Biology subjects

Qing, J.

Publications and source records attributed to Qing, J..

4 recordsLinked to original sources

bioRxiv2026

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq

BackgroundPseudotime inference has become a standard approach for reconstructing dynamic biological processes from single-cell transcriptomic data. However, after a pseudotemporal ordering has been established, systematically identifying and interpreting gene expression trends along pseudotime remains challenging. Existing approaches often rely on clustering-based heuristics or subjective parameter choices, which can compromise interpretability, reproducibility, and scalability in large datasets. ResultsWe present scTrends, an automated and interpretable framework for gene-level trend classification and strength quantification along a given pseudotime trajectory. Importantly, scTrends does not perform pseudotime inference; instead, it operates downstream of established pseudotime methods to characterize expression dynamics once a temporal ordering is available. scTrends models pseudotime-binned gene expression profiles using generalized additive models and assigns genes to predefined temporal trend categories through a hierarchical, rule-based procedure combined with empirical significance testing, data-adaptive parameter selection, and quantitative assessment of trend strength. This enables simultaneous identification of the direction, shape, and magnitude of gene expression changes along pseudotime. We applied scTrends tothree distinct datasets: human PBMC, human brain, and mouse pancreas, using three different pseudotime inference methods (CytoTRACE v2, Monocle3, and scVelo, respectively). scTrends systematically characterized gene expression dynamics during T cell differentiation, oligodendrocyte precursor differentiation, and pancreatic endocrine cell maturation. The analysis revealed diverse monotonic, non-monotonic, and complex expression patterns, with varying strengths, consistent with known biological processes. Benchmarking analyses further demonstrate that scTrends is computationally efficient and scalable to large single-cell datasets, with modest memory requirements, making it suitable for diverse applications across a range of biological systems. ConclusionsscTrends provides a systematic, automated, and resource-efficient solution for gene-level trend analysis in single-cell pseudotime studies, enabling reproducible characterization of dynamic expression patterns across diverse biological systems.

bioinformatics↗

bioRxiv2025

SCNT: An R Package for Data Analysis and Visualization of Single-Cell and Spatial Transcriptomics

BackgroundThe emergence of single-cell (SC) and spatial transcriptomics (ST) has revolutionized our understanding of gene expression dynamics in complex tissues. However, it also presents challenges for data analysis and visualization, particularly due to the complexity of ST data and the diversity of analysis platforms. The SCNT (Single-Cell, Single-Nucleus, and Spatial Transcriptomics Analysis and Visualization Tools) package was developed to address these challenges by providing an efficient and user-friendly tool for processing, analyzing, and visualizing SC and ST data. ResultsSCNT is an R-based package that integrates widely used tools such as Seurat and ggplot2, enabling seamless conversion between Seurat and H5ad formats. The package supports high-resolution spatial visualization, including customizable gene expression and clustering plots. SCNT also simplifies key data analysis steps, such as quality control, dimensionality reduction, and doublet detection, significantly enhancing workflow efficiency. We tested SCNT on publicly available PBMC dataset, Visum and Visium HD human kidney tissue data, demonstrating its effectiveness. ConclusionsSCNT offers a valuable tool for researchers exploring SC and ST data. Its simplicity, flexibility, and powerful visualization capabilities provide a streamlined workflow for both novice and advanced users. Future developments will focus on expanding support for additional ST platforms and enhancing mult-omics data integration.

bioinformatics↗

bioRxiv2024

PRMT7 mediated PTEN activation promotes bone formation in female mice

Although the epigenetic mechanisms underlying bone formation are recognized, their specific roles and regulatory pathways remain largely unexplored. In this study, we unveil PRMT7 as a novel epigenetic modulator of MSCs osteogenic commitment. The conditional knockout of Prmt7 in mice reveals significantly impaired osteogenesis and bone regeneration exclusively in females, affecting both long bones and craniofacial structures, with no discernible impact in males. Our findings demonstrate that PRMT7 orchestrates osteogenic differentiation through a methyltransferase-dependent manner. Mechanistically, PRMT7 modulates MSCs osteogenic differentiation through the activation of PTEN. Specifically, PRMT7 augments PTEN transcription by increasing H3R2me1 levels at the PTEN promoter. Furthermore, PRMT7 interacts with the PTEN protein, and its deficiency leads to the ubiquitination and subsequent degradation of nuclear PTEN, revealing an unprecedented pathway. Crucially, PTEN overexpression ameliorates the osteogenic deficits observed in Prmt7-deficient mice. Our research positions PRMT7 as a potential therapeutic target to enhance bone formation and offers novel molecular insights into the PRMT7-PTEN regulatory axis, underscoring its significance in bone biology and regenerative medicine. Subject Categories Developmental Biology, Musculoskeletal System, Epigenetics, Post-translational Modifications

developmental biology↗

bioRxiv2018

Disruption of IRE1α through its Kinase Domain Attenuates Multiple Myeloma

Multiple myeloma (MM) arises from malignant immunoglobulin-secreting plasma cells and remains an incurable, often lethal disease despite recent therapeutic advances. The unfolded-protein response sensor IRE1 supports protein secretion by deploying a kinase-endoribonuclease module to activate the transcription factor XBP1s. MM cells may coopt the IRE1-XBP1s pathway; however, the validity of IRE1 as a potential MM therapeutic target is controversial. Here we show that genetic disruption of IRE1 or XBP1s, or pharmacologic IRE1 kinase inhibition, attenuated subcutaneous or orthometastatic growth of MM tumors in mice, and augmented efficacy of two well-established frontline antimyeloma agents, bortezomib or lenalidomide. Mechanistically, IRE1 perturbation inhibited expression of key components of the ER-associated degradation machinery, as well as cytokines and chemokines known to promote MM growth. Selective IRE1 kinase inhibition reduced viability of CD138+ plasma cells while sparing CD138- cells from bone marrow of newly diagnosed MM patients or patients whose disease relapsed after 1 - 4 lines of treatment in both US- and EU-based cohorts. IRE1 inhibition preserved survival and glucose-induced insulin secretion by pancreatic microislets. Together, these results establish a strong therapeutic rationale for targeting IRE1 with kinase-based small-molecule inhibitors in MM.\n\nSignificance statementMultiple myeloma (MM) is a lethal malignancy of plasma cells. MM cells have an expanded endoplasmic reticulum (ER) that is constantly under stress due to immunoglobulin hyperproduction. The ER-resident sensor IRE1 mitigates ER stress by expanding the ERs protein-folding capacity while supporting proteasomal degradation of misfolded ER proteins. IRE1 elaborates these functions by deploying its cytoplasmic kinase-RNase module to activate the transcription factor XBP1s. The validity of IRE1 as a potential therapeutic target in MM has been questioned. Using genetic and pharmacologic disruption in vitro and in vivo, we demonstrate that the IRE1-XBP1s pathway plays a critical role in MM growth. We further show that IRE1s kinase domain is an effective and safe potential small-molecule target for MM therapy.

cancer biology↗