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The concept of RNA velocity has been recently developed that allowed to look at the otherwise static single-cell RNA sequencing data in a dynamic way, which permitted inferences about cell fates. However, the more precise parameters, such as the number of exons/introns, can also be determined using long-read methods. Comparing the numbers of exons and introns allows including more genes for downstream velocity analysis and resolves the precise cell fate. The recently developed concept of “RNA velocity” concerns with dynamic changes in mRNA expression and complements single-cell RNA sequencing (scRNA-seq) data, which are static snapshots of a certain cell state taken at a given time point1. RNA velocity measures the change in mRNA abundance by differentiating the newly transcribed unspliced pre-mRNAs from mature spliced mRNAs. The rapidly developing long-read sequencing technology lends itself for RNA velocity analysis of scRNA-seq data, which was previously performed primarily using second-generation sequencing.

Conventional approaches to studying 5mC marks in single cells or samples with picogram input DNA amounts usually suffer from low genome coverage due to DNA degradation. Many methods have been developed to optimize the library construction efficiency for bisulfite-treated DNA. However, most of these approaches ignored the amplification bias of bisulfite-treated DNA, which leads to shallow genome coverage. In this study, we developed the T7-assisted enzymatic methyl-sequencing method (TEAM-seq), which adopts enzymatic conversion to minimize DNA degradation and T7 polymerase-assisted unbiased amplification. We demonstrate that TEAM-seq delivered, to the best of our knowledge, the highest reported coverage(70% for 100pg, 35% for 20pg) of single cell genomes in whole-genome 5mC sequencing.

The COVID-19 pandemic is a global health disaster. Moreover, emerging mutated virus strains present an even greater challenge for existing vaccines and medications. One possible solution is to design drugs based on the properties of virus epigenome, which are more common among coronaviruses.Here, we reported an FDA-approved drug for myelodysplastic syndrome, azacytidine (5Aza), limited virus infection and protected mice against SARS-CoV-2. We demonstrated that this antiviral effect is related to 5Aza incorporation into viral RNA, which disrupt m5C RNA methylation modification profile. This work suggests that targeting viral epigenomes is a viable therapeutic strategy, potentially opening new pathways for treating COVID-19.

As one of the post-transcriptional regulatory mechanisms, transcription and translation’s uncoupling plays an essential role in development and adulthood physiology. However, it remains elusive how thousands of mRNAs get translationally silenced while stability is maintained for up to hours or even days before translation. In addition to oocytes and neurons, developing spermatids have significant uncoupling of transcription and translation for delayed translation. Therefore, spermiogenesis represents an excellentin vivomodel for investigating the mechanism underlying uncoupled transcription and translation. Through full-length poly(A) deep sequencing, we discovered dynamic changes in poly(A) length through deadenylation and re-polyadenylation. Deadenylation appeared to be mediated by microRNAs (miRNAs), and transcripts with shorter poly(A) tails tend to be sequestered into ribonucleoproteins (RNPs) for translational repression and stabilization. In contrast, re-polyadenylation allows for translocation of the translationally repressed transcripts from RNPs to polysomes for translation. Overall, our data suggest that miRNA-dependent poly(A) length control represents a novel mechanism underlying uncoupled translation and transcription in haploid male germ cells.

The molecular mechanism underlying epigenetic inheritance remains largely unknown. Previous studies have shown that transmission of paternal acquired traits through the male germline (i.e., sperm) requires functional DNMT2 to maintain normal profiles of sperm-borne tRNA-derived small RNAs (tsRNAs). Here we report that maternal transmission of a Kit paramutant phenotype (white tail tip) through the female germline (i.e., oocytes) also requires normal function of DNMT2 and normal profiles of DNMT2-dependent tsRNAs and other small noncoding RNAs (sncRNAs) in sperm. Specifically, ablation of DNMT2 leads to aberrant profiles of tsRNAs and other sncRNAs in sperm, which correlate with drastically dysregulated mRNA transcriptome in pronuclear zygotes derived from oocytes carrying the Kit paramutation and a complete blockage of transmission of the paramutant phenotype through the oocytes. Together with previous reports, the present study suggests that both paternal and maternal transmission of epigenetic phenotypes requires DNMT2-dependent tsRNAs in sperm.

Single-cell isoform sequencing can reveal transcriptomic dynamics in individual cells invisible to bulk- and single-cell RNA analysis based on short-read sequencing. However, current long-read single-cell sequencing technologies have been limited by low throughput and high error rate. Here we introduce HIT-scISOseq for high-throughput single-cell isoform sequencing. This method was made possible by full-length cDNA capture using biotinylated PCR primers, and by our novel library preparation procedure that combines head-to-tail concatemeric full-length cDNAs into a long SMRTbell insert for high-accuracy PacBio sequencing. HIT-scISOseq yields > 10 million high-accuracy full-length isoforms in a single PacBio Sequel II 8M SMRT Cell, providing > 8 times more data output than the standard single-cell isoform PacBio sequencing protocol. We exemplified HIT-scISOseq by first studying transcriptome profiles of 4,000 normal and 8,000 injured corneal epitheliums from cynomolgus monkeys. We constructed dynamic transcriptome landscapes of known and rare cell types, revealed novel isoforms, and identified injury-related splicing and switching events that are previously not accessible with low throughput isoform sequencing. HIT-scISOseq represents a high-throughput, cost-effective, and technically simple method to accelerate the burgeoning field of long-read single-cell transcriptomics.

Coronavirus disease 2019 (COVID-19) is a global pandemic and has been widely reported; however, a comprehensive systemic review and meta-analysis has not been conducted. We systematically investigated the clinical characteristics of COVID-19 in mainland China to guide diagnosis and treatment. We searched the PubMed, Embase, Scopus, Web of Science, Cochrane Library, bioRxiv, medRxiv, and SSRN databases for studies related to COVID-19 published or preprinted in English or Chinese from January 1 to March 15, 2020. Clinical studies on COVID-19 performed in mainland China were included. We collected primary outcomes including signs and symptoms, chest CT imaging, laboratory tests, and treatments. Study selection, data extraction, and risk of bias assessment were performed by two independent reviewers. Qualitative and quantitative synthesis was conducted, and random-effects models were applied to pooled estimates. This study is registered with PROSPERO (number CRD42020171606). Of the 3624 records identified, 147 studies (20,662 patients) were analyzed. The mean age of patients with COVID-19 was 49.40 years, 53.45% were male, and 38.52% had at least one comorbidity. Fever and cough were the most common symptoms, followed by fatigue, expectoration, and shortness of breath. Most patients with COVID-19 had abnormal chest CT findings with ground glass opacity (70.70%) or consolidation (29.91%). Laboratory findings shown lymphopenia, increased lactate dehydrogenase, increased infection-related indicators, and fibrinolytic hyperactivity. Antiviral therapy, antibiotic therapy, and corticosteroids were administered to 89.75%, 79.13%, and 35.64% of patients, respectively. Most clinical characteristics of COVID-19 are non-specific. Patients with suspected should be evaluated by virological assays and clinically treated.

Hundreds of thousands of putative circular RNAs have been identified through deep sequencing and bioinformatic analyses. However, the circularity of these putative RNA circles has not been experimentally validated due to limited methodologies currently available. We reported here that the template-switching capability of commonly used reverse transcriptases (e.g., SuperScript II) leads to the formation of artificial junction sequences, and consequently misclassification of large linear RNAs as RNA circles. Use of reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis is critical for the identification of physiological circular RNAs. We also report two methods, MonsterScript junction PCR and high-resolution melting curve analyses, which can reliably distinguish circular RNAs from their linear forms and thus, can be used to discover and validate true circular RNAs.Significance StatementThe vast majority of circular RNAs were identified through computational detection of junction sequences in the deep sequencing reads because these unique fusion sequences represent back-splicing events. We found that artificial junction sequences could be formed through template switching (TS) when MMLV-derived reverse transcriptases, e.g., SuperScript II, are used to synthesize cDNAs. Thus, many of the reported circular RNAs may not be RNA circles, but rather experimental artifacts. Fake circular RNAs can be avoided by using reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis. We developed two novel methods, MonsterScript junction PCR and high-resolution melting curve analyses, for distinguishing circular RNAs from their linear form.

SncRNA-Seq has become a routine for sncRNA profiling; however, software packages currently available are either exclusively for miRNA or piRNA annotation (e.g., miRDeep, miRanalyzer, Shortstack, PIANO), or for direct mapping of the sequence reads to the genome (e.g., Bowtie 2, SOAP and BWA), which tend to generate inaccurate counting due to repetitive matches to the genome or sncRNA homologs. Moreover, novel sncRNA variants in the sequencing reads, including those bearing small overhangs or internal insertions, deletions or mutations, are totally excluded from counting by these algorithms, leading to potential quantification bias. To overcome these problems, a comprehensive software package that can annotate all known small RNA species with adjustable tolerance towards small mismatches is needed. AASRA is based on our unique anchor alignment algorithm, which not only avoids repetitive or ambiguous counting, but also distinguishes mature miRNA from precursor miRNA reads. Compared to all existing pipelines for small RNA annotation, AASRA is superior in the following aspects: 1) AASRA can annotate all known sncRNA species simultaneously with the capability of distinguishing mature and precursor miRNAs; 2) AASRA can identify and allow for inclusion of sncRNA variants with small overhangs and/or internal insertions/deletions into the final counts; 3) AASRA is the fastest among all small RNA annotation pipelines tested. AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor miRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.Availability and Implementation:The AASRA software is freely available at https://github.com/biogramming/AASRA.