Biology subjects

Luo, X.

Publications and source records attributed to Luo, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

bioRxiv2025

Expression spectrum of TE-derived transcripts in human adult tissues

Transposable elements (TEs) are vital components of eukaryotic genomes and have played a critical role in genome evolution. Although most TEs are silenced in the mammalian genome, increasing evidence suggests that certain TEs are actively involved in gene regulation during early developmental stages. However, the extent to which human TEs drive gene transcription in adult tissues remains largely unexplored. In this study, we systematically analyzed 17,329 human transcriptomes to investigate how TEs influence gene transcription across 47 adult tissues. Our findings reveal that TE-derived transcripts are broadly expressed in human tissues, contributing to both housekeeping functions and tissue-specific gene regulation. We identified sex-specific expression of TE-derived transcripts regulated by sex hormones in breast tissue between females and males. Our results demonstrated that TE-derived alternative transcription initiation significantly enhances the variety of translated protein products, e.g., changes in the N-terminal peptide length of WNT2B caused by TE-derived transcription result in isoform-specific subcellular localization. Additionally, we identified 68 human-specific TE-derived transcripts associated with metabolic processes and environmental adaptation. Together, these findings highlight the pivotal evolutionary role of TEs in shaping the human transcriptome, demonstrating how conserved and human-specific TEs contribute to transcriptional and translational innovation in human genome evolution.

genomics↗

bioRxiv2025

Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcriptomics Clustering

The spatial transcriptomics (ST) clustering plays a crucial role in elucidating the tissue spatial heterogeneity. An accurate ST clustering result can greatly benefit downstream biological analyses. As various ST clustering approaches are proposed in recent years, comparing their clustering accuracy becomes important in benchmarking studies. However, the widely used metric, adjusted Rand index (ARI), totally ignores the spatial information in ST data, which prevents ARI from fully evaluating spatial ST clustering methods. We propose a spatially aware Rand index (spRI) as well as spARI that incorporate the spatial distance information. Specifically, when comparing two partitions, spRI provides a disagreement object pair with a weight relying on the distance of the two objects, whereas Rand index assigns a zero weight to it. This spatially aware feature of spRI adaptively differentiates disagreement object pairs based on their distinct distances, providing a useful evaluation metric that favors spatial coherence of clustering. The spARI is obtained by adjusting spRI for random chances such that its expectation takes zero under an appropriate null model. Statistical properties of spRI and spARI are discussed. The applications to simulation study and two ST datasets demonstrate the improved utilities of spARI compared to ARI in evaluating ST clustering methods. The R package to compute spRI and spARI is available at https://github.com/yinqiaoyan/spARI.

bioinformatics↗

bioRxiv2025

Chimera: Ultrafast and Memory-efficient Database Construction for High-Accuracy Taxonomic Classification in the Age of Expanding Genomic Data

The rapid growth of genomic data expands species diversity but also causes taxonomic imbalance, with certain species heavily overrepresented. Both data volume and imbalance challenge the accuracy and efficiency of metagenomic tools. Here, we present Chimera, a transformative tool harnessing the Interleaved Merged Cuckoo Filter (IMCF) and FairMin-Cap (FMC) strategy for next-level performance. It achieves the highest classification accuracy while providing an astonishing 162-fold faster database assembly than Kraken2, constructing the complete RefSeq genome database within mere minutes using under 32 GB of RAM, enabling rapid and cost-effective database updates. Furthermore, Chimeras universal memory scalability supports at least 300,000 species and potentially over 800,000 species in practical 1 TB systems, overwhelming traditional software solutions. Our results establish Chimera as a foundational tool for the next era of metagenomic research, laying a crucial cornerstone for the future of ultramassive genome datasets.

bioinformatics↗

bioRxiv2025

Phase separation of Polycomb-like (PCL) proteins drive PRC2 complex condensates to regulate gene expression

Polycomb group (PcG) proteins function as two major multicomponent protein complexes: Polycomb repressive complex 1 (PRC1) and 2 (PRC2) to repress key developmental genes and maintain epigenetic memory of cell identity during development. Phase-separation has long been implicated in PRC1 mediated gene silencing, but it is still unclear whether PRC2 also utilize a similar mechanism to regulate gene expression. Here we report that Polycomb-like (PCL) proteins, PRC2 accessory factors, can phase-separate in vitro and generate dynamic puncta in vivo. Biochemical and cellular analyses reveal that PCL proteins (hereafter referred to as PCLs) have intramolecular interaction between N- and C-terminal domains, which make PCLs into more compact conformations. The intramolecular interaction not only controls the size of the PCLs phase separation droplets, but also affects the chromatin association of PRC2. Finally, we show that CpG islands, key DNA regulatory elements in mammalian promoters, disrupt the N-C intramolecular interaction of PCLs to expose their middle intrinsically disordered regions (IDRs), which in turn trigger PCLs driven PRC2 condensates formation. Together, this study provides a new perspective on the regulation of PRC2 by PCLs, implicating PCLs read CpG islands to fine-tune their oligomerization to overcome the threshold for PRC2 recruitment to corresponding genomic loci via multivalent interaction with CpG islands chromatin.

biochemistry↗

bioRxiv2025

A pair of readers of histone H3K4 methylation recruit Polycomb repressive complex 2 to regulate photoperiodic flowering

In flowering plants, the transition from vegetative growth to reproduction or flowering, is often timed by seasonal changes in day length (photoperiod). In the model flowering plant Arabidopsis thaliana, the photoperiodic cue increasing day length or long day, through the photoperiod pathway, induces a daily rhythmic activation of the florigen gene FLOWERING LOCUS T (FT) to promote flowering. Under inductive long days (LDs), FT expression is activated around dusk, but to be repressed overnight and into the early afternoon the next day. The mechanism underlying the daily oscillation of FT repression to ensure long-day induction of flowering remains unclear. Here, we report that AtING1 and AtING2, Arabidopsis homologs of the mammalian Inhibitor of Growth (ING) proteins, read di- and tri-methylated histone 3 lysine 4 (H3K4me2/me3) on FT chromatin and further recruit Polycomb-repressive complex 2 (PRC2) to repress FT expression at night and into the early afternoon the next day, following FT activation at dusk in LDs. This prevents precocious flowering under inductive LDs. Our study reveals that a previously-undescribed chromatin-regulatory module: H3K4me2/me3-ING1/2-PRC2, which timely represses FT expression following the daily rhythmic FT activation at dusk by the long-day pathway, to prevent excessive FT expression and thus precisely control the timing of the transition to flowering in response to inductive photoperiodic signals.

plant biology↗

bioRxiv2025

Homoharringtonine Promotes FTO Degradation to Suppress LILRB4-Mediated Immune Evasion in Acute Monocytic Leukemia

Acute monocytic leukemia (AML-M5), a subtype of acute myeloid leukemia, is a highly aggressive malignancy characterized by a poor prognosis, primarily due to the ability of leukemic cells to evade immune surveillance. In this study, we demonstrate that homoharringtonine (HHT), an FDA-approved therapeutic agent for chronic myeloid leukemia (CML), inhibits this immune evasion by targeting the FTO/m6A/LILRB4 signaling pathway in monocytic AML. Utilizing RNA sequencing (RNA-seq) and various functional assays, we reveal that HHT treatment significantly reduces LILRB4 expression at both the RNA and protein levels, suggesting that the effects of HHT on LILRB4 are distinct from its well-established role as a protein synthesis inhibitor. Mechanistically, HHT treatment markedly increases global levels of RNA m6A in THP-1 cells by promoting the degradation of FTO, which subsequently diminishes the expression of its downstream targets, MLL1 and LILRB4. Furthermore, in vitro and in vivo analyses employing monocytic AML cell lines, mouse-derived AML xenograft models, and patient samples collectively support the conclusion that HHT suppresses immune evasion in monocytic AML by reducing LILRB4 expression. Importantly, the downregulation of LILRB4 resulting from HHT treatment enhances the susceptibility of THP-1 cells to CD8+ T cell cytotoxicity, accompanied by increased markers of immune activation. Overall, our findings position HHT as a promising clinical agent for enhancing CD8+ T cell-based cancer immunotherapy by mitigating immune evasion in monocytic AML. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=147 HEIGHT=200 SRC="FIGDIR/small/643466v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@e72e5dorg.highwire.dtl.DTLVardef@19519aeorg.highwire.dtl.DTLVardef@1c8f786org.highwire.dtl.DTLVardef@f2a963_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Targeting Modulated Vascular Smooth Muscle Cells in Atherosclerosis via FAP-Directed Immunotherapy

Vascular smooth muscle cell (VSMC) and immune cell diversification play a central role in driving atherosclerotic coronary artery disease (CAD)1-3. However, the molecular mechanisms governing cell state transitions within the neo-intima in human CAD remain poorly understood, and no lipid-independent therapies are currently approved for its treatment. Here, we performed multi-omic single-cell gene expression profiling, epitope mapping, and spatial transcriptomics from 27 human coronary arteries. Our analysis identified fibroblast activation protein (FAP) as a marker of modulated VSMCs within the neo-intima. Genetic lineage tracing in mice confirmed that FAP cells in the plaque originate from medial VSMCs. Additionally, non-invasive positron emission tomography (PET) imaging in patients with CAD revealed focal FAP uptake in atherosclerotic lesions. Spatial transcriptomics further delineated the distinct localization of VSMC and immune cell subsets within plaques, with FAP states enriched in the neo-intima. To explore the therapeutic potential of targeting de-differentiated VSMCs, we developed an anti-FAP bispecific T-cell engager (BiTE) and demonstrated that it significantly reduced the plaque burden in multiple mouse models of atherosclerosis. Collectively, our study provides the first single-cell and spatially resolved map of human CAD, establishes FAP as a marker of modulated smooth muscle cells, and demonstrates the broader potential of immunotherapeutics for lipid independent targets in atherosclerotic CAD.

genomics↗

bioRxiv2025

Basic-Leucine-Zipper Transcription Factors Regulate Selective Molecular Phenotypes in Regulatory T Cells During IL-2-Induced Activation

Regulatory T (Treg) cells have long been recognized as modulators of immunological tolerance and homeostasis. Previously, we used scRNA-seq to reveal significant Treg heterogeneity in response to IL-2-induced activation. Herein, we leveraged enrichment analyses, as well as bulk and single-nucleus multi-omics in splenic and lung Tregs, to uncover and confirm the importance of transcription factors (TFs) and chromatin remodeling in Treg activation. Multiple bZIP TF motifs showed increased chromatin accessibility post IL-2 treatment, with correlated transcriptional changes resembling Th1 and Th2 molecular phenotypes, further confirmed by spatial ATAC-seq. By combining gene perturbation and CUT&RUN assays before and after Treg stimulation, we show that bZIP TFs, such as BATF and BACH1, are critical to IL-2-induced Treg activation, coordinating epigenetic and transcriptional changes that selectively drive T-helper phenotypes and metabolic pathways.

genomics↗

bioRxiv2025

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

ImportanceMedical reporting guidelines are significant in improving the transparency, quality, and integrity of medical research, particularly in randomized clinical trials; adherence to these guidelines supports research interpretability and has direct implications for downstream applications, such as patient treatment. However, with over 600 distinct reporting guidelines, manual assessments are often time-consuming and labor-intensive. ObjectiveTo evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. Design, Setting, and ParticipantsThis study used large language models to design a retrieval augmentation generation architecture and collected published journal articles as training and validation sets to optimize prompts within the framework and comprehensively evaluate the performance of the framework. Medical reporting experiments were collected from 50 randomized controlled trials without the intervention of AI tools and 41 randomized controlled trials with the intervention of AI tools. Main Outcomes and MeasuresFor effective evaluation of the performance of this tool, a classification accuracy metric (Reported/Not Reported) defined as the number of correct judgments divided by all judgments and a content consistent score metric defined as the number of contents retrieved by the tool that are the same as those retrieved by researchers divided by the total number of judgments were calculated. ResultsThe RAPID tool uses the widely used Word document and Portable Document Format as an input file. Fifty published journal articles without the intervention of AI tools and 41 published journal articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. For these two reporting guidelines, a training set of 5 articles was selected from each dataset to refine the prompts used in the tools for CONSORT and CONSORT-AI reporting checklist. The validation set of the remaining articles was used to assess the performance of the RAPID. The RAPID tool used the Word document and Portable Document Format of the articles as input files. A RAPID graphical user interface was built using JavaScript and Vue. Conclusions and RelevanceThe RAPID tool is designed to assist in the reporting of various types of trials. RAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID may effectively save time and improve working efficiency for different user groups, for example, 1) simplifying the submission process and improving report quality by verifying manuscript completeness for medical authors; 2) facilitating evaluation of report quality for medical researchers; 3) expediting manuscript distribution for medical editors; and 4) identifying reporting deficiencies and providing deeper insights for review comments for reviewers. Key PointsO_ST_ABSQuestionC_ST_ABSCan large language model tools automatically generate medical reporting checklists for manuscripts of different types of clinic trials? FindingsAn automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, RAPID, was developed. RAPID was fully evaluated on all items across two separate datasets related to the Consolidated Standards of Reporting Trials (CONSORT) and CONSORT-AI reporting guidelines. In the first dataset corresponding to CONSORT, RAPID achieved an average accuracy of 92.11%, while in the second dataset associated with CONSORT - AI, it reached an average accuracy of 83.81%. Additionally, RAPID is highly scalable. It can be easily and smoothly adapted to different medical reporting guidelines without transfer learning on a large dataset. MeaningRAPID can effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.

bioinformatics↗

bioRxiv2025

TAC-C uncovers open chromatin interaction in crops and SPL-mediated photosynthesis regulation

Cis-regulatory elements (CREs) direct precise gene expression for development and environmental response, yet their spatial organization in crops is largely unknown. We introduce Transposase-Accessible Chromosome Conformation Capture (TAC-C), a method integrating ATAC-seq and Hi-C to capture fine-scale chromatin interactions in four major crops: rice, sorghum, maize, and wheat. TAC-C reveals that chromatin interaction frequency aligns with genome size and gene expression, exhibiting distinct loop structures between C3 and C4 plants, particularly in C4-specific enzymes coding genes. Integrating chromatin structure with population genetics data highlights that chromatin loops connect distal regulatory elements to phenotypic variation. Asymmetrical open chromatin interactions among subgenomes, driven by transposon insertions and sequence variations, contribute to biased homoeolog expression. Furthermore, TaSPL7/15 regulate photosynthesis-related genes through chromatin interactions, with enhanced photosynthetic efficiency and starch content in Taspl7&15 mutant. TAC-C provides new insights into the spatial organization of regulatory elements in crops, especially for SPL-mediated photosynthesis regulation in wheat. TeaserTAC-C Reveals Chromatin Interactions and SPL-Mediated Photosynthesis Regulation in Crops.

plant biology↗

bioRxiv2025

DNA origami directed integration of colloidal nanophotonic materials with silicon photonics

Incorporation of colloidal quantum emitters into silicon-based photonic devices would enable major advances in quantum optics. However, deterministic placement of individual sub-10 nm colloidal particles onto micron-sized photonic structures with nanometer-scale precision remains an outstanding challenge. Here, we introduce Cavity-Shape Modulated Origami Placement (CSMOP) that leverages the structural programmability of DNA origami to precisely deposit colloidal nanomaterials within lithographically-defined resist cavities. CSMOP enables clean and accurate patterning of origami templates onto photonic chips with high yields. Soft-silicification-passivation stabilizes deposited origamis, while preserving their binding sites to attach and align colloidal quantum rods (QRs) to control their nanoscale positions and emission polarization. We demonstrate QR integration with photonic device structures including waveguides, micro-ring resonators, and bullseye photonic cavities. CSMOP therefore offers a general platform for the integration of colloidal quantum materials into photonic circuits, with broad potential to empower quantum science and technology.

bioengineering↗

bioRxiv2025

Feature-Driven Whole-Tissue Imaging with Subcellular Resolution

Understanding how subcellular structures and events shape tissue-wide phenomena remains a central challenge in biology. We present Multiscale Cleared Tissue Axially Swept Light-Sheet Microscopy (MCT-ASLM), a platform combining cm-scale imaging with targeted high-resolution interrogation of intact tissues in human-guided or autonomous modes. Capable of capturing fields of view up to 21 mm at micron-scale resolution, MCT-ASLM can seamlessly transition to submicron (~300 nm) isotropic resolution for targeted imaging. This versatility enables comprehensive studies of hierarchical organization and spatially complex processes, including mapping neuronal circuits in rat brains, visualizing glomerular innervation in mouse kidneys, and examining metastatic tumor microenvironments. By bridging subcellular to tissue-level scales, MCT-ASLM offers a powerful method for unraveling how local events contribute to global biological phenomena.

biophysics↗

bioRxiv2024

HIV-SEQ REVEALS GLOBAL HOST GENE EXPRESSION DIFFERENCES BETWEEN HIV-TRANSCRIBING CELLS FROM VIREMIC AND SUPPRESSED PEOPLE WITH HIV

"Active" reservoir cells transcribing HIV can perpetuate chronic inflammation in virally suppressed people with HIV (PWH) and likely contribute to viral rebound after antiretroviral therapy (ART) interruption, so they represent an important target for new therapies. These cells, however, are difficult to study using single-cell RNA-seq (scRNA-seq) due to their low frequency and low levels of HIV transcripts, which are usually not polyadenylated. Here, we developed "HIV-seq" to enable more efficient capture of HIV transcripts - including non-polyadenylated ones - for scRNA-seq analysis of cells from PWH. By spiking in a set of custom-designed capture sequences targeting conserved regions of the HIV genome during scRNA-seq, we increased our ability to find HIV RNA+ cells from PWH by up to 44%. Implementing HIV-seq in conjunction with surface phenotyping by CITE-seq on paired blood specimens from PWH before vs. after ART suppression, we found that HIV RNA+ cells were enriched among T effector memory (Tem) cells during both viremia and ART suppression, but exhibited a cytotoxic signature during viremia only. By contrast, HIV RNA+ cells from the ART-suppressed timepoints exhibited a distinct anti-inflammatory signature involving elevated TGF-{beta} and diminished IFN signaling. Overall, these findings demonstrate that active reservoir cells exhibit transcriptional features distinct from HIV RNA+ cells during viremia, and underscore HIV-seq as a useful tool to better understand the mechanisms by which HIV-transcribing cells can persist during ART.

immunology↗

bioRxiv2024

RobustCell: A Model Attack-Defense Framework for Robust Transcriptomic Data Analysis

Computational methods should be accurate and robust for tasks in biology and medicine, especially when facing different types of attacks, defined as perturbations of benign data that can cause a significant drop in method performance. Therefore, there is a need for robust models that can defend attacks. In this manuscript, we propose a novel framework named RobustCell to analyze attack-defense methods in single-cell and spatial transcriptomic data analysis. In this biological context, we consider three types of attacks as well as two types of defenses in our framework and systemically evaluate the performances of the existing methods on their performance of both clustering and annotating single cells and spatial transcriptomic data. Our evaluations show that successful attacks can impair the performances of various methods, including single-cell foundation models. A good defense policy can protect the models from performance drops. Finally, we analyze the contributions of specific genes toward the cell-type annotation task by running the single-gene and group-genes attack methods. Overall, RobustCell is a user-friendly and extension-flexible framework for analyzing the risks and safety of analyzing transcriptomic data under different attacks.

bioinformatics↗

bioRxiv2024

PanTax: Strain-level taxonomic classification of metagenomic data using pangenome graphs

Microbes are omnipresent, thriving in a range of habitats from oceans to soils and even within our gastrointestinal tracts. They play a vital role in maintaining ecological equilibrium and promoting the health of their hosts. Consequently, understanding the strain diversity within microbial communities is crucial, as variations between strains can lead to distinct phenotypic expressions or diverse biological functions. However, current methods for taxonomic classification from metagenomic sequencing data have several limitations, including their reliance solely on species resolution, support for either short or long reads, or their confinement to a given single species. Most notably, the majority of existing taxonomic classifiers rely solely on a single linear representative genome as a reference, which fails to capture the strain diversity, thereby introducing single-reference biases. Here, we present PanTax, a pangenome graph-based taxonomic classification method that overcomes the shortcomings of single-reference genome-based approaches, because pangenome graphs possess the capability to depict the genetic variability present across multiple evolutionarily or environmentally related genomes. PanTax provides a comprehensive solution to taxonomic classification for strain resolution, compatibility with both short and long reads, and compatibility with single or multiple species. Extensive benchmarking results demonstrate that PanTax drastically outperforms state-of-the-art approaches, primarily evidenced by its significantly higher precision or recall (at both species and strain levels), while maintaining comparable or better performance in other aspects across various datasets. PanTax is a user-friendly open-source tool that is publicly accessible at https://github.com/LuoGroup2023/PanTax.

bioinformatics↗

bioRxiv2024

CelLink: Integrate single-cell multi-omics data with few linked features and imbalanced cell populations

Single-cell multi-omics technologies capture complementary molecular layers, enabling a comprehensive view of cellular states and functions. However, integrating these data types poses significant challenges when their features are weakly linked and cell population sizes are imbalanced. Currently, no method efficiently addresses these two issues simultaneously. Therefore, we developed CelLink, a novel single-cell multi-omics data integration method designed to overcome these challenges. CelLink normalizes and smooths feature profiles to align scales across datasets and integrates them through a multi-phase pipeline that iteratively employs the optimal transport algorithm. It dynamically refines cell-cell correspondences, identifying and excluding cells that cannot be reliably matched, thus avoiding performance degradation caused by erroneous imputations. This approach effectively adapts to weak feature linkage and imbalanced cell populations between datasets. Benchmarking CelLink on scRNA-seq and spatial proteomics datasets, as well as paired CITE-seq data, demonstrates its superior performance across various evaluation metrics, including data mixing, cell manifold structure preservation, and feature imputation accuracy. Compared to state-of-the-art methods, CelLink significantly outperforms others in imbalanced cell populations while consistently achieving better performance for balanced datasets. Moreover, CelLink uniquely enables cell subtype annotation, correction of mislabelled cells, and spatial transcriptomic analyses by imputing transcriptomic profiles for spatial proteomics data. CelLink sets a new milestone for multi-omics data integration. Its great ability to impute large-scale paired single-cell multi-omics profiles positions it as a pivotal tool for building single-cell multi-modal foundation models and advancing spatial cellular biology.

bioinformatics↗

bioRxiv2024

KLF7-Regulated ITGA2 as a Therapeutic Target for Inhibiting Oral Cancer Stem Cells

Cancer stem cells (CSCs) play crucial roles in tumor metastasis, therapy resistance, and immune evasion. Identifying and understanding the factors that regulate the stemness of tumor cells presents promising opportunities for developing effective therapeutic strategies. In this study on oral squamous cell carcinoma (OSCC), we confirmed the key role of KLF7 in maintaining the stemness of OSCC. Using chromatin immunoprecipitation sequencing and dual-luciferase assays, we identified ITGA2, a membrane receptor, as a key downstream gene regulated by KLF7 in the maintenance of stemness. Tumor sphere formation assays, flow cytometry analyses, and in vivo limiting dilution tumorigenicity evaluations demonstrated that knocking down ITGA2 significantly impaired stemness. When bound to its ECM ligand, type I collagen, ITGA2 activates several stemness-related pathways, including PI3K-AKT, MAPK, and Hippo. TC-I 15, which inhibits the ITGA2-collagen interaction, showed a synergistic anti-tumor effect when combined with cisplatin in both in vitro and xenograft models. In summary, we reveal that the KLF7/ITGA2 axis is a crucial modulator of stemness in OSCC. Our findings suggest that ITGA2 is a promising therapeutic target, offering a novel anti-CSC strategy. Highlights1) KLF7 as a key molecule in maintaining oral cancer stemness. 2) ITGA2as a key downstream gene regulated by KLF7 in the maintenance of stemness. 3) ITGA2 interacts with extracellular matrix type I collagen, activating stemness-related pathways and promoting YAP1 nuclear translocation to sustain OCSCs. 4) ITGA2 as a novel anti-CSC target, providing a new strategy to overcome OSCC drug resistance.

cancer biology↗