Arabidopsis thaliana, as well as meta-assembly of RNA-seq from single mouse embryonic stem cells, can produce reference-quality end-to-end transcript annotations.We developed Bookend, a package for transcript assembly that incorporates data from different RNA-seq techniques, with a focus on identifying and utilizing RNA 5′ and 3′ ends. We demonstrate that correct identification of transcript start and end sites is essential for precise full-length transcript assembly. Utilization of end-labeled reads present in full-length single-cell RNA-seq datasets dramatically improves the precision of transcript assembly in single cells. Finally, we show that hybrid assembly across short-read, long-read, and end-capture RNA-seq datasets from The online version contains supplementary material available at 10.1186/s13059-022-02700-3. Bookend is a generalized framework that utilizes RNA 5′ and 3′ end information in RNA-seq datasets to accurately reconstruct transcriptomes including those from single cells.The functions of genes depend on the amount and types of RNA molecules that they produce. Variation in transcript initiation, splicing, and polyadenylation can generate an array of RNA isoforms, and cataloging how these RNA variants change across development and disease provides insights into corresponding gene functions –3. LargeA wide array of RNA-seq protocols have been developed to profile different aspects of the transcriptome, from strand-specific coverage of gene bodies  to selecTranscript assembly is the effort to distill information from RNA-seq experiments into a comprehensive annotation of the transcript isoforms present in the corresponding samples. Depending on the method, RNA-seq reads contain a broad spectrum of information content. At one extreme, single-end reads from non-stranded RNA-seq protocols can be 50 nucleotides (nt) or shorter and sequenced from one end of a double-stranded cDNA fragment such that the resulting sequence is a random substring of an RNA molecule or its reverse complement. Paired-end reads contain two ends of a cDNA molecule, and typically, there is a gap of unknown length between the mate pairs. When aligned to a reference genome, paired reads may span more than one splice junction, indicating that these splicing events occurred in the same molecule. Some strand-specific RNA-seq protocols selectively sequence only first-strand or second-strand cDNA to preserve knowledge of the original mRNA molecule’s orientation . Other pTo determine whether RNA 5′ and 3′ end information can improve transcript assembly algorithms, we developed a generalized framework for identifying RNA ends in sequencing data and using this information to assemble transcript isoforms as paths through a network accounting for splice sites, transcription start sites (TSS), and polyadenylation sites (PAS). Because this software uses end information to guide transcript assembly, we named it Bookend. Importantly, Bookend takes RNA-seq reads from any method as input and after alignment to a reference genome, reads are stored in a lightweight end-labeled read (ELR) file format that records all RNA boundary features , as well as the sample of origin for that read  is an ideal model to benchmark transcript assembly in higher eukaryotes. The Arabidopsis genome is compact (~ 119 megabases), contains few repetitive elements, and the TAIR10 reference annotation was extensively curated from expressed sequence tag (EST) data  with library-specific settings. Arabidopsis Smart-seq2: --start_seq ACGGG --end_seq RRRRRRRRRRRRRRRRRRRRRRRRRRRRRR --mismatch_rate .2; Arabidopsis nanoPARE: --stranded -s --start_seq ACGGG --mismatch_rate .2; mouse SMARTer: --start_seq ACATGGG --end_seq AAAAARRRRRRRRRRRRRRRRRRRRRRRRR --mismatch_rate .25.Prior to alignment, reads were preprocessed with cutadapt  to removing STAR . Arabidohttps://github.com/PacificBiosciences/IsoSeq) with the command ccs --min-passes 2 --min-rq .9, and CCS reads were converted to full-length non-chimeric (FLNC) reads using lima and isoseq3 refine --require-polya --min-rq -1 --min-polya-length 10. FLNC reads were aligned to the Arabidopsis genome with the command minimap2 -G 5000 -H -ax splice --MD -C 5 -u f -p 0.9 --junc-bed [TAIR10 transcript BED12]. For cluster collapse, ToFU was installed with the cDNA_Cupcake package (https://github.com/Magdoll/cDNA_Cupcake), and the function collapse_isoforms_by_sam.py was run on a sorted BAM file of all FLNC reads with the arguments –flnc_coverage 2 –max_5_diff 200 –max_3_diff 200. Additionally, aligned unsorted SAM files were converted to ELR with the command bookend elr --stranded -s -e --start_seq ATGGG --genome [TAIR10.fa].Raw Arabidopsis PacBio reads were converted to Circular Consensus Sequences using Iso-seq3 software  First, reads are streamed in from an ELR file in sorted order and separated into overlapping chunks. (Tag Clustering) In each chunk, Start Tags and End Tags are clustered on each strand by grouping tags by genomic position and assigning each position a signal score of counts × proportion of total coverage. A signal threshold is set and positions below the threshold are discarded. Remaining positions are grouped within a user-specified distance to yield Start and End clusters on each strand.  Start/End clusters are added to a catalog of boundaries, which include splice donor/acceptor sites that are also filtered by a threshold of total overlapping coverage. Adjacent boundary pairs define a “frag”, and each read is assigned a Membership array that describes whether the read overlaps or excludes each frag. Redundant membership arrays are combined, and the unique set of elements is stored as the Membership Matrix.  A matrix describing the relationship between each element pair 1 and 2 is generated by asking (from left to right in genomic coordinates): can 1 extend into 2? Can 2 extend into 1? More formally, 2 extends 1 if they disagree in no column of the Membership Matrix and there exists at least one instance of a run of non-zero membership in 2 that ends at a higher index than the overlapping run in 1. Each comparison returns a pair of Overlaps, O1,2 and O2,1, respectively: 1 = extends, −1 = excludes, 2 = is contained by, 0 = does not overlap. The values −1 and 0 are symmetric, but 1 and 2 are directed relationships that can be used as edges in a directed graph. (Collapse Linear Chains) It is possible to identify and collapse non-branching sets of elements (“linear chains”) prior to assembly. Two graphs are constructed with elements as nodes: a directed graph with extensions as edges, and an undirected graph with exclusions as edges. A depth-first search is conducted by visiting each element in increasing order of information content (number of non-zero memberships). During a visit, the element’s edges are traversed recursively to record all traversed nodes’ exclusions. An element with no edges is assigned to a new chain. Otherwise, when an element’s edges are all traversed, the element is compared against its outgroup, the set of all elements reached. If all outgroup elements belong to one chain and the element and outgroup have the same set of exclusions, then the element is added to the same chain. If the element’s outgroup is assigned to multiple chains, the element begins a new chain. After completion of the search, each chain is combined to form a single reduced element. (Generate Overlap Graph) From the set of reduced elements, a second directed graph is constructed with a global source (Start+/End−) and sink (Start−/End+), where each node records the element weight (sequenced bases / genomic length), outgroup (extends to), ingroup (extends from), containments and exclusions. (Resolve Containment) All elements contained by one or more longer elements have their weight redistributed proportionally to their containers as long as not all containers exclude any single node the element does not already exclude. (Greedy Paths) All elements begin unassigned. Starting with the heaviest unassigned element, choose an extension (ingroup/outgroup pair) that maximizes a score that equally combines the following: maximal weight of the extension, maximal similarity of coverage distribution across samples between element and extension, minimal coverage variance across covered frags, and does not cause the source or sink to become unreachable. The highest-scoring extension is iteratively added to a path until both source and sink are reached or no further extensions are possible. Paths are generated in this manner until the total weight of unassigned elements falls below a given signal threshold.(Additional file 1: Figure S1. The Bookend workflow. Figure S2. Nucleotide-level precision of Arabidopsis assembly 5′ and 3′ ends. Figure S3. Artifacts in long-read data. Figure S4. Single mESC assembly details. Figure S5. Meta-assembly details. Table S1. Floral bud Smart-seq2 end-labeled read mapping statistics. Table S2. Long-read validation of floral bud assemblies by class. Table S3. Floral bud hybrid assembly details. Table S4. End-labeling and alignment of single mESCs. Table S5. GffCompare performance statistics for mESC meta-assemblies. Supporting notes. Detailed information about End Labeled Read file format and assembly algorithms.Additional file 2: Dataset 1. Bookend Floral Bud, hybrid assembly of Arabidopsis stage 12 inflorescence. Dataset 2. Classification of Bookend Floral Bud transcripts against TAIR10 and Araport11. Dataset 3. Tissue-specific quantification of TSSs from TAIR10, Araport11 and Bookend Floral Bud annotations. Dataset 4. Bookend mESC, hybrid assembly of mouse embryonic stem cells. Dataset 5. Classification of Bookend mESC transcripts against RefSeq and Gencode.Additional file 3. Bookend user guide: Detailed guidelines and instructions for using all utilities in the Bookend software package.Additional file 4. Review history.


Ischemic stroke is a primary origin of morbidity and mortality in the United States and around the world. Indeed, several research projects have attempted to discover new drugs or repurpose existing therapeutics to advance stroke pharmacotherapy. Many of these preclinical stroke studies have reported positive results for neuroprotective agents; however, only one compound (3K3A-activated protein C (3K3A-APC)) has advanced to Phase III clinical trial evaluation. One reason for these many failures is the lack of consideration of transport mechanisms at the blood–brain barrier (BBB) and neurovascular unit (NVU). These endogenous transport processes function as a “gateway” that is a primary determinant of efficacious brain concentrations for centrally acting drugs. Despite the knowledge that some neuroprotective agents  are substrates for these endogenous BBB transporters, preclinical stroke studies have largely ignored the role of transporters in CNS drug disposition. Here, we review the current knowledge on specific BBB transporters that either limit drug uptake into the brain  transporters) or can be targeted for optimized drug delivery  transporters). Additionally, we highlight the current knowledge on transporter expression in astrocytes, microglia, pericytes, and neurons with an emphasis on transport mechanisms in these cell types that can influence drug distribution within the brain. This paf 342 Da . Under nf 342 Da ,134,135.f 342 Da ,136,137,f 342 Da . The pref 342 Da . In addif 342 Da ,129,139,f 342 Da ,141, andf 342 Da ,143. Intf 342 Da ,145,146.Despite the knowledge that disruption of the tight junction can increase a non-selective paracellular “leak” at the BBB, proteins that facilitate the selective transport of small molecules can overcome these changes in microvascular integrity. This enables endogenous BBB transporters to remain as primary determinants of CNS drug disposition despite marked tight junction protein complex dysfunction. Relevant to ischemic stroke, our recent study with memantine shows that cimetidine can block its brain uptake in ipsilateral and contralateral cerebral cortices from both MCAO animals and Sham-operated controls; however, the magnitude of memantine uptake in the ipsilateral cortex was greater than that measured in the contralateral cortex under MCAO conditions . This fiThe BBB exists at the level of the brain microvascular endothelium. A central concept in BBB physiology is that endothelial cells cannot form barrier characteristics without coordinated communication networks with other CNS cell types ,150,151.Astrocytes are the most numerous CNS cell type and play an essential role in the development and maintenance of the BBB phenotype ,155,156.ABC transporters that are known to be expressed in astrocytes include P-gp ,164,165,Microglia are the innate immune cells of the brain and comprise 10–15% of all cell types in the CNS . MicroglAlthough previous studies have shown that microglia express ABC transporters, including P-gp, Mrp1, Mrp4, and Mrp5 ,184,185,ret/retPdgf-b mouse model was not conducted [In addition to glial cells, pericytes play a fundamental role in the control of BBB homeostasis ,188,189.onducted . FurtherTo date, data on the localization and functional expression of transporters in pericytes is limited. P-gp has been localized to the plasma membrane of pericytes in rat and human brain tissue fixed in situ . It is a4) are involved in inflammatory signaling and regulation [Neurons form the basic structural and functional component of the CNS. The primary function of neurons is to respond to stimuli by conducting electrical signals along conductive processes . The conduction of electrical impulses results in the release of neurotransmitters that further regulate (positively and negatively) nearby neural responses . This engulation . Becausegulation . Confocagulation . In vivogulation . Additiogulation , while Ogulation ,205. In gulation ,207,208.gulation . While tThe challenges of CNS drug delivery are best exemplified by ischemic stroke, which continues to be a significant cause of death and disability in the United States and worldwide. Since FDA-approved pharmacotherapy for ischemic stroke remains restricted to fibrinolytic therapy , safe and effective neuroprotective drugs are urgently needed. The application of these new chemical entities to the population of stroke patients is highly dependent upon transport across the BBB. Previous research by our laboratory has revealed several endogenous transporters with considerable potential to be targeted for optimization of CNS drug delivery; however, there are few studies in the preclinical stroke literature that describe the need to consider BBB transport mechanisms during therapeutic development. It is striking that statins and memantine, drugs that have shown varying degrees of success in stroke, are substrates for uptake transporters that are expressed at the human brain microvascular endothelium  while other compounds  that are not transported at the BBB have failed in the clinic . Our lab


Platelets are generated and released into the bloodstream from their precursor cells, megakaryocytes that reside in the bone marrow. Though platelets have no nucleus or DNA, they contain a full transcriptome that, during platelet formation, is transported from the megakaryocyte to the platelet. It has been described that transcripts in platelets can be translated into proteins that influence platelet response. The platelet transcriptome is highly dynamic and has been extensively studied using microarrays and, more recently, RNA sequencing (RNA-seq) in relation to diverse conditions . In this review, we focus on bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans. It has been noted that bulk RNA-seq has limitations when studying in vitro-generated megakaryocyte cultures that are highly heterogeneous, while single-cell RNA-seq has not yet been applied to platelets due to their very limited RNA content. Next, we illustrate how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakaryocytes and rare disease bioinformatics. Next, future perspectives are discussed on how this field of coding transcriptomics can be integrated with other next-generation technologies to decipher unexplained inherited platelet disorders in a multiomics approach. Megakaryocytes (MKs) are large polyploidic blood cells that originate in the bone marrow and, in their mature state, release platelets into the blood circulation. MKs differentiate from hematopoietic stem cells (HSCs) located in the bone marrow. The classical differentiation model for megakaryopoiesis states that HSCs divide asymmetrically into multipotent progenitor cells, which lose their self-renewal capacity and multipotency over time. This leads to bipotent megakaryocytic/erythroid progenitor (MEP) cells, which, as the name suggests, can differentiate into MKs or erythrocytes. In the former case, they differentiate into unipotent MK progenitor cells and then MK precursor cells ,2. ImmatMost studies use in vitro-cultured MKs because of the low yield and difficulty of extracting bone marrow-derived MKs. In vitro MKs can be procured by differentiating induced pluripotent stem cells (iPSCs) through sequential cytokine cocktails (directed differentiation) ,7,8,9 orPlatelets are the second most prevalent cell in the blood and play a pivotal role in cardiovascular diseases by maintaining a balance between hemostasis and blood clot formation. Platelets are small (2–4 µm in diameter), anuclear cells that circulate in the blood for 7–10 days in humans, after which they are eliminated in the spleen and liver . In 1947Platelets have on average 2.2 fg of RNA present in total, but this amount is 20–40-fold higher in young platelets and decreases with time ,42. PlatHere, we review bulk and single-cell RNA-seq studies that have used MKs and platelets to understand their differentiation processes and their function in normal and pathological conditions. Unlike microarrays, RNA-seq does not require transcript-specific probes and can therefore detect novel, rare and unknown transcripts, non-coding transcripts, alternative splicing, gene fusions, single nucleotide variants and indels.The study of healthy MKs enables the identification of genetic pathways regulating MK differentiation and thrombopoiesis. Given the dynamic nature of MK cultures, first being differentiated from HSCs and later producing platelets themselves, cell sorting is often used to isolate specific MK subpopulations representing different differentiation stages before applying bulk RNA-seq. Different cell-specific markers have been identified, such as CD41 or CD61, markers associated with early MK and erythroid progenitors, and CD42, a glycoprotein expressed during the later stage of MK differentiation ,58 diff. For mathttps://www.blueprint-epigenome.eu/, accessed on 5 June 2022) consortium has used bulk RNA-seq extensively in a broad effort to dissect the molecular traits that govern blood cell differentiation in general, thus also including megakaryopoiesis [The BLUEPRINT  depletion, and afterwards, the purity of the platelet population for RNA-seq should be confirmed by flow cytometry using platelet- and leukocyte-specific markers . RNA seqegulated . Anotheregulated . The plaegulated . Plateleegulated ,54. Spliegulated . Interesegulated . As withegulated . Hille eegulated  sorted pFor single-cell RNA sequencing (scRNA-seq) methods, the RNA content of each cell receives a unique barcode enabling cell-wise demultiplexing after sequencing. First, microwells were used to isolate single cells in plate-based assays such as smart-seq ,78. NeweTHBS1 has been identified as an early marker for this CD14+ MK-biased HSC subpopulation [In MKs and their precursors, scRNA-seq has been proven useful for studying their dynamic transcriptome throughout their various developmental trajectories ,91,94,96pulation . In Sun pulation ,94, for pulation ,99. The pulation  identifiAlthough scRNA-seq analysis has been carried out on MK differentiation in vivo ,90,91 anSingle-cell RNA-seq has not yet been used for platelets because of their extremely low RNA content (~2.2 fg/platelet). The leading edge of scRNA-seq assays might enable this in the future, with single-cell libraries currently being created from cDNA contents of 1–2 ng/cell by the much-used commercial 10X Chromium platform. Alternatively, the Smart-seq v4 Ultra Low Input RNA kit has been validated to an RNA concentration of 2 pg of total RNA per cell by the manufacturer. An scRNA-seq protocol called ultra-low RNA sequencing (ulRNAseq) was optimized for RNA concentrations as low as 0.5 pg per cell . scRNA-sChanges in the MK and platelet transcriptome have been studied in diverse pathological conditions, as recently reviewed by Davizon-Castillo et al. , who focNBEAL2 variants as the cause of gray platelet syndrome using platelet RNA-seq [NBEAL2 for platelets from a gray platelet syndrome patient due to a splice variant. A more recent bulk RNA study for gray platelet syndrome reported widespread differences in platelet, neutrophil, monocyte and CD4 lymphocyte transcriptomes between patients and controls, but loss of function of NBEAL2 does not seem to affect the transcriptional output of α-granule-associated genes in platelets [ETV6, a transcription factor that plays a central role in hematopoiesis and malignant transformation, have been implicated in thrombocytopenia [RNU4ATAC, which regulates minor intron splicing. Significant minor intron retention was detected in the patient transcriptomes for 354 MK genes, including many linked to the thrombocytopenia phenotype of platelets. Lentiviral-transduced CD34+ HSCs were generated for wild-type SRC and E527K-SRC and differentiated into MKs before applying bulk RNA-seq [RUNX1, found in patients suffering from familial platelet disorder with a predisposition to myeloid malignancy, was studied by inserting the patient variants into iPSC-derived HSCs and performing scRNA-seq [IKZF5 were found to be causal for thrombocytopenia. Comparison of platelet, monocyte, neutrophil and CD4+ T cell bulk RNA-seq data from patients and controls showed 1194 differentially expressed genes in platelets, while only 4 genes were differentially expressed in each of the other cell types. The pathways downregulated in platelets involved platelet function, hemostasis and membrane- and vesicle-mediated transport.The first RNA-seq-enabled discovery for an IPD was the identification of  RNA-seq . Abnormalatelets . Variantytopenia . Plateleytopenia  performe RNA-seq . The E52cRNA-seq . There wcRNA-seq , variantThe challenge ahead will be to now use platelet and MK RNA-seq datasets to discover new IPD genes, as, to date, most transcriptome studies have only endeavored to decipher defective platelets or MKs for known IPDs. Instead of using RNA-seq data for gene expression and pathway analyses, additional information can be retrieved, such as differences in splicing and monoallelic expression. More recently, the advent of specialized data analysis methods has been directed at finding and statistically assessing outliers in transcriptomic data, and this has stimulated progress in the field of transcriptomics in rare disease research. Outlier detection tools for gene expression  , aberranIt would be very interesting to compare the transcriptomes of platelets, primary MKs and in vitro-generated MKs from single donors using a similar RNA-seq technique. Ideally, since MK populations are very heterogeneous at different differentiation stages, sc-RNA-seq would be best, but this method is still not able to generate transcriptomes for platelets due to their low RNA content. As these methods improve, it is expected that this will become possible in the future. Such a comparative gene expression experiment would improve our current understanding of MK differentiation, thrombopoiesis and platelet function under healthy conditions. It will not be possible to generate such datasets for most patients with IPDs, as primary MKs are typically not available.HDAC6/GATA1 was detected as the cause of thrombocytopenia and autism [Another challenge is the interpretation and functional validation of RNA-seq results. Most studies focus on the detection of differentially expressed genes and pathways. However, it remains very difficult to understand the biological relevance of these changes. One can focus on a subset of the most significant up- or downregulated genes and validate these changes using qRT-PCR or immunoblot analysis, but this is of course a biased approach. It is also possible to validate a pathway using functional MK or platelet studies that have been designed to study the pathway of interest. The main issue remains that most studies are biased by highly expressed genes, while genes with weak expression but still important functions in MKs and platelets are typically missed. Better analytical approaches and larger datasets are needed. At least some of the challenges of associating IPDs with rare variants, in both coding and non-coding regions of the genome, can be overcome by the integration of multiple layers of information from different assay types. These different assays can be performed simultaneously in single cells by techniques such as G&T-seq  (gDNA and autism . It was d autism . Combinid autism ,107,108,d autism  can be ud autism , 115 und


Thermus thermophilus (TtCsm) for programmable capture and concentration of specific RNAs from complex mixtures. The target bound TtCsm complex primarily generates two cyclic oligoadenylates  that allosterically activate ancillary nucleases. To improve sensitivity of the diagnostic, we identify and test several ancillary nucleases . We show that Can1 and Can2 are activated by both cA3 and cA4, and that different activators trigger changes in the substrate specificity of these nucleases. Finally, we integrate the type III-A CRISPR RNA-guided capture technique with the Can2 nuclease for 90 fM (5×104 copies/ul) detection of SARS-CoV-2 RNA directly from nasopharyngeal swab samples.Type-III CRISPR-Cas systems have recently been adopted for sequence-specific detection of SARS-CoV-2. Here, we make two major advances that simultaneously limit sample handling and significantly enhance the sensitivity of SARS-CoV-2 RNA detection directly from patient samples. First, we repurpose the type III-A CRISPR complex from  While the complexity and turnaround times necessary for qPCR are acceptable for many diagnostic applications, the SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) pandemic reveals an urgent need for diagnostics that are easy to distribute, simple to perform, and fast enough to stop transmission of a contagious disease1. Although rapid antigen tests and isothermal amplification methods have helped address this need, these and other emerging methods have limitations related to sensitivity, versatility, or specificity3.Although qPCR (quantitative polymerase chain reaction) remains the “gold standard” for nucleic acid detection, it requires sophisticated equipment, trained personnel, efficient specimen transport to high-complexity labs, and reliable reporting systems5. The first CRISPR-based viral diagnostic came from Collins and colleagues in 2016, when they demonstrated that Cas9 could be used to discriminate between different variants of the Zika virus6. This approach relies on converting viral RNA to DNA using reverse transcriptase, followed by isothermal DNA amplification prior to sequence-based discrimination by Cas9. The exclusive recognition of double-stranded DNA (dsDNA) by Cas9 seemed to be an intrinsic limitation for diagnostic applications that require RNA detection. However, Beisel and colleagues recently developed a creative method that uses the trans-acting CRISPR-RNA (tracrRNA) to capture complementary RNA guides derived from RNA viruses7. In this system, the engineered tracrRNA-crRNA hybrid guides Cas9 to a complementary dsDNA reporter. While this approach enables RNA detection, Cas9 is a single turn-over enzyme, which may limit its sensitivity. In contrast to Cas9, target recognition by type V (Cas12-DETECTR) and type VI (Cas13-SHERLOCK) CRISPR-systems activates a multi-turnover non-sequence-specific “collateral nuclease” activity that amplifies the signal by cleaving thousands of reporter molecules for every target bound9.CRISPR RNA-guided diagnostics (CRISPR-dx) are a diverse group of nascent technologies that aim to address current limitations by providing a versatile and programmable platform that is sufficiently sensitive for clinical applications and stable enough for distribution12. The polymerase domain has been estimated to generate ~1000 cyclic oligoadenylates per bound RNA13, which trans-activate and allosterically regulate diverse multi-turnover ancillary nucleases that provide defense from invading genetic parasites15. This biochemical cascade exponentially amplifies the signal when a type III complex detects target RNA, suggesting that these systems have the potential to enhance the sensitivity of CRISPR-based diagnostics. However, initial efforts to implement this approach failed to be sufficiently sensitive for clinical applications without prior amplification of the target RNA18. The sensitivity of this first-generation diagnostic was in part limited by the use of Csm6 ancillary nucleases that also degrade the cyclic nucleotide activator23. Recently, Malcolm White’s lab demonstrated that alternative ancillary nucleases, which efficiently cleave reporters but do not cleave the signaling molecule, can be used to enhance the sensitivity of type III-based diagnostics24.Like type VI, type III systems also recognize complementary RNA. However, unlike any other CRISPR system, target recognition by type III complexes simultaneously activates polymerase and HD-nuclease domains in the Cas10 subunit3-cA6), test for ring nuclease activity and determine how cyclic oligoadenylates, as well as metal-preferences impact substrate cleavage activities. We show that the Can1 nuclease from T. thermophilus (TtCan1) and the Can2 ortholog from Archaeoglobi archaeon JdFR-42 (AaCan2) are activated by more than one cyclic nucleotide species  and that substrate specificity of these nucleases changes according to the bound activator. This observation helps to explain how diverse cyclic nucleotides  produced by a single type III surveillance complex integrate distinct activities from a single effector. Finally, we demonstrate how the type III complex can be used to bypass RNA extraction methods, and that coupling type III-based RNA capture with the AaCan2 nuclease further increases the sensitivity of SARS-CoV-2 RNA detection in patient swabs to 5×104 copies/ul.Despite innovations leading to new and improved CRISPR-based diagnostics, point-of-care testing requires new strategies that simplify the workflow and increase the sensitivity without prior RNA purification or amplification . Here, we bring CRISPR-dx closer to a deployable diagnostic by developing a type III CRISPR-based method for sequence-specific capture and concentration of RNA from heterogeneous samples. To improve the sensitivity, we purify several different ancillary nucleases , systemically test nuclease activation using a series of purified cyclic oligoadenylate standards  that form the oligomeric “backbone” of the complex26. Type III complexes release fragments of the cleaved target, which inactivates ATP polymerization by the Cas10 subunit26. Previously, we mutated residues in the Csm3 subunit responsible for target RNA cleavage (D34A), purified the RNase-dead complex (TtCsmCsm3-D34A), and showed that the mutant complex provides more sensitive detection of viral RNA than the wildtype complex16. To further increase the sensitivity, we set out to determine if TtCsmCsm3-D34A could be used to concentrate sequence-specific RNAs. To test this approach, we mixed 32P-labeled target or non-target RNAs with TtCsmCsm3-D34A, incubated for 20 minutes, and concentrated the His-tagged complex using nickel-derivatized magnetic beads . The beads were concentrated using a magnet, and RNAs were extracted from the bound and unbound fractions. The type III complex captured most of the radiolabeled target RNA (76±5.8%), while non-target RNA primarily remains in the supernatant . To determine if type III CRISPR-based RNA capture and concentration results in the synthesis of more cyclic nucleotides, we mixed Csm-beads with 120 μL of a sample containing SARS-CoV-2 RNA and total RNA extracted from HEK 293T cells . The type III CRISPR-based concentration increases the amount of cA3 and cA4, as compared to the reaction performed without RNA concentration .Type III CRISPR RNA-guided complexes  bind and cleave complementary single-stranded RNA (ssRNA) targets3T cells . After c4-activated ribonuclease, to generate a real-time fluorescent readout for Csm-based RNA detection16 (top). We reasoned that increased cA4 levels after RNA enrichment will boost the nuclease activity of TtCsm6 and therefore increase the sensitivity of the RNA detection. To test this hypothesis, we titrated 108 to 105 copies/μL of SARS-CoV-2 N-gene RNA into total RNA extracted from HEK 293T cells, concentrated the target RNA using TtCsmCsm3-D34A, resuspended the beads in a buffer containing ATP, and then transferred the polymerization products to a reaction containing TtCsm6 and a fluorescent RNA reporter . Csm-based RNA enrichment increased the sensitivity of the assay 100-fold compared to the assay without the pull-down  and a carboxy-terminal HEPN (Higher Eukaryotes and Prokaryotes Nucleotide-binding) domains31. Like Csm6 proteins, Can proteins also contain amino-terminal CARF domains, but the carboxy-terminal nucleases are distinct. The Can1 protein from Thermus thermophilus (TtCan1) has a unique monomeric architecture with two non-identical CARF domains, one nuclease-like domain (NLD) and one restriction endonuclease domain (PD-(D/E)XK)31, while Can2 nucleases contain a single CARF domain and form symmetrical homodimers30 (CRISPR ancillary nucleases (Can) are another family of recently identified proteins that are activated by cyclic oligoadenylates and lack ring nuclease activitydimers30 .431, the impact of other cyclic oligoadenylates on TtCan1 activity has not been reported. We purified TtCan1 and tested nuclease activity against plasmid DNA in the presence of five different cyclic oligoadenylates (cA2-cA6) . To our surprise, TtCan1 robustly degrades plasmid DNA to ~100 bp fragments in the presence of cA3 and Mn2+, while cleavage with cA4 is comparable to the background activity in the absence of an activator . To determine if the TtCan1 nuclease has any sequence preference, we deep-sequenced the cleavage fragments, aligned the reads, and identified cut sites. This analysis failed to identify common sequence motifs that define the cleavage site, suggesting that TtCan1 is a non-sequence specific DNase . Based on the unexpected activation of TtCan1 with cA3, we tested several other substrates and discovered that TtCan1 is a cA4-dependent single-stranded RNase (ssRNA) but does not cleave ssDNA . Taken together, our in vitro assays show that TtCan1 is a non-sequence specific double-stranded DNase when activated with cA3 and a single-stranded RNase when activated with cA4.To identify Can1 and Can2 orthologs compatible with the TtCsm complex, we generated profile Hidden Markov models (HMMs) to query publicly available microbial genomes and metagenomes from NCBI and JGI. This analysis identified 204 Can1 and 3,121 Can2 proteins. Based on this analysis, we selected TtCan1 and three Can2 orthologs from thermophilic organisms for cloning and expression . While pSupplementary Fig. 3a, b). We systematically tested the activities of AaCan2 against different substrates with a range of cyclic oligoadenylates . Like TtCan1, AaCan2 is also a Mn2+- and cA3-dependent dsDNase , or a ssRNase when activated with cA4. The ssRNase activity of AaCan2 is supported by either Mn2+ or Mg2+ . Reproducible cleavage of ssDNA is also detectable for AaCan2, but the activity is Mn2+-specific, and robust cleavage requires a higher concentration of cA4  . Cleavage of ssDNA produces a discrete band suggesting that the enzyme processes ssDNA to a minimal cleavage product or that the activity is sequence-specific . While cA4-dependent activities of AaCan2 are consistent with activities previously reported for the Can2 protein from Treponema succinifaciens29 .Can2 genes from Clostridium thermobutyricum (CthCan2), Thermus thermophilus (TtCan2), and Archaeoglobi archaeon JdFR-42 (AaCan2) were cloned and expressed in E. coli . HoweverSupplementary Table 1, Supplementary Fig. 4). Consistent with our gel-based assays, cA4-activated AaCan2 cleaves RNA reporters in the presence of either Mg2+ or Mn2+, but reactions with Mn2+ consistently result in higher fluorescent signal . While TtCan1 cleaves the same RNA reporters as AaCan2, cleavage by TtCan1 requires higher concentrations of cA4 and produces less fluorescent signal .To determine if incorporating TtCan1 or AaCan2 improves sensitivity of the Csm-based RNA detection assay, we screened a library of synthetic RNA reporters designed to identify sequences that might be preferred by these nucleases . AaCan2 produces a similar fluorescent signal to TtCsm6 when activated with 20-fold less cA4 (0.5 nM versus 10 nM)  drop in the activity. The distinction in activity between these enzymes is consistent with the ring-nuclease activity of TtCsm6 rapidly degrading its activator, while AaCan2 binds and preserves the cyclic nucleotide .Having established that AaCan2 is more active than TtCan1, we set out to compare AaCan2 to the sensitivity of TtCsm6, which we used previouslys 10 nM) . Moreove6 copies/μL of target RNA (5 copies/μL)  . While cpies/μL) , g, wherpies/μL) . This di4-activated collateral cleavage of ssRNA reporters, the TtCsmCsm3-D34A-complex also produces cA3 . We hypothesized that combining cA3- and cA4-sensing nucleases might enhance the sensitivity of TtCsm-based detection . The NucC from Clostridium tepidum (CtNucC) has the highest dsDNase activity and digests plasmidWhile our assay uses cAetection . NucC . Deep sequencing of cleavage fragments determined that all purified NucC nucleases have a preference for 5’-ANNT-3’ sequence motif, which is consistent with previously published work33 .DNA into 300–400 bp fragments in the presence of cACsm3-D34A. To perform fluorescent assays with CtNucC, we designed a 31-bp dsDNA reporter comprising six repeats of the optimal cleavage site (Supplementary Table 1). The lowest concentration of cA3 detected by CtNucC is 0.5 nM, which is 10-fold more sensitive than TtCan1 and 100-fold more sensitive than AaCan2 . While CtNucC is sensitive to cA3 activation, the TtCsm-complex may not produce sufficient concentrations of this cyclic nucleotide to increase sensitivity over AaCan2 detection alone.Next, we set out to determine if CtNucC and AaCan2 could be combined into a single reaction to improve the sensitivity of RNA detection with TtCsmn AaCan2 . HoweverSupplementary Fig. 9a). We used 3 μL of each RNA sample to perform the TtCsm-AaCan2 reaction and 120 μL as input for Csm-based RNA capture followed by a polymerization reaction and fluorometric detection with AaCan2. Only samples with the highest viral RNA concentration (Ct <17) tested positive in the TtCsm-AaCan2 reactions. However, adding the Csm-based RNA capture method increases the sensitivity ~100-fold and reliably detects SARS-CoV-2 RNA in patient samples with Ct values ≤23.2, which corresponds to ~104 copies/μL of viral RNA .RNA extracted from nasopharyngeal swabs of COVID-19 patients are complex mixtures of nucleic acids derived from the host, the virus, and microbial communities residing in the upper respiratory tract. To determine if TtCsm complex can capture SARS-CoV-2 RNA in such mixtures, we extracted total RNA from nasopharyngeal swabs of 17 positive and 6 negative patients diagnosed by RT-qPCR . We mixed Csm-beads with a mock sample made by spiking SARS-CoV-2 RNA fragment into SARS-CoV-2 negative nasopharyngeal swab, added lysis buffer, and incubated for 20 min at 65°C. This heat treatment inactivates SARS-CoV-2, promotes lysis, and allows RNA binding by TtCsm-complex and its downstream activities35. After pulling down Csm-beads with a magnet, we discarded the supernatant and performed polymerization reactions followed by a TtCsm6-based fluorescent readout. The TtCsm complex detects spiked RNA in the samples treated with Triton X-100 (0.025 – 0.1%) and EGTA (1 mM), while other buffers significantly inhibited Csm-based detection .RNA extraction kits are expensive, time-consuming, and require specialized equipment. To eliminate this step, we tested if the TtCsm complex can capture and concentrate target RNA directly from a nasopharyngeal swab sample without prior RNA extraction. To identify lysis conditions that do not inhibit activity of the TtCsm-complex, we tested 10 lysis buffer compositions with varying concentrations of detergents  and chelators  , which corresponds to 5×104 copies/μL and ~5-fold less sensitive compared to detection performed using purified RNA . To compare the efficiency of direct detection from lysed nasopharyngeal swab relative to detection from extracted RNA, we used three nasopharyngeal swab samples that previously tested positive for SARS-CoV-2 using RT-qPCR . All three samples tested positive using direct detection from nasal swabs, however direct detection from patient samples resulted in a higher signal-to-noise ratio. This difference suggests that further optimization of the lysis conditions may lead to higher sensitivity .Finally, to assess the sensitivity of direct SARS-CoV-2 RNA detection in swab samples using type III capture and AaCan2-based fluorescent detection , we used5. Development efforts have primarily focused on type V (Cas12) and type VI (Cas13) CRISPR-systems, and the sensitivity of these techniques have improved from picomolar36 to attomolar concentrations28. However, most CRISPR-based viral diagnostics described to date still require nucleic acid extraction and pre-amplification to reach clinically relevant sensitivities4.CRISPR-based diagnostics have been progressing at a remarkable pace17. More recent improvements using different type III complexes or different ancillary nucleases have been used to detect SARS-CoV-2 RNA in purified RNA samples with ~2–4 fM sensitivity24. Here, we contribute to the ongoing development of type III systems by developing methods for sequence-specific capture and concentration of target RNAs directly from unprocessed patient samples. This approach enables direct detection of 5×104 copies of SARS-VoV-2 RNA per μL (~90 fM) in clinical samples without laboratory-based RNA extraction or pre-amplification. While the sensitivity of the approach is still inferior to RT-qPCR, it is sufficient to identify infected individuals capable of spreading SARS-CoV-237 and is comparable to rapid antigen tests2.In 2021, the first attempts to repurpose type III CRISPR systems for SARS-CoV-2 diagnostics achieved 0.1 – 1 nM sensitivity of RNA detection without pre-amplification4. While Cas13-based methods are currently more sensitive (~50 aM), the intrinsic amplification of RNA recognition by type III system may ultimately improve sensitivity. Type III systems uniquely amplify RNA recognition in two sequential steps: first, through Cas10-mediated polymerization of cOAs and second, through cOA-mediated activation of multi-turnover effectors . In addition to the advantages that might come from consecutive stages of signal amplification, the separation of target recognition by the type III surveillance complex  from collateral cleavage by ancillary effectors also enables programmable RNA capture. Unlike Cas13, which relies on the same active site for target and non-target collateral cleavage38, the RNase-dead TtCsm complex (TtCsmCsm3-D33A) can be used to capture and maintain target RNA from a larger volume and concentrate these RNAs for various downstream applications. Incorporating RNA capture increases the sensitivity of type III CRISPR-based diagnostic and allows direct detection in clinical samples without RNA extraction, a prerequisite for most current platforms. We anticipate that further incorporation of type III-based RNA pull-down techniques to bypass RNA extraction, optimization of lysis conditions, and next generation of readouts  will further boost the sensitivity and minimize time-to-result, bringing type III CRISPR diagnostic to current standards of rapid molecular testing.Like Cas13, type III systems also recognize RNA, and the most sensitive detection methods developed to date for either approach rely on collateral nuclease activity to release a fluorescent signal3 and cA4, but none of the other tested cyclic oligoadenylates , activate TtCan1 and AaCan2 to cleave specific substrates. TtCan1 is primarily a cA3-dependent dsDNase, while AaCan2 is a cA4-dependent ssRNase. Can1 nucleases may have emerged from duplication and fusion of ancestral Can2 genes31, and we hypothesize that this fusion may enable the evolution of mechanisms for recognizing diverse  ligands that activate the effector. Similarly, SAVED (SMODS-Associated and fused to Various Effector Domains) domains appear to be derived from the fusion of two ancient CARF-like domains and are activated by cyclic trinucleotides39.Our work to improve type III diagnostics has also uncovered ancillary nuclease activities that are valuable for understanding the basic biology and augmenting applications for biotechnology. Both cA24. We showed that the TtCsm complex predominantly generates cA4, while cA3 is produced at a lower level. We hypothesize that cOA ratios generated by type III complexes have evolved as a fine-tuned immunomodulatory mechanism that regulates ancillary nuclease activities and infection outcomes. In fact, the genome of T. thermophilus (HB8 and HB27 strains) encodes both a cA4-activated Csm6 RNase and Can1 CARF-nuclease31 that is activated by cA4 (RNase) and cA3 (DNase). cA4 is the primary signal generated by target-bound TtCsm, and RNA cleavage by cA4-activated Csm6 nucleases results in growth arrest and facilitates clearance of invading genetic parasites15. However, failure to clear the infection through cA4-dependent RNase activity by Csm6 would result in continuous polymerization by Cas10 and accumulation of cA3, which will activate the TtCan1 DNase. The lack of sequence preference suggests that TtCan1 might degrade the host genome and induce abortive infection and cell death. More work is necessary to understand the diversity of nucleoside-based signal generators and the diversity of signal integrators.Target RNA binding by type III Csm- or Cmr-complexes triggers synthesis of several cyclic oligoadenylate species in varying ratiosClinical samples were obtained with local IRB approval (protocol #DB033020) and informed consent from patients undergoing testing for SARS-CoV-2 at Bozeman Health Deaconess Hospital. Nasopharyngeal swabs from patients that either tested negative or positive for SARS-CoV-2 were collected in viral transport media. RNA was extracted from all patient samples using the QIAamp Viral RNA Mini Kit (QIAGEN).26) were purchased from Biolog Life Science Institute. Fluorescent reporters (RNA and DNA) were purchased from IDT (Supplementary Table 1). The dsDNA reporter was ordered as a duplex from IDT. Target and non-target RNAs of SARS-CoV-2 N-gene were in vitro transcribed with MEGAscript T7 (Thermo Fisher Scientific) from PCR products generated from pairs of synthesized overlapping DNA oligos (Supplementary Table 1) (Eurofins). Transcribed RNAs were purified by denaturing PAGE. Total RNA from HEK 293T cells was extracted using TRIzol reagent.Sodium salts of cyclic di-, tri-, tetra-, penta- and hexa-adenosine monophosphates  obtained for RNAseP mRNA in RNA samples extracted from nasopharyngeal swabs (Supplementary Table 2). The RT-qPCR for RNase P mRNA was performed using CDC RP primers and probe .Thermus thermophilus , were a gift from Jennifer Doudna. Vector pCDF-5xT7-TtCsm was used as a template for site-directed mutagenesis to mutate the D33 residue in Csm3 to alanine (D33A) and inactivate Csm3-mediated cleavage of target RNA (pCDF-5xT7-TtCsmCsm3-D34A)35. The CRISPR array in pACYC-TtCas6–4xcrRNA4.5 was replaced with a synthetic CRISPR array (GeneArt) containing five repeats and four identical spacers, designed to target the N-gene of SARS-CoV-2 16. TtCas6 was PCR was PCR-amplified from the pACYC-TtCas6–4xcrRNA4.5 plasmid and cloned between the NcoI and XhoI sites in the pRSF-1b backbone (Millipore Sigma) (pRSF-TtCas6). Expression vector encoding TtCsm6 nuclease, pC0075 TtCsm6 His6-TwinStrep-SUMO-BsaI, was a gift from Feng Zhang (Addgene plasmid #115270)40.Plasmids encoding the type III-A Csm complex frm Thermus thermophilus , Can2 from Archaeoglobi archaeon JdFR-42  IMG gene accession=2730024700), Clostridium thermobutyricum , and Thermus thermophilus , were codon optimized for expression in E. coli, synthesized by GenScript, and cloned into pC0075 vector (Addgene #115270) in frame with the N-terminal His6-TwinStrep-SUMO tag using NcoI and XhoI restriction sites to replace the TtCsm6 gene. NucC from Clostridium tepidum BSD2780120874b_170522_A10 , Elioraea sp. Yellowstone  and Acidimicrobiales bacterium mtb01 , were cloned into pC0075 backbone using the same restriction sites as for Can1 and Can2 genes.Gene fragments encoding for Can1 from Csm3-D34A complex and TtCsm6 were performed as previously described16. TtCan1, AaCan2, CtCan2, TtCan2, CtNucC, EsNucC, and Amtb01NucC) were purified according to the following protocol. Each expression vector was transformed into Escherichia coli BL21(DE3) cells and grown in LB Broth (Lennox) (Thermo Fisher Scientific) at 37°C to an OD600 of 0.5. Cultures were then incubated on ice for 1 hour, and then induced with 0.5 mM IPTG for overnight expression at 16°C. Cells were lysed with sonication in Lysis buffer  and lysate was clarified by centrifugation at 10,000 ×g for 25 mins, 4°C. The lysate was heat-treated at 55°C for 45 minutes and clarified by centrifugation at 10,000 g for 25 mins at 4°C. His6-TwinStrep-tagged protein was bound to a StrepTrap HP column (Cytiva) and washed with Lysis buffer. The protein was eluted with Lysis buffer supplemented with 2.5 mM desthiobiotin and concentrated (10k MWCO Corning Spin-X concentrators) at 4°C. Affinity tags were removed from the protein using His-tagged SUMO protease (100 μL of 2.5 mg/mL protease per 20 mg of protein) during dialysis against SUMO digest buffer , 0.15% Igepal) at 4°C overnight. The tag and the protease were applied to HisTrap HP column (Cytiva), and the flow-through was concentrated using Corning Spin-X concentrators at 4°C. Finally, the protein was purified using a HiLoad Superdex 200 26/600 size-exclusion column (Cytiva) in storage buffer . Fractions containing the target protein were pooled, concentrated, aliquoted, flash-frozen in liquid nitrogen, and stored at −80°C.Expression and purification of the TtCsm32P]-ATP (PerkinElmer) for 30 min at 37°C. The kinase was heat inactivated by heating at 65°C for 20 min.Target (SARS-CoV-2 N1) and non-target RNAs were transcribed from PCR extended duplex oligos using home-made T7 RNA polymerase (Supplementary Table 3) (Eurofins). The IVT RNAs were gel purified and dephosphorylated with Quick CIP (NEB) for 20 min at 37°C in 1X CutSmart Buffer (NEB). The phosphatase was inactivated by heating at 80°C for 5 min before 5’ end-labeling the RNAs with T4 polynucleotide kinase (NEB) and .For the experiments shown in dead complex (25 nM) in 1X Binding Buffer (V=50 μL) and incubated for 30 min on ice. The beads with the complex (Csm-beads) were concentrated with a magnet and resuspended in 5 μL of 1× Binding Buffer.The HisPur Ni-NTA Magnetic beads (ThermoFisher) were washed two times with a 1X Binding Buffer . For one reaction, 5 μL of equilibrated beads were mixed with TtCsm10 copies/μL) or 3 μL of NTC were mixed with TtCsm Csm3-D34A complex (25 nM) and 250 μM ATP supplemented with [α−32P]-ATP (PerkinElmer) in the reaction buffer phosphine)), 5 mM magnesium sulfate). The reaction was incubated at 60°C for 1h. For the pull-down reactions, 120 μL of positive or negative samples were mixed with 5 μL of Csm-beads in Binding Buffer  for 10 min at 60°C. The Csm-beads were concentrated with a magnet and the supernatant was discarded. The Csm pellets were resuspended in 30 μL of the reaction buffer and 250 μM ATP supplemented with [α−32P]-ATP (PerkinElmer). Reaction products were phenol-chloroform extracted and resolved on silica TLC plates (Millipore).For the experiments shown in Samples (1 μL) were mixed with 100 mM sodium acetate, pH 5.2 (2 μL) and spotted 1.5 cm above the bottom of the TLC plate. The plate was placed inside a 2 L beaker filled to ~0.5 cm with developing solvent  and capped with aluminum foil. The plate was run for 2 h at room temperature and dried. TLC plate was exposed to a phosphor screen and imaged with Typhoon phosphor imager. Chemically synthesized standards (2μM) were resolved on the same TLC plate and visualized using UV shadowing.3 and cA4 hydrolysis in the presence of ancillary nuclease, radiolabeled cA3 and cA4 produced above were mixed with nuclease (500 nM) in the reaction buffer and incubated for 1 hour at 55°C. Reaction products were phenol-chloroform extracted and resolved using thin-layer chromatography (TLC) for 45 min as described above.To test cAdead complex, 300 nM of nuclease  with corresponding reporter in a reaction buffer phosphine)), 5 mM magnesium sulfate (for TtCsm6 and CtNucC) or 5 mM manganese(II) chloride (for AaCan2) in a 30 μL reaction. The reporter B8 (300 nM) was used for the reaction with TtCsm6, D7 (300nM) – with AaCan2, and dsDNA probe (300 nM) – with CtNucC. Reactions were incubated at 55°C. Cleavage of fluorescent reporters was detected by measuring fluorescence every 10 sec in a real-time PCR instrument QuantStudio 3 (Applied Biosystems).3 μL of RNA sample was mixed with 250 μM ATP, 25 nM TtCsmdead complex with the magnetic beads, the HisPur Ni-NTA Magnetic beads (ThermoFisher) were washed two times with a 1X Binding Buffer . For one reaction, 5 μL of equilibrated beads were mixed with TtCsmdead complex (30 nM) in 1X Binding Buffer (V = 50 μL) and incubated for 30 min on ice. The beads with the complex (Csm-beads) were concentrated with a magnet and resuspended in 5 μL of 1× Binding Buffer.To bind TtCsm120 μL of sample was mixed with 5 μL of Csm-beads in 1x Binding Buffer for 10 min at 60°C. The Csm-beads were concentrated with a magnet and the supernatant was discarded. The Csm-beads pellet was resuspended in 20 μL of the 1X reaction buffer phosphine)), 5 mM magnesium sulfate / manganese(II) chloride) containing ATP (250 μM). The reaction was incubated 10 min at 60°C, the Csm-beads were pelleted, and the supernatant (10μL) was transferred to a new reaction with TtCsm6 (300 nM) and B8 RNA Reporter (300 nM) or AaCan2 (300 nM) and D7 RNA Reporter (300 nM) in 1X reaction buffer (V = 30 μL) (Supplementary Table 1). Reactions were incubated at 55°C. Cleavage of the fluorescent RNA reporter was detected by measuring fluorescence every 10 sec in a real-time PCR instrument QuantStudio 3.120 μL of a nasopharyngeal swab was mixed with 5 μL of Csm-beads in 1X Lysis Buffer and incubated for 20 min at 65°C. Ten lysis buffers compositions were tested. All buffers contained 25 mM HEPES, pH 7.5, 150 mM NaCl, 1 mM TCEP and were supplemented with (A) 0.025% Triton X-100, (B) 0.025% Triton X-100 and 1 mM EDTA, (C1) 0.025% Triton X-100 and 1 mM EGTA, (C2) 0.05% Triton X-100 and 1 mM EGTA, (C3) 0.1% Triton X-100 and 1 mM EGTA, (I) 0.025% NP-40, (J) 0.025% NP-40 and 1 mM EDTA, (K1) 0.025% NP-40 and 1 mM EGTA, (K2) 0.05% NP-40 and 1 mM EGTA, or (K3) 0.1% NP-40 and 1 mM EGTA. Each of the supplements are lettered according to the results presented in Supplementary Fig. 9d. The Csm-beads were concentrated with a magnet and the supernatant was discarded. The Csm-beads pellet was resuspend in 20 μL of the 1x reaction buffer phosphine)), 5 mM magnesium sulfate or manganese(II) chloride) containing ATP (250 μM). The reaction was incubated 10 min at 65°C, the Csm-beads were pelleted, and the supernatant (10 μL) was transferred to a new reaction with TtCsm6 (300 nM) and B8 RNA Reporter (300 nM) or AaCan2 (300 nM) and D7 RNA Reporter (300 nM) in 1 × reaction buffer . Reactions were incubated at 55°C. Cleavage of fluorescent RNA reporter was detected by measuring fluorescence every 10 sec in a real-time PCR instrument QuantStudio 3.https://www.fda.gov/media/134922/download). In brief, 20 μL reaction included 8.5 μL of Nuclease-free Water, 1.5 μL of Primer and Probe mix , 5 μL of TaqPath 1-Step RT-qPCR Master Mix  and 5 μL of the RNA. Nuclease-free water was used as negative template control (NTC). Amplification was performed as follows: 25°C for 2 min, 50°C for 15 min, 95°C for 2 min followed by 45 cycles of 95°C for 3 s and 55°C for 30 s. To quantify viral RNA in the samples, standard curve for N1 primers was generated using a dilution series of a SARS-CoV-2 synthetic RNA fragment  spanning N gene with concentrations ranging from 10 to 106 copies per μL. Three technical replicates were performed at each dilution. The NTC showed no amplification throughout the 45 cycles of qPCR.RT-qPCR was performed using N1 and RP CDC primers . RNA was extracted from patient samples with QIAamp Viral RNA Mini Kit  and used for one-step RT-qPCR in ABI 7500 Fast Real-Time PCR System according to CDC protocols . Briefly, DNA was end-repaired with NEBNext Ultra II End Prep Enzyme Mix, which fills 5’- and removes 3’- overhangs. Next, end-repaired fragments were barcoded with Native Barcoding Expansion kit  using Ultra II Ligation Master Mix (NEB). Barcoded DNA fragments were pooled together and purified with magnetic beads . Freshly mixed 80% ethanol was used to wash magnetic bead pellet. Sequencing adapters (AMII) were ligated to barcoded DNA using NEBNext® Quick Ligation Module . Ligation reactions were purified with magnetic beads. SFB buffer  was used for washes. Resulting DNA library was eluted from the beads in 20 μL of EB buffer . DNA concentration was measured with Qubit dsDNA HS Assay , and 20 g was loaded on the Nanopore MinION (R9.4.1 flow cell). The flow cell was primed, and library was loaded according to Oxford Nanopore protocol (SQK-LSK109 kit). The sequencing run was performed in the high-accuracy base calling mode in the MinKNOW software.DNA cleavage fragments were sequenced using Oxford Nanopore with Ligation Sequencing Kit (SQK-LSK109). After incubation with TtCan1 or NucC nucleases, cleavage fragments were column-purified using DNA Clean & Concentrator-5 kit  as instructed. Next, for each sample 50 ng of purified DNA was used to prepare sequencing libraries with NEBNext® Ultrabamtobed function in bedtools package was used to generate BED files and read coordinates were extracted. Read end coordinates were used to calculate cleavage fragment length distributions and map frequencies of cuts at specific locations . To analyze the sequence preferences of each nuclease, 14 bp windows surrounding read ends were extracted with getfasta function from bedtools package. Resulting fasta files were used to calculate position weigh matrices (PWMs) with getPwmFromFastaFile function in DiffLogo R package. Finally, PWMs were plotted as sequence logos using ggseqlogo R package. Sequencing depth around the most frequent cut site for each nuclease was calculated with samtools depth function and plotted with ggplot2 package in RStudio.Sequenced reads were demultiplexed using guppy-barcoder (ONT) and aligned with minimap2 v2.17-r954-dirty (ax map-ont mode) to the reference plasmid sequence that was modified by adding 1000 bp overlaps at the 5’- and 3’- ends. Overlapping regions were introduced to account for circular nature of the plasmid. Resulting alignments (BAM files) were sorted and indexed using samtools v1.13. Next, NNNNNNAU-IABkFQ-3’; variable region underlined) or to produce a structured RNA . The Biostrings package in R was used to construct a library of reporter sequences containing each of the 64 unique trinucleotide combinations possible. Since multiple unique trinucleotides could be included in a single reporter , we narrowed our initial library of 64 reporters to remove redundant sequences. This resulted in a library of 24 unique reporter sequences, each of which were integrated into both a single-stranded RNA reporter and a structured RNA reporter (Supplementary Table 1). The R-script used to design these reporters is accessible on GitHub (WiedenheftLab/RNA_reporter_design).To determine the optimal RNA reporter for each cOA-activated nuclease, we constructed a library of variable RNA sequences tethering a FAM fluorophore to an Iowa Black quencher. These reporters were designed as single-stranded RNA molecules  plasmid with TtCan1, AaCan2 or CtNucC (15–200 nM) in the presence of cOAx (15–45 nM) in 10 μL reaction. After 5–15 min incubation at 60°C for TtCan1 and 55°C for both AaCan2 and CtNucC, Gel Loading Dye, Purple (6X) (NEB) was added and 4 μL was loaded on 1% agarose gel. For ssDNA and ssRNA cleavage assays, 0.425 μM of 71 nt DNA oligo  or 0.425 μM of 74 nt IVT RNA SARS-CoV-2 N-gene (Supplementary Table 3) were incubated with TtCan1 (200 nM) or AaCan2 (200 nM) in the presence of cOAx (20–45 nM) in 10 μL. After 5–15 min incubation at 60°C for TtCan1 and at 55°C for AaCan2, 2X RNA Loading Dye (NEB) was added and 10 μL was loaded on 12% UREA PAGE.31 to generate individual lists of closely related proteins with an e-value cutoff of 10−4 and 50% query coverage. The resulting sequences were then used as queries to initiate a PSI-BLAST search with an E-value cutoff of 10−4 and 50% query coverage. This step was repeated until convergence and redundant sequences were removed with CD-HIT v4.741. In case of Can1, sequences from a previously published dataset14 that contain two CARF domains and a nuclease domain were used to generate multiple sequence alignment of Can1-related proteins. In total, 29 sequences of Can1-related proteins and 2,531 sequences of Can2-related proteins were used separately to generate multiple sequence alignment with a local version of MAFFT v7.42942 . The generated alignments for Can1 and Can2 were curated with MaxAlign v1.143 to remove misaligned or non-homologous sequences. The resulting dataset—comprised of 29 Can1-like and 1,283 Can2-like proteins, respectively—were then individually realigned with MAFFT and HMMbuild44 (HMMER v3.2.1) was used to generate HMM profiles from each alignment. The resulting profiles were used to search a local database of prokaryotic genomes from NCBI  and list of sequences identified in BLAST search from previous steps. An initial search performed with these HMM profiles identified 1,442 Can1 and 5,431 Can2 homologs, which were manually filtered according to the presence of domains that define each protein, as well as the presence of conserved residues found in CARF and nuclease domains. The resulting set of 204 Can1 and 3,121 Can2 proteins were merged into a single file and aligned in MAFFT (LINSI option) for downstream phylogenetic analyses. Next, Trimal v1.445 was used to remove columns in the alignment comprised of ≥70% gaps. Thermostable homologs of Can1 and Can2 were annotated according to organisms that they are originated. ProtTest v3.4.246 was used to select an evolutionary model, and a phylogenetic tree was constructed in IQ-TREE v1.6.147 using the recommended model . The phylogenetic tree was plotted using the ggTree package in R48.A DELTA-BLAST was initiated, using previously described Can1 and Can2 proteins as queries32 generated a list of closely related proteins . The resulting dataset was filtered with CD-HIT v4.7 to remove redundant sequences. The resulting 1,230 NucC sequences were aligned with MAFFT , and poorly aligned and highly gapped sequences were removed with MaxAlign. The resulting set of 896 NucC sequences were re-aligned with MAFFT as previously described, and the resulting alignment was used to generate a NucC HMM profile which we used to search within prokaryotic genomes from NCBI. This search identified 1,774 hits, which were filtered according to the presence of restriction endonuclease-like domain , gate-loop and cA3 binding domains and were aligned with MAFFT. The remaining NucC homologs were curated according to organisms they are originated from to identify thermostable NucC homologs. The resulting alignment of 1,510 NucC proteins with 21 thermostable homologs was used to generate a phylogenetic tree with FastTree v2.1.1049 and was plotted using the ggTree package in R.A phylogenetic tree of NucC proteins was generated using the same methods as described above for Can1/Can2 proteins. Briefly, DELTA-BLAST and PSI-BLAST searches with previously identified NucC proteinsaov function in the stats R package. Multiple comparisons between positive samples and negative controls were performed using Dunnett’s test with multcomp R package. Reaction slopes were determined by extracting coefficients from linear models fitted to fluorescence data with lm function in R. The linear regions of the fluorescence curves were identified using rolling regression with auto_rate function in respR package. Patient samples (n = 17) for viral detection assays were randomly selected from a sample database (n = 858) with base R function sample. Statistical threshold for detecting SARS-CoV-2 in patient samples with Csm-based assay was set as mean of negative control ± 2.33 S.D., which captures 98% of variation in negative samples . Samples with z-score > 2.33 were considered positive for SARS-CoV-2. Z-scores were calculated in R using following formula: Z = (Fsample - μneg)/σneg, where Fsample is fluorescence measured in a sample, μneg is mean of the negative control, σneg is standard deviation of the negative control. Statistical significance levels used in the figures are *** p < 0.001, ** p < 0.01, and * p < 0.05.All statistical analyses were performed in RStudio. Analysis of Variance Models (ANOVA) were calculated with


RNA modifications play a major role in tumorigenicity and progression, but the expression and function in glioblastoma (GBM) have not been well described. In this study, we developed a GBM score based on the differentially expressed genes (DEGs) between groups showing RNA modification patterns. We assessed the association between the GBM score and tumor microenvironment (TME) characteristics. Based on the gene expression of these regulators, we identified two clusters with distinct RNA modification patterns. Kaplan–Meier survival curves showed that patients in cluster 1 had worse survival than those in cluster 2. Kaplan–Meier and multivariate Cox regression analyses showed that GBM scores (based on DEGs between RNA modification patterns) are an independent predictive biomarker for patient prognosis. Besides, we found that samples with high scores were significantly associated with epithelial-to-mesenchymal transition and immune checkpoints, while samples with low scores were associated with cell cycle regulation. Importantly, GBM-score markedly positively correlated drug resistance, while negatively correlated with drug sensitive. The responders of anti-PD-1/PD-L1 immunotherapy tend to have a lower GBM score than non-responders. In conclusion, our comprehensive analysis of multiple RNA modifications in GBM revealed that RNA modification regulators were closely correlated with TME. The 5-year survival rate of patients with GBM is only 5%, and the median overall survival time is just 12–15 months2. GBM is an extremely aggressive tumor with a high frequency of recurrence after surgery, radiotherapy, and chemotherapy2. Further, GBM is a highly heterogeneous neoplasm that displays a wide range of genetic and epigenetic changes3. A better understanding of the molecular mechanisms that underlie GBM may lead to more effective prognostic markers and therapeutic targets for GBM.Glioblastoma (GBM) is one of the most common and aggressive intracranial tumors in adults, comprising 45.6% of all primary malignant brain cancers5. The most common modification of mRNA is the addition of 6-methyladenosine (m6A), which regulates the biogenesis and function of non-coding RNAs, including long non-coding RNAs, microRNAs, and circular RNAs6. Three types of RNA modification regulators have been identified: “writers” (which add a specific modification), “erasers” (which remove a specific modification), and “readers” (which identify and bind modified nucleotides). Most RNA modifications are the result of writers. There is increasing evidence that there are multiple regulators for m6A modification that are important in tumor initiation, progression, immune modulation, and effective treatments7. The role of other RNA modifications, including 1-methyladenosine (m1A), Alternative polyadenylation (APA), RNA editing, 5-methylcytosine (m5C), and pseudouridine (Ψ), are poorly understood9.RNA modification is an important method of posttranscriptional gene regulation that affects many biological processes6. Zhang et al. reported that m6A modification patterns were predictive for prognosis, inflammation status, TME phenotype, and genetic variation10. Particular m6A modification patterns are linked to successful immunotherapy, guiding immunotherapy strategies10. Based on RNA m6A regulators, Lin et al. constructed a prognostic model for glioma that provides insight into the relationship between m6A modification and the infiltration of immune cells11. However, it is difficult to evaluate the role of RNA modification in cancers because different RNA methylation modifications interact; therefore, the study of a single type of RNA modification in tumors is not optimal.RNA modifications, especially m6A addition, influence the diversity and complexity of the tumor microenvironment (TME) and immune cell-infiltrating characteristics of the tumorIn this study, we explored the expression profile of RNA modification writers using data from The Cancer Genome Atlas (TCGA). Based on the expression of those regulators, we identified two distinct gene expression patterns of RNA modification writers(cluster 1 and cluster 2) using unsupervised clustering analysis. Both clusters were significantly correlated with differences in immune cell infiltration and prognosis of GBM patients. We developed a scoring model based on the differentially expressed genes (DEGs) between cluster 1 and cluster 2 to assess RNA modifications for individual patients. Here, we demonstrate the value of this scoring model in epithelial-to-mesenchymal transition (EMT) characteristics, drug sensitivity, and response to immunotherapy.In this study, we focused on the four main types of RNA modifications  and identified 26 RNA modification writer genes . Based on these DEGs, we classified GBM patients into distinct gene subgroups using an unsupervised clustering method and identified gene subgroup clusters A and cluster B .Thus, the GBM score can quantify the RNA modification pattern and may serve as a predictor for the prognosis for patients with GBM.The analysis of RNA modification patterns was based only on patient populations. Given the heterogeneity of RNA modifications in individuals, we developed a scoring model to evaluate the RNA modification pattern of individual patients with GBM, which we termed the GBM score model. Cluster 2 had a significantly lower median score compared to cluster 1, and cluster B had a lower median score compared to cluster A Fig. C,d. We s14. In cancer, reactivation of the EMT enhances the metastatic properties of tumor cells, including invasion, migration, drug resistance, and tumor initiation potential14. To determine the association between GBM score and clinical characteristics, we analyzed the characteristics of the EMT-related pathways in the high and low GBM score groups. The two groups of samples with different GBM scores had different pathway characteristics. In the TCGA dataset, samples with high scores were significantly associated with EMT and immune checkpoints, while samples with low scores were associated with cell cycle regulation  database. We discovered 21 pairs of significant correlations between the GBM score and drug sensitivity , partial response (PR), stable disease (SD), or progressive disease (PD), was linked to GBM scores. The responders had a lower GBM score than non-responders , where it mediates GSC maintenance and dedifferentiation by regulating SOX2 mRNA stability26. Many studies have focused on the role of a single regulator; however, RNA modification is an integrated process involving multiple regulators. In this study, we performed a comprehensive analysis of the role of RNA modification based on the primary regulator writers for two types of RNA modification. Notably, we found that RNA modification patterns correlated with immune cell infiltration and the prognosis for GBM patients.In GBM, an invasive human brain tumor with extremely poor survival27. Many immune cells, including glioma-associated macrophages (GAMs), CD4 + and CD8 + T lymphocytes, dendritic cells, T regulator cells, and NK cells, have an important role in GBM growth, migration, drug resistance, and response to immunotherapy27. GAMs, in particular, correlated strongly with tumor progression and clinical tumor grade28. GAMs are involved in remodeling the TME by inducing alterations between the M2 immunosuppressive state and the M1 pro-inflammatory, anti-tumor state. In this study, we found that patients in cluster 1 had worse survival than those in cluster 2. Although the expression plots of M1 and M1 seemed highly heterogeneous in cluster 1 and cluster 2, M2 macrophages tend to higher in cluster 1 than cluster 2, while the M1 macrophages tend to be more enriched in cluster 2. These results suggested that RNA modification patterns are closely correlated with the immune state and prognosis for GBM patients.Tumor heterogeneity contributes to the complex interaction of tumor cells and TMEs, as well as to GBM growth and aggressiveness29. While there is no FDA-approved immunotherapy for GBM, ongoing clinical trials, especially for checkpoint inhibitors, show promise for the treatment of GBM. A previous study found that tumor mutational burden and microsatellite instability might predict the therapeutic efficacy of PD-1/PD-L1 inhibitors in GBM patients29. Recent studies have revealed that RNA modifications are strongly correlated with the immune microenvironment and the effectiveness of immunotherapy in GBM patients31. Lin et al. developed a prognostic model based on m6A RNA methylation regulators, which provides a novel biomarker for TME phenotype and GBM prognosis11. Xu et al. found that m6A regulators were not only associated with the expression of immune checkpoint genes, such as CTLA-4, B7H3, PD-1, and PD-L1, but also that they predict the efficacy of immunotherapy in GBM13. Further, Li et al. established a scoring model based on m6A regulators and TME-related genes that has the potential to predict the efficacy of PD-L1 inhibitors in lung adenocarcinoma32. The role of m6A modification in immune regulation and immune response has also been evaluated in colon cancer12, gastric cancer10, bladder cancer33, and head and neck squamous cell carcinoma34. Additionally, Chen et al. demonstrated that cross-talk among the main types of RNA modification writers plays a role in TME formation and the efficacy of immunotherapy of colorectal cancer35.High immunosuppression is the primary reason for the failure of immune therapy in GBM patientsCollectively, these results indicate that RNA modification shapes the complexity of TME and may predict the response to immunotherapy. However, the role of many RNA modifications in cancer remains largely unknown. More research in vivo and in vitro is still needed to determine the role of RNA modifications in cancer.we performed a comprehensive analysis of the genetic alterations of RNA modification regulators in GMB. The expression of RNA modification regulators was associated with RNA modification patterns and immune cell infiltration in TME. In addition, we develop a scoring model based on the DEGs between clusters showing different RNA modification patterns to predict therapeutic efficacy and prognosis for patients with GBM.https://portal.gdc.cancer.gov/). The database platform has obtained ethical approval from all participants. All experiments were performed in accordance with the relevant guidelines and regulations. Data with missing information were removed. 16 of the 167 GBM samples had missing survival follow-up data, so we filtered these samples when we did the prognostic analysis.Data related to mRNA expression, miRNA expression, gene mutation, copy number variation (CNV), and clinicopathological information of 392 glioblastoma (GBM)) samples were collected from the TCGA database  is an immunotherapy cohort for urothelial carcinoma including both clinical and gene expression data37. The optimal number of clusters and their stability were determined by cophenetic, dispersion, and silhouette coefficients using ConsensusClusterPlus37. For the principle of the farthest distance between groups, we choose K = 2 as the final number of clusters, according to the smallest clustering within groups.We used a consensus clustering algorithm to identify RNA modification patterns based on the expression of the 26 RNA modification regulators as described previously38. The gene sets of “c2.cp.kegg.v7.1” were obtained from the MSigDB database for GSVA analysis. The clusterProfiler R package was used for functional annotation of the 26 RNA modification regulator genes with the cutoff value of false discovery rate < 0.0539.To determine the correlation between biological function and different RNA modification patterns, GSVA enrichment analysis was performed using the “GSVA” R packagehttp://cibersort.stanford.edu/) to determine the abundance of immune cells in GBM. CIBERSORT is a computational method for accurately estimating immune cell composition from tissue gene expression profiles40. The final CIBERSORT output estimates were normalized to directly compare cell fractions across different immune cell types. The optimal cut-off values for a fraction for each immune subset were defined as the point with the most significant split and were calculated using the web-based tool “cutoff Finder”41.To identify immune subsets in the TME, we used CIBERSORT  was used to construct an RNA modification gene signature. We then defined the RNA modification score (GBM score) with a method close to gene expression grade index (GGI):i × Expi), here β is the coefficient of the gene in the univariate Cox regression of the corresponding gene and i is the expression of RNA modification phenotype-related genes42.GBM score = ∑. The Wilcoxon test was used to calculate differences between the two subgroups. Student’s t-tests and one-way ANOVA were used to compare differences among three or more subgroups. Patient prognosis was tested using Kaplan–Meier analysis and the multivariable Cox regression model. Performance of the GBM scores was assessed using a receiver operating characteristic (ROC) curve. A Data was collected from public databases.Supplementary Figure 1.Supplementary Table 1.Supplementary Legends.


Adaptation to high-altitude hypobaric hypoxia has been shown to require a set of physiological traits enabled by an associated set of genetic modifications, as well as transcriptome regulation. These lead to both lifetime adaptation of individuals to hypoxia at high altitudes and generational evolution of populations as seen for instance in those of Tibet. Additionally, RNA modifications, which are sensitive to environmental exposure, have been shown to play pivotal biological roles in maintaining the physiological functions of organs. However, the dynamic RNA modification landscape and related molecular mechanisms in mouse tissues under hypobaric hypoxia exposure remain to be fully understood. Here, we explore the tissue-specific distribution pattern of multiple RNA modifications across mouse tissues.Ala, tRNAval, tRNAGlu, and tRNALeu. In vitro transfection experiments showed that the transfection of testis total tRNA-enriched fragments from the hypoxia group into GC-2spd cells attenuated the cell proliferation rate and led to a reduction in overall nascent protein synthesis in cells.By applying an LC-MS/MS-dependent RNA modification detection platform, we identified the distribution of multiple RNA modifications in total RNA, tRNA-enriched fragments, and 17–50-nt sncRNAs across mouse tissues; these patterns were associated with the expression levels of RNA modification modifiers in different tissues. Moreover, the tissue-specific abundance of RNA modifications was sensitively altered across different RNA groups in a simulated high-altitude (over 5500 m) hypobaric hypoxia mouse model with the activation of the hypoxia response in mouse peripheral blood and multiple tissues. RNase digestion experiments revealed that the alteration of RNA modification abundance under hypoxia exposure impacted the molecular stability of tissue total tRNA-enriched fragments and isolated individual tRNAs, such as tRNAOur results reveal that the abundance of RNA modifications for different classes of RNAs under physiological conditions is tissue-specific and responds to hypobaric hypoxia exposure in a tissue-specific manner. Mechanistically, the dysregulation of tRNA modifications under hypobaric hypoxia attenuated the cell proliferation rate, facilitated the sensitivity of tRNA to RNases, and led to a reduction in overall nascent protein synthesis, suggesting an active role of tRNA epitranscriptome alteration in the adaptive response to environmental hypoxia exposure.The online version contains supplementary material available at 10.1186/s12915-023-01537-x. EGLN1 gene  SDS) for 15 min each, followed by twice washes with high stringent buffer (0.1 × SSC with 0.1% [wt/vol] SDS) for 10 min each, and finally washed by washing buffer (1 × SSC) for 10 min. Then, the membrane was blocked in a blocking buffer  at room temperature for 3 h. After which, DIG antibody  was added into the blocking buffer at a ratio of 1:10,000 and incubated again for another 1 h at room temperature. The membranes were washed 4 times in DIG wash buffer  at room temperature for 15 min each, followed by incubation in developing buffer for 10 min at room temperature. Finally, the membranes were coated with CSPD reagent  at 37°C for 15 min in the dark and imaged by using a Bio-Rad system (USA). Original uncropped images of northern blot and denatured urea gel are shown in Additional file Isolated RNAs were denatured at 95°C for 3 min and separated using 15% Urea-PAGE. The gels were then stained with SYBR GOLD, imaged and immediately transferred to Roche Nylon Membranes , and UV-cross-linked twice at 0.12J energy. Membranes were pre-hybridized with Roche DIG hybridization buffer  for 60 min at 42°C shaker. DIG- labeled probes (5′-tRNA2 and 5% CO2 at 37 °C.Germ cell-2 spermatid (GC-2spd) cells were cultured in DMEM medium  supplemented with 10% fetal bovine serum (FBS)  and 1% antibiotics (100 U/ml penicillin and 100 U/ml streptomycin) in an incubator  containing 21% O6 GC-2spd cells per well into the 6-well plate and transfection was performed the next day when the cell reached 70–90% confluent. Four hundred nanograms of tRNA purified from testis was transfected into each well using LipofectamineTM 3000 Reagent  according to the instructions as previously reported [Seeding 0.5 × 10reported . Before Protein synthesis assay was performed at 48 h after transfection of testis tRNA to evaluate the effect of RNA modification change on tRNA function using Click-iT® Plus OPP Protein Synthesis Assay Kits  according to the manufacturer’s instructions. In brief, dilute Click-iT® OPP (component A) 1:1000 in 1 ml DMEM supplemented with 10% FBS and 1% antibiotics to prepare a 20-μm final working solution; remove the medium, add 1 ml per well of DMEM containing 20 μm Click-iT® OPP, and incubate cells at 37°C for 15 min; after incubation, remove the DMEM, wash the cells once with PBS, and then digest cells with pancreatin; wash cells with PBS and resuspend with 100 μl of 3.7% formaldehyde and incubate for 15 min at room temperature, followed by incubation of 0.5% Triton®X-100 for 15 min at room temperature; then, prepare 1 ml Click-iT® Plus OPP reaction cocktail consisted of 880 μl Click-iT® OPP reaction buffer, 20 μl Copper Protectant (component D), 2.5 μl Alexa Fluor® picolyl azide (component B), and 100 μl Click-iT® Reaction Buffer Additive, remove the permeabilization buffer, wash cells twice with PBS, and add 100 μl of above reaction cocktail to incubate 30 min at room temperature; finally, dye with DAPI for 20 min to perform flow analysis using Kaluza (Beckman Coulter).Western blot analysis was performed to evaluate the expression level of eukaryotic initiation factor 4A/E (eIF4A and eIF4E). In brief, lysates from cells mixed with 5 × SDS gel loading buffer were run in SDS-PAGE gels. Beta Actin Mouse McAb , eIF4A (C32B4) Rabbit mAb , and eIF4E  Rabbit mAb were used as primary antibody. Horseradish peroxidase (HRP)-conjugated goat anti-rabbit immunoglobulin G  and HRP-conjugated goat anti-mouse immunoglobulin G  were used as a secondary antibody. The blots were scanned using the Bio-Rad system (USA). Original uncropped images of the western blot are shown in Additional file t-test and one-way ANOVA multiple comparisons using GraphPad Prism 8 . Linear correlation analysis was also performed to evaluate the correlation between different RNA modifications or RNA modifications and RNA modification enzymes as determined by GraphPad Prism 8. Circos were drawn by R packages circlize, and the correlation between modification was calculated based on the method “Spearman.” Data were present as the mean ± standard error of mean (sem.), and p value < 0.05 was considered statistically significant. Each statistical test used for each figure is described in the legends. We independently repeated all the molecular biology experiments at least trice, and all attempts to reproduce the results were successful.Statistical analyses were conducted using an unpaired Student’s Additional file 1: Fig. S1. Detection of RNA modifications in mouse multiple tissues RNA. (A) Experimental procedures for detecting and quantifying RNA modifications in mouse tissues RNA. (B) List of applied nucleobase standards. (C) The percentage of modified and unmodified four nucleotides  in mouse mRNA and the relative proportion of detected RNA modifications across multiple tissues   The linear correlations analysis between RNA modifications in tRNA-enriched fragments and 17-50 nt sncRNAs across multiple tissues   Levels of RDW, red blood cell volume distribution width in peripheral blood .Additional file 5: Fig. S5. Purification of the single endo-tRNA from mouse liver. (A) The codon usage frequency of individual tRNA in mouse liver nuclear coding genes. (B) Purification diagram of liver individual tRNA. (C) Confirmation of purified individual endo-tRNA by northern blot and denatured PAGE. L, Leu; S, Ser; E, Glu; A, Ala; G, Gly; P, Pro; V, Val; K, Lys; R, Arg; T, Thr; Q, Gln; D, Asp; I, Ile; F, Phe; N, Asn; H, His; Y, Tyr; C, Cys; M, Met; W, Trp; *, Stop codon; NB, northern blot; DP, denatured PAGE.Additional file 6: Fig. S6. Scatter plot comparison of transcriptome between GC-2spd which transfected with NC group testis tRNA enriched fragments and hypoxia group testis tRNA enriched fragments.Additional file 7: Fig. S7. Original uncropped images for liver single RNA. (A-E) The original uncropped images of northern blot to identify the pull-down products of tRNAAla (A), tRNAVal (B), tRNAGly (C), tRNALeu (D) and tRNAGlu (E). (F-I) The original uncropped images of denatured urea gel to identify the purified tRNAGlu (F), tRNAGly (G), tRNAAla and tRNAVal (H) and tRNALeu.Additional file 8: Fig. S8. Original uncropped images for tRNA enriched fragments stability analysis. (A-D) The original uncropped images of native gel to analyze liver tRNA-enriched fragments (A), testis tRNA enriched fragments stability (B), liver tRNAAla and liver tRNAGly (C), and liver tRNAVal (D) stability against RNase A/T1 and fetal bovine serum.Additional file 9: Fig. S9. Original uncropped images for denatured urea gel, northern blot and western blot. (A) Denatured urea gel image of liver total RNA for Fig. Ala (B), tRNAVal (C), tRNAGly (D) for Fig. Ala for Fig. Additional file 10: Table S1-S5. Data for RNA modification levels in different RNA classes across normal control group mouse tissues. Table S1. Original data for RNA modification levels in total RNA of normal control mouse tissues. Table S2. Original data for RNA modification levels in tRNA-enriched RNA fragments of normal control mouse tissues. Table S3. Original data for RNA modification levels in 17-50 nt sncRNAs of normal control mouse tissues. Table S4. Original data for RNA modification levels in PolyA-enriched RNA of normal control mouse tissues. Table S5. Original data for RNA modification levels in liver single tRNA of normal control mouse tissues.Additional file 11: Table S6-S9. Data for RNA modification levels in different RNA classes across hypoxia group mouse tissues. Table S6. Original data for RNA modification levels in total RNA of normal control and hypoxia mouse tissues. Table S7. Original data for RNA modification levels in tRNA-enriched RNA fragments of hypoxia mouse tissues. Table S8. Original data for RNA modification levels in 17-50 nt sncRNAs RNA of hypoxia mouse tissues. Table S9. Original data for RNA modification levels in liver single tRNA of normal control mouse tissues.Additional file 12: Table S10. Data for routine analysis of blood.Additional file 13: Table S11-S12. Data for GC-2spd growth analysis. Table S11. Fold change data for cell number of GC-2spd growth analysis. Table S12. CCK8 data for GC-2spd growth analysis.