Biology subjects

Kang, Q.

Publications and source records attributed to Kang, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

Beyond Generic Signal Peptides: ApexSP Enables Cargo-Specific Secretion Design

Recombinant proteins are widely used in biopharmaceuticals, industrial manufacturing, and molecular diagnostics; however, efficient secretion remains a major bottleneck limiting their large scale production. As core elements controlling protein entry into secretion pathways, signal peptides do not function solely based on their own sequences, but rather depend on coordinated compatibility among cargo protein properties, secretion pathways, and host backgrounds. Current signal peptide engineering mainly relies on a limited number of commonly used signal peptides, empirical selection, and individual experimental screening, making it difficult to design efficient secretion elements tailored to specific expression systems. Here, we present ApexSP, a signal peptide design framework for secretion engineering tailored to individual cargo proteins. ApexSP is built upon a large scale, high quality signal peptide knowledge base and integrates a discrete diffusion generative model constrained by evolutionary information, a multitask biological filter incorporating topology information, and a signal peptide and mature protein compatibility ranking model to enable integrated design of signal peptides for specific expression systems. ApexSP achieved high accuracy in multiple attribute prediction tasks, including pathway classification and cleavage site prediction, reaching or exceeding the performance of existing signal peptide prediction tools. Moreover, the cargo protein aware ranking module improved the enrichment of candidates with high secretion performance. Experimental validation across multiple eukaryotic and prokaryotic expression systems demonstrated that screening only 10 candidate sequences generated by ApexSP yielded multiple designed signal peptides outperforming reference signal peptides, with the best performing design in the ApGA expressed Pichia pastoris system achieving a 1.71 fold increase in secretion performance compared with the factor signal peptide. Overall, ApexSP advances signal peptide research from sequence prediction toward secretion element design, demonstrating that a single round of designed signal peptides can achieve high secretion performance suitable for further engineering optimization.

synthetic biology↗

bioRxiv2026

A unified spatial transcriptome profiling of ten mouse organs

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs--including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus--encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 {micro}m x 25 {micro}m) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.

bioinformatics↗

bioRxiv2025

MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction

Microorganisms underpin global biogeochemical cycles and represent a vast, underexplored reservoir for sustainable biotechnology and human health. Their diversity, arising from the interplay of numerous genes within holistic genomic contexts, dictates complex ecophysiological traits across varied environments. To bridge the gap between complex genomic sequences and associated biological functions and phenotypic outcomes, we present MicroGenomer, a foundation model for transferable microbial genome representations enabling multi-scale genomic understanding and ecophysiological trait prediction. MicroGenomer leverages a hierarchical training strategy comprising pre-training on large-scale genomic sequences (234.5 billion base pairs), domain-specific mid-training using the GTDB-curated marker gene set, and task-specific post-training. Despite a streamlined architecture of 470 million parameters, MicroGenomer achieves performance on key tasks competitive with models nearly 85 times its size. Advancing beyond established gene-scale encoders, MicroGenomer generates robust embeddings at the genome scale for downstream modeling. Extensive evaluations demonstrate that MicroGenomer effectively captures phylogenetic structures in species space, excelling in geneand genome-scale understanding as well as ecophysiological trait prediction. The practical utility of these capabilities is further demonstrated by targeted wet-lab validation on newly isolated strains, indicating that MicroGenomers predictions provide reliable guidance for biological experiments. Collectively, MicroGenomer offers a high-performance, resource-efficient framework that transforms raw sequence data into actionable biological insights, providing a powerful foundation for microbiome research and biotechnology.

bioinformatics↗

bioRxiv2025

CellBin:a generalist framework to process spatial omics data to cell level

Spatial omics has rapidly expanded with increasingly diverse imaging modalities, molecular targets, and chip sizes. However, no general framework currently exists to construct cell level matrices that are robust across platforms and omics types. Here we present CellBin, a universal and scalable frame-work that unifies image stitching, cell segmentation, and spot-to-cell mapping for multiple spatial omics technologies. CellBin integrates a multi-field weighted stitching algorithm for large-area images, a family of U-Net-based models trained across diverse staining modalities, and an optimized computational architecture for high-throughput processing. Across five technological platforms and three omics data types, CellBin achieves robust segmentation and accurate single-cell matrix construction, consistently outperforming seven state-of-the-art methods in F1-score, cell size precision, and annotation accuracy. By providing a generalizable, cross-platform solution, CellBin bridges multiple spatial omics, enabling unified, high-resolution cell level analyses across technologies.

bioinformatics↗

bioRxiv2025

Prediction of soil probiotics based on foundation model representation enhancement and stacked aggregation classifier

Soil probiotics are indispensable in agro-ecosystem, which enhances crop yield through nutrient solubilization, pathogen suppression, and soil structure improvement. However, reliable prediction methods for soil probiotics remain absent. In this study, we utilize genomic foundation models to generate representations from samples sequences, and then, enhance them by deeply integrating domain-specific engineered features. The enhanced representations enable training a powerful classifier for a target task, instead of common parameter fine-tuning. Inspired by the stacking ensemble learning framework, we also design a stacked aggregation classifier. It predicts the label of a sample with only leveraging partial sequence segments from this sample, effectively addressing the challenges in processing long sequences. The proposed method is applied on prediction of soil probiotics and obtains 97.50% accuracy and 0.9807 AUC value on balanced and imbalanced test sets, respectively. Potential functional genes are revealed from the predicted probiotics, providing biologically insights for more related studies.

bioinformatics↗

bioRxiv2025

Functional loss of CHS2 confers high levels resistance to Bacillus thuringiensis Vip3Aa, but with significant fitness costs in five lepidopteran species

Bacillus thuringiensis (Bt) crops, engineered to produce insecticidal proteins such as Vip3Aa and Cry toxins, have revolutionized pest management by providing a sustainable alternative to chemical pesticides. However, the development of resistance to these toxins has driven the investigation of underlying mechanisms to better understand how pests evade toxicity and to develop more effective resistance management strategies. Recently, a laboratory-selected Spodoptera frugiperda strain exhibited a high level of resistance to Vip3Aa (resistance ratio: 5562-fold), with the mutation of the chitin synthase gene, SfCHS2, identified as a key factor. In this study, we extend these findings to additional lepidopteran species, including Spodoptera exigua and Agrotis ipsilon. Our results show that CHS2 knockout strains lack the peritrophic matrix (PM), while the resistant Sfru_R3 strain retains its PM. Knockout of this gene resulted in high levels resistance to Vip3Aa. These findings further validate the role of the CHS2 gene in Vip3Aa resistance and highlight its potential as a target for resistance management in lepidopteran pests. This study advances our understanding of the molecular mechanisms behind resistance to Vip3Aa and supports the development of strategies to delay resistance in Bt crop management.

genetics↗

bioRxiv2025

Larvicidal activity of Trichoderma atroviride (Hypocreales: Hypocreaceae) against Aedes albopictus (Diptera: Culicidae)

Larviciding is an important part of effective integrated mosquito management. However, growing resistance to chemical and bacterial-based insecticides requires biocontrol agents with novel modes of action. Entomopathogenic fungi are good candidates for larval control due to their capability to infect mosquito larvae and their production of larvicidal compounds. In this study, we isolated a strain of Trichoderma atroviride from Aedes albopictus larvae collected in Manhattan, KS, USA. We used a laboratory-based microcosm assay to expose L3 Ae. albopictus larvae to T. atroviride conidia and culture supernatant treatments. Larvae were monitored daily for survival and development to pupae and adults. In addition, adult survival was monitored for ten-days post pupation, and wing lengths were measured to assess mosquito size. Our results revealed that T. atroviride culture supernatant was a potent larvicide towards Ae. albopictus. However, conidia by themselves were not larvicidal, indicating the major mode of killing was through toxicity exerted by the culture supernatant. We further showed that larval exposure to T. atroviride supernatant delayed larval development to pupae. Sex-specific adult survival was not affected by larval exposure to T. atroviride. However, wing length of male and female mosquitoes were reduced, indicating a reduction in adult mosquito body size as compared to the control. Taken together, this study identifies the culture supernatant from a novel strain of T. atroviride as a potent larvicide of Ae. albopictus, potentially expanding our toolbox for biological control of mosquitoes.

microbiology↗

bioRxiv2024

CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models

In recent years, cell segmentation techniques have played a critical role in the analysis of biological images, especially for quantitative studies. Deep learning-based cell segmentation models have demonstrated remarkable performance in segmenting cell and nucleus boundaries, however, they are typically tailored to specific modalities or require manual tuning of hyperparameters, limiting their generalizability to unseen data. Comprehensive datasets that support both the training of universal models and the evaluation of various segmentation techniques are essential for overcoming these limitations and promoting the development of more versatile cell segmentation solutions. Here, we present CellBinDB, a large-scale multimodal annotated dataset established for these purposes. CellBinDB contains more than 1,000 annotated images, each labeled to identify the boundaries of cells or nuclei, including 4,6-Diamidino-2-Phenylindole (DAPI), Single-stranded DNA (ssDNA), Hematoxylin and Eosin (H&E), and Multiplex Immunofluorescence (mIF) staining, covering over 30 normal and diseased tissue types from human and mouse samples. Based on CellBinDB, we benchmarked seven state-of-the-art and widely used cell segmentation technologies/methods, and further analyzed the effects of four cell morphology indicators and image gradient on the segmentation results.

cell biology↗

bioRxiv2024

Single-cell multi-omics and spatial multi-omics data integration via dual-path graph attention auto-encoder

Single-cell multi-omics data integration enables joint analysis of the resolution at single-cell level to provide comprehensive and accurate understanding of complex biological systems, while spatial multi-omics data integration is benefit to the exploration of cell spatial heterogeneity to facilitate more diversified downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration on spatial information, and still have the room for performance improvement. A reliable multi-omics data integration method that can be applied to both single-cell and spatially resolved data is necessary and significant. We propose a single-cell multi-omics and spatial multi-omics data integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct neighborhood graphs based on single-cell expression data and spatial information respectively, and perform self-supervised learning for data integration through the graph attention auto-encoders from two paths. SSGATE is applied to data integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.

bioinformatics↗

bioRxiv2023

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Stereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we update our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify that STCellbin can be applied on the mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets and outperforms other competitive methods. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology. Availability & ImplementationThe source code of STCellbin is available at https://github.com/STOmics/STCellbin.

bioinformatics↗

bioRxiv2023

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interaction network within the spatial texture, thus bridging intercellular communications and intracellular regulations to unravel the intricate regulatory mechanisms that govern cellular behavior. Overall, Stereopy serves as both a bioinformatics toolbox and an extensible framework that provides researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

BatchEval Pipeline: Batch Effect Evaluation Workflow for Multiple Datasets Joint Analysis

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results. Availability & ImplementationThe source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

bioinformatics↗

bioRxiv2023

An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

bioinformatics↗

bioRxiv2023

StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset

BackgroundOwing to recent advances in resolution and field-of-view, spatially resolved transcriptomics sequencing, such as Stereo-seq, has emerged as a cutting-edge technology for the interpretation of large tissues at the single-cell level. To generate accurate single-cell spatial gene expression profiles from high-resolution spatial omics data, a powerful computational tool is required. FindingsWe present CellBin, an image-facilitated one-stop pipeline for high-resolution and large field-of-view spatial transcriptomic data of Stereo-seq. CellBin provides a comprehensive and systematic platform for generating high-confidence single-cell spatial gene expression profiles, which specifically includes image stitching, image registration, tissue segmentation, nuclei segmentation and molecule labeling. CellBin is user-friendly and does not require a specific level of omics and image analysis expertise. ConclusionsDuring image stitching and molecule labeling, CellBin delivers better-performing algorithms to reduce stitching error and time, in addition to improving the signal-to-noise ratio of single-cell gene expression data, in comparison with existing methods. Additionally, CellBin has been shown to obtain highly accurate single-cell spatial data using mouse brain tissue, which facilitated clustering and annotation.

bioinformatics↗

bioRxiv2022

The potential of the Beauveria bassiana MHK isolate for mosquito larval control

The African malaria mosquito, Anopheles gambiae Giles (Diptera: Culicidae), and the Asian tiger mosquito, Aedes albopictus Skuse (Diptera: Culicidae) are of public health concern due to their ability to transmit disease-causing parasites and pathogens. Current mosquito control strategies to prevent vector-borne diseases rely mainly on the use of chemicals. However, insecticide resistance in mosquito populations necessitates alternative control measures, including biologicals such as entomopathogenic fungi. Here we report the impact of a Beauveria bassiana (Balsamo) Vuillemin (Hyprocreales: Cordycipitaeceae) isolate, MHK, isolated from field-collected Ae. albopictus larvae on mosquito survival and development. Larval infection bioassays using three B. bassiana conidial doses were performed on the second and third larval instars of An. gambiae and Ae. albopictus mosquitoes. Larvae were monitored daily for survival and development to pupae and adults. Our results show that B. bassiana MHK was more effective in killing An. gambiae than Ae. albopictus larvae. We further observed delays in development to pupae and adults in both mosquito species exposed the varying doses of B. bassiana as compared to the water control. In addition, larval exposure to B. bassiana reduced adult male and female survival in both mosquito species, further contributing to mosquito population control. Thus, this study identifies the locally isolated fungus, B. bassiana MHK, as a possible biological control agent of two mosquito species of public health concern, increasing the arsenal for integrated mosquito control.

microbiology↗

bioRxiv2022

Specific Deletion of Axin1 Leads to Activation of β-Catenin/BMP Signaling Resulting in Fibular Hemimelia Phenotype in Mice

Axin1 and Axin2 are key regulators of canonical Wnt signaling pathway. The roles of Axin1 and Axin2 in skeletal development and in disease development have not been fully defined. Here, we reported that Axin1 and Axin2 are essential for lower limb development. Specific deletion of Axin1 in limb mesenchymal cells leads to fibular hemimelia (FH)-like phenotype, associated with tarsal coalition. Further studies demonstrated that FH disease is associated with additional defects resembling to the proximal femoral focal deficiency (PFFD) in Axin1/Axin2 double knockout (KO) mice. We then provided in vivo evidence showing that Axin1 controls limb development through both canonical {beta}-catenin and BMP signaling pathways. We demonstrated that inhibition of {beta}-catenin or BMP signaling could significantly reverse the FH phenotype in mice. Together, our findings revealed that integration of Wnt and BMP signaling by Axin1 is required for lower limb development. Defects in Axin1 and Axin2 signaling could lead to the development of FH disease.

developmental biology↗

bioRxiv2021

SmProt: a reliable repository with comprehensive annotation of small proteins identified from ribosome profiling

Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORFs translation events or sequences, and significantly increased data volume. More components such as non-AUG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets and collected from the literature and other sources originating from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.

bioinformatics↗

bioRxiv2021

Genome-wide analysis of mobile element insertions in human genomes

Mobile element insertions (MEIs) are a major class of structural variants (SVs) and have been linked to many human genetic disorders, including hemophilia, neurofibromatosis, and various cancers. However, human MEI resources from large-scale genome sequencing are still lacking compared to those for SNPs and SVs. Here, we report a comprehensive map of 36,699 non-reference MEIs constructed from 5,675 genomes, comprising 2,998 Chinese samples ([~]26.2X, NyuWa) and 2,677 samples from the 1000 Genomes Project ([~]7.4X, 1KGP). We discovered that LINE-1 insertions were highly enriched at centromere regions, implying the role of chromosome context in retroelement insertion. After functional annotation, we estimated that MEIs are responsible for about 9.3% of all protein-truncating events per genome. Finally, we built a companion database named HMEID for public use. This resource represents the latest and largest genomewide study on MEIs and will have broad utility for exploration of human MEI findings.

genomics↗