RNA is a critical analyte for unambiguous detection of actionable mutations used to guide treatment decisions in oncology. Currently available methods for gene fusion detection include molecular or antibody-based assays, which suffer from either being limited to single-gene targeting, lack of sensitivity, or long turnaround time. The sensitivity and predictive value of next generation sequencing DNA-based assays to detect fusions by sequencing intronic regions is variable, due to the extensive size of introns. The required depth of sequencing and input nucleic acid required can be prohibitive; in addition it is not certain that predicted gene fusions are actually expressed.Herein we describe a method based on pyrophosphorolysis to include detection of gene fusions from RNA, with identical assay steps and conditions to detect somatic mutations in DNA , permittThe limit of detection was under 6 molecules/ 6 µL target volume. The workflow and instrumentation required are akin to PCR assays, and the entire assay from extracted nucleic acid to sample analysis can be completed within a single day.The online version contains supplementary material available at 10.1186/s12920-022-01363-0. ALK, ROS1, and RET, making this class of mutations critical for clinical decision making, but robustly and accurately identifying these patients remains challenging using current technology. Most commercially available targeted next generation sequencing (NGS) methods that analyse variations in exons from genomic material enable many common DNA mutations to be detected, but fail to provide a full dataset for actionable RNA mutations, as they rely on DNA-based sequencing to infer intronic mutations that are predicted to lead to fusion events manifest in the RNA [As precision oncology becomes routine, identification of patients most likely to benefit from targeted therapeutics requires a diagnostic technology with fast turnaround time, low-cost and appropriately sized panels of relevant targets necessary for timely clinical decision-making, for both first-line treatments and at progression. RNA is an under-utilised but critical analyte for oncology, and contains information on transcript variations and expression levels, and is affected by genomic changes outside of exons , 3. Gene the RNA , 6. The ALK, ROS1, RET, MET exon 14 skipping, and NTRK1, NTRK2 and NTRK3 (Supplementary Table 1). We show detection of targets in commercially sourced synthetic samples, with detection down to single molecule copy numbers. High specificity is shown using normal formalin-fixed paraffin-embedded (FFPE) lung tissue samples with a final demonstration of end-to-end execution of our assay from lung tissue to result using confirmed RNA fusion-positive FFPE samples.Previously, we demonstrated that detection of a panel of somatic variants in DNA from tumor tissue was possible down to single-molecule levels of sensitivity by using a simple, fast and low-cost method incorporating Allele-Specific PYrophosphorolysis REaction (ASPYRE) . Herein The 18-plex Seraseq Fusion RNA Mix v4 reference standard sample was purchased from LGC Seracare . Synthetic RNA oligonucleotides were manufactured by GenScript , and diluted in 0.2 µg/µL polyA carrier RNA . Purified total lung RNA (AM7968) was obtained from ThermoFisher Scientific  and used as background RNA.De-identified samples supplied by biobanks were from sites worldwide, collected under local IRB approved informed consents or through waiver of consent after 10 years storage, consistent with IRB and Ethics Committee guidelines. Lung tissue FFPE blocks from normal lung tissue were procured from Discovery Life Sciences, Santa Barbara, USA and AMS Bio, Abingdon, UK. Blocks were sectioned using a microtome, producing three curls of 12 µM per replicate. Lung tissue FFPE curls from patients with confirmed RNA fusions were obtained from Azenta Life Science .RNA and DNA was extracted using the parallel protocol with the Quick-DNA/RNA™ FFPE miniprep kit  and stored at -20 °C until analysis. Nucleic acid concentrations were measured using Qubit™RNA and DNA High Sensitivity kits (ThermoFisher).Target amplification, enzymatic digestion, pyrophosphorolysis and ligation, and rolling circle amplification were carried out essentially as described in Silva et al. with modifications described below:4 (NEB), and 6 µL of RNA target. The thermocycling conditions used were 37 ˚C 1 min; 55 ˚C 10 min; 98 ˚C 1 min; 50 cycles of 98 ˚C 10 s, 63 ˚C 15 s, 72 ˚C 15 s; and 72 ˚C 5 min. Proteinase K digestion and pyrophosphorolysis were performed essentially as described previously [4SO4, 3.72 mM KCl, 3.49 mM MgSO4, 0.0567% Tween-20), 300 U/mL Bst 3.0 WarmStart , 0.8 mM dNTPs with dUTP (Promega), 0.3% polydimethylsiloxane emulsifier , and 0.253 µM primer mix. 5 µL PPL reaction sample was added to 10 µL of detection mix in a 384-well plate. Samples were incubated at 57 °C for 200 min in a QuantStudio5 RealTime PCR System (ThermoFisher). The fluorescence read-out was taken every minute in the FAM, JUN, VIC and Cy5 channels.The PCR mastermix from  was modiQuantification of synthetic RNA oligonucleotides by RT-dPCR was performed in a QIAcuity One (Qiagen) using the RT-PCR mix from the ASPYRE assay with the following additions: 0.1% Tween-20 (Sigma-Aldrich), EVAgreen (Biotium) 3X, AlexaFluor 700 (ThermoFisher) 0.3 ng/µl. The number of amplification cycles was reduced to 40.A baseline correction was applied to fluorescence data to correct for drift, and Cycle Sigmoid midpoint (CSm) values were identified using fits similar to those described previously . Threshohttp://www.r-project.org/) and the ‘tidyverse’ library for data organisation and visualisation [For statistical analyses we used the statistical software package R . The forward primer targets the 5’ partner of these genes, engendering the specificity of the reaction since only samples with a gene fusion present will exponentially amplify.The ASPYRE assay comprises four simple sequential enzymatic steps, requiring only reagent transfer. The stages were described previously for DNA: 1) PCR, 2) enzymatic cleanup, 3) exonuclease digestion, hybridisation, pyrophosphorolysis (PPL), and ligation; and 4) rolling circle isothermal amplification . The re0 wells  and quantified by digital RT-PCR. The commercial reference standard Seraseq Fusion RNA mix v4 contains a mix of 18 RNA targets quantified by dPCR by the supplier. To confirm the quality and validity of our dRT-PCR quantified samples, a comparison of the outcomes of the ASPYRE-Lung RNA assay with single primers and probes against a selection of five targets using both the in-house quantified RNA oligos and the Seraseq Fusion mix was performed. The results  within the 3’ gene fusion families. Altogether these families collectively account for the approximately 11.3% of NSCLC cases that are gene-fusion positive [ALK and RET: rho = 0.857 ), P = 0.01 (ROS1: rho = 0.985) and P = 0.06 (NTRK: rho = 0.955), demonstrating that detection using ASPYRE is consistent with single molecule detection.Supplementary Table  2 compares the results to a theoretical distribution assuming a reaction efficiency of 100% at the copy numbers given. The correlation of experimental and theoretical results were significant at P = 0.029  was used as a positive control to ensure that detectable levels of RNA were added to the reaction, and gave a positive result for all replicate curls of all tissue samples for both users, except one at 10 ng probably due to pipetting error. Under standard operating procedures this would trigger an invalid result, and reanalysis. The overall analysis returned negative results for all replicate curls of all samples for both users across all RNA fusions, as expected for samples that contained normal lung tissue, with a negative predictive value of 100%.ROS1-positive, and one ALK-positive) previously detected via fluorescence in-situ hybridization (FISH) and immunohistochemistry (IHC) respectively. RNA was extracted from curls, and sequential input amounts  were processed through our standard multiplexed ASPYRE-Lung fusion assay  gave a positive signal commensurate with the ROS1-positive control at all input levels of RNA tested  gave a positive signal commensurate with the ALK-positive control at all input levels of RNA tested  out of the eight performed yielded a false-positive result for ACTβ. Under standard operating procedures this would trigger an invalid result for the plate, and reanalysis.Sample NSCLC_151 smokers, incurring delays with each test required [Across 102 countries, under half of patients diagnosed with lung cancer have access to molecular testing analysis at all . For thorequired . NGS tarrequired , 20. RNArequired .ALK, ROS1, RET and NTRK1, NTRK2 and NTRK3. No changes were made to the ASPYRE workflow, with the only adaptation being the addition of a reverse transcriptase to one mastermix formulation. As a result, DNA and RNA samples can be run concurrently with no additional steps required from the user, on the same plate in the same thermal cycler and qPCR machine. The entire post-extraction workflow, from RT-PCR through to result can be completed in under a day. Up to 192 samples can be processed through this fusion assay in a single real-time PCR instrument run, or 16 samples when combined with the 20 wells used for detection of mutations from DNA (data not shown).In our previous study introducing the ASPYRE technology, we reported the design of multiplex panels to target somatic variations in DNA that can detect mutations at single molecule levels. Herein we extend the capabilities of ASPYRE to include detection of targets from RNA, specifically gene fusions in groups including 3’ partners of A combination of multiplexed primers  allows amplification of all targets in our RNA panel to be run from a single RT-PCR reaction. This panel includes all 3’ partner classes of actionable mutations listed in current NSCLC treatment guidelines  for advanced/metastatic disease. Collectively, these targets represent just over 11% of reported 3’ classes of mutations found as RNA gene fusions in NSCLC [RNA is an under-utilised resource for diagnostic testing of patients with NSCLC; yet the availability of targeted therapy for specific gene fusions renders the need urgent for accurate, fast, cost-effective diagnostics that are scalable and easy to integrate into existing laboratory workflows. Access to a simple, fast, local and low-cost panel that can provide a genotype for all actionable mutations of NSCLC would be transformative for patients. Equally, a panel that does not require extensive investment in specialised equipment and training for end-users and care providers to implement into routine clinical workflow to improve patient outcomes would be practice changing. We expect that the ASPYRE technology has the potential to transform current care pathways, both for NSCLC and future oncology applications.Below is the link to the electronic supplementary material.Supplementary Material 1


S-adenosyl-l-methionine (SAM) are being developed as bioconjugation tools for targeted transfer of functional groups and labels to increasingly visible targets. Secondly, SAM-derived compounds are being investigated for their ability to act as inhibitors of RNA MTases. Drug development is moving from derivatives of cosubstrates towards higher generation compounds that may address allosteric sites in addition to the catalytic centre. Progress in assay development and screening techniques from medicinal chemistry have led to recent breakthroughs, e.g. in addressing human enzymes targeted for their role in cancer. Spurred by the current pandemic, new inhibitors against coronaviral MTases have emerged at a spectacular rate, including a repurposed drug which is now in clinical trial.RNA methyltransferases (MTases) are ubiquitous enzymes whose hitherto low profile in medicinal chemistry, contrasts with the surging interest in RNA methylation, the arguably most important aspect of the new field of epitranscriptomics. As MTases become validated as drug targets in all major fields of biomedicine, the development of small molecule compounds as tools and inhibitors is picking up considerable momentum, in academia as well as in biotech. Here we discuss the development of small molecules for two related aspects of chemical biology. Firstly, derivates of the ubiquitous cofactor  Concomitant with a surge in research and publication activity about a decade ago, the field of RNA modification was dubbed ‘epitranscriptomics’ ,2. This Especially in comparison to other enzymes catalyzing group transfer reactions, such as kinases , literatS-adenosyl-l-methionine (SAM) analogues for group transfer was first developed with DNA, before being applied to RNA as well, therein recapitulating the dynamic research interest in the fields of epigenetics and epitranscriptomics.Because this recent development is liable to entail increased activity in the field of medicinal chemistry, we decided to collect and compile pertinent literature and to summarize the state of science. In doing so, we acknowledge that, in addition to a relatively low number of small molecules available in the literature for modulating RNA MTase activity, there is a related field that provides valuable insight in terms of structure–activity relationships (SAR). Interestingly, the use of functional N5,N10-methylenetetrahydrofolate (MTHF) (The vast majority of MTases utilizes SAM as cofactor  with fewe (MTHF) . SAM MTae (MTHF) ,17–19. Ae (MTHF) ,17,19,20e (MTHF) . The clae (MTHF) . NoteworOn the contrary, MTHF-dependent MTases have not been put into a generally accepted classification system, even though they too, are found in all three domains of life , methylaSAM dependent MTases are known to methylate a wide variety of biomolecules, e.g. DNA , RNA 3434, proteAmong the first attempts to exploit or manipulate the transfer activity of MTases was the use of SAM analogues with moderately modified side chains. Early work found that, although transferring saturated chains longer than methylgroups was possible, transfer ability decreased with increasing chain length , suggestN-mustard derivatives for the in situ generation of aziridines have been developed . Two approaches to improve this activation were developed. On one hand, the sulfur was replaced with higher chalcogenes (i.e. selenium and tellurium), on the other hand, unsaturated systems were introduced to stabilize the transition state of the transfer reaction.To avoid this problem, the field turned back to the transfer of SAM analogue side chains, rather than of the entire cofactor. The activation of the transferable alkyl chain resulting from a central trivalent sulfur atom had shown to be adequate for methylgroups, but insufficient for transfer of longer saturated alkyl chains  mechanism, implying the occurrence of an sp2 hybridized transition state on the α atom , group transfers of various reactive groups (Utilizing this e groups  as well e groups  to DNA hS-adenosyl-l-homocystein (SAH) or its respective analogues. The central chalcogen typically acts as a nucleophile in a simple nucleophilic substitution, usually of the SN2 type, with e.g. an alkylhalogenide as electrophile. A major drawback of this reaction is the racemic nature  was engineered to act more promiscuously in the preparation of artificial SAM analogues. This enzyme family's biological role is to synthesize SAM from ATP and methionine. Through enzymatic cascade reactions, starting from the corresponding methionine analogues, various groups were not only transferred to small molecules, proteins and DNA, but also to RNA . Using aSAM analogues featuring a central selenium atom were well accepted ,65. SincCryptosporidium hominis (ChMAT) was also used to synthesize SAM derivatives for subsequent MTase mediated labelling of minimal RNA-cap analogues, either in its native form  with a V34A mutation , and specific photoclick reactions Figure . For thiAnother double bond bearing group transferred to RNA is the benzyl-vinyl group, which was introduced to RNA with GlaTgs2-Var1 ,74,85, EInterestingly, even bulky residues such as norbornene derivatives with benzylic linkers have been successfully transferred from corresponding SAM analogues to RNA caps with the Ecm1 enzyme. The norbornene double bond was exploited for the attachment of biotin or fluorescence labels using the IEDDA reaction .N-hydroxysuccinimide  particular interesting, since its substrate specificity is programmable via a guide RNA and can thus be targeted to variable sites on RNA where it modifies 2′-O residues. This way, a pre-mRNA and tRNALeu could be modified with a propargylic residue at various sites, each addressed by suitable guide RNAs and the corresponding seleno-derivative of SAM  and para-nitrobenzyl (PNB), were successfully transferred from SAM derivatives to RNA cap analogues by Ecm1 but could not be removed upon irradiation . ONB, asand Ecm1 .Internal RNA modification with photocaging groups was achieved by using METTL3-METTL14, which was able to transfer ONB and a 6-nitropiperonyl group (NP) to model mRNAs. Contrary to the findings with Ecm1 modified RNA caps, both groups could be removed successfully after irradiation with wavelengths of 365 or 405 nm (ONB) and 420 nm (NP). Furthermore, the ONB modification blocked reverse transcriptase activity on the labelled RNA, which was restored after light induced uncaging .As for chemical reactive groups, various photoreactive groups have been transferred to RNAs, the extent of used enzymes however, is very limited. Since the requirement of spacious conjugated π electron systems for photoreactivity naturally increases the size of the residues, the requirements for the MTases are more demanding, making this limitation understandable. Nevertheless, a broader selection of enzymes might be wish worthy since the high utility of photoreactive groups in RNA research.N1-N6-cyclization of the modified adenine base but was inert with respect to unmodified adenosines. The resulting bulky tricyclic RNA modification caused misincorporation during reverse transcription, which was then identified by high-throughput sequencing. This technique thus revealed the site-specificity of alkylation by the METTL3–METTL14, implying similar specificity in the biogenesis of m6A  pair, or with biotin and a fluorescent dye . Since min vivo, is the potentially low selectivity of SAM analogues in the presence of multiple MTases. To tackle this, ATP analogues with a derivatized adenine bases and 2′-O positions were prepared. In enzymatic cascade reactions using MAT and various MTases, these were first transformed to the corresponding SAM analogues, which then transferred their residues onto the RNA. Remarkably, partial selectivity towards certain enzymes was achieved with specific residues . After the methylation reaction, macromolecules like enzymes and RNA are typically precipitated, followed by chromatographic analysis of the supernatant. Chromatographic quantification methods for SAM and SAH have been described in conjunction with either UV , fluoresFor the development of techniques with higher throughput in combination with HPLC, e.g. in a 96-well plate format or higher, a staggered parallel scheme was developed. In this approach, multiple HPLC systems are connected to one mass-spectrometer and are run with a time delay to allow the MS system to switch to incoming analytes, thus minimizing the unused time of the detector ,122.In the RapidFire approach the HPLC column is replaced by an automated solid-phase-extraction. This lowers the throughput time for the purification drastically before analytes are quantified with a mass-spectrometer ,124. RapPlate formats are also frequently applied for spectrophotometric methods, thus methods suitable for plate reader equipment are desirable, that typically rely on either absorption , luminesS-ribosylhomocysteine lyase (LuxS) releases homocysteine which can be spectrometrically quantified after coupling to reactive stains like ThioGlo1, DTNB, or monobromobimane  or a combination of AdoHcy nucleosidase (mtnN) and mobimane ,133.On the other hand, adenine released by cleavage through mtnN, can further be converted into hypoxanthine via adenine deaminase, and then spectrometrically quantified . AlternaThe advantage of high-throughput numbers offered by concatenated enzymatic conversion methods is partially offset by potential side reactions and interfering substances in each enzymatic conversion. Hence, such assays frequently come into play when an MTase system is already well understood, and the time required for their optimization correlates with an expected return from high-throughput screening (HTS).3H-SAM. After separation from the tritiated cosubstrate, methylated product can be directly quantified via scintillation counting. For example, methylated RNA can be separated from 3H-SAM by precipitation with trichloroacetic acid (TCA) on paper (In early assay development and low-throughput screening (LTS), the arguably most direct and robust way to detect a methylation reaction consists in tracing isotope-labelled methylgroups during their transfer from SAM onto the substrate. A widespread method for RNA MTases is based on radioactive labelled on paper . Small fon paper .Scintillation proximity assays (SPA) represent an approach to improve throughput numbers when working with radioactivity. Using a similar detection principle, these can be carried out in 96 or higher well plate formats. Methylated, tritium-containing RNA is being separated from remaining labelled SAM by binding to scintillator beads. Binding can be mediated by unspecific electrostatic interactions or by exploiting specific affinity pairs such as biotin/streptavidin when using biotinylated substrate RNA. Radioactive decay from tritium in proximity to the beads then causes excitation of the scintillator and a subsequent emission that allows quantification of bound tritiated RNA .Many of the currently known RNA MTase inhibitors have been developed as substrate analogues, either by uninformed library screening, or by screening of focussed libraries, compiled with the help of computer-aided docking studies. Prior to the advent of X-ray structures, which might have allowed the design of non-substrate analogues as inhibitors, numerous derivatives of SAM and of SAH, itself an efficient inhibitor of many enzymes, were tested Figure . Such suStreptomyces griseolus, SFG features a central nitrogen en lieu of the typical trivalent sulfur atom and is therefore more stable against hydrolysis  nsp14 or RlmJ  campaigns up to millions of compounds can be tested to find new hits, provided a robust and sensitive assay format is available . A diffeIn a next step, such fragments are then expanded based on the co-structure with their respective target. In this ‘fragment growing’ step, potential additional interactions in proximity to the fragment binding site are explored by addition of matching functional groups to the fragment. Provided the new functional groups do indeed improve binding parameters, this step can be repeated several times, leading to bigger compounds which usually have high affinities coupled with high ligand efficiencies.Alternatively, multiple fragments binding to distinct sites can be linked in a search for synergy akin to bidentate ligands. Of course, the growth process outlined for fragments can be applied to the resulting multi-fragment compounds as well. Advantages relative to HTS include smaller libraries and better understanding of the chemical, physical and biological properties of compounds, as they emerge from the rational fragment growing process .in silico prior to experimental testing  approaches aim to select promising molecules  testing . Librariolecules ,173, whiolecules .As the success rates of both HTS and VS rely on the quality of the screened molecular libraries, general rules like drug-likeness, lead-likeness, or removal of unwanted (reactive) groups and awareness for pan-assay interference compounds (PAINS) are regularly applied in library design to avoid false-positive hits .The treatment of bacterial infections with multiple antibiotic resistances is nowadays a significant and growing challenge. New strategies involve both, overcoming resistance mechanisms, and developing antibiotics against new targets. Both strategies are also being pursued with prokaryotic RNA methyltransferases as targets, which are displayed in Table An interesting target with respect to overcoming resistances is the erythromycin resistance methyltransferase (Erm) family, as their enzymes modify rRNA at binding sites for antibiotics such as macrolides, lincosamides and streptogramin, thus simultaneously causing resistance to multiple antibiotics  Figure . Therefo50 = 0.45 μM) could be identified. Inhibition showed the sensitization of resistant bacteria to azithromycin  screening approach. Optimisation of hits by parallel synthesis led to inhibitors in the low micromolar range that bind competitively in the SAM pocket .N1 of G37, has been extensively studied in the more recent past. Various SAH-like as well as non-SAH-like inhibitors were tested against TrmD from different prokaryotes including E. coli   , and on infantum .Trypanosoma brucei  was tested. Remarkably, only one compound Table  showed ane Table .in silico , it has become a central target for new cancer therapies. Table 3n silico ,221 to hchniques ,222.in silico-based study, followed up by in vitro ³H-SAM methylation assays and cell experiments of selected ligands. Interestingly, several of the latter turned out to activate rather than inhibit the enzymatic activity by a yet unknown mechanism , dengue virus (DENV) and most recently the SARS-CoV-2, feature an RNA genome , are the origin of as many as 400 million infections per year, causing serious diseases like encephalitis, hepatitis and fetal death  functionality. Accordingly, the NS5 protein features separate binding sites for its multiple substrates and cofactors. Besides the RNA binding pocket, the SAM binding site is of particular interest for MTase activity, while a GTP binding site is present for the guanylyl transferase part  MTase activity (both N7 and 2′-O) with Ki values in the sub micromolar range, clearly outperforming SAH. Furthermore, some selectivity towards human MTases was observed . 500 drug-like fragments were screened of which 32 fragments were retained. Five fragments were able to inhibit at least one of the MTase activities, while seven of these hits could be co-crystallized with the enzyme. The fragments bound to four distinct sites, one corresponding to the GTP binding pocket of the enzymes guanylyl transferase functionality. Interestingly, the other fragments bound to three previously unexplored binding sites. Two of these were in close proximity to each other and to the SAM binding site .vide supra) to fill out their respective binding sites, without reaching into the SAM binding site. This led to compounds which did not affect the N7 MTase function, but exclusively inhibited the 2′-O function. IC50 values were in the 3-digit micromolar range, with the best compound displaying an IC50 value of around 90 μM. The newly designed inhibitors were also able to reduce west nile virus and zika virus MTase activity with IC50 values in the same order of magnitude , was also applied to inhibition of SARS-CoV and SARS-CoV-2 MTases  . The best RNAs were found to bind in the low nanomolar range and inhibited the N7 and 2′-O MTase functions. In cells, which were co-transfected with the JEV genome and an aptamer against JEV, replication of the viral RNA was impeded pointing to a potential antiviral activity of the aptamer (Aptamers are structured RNAs whose specific binding to targets can be directed during their genesis in an ichment) ,274. Suc aptamer ,274.5C MTases .chirality molecules which cannot be overlaid with their mirror images by rotation, as the left and right hand. The two different isomer forms are dubbed R- and S-isomers.click chemistry various reaction types used for bioconjugation. Relevant reactions are displayed in Figure double activated SAM analogues SAM analogues containing → β,γ-unsaturated side chains used in → mTAG RNA labelling (Figures  Figures  and 3A.epitranscriptomics research centered around RNA modifications.FBDD fragment-based drug design/discovery, drug-design method in which small molecular fragments are first screened and then rationally improved towards their respective targets.HTS high throughput screening, drug-design method in which thousands of molecules are screened towards their respective targets to identify novel enzyme inhibitors.in silico experiments conducted on a computer.in situ emerging of the molecule in question directly ‘on site’, i.e. in the reaction mixture.50IC half maximal inhibitory concentration, a widely used value for quantifying the potential of inhibitiors.Ki equilibrium constant describing binding affinity of the enzyme–inhibitor complex as calculated by the Cheng–Prussov equation, a widely used value for quantifying the potential of inhibitiors.mTAG methyltransferase-directed transfer of activated groups, labelling technique for biomolecules exploiting promiscuous MTases and → double activated SAM analogues (Figure s Figure .N-mustard derivatives compounds used for → SMILing which generate the corresponding aziridine derivatives → in situ as displayed in Figure orthogonal click reactions → click reactions, that can independently and selectively address different functional groups and thus allow multiple labelling.π-orbital oribtals responsible for double- and triple-bonds located above the binding plane (Figure e Figure .racemate/racemic mixture an even mixture of corresponding R- and S-isomers of the same, → chiral molecule (Figure e Figure .SAR structure–activity relationship, correlation between the molecular structure of a compound and its biological activity.SMILing sequence-specific methyltransferase-induced labelling, labelling technique for biomolecules exploiting promiscuous MTases and aziridine derivatives of SAM (Figure M Figure .2 hybridisationsp electron distribution as found in double bonds leading to their planar shape. In carbon atoms three sp2-hybrid orbitals exist together with one p-orbital.All relevant data are available in the manuscript itself.


This study aimed to investigate the usefulness of computed tomography (CT) texture analysis in the diagnosis of lateral pelvic lymph node (LPLN) metastasis of rectal cancer.This was a retrospective cohort study of 45 patients with rectal cancer who underwent surgery with LPLN dissection at Tokushima University Hospital from January 2017 to December 2021. The texture analysis of the LPLNs was performed on preoperative CT images, and 18 parameters were calculated. The correlation between each parameter and pathological LPLN metastasis was evaluated. The texture parameters were compared between pathologically metastasis-positive LPLNs and metastasis-negative LPLNs.p value 0.018) and GLCM Entropy_log2 (a texture parameter) . The combination of both parameters improved the diagnostic specificity while maintaining the sensitivity compared with each parameter alone.A total of 40 LPLNs were extracted from 25 patients by preoperative CT scans. No LPLNs could be identified in the remaining 19 patients. Eight of the 25 patients had pathologically positive LPLN metastasis. Extracted LPLNs were analyzed by the texture analysis. Pathologically metastasis-positive LPLNs had significantly lower mean Hounsfield unit, gray-level co-occurrence matrix (GLCM) energy, and GLCM Entropy_log2 values, and a significantly larger volume than pathologically metastasis-negative LPLNs. Multivariate analysis revealed that the independent predictive factors for LPLN metastasis were volume  (odds ratio 7.81, 95% confidence interval 1.42–43.1, Combining the CT texture analysis with conventional diagnostic imaging may increase the accuracy of the diagnosis of LPLN metastasis of rectal cancer. Colorectal cancer is the third most common cancer in men and the second most common cancer in women worldwide, with high morbidity and mortality rates . FurtherCurrently, the main method used to predict the LPLN metastasis is size-based diagnosis using the largest short-axis diameter of the lymph node on computed tomography (CT) or magnetic resonance imaging (MRI) . HoweverThe texture analysis is an image-processing algorithm that quantifies tissue heterogeneity by evaluating the distribution of the texture coarseness, density, and irregularity within a lesion and is expected to provide a more detailed and quantitative characterization of lesions than visual analysis. Several studies have reported the usefulness of the texture analysis in predicting lymph node metastasis of various cancers . However2/90 min, leucovorin: 200 mg/m2 for 120 min, oxaliplatin: 85 mg/m2 for 120 min, 5-fluorouracil: 2400 mg/m2 for 44 h) regimen with bevacizumab (5 mg/kg for 90 min) or panitumumab (60 mg/kg for 60 min). All patients who underwent preoperative chemoradiotherapy (CRT) received S-1 orally  and infusions of oxaliplatin  and bevacizumab (5 mg/kg on days 1 and 15). The total radiation dose was 40 Gy delivered in daily fractions of 2 Gy via the four-field technique. Surgery was performed 6–9 weeks after completing preoperative CRT.This was a retrospective cohort study of 45 patients with rectal cancer who underwent surgery at Tokushima University Hospital from January 2017 to December 2021. The inclusion criteria were lower rectal cancer of clinical stage ≥ T2 or LPLN metastasis in patients with recurrent rectal cancer who underwent LPLN dissection. The exclusion criterion was the absence or unavailability of contrast-enhanced CT images. All patients who underwent preoperative chemotherapy were treated with FOLFOXIRI  was used. The scanning parameters were tube voltage, 120 kV; tube current, auto exposure control; tube rotation speed, 0.5 per second; slice thickness, 0.5 mm; reconstruction kernel, FC14; image reconstruction interval, 0.5 mm; helical pitch, 129; and interpolation method, 180°. Dynamic contrast-enhanced CT was performed using a bolus-tracking technique, where a region of interest (ROI) was placed on the abdominal aorta and the trigger threshold inside the ROI was set at 120 Hounsfield units (HU). The first-phase scan was started 8 s after the threshold was achieved following the administration of contrast material. The second-phase image was obtained 15 s after the first arterial phase. CT was performed within 1 month prior to surgery.http://www.lifexsoft.org/). All clinical results were blinded during the measurement. The ROI was manually traced on the axial slice of the first-phase CT image that yielded the maximum LPLN area, as already reported  parameters. The minimum (HU min), mean attenuation (HU mean), standard deviation (HU std), and maximum values of HU (HU max) were evaluated as conventional parameters. The skewness, kurtosis, entropy_log10, entropy_log2, and energy values were evaluated as histogram-based parameters. The volume and sphericity values were evaluated as shape-based parameters. The homogeneity, energy, contrast, correlation, entropy_log10, entropy_log2, and dissimilarity values were evaluated as GLCM parameters.All patients underwent robot-assisted surgery with LPLN dissection, and all resected tumors and lymph nodes were evaluated by pathologists in accordance with the Seventh Edition of the American Joint Committee on Cancer TNM grading system.U test. Multivariate analysis was carried out based on the logistic regression model using a forward stepwise selection. The P values of < 0.05 were considered statistically significant.All statistical analyses were performed with EZR (version 1.54) , a graphical user interface for R (version 4.03)  . EZR is A total of 45 patients were evaluated. A total of 40 LPLNs were extracted from 25 patients by preoperative CT scans. No LPLNs could be identified in the remaining 19 patients. Eight of the 25 patients had pathologically positive LPLN metastasis. The comparison of patient characteristics between the patients with pathologically positive LPLN metastasis and the patients with pathologically negative LPLN metastasis is summarized in Table Extracted LPLNs were analyzed by texture analysis. Fifteen of the 40 LPLNs showed pathologic metastasis. None of the patients whose LPLNs could not be identified by CT scans had pathologic lymph node metastasis. The texture parameters were compared between pathologically metastasis-positive LPLNs and metastasis-negative LPLNs Table . PatholoThe cutoff values for the prediction of LPLN metastasis were 2.546 for the mean HU, 85.0 for the volume, 5.164 for the GLCM Energy, and 1.73 for the GLCM Entropy_log2; the areas under the receiver operating characteristic curves for each parameter were 0.739, 0.773, 0.773, and 0.792, respectively  and GLCM Entropy_log2 (a texture parameter) as independent predictive factors for LPLN metastasis. The cutoff value of the volume was 85.0 ml, with a sensitivity and specificity of 80.0% and 76.0%, respectively. Some studies have reported the size-based diagnosis of LPLN metastasis on CT or MRI. Sato et al. reported that the maximum short-axis LPLN diameter was significantly larger in metastasis-positive cases than in metastasis-negative cases ; the cutThe present study showed that the GLCM Entropy_log2 value may be useful in the diagnosis of LPLN metastasis using CT texture analysis. The cutoff value of the GLCM Entropy_log2 was 1.730, and the sensitivity and specificity were 93.3% and 60.0%, respectively. Texture analysis has recently been widely applied in diagnostic imaging. Liu et al. evaluated a total of 1054 lymph nodes in patients with colorectal mucinous adenocarcinoma and demonstrated that some CT texture parameters were of significance in differentiating lymph node metastasis . Song anThe combination of the volume and the GLCM Entropy_log2 value showed better predictive ability for LPLNM in this study. Following the results of this study, we consider that positive for both factors is an absolute indication o LPLND, that positive for either one of the indicators is a relative indication of LPLND, and that a negative for both factors indicates the possibility of omitting LPLND.The present study had some limitations. First, the sample size was small. Smaller sample sizes may affect the reliability of results due to increased variability and possible bias. Second, this study included patients who underwent preoperative chemotherapy or chemoradiotherapy. Zhang et al. have reported that histogram and GLCM features were associated with the response to chemotherapy for liver metastasis of colorectal cancer . Liu et In the present study, the combination of the volume and the GLCM Entropy_log2 value showed better predictive ability for LPLNM in this study. The specificity was improved by the positivity of both factors, and the sensitivity was improved by the negativity of both factors. Combining texture analysis with conventional diagnostic imaging may lead to a more accurate prediction of LPLN metastasis of rectal cancer.


Purpose: In this study, we analyzed the differences between hypertension patients with ocular metastasis of liver cancer and those with metastases to other sites, the correlation between history of HBV and liver cancer metastasis, and independent risk factors for ocular metastasis.Methods: We used treatment records from 488 patients with metastases of primary liver cancer from August 2001 to May 2015, divided into two groups based on metastatic sites: OM (ocular metastasis) and NOM  groups. The Student’s t-test and Chi-square test were used to assess the significance of differences between the groups and define the relationship between history of HBV and ocular metastasis of liver cancer. Binary logistic regression analysis was used to identify indicators of ocular metastasis of liver cancer and receiver operating curve (ROC) analyses to estimate their diagnostic value.Results: No significant differences in sex, age, tumor stage, pathological type, or treatment were identified between the OM and NOM groups, while the prevalence of HBV was higher in the former than that in latter. Binary logistic regression demonstrated that AFP and CA-125 were independent indicators of liver metastasis (both p < 0.001). ROC curve analyses generated cut-off values for AFP and CA-125 of 957.2 ng/ml and 114.25 U/ml, respectively, with corresponding AUC values of 0.739 and 0.810. The specificity of the combination of AFP and CA-125 was higher than either factor separately.Discussion: To explore the diagnostic value of AFP and CA125 in predicting the development of ocular metastases of hypertensive patients with liver cancer, which will help us to diagnose the occurrence and development of the disease more accurately and make the best clinical diagnosis and treatment measures. Hypertension is a chronic disease with functional or organic damage of heart, brain, kidney, and other organs, which brings a huge health burden to the society. Primary liver cancer (PLC) is a common disease worldwide, particularly in developing countries . Hepatocn = 488), admitted from August 2001 to May 2015, were enrolled in the study. The inclusion criteria are: 1) hypertension (systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg); 2) without contraindications in MRI, CT and other imaging examinations; 3) Canceration of liver tissue; 4) ocular metastasis of hepatocellular carcinoma , blood lipid indicators {lipoprotein a [Lp(a)], apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol (TC)}, serum calcium concentration, hemoglobin concentration, ferritin, alkaline phosphatase (ALP), and other serum indicators. All clinical data were collected at initial diagnosis.t-tests and Chi-square tests were used to evaluate the significance of differences in clinical characteristics between the OM and NOM groups. Binary logistic regression analysis was used to identify indicators of ocular metastasis of liver cancer. ROC curve analysis was conducted and the area under the curve (AUC) used to assess the accuracy of risk factors as diagnostic indicators. p < 0.05 was considered statistically significant. SPSS 25.0 , MedCalc 19.0.5 , and Excel 2019  software were used for statistical analyses.Student’s t-test and Chi-square test). The OM group was significantly older (57.7 ± 12.2 years) than the NOM group (51.5 ± 13.8 years) (p < 0.05). There was also a significant difference in history of HBV between the two groups (p < 0.05), confirming that ocular metastasis of PLC is associated with history of HBV. The clinical details of all subjects are presented in In this study we reviewed data from 488 patients, including 21 in the OM and 467 in the NOM groups. Clinical characteristics, including sex, clinical stage, pathological type, and treatment methods did not differ significantly between the OM and NOM groups (Student’s p < 0.001) , calcium, Hb, ferritin, ALP, and other serological indicators did not differ significantly between the OM and NOM groups, while AFP and CA-125 were significantly higher in the OM group (< 0.001) . Binary < 0.001) .Liver carcinoma ranks second among the causes of cancer mortality globally . PLC patThe incidence and mortality of HCC are increasing. Cirrhosis caused by HBV and hepatitis C is a significant risk factor for HCC globally ; however. It is clear that HBV contributes to liver cirrhosis and even liver cancer, and the former can cause ocular complications; therefore, there is evidence for an indirect relationship between history of HBV and ocular lesions. The mechanism underlying this relationship is currently unclear; however, experiments in mice confirmed that SMAD3 mediates the majority of profibrotic activity, since liver cirrhosis, proliferative vitreoretinopathy, and ocular capsule injury are alleviated in SMAD3-null mice , likely because elderly patients have a higher prevalence of diabetes, poorer physical fitness, and cannot tolerate higher-intensity treatment , including distant ocular metastases, have clear primary sites . The mosreatment . In addiLp(a), ApoB, ApoA1, LDL, HDL, TG, TC, hemoglobin, ferritin, and ALP are commonly used as indicators for evaluating blood lipids and liver function. Statistical analysis did not identify any significant differences between the above indicators in the OM and NOM groups. The serum biomarkers, CA-153, CA-199, CA-125, CEA, AFP, and CA-724 are tumor markers and have clear predictive significance for a variety of tumors . In patiNon-invasive standard imaging methods, such as MRI, dynamic multi-phase multi-row computed tomography, and ultrasound, are used to diagnose HCC . Serum bFinally, we determined the value of AFP, CA-125, and AFP + CA-125 as diagnostic indicators for ocular metastasis of liver cancer by binary logistic regression analysis and plotting ROC curves. The results indicate that AFP has the highest sensitivity and can be used for early screening of ocular metastases from liver cancer. If AFP rises above 957.2 U/ml, the patient has a higher probability of terminal ocular metastasis. AFP + CA-125 has the highest specificity, and is of great significance for the diagnosis of ocular metastasis from liver cancer.Our research has certain limitations. First, the sample size of this retrospective study was small, particularly for the OM group. Second, because all patients in the OM group had other sites of metastases simultaneously, confounding factors were inevitable. Furthermore, there were missing items in various clinical statistical analyses, which will have reduced the accuracy of the results. Finally, all subjects were diagnosed and treated in the same hospital, and it is difficult to exclude selection bias; therefore, the accuracy of the conclusions from the results of this study require confirmation in investigations with large samples and multiple centers.In summary, we found that patients who had PLC metastasis with a history of HBV were more likely to have ocular metastases than those without. Increases in the serum biomarkers, AFP and CA-125, are associated with an increased likelihood of ocular metastasis. AFP, CA-125, and AFP combined with CA-125 can be used as diagnostic indicators for ocular metastasis in patients with PLC with metastasis.


Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an   Since many of these RNAs can be measured in peripheral blood, circulating transcriptome markers are a promising source to molecularly phenotype patients and potentially improve diagnosis, prognostic power, and treatment.2 Although a large number of discovery studies have identified candidate RNA biomarkers, only a minority of these markers have progressed towards implementation into clinical practice. The slow development of RNA biomarkers is mainly caused by the complex role of RNAs in disease, discrepant results, difficult and diverse measurement techniques with limited standardization, and lack of samples for large-scale validation.The treatment and outcome of patients with cardiovascular disease (CVD) substantially improved in the past decades. Nevertheless, there is considerable room to improve the diagnosis of CVD, predict disease progression, and tailor therapies accordingly. Recent calls have emphasized this unmet need to improve precision-based approaches in CVD.Figure 1). We systematically list the important steps and pitfalls in the development of RNA biomarkers for CVD. We discuss the unmet clinical needs and address the issues of the different sample types and measurement techniques in the sample matrix. We list potential effect of demographical factors and patient comorbidities, which should be mentioned in each biomarker study. We finally discuss statistical and regulatory issues for translation of the candidate RNA into a clinically validated assay.The EU-CardioRNA Action CA17129 is an international consortium supporting collaboration and research on RNAs in CVD, to improve our knowledge of the pathophysiology of RNAs, and to translate this into clinical practice. In this position paper, we describe the translational RNA biomarker development track , contractility, and anti-hypertrophic transcription factors (FOXO3). In a preclinical porcine model, monthly intravenous administration of CD132L, an antisense miR-132 inhibitor, improved cardiac function and reversed cardiac remodelling.4 In a phase Ib clinical study, CD132L resulted in a dose-dependent miR-132 suppression in patients with heart failure and was well tolerated.5Coding and non-coding RNAs have important biological functions, which makes them promising biomarkers. Heart- or muscle-enriched microRNAs (miRs) are involved in cardiac development, proliferation, hypertrophy, and failure. For instance, miR-1 and miR-133a are downregulated in cardiac hypertrophy.Figure 2). Because of their important biological function, RNAs may help to identify patients benefitting most from new therapies.Although currently available biomarkers had a significant positive impact on the clinical care of patients with CVD, e.g. high-sensitive assays for cardiac troponins and assays for natriuretic peptides, the prognosis is still poor in many areas of CVD. We next discuss a number of unmet clinical needs where RNAs can potentially improve diagnosis and outcome  dysfunction post-MI, predict recurrent ischaemic events, and eventually guide new cardioprotective therapies. In patients with ST-elevation MI, adding miR-26b-5p, miR-660-5p, and miR-320a improved the prediction of recurrent ischaemic events.9 Thrombo-miRs may also provide new opportunities to tailor antithrombotic therapy in secondary prevention, since tailoring based on platelet function testing did not improve clinical outcome.10–12 RNAs may also allow us to better understand the pathophysiology of myocardial infarction with no obstructive coronary artery disease (MINOCA) or type 2 MI, for which no evidence-based therapies are currently available.13 Finally, many RNAs related to inflammation and metabolic changes have been identified as promising markers of atherosclerosis and coronary artery disease (CAD).14In ischaemic heart disease, muscle- or heart-enriched miRs have been shown to be useful as biomarker to diagnose acute myocardial infarction (MI).QSOX1, LIPCAR, and MICRA have been identified and relate to post-MI LV dysfunction.15–17 In contrast to muscle- or heart-enriched miRs, not all differentially expressed RNAs are necessarily released from the heart or mirror changes at the cardiac level.18 Even in patients with non-ischaemic heart failure, improved molecular phenotyping is required to identify patients who might benefit from upfront intensified neurohormonal blockade or cardiac resynchronization therapy.In heart failure, candidate RNAs such as HOTAIR and TUG1) are dynamically regulated in aortic valve interstitial cells and are associated with inflammation and calcification in patients with aortic stenosis (AS).20In valvular heart disease, the decision to perform a surgical or transcatheter valve intervention is currently made in a multidisciplinary heart team, based on symptoms, echocardiographic parameters, and natriuretic peptides. To optimize the decisions for early intervention, there is a need to improve the identification of patients at high-risk for disease progression, e.g. in aortic stenosis or mitral regurgitation. Both miRs and long non-coding RNAs , besides echocardiographic signs, no biomarkers are available to predict progression of paroxysmal to permanent AF, although treatment options are different. Since the different stages of the disease are accompanied by structural and transcriptomic changes in the left atrium, circulating RNAs (e.g. miR-21-5p and miR-150-5p) may guide us for early detection of progressive disease.22 Very recently, hsa-miR-Chr8:96 has been shown a promising marker for the diagnosis of myocarditis, discriminating patients with myocarditis from healthy subjects and patients with MI.23 The marker was validated in a preclinical model and in three moderately sized independent cohorts. However, as for almost all of the aforementioned examples, it remains unknown to what extent these markers may improve treatment decisions and improve outcome.Since circulating RNAs act as intercellular communicators, they have the potential to improve the diagnosis of clinical entities involving interactions between the heart and other organs, e.g. cardiorenal syndrome. Similar gaps in the understanding of the pathophysiology of Takotsubo cardiomyopathy, cardiogenic shock, cardiac arrest, and myocarditis lead to poor outcomes, even in the current era.Table 1). Although RNAs from cellular subfractions or extracellular vesicles (EVs) yield more specific RNA expression patterns, they are more time consuming to process and to store compared to plasma or whole blood. Many factors in sample collection and processing can result in increased preanalytical variation with subsequent deleterious impact on the quantification of the biological variability. These preanalytical variables should be reported in each biomarker study .Sample collection is a crucial step when using RNA-based biomarkers. Different sample types are available to collect cell-free or cellular RNA .12 The use of citrate–theophylline–adenosine–dipyridamole as anticoagulant has been suggested to decrease contamination related to platelet activation.25 Platelet-poor plasma can also be used to decrease contamination by platelets and their RNA content. Heparin as an anticoagulant should be avoided since it has a dose-dependent inhibitory effect on enzyme-based RNA quantification.26 The same recommendation should be taken into account for samples from patients treated with heparin as anticoagulant.27 The use of heparinase to remove heparin from the samples may be considered.7 Finally, since blood cells are a significant source of RNAs, perturbations in blood cell counts and haemolysis may alter plasma miR levels by up to 50-fold.18 Different methods have been developed to screen serum and plasma samples for haemolysis, e.g. including miR-16, miR-451, and miR-486, as quality control downstream the RNA quantification process.28 To reduce haemolysis caused by blood sampling, a needle of adequate gauge should be used.For the collection of cell-free RNA, the use of serum tubes or tubes with anticoagulants  differentially affects the concentration of miRs. The repertoire and concentrations of RNAs found in serum samples differs to those of corresponding plasma samples due to RNAs released during the coagulation process.30 Köberle et al.31 found that levels of miR-1, miR-16, and miR-21 were increased in EDTA and serum collection tubes incubated for 1–3 h at room temperature prior to separation of plasma or serum from cell components, even in the absence of significant haemolysis. Differences in temperature and coagulation times may increase the variability between samples. As such, delayed processing of samples and inconsistency in storage temperature may introduce significant technical variation. A rapid specimen processing, i.e. within 1–4 h as recommended by some standard operating procedures, and consistency in storage temperature is recommended.32 Nonetheless, this is not always possible, e.g. in multi-centre setting. A meticulous storage documentation and detailed quality control experiments are therefore imperative. The use of RNA stabilization reagents may also be considered.33 Finally, the contamination of blood with epithelial cells during puncture can be avoided by discarding the first 1–2 mL of blood drawn.34The storage conditions of the blood samples as used in clinical routine may affect the extracellular RNA profile before the isolation of cell-free fractions. Although some families of RNAs, such as miRs and circular RNAs, have been reported as highly stable in clinical specimens due to their remarkable resistance to nucleases, both miR degradation and release of RNA from cellular components influence the cell-free RNA profile.35 RNA profiles obtained from isolated cells, e.g. CD14+ monocytes or specific lymphocytes subsets, using fluorescence-activated cell sorting (FACS) or magnetic bead isolation, may be more specific but they are time consuming to collect and process.36Besides cell-free RNA fractions, whole blood is also an excellent source of RNA-based biomarkers. The transcriptomic signatures of blood cells could provide valuable information on disease status and progression. The development of commercially available blood collection systems allowing RNA stabilization and facilitating the storage of the samples makes whole blood very convenient for clinical practice. Subtle or cell-specific changes in RNA expression may not be detected since the bulk RNA profile is measured of the entire blood compartment. Depletion of abundant red blood cell-derived RNAs  may be required to improve the sensitivity of micro-array or RNA sequencing studies.37 EVs therefore have a good potential to translate into minimally invasive biomarkers (liquid biopsy).In addition to the aforementioned sample types, EVs are a promising source of circulating biomarkers with specific characteristics. EVs exist in different sizes and are released by the parent cells into the circulation, shuttling molecular information, including miRNAs, from the parent cells to the extracellular space.Larger EVs, such as microparticles, possess a panel of surface markers, which allow tracking of their cellular origin, particularly from platelets, endothelial cells, leucocytes, and erythrocytes. Despite some initial attempts, protocols enabling tracking of the cellular origin of the smaller EVs populating the biofluids of patients are still lacking. This could improve with recently introduced platforms, such as the ExoView R100  and the Flow Nanoanalyzer . The former is an automated platform, providing multi-level measurements for exosomes and other small EV particle size and concentration analyses, and membrane antigens colocalization. The latter reportedly acts as a high sensitivity flow cytometer combining light scattering and fluorescence detection with distributions of particle size. These techniques could potentially support the use of EVs as circulating biomarkers without the need to purify them. While researchers have mostly focused on the internal cargo of EVs, potential biomarkers are indeed also present at the level of the EV external surface.N-glycans.37 The molecular signature of individual EVs is still debated, with some papers suggesting not more than one miRNA present per every 1–100 EVs.38 Definitive conclusions are understandably difficult, given the current infeasibility of profiling the molecular content at single EV level, and when considering that different protocols for EV isolation and RNA or protein extraction and analysis produce different profiles from the same biofluids.40 These discrepancies will be probably resolved as technologies advances.To study the molecular content of EVs, the best approach is still to enrich these vesicles from the biofluid before performing molecular analyses. The comprehensive analysis of several studies on pools of EVs extracted from a biofluid suggests EVs could contain proteins, microRNAs, other RNAs, and DNAs as well as metabolites, such as sugars, amino acids, lipids, nucleotides, and 41 These include storage temperatures of the source (e.g. plasma), the volume used for EV extraction, the extraction method with the trade-off between recovery, and specificity and quantification by at least two methods. Recommendations on EV isolation and RNA extraction are summarized in Table 1. The most used isolation methods remain density gradient centrifugation, ultracentrifugation, and their combination with size exclusion chromatography. However, these techniques are time consuming, require specialized equipment, and are difficult to automate, which poses a limitation for the use in clinical practice. The effect of using more simplified, cost-effective, and commercially available methods (often resulting in higher recovery but lower EV specificity) needs to be assessed when developing an EV-based biomarker. Contamination caused by the method of EV extraction also needs to be considered, especially for preparation of serum or plasma EVs, which usually carry lipoproteins.Recommendations on minimal information that needs to be reported when performing studies with EVs have been published by International Society for Extracellular Vesicles.MTCOI) related to poor outcome in patients with stable coronary disease.42 In contrast to the oncological field, clinical studies with EVs in CVD are still in the discovery or recruitment phase .Despite these technical limitations, work on pooled circulating EVs clearly show disease-associated molecular changes and suggest the potential of EVs as diagnostic and prognostic biomarkers. For instance, the number of monocyte-derived microparticles related with outcome in patients with ST-segment MI, while the specific cargo in monocyte-derived EVs (e.g. decreased Table 1). RNAs are generally extracted using either a traditional phenol-based nucleotide extraction method (e.g. TRIzol) or silica column-based extraction kits. In this latter case, the RNA species being tested  guides the choice of the kit with the appropriate size cut-off. Among plasma extracellular miRNA isolation kits, the miRNeasy kit from Qiagen  performs well in terms of purity and recovery, but results in a high amount of unmapped reads in RNA-sequencing.43 The low RNA yields derived from plasma, serum, and EVs are a significant limiting factor. Adding carriers, such as glycogen, may also improve yield in RNA-precipitation-based protocols. Due to the membranous nature of the exosomes, lysis should be performed before RNA isolation from EVs following the manufacturer’s protocol. A pure RNA extract for downstream analysis is still preferred above preamplification, since the latter step may increase variability and bias. It is important to be consistent when performing RNA extraction and measurement: only when using standardized protocols, the results can be interpreted and compared correctly.44 For whole blood samples, the inference of haemoglobin should be considered, which, along with heparin, is one of the major sources of inhibitors for downstream RNA amplification. RNA quantification and quality checks are generally performed using spectrophotometric (e.g. 260 and 260/280 nm absorbances) and electrophoresis  techniques. However, this step is generally not possible for RNAs derived from plasma or serum, due to the low amounts recovered. A commonly used strategy consists in extracting the same volume of plasma or serum, and monitor the efficiency of the extraction and measurement steps [e.g. reverse transcription and polymerase chain reaction (PCR)] using synthetic spike-ins .45 The detection of synthetic spike-ins seems to be particularly sensitive to heparin inhibition.After having assessed sample collection and the appropriate sample type, RNA extraction poses challenges that vary according to the sample type, analytical method, and future automation requirements ] is strongly advised.RNA measurement techniques can be divided between high-throughput and targeted approaches. Among the first, RNA-sequencing and microarrays allow to investigate the whole transcriptome or a large portion of the sample, respectively, providing a broad analysis that is particularly useful during the biomarker discovery phase.Among the targeted measurement techniques, qPCR represents the gold standard. This amplification-based assay is an accurate method to measure RNA and sufficiently sensitive to be reliably used in plasma and serum samples. It is also cost-effective and not particularly challenging from a technical and data-analysis point of view. For the detection and accurate quantification of low-abundance RNAs , digital PCR (dPCR) is particularly indicated, as it provides direct, absolute, and precise measures of target sequences, although in a less cost-efficient manner compared to qPCR.51 While exogenous spike-ins are very useful in monitoring the efficiency of extraction and measurement, they do not account for the quality of the original sample. Endogenous normalizers are, theoretically, the best choice, and should have low deviation of expression levels across samples, be minimally affected by storage conditions and sample processing, and display a maximal efficiency of extraction.45 A commonly used normalizer is miR-16. However, this miRNA is present in significant levels in red blood cells and its levels in the plasma are proportional to the degree of haemolysis. Thus, its value as normalizer is limited to sample sets in which haemolysis is tightly controlled. Small non-coding RNAs RNU6A and RNU6B are among the reference genes most frequently used as normalizers. Although they have many positive characteristics, they are not miRs and their normalization value can always be questioned.45 When an RNA expression panel is available from a profiling study, the total RNA content of the sample can be used for global normalization or the panel can be used to identify the most suitable endogenous normalizer for the specific experimental setting. In order to compensate the potential shortcomings of each individual normalization method, a combination of endogenous and exogenous controls is recommended.The next crucial issue is the choice of the correct reference RNAs for expression level normalization. This is particularly challenging for plasma/serum miR studies, where exogenous spike-in RNAs are still frequently used because of the lack of widely accepted endogenous normalizers.Each of the above-mentioned RNA detection techniques currently requires considerable time and advanced expertise for sample preparation and analysis, making them adequate platforms for centralized laboratories, but not suitable for point-of-care diagnostics. Alternative technologies hold great promise, such as isothermal amplification , nanobead-based, oligonucleotide-templated reaction-based, electrochemical signalling-based, lateral flow assay-based, and microfluidic chip-based strategies.Figure 3). We will here discuss those variables with sufficient evidence to at least partially affect RNA expression in cellular or cell-free fractions (Table 2).Cardiovascular risk factors and comorbidities (e.g. diabetes and obesity) act, at least in part, through alterations of metabolic and inflammatory profiles. A growing body of literature also suggests that these factors, together with demographic factors, influence levels of peripheral blood RNAs, highlighting the importance of documenting, reporting, and if necessary adjusting for these confounding variables in biomarker studies  might affect miR expression. Several rhythmically expressed miRs have been documented in various tissues including heart and blood plasma in animal studies.95 Robust diurnal oscillations have also been found for tissue Dicer expression (endonuclease controlling miR processing) in mice.96 Only a limited number of studies focused on the diurnal variations in humans. While in few studies, no circadian variations in levels of circulating RNAs were found, Heegaard et al. found clear rhythmicity in circulating levels of 26 miRs showing out two main diurnal phase patterns.97External factors such as seasonal changes have been shown to influence both miR and mRNA profiles mostly through widespread seasonal changes of immune cells and their gene expression.98 While Rekker et al. and Max et al. found no changes in circulating miRs levels within the menstrual cycle in healthy women, Eisenberg et al. found elevated serum levels of selected miRs in early follicular phase as compared to early luteal phase of the cycle.99 More generally, sex is considered to significantly affect expression levels.62Hormonally-controlled cycles, primarily menstrual cycle in women, represent additional physiological rhythmicity possibly influencing miRs expression. Bovine plasma levels of selected miRs distinctly increased during oestrus as compared to other stages of the cycle, while data on miR expression during the hormonal cycle in humans is inconclusive so far.100 Whole blood gene expression data identified specific mRNA transcripts that were upregulated amongst obese individuals, most of which belong to pathways associated with the metabolic state and inflammatory responses.101 Many studies have identified changes in circulating RNA expression in patients with diabetes mellitus, with significant overlap with obesity, metabolic syndrome, and CAD.102 Whole blood mRNA expression signatures were also associated with hypertension.68 The effect of LDL cholesterol levels on the expression profile were linked to oxidized low-density lipoprotein (ox-LDL), with ox-LDL-loaded cells showing transcriptional changes in expression of pro-inflammatory genes.103 The evidence for other cardiovascular and non-cardiovascular comorbidities and drug therapies is further summarized in Table 2.Cardio-metabolic risk factors, which include body mass index, hyperlipidaemia, diabetic status, and hypertension, have been shown to influence both mRNA and miR levels in research subjects from the Framingham Heart Study selected to exclude those on medication for hypertension, dyslipidaemia, or diabetes.Figure 4). For each of the steps, there is a plethora of available statistical methods and there is ongoing discussion, which performs best in which situation.State-of-the-art detection and validation of biomarkers and related scoring systems relies on thorough data analyses comprising multiple steps. Although there is no common, generally accepted standard of this process, most biomarker research papers follow a general approach  available in a small number of individuals . Therefore, in the first step of biomarker discovery, dimensionality of the problem is often reduced by selecting or prioritizing markers for the subsequent analysis steps. This step is not mandatory and could be combined with the model identification step (see below). Methods to deal with this problem are from the class of (univariate) variable importance analyses and comprise for example methods of univariate hypotheses testing, explained variance analysis, regression techniques, or methods of discrimination analysis.106In the next step, markers selected at Step 1 are combined to a prediction model, i.e. a formula or decision tree combining the information of several features to one predictive framework. Established biomarkers or clinical covariates can also be included. This step typically includes model selection and parameter estimation for which several methods and concepts are available comprising for example multivariable regression and classification approaches, which could be linear , non-linear , or based on decision trees or graphical models. Advantages and disadvantages of these methods are compared elsewhere.107 The goal is to find a trade-off between the accuracy of the prediction in the training set  and a new data set (out-of-sample accuracy).Overfitting is a serious concern during establishing a classifier, i.e. the classifier typically performs better in the data set where it was developed (training set) than in a new data set . Several measures are applied to reduce this issue as much as possible comprising for example penalization approaches  or prior limitation of the degrees of freedom of a model.108 This means that the splitting of data is repeated in a systematic manner and a consensus model is constructed. Combining cross-validation with resampling techniques for improved out-of-sample prediction is proposed.110Estimation of the out-of-sample accuracy requires an independent data set comparable to that of the training set. If such a data set is not available, the set of samples can be split into a training set and a validation set, i.e. a subset of samples is not used for the analyses in Step 2 but reserved for assessing the prediction accuracy of the model in Step 3. To avoid dependence of model identification and validation on a specific split of the data, methods of cross-validation are often applied.Assessing the potential clinical impact of a new classifier is an important step towards bedside translation. Several aspects are of interest. First, the discriminative power of the test needs to be evaluated, e.g. by receiver operating characteristics (ROC) allowing assessing sensitivity and specificity of the test for a given cut-off. Often, defining a cut-off is not straightforward due to uncertainty with respect to the costs of false negatives and positives. Therefore, the area under the curve (AUC) is typically considered as a measure of discriminative strength. A complementary characteristic are precision-recall curves displaying the relation between the positive predictive value (precision) and the sensitivity . Compared to ROC, this curve is not affected by imbalanced case/control samplings, e.g. enrichment of controls with low classifier values.111 It is recommended to calculate this statistic separately for cases and controls.112 The Integrated Discrimination Index (IDI) assesses the gain in sensitivity and specificity over all possible cut-offs compared to another test.113 It was pointed out recently that NRI and IDI should not be used for significance testing.114 Finally, the effectiveness of a new classifier in a given population can be evaluated by Numbers Needed to Screen, which is the average number of tests required to prevent one event.115 However, this statistic also depends on the effectiveness of treatment measures applied to test-positives.In the next step, new classifiers are compared with current standards . Although this can be done by comparing, e.g. AUC of ROC, there are clinically more intuitive indices available for this purpose. Net Reclassification Improvement (NRI) compares the correct re-classifications between two tests.Generalization is a major issue to improve clinical utility of a developed classifier. Often, the classifier was developed in a specific selection of cases and controls (i.e. with specific inclusion/exclusion criteria and highly standardized testing environments), which does not mirror the general population or conditions in general health care. Thus, the classifier might require re-calibration to new situations by re-weighting the components of the prediction model, e.g. by using appropriate regression techniques to determine new weights or by Bayesian approaches using the established weights as priors.in-vitro diagnostic (IVD) tests and taken up into clinical practice. Thus, we observe that only few promising projects are continued beyond the publication of interesting biomarker discovery and validation studies, or even the RoU status of development. Besides the need for reproducibility in broader validation cohorts, there are other important milestones that need to be achieved in order to translate these biomarker candidates into clinically useful IVD tests. We next describe these other crucial steps that need to be taken to go from biomarker validation to approval as an IVD test . Some of these steps may already have been partially addressed during the discovery or validation phase of the study.Once potential biomarkers have been evaluated in a discovery phase, steps have to be made towards the development of a clinically useful assay. Only few candidate markers from a discovery phase are translated to the status of research use only (RoU) tests. Even more rare is that RoU tests are validated as The first essential step, if not done already at the beginning of the discovery phase, is to define the ‘intended-use’ of the test. This seems a trivial action but is highly important as it will subsequently affect the regulatory pathway and efforts that need to be taken during analytical and clinical validation of the biomarker. The intended-use must include a description of the (patient) target population, the biological matrix, the type of assay , and target molecules, and potentially the clinical action that is triggered by the test. From our experience, the clinical uptake of a test strongly depends on the clear definition how the test result will support decision-making of physicians.116In the next step, the requirements for assay type and sample logistics can be delineated from the intended use. Typically, the assay platform for biomarker discovery and validation is selected based on the availability of instruments and know-how in the research lab, and commonly RNA biomarkers are measured using various types of laboratory assays such as qPCR, dPCR, RNA sequencing, or other targeted hybridization-based techniques. However, the turn-around-time that is required for the test result to be clinically useful must be considered as well. In case the turn-around-time can be 6–8 h or more, conventional ‘laboratory assays’ such as qPCR are suitable. In this case, the pre-analytical logistics such incubation times before sample processing, and storage temperatures and analyte stability must be determined alongside analytical validation of the assay.117 This will likely require additional assay development efforts and bridging studies to demonstrate comparability to the results obtained from the laboratory tests. Once the assay and sample type have been determined, manufacturing protocols according to for example ISO13485 standards must be implemented. The prototype assays need to be taken through analytical validation studies to determine key parameters such as reproducibility, accuracy, specificity, and sensitivity of the test. Analytical validation protocols should be defined taking into account the national or regional legal requirements for IVD approval.In case the turn-around-time should be shorter, rapid diagnostic test platforms or point-of-care platforms must be used for the IVD test.In-Vitro Diagnostics Regulation (IVDR), which was released in April 2017 and will fully replace the In-Vitro Diagnostics Directive after 26 May 2022. Importantly, the IVDR includes a risk-based classification of IVDs with four risk classes , which take into account the intended purpose of a test as well as its inherent risk. For example, tests for determining blood type to determine the suitability of blood specimens for transfusion are considered high risk, since a wrong test result will have severe impact on the health of the test person. A major change compared to the previous directive is the risk classification system, since the IVDR risk classification is now based on rules, which means that the intended-use of the test together with assay type define the risk class. For example, most ‘omics-based’ technologies for diagnosis, prognosis, staging, or guiding the treatment of a disease will from now on fall into the second highest class C, which means mandatory involvement of notified bodies  and conduction of clinical performance evaluation studies using the analytically validated test device. The extent of such studies depends on the intended-use (i.e. the type and size of the disease population) and the already available amount of data.In Europe, the legal basis for placing IVD tests on the market is the In the United States, the FDA is tasked with the approval of molecular diagnostic IVD tests and distinguishes three risk classes. Molecular diagnostic tests without predicate devices, i.e. already approved tests with the same intended-use and technology, will most likely require clinical studies. In order to determine the exact regulatory pathway [510(k) pre-market notification vs. pre-market approval] a pre-submission meeting with the FDA is highly recommended.118 First, inclusion into patient management and treatment guidelines by any professional society, and second reimbursement of the test cost by medical insurances. In order to achieve this, the impact of any new IVD test on health of the target population and the budget of the paying stakeholder needs to be evaluated. This can be done using the principals of cost-utility  studies, which model the impact of test uptake on health outcomes such as mortality, morbidity, life-years, and quality-adjusted life years.119 The change (improvement) in health outcomes is then contrasted with the expected impact on budget for the IVD test, additional (or avoided) treatment, and/or hospital interventions, etc. Cost-utility is then calculated as the incremental cost-efficiency ratio between the change in health (H) and change in budget (B) before and after uptake of the IVD test: Once the technical documentation of the analytical and clinical performance of an IVD test have been reviewed and approved by the regulatory agencies, IVD tests can be placed on the market. However, this does not necessarily result in immediate uptake of the test by its target audience. Uptake of a novel test can be catalysed by two main achievements.®  to rule-out acute cellular rejection after cardiac transplantation and Corus® CAD  to rule-out CAD in non-diabetic patients. Both tests were developed using an RNA micro-array in the discovery phase, followed by qPCR to confirm the identified genes (on the same or new samples) and to establish the classifier .120 Importantly, preanalytical bias caused by clinical variables affecting expression of the transcripts was timely identified and corrected for . The classifier was next validated in independent cohorts.78 The clinical benefit of AlloMap® was shown in the IMAGE study, with a reduction in the number of endomyocardial biopsies without an increase in adverse events.121 This clinical benefit, together with continued validation (CARGO II) and regulatory approval, led to active clinical use of this RNA-based test.122 For Corus® CAD, additional validation studies were performed, but the clinical benefit was less convincing and no regulatory approval was obtained.124 Together with the availability of other non-invasive tests to rule-out CAD (such as coronary CT angiography), this led to the discontinuation of the test.To our knowledge, only two clinically relevant RNA-based IVD tests have been made commercially available: AlloMapThere is a clear need for new molecular biomarkers for clinical use. However, only a well-orchestrated approach using standardized techniques and showing clinical benefit can lead to a successful IVD test. With COVID-19, we have seen an unprecedented interest in the use of RNA-based vaccines. These developments will certainly pave the way for future developments of other RNA-based applications.LIPCAR or QSOX1, while for others, e.g. muscle-enriched miRs, it is clearer. Therefore, to facilitate uptake by the clinical community and approval by the regulatory bodies, independent validation of associations between RNA expression values and disease outcome in properly sized populations is of paramount importance. A database gathering existing cohorts of patients with CVD available for sharing will help designing independent validation strategies.125 Since some RNAs have a significant variability in expression levels, prediction models based on artificial intelligence and combining both RNA data and clinical data of patients may constitute future tools for personalized medicine.126The potential of RNAs to aid in personalizing health care has been suggested by many studies, yet RNA-based biomarker applications are still at a preclinical research level. Challenges linked to the measurement of RNAs in biological fluids are currently being addressed and great efforts are being made regarding their reproducibility, accuracy, time- and cost-effectiveness, and acceptance by the clinical community. Because of the complex RNA biology, the exact mechanism which results in a dysregulated peripheral blood level of a candidate RNA biomarker is often uncertain, e.g. as for With these recommendations, we aim to promote standardization in the development process and catalyse efforts to investigate novel RNAs that may improve the treatment of CVDs in a precision-medicine approach. Overall, as shown for COVID-19 vaccines, there is great hope that RNA constitutes the next family of RNA biomarkers for clinical practice.Significant contribution to the manuscript content: M.V., R.A., M.B., S.B.-A., T.B., D.d.G.-C., C.E., R.F., J.G., M.H., B.K., F.M., M.S., S.B.W., Y.D. Manuscript drafting: M.V., Y.D. Manuscript review and revision: M.V., R.A., M.B., S.B.-A., T.B., D.d.G.-C., C.E., R.F., J.G., M.H., B.K., F.M., M.S., S.B.W., Y.D.


Maintenance of germline genomic integrity is critical for the survival of animal species. Consequently, many cellular and molecular processes have evolved to ensure genetic stability during the production of gametes. Here, we describe the discovery, characterization, and emerging molecular mechanisms of the protein Asterix/Gametocyte‐specific factor 1 (GTSF1), an essential gametogenesis factor that is conserved from insects to humans. Beyond its broad importance for healthy germline development, Asterix/GTSF1 has more specific functions in the Piwi‐interacting RNA (piRNA)–RNA interference pathway. There, it contributes to the repression of otherwise deleterious transposons, helping to ensure faithful transmission of genetic information to the next generation.Regulatory RNAs/RNAi/Riboswitches > RNAi: Mechanisms of ActionRegulatory RNAs/RNAi/Riboswitches > Regulatory RNAsRNA Interactions with Proteins and Other Molecules > RNA‐Protein ComplexesRNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional ImplicationsThis article is categorized under: Asterix/GTSF1 These cA prime example of these damaging genes are transposons, mobile genetic elements that can insert into distant genetic loci and potentially disrupt coding or regulatory regions. Widespread expression of transposons causes genomic instability and therefore must be repressed  was identified, its roles in development, and the findings that implicate it in piRNA‐directed silencing. This is presented first in the context of mammals followed by that of insects and other model systems. We then describe our current knowledge of Asterix/GTSF1 structural biology and biochemistry and finally pose unanswered questions regarding its action.2As mentioned above, the piRNA pathway is a germline‐specific silencing mechanism that is largely dedicated to transposon repression. Like other RNA interference pathways, the central silencing complex is an Argonaute family protein bound to a single‐stranded nucleic acid guide  and Ago3 localize to cytoplasmic foci (termed “nuage” in Drosophila), and participate in an adaptive, “ping–pong” amplification cycle to silence transposons post‐transcriptionally  or RNA sequencing data. Yoshimura et al.  performe4Gtsf1 transcription is observed beginning around embryonic day 12.5 (E12.5), remains relatively constant until birth, then plateaus around postnatal day 14 (P14)  spermatocytes. RNA expression levels increase during the round spermatid stage, reach a maximum in late round spermatids, then decrease with the progression of elongation  and IAP (an LTR retrotransposon) were significantly higher in the mutant background in mouse prospermatogonia  spermatocytes and round spermatids (meiosis II)  localize normally  was sufficient for inclusion in these complexes , and the fraction of secondary piRNAs—distinguished by an enrichment of adenosine at the tenth nucleotide—is lower. Intriguingly, while GTSF1 properly localizes to piP‐bodies in MIWI2  knockout mice, it fails to do so in a MILI (a pi‐body component that is involved in primary piRNA processing) null context  compared to Gtsf1. Gtsf2, on the other hand, is slightly later (E15.5–E18.5). Both of these orthologs are expressed again postnatally, beginning around P20 and P14, respectively  was found through the identification of genes involved in retrotransposon silencing. In 2013, transgenic RNAi screens by multiple groups identified a previously uncharacterized protein, CG3893, as a critical component for transposon silencing in the female germline in Drosophila melanogaster  owing to this organism's expanding role as a piRNA biology model system. Regardless of the species, similar to its role in mammals, Asterix/GTSF1 is involved in germ cell development in insects with its expression restricted to the gonads.Owing to the protein's <20 kDa size but potent phenotype when knocked out, the protein was named 11Gtsf1 seems to be primarily involved in male gametogenesis, in Drosophila data focus on female fertility owing to the fact that Asterix expression is highest in the ovary   , OnGtsf1 is specifically expressed in female oogonia and oocytes, and is a marker for sex determination , and the necessity for both of these has been demonstrated by numerous studies ; visualization (lead); writing – original draft (lead); writing – review and editing . Leemor Joshua‐Tor: Funding acquisition (lead); supervision (lead); writing – review and editing .Small noncoding RNAs and male infertility