Biology subjects

Zhan, Q.

Publications and source records attributed to Zhan, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

The in vivo inhibitory function of the MHC-I alpha3 domain/CD8alpha interaction

The interaction between the Major Histocompatibility Complex Class I (MHC-I) 3 domain and CD8 has classically been viewed as a positive coreceptor interaction that stabilizes TCR signaling during antigen recognition. However, its physiological function in mature peripheral CD8+ T cells in vivo remains incompletely understood. Here, we identify the MHC-I 3 domain-CD8 interaction as a previously unrecognized inhibitory pathway that tonically restrains peripheral CD8+ T-cell activation and maintains T-cell tolerance in vivo. Antibody-mediated disruption of the MHC-I 3 domain-CD8 interaction induced spontaneous activation of peripheral CD8+ T cells without impairing their survival, lowered the threshold for antigen-induced activation, and enhanced responsiveness to cognate peptide stimulation. In peptide-induced OT-I T-cell anergy models, blockade of either H-2Db or H-2Kb 3 domain interactions with CD8 prevented the induction of anergy and restored responsiveness of previously anergic T cells. Notably, blockade of the H-2Kb 3 domain enhanced OT-I responses despite simultaneously disrupting the classical positive coreceptor interaction within the TCR-peptide-MHC complex, indicating that tonic inhibitory signaling mediated by the MHC-I 3 domain predominates under these conditions. Together, these findings redefine the classical MHC-I-CD8 interaction as a bidirectional pathway that not only supports antigen recognition but also imposes tonic inhibitory control over peripheral CD8+ T cells. These results identify the MHC-I 3 domain-CD8 axis as a potential target for reversing T-cell tolerance and enhancing antitumor or antiviral immunity.

immunology↗

bioRxiv2026

Faithful Supervised Dimensionality Reduction for Biomedical Data via Decision Geometry

Unsupervised dimensionality reduction methods aim to preserve intrinsic data geometry by maintaining local neighborhoods and approximate global relationships in low-dimensional embeddings, but they do not use label information and therefore may fail to reflect task-relevant class structure in biomedical and health applications. Supervised dimensionality reduction (SDR) incorporates labels to improve class organization, yet existing approaches often face a trade-off between discrimination and geometric faithfulness. Linear supervised methods are stable and interpretable but are limited in their ability to capture nonlinear structure, whereas many nonlinear methods impose supervision directly in the embedding space, which can over-separate classes and distort the underlying manifold. In biomedical applications, labels such as cell types in single-cell data or patient status in clinical cohorts provide meaningful biological signal, and supervised dimensionality reduction can use this information to produce more informative low-dimensional representations. Here we propose a new framework, DG-UMAP (Decision-Geometry UMAP), for faithful supervised dimensionality reduction via decision geometry. We first fit a classifier in the original feature space and use its boundary-local decision geometry to construct a low-rank metric deformation that emphasizes discriminative directions while limiting geometric distortion. Parametric UMAP is then applied to the transformed space, so supervision acts through the ambient geometry rather than by directly forcing class separation in the embedding. Across synthetic and multiple real-world biomedical datasets, our method yields embeddings with improved agreement with class structure and global organization while preserving local neighborhood quality.

bioinformatics↗

bioRxiv2026

A feed-forward loop between niche adenosine and Gzmk⁺ CD8 T cells propagates systemic inflammaging

The causal link between the aging microenvironment and T cell aging remains elusive. Here, we demonstrate that adenosine within aging tissues actively reprograms CD8+ T cells into a pro-aging Granzyme K+ (Gzmk+) population. Mechanistically, senescent cells create an adenosine-rich niche via p16-dependent CD39 upregulation, triggering A2aR signaling to induce Gzmk+ T cell differentiation. Once released, Gzmk promotes systemic inflammaging through PAR1 and complement activation. Crucially, targeting this axis--either via genetic Gzmk ablation or pharmacological A2aR blockade--reverses multi-organ aging phenotypes and significantly extends healthy lifespan in mice. Human analysis reveals age-dependent Gzmk+ T cell accumulation in multi organs, while coffee intake (an A2aR antagonist) inversely correlates with plasma Gzmk levels. Our findings uncover how metabolic niche changes drive T cell aging and establish the adenosine-Gzmk axis as a pivotal therapeutic target for combating age-related diseases.

immunology↗

bioRxiv2025

Evolutionary trade-offs between functional and immune selection shape multigene families in pathogens

Major surface antigens in many pathogens are encoded by rapidly diversifying multigene families, generating fitness variation through antigenic and functional differences. These variations align with the niche and absolute fitness axes of Modern Coexistence Theory (MCT). Yet, how such gene families evolve along these axes under competition for hosts and across transmission gradients remains poorly understood, as prior MCT studies have not explicitly accounted for evolutionary dynamics in high dimensions. We use a stochastic computational model of Plasmodium falciparum transmission to examine how transmission intensity and selection shape var multigene family evolution and composition within parasite genomes. Results show that selection alone cannot maintain the observed stable ratio of two gene groups within parasite genomes, indicating that group-based classifications do not clearly reflect transmission strategy or virulence. When a trade-off exists between diversification rates and absolute fitness, strong immune selection under high transmission favors fast-recombining genes while attenuating functional selection on R0-associated traits. In general, stronger immune selection increases the invasion probability of novel antigens and the niche differentiation among parasite genomes, while reducing the variance in gene-level transmissibility and expression duration, and therefore R0. This outcome, combining enhanced niche differentiation and reduced absolute fitness variation, departs from MCT predictions.

evolutionary biology↗

bioRxiv2024

Graph-Regularized Optimal Transport for Single-Cell Data Integration

BackgroundRecent advancements in single-cell omics technologies have enabled detailed characterization of cellular processes. However, coassay sequencing technologies remain limited, resulting in un-paired single-cell omics datasets with differing feature dimensions; Findingwe present GROTIA (Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis), a computational method to align multi-omics datasets without requiring any prior correspondence information. GROTIA achieves global alignment through optimal transport while preserving local relationships via graph regularization. Additionally, our approach provides interpretability by deriving domain-specific feature importance from partial derivatives, highlighting key biological markers. Moreover, the transport plan between modalities can be leveraged for post-integration clustering, enabling a data-driven approach to discover novel cell subpopulations; ConclusionsWe demonstrate GROTIAs superior performance on four simulated and four real-world datasets, surpassing state-of-the-art unsupervised alignment methods and confirming the biological significance of the top features identified in each domain. The software is available at https://github.com/PennShenLab/GROTIA.

bioinformatics↗

bioRxiv2024

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The datas innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

bioinformatics↗

bioRxiv2024

CellDemux: coherent genetic demultiplexing in single-cell and single-nuclei experiments.

Multiplexed single-cell experiment designs are superior in terms of reduced batch effects, increased cost-effectiveness, throughput and statistical power. However, current computational strategies using genetics to demultiplex single-cell (sc) libraries are limited when applied to single-nuclei (sn) sequencing data (e.g., snATAC-seq and snMultiome). Here, we present CellDemux: a computational framework for genetic demultiplexing within and across data modalities, including single-cell, single-nuclei and paired snMultiome measurements. CellDemux uses a consensus approach, leveraging modality-specific tools to robustly identify non-empty oil droplets and singlets, which are subsequently demultiplexed to donors. Notable, CellDemux demonstrates good performance in demultiplexing snMultiome data and is generalizable to single modalities, i.e. snATAC-seq and sc/snRNA-seq libraries. We benchmark CellDemux on 187 genetically multiplexed libraries from 800 samples (scRNA-seq, snATAC-seq, CITE-seq and snMultiome), confidently identifying and assigning cells to 88% of donors. In paired snMultiome libraries, CellDemux achieves consistent demultiplexing across data modalities. Moreover, analysis of 38 snATAC libraries from 149 samples shows that CellDemux retains more genetically demultiplexed nuclei for downstream analyses compared to existing methods. In summary, CellDemux is a modular and robust framework that deconvolves donors from genetically multiplexed single-cell and single-nuclei RNA/ATAC/Multiome libraries.

genomics↗

bioRxiv2023

A paradoxical population structure of var DBLα types in Africa

The var multigene family encodes the P. falciparum erythrocyte membrane protein 1 (PfEMP1), which is important in host-parasite interaction as a virulence factor and major surface antigen of the blood stages of the parasite, responsible for maintaining chronic infection. Whilst important in the biology of P. falciparum, these genes (50 to 60 genes per parasite genome) are routinely excluded from whole genome analyses due to their hyper-diversity, achieved primarily through recombination. The PfEMP1 head structure almost always consists of a DBL-CIDR tandem. Categorised into different groups (upsA, upsB, upsC), different head structures have been associated with different ligand-binding affinities and disease severities. We study how conserved individual DBL types are at the country, regional, and local scales in Sub-Saharan Africa. Using publicly-available sequence datasets and a novel ups classification algorithm, cUps, we performed an in silico exploration of DBL conservation through time and space in Africa. In all three ups groups, the population structure of DBL types in Africa consists of variants occurring at rare, low, moderate, and high frequencies. Non-rare variants were found to be temporally stable in a local area in endemic Ghana. When inspected across different geographical scales, we report different levels of conservation; while some DBL types were consistently found in high frequencies in multiple African countries, others were conserved only locally, signifying local preservation of specific types. Underlying this population pattern is the composition of DBL types within each isolate DBL repertoire, revealed to also consist of a mix of types found at rare, low, moderate, and high frequencies in the population. We further discuss the adaptive forces and balancing selection, including host genetic factors, potentially shaping the evolution and diversity of DBL types in Africa.

genetics↗

bioRxiv2023

Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging

Assessing fertilized human embryos is crucial for in vitro-fertilization (IVF), a task being revolutionized by artificial intelligence and deep learning. Existing models used for embryo quality assessment and chromosomal abnormality (ploidy) detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we developed and compared various embryo ploidy status prediction models across distinct embryo development stages. We present BELA (Blastocyst Evaluation Learning Algorithm), a state-of-the-art ploidy prediction model surpassing previous image- and video-based models, without necessitating subjective input from embryologists. BELA uses multitask learning to predict quality scores that are used downstream to predict ploidy status. By achieving an AUC of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists manual scores. While not a replacement for preimplantation genetic testing for aneuploidy (PGT-A), BELA exemplifies how such models can streamline the embryo evaluation process, reducing time and effort required by embryologists.

developmental biology↗

bioRxiv2023

Depletion-assisted multiplexing cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicle

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilized a meticulously tailored set of customized guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimize plasma requirements. Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs, and viral RNAs, while EVs showed enrichment in mRNAs and srpRNAs. Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration, and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost-effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of extracellular vesicles and liquid biopsy.

molecular biology↗

bioRxiv2022

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high-transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low- and moderate-transmission. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs. Author SummaryDespite control and elimination efforts, malaria continues to be a serious public health threat especially in high-transmission regions. Molecular tools for evaluating these efforts include those seeking to estimate multiplicity (or complexity) of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, a key epidemiological parameter. MOI estimation remains challenging in high-transmission regions where hosts typically carry multiple co-infections by Plasmodium falciparum. THE REAL McCOIL and the varcoding are two cost-effective methods relying on distinct parts of the parasite genome, those respectively under neutrality and selection. The more recent varcoding approach relies on the var multigene family encoding for the major blood-stage antigen and contributing to a complex immune evasion strategy of the parasite. We compare the performance of the two methods by simulating disease dynamics under different transmission intensities with a stochastic agent-based model tracking infection by different parasite genomes and immune memory in individual hosts, then sampling resulting infections to estimate MOI. Although THE REAL McCOIL provides reasonable estimates for low- and moderate-transmission, varcoding allows more robust estimates especially under high-transmission. Defining the most suitable approach to estimate MOI based on local transmission intensity is highly recommended for hyper-diverse pathogens such as malaria.

evolutionary biology↗

bioRxiv2021

Wound-like tumor periphery in human breast cancer predicts a convergent drug nonresponse

A significant portion of breast cancer patients are nonresponsive to well-established drugs and destined for a poor outcome regardless of molecular subtype. Although several (multiparameter) molecular markers have predicted their resistance to some of these drugs, profound uniparameter markers predictive of a convergent nonresponse to all these drugs remain elusive. We employ co-registered standard-multiphoton histology to representatively sample a few peripheral niches of the primary tumor, so that hundreds of patients can be stratified with either a wound-like or non-wound tumor periphery. With no fitting variable, this simple uniparameter morphological marker is: (a) highly sensitive and specific to predict a multidrug-nonresponsive phenotype that accounts for the majority of recurrence or death, independent of the molecular subtype or related adjuvant drug selection, clinical endpoint (disease-free versus overall survival), and hosting medical center; (b) robust against intratumor heterogeneity and valid at the earliest clinicopathological stage; and (c) dominant in predicting prognosis in the context of routine clinicopathological markers. Considering the mechanistic link between a wound-like extracellular matrix and a microenvironment supporting migratory or mesenchymal tumor cells, we attribute these unusual capabilities to an epithelial-mesenchymal transition nature of the morphological marker long sought after by pathologists.

pathology↗

bioRxiv2020

Large-scale analysis of Drosophila core promoter function using synthetic promoters

The core promoter, the region immediately surrounding the transcription start site, plays a central role in setting metazoan gene expression levels, but how exactly it computes expression remains poorly understood. To dissect core promoter function, we carried out a comprehensive structure-function analysis to measure synthetic promoters activities, with and without an external stimulus (hormonal activation). By using robotics and a dual-luciferase reporter assay, we tested [~]3000 mutational variants representing 19 different Drosophila melanogaster promoter architectures. We explored the impact of different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, positioning, and flanking sequences. We observe strong effects of the mutations on activity, and a linear combination of the individual motif features can largely account for the combinatorial effects on core promoter activity. Our findings shed new light on the quantitative assessment of gene expression, a fundamental process in all metazoans.

systems biology↗

bioRxiv2020

Resolving an underrepresented circulating tumor cell population in lung cancer enabled by Hexokinase 2 analysis

Unlike other epithelial cancer types, circulating tumor cells (CTCs) are less frequently detected in the peripheral blood of non-small cell lung cancer (NSCLC) patients using epithelial marker-based detection approaches, despite the aggressive nature of NSCLC. Here we demonstrate hexokinase-2 (HK2) as a metabolic function-associated marker for detection of CTCs, with significantly improved detection rates and high specificity, in 33 NSCLC patients. Use of the HK2 marker identified underrepresented cytokeratin-negative (HK2high/CKneg) CTCs present in many blood samples but rarely detected in pleural effusions or cerebrospinal fluids of NSCLC patients. HK2high/CKneg CTCs exhibited smaller sizes but consistent copy number variation profiles compared to CKpos CTCs. Surprisingly, CK expression levels were found to be independent of CTC epithelial-to-mesenchymal transition (EMT) status as measured by single-cell transcriptome profiling, challenging the long-standing association between CK expression and EMT. Our approach improves sensitivity of CTC detection in NSCLC and can potentially resolve a more complete spectrum of CTCs, regardless of their CK expression levels or epithelial traits.

cancer biology↗

bioRxiv2020

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

bioinformatics↗

bioRxiv2019

RADAR: Differential analysis of MeRIP-seq data with a random effect model

Epitranscriptome profiling using MeRIP-seq is a powerful technique for in vivo functional studies of reversible RNA modifications. We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real datasets analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at https://github.com/scottzijiezhang/RADAR.

bioinformatics↗

bioRxiv2019

A Single-Cell Immune Atlas of Triple Negative Breast Cancer Reveals Novel Immune Cell Subsets

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, which recently attracts great interest for immune therapeutic development. In this context, in-depth understanding of TNBC immune landscape is highly demanded. Here we report single-cell RNA sequencing results of 9683 tumor-infiltrated immune cells isolated from 14 treatment naive TNBC tumors, where 22 immune cell subsets, including T cells, macrophages, B cells, and DCs have been characterized. We identify a new T cell subset, CD8+CXCL8+ naive T cell, which associates with poor survival. A novel immune cell subset comprised of TCR+ macrophages, is found to be widely distributed in TNBC tumors. Further analyses reveal an up-regulation of molecules associated with TCR signaling and cytotoxicity in these immune cells, indicating TCR signaling activation. Altogether, our study provides a valuable resource to understand the immune ecosystem of TNBC. The novel immune cell subsets reported herein might be functionally important in cancer immunity.\n\nSIGNIFICANCEThis work demonstrates a single-cell transcriptome atlas of immune cells in treatment naive TNBC tumors, revealing novel immune cell subsets. This study provides a valuable resource to understand the immune ecosystem of TNBC, which will be helpful for the immunotherapeutic strategy design of TNBC.

immunology↗

bioRxiv2019

In vitro Reconstitution Unveils the Phospho-regulated Ska-Ndc80 Macro-complex

The human Ska (spindle and kinetochore associated) complex is essential for proper kinetochore-microtubule interactions. Its loading onto the kinetochore complex Ndc80 (Ndc80C) plays a critical role in such function; however, the key determinant that docks Ska onto Ndc80C still remain elusive. Here, we address this question using biochemical reconstitution followed by functional analysis. We identified six Cdk1 sites in Ska3 distributed in three conserved regions. In vitro, Cdk1 phosphorylation on the Ska complex enhanced WT, not phospho-deficient 6A, binding to Ndc80C. Strikingly, the phospho-mimetic Ska 6D complex formed a stable macro-complex with Ndc80C, but Ska WT failed to do so, suggesting that Cdk1 phosphorylation is necessary and sufficient for the formation of the Ska-Ndc80 macro-complex. In cells, Ska3 6A completely lost its localization to kinetochores and its functions in chromosome segregation; whereas Ska3 6D partially restored them. Altogether, our findings not only for the first time reveal the key phospho-regulated Ska-Ndc80 macro-complex essential for faithful chromosome segregation, but also pave the way for mechanistic analyses of kinetochore-microtubule interactions.

cell biology↗