Cancer is a multifaceted, life-threatening, and genomically complex disease. The worldwide prevalence of cancer is so high that one in three people will develop cancer during their lifetime. Although the use of RNA therapy is promising to fight cancer, its efficient and safe delivery is still one of the significant challenges hampering its therapeutic application. Thus, the aim of the present review was to highlight the most recent developments in the field of nanomedicine RNA-associated therapies to fight cancer.The complexity, and the diversity of the different types of cancers allied to the tendency to form metastasis make treatment efficiency so tricky and often impossible due to the advanced stage of the disease in the diagnosis. In recent years, due to tremendous scientific breakthroughs, we have witnessed exponential growth in the elucidation of mechanisms that underlie carcinogenesis and metastasis. The development of more selective therapies made it possible to improve cancer treatment. Although interdisciplinary research leads to encouraging results, scientists still have a long exploration journey. RNA technology represents a promise as a therapeutic intervention for targeted gene silencing in cancer, and there are already some RNA-based formulations in clinical trials. However, the use of RNA as a therapeutic tool presents severe limitations, mainly related to its low stability and poor cellular uptake. Thus, the use of nanomedicine employing nanoparticles to encapsulate RNA may represent a suitable platform to address the major challenges hampering its therapeutic application. In this review, we have revisited the potential of RNA and RNA-associated therapies to fight cancer, also providing, as support, a general overview of nanoplatforms for RNA delivery. With increasing awareness of the interdisciplinarity needed for a comprehensive characterization of the underlying mechanisms of cancer research, we have witnessed groundbreaking discoveries in various facets of molecular oncology. Compelling experimental evidence obtained from high-throughput technologies has offered a window into previously intractable problems in our comprehension of cancer genetics/epigenetics, deregulated cell signaling pathways, noncoding genome, and tumor heterogeneities and provided new insights into therapeutic options. Rapidly evolving understanding has shown that by gaining deeper insights into nano-bio interactions and personalization of nanomedicines, and through the applications of nanotechnology to emerging and existing therapeutic modalities, we have started to realize the true potential of nanomedicines in cancer ,2,3,4.Recent studies have provided evidence of an upsurge in cytokines after the administration of positively charged nanoparticles. There is sufficient proof of the correlation of Compliment activation with nanoparticle administration. Importantly, nanoparticles with a positive surface charge triggered activation of the classical compliment pathways, whereas negatively charged particles “switched on” the alternative (lectin) pathways ,6.It is becoming sequentially more understandable that after intense experimental and clinical evaluation of protein biologics and small molecules, gene therapy and RNA medicines represent promising models of drug innovation. RNA vaccines against different cancers showcase an efficient technology, as they are easier and faster to develop and manufacture compared to conventional vaccines. Importantly, RNA vaccines are completely synthetic and do not require cell cultures.In this review, we have attempted to highlight the most recent developments in the field of nanomedicine.RNA therapy acts on messenger RNA (mRNA) by using oligonucleotides that can interfere with different metabolic processes of a polynucleotide, such as splicing, the mature process starting from pre-mRNA, transport, translation, and degradation  DifferenTheir easy diffusion and absorption through tissues allows them to directly bind to targets after being injected into patients . ASO may act at their target mRNAs by activating RNase H or inhibiting translation by preventing ribosomes’ action through a steric effect . ASO mayThe benefit of shRNA is that it can be incorporated into specific plasmid vectors, permitting cell-type-specific or inducible promoters integrated into genomic DNA for longer-term or stable expression, and thus more prolonged knockdown of the target mRNA . miRNAs The development of RNA-based therapeutics has experienced a boost since the 1990s owing to the increasing knowledge of nucleic acid chemistry and the decline in production costs of mRNA .The therapeutic potential of RNA largely depends on its ability to reach the desired target cells and express the proteins of interest. However, RNA presents limited stability in serum, suffers from rapid blood clearance, off-target effects, and poor cellular uptake, and may activate immune responses ,22,23. TThe translation efficiency and stability of exogenous RNA can be enhanced by several methods, such as UTR (untranslated regions) manipulation, codon optimization, and chemical modification of the poly(A)tail of RNA ,25. FurtOne of the most advanced RNA delivery methods is co-formulation into lipid nanoparticles (LNP) ,43. LNP The inclusion of helper lipids, such as DOPE or cholesterol, generally increases the in vitro transfection efficiency of the lipoplexes . HoweverZ,9Z,28Z,31Z)-heptatriaconta-6,9,28,31-tetraen-19-yl-4-(dimethylamino) butanoate  [More recent studies have focused on using LNP based on pH-dependent ionizable cationic lipids, which have been shown to efficiently transfect mRNA to express therapeutic proteins. These lipids are positively charged at acidic pH but neutral at physiological pH. The resulting LNP display low surface charge at physiological pH and are relatively non-toxic and non-immunogenic. The structure of pH-dependent nanoparticles may be destabilized in environments with pH values lower than 6.5, facilitating the release of encapsulated cargoes within the more acidic tumor microenvironment ,53,54. IMA, MC3) . SeveralMA, MC3) ,57. EthaMA, MC3) ,59. BiodMA, MC3) ,59,60.Further, the use of unsaturated lipid tails in the lipid structure has been shown to increase fluidity and introduce structural defects in the cell membrane, facilitating fusion of the LNP with the cell membrane, as well as endosomal escape . HoweverThe development of lipid nanoparticles based on serine-derived gemini surfactants and monoolein (MO) as helper lipids for siRNA delivery has also been reported. The use of amino acids as polar headgroups in the design of surfactants leads to enhanced biological properties (biocompatibility and toxicity) compared to conventional quaternary ammonium-based surfactants . The lipSolid lipid nanoparticles (SLN) containing cationic lipids were also reported as RNA carriers for cancer therapy. They can be produced without the need for organic solvents, lyophilized, and the dehydrated SLN are stable for up to 9 months when stored at temperatures up to 30 °C . In addiMore recently, miRNA-loaded exosomes have been engineered as cancer therapeutics  ,70,71,72Polymeric-based non-viral vectors represent another class of nanoscale platforms for RNA delivery. Specifically, cationic polymers can bind to nucleic acids to form polyplexes. Polymers can efficiently protect RNA from nucleases and promote cellular uptake and endosomal escape, leading to higher RNA delivery efficiency. Representative polymers of this class include chitosans, polyethyleneimine (PEI), dendrimers, and nanogels .Chitosans are naturally derived cationic polysaccharides, differing in the degree of N-acetylation and molecular weight (50–2000 kDa). They are readily available, biodegradable, easy to modify, and possess unique biological properties associated with their polycationic nature [N-alkylation and quaternization enhance colloidal stability and transfection efficacy of the nanoparticles–and ligand conjugation–peptides, proteins, and non-proteinaceous ligands, like carbohydrates, folic acid and hyaluronic acid are commonly used for chitosan vector conjugation [c nature ,77,78. Cc nature ; and  has been reported to decrease its toxicity. For example, PEI-β-cyclodextrin conjugates show low toxicity while being highly efficient in promoting cell uptake and endosomal escape, leading to enhanced transfection efficiency .Chemical modification of low molecular weight PEI (<2 kDa) with salicylamide or stearic acid has also been reported to reduce the toxicity and enhance transfection efficiency of the resulting polyplexes/micelles ,91.Dendrimers are polymeric materials with a highly branched 3D structure [tructure . They cotructure ,94,95. Dtructure ,97,98.Dendrimers have also been used as templates for gold nanoparticles (AuNPs) to control their size and shape, which are known to influence the efficiency of AuNPs in biological systems highly ,100.Nanogels (NG) are gel particles with a three-dimensional hydrophilic network structure. They are commonly made up of polyacrylic acid, polyacrylamides, polyaminoacids, and other high molecular weight polymers. NG-based drug delivery systems possess a large surface area, structural stability, and the ability to swell. They present a high loading capacity, encapsulating either hydrophobic or hydrophilic drugs. Their shape and size can be finely tuned, and they are sensitive to pH, temperature, ionic strength, and other external stimuli, which confers them adequate controlled drug release capacities [pacities ,102,103.A thiolated PEI-dextrin NG was shown to efficiently deliver siRNA to cancer cells without inducing compromising toxicity. In any case, the variable morphology of the nanoparticles, as well as their size distribution, allied to the lack of data on their clinical safety and efficacy, are issues to be overcome for a broad application of this nanoplatform to be feasible .Inorganic nanomaterials  are promising carrier platforms for RNA delivery due to their unique physicochemical properties, which endow them exciting attributes, such as long-term stability, high loading capacity, and optical responsiveness. These inorganic nanoparticles are usually easier to synthesize and scale up than organic-based ones and have been the subject of much research concerning their potential as nanocarriers for nucleic acid delivery. The calcium phosphate (CaP) composite is the oldest non-viral gene carrier, introduced in 1973. It is biocompatible and biodegradable and forms complexes with nucleic acids, successfully delivering them to cells . HoweverGold nanoparticles, AuNPs, possess high chemical stability and attractive optical properties, are easy to functionalize, and are of particular interest for biomedical applications [ications . They haications . Their eications ,100,113.ications .Carbon nanotubes (CNT) can be categorized into single-, double-, or multi-walled  according to the number of graphene layers in their structure. They are promising nanocarriers for nucleic acid delivery, as they can avoid the endosomal barrier through an endocytosis-independent cell penetration pathway. CNT are poorly water-soluble; however, they offer a large surface area, which can be modified with functional groups and loaded with drugs or nucleic acids, thus enhancing their aqueous dispersibility [sibility ,116. CNTsibility  or throusibility ,120,121.sibility .Mesoporous silica-based nanoparticles (MSNP) were first applied for drug delivery in 2001 [ in 2001 . They pr in 2001 ,125. Nga in 2001 . Functio in 2001 . Further in 2001 .Iron oxide magnetic nanoparticles have also been proposed as vehicles for the delivery of nucleic acids [ic acids . Magnetiic acids . This teic acids ,132. Nanic acids .The methods described above represent some of the most used nanoplatforms for RNA delivery. Still, others exist that were not referred to, as the aim was to provide a general, although not extensive, overview of the wide variety of available delivery systems, specifically for RNA delivery. The classifications of the delivery systems may sometimes overlap, as, e.g., in the case of inorganic gold nanoparticles functionalized with polymers. Whether they fit in the class of AuNP, or of polymers, or may be considered as hybrid nanoparticles, the most important thing is to be aware of the many considerations that have to be taken into account when designing novel vectors for biomedical applications. Efficacy concerning the proposed aim must go hand-in-hand with biocompatibility and safety; otherwise, translation into clinical use may be at cost.In TP53, NF1, and ARID1A. Moreover, RNA sequencing showed elevated expression of PLK1 compared to the normal adrenal RNA control. Two patients completed at least six cycles. Treatment was well tolerated, with the most frequent adverse events (AEs) being pyrexia (56%), chills (50%), back pain (31%), infusion reaction (31%), and nausea (25%) [Lipid-based nanoparticles (LNPs) that include liposomes and lipid NPs have been tested in animal models of tumors with promising results . Therefoea (25%) . Later, ea (25%) .MYC oncogene has also been a target of the siRNA therapeutic approach. Dicerna Pharmaceutical developed DCR-MYC, a lipid particle with double-stranded RNA targeting the MYC oncogene and suppressing cancer progression [MYC in cancer metabolism, FDG-PET was performed after one cycle to evaluate metabolic responses. At the same time, tumor shrinkage measured with RECIST criteria was assessed every two cycles. A patient with PNET had dose-limiting toxicity after one cycle at 0.1 mg/kg with a transient increase of aspartate aminotransferase and fatigue. However, this patient obtained a complete metabolic response sustained for more than eight months without further treatment. The most common AEs were fatigue, nausea, and infusion reactions [The gression . DCR-MYCeactions . DCR-MYCeactions .Some trials using RNA-loaded NPs are ongoing. A nanoliposomal targeted therapeutic against EphA2 has been developed to reduce cancer cell proliferation and slow tumor growth. EphA2 is a tyrosine-kinase receptor with overexpression in breast, lung, prostate, ovarian, pancreatic, and endometrial cancer. A phase I study evaluating the role of EphA2 siRNA in patients with advanced solid tumors is currently active but not recruiting (NCT01591356). The companies ModernaTX and AstraZeneca have developed LPNs loaded with mRNA-2752. This mRNA encodes for OX40L, a T-cell co-stimulator. Patients with solid tumors have received mRNA-2752 or a combination with durvalumab. Responses in the preclinical setting using mRNA-2752 in monotherapy or in combination with durvalumab led to a phase I study activation in participants with relapsed/refractory solid tumor malignancies or lymphoma recruiting (NCT03739931).® MUTANOME immunotherapy–IVAC_M_uID). The scientific rationale for combining the two IVAC® approaches assumes that immunotherapies that acknowledge tumor heterogeneity on a single-patient level and target the whole range of antigens selectively expressed on tumors bear the highest potential to constitute an effective treatment (NCT02316457). Another LNP encapsulating mRNA, mRNA-2416, is being evaluated in treating patients with solid malignancies in a phase I-II study alone and in combination with fixed doses of durvalumab (NCT03323398). MRNA-2416 targets OX40, which strongly activates T-cell responses against tumoral cells. Lipo-MERIT is a cancer vaccine of four mRNAs encoding for NY-ESO-1, MAGE-A3, tyrosinase, and PTE. Lipo-MERIT has a splenic tropism, is taken up by dendritic cells and macrophages, and activates NK, B, CD4+ and CD8+ T-cells. Lipo-MERIT is now under evaluation in a phase I study of patients with advanced melanoma (NCT02410733). The mRNA-4157 LNP cancer vaccine is being tested in a phase I study (KEYNOTE-603) alone in patients with resected solid tumors and in combination with pembrolizumab in patients with unresectable solid tumors. After administration, this LNP is uptaken by antigen-presenting cells and induces cytotoxic T-lymphocyte and memory T-cell-dependent immune activation (NCT03313778). In contrast, KEYNOTE-942 is testing mRNA-4157 LNP together with pembrolizumab in patients with cutaneous melanoma completely resected and with a high risk of recurrence (NCT03897881). The OLIVIA trial is a phase-I study currently evaluating a combination of three ovarian cancer tumor-associated antigen mRNAs encapsulated in a liposome. Patients with ovarian cancer are vaccinated during neoadjuvant and adjuvant chemotherapy  to increase the T-cell-mediated systemic immune response against cancer cells (NCT04163094). Another phase I study (TNBC-MERIT), currently active but not recruiting, is evaluating the treatment outcome with a combination of immunotherapy with patient-specific RNA-liposome complexes tailored to the antigen-expression profile of any given patient’s tumor (WAREHOUSE immunotherapy–IVAC_W_bre1_uID) and de novo synthesized RNAs targeting up to 20 individual tumor mutations (IVACFor clinical translatability of promising nano-enabled technologies, well-defined initiatives have already been initiated aiming to improve patient-specific therapeutic outcomes. We have witnessed encouraging results in laboratory settings related to delivery strategies for RNA molecules. However, delivery of tumor suppressor non-coding RNAs is still in its infancy. There is a need to identify highly promising miRNAs, lncRNAs, and circular RNAs for delivery in animal models for disease prevention and treatment. Through continuous and tireless investigation of nanoparticle delivery technologies in laboratory settings, researchers have unique opportunities to conceptually interpret and analyze outcomes to add to an ever-expanding library of known design-function relationship trends in the rapidly evolving field of nanomedicine. However, it is worthwhile that the trends observed at the benchtop must be contextualized before making any attempt to generalize experimental evidence broadly, as minor differences in nanoparticle compositions, animal models, and disease pathology may modify the functions of nanoparticles and must be considered during the clinical translation of nanoparticle technology.


Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data.We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes . The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples.We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.The online version contains supplementary material available at 10.1186/s12920-022-01355-0. For decades, clinical biospecimens have been typically fixed in formalin then embedded in paraffin wax to make formalin-fixed paraffin-embedded (FFPE) tissue blocks for diagnosis and long-term storage. FFPE tissue archiving has many advantages, including room temperature stability, long-term storage, and suitability for subsequent immunohistochemical (IHC) analyses, which had led to use of FFPE in IHC-based biomarker investigations , 2. HoweOur study aim is to compare two commonly used RNA library preparation protocols for FFPE samples, and to provide a recommendation on RNA input metrics, including RNA concentration and library concentration, to achieve adequate RNA-seq data for downstream bioinformatics analysis.For the first part of the study, we evaluated two commonly used RNA library protocols for FFPE samples using seven paired FFPE and fresh frozen (FFzn) samples. All samples were prepared through both protocols and compared based on bioinformatics metrics, including alignment, SNP concordance, junction coverage and sample-wise correlation. For the second part of the study, we sequenced 130 benign breast disease (BBD) samples along with technical replicates in ten sequencing batches. Thorough bioinformatics quality control was performed to identify QC-failed samples. Finally, a decision tree model was constructed to correlate pre-sequencing metrics with QC status defined by bioinformatics metrics.Institutional Review Board approval was obtained for research use of human samples in this project (#IRB 75–87). A pilot study was performed using FFPE and fresh frozen pairs for seven women diagnosed with benign breast disease to evaluate the performance of two library preparation protocol, Illumina’s TruSeq RNA Exome and NEBNext rRNA Depletion Fig. a. To evaTotal RNA concentration was determined using the Qubit 2.0 Fluorometer and RNA HS Assay . RNA integrity was assessed and recorded with DV50, DV100, and DV200 values using the RNA 6000 Nano Kit on an Agilent 2100 Bioanalyzer , but was not used for sample exclusion in the library preparation. DV values are a commonly used metric that represents the proportion of RNA fragments in a sample with greater length than the numeric value .40–100 ng of experimental FFPE RNA, FFPE control RNA, or 20 ng fresh frozen control RNA was used for library preparation using the TruSeq RNA Library Prep for Enrichment and the Illumina Exome Panel-Enrichment Oligos kit  following the manufacturers protocol for FFPE RNA or high-quality total RNA respectively. As per the protocol, fragmentation of FFPE RNA was not performed. Following adaptor ligation and enrichment, the libraries were quantitated by Qubit and pooled for subsequent exome capture based on available yield. Up to a 4-plex pooling strategy was used for the exome capture, with capture groups consisting of 200 ng, 100 ng, 50 ng, 40 ng, and 30 ng of input library for each sample in the pre-capture pool. Up to 12 samples (3 pools) were batched for sequencing and included a paired FFPE control RNA and fresh frozen control RNA in each batch. Following two rounds of hybridization to the capture probes, the pools were PCR amplified and purified using AMPure XP beads. The amplified and enriched libraries were quality assessed using a combination of the Qubit dsDNA HS Assay (Invitrogen), the Bioanalyzer DNA 7500 Assay (Agilent Technologies), and KAPA Library Quantification Kit for Illumina . The three capture pools for each batch were combined in equal molar amounts and sequenced across 3 lanes of an Illumina NextSeq 500 High Output flowcell using 75 × 2 bp paired end reads. Each flowcell generated a minimum of 700 million reads passing filter.20–100 ng of FFPE RNA and paired fresh frozen RNA was used for library preparation using the NEBNext rRNA Depletion Kit (Human/Mouse/Rat) and Ultra II Directional RNA Library Prep Kit for Illumina , following the manufacturers protocol for highly degraded (RIN ≤ 2) or intact (RIN > 7) samples respectively. Fragmentation is based on RIN value of RNA input and conducted as outlined in the protocol. Fragmentation for FFPE RNA was not performed. Experimental FFPE RNA and paired fresh frozen RNA from the same patient was used if available using similar input amounts for each sample type. A total of 13 libraries were prepared, including six patient pairs. Libraries were quality assessed using a combination of the Qubit dsDNA HS Assay (Invitrogen), the Bioanalyzer DNA 7500 Assay (Agilent Technologies), and KAPA Library Quantification Kit for Illumina . Libraries were combined in equal molar amounts and sequenced across three lanes of an Illumina NextSeq 500 High Output flowcell using 75 × 2 bp paired end reads. Each flowcell generated a minimum of 800 million reads passing filter.Three fresh frozen samples were submitted for whole exome sequencing at Mayo Clinic molecular genomic facility. In brief, paired-end libraries were prepared with 1.0 μg of genomic DNA in accordance with the manufacturer's protocol . Whole-exon capture was performed with 750 ng of the prepped library following the protocol for the SureSelect Human All Exon v5 + UTRs 75 Mb kit . The purified capture products were then amplified with use of SureSelect Post-Capture Indexing forward and Index polymerase chain reaction reverse primers  for 12 cycles. Concentration and size distribution of the completed captured libraries were assessed on Qubit  and Bioanalyzer DNA 1000 chip . Libraries were sequenced at an average coverage of about 80× in accordance with standard protocol of the cBot and HiSeq 3000/4000 PE Cluster Kit . The flow cells were sequenced as 150 × 2 paired end reads on the HiSeq 4000 with the HiSeq 3000/4000 sequencing kit and collection software . Base calling was performed with Real-Time Analysis version 2.7.3 (Illumina). All procedures were performed in accordance with the manufacturer's instructions.After sequencing procedure, raw FASTQ files were processed through Mayo’s internal MAP-RSeq pipeline   between either technical or study replicate samples was calculated as:Ntotal denotes the total number of genes and NMAD>lfc denotes the number of genes with maximum absolute difference (MAD) above a certain logarithm fold change cutoff.SNPs were identified using GATK haplotype caller  and furtSample-wise median gene expression correlation smaller than 0.75Gene mapped reads smaller than 25 million gene mapped reads,Less than 11,400 # of detected genes with TPM > 4For each of the sequencing batch, if the technical controls/replicates failed QC, the whole batch of samples will be flagged as QC-fail as well. For our study, all technical controls/replicates have passed QC. A decision tree model was built based on CART Modeling via rpart R package to learn the relationship between pre-sequencing QC metrics, such as RNA qubit or pre-capture library qubit, and QC pass/fail status predicted by post sequencing bioinformatics metrics. Samples were split into training and testing set with a ratio of 7:3. Repeated cross validation were performed to optimal parameters (complexity parameter) during model training. Similarly, an alternative model based on logistic regression was also constructed:We defined three bioinformatics metrics for QC purpose, including sample-wise median correlation of gene expression (median_cor_expr), number of genes mapped to genic regions (gene_reads), number of detectable genes with transcript per million (TPM) larger than 4 (gene_tpm4). TPM was calculated based on: earlier . For eac package  and all We evaluated two RNA-seq library preparation protocols optimized for low quality, highly degraded samples (such as FFPE): Illumina’s TruSeq RNA Exome protocol and NEBNext rRNA Depletion protocol Fig. a. FFPE aAll study samples and technical controls were submitted for library preparation using RNA Exome protocol. Library concentration was gathered on the individual samples prior to hybridization capture and is hereafter referred to as the “pre-capture” library. 40 samples failed this step due to low pre-capture library output. The remaining samples were submitted for RNA sequencing in ten batches as detailed in the methods section Fig. b. BioinfWe next examined the relationship between pre-sequencing RNA metrics and post sequencing bioinformatics metrics. Among those pre-sequencing lab metrics, pre-capture library qubit values showed high correlation with bioinformatics metrics, including median_cor_expr, gene_tpm4 and gene_reads , number of gene mapped reads (gene_reads), number of detectable genes with transcript per million (TPM) larger than 4 (gene_tpm4). Finally, a decision tree-based model was built to examine the relationship between pre-sequencing lab metrics and qc-status as defined by post-sequencing bioinformatics metrics. Based on the model, we recommend a minimum of 25 ng/ul for RNA concentration and 1.7 ng/ul for pre-capture library concentration for FFPE samples to generate good quality RNA-seq data for bioinformatics analysis. We also demonstrated that FFPE replicates have similar reproducibility compared to FFzn replicates across sequencing batches. However, genes with short length or high GC content are more likely to be influenced by the FFPE procedure.Clinical biospecimens are typically stored as FFPE blocks, representing an invaluable source of material for biomedical research. FFPE blocks enable prolonged storage of clinical samples, preserving both tissue morphology and nucleic acids information. However, FFPE processing and tissue storage have been shown to affect RNA quality, thus limiting gene expression quantification by technologies like RNA sequencing. Our study provides a guideline for future research that utilizes FFPE samples for RNA-seq. By following these recommendations, sequencing samples with RNA and library input higher than our recommended values will not only help yield a better success rate for RNA sequencing, but also help to prevent unnecessary cost for sequencing.https://github.com/Liuy12/FFPEinput. Thirdly, the concentration of RNA in the original samples is highly dependent on the amount of input tissue, original handling and storage of the sample, the extraction method used, and perhaps most importantly, the elution volume used following extraction and purification. It is difficult to compare these amounts across samples or studies unless all these factors are controlled. The library concentrations are more comparable since they are based on a consistent total RNA amount going into the library prep. Finally, bioinformatics metrics in this study were derived from breast tissue and might not be readily applicable to other tissue types, but our recommendations for study design and bioinformatics QC procedure can be tailored for other studies involving different tissue types.There are several limitations for our study. Firstly, we benchmarked two commonly used library preparation protocols for FFPE samples using bioinformatics metrics, including SNP confirmation rate. SNP confirmation rate (precision) was calculated as the percentage of true SNPs  within the SNPs identified by RNA-seq for the same sample. This does not consider RNA specific mutations introduced by events like RNA editing. However, RNA editing events are considered very rare and the expected SNP confirmation rate should be very close to our calculation in Additional file Other than RNA and library input metrics, we also investigated other pre-sequencing lab metrics including DV50, DV100, DV200 values. The recommended quantities of starting FFPE material according to the vendor corresponds to a range of DV200 values, with the lowest recommended quality at DV200 of 30–50%. Recommendations for input using DV50 or DV100 values has not been evaluated by the vendor. Due to the RNA input limit, we were only able to quantify around 70% of all study samples for DV metrics. Based on those limited data, we observed that DV50 is highly correlated with DV100 values. Both DV50 and DV100 have moderate correlation with DV200, a conventional metric for measuring RNA quality (Additional file We benchmarked two commonly used library preparation protocols for FFPE samples. The TruSeq RNA exome capture protocol showed a superior performance. We also provide a common bioinformatics quality control recommendation for FFPE samples. Based on our defined bioinformatics criteria, we recommend a minimum of 25 ng/ul for RNA concentration and 1.7 ng/ul for pre-capture library concentration for FFPE samples to achieve adequate RNA-seq data for downstream bioinformatics analysis.Additional file 1. Bioinformatics evaluation of library preparation protocols using FFPE and FFzn pairs.Additional file 2. Evaluation of sample size on the calculation of sample wise median correlation.Additional file 3. Pair-wise scatter plot among pre-sequencing lab metrics and post sequencing bioinformatics metrics.Additional file 4. Correlation of pre-capture library concentration with bioinformatics metrics for all samples excluding FFzn controls.Additional file 5. Evaluation of false positives based on FFPE and FFzn replicates.Additional file 6. Pair-wise scatter plot among pre-sequencing lab metrics and DV50/100/200 metrics.Additional file 7. A decision tree model to predict QC pass/fail based on DV50.Additional file 8. All sample related metrics including pre-sequencing lab metrics and post sequencing bioinformatics metrics.


WRKY29 , RbohD (respiratory burst oxidase homolog D), EDS5 (enhanced disease susceptibility 5), SERK3 (somatic embryogenesis receptor kinase 3) encoding brassinosteroid-insensitive 1-associated receptor kinase 1 (BAK1), and PR-1b (pathogenesis-related gene 1b). With respect to virus infections, PVY dsRNA suppressed only PVY replication but did not exhibit any effect on PVX infection in spite of the induction of PTI-like effects in the presence of PVX. Given that RNAi-mediated antiviral immunity acts as the major virus resistance mechanism in plants, it can be suggested that dsRNA-based PTI alone may not be strong enough to suppress virus infection. In addition to RNAi- and PTI-inducing activities, we also showed that PVY-specific dsRNA is able to upregulate production of a key enzyme involved in poly(ADP-ribose) metabolism, namely poly(ADP-ribose) glycohydrolase (PARG), which is regarded as a positive regulator of biotic stress responses. These findings offer insights for future development of innovative approaches which could integrate dsRNA-induced RNAi, PTI and modulation of poly(ADP-ribose) metabolism in a co-ordinated manner, to ensure a high level of crop protection.In this work we developed and exploited a spray-induced gene silencing (SIGS)-based approach to deliver double-stranded RNA (dsRNA), which was found to protect potato against potato virus Y (PVY) infection. Given that dsRNA can act as a defence-inducing signal that can trigger sequence-specific RNA interference (RNAi) and non-specific pattern-triggered immunity (PTI), we suspected that these two pathways may be invoked via exogeneous application of dsRNA, which may account for the alterations in PVY susceptibility in dsRNA-treated potato plants. Therefore, we tested the impact of exogenously applied PVY-derived dsRNA on both these layers of defence (RNAi and PTI) and explored its effect on accumulation of a homologous virus (PVY) and an unrelated virus . Here, we show that application of PVY dsRNA in potato plants induced accumulation of both small interfering RNAs (siRNAs), a hallmark of RNAi, and some PTI-related gene transcripts such as  Given the versatile tunability of the properties and activities of RNA, novel RNA-based technologies are extensively emerging. A growing body of research confirm their successful applications via both host-induced (transgenic) gene silencing (HIGS) and spray-induced gene silencing (SIGS) techniques in plant protection against various plant pathogens, including viruses. Gene silencing or RNA interference (RNAi) is a pervasive nucleotide sequence-specific mechanism of gene regulation which involves production of small RNAs that induces the silencing machinery to target complementary DNA or RNA for transcriptional  or post-transcriptional  silencing, respectively ,2,3,4. INicotiana benthamiana plants could also be protected from PVY and potato virus X (PVX) by using corresponding exogenous dsRNA targeting the homologous virus (either PVY or PVX). Moreover, it was previously indicated that host-encoded RDR6 and the combined activities of DCL2 and DCL4 are required to promote efficient resistance to virus infection conferred by topical application of dsRNA -labelled RNA probes corresponding to the 918 bp (nts 7338–8255) fragment of PVY RNA were generated from PCR products obtained from the recombinant L4440 vector described above, using the mMESSAGE mMACHINE T7 kit  with a random-priming DNA labelling system (Invitrogen). As a loading control, equal fractions of each sample were resolved on a 1% agarose gel and stained with ethidium bromide (EtBr). Similar results were obtained in three independent experiments.Leaf tissues (1 to 2 g) were frozen in liquid nitrogen and ground to fine powder in a mortar and pestle, and total RNA was extracted using TRI REAGENT according to the manufacturer’s recommendations . RNA was suspended in 50 μL DEPC-treated water. Low-molecular weight (LMW) RNAs were isolated and resolved by electrophoresis in polyacrylamide/7M urea gels, which were then electroblotted onto Hybond N membranes prior to UV cross-linkage using a StrataLinker  as described by Taliansky et al. . The [32S. tuberosum genes: StWRKY29, StRbohD, StPR-1b, StEDS5, StSERK3/BAK1, StPARP1, StPARG, StEF-1α and StCox. However, for simplicity, in places in the manuscript this “St” nomenclature is not used for genes, proteins or mRNAs. Total RNA was isolated as described above. DNase-treated RNA was reverse-transcribed into cDNA using the SuperScriptTM First-Strand Synthesis System for RT-PCR (Invitrogen), with either an oligo-dT primer (for host plant-specific mRNAs) or PVY- or PVX-specific primers  and PRIMER EXPRESS software . The Ct values for PVY RNA, PVX RNA and each mRNA of interest were normalized using two internal reference genes encoding StEF-1α [Species-specific prefixes (St) are used in the text and mers see . The primers see  were des StEF-1α  and cyto StEF-1α . The ave2P04 (pH 9.5), 0.01% aniline blue for 2 h [http://rsb.info.nih.gov/ij/, accessed on 25 May 2022). More than 10 adjacent fields of view along the length of the leaf (not including the midvein or leaf edge) were analysed and averaged.To detect callose, potato leaves were cleared with 95% ethanol overnight and stained with 150 mM K for 2 h . Leaves For the isolation of plant nuclei and nuclear protein extraction the CelLytic PN kit  was used . The prop-value was <0.05.Statistical analysis was performed on four independent biological replicates. Each replicate was composed of samples from three plants pulled pooled together (two leaves per plant). Statistical analyses and bar plots were made using Python version 3.7.5 . For two


Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release.Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system.We employ minos-mediated integration cassette (MiMIC)-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated GFSTFunc-13 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13GFSTF, Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13GFSTF subclusters that move toward the AZ center during PHP with unaltered Unc-13GFSTF protein levels.Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in  N (N and its dynamics are currently a topic of intense investigations and have been linked to the AZ protein (M)unc-13  to analyze the general expression pattern of this important release site marker.Synapses enable fast and efficient signaling in combination with structural miniaturization . NeurotrN . The molM)unc-13 . The (M) release . MultiplSyntaxin . Due to Syntaxin . Furtherion PHP, . Two Unctibodies . We deciDrosophila melanogaster various tools emerged during the last decade speeding up the generation of genetically marked constructs. Among them is the Minos-mediated integration cassette (MiMIC) collection  tag into the fly genome using a previously described injection method , Bloomington, IN, United States]. GFSTFunc-13: y[1] w[*]; Mi{PT-GFSTF.0}unc-13[MI00468-GFSTF.0] (stock sent out to BDSC). Original MiMIC-strain (MI00468): y[1] w[*]; Mi{y[+ mDint2] = MIC}unc-13[MI00468]/In(4)ci[D], ci[D] pan[ciD] (BDSC #31015).Wildtype: pBS-KS-attB1-2-PT-SA-SD-0-EGFP-FlAsH-StrepII-TEV-3xFlag  event were performed by the company. y[1] w[*]; Mi{PT-GFSTF.0}unc-13[MI00468-GFSTF.0] (GFSTFunc-13) was shipped as an unbalanced, homozygous viable stock. Molecular confirmation of precise incorporation of the EGFP-FlAsH-StrepII-TEV-3xFlag  + fg_31r (CTGCTTCATGTGATCGGGGT), fg50_f (TGGATGGCGA CGTGAAC) + fg51_r (GGTTCCATGCAGCATCC) and fg_58f (CACAACGTGTACATCACCGC) + fg_59r (CTTGA GAACCTGCCGTCCAT). The PCR products were sequenced with fg_31r, fg50_f, and fg_58f.The Minos-mediated integration cassette (MiMIC) strain MI00468 was used to generate the GFP-tagged Unc-13 line . Microin°C. After two short and three long washing steps with PBT , rabbit-α-GFP , mouse α-GluRIIA , and Cy5-conjugated α-horseradish-peroxidase .For immunofluorescence imaging of larval neuromuscular junctions and ventral nerve cords, larvae were dissected in ice-cold hemolymph-like solution HL-3, , fixed wNc82, 1:100) and rabbit-α-GFP .For immunofluorescence imaging of adult brains, fly heads were dissected and brains removed in ice-cold PBS, fixed in 4% PFA in PBS for 30 min and blocked for 30 min with 0.6% PBT including 5% NGS. Incubation in primary antibodies, washing in 0.6% PBT, incubation in secondary antibodies and again washing followed the protocol explained in detail above. The same primary antibodies were used in the following concentrations: mouse α-Brp  and goat α-mouse conjugated Alexa Fluor488 . Larval preparations were mounted in Vectashield  for Axiovert imaging or Prolong Glass  for structured illumination microscopy. Images were acquired at room temperature from NMJs on muscles 6/7 in segments A2 and A3. An Apotome System  was used for low-resolution characterization of larval NMJs, larval ventral nerve cords and adult brains . For SIMdirect stochastic optical reconstruction microscopy imaging of the specimen was performed essentially as previously reported  and goat α-mouse IgGs labeled with Alexa Fluor532 . After staining, larval preparations were incubated in 100 mM mercaptoethylamine  in a 0.2 M sodium phosphate buffer, pH ∼7.9, to allow reversible switching of single fluorophores during data acquisition  were masked in the reconstructed, binned images from rapidSTORM using FIJI . The function was evaluated in nm steps for radii from 0 to 120 nm and without correction for edge effects. A second HDBSCAN to extract the individual Unc-13GFSTF subclusters (SC) was performed with minimum cluster size seven and minimum samples two to get SCs with a radius that matches the maximum of the H function. Unc-13GFSTF SCs were assigned to individual Brp clusters by computing the euclidian distance between the center of mass (c.o.m.) of each SC and the c.o.m. of each Brp cluster and selecting the lowest distance. In that way, the number of SCs per AZ could be quantified. The distance between the c.o.m.s is referred to as radial distance and was computed for each individual AZ as mean of the assigned Unc-13GFSTF SCs. To quantify cluster areas, we computed 2D alpha shapes using CGAL 5 in Python. To get the alpha shapes of Brp clusters and Unc-13GFSTF SCs we choose α-values of 800 and 300 nm2, respectively. Exclusion criteria for Brp clusters were area <0.03 and >0.3 μm2  or –60 mV (evoked EPSCs). To evoke synaptic currents, nerves were stimulated via a suction electrode with pulses of 300 μs length and typically at 12 V . Signals were low-pass filtered at 10 kHz and analyzed in Clampfit 11.1 . Paired-pulse recordings were performed with interstimulus intervals of . Between recordings, cells were given a 10 s rest. For analysis, 5–10 traces per interval were averaged. The amplitude of the second response in 10 ms interpulse recordings was measured from the peak to the point of interception with the extrapolated first evoked excitatory postsynaptic current (eEPSC) as described previously  were obtained from abdominal muscle 6 in segments A2 and A3 as previously described . All meaeviously . To asseeviously .°C. For each experiment, the respective volume was further diluted with freshly prepared HL-3 to a final PhTx concentration of 20 μM in 0.5% DMSO. Control experiments were performed with the same DMSO concentration in HL-3. PhTx treatment of semi-intact preparations was performed essentially as described previously (dSTORM imaging.Philanthotoxin 433 tris (trifluoroacetate) salt  was dissolved in dimethyl sulfoxide (DMSO) to obtain a stock solution of 4 mM and stored at –20eviously . In briep-values and samples sizes.Statistical analyses were performed with Sigma Plot 13  or GraphPad Prism 9 . Shapiro-Wilk was used to test normality. If data were not normally distributed, we used the non-parametric Mann-Whitney rank sum test for statistical analysis and reported data as median (25th–75th percentile). If data were normally distributed, they were reported as mean ± SD unless indicated otherwise. In box plots, horizontal lines represent median, boxes quartiles and whiskers 10th and 90th percentiles. Scatter plots show individual data points unless indicated otherwise. Bin counts in histograms were normalized to the total number of observed events which was set to one. All plots were produced with Sigma Plot. Figures were assembled using Adobe Illustrator . The authors declare that custom written Python code and all data sets supporting the findings of this work are available from the corresponding authors.unc-13 locus, we screened the previously established MiMIC library (yellow+ and enhanced GFP [EGFP]) and two inverted attP sites for recombinase-mediated cassette exchange (RMCE), as well as a gene-trap cassette  for detection of the sfGFP within the GFSTF-tag and a well-characterized, highly specific monoclonal antibody BrpNc82 mapping to the C-terminal region of Bruchpilot  in GFSTFunc-13 animals compared to control larvae  matching the maximum of the H function with diameters of ∼26 nm  and DMSO controls , each containing several Unc-13GFSTF SCs -tag within a coding exon of the s system . Using spression , 3. Furtring PHP .2C domain are necessary for efficient vesicle binding to lipid bilayers, thus, nano-clustering can be considered an inherent property of Munc13-1. However, the size of (M)unc-13 nano-assemblies is still debatable. dSTORM imaging and the glutamatergic Drosophila NMJ in a newly generated GFSTF-tag MiMIC line we here describe individual Unc-13GFSTF SCs of 26 nm diameter . Distinct labeling strategies, experimental organisms and synapses as well as the localization precision of the applied microscopic techniques might explain the different dimensions of Unc-13 clusters in our and previous work. dSTORM as a variant of localization microscopy in principle may allow to count molecules  localizations per Alexa Fluor647-labeled F(ab’)2 fragment .In mammals, Munc-13 exists in nano-assemblies of 5–10 protein copies at the presynaptic AZ and is responsible for recruiting Syntaxin-1 to promote vesicle exocytosis . It has , 2 area . Differe, 2 area  we found, 2 area . The twoolecules . In thisfragment  we assumfragment  may corrGFSTF SpCs per AZ, each containing about three SCs unc-13 ring. The ring and it’s MUN domains are aligned to 18 Synaptotagmin C2B domains and were proposed to serve as a platform for SNAREpin assembly and the subsequent fusion of a synaptic vesicle  will also change stimulation-dependent. This study implements a newly generated and highly suitable tool, the Unc-13GFSTF-tag MiMIC line, to address this question of structure-function relationship in future studies. We show that Unc-13GFSTF expression at the Drosophila NMJ is heterogeneous and varies between individual AZs 2 fragment. Further work using nanobody-staining to visualize the EGFP-tag or labeling strategies detecting the Strep-, FlaSH-tag, or TEV-tag within the GFTSF-multi-tag are conceivable.Furthermore, the number of Unc-13t manner . Previouse faces  suggestidual AZs . This fingements . In thisngements . Relatinphila AZ . We testonal PHP . Specifional PHP  with ouronic PHP , thus, wntire AZ . These cntire AZ . The repntire AZ . Future GFSTF MiMIC line with knock-out strains of crucial AZ proteins such as RIM or RBP to elucidate their effects on the amount or expression pattern of Unc-13 appears promising. Due to the interaction of Munc-13, Rab3, and RIM in mammals (Drosophila as formation of the tripartite complex is lacking. The RBP knock-out should affect Unc-13GFSTF expression in the fly (Drosophila NMJ.Combining the Unc-13 mammals  a RIM kn the fly  which caThe original contributions presented in this study are included in the article/SD, AM, MH, and MMP designed experiments. SD, AM, FG, MP, FK, and MMP performed experiments. SD, AM, FG, MP, PK, MS, and MMP analyzed the data. SD, AM, and MMP wrote the manuscript with help of all co-authors. MH and MMP coordinated the study and provided funding. All authors contributed to the article and approved the submitted version.


Cell-specific RNA sequencing has revolutionized the study of cell biology. Here, we present a protocol to assess cell-specific translatomes of genetically targeted cell types. We focus on astrocytes and describe RNA purification using RiboTag tools. Unlike single-cell RNA sequencing, this approach allows high sequencing depth to detect low expression genes, and the exploration of RNAs translated in subcellular compartments. Furthermore, it avoids underestimation of transcripts from cells susceptible to cell isolation procedures. The protocol can be applied to a variety of cell types.For complete details on the use and execution of this protocol, please refer to   •A protocol for investigating cell-specific translatomes in mouse brain cells•Purification of RNA from genetically targeted cells using RiboTag tools•Recommended antibodies and oligonucleotides to assess brain-cell specificity•Sample quality assessment tips for generating the most reliable data Cell-specific RNA sequencing has revolutionized the study of cell biology. Here, we present a protocol to assess cell-specific translatomes of genetically targeted cell types. We focus on astrocytes and describe RNA purification using RiboTag tools. Unlike single-cell RNA sequencing, this approach allows high sequencing depth to detect low expression genes, and the exploration of RNAs translated in subcellular compartments. Furthermore, it avoids underestimation of transcripts from cells susceptible to cell isolation procedures. The protocol can be applied to a variety of cell types. This protocol describes how to purify RNA from specific cells using RiboTag tools. RiboTag is a technique that consists of expressing Human influenza hemagglutinin (HA) tagged ribosomal subunit RPL22 in a cell-specific manner . The expCRITICAL: Any experiments on live vertebrates or higher invertebrates must be performed in accordance with relevant institutional and national guidelines and regulations. To perform this protocol, please make sure that you have acquired the permissions from the relevant institutions.Animal experiments were conducted in accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals and were approved by the Chancellor’s Animal Research Committee at the University of California, Los Angeles. All mice were housed with food and water available ad libitum in a 12 h light/dark environment. All animals were culled during the light cycle, and none were involved in previous studies.Timing: 3 weeks1.Rpl22 endogenous promoter, upon Cre recombination. Cell specificity can be controlled by breeding this line to cell-specific Cre expressing mouse lines, e.g., Aldh1l1-CreERT2 for astrocytes  A and 1B.The protocol below details the specific steps for purifying astrocyte RNA from mouse striatum. The protocol is described to generate a total of four replicates, each replicate corresponds to the striata of a single mouse. Nonetheless, this protocol can be adjusted to more samples or used to purify RNA from other cells as long as the mouse or AAV tools needed are available. For other applications of RiboTag please see Timing: 2 weeks3.We recommend checking the cell specificity of RiboTag expression with immunohistochemistry, especially if using new brain regions or Cre mouse lines to explore cells that have not been tested in this setting before. Colocalization of RiboTag and brain cells can be assessed combining anti-HA and cell specific antibodies . A list •1.5 M, pH 7.4 Tris in RNase free water.•1 M KCl in RNase free water.•2 in RNase free water.1 M MgCl•5 mg/mL Cycloheximide in RNase free water.•100 mg/mL Heparin in RNase free water.CRITICAL: Cycloheximide is considered a hazardous chemical according to Regulation (EC) No 1272/2008; wear personal protective equipment when handling. Cycloheximide waste needs to be collected and disposed of according to institute regulations.Stock solutions that will be necessary to prepare the homogenization and wash solutions described below can be prepared in advance and stored up to 1 year at 4°C.•Homogenization buffer (HB) – It can be stored at 4°C for two months.•Supplemented homogenization buffer (S-HB) – It should be made fresh on the day of the experiment and kept at 4°C.CRITICAL: DTT is considered a hazardous chemical according to Regulation (EC) No 1272/2008; wear personal protective equipment when handling. DTT waste needs to be collected and disposed of according to institute regulations.•High salt buffer – It can be prepared fresh on day 1 or at the beginning of day 2 and kept at 4°C.Note: Molarity of pure water calculated with a density of 1 g per mL.•○https://www.qiagen.com/gb/resources/resourcedetail?id=e112adfa-cc06-4e29-87f8-4820062ae44e&lang=en). Example for 4 mL: 4 mL of RLT + 40 μL of β- mercaptoethanol.To make RNeasy Micro QIAGEN kit lysis buffer, mix β-mercaptoethanol with RLT QIAGEN buffer in a 1:100 ratio as per manufacturer recommendation  for 6 h or at 4°C for up to two days.CRITICAL: β-Mercaptoethanol is considered a hazardous chemical according to Regulation (EC) No 1272/2008; wear personal protective equipment when handling. Work under chemical hood. β- Mercaptoethanol waste needs to be collected and disposed of according to institute regulations.Prepare the solutions that will be necessary to perform the protocol. These solutions should be kept at 4°C or on ice.RNA is highly sensitive to RNases. RNases can be present on your skin and surfaces.CRITICAL: Wash surfaces, pipettes, tube racks, and pipette boxes before starting using Vikron, 1% bleach or RNase Away and do not touch anything without gloves from here on.CRITICAL: Keep everything cold.CRITICAL: Use pipette tips with filters.CRITICAL: Use DNase/RNase free 1.5 mL tubes, water and PBS.CRITICAL: Frequently change gloves to ensure they are clean.To avoid RNase contamination of your samples and to reduce their activity as much as possible, please adhere to the following tips:Timing: 1–1.5 h1.Cull and decapitate the mouse following institutionally approved procedures. Extract the brain and put it on a petri dish filled with RNase free PBS under the dissection microscope.2.a.Cut the brain sagittally in two halves .b.For each half, remove the thalamus and hippocampus. When the striatum is exposed, scoop the striatum to detach it from the cortex. This can be achieved with fine curved forceps .c.Remove the corpus callosum that may have remained bound to the striatum until is clean .Dissect the area of interest. For striatum:3.a.Homogenize first with pestle “A” up and down 10 times.b.Homogenize then with pestle “B” for 15–20 times.Put the two striata from a mouse into the Dounce homogenizer and homogenize the samples at 2%–10% weight/volume, i.e., 1 mL of S-HB solution for two striata from the same brain (30 mg).Note: In between homogenization steps the Douncer can be kept on ice to avoid heating of the sample due to the friction.Note: Homogenization steps can be repeated if the tissue does not appear to homogenize, as long as the sample stays cold.4.Transfer the homogenized sample to a 1.5 mL tube labeled with the sample ID.CRITICAL: Between samples, clean the Douncer with RNase free water 3 times (5–10 strokes per pestle each time) to avoid contamination between samples.CRITICAL: During tissue dissection and homogenization, speed is very important. RNAs are exposed to RNases as soon as the mouse is culled. Even though the homogenization buffer contains RNase inhibitors, the probability of RNA degradation increases with time. The homogenized samples should be kept on ice until the entire group is processed. We recommend spending no more than 90 min for the dissection and homogenization step to preserve RNA integrity.In this step, we describe how the brain region of interest, striatum in this case, can be dissected and homogenized , and 4.1Timing: 4.5 h5.Centrifuge the samples in 1.5 mL tubes at 4°C/10,000 g/10 min.6.Transfer supernatant to the tube labeled IP+HA.7.a.Note: With 5% of the supernatant for the INPUT sample, we usually obtain 4 times the RNA yield of its corresponding IP sample. If more INPUT RNA is needed, up to 10% of the supernatant can be taken. We do not recommend taking more than 10% since it would decrease significantly the IP RNA amount.Take 5%–10% of the supernatant in the tube IP+HA and transfer it to the INPUT sample tube. If the striata were homogenized with 1 mL of S-HB, 50–100 μL can be transferred to the INPUT tube.b.Add 400 μL of Lysis Buffer (1:100 β-mercaptoethanol:RLT buffer from RNeasy kit).c.Vortex 10–30 s.d.Freeze in liquid nitrogen or dry ice and store at −80°C.INPUT SAMPLE.8.a.Note: We typically use mouse anti-HA from BioLegend but rabbit anti-HA from Cell signaling also works (see orks see .Add 5 μL of anti-HA antibody to each IP sample (in the IP+HA tube), this is a ratio of ∼1:200 vol/vol and 1 μL of antibody for 6 mg of tissue.b.Incubate for 4 h at 4°C on a gentle spinner at 20 revolutions per minute .c.i.Vortex the bead stock vigorously before using to homogenize the beads.ii.Add 200 μL of beads to the IP+BEADS 1.5 mL tubes.iii.Place the tubes on the magnet and discard the storage buffer solution.iv.Add 800–1,000 μL of HB to the beads immediately.v.Vortex 10–30 s.vi.Incubate the beads at 4°C on a gentle spinner for 10 min at 20 rpm.Pierce beads preparation (about 30 min before the 4 h IP+HA incubation ends):d.Place the IP+BEADS tube on the magnet and discard the buffer.e.Quickly add the IP+HA sample to its corresponding IP+BEADS tube.f.CRITICAL: Do not let the beads dry between washes.Note: Protocol could be optimized for shorter incubation times. However, RNA amount and quality should be checked first.Incubate 14–16 h at 4°C on a gentle spinner at 20 rpm.IP SAMPLE.On day 1, the sample processing involves clearing the homogenized samples from brain debris, collecting a whole tissue (INPUT) sample for RNA extraction, e incubating the rest of the sample with anti-HA antibody and magnetic beads to pull down cell specific RNAs on Day 2 C and 3. Timing: 1 h9.Place IP+BEADS tubes on the magnetic rack.10.Discard the supernatant; the HA-ribosome/RNA complex will be bound to the beads.11.Wash the beads adding 800 μL of high salt buffer to each sample immediately after removing the liquid.12.Incubate at 4°C on a gentle spinner for 10 min.13.Repeat the washes a total of 3 times.14.Place tubes on the magnetic rack.15.Discard the high salt buffer.16.Add 350 μL of Lysis Buffer (1:100 β-mercaptoethanol:RLT buffer from RNeasy kit) to the beads of each sample immediately.17.Vortex vigorously for 30 s (to break apart the antibody-bead-protein bond).18.Freeze in liquid nitrogen or dry ice and store at −80°C.CRITICAL: Do not let the beads dry between washes.On day 2, the IP samples will be washed several times before mixing them with the lysis buffer for RNA extraction C and 3.9Timing: 1 h19.Thaw the INPUT and IP+BEADS samples on ice.CRITICAL: For the IP RNA, remove the beads (now unbound from the ribosomes and RNA) placing the tubes on the magnetic rack and use the supernatant for RNA purification with RNeasy Micro QIAGEN kit.20.https://www.qiagen.com/gb/resources/resourcedetail?id=e112adfa-cc06-4e29-87f8-4820062ae44e&lang=en).Proceed with the RNeasy Micro QIAGEN kit following the manufacturer’s instructions . This way the sample that will be used for the translatome assessment does not need to be thawed until the day of the experiment.RNA purification will be performed following QIAGEN recommendations. It can be done any time after the samples processing steps D. NonethTiming: 1 h21.RNA integrity and abundance can be assessed using for example an Agilent Bioanalyzer.Note: This instrument performs a miniature electrophoresis with 1–2 μL of the RNA sample to determine its integrity based on the electrophoresis band pattern that is analyzed by the software. It provides an RNA integrity number (RIN) and an estimation of the RNA concentration. The RIN scale goes from 0, highly degraded RNA, to 10, RNA with high integrity. RNA samples with RIN > 8 are ideal for qRT-PCR or RNA-seq experiments . With this enzyme we reverse transcribe 20 ng of RNA.a.Once the cDNA is generated, make a 1:10 dilution of the cDNA stock.b.Use 5 μL of the cDNA stock for each qRT-PCR reaction.Transcribe complementary DNA (cDNA) from the purified RNA using SuperScript™ IV Reverse Transcriptase following manufacturer instructions .23.https://tools.thermofisher.com/content/sfs/manuals/cms_046776.pdf).a.For the reaction to take place, Fast Sybr Green Master Mix can be mixed with 5 μL of the 1:10 diluted cDNA stock per reaction and a pair of gene specific oligonucleotides .Table 1OqRT-PCR can be performed in any qRT-PCR system. To measure the amplification of cDNA during qRT-PCR you can use Fast Sybr Green Master Mix or similar and follow manufacturer protocol  – Ct (housekeeping gene)  .Note: Relative cell enrichment calculations, e.g., expression of an astrocyte gene/neuron gene, can be misleading because they indicate enrichment of a marker over another cell marker but do not show if the level of expression of genes from the non-targeted cell are considerable. Instead, we recommend assessing and presenting the expression of cell-specific genes relative to one or two housekeeping genes. They can also be presented as a ratio of enrichment in the IP vs its corresponding INPUT samples. This cell specificity assessment should not be taken as a substitute for testing with immunohistochemistry, detailed above .b.Check if the variance is homogeneous between conditions with tests that assess homogeneity of variance .c.If data are normally distributed and variances are homogeneous between conditions, use repeated measure (RM) one-way ANOVA test.d.If not, use Friedman’s test.e.If c or d show statistical significance, differences between specific groups can be assessed with post-hoc tests like Tukey’s or Dunn’s respectively.For the statistical analysis of the qRT-PCR results:Note: Since several genes are measured in the same sample it is important to take into account the matching effect between them. Both RM one-way ANOVA and Friedman’s test compare the means of three or more matched groups.We recommend checking the cell specificity of the RNA purified if the protocol is performed for the first time, you are investigating new brain regions, or using new CRE mouse lines D. To cheAldh1l1-CreERT2 mice is 200–1,000 ng. The yield may increase if the RiboTag gene is in homozygosity. Using the RiboTag AAVs, striatum astrocyte RNA yield is between 500–1,500 ng per mouse.The RNA yields expected from the astrocytes of one mouse striata when using RiboTag::RNA quality, lack of degradation or fragmentation, is quantified through the RIN. RNA samples should display a RIN > 7.5. As an example, in our latest experiment, 92% of our samples showed a RIN > 7.5 (24 out of 26 samples) .When the cell enrichment is pure enough, the expression of genes of non-targeted cells is close to 0 and the expression of your cell of interest is at least one order of magnitude higher than the expression of other cell markers .The number of genes identified by RNA-seq in striatal samples was of 20263 . We deteOur protocol allows the study of cell-specific transcriptomes with RNA-seq at high sequencing depth, i.e., it allows assessment of genes that are not highly expressed and usually missed when performing single-cell (sc)RNA-seq. Moreover, unlike scRNA-seq, cell dissociation is not needed preceding the RNA purification. This has two main advantages, (1) our method captures RNAs that are translated in subcellular compartments such as the astrocyte endfeet that are ripped off with cell isolation protocols, (2) the transcriptome detection is not biased by lower sampling of cells that are more susceptible to dissociation treatments in healthy brain or that become more fragile in disease models. In addition, this method can be used to investigate the translatome of cells that are traditionally challenging to study through scRNA-seq due to the difficulty of obtaining enough cells of that type, e.g., pericytes or stem cells. However, this protocol has some limitations to consider. RiboTag only allows the cell-specific translatome assessment of one cell type per mouse. The variety of cell types that you can assess is dependent on the availability of Cre mouse lines or AAV promoters. If Cre mice need to be used, breeding schemes can be time and resource consuming. In addition, the description of intraregional cell subpopulations may be missed. Nonetheless, to gain understanding on cell subpopulations, our protocol can be combined with immunohistochemistry or RNA-scope; this has provided insightful information about astrocyte subpopulations . The devRiboTag only expresses in a fraction of the cells you aim to target “” sectionAB_10693385) in S100β positive astrocytes . If it appears that not as many cells as expected with your Cre line express RiboTag, tamoxifen dose could be increased to ensure wide expression of RiboTag in your cell of interest. This will avoid an unintentional sampling of transcriptomes of certain cell subpopulations.The spread of expression of RiboTag should be assessed in advance with immunohistochemistry, e.g., measuring RiboTag-HA expression  is not expired or have been left at RT.Low RNA yield  to be able to perform RNA-seq even when the RNA yield is very small.There are several solutions to low RNA yields.b.diaz-castro@ed.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Blanca Díaz-Castro  and the RiboTag and astrocyte specific Cre mouse lines at Jax Cat# JAX:011029, RRID: IMSR_JAX:011029 and Cat# JAX:031008, RRID: IMSR_JAX:031008 respectively.


This mechanism of action has since been further elucidated and additional antiviral activities of ISG20 highlighted, including direct degradation of deaminated viral DNA and translational inhibition of viral RNA and nonself RNAs. This review focuses on the current understanding of the main molecular mechanisms of viral inhibition by ISG20 and discusses the latest developments on the features that govern specificity or resistance to its action.Interferon‐stimulated gene 20 kDa protein (ISG20) is a relatively understudied antiviral protein capable of inhibiting a broad spectrum of viruses. ISG20 exhibits strong RNase properties, and it belongs to the large family of DEDD exonucleases, present in both prokaryotes and eukaryotes. ISG20 was initially characterized as having strong RNase activity  in vitro. However, a precise understanding of its mechanism of action is lacking. In this review, we discuss the latest developments on this fascinating innate defense factor.ISG20 is a broad antiviral inhibitor and a potent RNase  The interferon system represents the first line of response against microbial infections , 2, 3. D, one β gene situated syntenically (IFNβ1) [IFNε, IFNκ, and IFNω). Members of the IFN family exhibit from 30 to 80% sequence identity, and all are ligands for the cell surface receptors IFNα receptor subunits 1 and 2 (IFNAR1/2). The ligand–receptor interaction leads to the activation of the Janus kinase signal transducer and activator of transcription (JAK‐STAT) signaling pathway and to the upregulation of several hundreds of genes, referred to as interferon‐stimulated genes (ISGs). These genes exert numerous effects aimed at blocking the microbe at multiple steps  [In humans, the IFN locus on human chromosome 9p contains 13 functional α genes In a very simplified manner then, this innate defense mechanism recognizes common features present in a given class of pathogens  in cells that are compromised. Through the rapid diffusion of signals (the IFNs), this leads to a generalized antimicrobial cellular state in which cells become more resistant to infection. This intrinsic cellular resistance is ultimately mediated by interferon‐sensitive genes (ISGs) that act at multiple layers to inhibit pathogen replication either by directly targeting it or by indirectly affecting cellular processes normally required for its replication.If the mechanism of action of some ISGs is well described in the literature, the role of many others remains either unknown or unclear.Among ISGs that act as antiviral effectors and that are thus thought to inhibit virus infection by directly binding or altering one of their components is the interferon‐stimulated gene 20 kDa protein (ISG20), also known as human estrogen‐regulated transcript 45 protein (HEM45) , 8. ISG2ISG20 was discovered in 1997 in Daudi cells as a new interferon‐regulated protein , 12, 13.isg20 is located on the chromosome 15q26.1 and codes for a 181 amino acid protein [isg20l1 lays just next to isg20 and has been involved in apoptosis, hence its name of apoptosis‐enhancing nuclease (AEN), while isg20l2 is located on chromosome 1q23.1 and has been involved in ribosome biogenesis. At this time, neither ISG20L1 nor ISG20L2 has been reported to possess antiviral activities [In humans,  protein , 21. Humtivities , 23, 24.The DEDD superfamily of 3'‐exonucleases includes a large number of prokaryotic and eukaryotic enzymes that cleave one at a time from the 3’‐RNA and/or DNA terminus . Exoriboin vitro with a strong preference for RNA [1WLJ). In this structure, a five‐stranded β‐sheet core is surrounded by two clusters of α‐helices: α3, α4 and α1, α2, α5, α7 . Only the Mn2+ showed stimulatory effects on ISG20 activity, whereas other bivalent cations such Mg2+, Ca2+, or Zn2+ did not [ISG20 belongs to the DEDDh subgroup of 3’ to 5’ exonucleases as a large number of independent RNases such as the ribonuclease T and D, the three prime repair exonuclease 1 (TREX1), or the yeast RNA exonuclease 4 homolog (REX4) Fig. . Because for RNA . The cry for RNA  that deaminates cytosines on viral genomes . This acRhabdoviridae or Flaviviridae  to Retroviridae such as the human immunodeficiency type 1 virus (HIV‐1), the genome of which is reverse‐transcribed from a single‐stranded RNA to double‐stranded DNA [In light of its exquisite activity against RNA, it is perhaps not surprising, although simplistic, that ISG20 has been described to interfere with the replication of a large spectrum of RNA viruses. This list ranges from negative‐ or positive‐strand RNA viruses such as nded DNA , 38, 39 Retroviridae and Hepadnaviridae that undergo a reverse transcription phase that leads to the formation of linear or circular DNA  and a 3’ poly A tail.Even through the lenses of this simplified view of their mechanisms of replication, it is difficult to reconcile a single mechanism of inhibition based on the direct degradation of viral RNA against such diverse viruses. Indeed, while most RNA viruses targeted by ISG20 carry out their replication cycle entirely in the cell cytoplasm, some, as  nucleus , 46, 47.How can this enzyme distinguish between cellular and viral RNAs? One possible hypothesis would be that ISG20 is capable of discriminating RNAs based on specific features present, absent, or differentially present in one versus the other RNA species, as, for example, epitranscriptomic modifications, 5’ cap, or poly A tails. However, while some of the viruses targeted by ISG20 lack some of these canonical features present on cellular RNAs, other viruses do possess them Table . As suchin vitro; this enzyme inhibits the replication of numerous viruses and its enzymatic activity is key for this property, given that a single point mutation that disrupts the catalytic site of ISG20 both abolishes its ability to degrade RNA in vitro and impairs its antiviral behavior. The explanation of how this behavior underlines the antiviral mechanism of action of ISG20 is still a matter of debate, and currently, four models have been proposed: two in which ISG20 directly degrades its viral target and two in which ISG20 blocks the translation from viral RNA without degrading them. Given that the RNase activity of ISG20 remains nonetheless important for this translational block, it follows that ISG20 must target a cellular RNA, whose identity remains, however, unknown.At present, the results obtained and confirmed by numerous laboratories concur in three experimental evidences: ISG20 behaves as a potent RNase in vitro.The first model proposed on the basis of the experimental evidence described above was that ISG20 directly degraded viral RNA , 48, 49 in vitro [In addition to the fact that the direct binding of WT ISG20 to viral RNA has not been formally demonstrated in cells, the major caveat of this model is that it fails to provide the basis through which ISG20 can distinguish viral from cellular RNA substrates. Indeed, ISG20 is capable of degrading most ssRNA in vitro , 29, notin vitro . Howeverin vitro , it is din vitro, a recent study indicated that it could be increased in the presence of deoxyuridines (dU) [While the activity of ISG20 on ssDNA is generally lower when compared to ssRNA nes (dU) . In so dMembers of the APOBEC3 family (A through H in humans) are prototype antiviral factors well studied in the context of retroviral infection , 53. In Retroviridae: first because it occurs in the nucleus and is carried out by APOBEC3A, which is together with APOBEC3B, the sole nuclear APOBEC3 member; and second, because it is carried out on double‐stranded covalently closed circular DNA (cccDNA) [The mechanism of cytosine deamination described here in the case of HBV is distinct from the one of (cccDNA) . Its out(cccDNA) . Given t(cccDNA) , 57, it As said before, this mechanism of action is of interest because it models the concerted action of two antiviral factors. Several questions remain, however, unaddressed around the specificity of action of ISG20. First, the major enzymatic functions of ISG20 are directed against RNA, and second, most DNA viruses do replicate in the nucleus, but for the most part seem resistant to ISG20 , 39. It For most highly replicating RNA viruses, the first round of translation of viral proteins is capital to ensure viral RNA amplification. As such, an alternative explanation for the lower accumulation of viral RNA observed in numerous studies in the presence of ISG20 could be an inhibitory effect of the first round of viral translation, which would severely undermine the extent of viral RNA amplification.Translation inhibition has been reported in mouse embryonic fibroblasts (MEF) overexpressing the murine ISG20 (mISG20) that shares 82% identity with human ISG20 Fig. . In thisThis model leaves for the moment unanswered two major questions. First, ISG20 is itself an IFN‐regulated protein so that its reported induction of IFN would necessarily occur in an environment already primed by IFN. As such, either ISG20 acts as a positive regulator to potentiate or sustain IFN responses over time , or this mechanism is restricted to cell types in which ISG20 expression is induced by other signals, as, for example, estrogens. Second, this model does not explain the broad mechanism of viral inhibition of ISG20, as the spectrum of viruses inhibited by ISG20 is larger than those targeted by IFIT1 and it extends to viruses with a cellular‐like m7G‐5’ cap. Importantly, given that the exonuclease activity of ISG20 is important for this behavior, the identity of the RNAs  targeted by ISG20 to induce IFN responses remains to be identified.Mechanism 4: Translation inhibition of nonself RNAs in the absence of IFN induction. This mechanism was proposed recently by our laboratory following experimental observations that ISG20 exerted a strong impairment in the translation of viral proteins following VSV infection in spite of a very mild decrease in viral RNAs Fig.  and in tInterestingly, a similar behavior could be reproduced upon ectopic transfection of plasmid DNAs independently from the nature of the reporter gene examined . On the contrary, ISG20 had no effect when this exact reporter cassette was stably integrated into the host genome.CrPV) 5’‐untranslated region that drives initiation of factorless translation [Translation inhibition was independent from initiation factors as it affected equally reporter genes under the control of the cricket paralysis virus  or a narrow set  of RNA transcripts, a situation that is unusual for the cell and often associated with viral infection.VSV infection and transient DNA transfection do not share many features in common. However, they both represent the massive flooding of the cell with Given the absence of target RNA degradation, but in light of the importance of an intact RNase activity, we proposed that ISG20 could affect cellular translation by degrading a cellular RNA substrate, which affected proportionally the most actively translated RNAs, working as a threshold translational controller.This model is of interest because it is relatively independent from a single RNA feature. However, it can be influenced by all of them and in particular by the extent of epitranscriptomic modifications that are added cotranscriptionally and thus depend on the chromatin status of the target gene.In this respect, rRNAs and small nucleolar RNAs  represent interesting potential targets with which ISG20 could alter the overall translational abilities of the cell. snoRNAs are small nonprotein coding RNAs (60‐300 nt) involved in a number of physiological processes among the post‐translational modification of rRNAs, process in which they guide the methylase fibrillarin on specific sites of rRNAs promoting 2’‐O‐methylation modifications , 66. TheThis model would accommodate inhibition of a broad spectrum of viruses targeted by ISG20 according to a single mechanism of action. However, also in this case the identity of the RNAs targeted by ISG20 remains unidentified.The intracellular distribution of IGS20 has not been the subject of intense studies so far. Its small size (around 20 kDa) allows it to be fairly well distributed in both cell cytoplasm and nucleus. A subtler localization of ISG20 has been described upon strong detergent permeabilization in promyelocytic leukemia (PML) bodies, nucleolus, and Cajal bodies , 70. PMLIn the case of translation inhibition, our laboratory also reported that a fraction of cytoplasmic ISG20 could migrate (or induce) processing bodies (P bodies), which are cytoplasmic membraneless compartments involved in RNA decay and/or control of translation by RNA storage , 79, in However, the dynamics of ISG20 and an extensive characterization of its intracellular distribution during homeostasis or during the context of infection are cruelly lacking and this may yield important clues as to the mechanisms of inhibition of ISG20. For instance, with few exceptions , a singlPhenuiviridae [in vitro ‐driven evolution toward resistance to ISG20 in Bunyamwera orthobunyavirus (BUNV), prototype virus of Peribunyaviridae family, has led to interesting hints about the determinants that may modulate susceptibility and resistance to this restriction factors [cis‐acting elements on the viral RNA may influence susceptibility to ISG20.While a large number of viruses are susceptible to the action of ISG20, several remain resistant to it. This includes not only DNA viruses such as adenoviruses or the Epstein–Barr virus but also RNA viruses such as several, although not all, members of iviridae , as welliviridae . in vitr factors . SeveralViral proteins may indeed, as cellular proteins, shield viral RNAs from the degradative action of ISG20, and similarly, particular stem–loop structures may also protect RNA, as ISG20 displays no reported activity against double‐stranded RNA. In this respect, the RNA of bunyaviruses is not polyadenylated, but contains a conserved 3’ stem–loop structure that may be important to mediate the resistance of some, although not all, members of this viral order.6A, and 5‐methylcytosine  tail in the 3’ end, cellular mRNAs are also heavily modified either cotranscriptionally or shortly after transcription by the addition of the following modifications: ribose 2'‐O‐methylation, pseudouridine, inosine, mine Fig. . CollectThese modifications are mentioned here because an increasing number of reports indicate that viruses themselves can hijack these processes to their advantage to modulate the metabolism of their own RNAs, in addition to negative impact cellular defenses , 90, 91.However, we can hypothesize that the extent and or abundance of epitranscriptomic marks in a given RNA could modulate either directly or indirectly through RNA‐binding proteins the action of ISG20. The weight of these modifications in the antiviral functions of ISG20 can be direct, for instance, by providing protection from direct viral RNA degradation, or indirect, for example, by altering the efficiency of translation by specialized subsets of ribosomes, as proposed in the translation inhibition models.Overall, several studies indicate that ISG20 is an important and broad antiviral effector during innate immune responses. While the exonuclease activity of ISG20 is deemed necessary for its antiviral activity toward multiple viruses, the identity of the nucleic acids targeted by ISG20 remains unclear, ranging from viral RNA to deaminated single‐stranded DNA or to cellular RNA targets that may influence translation. Rarely mentioned, high levels of ISG20 expression lead to cell death , 92 and Despite the fact that the underlying molecular mechanism leading to viral inhibition remains to be defined precisely, ISG20 has shown potential for use as broad antiviral inhibitor after vehiculation inside the cell as fusion protein with antibodies (Fc fragment) or with Tat cell‐penetrating peptides , 94. In The authors have declared that there is no conflict of interest.SD, CL, FF, and AC conceived and wrote the manuscript.


In human rRNA, at least 104 specific uridine residues are modified to pseudouridine. Many of these pseudouridylation sites are located within functionally important ribosomal domains and can influence ribosomal functional features. Until recently, available methods failed to reliably quantify the level of modification at each specific rRNA site. Therefore, information obtained so far only partially explained the degree of regulation of pseudouridylation in different physiological and pathological conditions. In this focused review, we provide a summary of the methods that are now available for the study of rRNA pseudouridylation, discussing the perspectives that newly developed approaches are offering. Ribosome biogenesis is a highly complex process that requires the accomplishment of different steps. One of these fundamental steps is represented by the modification of ribosomal RNA (rRNA) at specific sites. These modifications are required for the proper maturation and folding of rRNA and impact its stability . It is aAlterations of pseudouridylation in rRNA are also known to play an active role in the development of human disorders such as cancer  and a raDespite its clear role in enabling proper ribosome function and its involvement in human pathology, the mechanism underlying the contribution of rRNA pseudouridylation in the development of human disorders remained underexplored for a long time. This relatively limited knowledge may be considered the consequence of the lack of proper reliable technical approaches. In fact, site-specific quantification of pseudouridine on a wide scale represented a major technical issue for a long time, but recent developments in high throughput sequencing approaches enabled us to obtain increasingly precise and reliable quantifications on a transcriptomic scale.This review aims to give an updated perspective of the available technical approaches that can be employed to fully characterize the role of altered rRNA pseudouridylation in the development of different human pathologies.Until recently, the study of pseudouridylation was hampered by biological and technical issues, because the isomeric form of pseudouridine possesses no distinguishable features nor mass changes. This limited the availability of methods for site-specific detection of pseudouridine, required a previous knowledge of the specific position of the pseudouridine in the sequence analyzed, and was suitable only for highly represented transcripts.Over the last few years, different approaches were developed for the quantitative and site-specific identification of the pseudouridine landscape in cellular models and tissue samples. They may be low throughput, for the identification or validation of specific pseudouridine sites, and high throughput for an overview of all sites of modification in a specific class of RNA or the whole transcriptome.The major features of the techniques reported for the detection of pseudouridines are summarized in in vivo deuterium labeling was successfully applied for direct determination of Ψs in cellular non coding RNA as rRNA and small nuclear RNA (and in such case at subpicomole amounts)  . Since pamounts) . This meamounts) . Using tamounts) . One of amounts) .More recently, using a mass spectrometry-based technology termed SILNAS , all ribosomal modifications of the 80S ribosome from yeast and subsequently from human were mapped . This meThe first discovered and most widely exploited method for the study of pseudouridylation was introduced by the use of the chemical reaction of N-cyclohexyl-N′-β-(4-methylmorpholinium) ethylcarbodiimide p-tosylate (CMC) to pseudouridine, which forms a stable adduct blocking reverse transcriptase and hence producing truncated complementary DNA (cDNA) . This mede novo identification of pseudouridine sites. These approaches combine RNA deep sequencing technologies together with selective chemical derivatization of pseudouridine by CMC. These CMC-based methods, namely Pseudo-Seq  was applied for the study of cellular ncRNAs and mRNAs . The PA-Other sequencing methods have been developed to overcome the many limitations of the CMC-RT approach. The first is based on bisulfite RNA treatment, termed RBS-Seq, which can detect transcriptome-wide at single-base resolution pseudouridine, in addition to m1A and m5C . RegardiThe second CMC-alternative high throughput method relies on RNA cleavage at uridine residues by hydrazine, followed by aniline treatment, and resistance of pseudouridine to chemical treatment . This meAll the described techniques have the great drawback of modifying RNA molecules and using customized protocols to map each specific modification individually. Furthermore, cluster sequencing involves an amplification step, experiencing potential bias and providing only an average picture of modifications in a population of RNA molecules  technology using nano wells  and nanopore sequencing  . The mai16S rRNA  and tRNA16S rRNA  were det samples . Among t samples  NanoPsu  samples  and Peng samples . However samples  for natiFinally, single-particle cryo-electron microscopy (cryo-EM) is a rapidly developing technique, which already depicted the structure of the 80S ribosome, together with rRNA modifications at an average of 3Å resolution . By thisGiven these recent developments, identifying the modifications in the chemical pattern of rRNA may well represent a new path towards formulating a true picture of the functional specialization of ribosomes. As described above, ribosome function and activity can be impacted by the composition in ribosomal proteins and the plasticity in rRNA which encompasses diversity in modifications and interaction with certain factors. Any changes in ribosomal function or activity may affect the regulation of gene expression in both normal and abnormal conditions. Although the exact mechanism through which an altered or oncogenic ribosome may affect tumor progression is not yet fully defined, a correlation between anomalies in ribosome biogenesis or function and cancer has been reported. This has helped in defining the link between alterations in translation apparatus and cancer etiology.As previously stated pseudouridylation is one of the most abundant chemical modifications affecting ribosome function. In general, these modifications can be driven by either stand-alone pseudouridine synthases or snoRNA H/ACA box guided pseudouridylation complexes. Pseudouridine synthases are classified into six families and are known to recognize the substrate and catalyze the conversion of uridine to pseudouridine without using any cofactors.On the other hand, RNA dependent pseudouridylation is carried out by H/ACA box snoRNA. H/ACA box snoRNAs, also known as SNORAs , possesIn numerous studies snoRNAs have also been directly related to cancer initiation and progression. Schulten and others explored the snoRNA expression in patients with breast cancer brain metastasis in comparison to non-metastatic breast cancers. The study revealed that 13 SNORAs were differentially expressed and possibly involved in supporting metastatic progression . Similarin vitro studies conducted on prostate cancer cells (However, despite the growing link of snoRNA with disease prognosis and progression very little is known about the method of action and the pathways adopted by these molecules to carry out the effect. Increased levels of DKC1 and H/ACA snoRNA together with elevated levels of pseudouridine were reported er cells . Anotherer cells . This deer cells .It would be worth mentioning here that the ribosomes can be targeted using drugs with different active molecules . ConsequTo sum up, we can expect that the recent advancement in the techniques that could identify the chemical modifications in rRNA molecules reported here could in perspective take us one step forward in better understanding the role played at the molecular level by altered rRNA pseudouridylation in disease development and sequentially in the development of strategies for better disease management and the development of targeted therapies snoRNA- or ribosome-centered .