Biology subjects

Zheng, Y.-F.

Publications and source records attributed to Zheng, Y.-F..

5 recordsLinked to original sources

bioRxiv2026

Multiplexed direct RNA sequencing using RB-dRNAseq with RNA barcodes

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.

genomics↗

bioRxiv2026

A T-cell engager platform induces deep plasma cell depletion in autoimmune dry eye

Bispecific T cell engagers (TCEs) have transformed targeted immunotherapy but remain constrained by ex vivo manufacturing and systemic delivery, limiting their application in autoimmune disorders. Here, we introduce the first in vivo TCE platform, utilizing a T cell-targeted integration-deficient lentiviral vector (T-IDLE) to reprogram endogenous T cells into engineered T cells secreting BCMAxCD3 TCEs (STCEs) directly in vivo. In murine Sjogrens syndrome-associated dry eye model, a single intravenous administration of T-IDLESTCE achieved deep and sustained depletion of plasmablasts and plasma cells, reversing lacrimal gland inflammation and restoring tear production without detectable off-target transduction. Longitudinal studies in cynomolgus monkeys confirmed translational promise, demonstrating potent plasma cell clearance and favorable safety profiles over 12 weeks post-vector dosing. Mechanistic analyses demonstrated attenuation of Th1/Th17-mediated inflammation and controlled cytokine responses, accompanied by negligible vector integration in non-T cell lineages, thereby substantiating the safety profile of the integration-deficient design. This first-in-class in vivo TCE approach represents a manufacturing-free, scalable immunotherapy strategy for autoantibody-mediated diseases.

bioengineering↗

bioRxiv2024

Have AI-Generated Texts from LLM Infiltrated the Realm of Scientific Writing? A Large-Scale Analysis of Preprint Platforms

Since the release of ChatGPT in 2022, AI-generated texts have inevitably permeated various types of writing, sparking debates about the quality and quantity of content produced by such large language models (LLM). This study investigates a critical question: Have AI-generated texts from LLM infiltrated the realm of scientific writing, and if so, to what extent and in what setting? By analyzing a dataset comprised of preprint manuscripts uploaded to arXiv, bioRxiv, and medRxiv over the past two years, we confirmed and quantified the widespread influence of AI-generated texts in scientific publications using the latest LLM-text detection technique, the Binoculars LLM-detector. Further analyses with this tool reveal that: (1) the AI influence correlates with the trend of ChatGPT web searches; (2) it is widespread across many scientific domains but exhibits distinct impacts within them (highest: computer science, engineering sciences); (3) the influence varies with authors who have different language speaking backgrounds and geographic regions according to the location of their affiliations (Italy, China, etc.); (4) AI-generated texts are used in various content types in manuscripts (most significant: hypothesis formulation, conclusion summarization); (5) AI usage has a positive influence on papers impact, measured by its citation numbers. Based on these findings, suggestions about the advantages and regulation of AI-augmented scientific writing are discussed.

scientific communication and education↗

bioRxiv2020

HIT-scISOseq: High-throughput and High-accuracy Single-cell Full-length Isoform Sequencing for Corneal Epithelium

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.

genomics↗

bioRxiv2020

Fast and accurate assembly of Nanopore reads via progressive error correction and adaptive read selection

Although long Nanopore reads are advantageous in de novo genome assembly, applying Nanopore reads in genomic studies is still hindered by their complex errors. Here, we developed NECAT, an error correction and de novo assembly tool designed to overcome complex errors in Nanopore reads. We proposed an adaptive read selection and two-step progressive method to quickly correct Nanopore reads to high accuracy. We introduced a two-stage assembler to utilize the full length of Nanopore reads. NECAT achieves superior performance in both error correction and de novo assembly of Nanopore reads. NECAT requires only 7,225 CPU hours to assemble a 35X coverage human genome and achieves a 2.28-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line showed an NG50 of 29 Mbp. The high-quality assembly of Nanopore reads can significantly reduce false positives in structure variation detection.

bioinformatics↗