Zhuo-Xing Shi

Nanopore direct RNA sequencing (dRNA-Seq) simultaneously resolves RNA sequences, poly(A) tail lengths, and base modifications at the single-molecule level. Here, we developed RB-dRNAseq, a multiplexed dRNA-Seq method using RNA-barcoded reverse transcription adapters (RTAs) compatible with Oxford Nanopore Technologies (ONT) workflows, enabling highly efficient sample demultiplexing based solely on basecalled sequences. On the RNA004 platform, RB-dRNAseq achieved >99.5% demultiplexing accuracy, with 81% of reads assigned in 6-plex samples. This represents a 16%-24% relative increase in read recovery compared to conventional DNA barcoding methods that rely on basecalling-based demultiplexing. To evaluate the sensitivity of RB-dRNAseq, we performed sequencing with low-input samples, demonstrating that even with 1 ng of total RNA, gene expression (r=0.84) and m6A modification (r=0.91) profiles remained highly consistent with high-input benchmarks. We further explored the feasibility of RB-dRNAseq in ultra-low input scenarios by applying it to mouse zygotes and 2-cell stage embryos. The method successfully captured dynamic shifts in gene isoform expression and multiple RNA modifications (m6A, m5C, pseU, and Inosine) during zygotic genome activation (ZGA), uncovering site-specific regulatory patterns.

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants that accumulate in edible crops, posing threats to food safety and human health. Breeding varieties with reduced PFAS accumulation offers a promising mitigation strategy, yet the underlying cellular mechanisms remain elusive. Here, we integrated short- and long-read single-cell RNA sequencing to generate a high-resolution transcriptomic atlas of lettuce (Lactuca sativa L.) root tips, comparing varieties with contrasting perfluorooctanoic acid (PFOA) accumulation capacities. Our analyses uncovered a synergistic mechanism underlying PFOA uptake: up-regulation of aquaporin genes in epidermal and xylem cells, coupled with down-regulation of cell-wall biosynthesis genes specifically in epidermal and xylem. Furthermore, we identified distinct RNA isoforms-including variants with altered coding potential-that may contribute to PFOA transport and deposition through structural modulation of the encoded proteins. This study represents the first application of single-cell sequencing to elucidate the cellular and molecular basis of crop responses to PFOA, providing new insights into pollutant-crop interactions and informing strategies for sustainable agriculture and food safety.

Motivation: The analysis of RNA isoforms using long-read single-cell RNA sequencing (scRNA-seq) represents a frontier in gene expression research, offering deeper insights beyond traditional gene-level analysis. However, specialized analytical methods tailored for this advanced technology remain scarce, underscoring the urgent need for novel tools to match the rapid pace of its development. Results: Here, we present IsoDiffR, a robust tool designed to identify RNA isoforms with expression patterns that differ from their corresponding genes or major isoforms across cell types, enabling both pairwise and multicell-type comparisons. Using IsoDiffR, we conducted benchmark tests using simulated data and analyzed long-read scRNA-seq data from the corneal limbus of Macaca fascicularis and the human frontal cortex, uncovering previously unrecognized cell-type-specific isoforms that were not detectable using conventional approaches. Additionally, we explored the structural and functional properties of these isoforms, from their nucleotide sequences to their corresponding protein isoforms, revealing their potential biological roles. Our findings offer new perspectives on gene expression regulation at the single-cell level and provide a methodological framework for future investigations of isoform-specific functions in diverse biological contexts. Availability and implementation: The R package is available on https://github.com/Eveqian98/IsoDiffR.

Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical treatments. During nanopore sequencing, methylation modifications on DNA bases cause changes in electrical current intensity. Therefore, constructing deep neural network models to decode the electrical signals of nanopore sequencing has become a crucial step in methylation site identification. In this study, we utilized nanopore sequencing data containing diverse DNA methylation types and motif sequence diversity. We proposed a feature encoding method based on current signal clustering and leveraged the powerful attention mechanism in the Transformer framework to construct the PoreFormer model for identifying DNA methylation sites in nanopore sequencing. The model demonstrated excellent performance under conditions of multi-class methylation and motif sequence diversity, offering new insights into related research fields.

Corneal wounding healing is critical for maintaining clear vision, however, a complete understanding of its dynamic regulatory mechanisms remains elusive. Here, we used single-cell RNA sequencing (scRNA-seq) to analyze the cellular activities and transcriptional changes of corneal limbal epithelial cells at different stages after wound healing in cynomolgus monkeys, which exhibit a closer transcriptomic similarity to humans. Corneal limbal tissues were collected during uninjured, 1-day and 3-day healing stages, dissociated into single cells, and subjected to scRNA-seq using the 10× Genomics platform. Cell types were clustered by graph-based visualization methods and unbiased computational analysis. Additionally, cell migration assays and immunofluorescent staining were performed on cultured human corneal epithelial cells. We characterized nine cell clusters by scRNA-seq analysis of the cynomolgus monkey corneal epithelium. By comparing heterogeneous transcriptional changes in major cell types during corneal healing, we highlighted the importance of limbal epithelial cells (LEPCs) and basal epithelial cells (BEPCs) in extracellular matrix (ECM) formation and wound healing, as well as suprabasal epithelial cells (SEPCs) in epithelial differentiation during the healing processes. We further identified five different sub-clusters in LEPC, including the transit amplifying cell (TAC) sub-cluster that promotes early healing through the activation of thrombospondin-1 (THBS1) expression. Our study represents the first comprehensive exploration of the detailed transcriptome profile of individual corneal cells during the wound healing process in nonhuman primates. We demonstrate the intricate mechanisms involved in corneal healing and provide a promising avenue for potential therapies in corneal wound healing.

Lettuce is one of the most widely cultivated and consumed dicotyledonous vegetables globally. Despite the availability of its reference genome sequence, lettuce gene annotation remains incomplete, impeding comprehensive research and the broad application of genomic resources. Long-read RNA isoform sequencing (Iso-Seq) offers substantial advantages for analyzing RNA alternative splicing and aiding gene annotation, yet it faces throughput limitations. We present the HIT-ISOseq method tailored for bulk sample analysis, significantly enhancing RNA sequencing throughput on the PacBio platform by concatenating cDNA. Here we show, HIT-ISOseq generates 3-4 cDNA molecules per CCS read in lettuce, yielding 15.7 million long reads per PacBio Sequel II SMRT Cell 8 M. We validate its effectiveness in analyzing six lettuce tissue samples, including roots, stems, and leaves, revealing tissue-specific gene expression patterns and RNA isoforms. Leveraging diverse tissue long-read RNA sequencing, we refine the transcript annotation of the lettuce reference genome, expanding its GO and KEGG annotation repertoire. Collectively, this study serves as a foundational reference for genome annotation and the analysis of multi-sample isoform expression, utilizing high-throughput long-read transcriptome sequencing.

Graph learning models have received increasing attention in the computational analysis of single-cell RNA sequencing (scRNA-seq) data. Compared with conventional deep neural networks, graph neural networks and language models have exhibited superior performance by extracting graph-structured data from raw gene count matrices. Established deep neural network-based clustering approaches generally focus on temporal expression patterns while ignoring inherent interactions at gene-level as well as cell-level, which could be regarded as spatial dynamics in single-cell data. Both gene–gene and cell–cell interactions are able to boost the performance of cell type detection, under the framework of multi-view modeling. In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.

Mice have emerged as a widely employed model for investigating various retinal diseases. However, the availability of comprehensive datasets capturing the entire developmental and aging stages of the mouse retina, particularly during the elderly period, encompassing integrated lncRNA and mRNA expression profiles, is limited. In this study, we assembled a total of 18 retina samples from mice across 6 distinct stages of development and aging (5 days, 3 weeks, 6 weeks, 10 weeks, 6 months, and 15 months) to conduct integrated lncRNA and mRNA sequencing analysis. This invaluable dataset offers a comprehensive transcriptomic resource of mRNA and lncRNA expression profiles during the natural progression of retinal development and aging. The discoveries stemming from this investigation will significantly contribute to the elucidation of the underlying molecular mechanisms associated with various retinal diseases, such as congenital retinal dysplasia and retinal degenerative diseases.