often acquired several days before the start of treatment, increasing the susceptibility of the plan to patient anatomical changes. The IMS should, therefore, allow for real-time monitoring of a CIRT treatment delivery. Such control could enable beam delivery interruption in the event that a threshold-exceeding divergence arises between the planned and real-time measured value of a monitored marker.The incentives of the present study were to recognize technology limitations and cost-effectiveness as key factors when designing a prospective detector system solution. The system's underlying goal is to achieve stable and persistent monitoring in clinical settings. From the detection feasibility standpoint, a simple and fast-response monitoring system would require (1) minimal signal processing, relinquishing expensive transformations such as pulse height analysis; (2) no particle-type discrimination or identification, removing the need for sophisticated equipment to measure energy loss and/or time of flight; and (3) a flexible data acquisition format in lieu of binned frame mode imaging.In this first conceptual IMS study, we show that CIRT fragment identification is not necessary for dose delivery verification, augmenting the work of Henriquet et al , which aThe IMS proof-of-concept was investigated for a brain CIRT case: glioblastoma multiforme, a primary brain tumor with markedly poor prognosis and hallmarks of radioresistance , 23.Monte Carlo (MC) simulations were used to generate the IMS detected signals. These signals were analyzed in a manner emulating the response of a realistic detector system to infer potential plan relevant monitoring markers.The TOPAS MC code version 3.2 , back-enFigure 2) transported the carbon ion beam in air and consisted of horizontal (Y) and 2 vertical (X) spot steering dipole magnets, vacuum section pipes and windows, and a diagnostics block. The latter is composed of a beam spot scanning monitor (SPM), a multi-wire proportional gas chamber, dose monitors, and an energy modulation “mini-ridge” filter.Detailed MC simulations of prospective AS-CIRT nozzle designs were completed in a separate study and will be reported in a future work. The investigated nozzle system (X: 1.3 mm × σY:1.6 mm in air at the isocenter. The beam energy straggling was 0.96% at 220 MeV/u (WET = 10.3 cm). The spread-out Bragg peak width for 1 energy layer at 220 MeV/u in the studied phantom was 2.8 (± 0.2) mm. All simulations considered 106 histories per scanned beam spot.The carbon beam phase-space was sampled from a Twiss Gaussian source, with emittance parameters provided by the vendor. For a 20-cm nozzle tip clearance, the AS-CIRT pencil-beam focal spot size was σ3) was implemented to represent a human head. The phantom was modeled as grey matter brain tissue (voxel size of 1 mm3) surrounded by a 6.75-mm-thick cortical bone shell.A voxelized geometric phantom , wh, wh2, 50tch , hit time stamp (t), and hit pixel (XYZ), that is, spatial coordinates. The fragment trajectories were constructed by requiring a front-to-back detector coincidence within a preset timing gate window on a stack and event-by-event basis. The trajectories of the primary beam ions were taken from the diagnostics SPM considering a 5×5-cm1,2,3H, 3,4He) energy spectra  = 240 MeV/u, WET = 11.9 cm) suggests the latter may scatter relatively minimally in tissues as they exit the target, thus allowing their trajectories to be reasonably approximated by 3D lines. The interaction vertex was defined as the point of closest approach (PCA) or minimal Cartesian distance between the fragment and beam ion trajectories.A beam axis back-projection principle method was implemented to reconstruct the reaction fragments' production points, that is, vertices. The hardness of the dominant fragments' , simulating a target centered at 1.5 cm downstream from the middle (depth Z = 9.025 cm) of the head. Assuming a 100% detection efficiency, the dominant fragments detected in the front tracker amount to 1.6% of the transmitted beam events, a statistics that is further reduced by a 47% intrastack, that is, front-back, coincidence efficiency.The reconstructed vertex yield distributions are shown in 12C beam vertex, suggesting secondary particle –induced reaction productions. Protons emanating from secondary vertices generated detector hit multiplicities of 2 and 3 and triggered uncertainties in the reconstruction algorithm. The 4He fragments on the other hand were dominantly primary (∼90%) in origin (compared to ∼50% for 3He), consistent with the prompt 3-α cluster break-up of 12C, which exhibits a threshold energy at 7.275 MeV. Furthermore, nearly 30% of detected 4He event vertices stemmed from out-of-field interactions of the beam with the nozzle's diagnostics.The MC data were exploited independently to verify and interpret the IMS reconstructed events. To illustrate the secondary radiation field produced in CIRT, at 240 MeV/u, about 50% of proton signals originated from the primary ) can be attributed to light particles  that scatter while leaving the target and to those particle tracks created away from the primary beam axis. As explained in “Detector and Data Acquisition System,” the beam finite spot size (a Gaussian shape) was not considered ad hoc in the reconstruction procedure.The phantom boundaries coincide with the rise and fall-off of the reconstructed vertex depth distributions. The broadening of the emission distribution . The expected CIRT dose fall-off is clearly discernible, as illustrated for a 240-MeV/u irradiation, and qualitatively corresponds to the primary ion beam range. The range to vertex fall-off correlation holds at the studied therapeutic energies. Strikingly, the BP location in the phantom is similarly recreated from a Lego plot of the vertex depth and width coordinates .The projected vertex distribution along the phantom depth correlates with the delivered dose profile, represented by the overlaid Bragg curve for reference (Equation 1) based on the complementary error function (erfc) [z is the phantom depth; ip, i = 0-3, are free parameters to be determined in the fitting procedure; and p3 represents the inflection point of erfc.To quantify the ion range correlation, an analytical parametrization (n (erfc)  was usedIn the in silico benchmark study we used to develop the IMS, which considered a water phantom (15-cm depth), the average difference between the vertex-profile–fitted inflection point and actual beam range was below 1.5 cm. The regions of best agreement were observed near the middle of the phantom. We considered that the inflection point did not correlate directly, in sufficient precision, with the beam range.Figure 4 reports the beam range calibration as a function of Z50 performed for incident energies between 150 and 290 MeV/u, spanning ranges of 4.22 to 14.72 cm.To improve the ion range verification accuracy, a depth marker “Z50” was calculated instead. This maker is defined at the depth corresponding to 50% of the maximum height of the fitted vertex depth distribution. A direct linear relationship was found between Z50 and the ion beam range. 2/ndf = 1.2 :The beam range and Z50 marker are expressed relative to the phantom depth, that is, the zero value on the z-axis corresponds to the middle of the phantom. A linear fit to the data produces the following formula with a fit goodness χEquation 2 was applied to a pseudo validation set obtained at irradiation energies between 160 and 280 MeV/u, which were not used in the calibration procedure. The beam range was determined within 3-mm accuracy, with an uncertainty ≤ 10% across the studied tissue depths.To mimic CIRT online monitoring where Z50 is extracted for each spot-scanned position, ) was fitted with a Gaussian to determine the distribution mean (Yvertex). The beam position (Ybeam) along the entrance path before delivery was measured by a virtual detector placed −10 cm upstream of the isocenter. The precision of the beam positional verification ΔY = Ybeam − Yvertex in the lateral  direction is shown in Figure 5 at 220-MeV/u incident energy. The error bars are of statistical origin. A direct linear dependence relates ΔY to the shifted beam position as shown in Equation 3. This dependence holds at intermediate energies as well as for the vertical direction as discussed subsequently.In the course of CIRT plan delivery, the pencil-beam position is monitored by the SPM. The real-time sensitivity of the fragment vertex distributions to the beam position in the target was similarly studied. Shifts of the beam spot in the phantom were induced by the steering dipole magnets in submillimeter (<0.5-mm) and centimeter-scale increments. The beam was shifted away from the IMS setup for the lateral position verification and towards the bottom of the phantom for the vertical direction. The projected vertex profile along the phantom width , a convolution of the intrastack and interstack coincidence efficiencies, resulted in substantially limited statistics. We concluded that the coincidence IMS's feasibility requires a larger detector aperture (10×10 cm2).As shown in Figure 4) stem from poorer fits of the reconstructed vertex depth distributions. Errors on the fit free parameters propagate to the Z50 marker calculation and therefore to the inferred range. The steepness of the vertex distribution fall-off was found to depend on the beam range. The slope of the tangent to the distal fall-off is higher at the proximal regions of the phantom, gradually decreasing with increasing depths. Also, at lower incident beam energies, the fragment energies are lower while they must traverse longer distances to reach the IMS detector. These effects influence the depth profile modeling with its analytical form a priori unknown. The incurred additional interactions alter the shape of the vertex track distribution. While the range verification uncertainties limit the IMS technique's accuracy, this could in principle be improved by investigating a more adapted analytical model or alternatively using numerical fits of the vertex distribution.For the range verification, the comparably larger uncertainties on certain calibration points , the vertex profile exhibits sensitivity to multiple scattering undergone by fragments escaping the target. Therefore, the X vertex distribution  deviates from a standalone normal profile, which reflects in larger uncertainties on ΔX  compared to the lateral direction. Despite this, the deduced spot shift linear relation (Equation 3) persists with quite comparable slopes (−0.02 to −0.03). These results support that the beam position in the CTV could be inferred with relatively good precision. However, the finite single IMS detector aperture limits only fragments incoming within that acceptance to be detected.For the beam spot in vivo location, at intermediate energies and in the lateral and vertical directions, beam shifts of < 3 cm were verified with an overall accuracy of better than 0.6 mm. At shallower irradiation depths , the precision is limited in average to 0.2 mm (35% error) and the linear dependence observed in The present proof-of-concept study has several limitations. Particle beams manifest a distinct sensitivity to changes in geometry and tissue density. Hence, an apparent clinical limitation is the IMS performance in presence of patient tissue heterogeneities. In this work, the particle beam was made to traverse a bone to soft-tissue–like interface, yielding a range determination accuracy superior to 5 mm. Future investigations are necessary to assess the impact of complex density heterogeneities on the achievable range verification precision. Similarly, an additional future test for the IMS imaging lies in probing range uncertainties for targets exhibiting respiratory motion. We note that the attainable range verification accuracy might also depend on the treated volume size. While the IMS detects the 2D  beam position in the phantom, range verifications for large mesh volumes may only be available as averaged over few beam spots.From the hardware perspective, a stringent limitation of the IMS technique is the achievable detector timing resolution. As noted, the reconstruction algorithm rejected multiplicity (pile-up) events for insufficient precision. The IMS data throughput, on the other hand, is plausibly manageable by modern high-performance digital front-end electronics . Also, rWe reported a nuclear fragmentation imaging conceptual study for a prospective AS-CIRT nozzle using the TOPAS-Geant4 MC simulation framework. Based on a simple detector-acquisition system, basic vertex track reconstruction, and data analysis, the IMS imaging technique yielded promising capabilities for verifying in vivo, in real time, and per scanned spot with at least 4 treatment delivery monitoring features: depth dose profile, beam ion range, beam position, and entrance-exit planes of the irradiated target. The presented technique provides physical proof-of-concept and guidance for future monitoring hardware prototype development efforts.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Extracellular vesicles (EVs) represent a valuable tool in liquid biopsy with tremendous clinical potential in diagnosis, prognosis, and therapeutic monitoring of gliomas. Compared to tissue biopsy, EV-based liquid biopsy is a low-cost, minimally invasive method that can provide information on tumor dynamics before, during, and after treatment. Tumor-derived EVs circulating in biofluids carry a complex cargo of molecular biomarkers, including DNA, RNA, and proteins, which can be indicative of tumor growth and progression. Here, we briefly review current commercial and noncommercial methods for the isolation, quantification, and biochemical characterization of plasma EVs from patients with glioma, touching on whole EV analysis, mutation detection techniques, and genomic and proteomic profiling. We review notable advantages and disadvantages of plasma EV isolation and analytical methods, and we conclude with a discussion on clinical translational opportunities and key challenges associated with the future implementation of EV-based liquid biopsy for glioma treatment. However, these membrane-bound nanoparticles play an essential role in cell-to-cell communication via delivering proteins, nucleic acids, and metabolites. In addition, EVs have been shown to regulate a number of cellular processes, including proliferation, apoptosis, and autophagy. Given their membrane-bound nature, EVs remain relatively stable in circulation, increasing the average lifespan of the encapsulated material. There is substantial evidence of glioma pathology represented in EVs circulating in biofluids like cerebrospinal fluid (CSF) and blood, highlighting their potential in liquid biopsy.7 Other analytes that can be isolated from biofluids include circulating tumor cells (CTCs), cell-free DNA, and circulating nucleic acids. However, the concentration of EVs isolated from patient biofluid is significantly higher and more likely to be stable in circulation. For instance, CTC isolation requires a starting input of a large volume of fresh blood followed by immediate processing due to rapid deterioration of cell viability. Plasma is a preferred sample type for EV biomarker discovery, given that blood collection is minimally invasive, compared to CSF collection, and unlike serum, plasma does not contain coagulation factors, which potentially confound glioma-specific EV isolation and analysis.8 Here, we review prevalent EV isolation, quantification, and characterization strategies with a focus on plasma-derived EVs from patients with glioma (Extracellular vesicles (EVs) are small, membrane-bound nanoparticles released into the extracellular environment via cell shedding and non-apoptotic blebbing.h glioma .9 This in turn allows for isolation of glioma-derived EV subpopulations from plasma and downstream analysis for tumor-specific EV profiling.10 NanoFACS relies on target protein or cargo expression for EV isolation.11 Unlike IA capture, EVs remain in a single EV suspension for downstream analysis.12The current standards of EV isolation include bulk and specific isolation. Bulk isolation consists of centrifugation, membrane affinity, size exclusion, and polymerization precipitation strategies. Although successful at eliminating debris during isolation, they limit glioma-specific EV enrichment due to the heterogeneous healthy cell-derived EVs in plasma. It is crucial to differentiate tumor-specific EVs from this background. Specific EV isolation methods, such as immunoaffinity (IA) capture and nano-fluorescence activated cell sorting (nanoFACS) harness the EV phenotype to enrich glioma EVs. IA capture isolates EVs based on the expression of target surface proteins, such as EGFRvIII, which bind to antibodies conjugated to magnetic beads or surfaces.Quantification and characterization of whole EVs are broadly categorized into total EV and EV subpopulation studies . Analysi18 Similarly, exploration of the cancer-specific EV landscape can be performed via labeling of glioma-related mutations, such as IDH1, EGFRvIII,21 and survivin.22 Although promising, this method of identification is burdened by antibody labeling standardization and processing. Recent studies have explored the use of endogenous fluorophores for the labeling of glioma-produced EVs. Utilizing the downstream features of 5-aminolevulinic acid  metabolism, a subpopulation of EVs produced by 5-ALA-dosed malignant glioma tumors have demonstrated endogenous fluorescence via IFC.23 These fluorescent EVs have successfully been sorted from plasma background.24 The quantification of fluorescence intensity, EV concentration, and EV cargo from plasma provides a venue for EV-based glioma biomarker development.Enrichment, in both isolation and analysis, of glioma-specific EV subpopulations relies primarily on EV labeling. Labeling using fluorescent markers indicative of intact biological environments  has been reported to quantify total EV populations in FC applications. Further analytical depth can be achieved via fluorescent conjugated antibodies for surface protein marker labeling, such as CD63 and CD9, for phenotypic characterization of glioma-derived EVs.Phenotype-based EV isolation and quantification methods are considered low throughput, but these modalities provide the highest specificity for glioma EV isolation and enrichment from a heterogeneous background for enhanced downstream analysis.Once glioma-specific EVs are isolated and purified, downstream detection and functional characterization of cargo are achieved by a careful selection of the relevant method. The EV cargo is composed of multiple proteins  and cytosolic analytes, including nucleic acids , lipids, and metabolites.25 It is the most widely used platform for the extraction of purified EV RNA for downstream mutation detection and genomic interrogation. Inclusion of a standard extraction protocol improves reproducibility and design of large-scale validation studies .Extraction of the encapsulated nucleic acids  can be performed using a number of commercially available kits leading to a variation in the yield and size distribution. Of the available techniques, ExoRNeasy (Qiagen) represents the most efficient method of EV RNA extraction from a range of volume of patient plasma with minimal binding to ex-RNA-containing particles like ribonucleoprotein complexes.Polymerase chain reaction (PCR) methods represent one of the earliest methods of targeted mutation detection using patient-derived plasma. Both real-time and digital PCR methods rely on dye-based fluorescent quantification of cDNA and/or EV DNA via reagents such as the, most commonly using SYBR Green or TaqMan, of cDNA and/or EV DNA. Droplet digital PCR (ddPCR), a more recent platform employs an ultrasensitive fluorescent technique, which has enabled absolute quantification of mutant events in partitioned samples  based on Poisson’s distribution. This approach has several advantages compared to qPCR: measurement of low abundance transcripts, tolerance to PCR inhibitors, less dependence on reference genes, higher signal-to-noise ratio, and higher sensitivity, thereby making digital bioassays a more reliable tool for detection of rare glioma-specific mutations.26 Similarly, ddPCR has been used successfully to detect TERT promoter mutations in EV DNA with a sensitivity of >70%.27 EGFRvIII is another important mutation that serves as a reliable diagnostic marker to distinguish glioma from healthy states.28 Studies have reported assays to detect this mutation in EV-derived mRNA from plasma  of serum and not plasma.33 Currently, plasma is the preferred medium for the isolation and analysis of tumor-specific analytes despite the risk of clotting at room temperature, which increases the risk of EV lysis and degradation.34Genetic profiling of exosomes has elucidated details on the genomic and epigenetic landscape of gliomas. The presence of certain RNA populations, namely mRNAs, miRNAs, and lncRNAs, has been demonstrated using PCR methods. Recently, however, high-throughput transcriptome analysis of EVs has led to the discovery of diverse RNA species, including snRNA, snoRNA, piRNA, scRNA, and SRP-RNA, and their role in mediating the biological effects of EVs on recipient cells.35 The majority of library preparation protocols, however, do contain a PCR amplification step with specific primers to improve the sensitivity of detection and quantification of low-level EV analytes. Additionally, library preparation kits have been tailored for size selection of short vs long RNA fragments.25 Medium length (60-300 nt) RNA sequencing requires the use of kit-free protocols. Common sources of bias in sequencing include adaptor dimers in ligation technique, size selection after cDNA synthesis, choice of sequencing platform, and subsequent bioinformatics analysis.25 Validation of obtained results using additional methods  can reduce bias and improve robustness.Unlike conventional PCR, NGS allows detection and monitoring of both known and unknown molecular alterations.36 It can also elucidate the role of RNA modifications in gliomagenesis and their interactions with key molecular alterations . For instance, m6A methylation can now be detected and mapped using MeRIP-seq, a technique that combines NGS with m6A-methylated RNA immunoprecipitation.37Given the availability of novel high-throughput methods, it is important to explore the potential use of these technologies for plasma-derived EV cargo transcriptomic and genomic interrogation. Compared to cDNA sequencing, direct RNA sequencing (Oxford Nanopore Technologies [ONT]) offers many opportunities: low input requirement (<1 µg), elimination of PCR bias, detection of ultra-long RNA fragments, and identification of isoforms, gene fusions, and novel transcripts.EV cargo represents a complex composition of RNA populations of varying lengths. However, a comprehensive overview of different classes of RNA encapsulated in EVs and their functional significance is still lacking. Due to the enrichment of small RNAs in EVs, most studies have focused on miRNA and small non-coding RNA. While most of the RNA is fragmented, little is known about the longer fragments present and their role in glioma progression. This population has not been previously explored due to the inherent limitations of conventional library preparation kits, namely low-depth coverage and low precision of sequencing. ONT, therefore, represents a promising platform for a more comprehensive analysis of coding, non-coding, and regulatory RNA populations in plasma-derived EVs.38 Using this, a study measured the expression of tropomyosin kinase receptor (TrkB) in plasma-derived exosomes and its correlation to aggressiveness and gliomagenesis.39 Furthermore, key cytokines  have been similarly identified and shown to be dysregulated in gliomas.15 Some limitations of this method include inability to multiplex, requirement of a large input of EV protein, and limited reproducibility.40Protein components of EVs have been cataloged using a number of mass spectrometry-based modalities . It is c46 allowing for isolation of a broader spectrum of EV antigens and potential biomarkers.47Another approach utilizes integrated immuno-based microfluidic isolation and protein analysis. Microfluidics devices allow fluorescent antibody-based detection of EVs on a chip rather than on a membrane or magnetic beads,48 Targeted (hypothesis-driven) proteomic analysis is mostly conducted using multiple reaction monitoring (MRM), which allows parallel monitoring of up to a hundred predetermined peptides at different retention times.48 This approach has several advantages: improved sensitivity and specificity, ability to multiplex, and low input requirement.49 The existing studies have highlighted a few candidate proteins, however, a more extensive correlation study between exosome protein levels and glioma cell of origin is needed to delineate disease specific from exosome-enriched proteins.50 To fully harness the clinical potential, we need candidate markers to differentiate between low-grade and high-grade glioma. Syndecan-1 (SDC1), an exosome protein, represents an important example in this application, with the mRNA expression levels measured by MS and enzyme-linked immunosorbent assay (ELISA) were shown to be significantly different in GBM (glioblastoma) vs low-grade glioma cohort.41Mass spectrometry (MS) analysis can be used for global and/or targeted proteomics. The general principle involves digestion of extracted proteins followed by the separation of peptides using gel-based (1D/2D gel electrophoresis) or gel-free platform (liquid chromatography). We can therefore deduce quantity and sequence details. Global (discovery-driven) proteomic approach achieves ionic selection either based on prevalence (data-dependent acquisition [DDA]) or predefined mass range (data-independent acquisition [DIA]).No studies have investigated plasma EV-derived lipids or metabolites as putative biomarkers in glioma.EV-based liquid biopsy has tremendous clinical potential in establishing a minimally invasive and cost-effective platform for characterizing tumors using circulating analytes. However, despite the significant progress in this field, it is yet not recognized as a standard of clinical care. There are a number of factors to consider including the presence of technical and biological variability in preanalytical and analytical stages as outlined in


The repeat expansions are the main genetic cause of various neurodegeneration diseases. More than ten kinds of repeat sequences with different lengths, locations, and structures have been confirmed in the past two decades. G-rich repeat sequences, such as CGG and GGGGCC, are reported to form functional G-quadruplexes, participating in many important bioprocesses. In this review, we conducted an overview concerning the contribution of G-quadruplex in repeat expansion disorders and summarized related mechanisms in current pathological studies, including the increasing genetic instabilities in replication and transcription, the toxic RNA foci formed in neurons, and the loss/gain function of proteins and peptides. Furthermore, novel strategies targeting G-quadruplex repeats were developed based on the understanding of disease mechanism. Small molecules and proteins binding to G-quadruplex in repeat expansions were investigated to protect neurons from dysfunction and delay the progression of neurodegeneration. In addition, the effects of environment on the stability of G-quadruplex were discussed, which might be critical factors in the pathological study of repeat expansion disorders. Neurological degenerative diseases, which are devastating diseases without efficient treatments, have attracted the interest of many researchers and progressed rapidly in the past decades. Dementias have become a globally problem as one of the World Health Organization’s (WHO) top 10 causes of death in 2016 . On the With the analysis of human genomes, many human diseases, including both familial and sporadic cases, are found to generate from genetic mutations. The investigations at genetic level deepen our understanding of disease pathogenesis and provide new inspirations in the development of novel therapies. Repeat expansion is one of pathogenic gene mutations, which has been demonstrated to cause many neurodegeneration diseases ,7,8,9,10An important feature of repeat expansion is the unique secondary structure. The repeat sequences can form multiple non-canonical structures, for example, hairpins, triplexes, and quadruplexes, increasing the gene instability and affecting gene expression processes including DNA replication, repair, and transcription. As these repeats usually have high G/C contents, G-quadruplex becomes one of the common non-canonical structures in repeat expansions. G-quadruplex is composed of π–π stacked G-quartets, where guanines interact with each other through Hoogsteen base pairing to form plane structures. Both DNA and RNA G-quadruplexes have been demonstrated to play important roles in many bioprocesses. DNA G-quadruplexes widely exist in the regulatory regions of genome and participate the regulation of replication and transcription. Similarly, the formation of G-quadruplex in RNA also modulates the process of translation and results in the change in protein expression level. Especially, RNA G-quadruplexes present multiple functions in neurodegenerative diseases, affecting not only RNA–RNA interactions, but also RNA–protein interactions and altered the formation of membrane-less granules and RNA foci. The investigation of DNA/RNA G-quadruplexes in repeat expansion disorders will be helpful for our understanding of disease mechanism and the design of novel therapeutic strategies. In this review, G-quadruplex-forming repeats in neurodegeneration were summarized to reveal their roles in disease occurrence and development, and the potential of G-quadruplex binding ligands and proteins in the therapy of repeat expansion disorders was discussed.In the past decades, multiple repeats have been confirmed to be the genetic causes of neurodegeneration diseases. The association between repeat expansions and neurodegeneration diseases was first reported in 1991. A long CGG repeat located in FMR-1 gene was involved in the phenotypic expression of the fragile X syndrome . Soon afThe non-canonical structures of these repeats are described as the main causes of expansion , attractG-quadruplex is one of the most frequently mentioned non-canonical structures in G-rich repeat expansions. CGG repeat expansion in 5′ UTR of FMR1 gene is the genetic cause of fragile X syndrome (FXS) and fragile X-associated tremor/ataxia syndrome (FXTAS) . The num+, which were characterized by both CD spectrum and native polyacrylamide gel electrophoresis [In 2011, two independent groups published two individual works in Neuron, revealing the relationship between ALS/FTD and the GGGGCC repeat expansions in the intron of C9orf72 gene . Later, phoresis . In furtphoresis ,61. As wphoresis . Zhang ephoresis . Moreovephoresis . A reporphoresis .2 [SCAs are a group of neurodegenerative disorders, which have diverse mutation types . In 20112 .Researchers have been working on the mechanism of repeat expansion disorders in recent years. Three main mechanisms have been proposed: the accumulation of toxic RNAs with repeat sequences; the loss-of-function of corresponding proteins; the gain-of-function peptides and proteins transcribed from repeat-containing RNAs . In neurThe sizes of repeat expansions vary in a large range from dozens to thousands, which potentially correlates with the symptoms of neurodegeneration diseases, such as the onset age, risk, and the severity of diseases . The repIn the replication of repeat sequences, the formation of non-canonical structure results in the expansion or contraction . During The formation of G-quadruplex structures in repeat expansions also greatly affect the process of transcription. One feature of repeat expansions is the bidirectional transcription, generating both sense and antisense RNA products ,69,70. TMost repeat expansion disorders suffer from the formation of toxic RNA foci regardless of the related repeats located in exons, introns, or UTRs. The RNA foci formed by repeat expansions in nucleus was observed in both cells and tissues. Generally, the formation of RNA foci increased the stress of nucleus ,80, sequThe secondary structures play important roles in the accumulation of RNA repeats. Similar to homologous DNA repeat, RNA repeats also form various non-canonical structures, including hairpin structures, triplexes, and tetraplexes. For example, the RNA repeats of CMG  also formed hairpins with a stem composed of C–G base pairing and non-canonical M–M interaction . Based oIn 2017, CAG repeats and GGGGCC repeats were observed to form RNA gels by phase transitions . The RNAThe secondary structures and intermolecular interactions of RNA repeats during RNA gelation have shown their importance in the pathological study of neurodegeneration. Zhang et al. found G-quadruplex structure triggered RNA phase separation, emphasizing the importance of G-quadruplex formation in RNA-driven phase separation . SimilarAs the crucial roles that almost involved in all life processes, peptides and proteins assumed many functions within organisms. In repeat-associated neurodegeneration diseases, the abnormalities in peptides and proteins are critical parts in pathological study. Repeat expansions have been demonstrated to affect the expression of proteins. The loss/gain of function of proteins and peptides was described as another main pathogenesis of repeat expansion disorders, which showed different features according to the position of repeat expansion in disease-related gene ,87,88,89The loss of function of proteins is usually the result of the downregulation or inhibition of related gene expression caused by repeat expansions. Non-canonical structures have been demonstrated to efficiently affect the processes of transcription and translation in previous reports ,90,91,92The gain of function of peptides and protein is another major pathogenesis of repeat expansion disorders. The repeat-associated non-ATG translation (RAN translation) is discovered to occur without an ATG start codon, leading to the production of polypeptides that are potentially toxic to neurons . The detMoreover, the aggregation of peptides and proteins was also affected by the presence and structure of RNA through phase separation. In 2018, researchers have demonstrated the effects of RNA on the phase separation of RNA-binding proteins from different points of view. Maharana et al. found the solubility of prion-like RNA binding proteins, such as TDP-43 and FUS, regulated by RNA concentration . The redThe formation of G-quadruplex in repeat sequence is important for our understanding of repeat expansion disorders, which are highly involved in many pathogenic processes, such as repeat expansion, regulation of gene expression, the accumulation of RNA and peptides. Therefore, targeting on G-quadruplex provide a new perspective for pathological research and the development of new therapeutic methods. Small molecule G-quadruplex binding ligands and G-quadruplex binding proteins are frequently utilized for targeting G-quadruplex.Small molecules, which can selectively bind to G-quadruplexes, have been widely studied in anticancer and antiviral strategies ,114,115.G-quadruplex binding proteins are attractive research objects to target G-quadruplexes because they are involved in many biological processes. The interaction between G-quadruplex and related G-quadruplex binding proteins are crucial for pathogenic mechanisms of repeat expansion disorders. For example, nucleolin was found to specifically bind to G-quadruplex formed by GGGGCC repeats, resulting in nucleolar stress . HelicasIn addition to the two strategies above, antisense oligonucleotides (ASO) and gene editing with CRISPR/Cas9 system are also promising therapeutic approaches of repeat expansion disorders. Short complementary sequences were observed to reduce the formation of RNA foci by disrupting the base pairing in the assembly process . Liu andThe neurodegeneration diseases that are caused by repeat expansions have been studied for more than 20 years since the first discovery of the relationship between long CGG repeats and FXS in 1991. Though there are still many problems that remained unsolved, we have gained a lot of significant information associated with pathology and therapy. The neurodegeneration diseases associated with repeat expansions have many similar characters and processes, but they also show their own specific mechanisms and pathologies. These studies involve many scientific fields in chemistry, biology, and medicine, and they still need a long way to go. The formation of G-quadruplex is demonstrated to be an important factor in neurodegeneration with G-rich repeat expansion, associated with repeat expansion in replication, reduction of mRNAs in transcription, formation of toxic RNA foci, and production of toxic polypeptides by RAN translation. Targeting on the G-quadruplex in repeat expansion is a significant strategy to protect neurons from dysfunction and delay the progression of neurodegeneration. More evidence and information are expected to provide insight into disease mechanisms and to achieve successful therapeutic methods.Currently, problems remain in the explanation of disease mechanisms. For example, researchers still have some doubts about the relationship between repeat lengths and neurodegeneration. In one of the initial studies of the relationship between repeat expansions in C9orf72 and ALS/FTD, though the comparison of ALS/FTD patients and controls showed an obvious difference in the region where repeat numbers are more than 30, there are still 38.1% cases carried the same repeat expansion as healthy people. In a recent stdy, the authors summarized 49 identified studies of C9orf72-related ALS/FTD and found that the lengths of C9orf72 hexanucleotide repeats were associated not with a higher risk of disorders, but a higher frequency of neuropsychiatric symptoms . In addiAccording to our knowledge, the structures of nucleic acids are greatly affected by the surrounding environments, especially the non-canonical structures, such as G-quadruplex under crowded condition. Researchers previously reported various changes in neurological functional biomolecules in cellular environments. For example, in the serum and spinal fluid of patients, a higher level of glutamate, the main excitatory neurotransmitter, was observed . There a


RNA editing is a post-transcriptional process that alters RNA sequence in a site-specific manner. A-to-I editing is the most abundant as well as the most well-studied type of RNA editing. About 0.5% of A-to-I editing sites were located in the coding regions. Despite of thousands of identified A-to-I nonsynonymous editing sites, the function of nonsynonymous editing was poorly studied. Here, we found that the nonsynonymous editing was significantly enriched in the ubiquitination site, compared to the synonymous editing. This enrichment was also in a modification type dependent manner, since it was not significantly enriched in other modification types. This observation was consistent with previous study that the codons for lysine (AAG and AAA) were enriched in the preferred deamination site for RNA editing. The peptides from proteomic data in CPTAC supported that mRNAs harboring edited ubiquitination sites can be translated into protein in cells. We identified the editing sites on ubiquitination site were significantly differential edited between tumor and para-tumor samples as well as among different subtypes in TCGA datasets and also correlated with clinical outcome, especially for the nonsynonymous editing sites on GSTM5, WDR1, SSR4 and PSMC4. Finally, the enrichment analysis revealed that the function of these above genes was specifically enriched in the immune response pathway. Our study shed a light on understanding the functions of nonsynonymous editing in tumorigenesis and provided nonsynonymous editing targets for potential cancer diagnosis and therapy. RNA editing was first discovered by Benne et al. and they found that the mitochondrial pre-mRNAs in trypanosomes could be post-transcriptionally edited by the insertion or deletion of uridylate (U) residues2. With the deepening of research, people have discovered that RNA editing is a widespread process and has been observed to occur on mRNAs, tRNAs, rRNAs and RNAs in nucleus, mitochondrial and chloroplast1.RNA editing is one of the post-transcriptional processes which alter RNA sequence in a site-specific manner3. The two of the most widespread types of RNA editing are adenosine-to-inosine (A-to-I) editing and cytosine-to-uracil (C-to-U) editing in mammals4. Among these two types of RNA editing, A-to-I editing is the most abundant as well as the most well-studied RNA editing type5. In mammals, ADAR genes, ADAR, ADARB1 and ADARB2, are responsible for the A-to-I editing7. ADARB2 are uniquely expressed in specific tissues, ADAR and ADARB1 is widely expressed in various tissues and responsible for the majority of editing activity3. It is well known that most RNA editing in human occurs in the Alu sequences5. AG-rich motif was witnessed in the local sequence for A-to-I editing site9.To date, there are more than one hundred types of RNA editing which have been identified12. Among these A-to-I editing sites, around 50% of editing sites were located on 3’UTR of genes and only about 0.5% of A-to-I editing sites were located in the coding regions14. Several hypothesizes were proposed to explain the function of nonsynonymous editing, such as resulting in the alteration of splicing pattern and contributing to the proteomic diversity16.Taking the advantage of high-throughput sequencing methods, tens of thousands of A-to-I editing sites were identified in several species including human, mouse and rat19. Hundreds of A-to-I RNA editing sites were identified as differential editing sites and correlated with clinical outcome in cancer13. Besides, several previous studies showed that alteration of amino acid due to RNA editing could create novel antigen site (neo-antigens) and may contribute to the immune therapy in cancer21. However, only a small number of A-to-I nonsynonymous editing were uncovered to function in cells, despite of hundreds of discovered nonsynonymous editing events26. The function of most of A-to-I nonsynonymous editing sites was largely unknown.The A-to-I RNA editing is also reported to be involved in tumorigenesis by mediating several important biological processesHere, we reported that the nonsynonymous A-to-I editing sites were significantly enriched in the ubiquitination sites. This enrichment was specific, compared to the other types of modification including phosphorylation, acetylation and etc. We also confirmed this significant and specific enrichment in The Cancer Genome Atlas (TCGA) datasets. By analyzing the proteome data in CPTAC datasets, we identified peptides harboring the ubiquitination sites altered by RNA editing. This observation supported that these nonsynonymous editing sites can be translated into peptide instead of degradation before translation. Furthermore, the editing level of some editing sites on ubiquitination site was significantly different between tumor and para-tumor as well as among different subtypes. Survival analysis revealed these sites were also significantly correlated with clinical outcome, indicating its potential functions in cancer. The GSEA enrichment analysis show that the genes exposed to the nonsynonymous editing on the ubiquitination sites are significantly and uniquely enriched in the functions related to the immune response pathway.Our analysis revealed a possible novel function of nonsynonymous A-to-I RNA editing to regulate ubiquitination and shed a light on understanding its functions in tumorigenesis.12. We also incorporated an A-to-I RNA editing dataset from a recent study and named it as “Gabay2022”8. The aim of these three databases is to establish comprehensive records of A-to-I RNA editing events independent of tissue, disease and patients. Especially for REDIportal, it recorded over 15 million of A-to-I RNA editing events derived from 9642 human RNA-seq samples from 549 individuals (31 tissues and 54 body sites) of the GTEx project which help us to investigate the potential function of nonsynonymous A-to-I RNA editing in an unbiased manner.In order to explore the potential function of nonsynonymous A-to-I RNA editing in the human genome, we downloaded the A-to-I RNA editing sites from DARNED and REDIportal databases9.From here on, we called A-to-I RNA editing as RNA editing for short. Firstly, we summarized the counts of changes of amino acids (AAs) due to the nonsynonymous RNA editing in three databases and sorted it in descendent order. The results showed that the several AAs frequently affected by nonsynonymous RNA editing in all three databases were the potential target sites for protein modification Fig. , 2 and 327. The PhosphoSitePlus collected modification sites from near 10,000 previous studies, some of which were high-throughput MS data  were enriched in the motif for RNA editingTo rule out this possibility, we focused on the acetylation, since almost of all acetylation and ubiquitination occurred on K and the number of modification sites for acetylation and ubiquitination were roughly proximal  and ovarian (OV) cancer in clinical proteomic tumor analysis consortium (CPTAC) proteomic data. Indeed, we identified four peptides harboring mismatches on ubiquitination sites from two genes in BRCA and five peptides from four genes in OV  and identified as prognostic editing sites in Glioblastoma multiforme (GBM), Bladder Urothelial Carcinoma (BLCA) and Kidney renal clear cell carcinoma (KIRC), respectively Fig. C.In summary, the RNA editing on ubiquitination site posed an impact on clinical features and correlated with clinical outcome, especially for the sites on GSTM5, WDR1, SSR4 and PSMC4. It strongly indicated that RNA editing on ubiquitination site may play a vital role in tumorigenesis and correlated with clinical outcome. Together, above observations could help us understand the role of nonsynonymous RNA editing on ubiquitination site involved in the tumorigenesis.Next interesting question was how these genes harboring RNA editing on ubiquitination site function in cell and have a significant impact on the tumorigenesis. To answer this question, we explored the possible function of these genes. The GSEA enrichment analysis revealed that these genes harboring RNA editing sites on ubiquitination site were enriched in the immune response against all genes harboring ubiquitination sites as background Fig. A. We useTo confirm the enrichment analysis, we also investigated the distribution of RNA editing on ubiquitination site among different immune subtypes in TCGA dataset. Three of these sites, chr7:100887329 on FIS1 in HNSC, chrX:153062943 on SSR4 in LUSC and chr4:10080600 on WDR1 in PRAD, were significant different among immune subtypes (FDR < 0.1) Fig. B.28. Besides, ubiquitination also carried out several important functions in DNA repair and the activation of protein kinases29. Furthermore, the previous studies showed that the ubiquitination regulate multiple signaling pathways in cell, especially in cancer32.In this study, we explored the potential function of nonsynonymous RNA editing and found that it was significantly overlapped with ubiquitination site. Since the AA altered by nonsynonymous RNA editing was not the target for ubiquitination, it indicated that the nonsynonymous RNA editing could control the level of ubiquitination. The most well studied role of ubiquitination is targeting proteins for degradation by the proteasomeSince almost of all ubiquitination events occurred on K not on E or R . The A-to-I RNA editing sites for human were downloaded from REDIportal (http://srv00.recas.ba.infn.it/atlas/download.html) and DARNED database12 (https://darned.ucc.ie/). The A-to-I RNA editing sites in Gabay2022 were downloaded from the supplementary data in their paper8. The RNA editing data for TCGA dataset was downloaded from Synapse under the accession number syn237437513https://www.synapse.org/#!Synapse:syn2374375 . The coordinates of A-to-I RNA editing sites analyzed in our work were based on hg19 human genome. The coordinates of A-to-I RNA editing sites in Gabay2022 were converted into hg19 human genome by liftover. The proteomic data for breast and ovarian cancer was downloaded from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) project39 . The somatic mutation data to check mismatched sites on peptides was downloaded from UCSC Xena (https://xenabrowser.net/datapages/).The protein modification data including acetylation, methylation, O-GalNAc, O-GlcNAc, phosphorylation, sumoylation and ubiquitination were downloaded from PhosphoSitePlus40. We analyzed the amino acid changes by the codon sequence before and after editing. Then the genomic position of modification site was overlapped with the position of nonsynonymous RNA editing site in REDIportal, Gabay2022 and DARNED database as well as in seventeen cancer types in TCGA dataset. We also counted and summarized the percentage of modified AA in PhosphoSitePlus database as the possibility of specific AA to be modified for each modification type.The position of modification sites on protein level were extracted from the data downloaded from PhosphoSitePlus. The genomic range of corresponding amino acids for each modification site were mapped onto the hg19 human genome by “biomaRt” package in Bioconductor41. Taking the overlaps between (non)synonymous RNA editings and ubiquitination sites in DARNED database and ubiquitination sites in PhosphoSitePlus database as an example, we tabulated the counts of (non)synonymous RNA editings overlapped and non-overlapped with ubiquitination site. The resulting table is 2X2 matrix and contains four digitals which are the counts of nonsynonymous RNA editings overlapped with ubiquitination site, the counts of nonsynonymous RNA editings nonoverlapped with ubiquitination site, the counts of synonymous RNA editings overlapped with ubiquitination site and the counts of synonymous RNA editings nonoverlapped with ubiquitination site. The one-side Fisher’s exact test was performed to test the significance between the distributions of (non)synonymous RNA editings overlapped with ubiquitination site.We applied one-sided Fisher’s exact test in R to test the significance between two percentages, which was commonly used to test the significant overlaps in genomic study20. The MS data was converted by “MSConvert” in ProteoWizard with unchecking the “Use zlib compression” option.The BRCA and OV MS datasets were downloaded from The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) in mzML-format. The data processing was basically followed the previous works42. We separately constructed two mismatch searching lists for identification of the nonsynonymous RNA editing events in BRCA and OV based on GENCODE gene annotation which was downloaded from UCSC Table browser (GENCODE.V37lift37). The coordinates of gene annotation were based on hg19 human genome. The searching lists for BRCA were derived from BRCA dataset and used for searching peptides comprising mismatching AA in BRCA MS data, respectively. Since there was no RNA editing data for OV, we merged all RNA editing events in available seventeen TCGA datasets and constructed the mismatch searching list for OV based on the merged list.Then we used “sapFinder” in Bioconductor for the database construction, peptide matching and identification of peptides comprising mismatching AA due to RNA editingFor mismatch searching, we added two more fixed modifications, iTRAQ 4-plex of N-terminal and lysine (144.10 Da) and oxidation on methionine (15.99 Da) and two variable modifications, acetylation of protein N-term (42.01 Da) and deamination of asparagine (0.98 Da). The other options in “sapFinder” were kept as default parameters. We read the final output from “peptideSummary.txt” file and picked the peptides comprising mismatching AA due to RNA editing. Since the limited number of edited peptides were identified. We just manually checked the information of somatic mutations in VCF format files for corresponding patient to make sure that the mismatched AA in these peptides was not due to the somatic mutation.44. The significance was computed by “wilcox.test” function in R. The raw P-value was adjusted by “p.adjust” function with “method = fdr” in R for each cancer type.The information of subtype for each patient in TCGA dataset was download from previous studySince there are lots of editing sites which editing level are not available due to sequencing depth in some samples, we only kept the sites available in at least three samples in any groups  when identifying the differential editing sites.46. For each cancer dataset, survival information was retrieved from the “TCGA-CDR” dataset47. Since the information of disease-specific survival is approximated in most of TCGA datasets, we excluded it from our analysis and kept other three endpoints , disease-free interval (DFI), and progression-free interval (PFI)).The survival analysis was basically performed as previously studiesCox models were run with the “coxph” function from the “survival” package in R. For survival plots, we divided the samples into two groups based on the editing level. The cutoff to classify the groups was the median value of editing level. Then Cox models were run with the “coxph” function, and the equation is “coxph ∼ group).” We picked the editing sites with Logrank p-value ≤ 0.05 and considered that these editing sites as the sites in which the editing level correlated with different survival outcome.48. The background gene set for enrichment analysis was the genes with corresponding modification annotated in PhosphoSitePlus database. The gene sets for enrichment analysis were immunologic signature gene sets (C7) from the Molecular Signatures Database (MSigDB)49 (http://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp).Gene Set Enrichment Analysis (GSEA) was performed using genes harboring nonsynonymous RNA editing on specific modification in “clusterProfiler” in RSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.


Even for essential splice-site variants that are almost guaranteed to alter mRNA splicing, no current method can reliably predict whether exon-skipping, cryptic activation or multiple events will result, greatly complicating clinical interpretation of pathogenicity. Strikingly, ranking the four most common unannotated splicing events across 335,663 reference RNA-sequencing (RNA-seq) samples (300K-RNA Top-4) predicts the nature of variant-associated mis-splicing with 92% sensitivity. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events and 86% of cryptic splice sites for 140 clinical cases subject to RNA testing, showing higher sensitivity and positive predictive value than SpliceAI. Notably, RNA re-analyses showed we had missed 300K-RNA Top-4 events for several clinical cases tested before the development of this empirical predictive method. Simply, mis-splicing events that happen around a splice site in RNA-seq data are those most likely to be activated by a splice-site variant. The SpliceVault web portal allows users easy access to 300K-RNA for informed splice-site variant interpretation and classification. Re-analysis of published RNA-sequencing samples finds that unannotated splicing events predict, with high sensitivity, the activation of exon skipping and cryptic splicing by splice-site variants. Pre-mRNA splicing is a process that precisely removes noncoding intronic sequences and connects coding exons together to make the mature mRNA blueprint that encodes each protein (or noncoding RNA). The splicing reactions are performed by a large, protein–RNA complex called the spliceosome3, which recognizes conserved splice-site motifs at either end of the intron. There are the following three main ways that mis-splicing can occur: one or more exons may be skipped, one or more cryptic splice sites may be activated or one or more introns may be retained, or a mixture.Genetic variants that induce mis-splicing of precursor messenger RNA (pre-mRNA) are emerging as a common cause of inherited disorders4  guidelines) allow an ES variant to be considered under the ‘null variant’ code, called very strong evidence for pathogenicity (PVS1). PVS1 is used to classify single nucleotide variants (SNVs) and small insertions and deletions that similarly introduce a PTC4. In 2018, revised PVS1 guidelines recommended application of the PVS1 code for ES variants, at varying strengths, based upon theoretical assertion of the likelihood of ‘null outcomes’ arising from exon skipping, intron retention and use of any cryptic splice site within 20 nt (ref. 5). However, only ~20% of variant-activated cryptic donors are within 20 nt (ref. 6). Consideration of cryptic splice-site activation across a larger distance window is unfeasible in diagnostic genetic pathology, due to the large number of potential cryptic splice sites present in the genome. In addition, factors that induce multi-exon skipping (or retention of multiple introns) associated with some splice-altering variants are unknown. Currently, there is no reliable way to know exactly how a variant will disrupt pre-mRNA splicing, to assert with confidence if outcomes are likely to encode a PTC, or not.Variants impacting essential splice sites (ESs), the almost invariant GT-AG flanking each intron, are almost guaranteed to induce mis-splicing. Variants affecting the wider splice-site motif can also disrupt pre-mRNA splicing, although because of greater sequence diversity, it is much harder to be certain if ‘extended splice-site’ variants will elicit mis-splicing. Due to triplet codons encoding each amino acid, mis-splicing of pre-mRNA that inserts or deletes nucleotides (nt) commonly induces a frameshift or encodes a premature termination codon (PTC). Consequently, the clinical guidelines for classifying genetic variants7. We routinely interrogate RNA sequencing (RNA-seq) data from controls or disease controls 8 or ENCODE9) to assess alternative splicing of the target gene between the manifesting tissues and specimens commonly collected in a clinical setting, to determine their suitability for RNA testing. We observed that the predominant, variant-associated mis-spliced transcript(s) identified in specimens from affected individuals and heterozygotes were often observed as rare, stochastic splice junctions in control RNA-seq data. Brandão and colleagues detailed a similar finding, with dominant variant-induced mis-spliced BRCA1 or BRCA2 transcripts often seen as rare events in disease controls10. Additionally, Kremer et al. 11 found that splicing ‘noise’ often forecasts the location of variant-activated pseudoexons and reasoned that a population-based RNA-seq compendium could aid in variant prioritization.Our laboratory is an Australasian reference center for RNA diagnostics, where we test RNA isolated from human specimens  to experimentally validate if and how a candidate splice-altering variant identified by genomic sequencing alters pre-mRNA splicing6, we analyzed 5,145 variants activating cryptic splice sites and established that 87% of activated cryptic splice sites are those detected as rare, unannotated splice junctions in 40,233 RNA-seq samples from GTEx8 and Intropolis12 (40K-RNA database6). The key insight is that cryptic donors activated by genetic variants are also seen as rare events in population-based RNA-seq data, which led us to explore whether other forms of variant-associated mis-splicing may be predicted by quantifying the relative prevalence of stochastic, natural and unannotated splicing events (referred to hereafter as mis-splicing events).In ref. 13 and RefSeq14 transcripts, based on splice junctions detected across 335,663 publicly available RNA-seq samples from GTEx8 and Sequence Read Archive (SRA)15, uniformly processed in the recount3 project16 (300K-RNA). 300K-RNA is updated to the GRCh38 genome assembly and is hosted in a web resource called SpliceVault, together with 40K-RNA (GRCh37)6. Unannotated splice junctions in 300K-RNA constitute evidence that a splicing event is biologically possible, with the requisite constellation of features for the splicing reactions to be executed, for example, recognizable and usable splice sites, suitable exon and intron lengths17, the correct balance of exonic and intronic splicing enhancer and suppressor elements18. Our central hypothesis is that a genetic variant impeding spliceosomal use of an annotated splice site is most likely to enhance mis-splicing events that occur naturally.We therefore created 300K-RNA, an expanded resource detailing the most common unannotated splicing events local to each exon–intron junction of Ensembl19, applying custom interpretive rules to SpliceAI Δ-scores ±5,000 nt of variants to infer predictions of exon skipping, intron retention and cryptic splice-site activation.We demonstrate that by knowing what mis-splicing events most commonly happen around a splice site, we can predict what mis-splicing events will happen when a genetic variant disrupts that splice site. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events (including multi-exon skipping) and 86% of activated cryptic splice sites induced by 88 variants in 74 genes for 140 affected individuals or heterozygotes subject to RNA diagnostics. We additionally compare the 300K-RNA empirical method with the deep-learning algorithm SpliceAI7 to disrupt pre-mRNA splicing (Supplementary Table 6) were excluded.We performed a retrospective analysis of 88 splice-site variants across 74 genes that are confirmed by RNA diagnostics23 , skeletal muscle (n = 22) or malformation syndrome (n = 9) phenotypes. Thirty-two percent of variants affect the essential GT (n = 19) or AG (n = 9) splice sites and 68% affect the extended donor or acceptor splice-site regions. The dataset included 76 SNVs, four insertions, six deletions and two deletion–insertion variants  and/or RNA-seq were performed on RNA isolated from clinically accessible specimens from 140 affected individuals or heterozygotes with diverse Mendelian conditionsnts Fig. .Fig. 1VaHalf of the variants (44/88) induced two or more mis-splicing events Fig. . Variant8 and SRA15, collected in the recount3 resource16  for 300K-RNA predictions were obtained by applying a filter of a maximum of two exons skipped and cryptic splice sites within 600 nt  a paired donor and acceptor from different introns, reflecting skipping of one or more consecutive exons normally present in that transcript . However, only 20% of 300K-RNA Top-4* events are present in all four clinically accessible tissues in GTEx  identifies only 15% (180 K) of the 1.16 M total splice junctions detected in 504 GTEx v8 fibroblast specimens . For comparison, in-house RNA-seq data from seven fibroblast specimens  subject to cycloheximide (CHX) treatment identify 36% (420 K/1.16 M), substantially more than DMSO treated specimens (340 K/1.16 M) or in two randomized sets of seven fibroblast specimens from GTEx v8 (245 K/1.16 M). This preliminary evidence indicates CHX treatment and high read depth of RNA from human cell lines may substantially enhance our detection of unannotated mis-splicing events through inhibition of nonsense-mediated decay (NMD).Long-read RNA-seq may prospectively assist the evaluation of new methods to improve the fidelity of short read alignment and reduce splice-junction artifacts in 300K-RNA, although it has its own bioinformatic and technical challenges. Extended Data Fig. 19 is a deep-learning algorithm that predicts the location of splice sites, using up to 10,000 nt of mRNA sequence context for each prediction. SpliceAI is trained on the pre-mRNA sequences surrounding 130,796 annotated donor–acceptor pairs19. By default, SpliceAI scans ±50 nt of a variant entered and reports four delta scores (Δ-score), the maximum difference in score with and without the variant for acceptor loss, acceptor gain, donor loss and donor gain. The authors recommend a threshold of Δ ≥ 0.20 for high sensitivity of splicing alterations, Δ ≥ 0.50 for more general purposes and Δ ≥ 0.80 for high specificity19.SpliceAI19 high sensitivity threshold of Δ ≥ 0.20 correctly predicted mis-splicing for 76/86 (88%) variants and for 63/86 (73%) variants with the Δ ≥ 0.50 threshold. Preliminary investigations using the default SpliceAI window of ±50 nt and high sensitivity Δ ≥ 0.20 threshold identified 13/44 variant-activated cryptic splice sites, noting only 19/44 activated cryptics lie within 50 nt, and 3/73 exon-skipping events (inferred by donor or acceptor loss of Δ ≥ 0.20 for both annotated splice sites flanking an exon), while the remaining 70 skipped exons were longer than the 50 nt scanned window. Therefore, we reasoned SpliceAI output could offer improved predictions of mis-splicing outcomes by extending the default scanned window to the maximum length of ±5,000 nt of the variant, developing custom interpretive rules to include predictions of multi-exon skipping, and optimizing the Δ-score threshold for this particular purpose  of detected exon-skipping events while SpliceAI Δ ≥ 0.011 predicts 85% (61/73) Fig. . Eighty-Sensitivity (S) and PPV of Top-4* and SpliceAI (using Δ ≥ 0.011 threshold) were similar across two additional sets of variants curated from literature; 58 variants tested in patient specimens  and 63 variants tested using midi-gene assays   indicates this will yield a high false-positive rate of >50%.We also emphasize that while a SpliceAI threshold of 0.011 was effective for this bespoke application to forecast the likely nature of any variant-elicited mis-splicing, we do not recommend the use of a 0.011 delta score threshold as a prediction for mis-splicing generally, as our evidence from experimentally confirmed splice neutral variants 7. We identified or clarified variant-associated enhanced use of 1/4 multi-exon-skipping events , 4/4 cryptic splice sites  and one single-exon-skipping event . Skipping of multiple exons associated with SPG11 c.2317-13C>G was not detected initially by RT-PCR due to primer placement in exons too proximal to the splice variant and undetected by RNA-seq due to low read depth exacerbated by NMD. Activation of two cryptic donors and two cryptic acceptors associated with GSDME c.1183+5G>A and EMD c.266-3A>G, respectively, was missed initially due to competition inherent with multitemplate PCRs, heteroduplex formation and challenges resolving multitrace chromatograms by Sanger sequencing.We noted many Top-4* events not detected in our early RNA diagnostics cases (before 40K-RNA or 300K-RNA) involved double-exon-skipping events 42%) or cryptic activation events further than 250 nt from the annotated splice site (11%), which may have been missed on initial analysis. Before the development of 40K-RNA% or crypForty-nine percent (27/55) of ES variants across the three variant sets induced at least one in-frame event, with a similar proportion of 51% (45/88) for all variants in our cohort  criterion to ES variants6. In addition, although intron retention cannot yet be quantified and ranked by 300K-RNA, Extended Data Fig. Our empirical method of using 300K-RNA Top-4* accurately predicts the nature of variant-associated mis-splicing with 92% sensitivity for 88 variants across a broad range of genes and disorders, outperforming SpliceAI on average to correctly predict exon-skipping, double-exon skipping and cryptic splice site activation. We emphasize that Top-4* cannot be used for variants creating or modifying the ES motif of a cryptic splice site and recommend use of SpliceAI for this category of variantIt is important to acknowledge the low PPV of Top-4* when used as a prediction of the nature of mis-splicing. However, we feel that prioritizing sensitivity is of the greatest importance, to avoid false-negative predictions. RNA re-analysis of four cases with one or more undetected Top-4* events via our initial RNA diagnostic testing revealed we had missed 6/9 of these events, due to experimental design and/or technical limitations. A priori knowledge of Top-4* mis-splicing events has been transformative for our research-led clinical RNA diagnostics program, facilitating both variant curation and strategic experimental design of RNA assays to specifically target probable mis-splicing events, expressly important for RT-PCR where primer design and extension times strongly influence which products may be amplified.Our re-interrogation of early cases, showing we had missed several rare events, raises the possibility that Top-4* PPV could be higher than we currently estimate. It also reinforces clinical benefits of being able to reliably predict probable mis-splicing events to improve the completeness and accuracy of conclusions drawn from RNA diagnostics. Notably, we cross-checked all other early cases (before 300K-RNA) to confirm that interpretation of likely pathogenicity would not be impacted by any undetected Top-4* events that may have resulted .25. We suspect the predictive accuracy of our ranking method may be improved further by the following: (1) higher read depth RNA-seq data across the breadth of manifesting tissues in rare disorders , (2) incorporation of RNA-seq data from human fetal samples to catalog developmental alternative splicing, (3) use of CHX to inhibit nonsense-mediated decay, (4) a better understanding of contexts that influence variant-associated double-exon skipping, (5) an ability to rank likelihood of variant-activated intron retention and (6) improved bioinformatic methods in sequencing read alignment. Consideration of Top-3* events in specific manifesting tissues, if shown to maintain >90% sensitivity, could substantially improve PPV and clinical utility for variant classification.Top-ranked unannotated splicing events are highly concordant between tissues and between GTEx and SRA Fig. , in line5. Theorized consideration of intron retention and Top-4* events by genetic pathology workforces provides a pragmatic, evidence-based method to reliably assess variant-associated exon skipping and/or cryptic splice site use within a larger distance window of 600 nt. Over 2022–2023, the Australasian Consortium for RNA diagnostics (SpliceACORD)7 will rigorously evaluate the clinical accuracy and usefulness of Extended Data Fig. To our knowledge, 300K-RNA Top-4* is the first evidence-based method for predicting the nature of variant-associated mis-splicing and will assist clinical classification of splice-site variants and/or guide RNA diagnostic testing strategies to make sure no likely mis-splicing outcome is missed due to design flaws or technical limitations. Informed by the current investigation, Extended Data Fig. https://kidsneuro.shinyapps.io/splicevault/). Users require no bioinformatics expertise and can retrieve stochastic mis-splicing events for any splice junction annotated in Ensembl or RefSeq. Default settings display 300K-RNA Top-4* output according to the optimized parameters we describe herein, with the option to return all events, customize the number of events returned, distance scanned for cryptic splice sites, maximum number of exons skipped or list tissue-specific mis-splicing events. We hope SpliceVault will improve the ability to classify and study splicing variants with accuracy and completeness, avoiding the non-actionable diagnostic endpoint of a variant of uncertain significance.We provide SpliceVault, a web portal to access 300K-RNA (and 40K-RNA in hg19), which quantifies natural variation in splicing and potently predicts the nature of variant-associated mis-splicing ) with informed, written consent for all participants.8 and SRA15 aligned using a unified Monorail pipeline from ref. 16. Splice junction read counts derived from 316,449 human RNA-seq samples from SRA and 19,214 human RNA-seq samples from GTEx were downloaded from the public resource recount3 (ref. 16). We then filtered split reads to those that span at least one annotated splice site, and for each splice junction detected we tallied the number of samples it occurred in across the two data sources. For each splice junction, we filtered associated detected exon-skipping and cryptic-activation events according to the rules in Fig. The 300K-RNA database consists of splice junctions from 335,663 publicly available RNA-seq samples from GTExUnannotated splicing events were ranked according to the number of samples in which the event was detected. This ranking process was completed with respect to each annotated splice junction in Ensembl transcripts (v104) and Refseq transcripts .27 was used to retrieve information on individual samples for Fig. The 300K-RNA enables customized access to ranked splice-junction data from individual GTEx tissue subtypes. SRA metadata precludes breakdown into specimen subtypes. As SRA contains data from cancer specimens , maximum read-counts output for each splice junction is derived from GTEx data. The R package Snapcount (v1.8.0)https://github.com/Illumina/SpliceAI, ‘3. Can SpliceAI be used to score custom sequences?’) into an API to allow us to easily retrieve scores . As input, we used the pre-mRNA sequence ±5,000 nt of the variant. Two Δ-scores returned at each base (variant nucleotide versus reference nucleotide) generated up to 20,002 Δ-scores per variant, of which we excluded all Δ-scores ≤ 0.001 as neutral impact.To adapt SpliceAI to the prediction of mis-splicing, we retrieved all Δ-scores ±5,000 nt of each variant, adapting a script from the SpliceAI GitHub  and Online Mendelian Inheritance in Man database . Genes were extracted from the Genomics England PanelApp using the Swagger PanelApp API (v1) (panelapp.genomicsengland.co.uk/api/docs/), excluding disease susceptibility panels; all genes were below a confidence level 3 . OMIM-listed genes were excluded when their only phenotype associations were nondiseases, susceptibilities, provisional links and somatic mutations.Clinically relevant genes were extracted from the Genomics England PanelApp−1; Gibco). Fibroblasts were cultured in high glucose DMEM (Gibco), 10% FBS  and Gentamicin . PBMCs and primary fibroblasts were treated with dimethyl sulfoxide (Sigma-Aldrich) or 100 μg ml−1 cycloheximide (Sigma-Aldrich) for 6 h before harvesting in 600 µl RLT buffer (Qiagen) for RNA extraction using the Qiagen RNeasy mini kit. SuperScript IV first-strand synthesis system (Invitrogen) was used to make cDNA from 500 ng of RNA according to kit instructions. Recombinant Taq DNA polymerase (Invitrogen) and MasterAmp 2X PCR PreMix D (Epicentre Biotechnologies) were used for PCRs. Thermocycling conditions were 94 °C for 3 min, 35 cycles 94 °C 30 s, 58 °C 30 s, 72 °C 90 s per kilobase, then 72 °C for 10 min. All PCR products were analyzed on a 1.2% agarose gel. Amplicons were manually excised from an agarose gel with a scalpel and cDNA was purified using GeneJET gel extraction kit (Thermo Fischer Scientific) according to the manufacturer’s instructions. Further, 8–75 ng of purified cDNA and 1 pmol of sequencing primer were subject to Sanger sequencing. Sanger sequencing chromatograms were analyzed using Sequencher DNA sequence analysis software (Gene Codes). See Supplementary Table Whole blood was collected in a PAXgene  blood RNA tube and RNA was isolated according to kit instructions. Peripheral blood mononuclear cells (PBMCs) were isolated using SepMate-15 tubes (StemCell Technologies) and Ficoll Paque Plus . PBMCs were cultured in RPMI 1640 Medium (Gibco), 10% fetal bovine serum  and penicillin-streptomycin (100 U µg mlNo statistical method was used to predetermine the sample size. All splicing variants were included in this study for which robust RNA assay data were available and met the following ascertainment criteria. Variants affecting an annotated splice site and demonstrated to activate exon-skipping or cryptic splice-site use were included in this study. Variants creating or modifying a cryptic splice site were excluded. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01293-8.Reporting SummaryPeer Review FileSupplementary Tables 1–4.Table 1—Variants experimentally verified to disrupt pre-mRNA splicing from our RNA diagnostics program. Table 2—Published variants experimentally verified to disrupt pre-mRNA splicing in patient specimens. Table 3—Published variants experimentally verified to disrupt pre-mRNA splicing using midi-gene assays. Table 4—Primers used for RNA re-analysis.


Non-coding RNAs (ncRNAs), such as microRNAs or long ncRNAs, have brought about a new paradigm in the regulation of gene expression. Sequencing technologies have detected transcripts with tremendous sensitivity and throughput and revealed that the majority of them lack protein-coding potential. Myriad articles have investigated numerous ncRNAs and many of them claim that ncRNAs play gene-regulatory roles. However, it is questionable whether all these articles draw conclusions through cautious gain- and loss-of function experiments whose design was reasonably based on an ncRNA’s correct identity and features. In this review, these issues are discussed with a regulatory ncRNA, nc886, as an example case to represent cautions and guidelines when studying ncRNAs. Non-coding RNAs (ncRNAs) are RNA molecules without protein-coding potential. The central dogma of modern molecular biology indicates that the flow of genetic information is from DNA, via RNA as an intermediate, to proteins that ultimately have catalytic roles and thus, are responsible for most cellular metabolism. Therefore, in the authentic central dogma viewpoint, ncRNAs could even be considered to be odd. Currently, the ubiquitous existence of ncRNAs has become common knowledge. The majority of the human genome is transcribed, and most transcripts are non-coding . FurtherTherefore, in this critical review, I would like to discuss the caveats of ncRNA studies, based on my experience while investigating ncRNAs, especially nc886, which has been my main research theme for over ten years. Based on lessons from nc886, I want to suggest several points to be cautious of in designing experiments and interpreting data. Although a significance number of references have been critically cited here, I should mention that they convey valuable information and have contributed to the growth of our knowledge. Importantly, most of them have precisely described experimental procedures so that I could rely on the data and interpret them correctly. I welcome any comments from the authors and readers and am open to constructive discussion.https://www.mirbase.org/, accessed on 1 March 2022; nc886 is a 101 nucleotide (nt) long ncRNA whose aliases include vault RNA2-1, pre-miR-886, etc. As one of its aliases indicates, nc886 was mis-annotated as an miRNA precursor, and its mature miRNAs, miR-886-5p and -3p, had been registered in the miRNA database  by Dicer . A long In conclusion, RNA fragments corresponding to miR-886-5p or -3p exist, but they are most likely to be degradation products whose level is negligible compared to the intact, 101 nt long nc886 . NonetheThe majority of the papers on miR-886-5p or -3p came from an attempt to find an miRNA that played a role in a biological situation of their interest. miRNA study typically begins with array experiments to screen an miRNA whose expression levels are altered. In hybridization-based array platforms, probes for miR-886-5p or -3p will also detect nc886. PCR-based arrays, which usually employ the TaqMan PCR technique , cannot When researchers have selected an miRNA of their interest, the next step is typically to examine cellular phenotypes and target mRNAs by performing overexpression or knockdown (KD) experiments. The most prevalent method is to transfect an miRNA mimic or inhibitor. Caution is required, especially when using an miRNA mimic. It should be noted that miRNA mimics are a chemically synthesized RNA duplex, whereas natural miRNAs are single-stranded .To understand this discrepancy, a story about miRNA and RNA interference (RNAi) needs to be told (reviewed in ). In theThe above story provided a theoretical background to justify the use of an siRNA-like duplex as the functional mimic of a miRNA . AlthougFrom overexpression data, several papers claimed that they found a cellular role of miR-886-5p or -3p and identified target mRNAs ,44,45,46In summary, from data that have been published so far, nc886 does not seem to produce functional miRNAs; experimental evidence is not sufficient to support an miRNA role for miR-886-5p or -3p. Overexpression data from a synthetic duplex should be interpreted with great caution . I do noPlasmid vectors are the most common tool for the ectopic expression of a gene. nc886 is silenced in a number of cancer cell lines. When attempting its ectopic expression in these cell lines, the vector-based method should certainly be the primary choice. In this strategy, knowledge of nc886 is needed for optimal experimental design.nc886 is transcribed by RNA polymerase III (Pol III), but not by RNA polymerase II (Pol II) ,48,49,50Commonly used expression vectors have a strong promoter, which is usually adopted from viruses such as cytomegalovirus (CMV) or simian virus 40 (SV40). A couple of studies employed these vectors to construct nc886-expression plasmids ,55. It sIn one report, a sufficient length (>200 nt) of the 5′-upstream sequence, together with the nc886 RNA region (101 nt), was inserted downstream of a Pol II promoter . Such a RNAi-based methods and modified antisense oligonucleotides (ASO) are commonly used for KD of a gene (reviewed in ). RNAi-bThe standard method for nc886 KD in my laboratory is to transfect a 20 nt long ASO, having five 2′-O-methyl ribonucleotides at both ends . The bacBecause it was mis-identified as an miRNA, several papers intended the KD of miR-886-5p or -3p using commercially available miRNA inhibitors ,41,44,68When choosing a KD method in actual experiments, the measurement of KD efficiency by reliable assays was more important than the theoretical principle of a KD method. I was surprised to see that the DNA-RNA mixmer ASO was highly efficient for nc886 KD. Conceivably, RNaseH-mediated cleavage will occur dominantly in the nucleus to destabilize a target RNA . On the These nc886 episodes highlight the importance of assays. Without knowing nc886′s role in PKR, anyone would have concluded that KD failed in the cholangiocarcinoma cells. It should be also noted that the examination of nc886 by Northern hybridization was the most convincing way to validate the KD efficiency. In qRT-PCR assays, I would have a concern about a false-positive result where inefficient KD is mistakenly assessed to be successful. A huge amount of an ASO could interfere with cDNA synthesis or PCR amplification, since the ASO will exist in the RNA preparation and possibly re-associate with the target RNA during these enzymatic steps. To avoid this possibility, the best way will be to design an ASO outside of a PCR amplicon. However, such a design may yield a false-negative result, in which KD is actually successful, but looks like a failure. Another complication is that an ASO could inhibit the activity of an ncRNA without affecting its expression level, as in the case of nc886 in the cholangiocarcinoma cells . TherefoBased on the experiences of nc886, I would like to bring some points to attention when studying an ncRNA.As compared to protein-coding RNAs, extra precautions are required when defining an ncRNA’s identity because the RNA molecule itself is a functional entity.The primary element of identity is a precise RNA sequence from the 5′-end to the 3′-end. In the case of protein-coding genes, the functional entity is the translation products, and their sequences are usually unambiguously defined by start and stop codons. This information on DNA can be complemented by peptide sequencing. In the case of ncRNAs, it is highly questionable whether their sequences in public databases represent their precise 5′- and 3′-ends. I was surprised to see that even the nc886 sequence is incorrectly displayed in one of the most authoritative databases, the National Center for Biotechnology Information (NCBI) database . nc886’sThe molecular shape of an ncRNA is determined by other features beyond its precise nt sequence. These include a 5′-end structure , an nt addition at the 3′-end (oligo- or poly-adenylation or uridylation), and base modification, as in the case of tRNAs. All these factors can determine an ncRNA’s function. When most research laboratories study an ncRNA, initial screening comes from NGS data, and validation is done by PCR-based techniques. Strictly speaking, NGS and PCR methods measure only a part in an entire ncRNA molecule, and not any of features described above.To overexpress an ncRNA, a common method is to clone an ncRNA sequence referred from databases into an expression vector. As mentioned earlier, sequences in databases cannot guarantee their genuine 5′- to 3′-end, presumably in many cases. I would like to state again that, for ncRNAs, it is an RNA molecule itself that functions. For better understanding, we can compare RNA with a protein. Do you expect a chance that a truncated polypeptide would phenocopy the full-length wild type protein? Rather, one has to be concerned about the possibility that the truncated polypeptide might suppress the wild type protein in a dominant-negative manner.Not only the identity, but the quantity also matters. The natural expression levels of most ncRNAs are very low . When thIn the case of small ncRNAs which are not miRNAs, the transfection of a synthetic RNA can be considered for overexpression. The easiest design would be a plain single-stranded RNA oligonucleotide, but they are usually fragile when introduced into cells . TherefoFunctional assays in the cancer cell biology field, as an example, include cell proliferation, apoptosis, colony formation, migration, and invasion assays. Although these are routinely performed assays, the measurement of specific genes, protein activities, or pathways  is more desirable as a functional assay. Gene expression profiling data upon overexpression and KD/KO of an ncRNA will provide a hint as to which should be measured, and further, a clue about its molecular function.For loss-of-function approaches, I would consider modified ASO as a higher priority than siRNA. The adverse effects of an siRNA (off-target activity and saturation of the miRNA machinery) are obvious ,72. TherA gene editing technique called clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) has provided a method for gene knockout (KO) . KO expe-What is its exact sequence? The precise 5′- to 3′-end should be determined in order to correctly design overexpression and KD experiments.-Which RNA polymerase  transcribes it?-Where is it located ?-Is it a de novo transcript or a processing product from a pre-existing RNA?-How abundant is it in a cell?-In a plasmid-based overexpression system, the precise sequence should be placed under a correct promoter of reasonable strength.-For small ncRNAs, a synthetic modified oligoribonucleotide can be considered.-For small ncRNAs without concrete evidence that they are bona fide miRNAs, an siRNA-like duplex must not be used.-For KD, modified ASO should be considered with a higher priority than an siRNA.-KO by CRISPR-Cas would be the best, if KO cells can be created.-Overexpression or KD should be validated by a proper measurement; Northern hybridization is better than qRT-PCR.-In addition to measurement, validation by a functional assay is desirable.-A functional assay is absolutely required when a synthetic RNA is used for overexpression.-A functional assay can include the use of cellular phenotypes, gene expression profiles, or the measurement of a particular protein activity or pathway. The more specific, the better.-The phenotypic data from overexpression and from a KD or KO experiment should complement each other.ncRNAs have been a prevailing research topic. Numerous ncRNAs have been detected and accordingly, the ncRNA field has grown enormously, with approximately 200,000 papers published so far. Among these, nearly three-quarters are miRNA papers, owing to handy tools for miRNA research: miRNA detection, overexpression, inhibition, and target prediction are all straightforward. However, for most other types of ncRNAs, it is a challenging task and a long journey to find an ncRNA’s genuine biological role. In case of orphan ncRNAs, it is understandably a difficult job. For an ncRNA in a particular class, it is no easier. I spent a significant amount of effort proving that nc886 (a.k.a. vtRNA2-1) is not an miRNA precursor, nor does it have a vault-related function, although it is known as a vtRNA paralog.Although those approximately 200,000 published papers have vastly expanded our knowledge, we should be careful in accepting the alleged role of an ncRNA therein. When a researcher obtains a different, or even conflicting, result during the investigation of an ncRNA, it would be worth revisiting the original article with a thorough inspection of the experimental procedures and the critical interpretation of the data, instead of accepting the conclusions of the paper. For on-going studies, evidence should be provided by prudently designed experiments to concretely prove an ncRNA’s role. This will prevent the situation where “bad money drives out good” from occurring in the ncRNA field. I hope that this review contributes to that prevention.