Biology subjects

Nie, Q.

Publications and source records attributed to Nie, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

Building virtual cell dynamics that evolve with biological measurements

Virtual cell models built from single-cell measurements need to predict how cells change over time, but new biological measurements often reveal dynamical relationships that existing models cannot express. Translating such measurements into dynamics therefore requires deciding what biological relation should be modeled, where it should enter the formulation and how its recovery should be validated. Here we present CellCompass, an agentic AI system that couples a scientific agent with an extensible dynamical engine for modeling state transitions and population change, along with fate and molecular readout. CellCompass reuses existing formulations when sufficient and designs, implements and refines new ones when experimental measurements introduce additional constraints. To assess this capability, we developed DynBench100, a benchmark of 100 simulated single-cell time-course scenarios with hidden dynamical ground truth. CellCompass achieved the highest aggregate performance among the tested general-purpose and biomedical agents across six dynamical tasks. Across four real single-cell studies, it retained an existing velocity-growth model to reconstruct context-dependent epithelial-mesenchymal transition and developed three new dynamical formulations for distinct experimental measurements. In drug-response data, incorporating metabolic RNA labeling reduced pulse-control identifiability error by 31.1%, revealing staged response programs and generating testable predictions for perturbing a candidate adaptive program. In hematopoietic lineage tracing, incorporating clonal barcodes improved clone-fate agreement by 54% over the strongest dynamical baseline, identifying early lineage priming among transcriptionally similar progenitors. Finally, in time-resolved CITE-seq data, CellCompass reduced future surface-protein distribution error by 13.2% for RNA-initialized trajectories, uncovering lineage-specific temporal offsets between transcriptional and protein programs. Together, CellCompass provides a framework in which virtual-cell dynamics can expand with the biological measurements used to constrain them.

bioinformatics↗

bioRxiv2026

Urea is a dynamic and functional component of mammalian breast milk

Urea is a constituent of human milk, yet its regulation and interactions with the infant gut microbiome remain unclear. Combining three human cohorts, a controlled mouse model, and complementary microbiome analyses, we show that milk urea increases across lactation, is conserved across mammals, and is not explained by circulating urea levels or maternal diet. Instead, we identify lactation-dependent remodeling of mammary urea-metabolism and transport pathways, including dynamic AQP3 localization in secretory epithelium, indicative of regulated transfer of circulating urea into breast milk. Alongside urea, maternal milk and stool provide complementary reservoirs of urea-metabolizing bacteria that help establish the infant "ureabiome", whose urea-utilization capacity is supported by metagenomics. This maternal-infant axis is context-dependent: infant formulas provide substantially higher urea levels than human milk, while functional assays show that urea supports both commensal and opportunistic bacteria in a concentration-dependent manner. Together, these findings define a maternal nitrogen axis through which lactation can shape the assembly and function of the infant gut microbiome.

microbiology↗

bioRxiv2026

Immunizing small cell lung cancer mice with isoaspartylated Elavl4 after chemotherapy mimics improved survival of anti-ELAVL4 antibody-positive small cell lung cancer patients

Introduction: Small cell lung cancer (SCLC) patients have an ~8% 5-year survival; new therapies are urgently needed. Approximately 15% of SCLC patients have naturally-occurring low-titer antibodies against neuronal ELAVL proteins, associated with improved response to therapy and significantly improved survival. We previously determined that the anti-ELAVL4 response is triggered by isoaspartylation in the unstructured ELAVL4 N-terminal region. Methods: We used a Tp53fl/fl;Rb1fl/fl inducible SCLC mouse model to test whether 1) immunization with isoaspartylated Elavl4 (isoAsp-Elavl4) prior to SCLC induction improves survival in the absence of any other treatment, and 2) immunization with isoAsp-Elavl4 following completion of 3 rounds of cisplatin+etoposide therapy improves survival. Immunizations contained incomplete Freund's adjuvant with either a recombinant N-terminal fragment of Elavl4 (amino acids 1-117), incubated under isoaspartyl-inducing conditions, or phosphate-buffered saline (used as the negative control, since Elavl4 spontaneously isoaspartylates). Mice were monitored by blinded assessors until euthanasia was indicated. Results: IsoAsp-Elavl4-immunized animals all became immune responsive, and spontaneous anti-isoAsp-Elavl4 antibodies were observed in 7% of the control animals. Kaplan-Meier analyses revealed that pre-SCLC immunization with isoAsp-ELAVL4 in the absence of other treatments did not affect survival. In contrast, immunization of SCLC mice following chemotherapy significantly improved survival. Conclusions: An anti-isoAsp-ELAVL4 response can be actively induced in mice and significantly increases SCLC survival when given following chemotherapy. This indicates that the anti-isoAsp-ELAVL4 immune response can be leveraged to develop new therapies for SCLC patients.

cancer biology↗

bioRxiv2026

Deciphering interaction syntax via decoupling intrinsic lineages and niche pressure

Spatial transcriptomics enables the mapping of gene expression within intact tissues, yet a fundamental gap remains between knowing where cells are and understanding how they interact. A cells measured transcriptome reflects both its intrinsic lineage identity and niche pressure. Here we introduce TRINUS, a self-supervised model that deciphers interaction syntax by generative decoupling of a cells intrinsic lineage identity from the extrinsic niche pressure. TRINUS maintains a library of context-free cell prototypes to isolate lineage identity while modeling cooperative interaction dependencies among neighbors. We validated TRINUS on synthetic datasets with known interaction logic and benchmarked it against existing methods with superior performance in cell clustering and spatial domain detection. Applied across diverse platforms and biological systems, TRINUS resolves multi-level interaction syntax and maps tissue-wide interaction patterns in colorectal cancer, and identifies stage-specific signaling dependencies and time-dependent receptor windows during mouse organogenesis. We also show TRINUSs bidirectional in silico engineering capability in the ovarian tumor microenvironment, where forward perturbation revealed subtype-specific macrophage immunosuppressive programs via virtual transplantation and inverse design identified molecular modifications in macrophages predicted to rescue adjacent T-cell function. Collectively, TRINUS provides a practical tool for interaction syntax discovery and predictive tissue engineering on spatial transcriptomics data.

bioinformatics↗

bioRxiv2026

Robust identification of cell-cell communication heterogeneity in single cells

Communication between cells modulates cell fate decisions by relaying information across tissues and inducing intracellular responses mediated by gene regulatory networks. Inference of cell-cell communication from high throughput data such as single cell transcriptomics is gaining popularity due to the high data availability and ease to automate modeling over hundreds of signaling pathways. Studying how cell-cell communication operates across biological scales and influences cell fate decisions, however, remain a major open question. Here, we present scRICH, a framework and package that integrates mechanism-based, multiscale mathematical modeling with learning strategies to capture the complexity of cell-cell communication from single-cell and spatial transcriptomics data. scRICH unravels the heterogeneity of communication behavior within cell types, links cell-cell communication to cell fate decisions by incorporating dynamical information of RNA splicing, and connects the scales of cell-cell interactions and intracellular response by constructing multilayer regulatory networks. We validate scRICH with new experiments on EGF ligand/receptor co-expression in keratinocytes from skin-equivalent organoid, and compare these computational predictions against existing CCC inference methods. Applying scRICH to multiple biological scenarios demonstrate its ability to capture emerging relations between distinct cell-cell communication pathways, interactions at the onset of cell fate decision, and emerging trends in cell-cell communications along cell lineages and in space.

bioinformatics↗

bioRxiv2026

Single-cell profiling of synchronous multi-organ metastasis reveals a systemic CD74+ lipid-associated macrophage niche driving polymetastatic breast cancer

Systemic, multi-organ metastasis is the primary cause of breast cancer mortality, yet the biological mechanisms that allow disseminated tumor cells to simultaneously colonize physiologically diverse tissues remain poorly understood. Current paradigms focus on organ-specific tropism, largely overlooking the potential for systemic, conserved and synchronized programs that facilitate widespread colonization. Here, we present a high-resolution, multi-organ atlas of metastatic ecosystems and their niches using a synchronous model of brain, lung, liver, and bone metastasis combined with in vivo proximal niche labeling and single-cell RNA sequencing. We identify a remarkably conserved proximal niche program defined by the accumulation of CD74+ lipid-associated, metastasis-associated macrophages (LA-MAMs) across all metastatic sites. CD74+ LA-MAMs are characterized by a unique metabolic-immune signature and drive T cell suppression. We show that the cytokine Macrophage Migration Inhibitory Factor (MIF), secreted by metastatic cells, acts as the universal paracrine mediator that instructs the LA-MAM phenotype via the CD74 receptor. Interference of the MIF-CD74 axis effectively disrupts the LA-MAM niche, mitigates T cell exhaustion, and reduces metastatic burden across all organs. Analysis of a 100-patient cohort of metastasis samples from different sites confirms that the MIF-CD74 axis is a hallmark of human multi-organ colonization and independently predicts poor post-metastasis survival. Our findings define a synchronized and systemic metastatic niche that can be targeted, providing a mechanistic rationale for neutralizing the MIF-CD74 axis to treat polymetastatic breast cancer.

cancer biology↗

bioRxiv2026

Single-cell characterization of skin response to a bite by West Nile virus-infected mosquito reveals fibroblast-mediated barrier to transmission

Cutaneous events at the mosquito bite site that determine orthoflavivirus transmission efficiency remain largely uncharacterized. Here, we report single-cell RNA-sequencing of skin from immunocompetent mice bitten by West Nile virus-infected mosquitoes, capturing early response at a critical transmission bottleneck. Fibroblasts were the dominant cells exposed to infectious saliva. While neutrophils and Folr2+ macrophages diminished, monocyte-derived and antigen-presenting macrophages, and lymphocytes became more abundant. Cell-cell communication analysis revealed the central roles of fibroblasts and myeloid cells in induced immune-related signaling. Transcriptional profiling defined cell type-specific responses to infectious bite integrating immune modulation, skin repair and metabolic remodeling. Using in vivo gene silencing, we demonstrated that fibroblast-expressed LRRC15 (leucine rich repeat-containing 15) functions as a cutaneous restriction factor, limiting viral replication in skin, viral dissemination to draining lymph nodes, and disease severity. Collectively, our analyses provide cellular and molecular understanding of bite-initiated arboviral transmission, establishing skin-resident fibroblasts as frontline defender cells. HIGHLIGHTSO_LISingle-cell profiling captures early cutaneous response to West Nile virus-infected mosquito bites with cell type resolution. C_LIO_LISkin-resident fibroblasts are the dominant frontline cells exposed to infectious mosquito saliva. C_LIO_LIInfectious bite reconfigures cutaneous immune cells, and activates multi-directional signaling between immune and structural skin cell types. C_LIO_LIBite-induced fibroblast-expressed LRRC15 restricts viral transmission and attenuates disease severity. C_LI

zoology↗

bioRxiv2025

Epigenetic Coalitions Couple Tissue Growth to Generate Periodic Colour Patterns in Birds

Periodic patterning is fundamental to biological organization. In birds, colour stripes and spots on growing embryonic surface and elongating feather filaments provide a unique system to study continuous periodic patterning in expanding domains. Agouti-signaling protein (ASIP) directly reports epigenomic activity during colour patterning. Using a neural-network-like architecture, we show that ASIPs cis-regulatory landscape functions as hidden layers, integrating morphogen feedback, chromatin topology, and tissue geometry to generate discrete colour outputs. Single-nucleus multiome and Micro-C uncover stage- and context-specific enhancer-silencer coalitions. Epidermal Wnt ligands activate ASIP while inducing Wnt inhibitors in fibroblasts, forming a negative-feedback loop coupling periodic patterning to domain expansion. Comparative cross-tissue and cross-species analyses define cis-regulatory modules comprising an epigenetic grammar; functional assays highlight retrotransposon co-option expanding ASIPs cis-regulatory repertoire, potentially contributing to colour pattern evolution. Together, these findings motivate a Turing-principle-based communication model: ASIP reflects epigenetic coalitions shifting to drive diverse, environmentally tunable colour motifs for adaptation.

developmental biology↗

bioRxiv2025

Morphogen-guided neocortical organoids recapitulate regional areal identity and model neurodevelopmental disorder pathology

The human neocortex exhibits characteristic regional patterning (arealization) critical for higher-order cognitive function. Disrupted arealization is strongly implicated in neurodevelopmental disorders (NDDs), but current neocortical organoid models largely fail to recapitulate this patterning, limiting mechanistic understanding. Here, we establish a straightforward method for generating arealized organoids through short-term early exposure to anterior (FGF8) or posterior (BMP4/CHIR-99021) morphogens. These treatments created distinct anterior and posterior signaling centers, supporting long-lasting polarization, which we validated with single-cell RNA sequencing that revealed area-specific molecular signatures matching prenatal human cortex. To demonstrate the utility of this platform, we modeled Fragile X Syndrome (FXS) in organoids with distinct anterior and posterior regional identities. FXS organoids showed highly disrupted SOX4/SOX11 expression gradients along the anterior-posterior axis, consistent with alterations found in autism spectrum disorder (ASD) and demonstrate how regional patterning defects may contribute to NDD pathology. Together, our study provides a robust platform for generating neocortical organoids with anterior-posterior molecular signatures and highlights the importance of modeling NDDs using experimental platforms with neuroanatomic specificity.

developmental biology↗

bioRxiv2025

Designing a broad-spectrum four-helix bundle targeting different strains of SARS-CoV-2 Spike Receptor Binding Domains with ACE2-like binding interface

One major strategy for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to evade antibody drugs or preventive vaccines is high mutation rate of the spike receptor binding domain (RBD). Because variable RBDs of different SARS-CoV-2 strains must bind to the same human receptor angiotensin-converting enzyme 2 (hACE2) for viral cell entry and infection, we hypothesize that designing a protein with the same or very similar hACE2 binding interface might have a broad-spectrum effect against various SARS-CoV-2 strains. The designed protein binds specifically to the WT-RBD (with micromolar affinity) but not to RBDs from other SARS-CoV-2 strains. However, two rounds of the E. coli display and Magnetic Cell Sorting (MACS) selection are sufficient to yield a protein named CYN1 with nanomolar binding affinities not only to the WT-RBD but also to those of Omicron BA.1, XBB.1.16, and JN.1. Molecular dynamics simulations and free-energy hotspot analysis revealed that CYN1s broader spectrum capability stems from its engagement of essentially all ACE2 hotspot residues critical for WT-RBD binding, unlike the designed protein. The discovery of CYN1, differing by only four mutations from the designed protein, confirms that targeting the small interface of human viral receptors--rather than the entire receptor--offers a viable strategy for developing broad-spectrum inhibitors. This approach minimizes potential off-target effects arising from receptor multifunctionality.

molecular biology↗

bioRxiv2025

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics

Spatial transcriptomics (ST) technologies offer rich spatial context for gene expression, with varying spatial resolutions and gene coverages. However, aligning and comparing multiple ST slices, whether derived from the same or different platforms, remains challenging due to nonlinear distortions and limited spatial overlap caused by tissue processing. We present CODA, an integrative framework for CrOss-sample alignment and spatially Differential gene Analysis. CODA first learns a shared low-dimensional latent feature space across samples. Within the latent space, CODA performs global rigid alignment, applies transformer-based feature matching to identify common spatial domains, and utilizes local nonlinear refinements via large deformation diffeomorphic metric mapping, enabling a robust cross-sample comparison and extraction of spatial gene expression patterns. Benchmarking across various ST platforms demonstrates that CODA outperforms the existing methods in alignment accuracy, computational efficiency, and memory usage. Applications to vascular and brain datasets shows CODAs ability to uncover spatially informative genes associated with disease and sleep regulation. These results highlight CODAs broad applicability and effectiveness in ST analysis.

bioinformatics↗

bioRxiv2025

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalks capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalks capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways.

systems biology↗

bioRxiv2025

Cell signaling pathways discovery from multi-modal data

Deciphering cell signaling pathways is key to understanding biology, disease mechanisms, and developing new therapies. Although advances in multi-omics technologies provide richer insight into signaling, the data remain high-dimensional, heterogeneous, and difficult to interpret, and current computational tools for inferring signaling pathways are limited. To address this, we developed Incytr, a method for efficient discovery of cell signaling pathways through integration of diverse data modalities, including transcriptomics, ATAC-seq, proteomics, phosphoproteomics, and kinomics. We demonstrate its application in COVID-19, Alzheimers disease, and cancer, where it successfully recovers known pathways and generates novel, cell-type-specific hypotheses supported by multiple data types. We further show how integrating Incytr-derived pathways with biomarker and drug databases can support target and drug discovery. Finally, we show that using Incytr-derived signaling pathways as training data for simple natural language processing models can deepen our understanding of cell-cell communication and immune cell dynamics, while helping identify new therapeutic targets.

bioinformatics↗

bioRxiv2024

Statistically principled feature selection for single cell transcriptomics

The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes (feature selection) for downstream analysis (e.g., unsupervised cell clustering). The evaluation of different approaches to feature selection is hampered by the fact that, as we show here, the performance of feature selection methods varies greatly with the task being performed. For routine cell type identification, even randomly chosen features can perform well, but for cell type differences that are subtle, both number of features and selection strategy can matter strongly. Here we present a simple feature selection method grounded in an analytical model that, without resorting to arbitrary thresholds or user-defined parameters, allows for interpretable delineation of both how many and which features to choose, facilitating identification of biologically meaningful rare cell types. We compare this method to default methods in scanpy and Seurat, as well as SCTransform, showing how greater accuracy can often be achieved with surprisingly few, well-chosen features.

bioinformatics↗

bioRxiv2024

Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multicompartmental alterations in gene expression and signaling cross-talk

Vulvar diseases are a critical yet often neglected area of womens health, profoundly affecting patients quality of life and frequently resulting in long-term physical and psychological challenges. Lichen sclerosus (LS) is a chronic inflammatory skin disorder that predominantly affects the vulva, leading to severe itching, pain, scarring, and an increased risk of malignancy. Despite its profound impact on affected individuals, the molecular pathogenesis of vulvar LS (VLS) is not well understood, hindering the development of FDA-approved therapies. Here, we utilize single-cell and spatial transcriptomics to analyze lesional and non-lesional skin from VLS patients, as well as healthy control vulvar skin. Our findings demonstrate histologic, cellular, and molecular heterogeneities within VLS, yet highlight unifying molecular changes across keratinocytes, fibroblasts, immune cells, and melanocytes in lesional skin. They reveal cellular stress and damage in fibroblasts and keratinocytes, enhanced T cell activation and cytotoxicity, aberrant cell-cell signaling, and increased activation of the IFN, JAK/STAT, and p53 pathways in specific cell types. Using both monolayer and organotypic culture models, we also demonstrate that knockdown of select genes, which are downregulated in VLS lesional keratinocytes, partially recapitulates VLS-like stress-associated changes. Collectively, these data provide novel insights into the pathogenesis of VLS, identifying potential biomarkers and therapeutic targets for future research.

genomics↗

bioRxiv2024

A novel bifunctional peptide VAMP mined from hemp seed protein hydrolysates improves glucose homeostasis by inhibiting intestinal DPP-IV and increasing the abundance of Akkermansia muciniphila

Discovery of new dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from natural protein resources capable of regulating glucose metabolism in type 2 diabetic populations has been a significant challenge. In this study, we constructed a molecular docking- and machine learning-aided DPP-IV inhibitory peptide library and combined a functional screening approach based on intestinal organoids to discover efficient and new DPP-IV-inhibiting peptides from hemp seed protein hydrolysates. A novel tetrapeptide, VAMP, was then identified to strongly inhibit DPP-IV (IC50=1.00 M in vitro), which competitively binds to DPP-IV and improves glucose metabolism in vivo with high safety by increasing active glucagon-like peptide-1 (GLP-1) levels in obese mouse models. Interestingly, VAMP specifically promoted the growth and abundance of intestinal Akkermansia muciniphila in vivo, at the same time, which was responsible for the improved intestinal barrier function and insulin resistance. Our study demonstrated that the novel bifunctional VAMP can effectively target the DPP-IV-GLP-1 axis and simultaneously regulate the abundance of the gut microbial A. muciniphila, to regulate glucose homeostasis, providing a promising nutraceutical and therapeutic tetrapeptide for hyperglycaemia treatment by targeting the gut-microbiata axis. TeaserVAMP improves glucose metabolism by increasing the active GLP-1 level and promoting the growth of A. muciniphila to improve intestinal barrier function.

molecular biology↗

bioRxiv2024

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However, understanding how transcriptional bursting, and consequently, cell fate, responds to DNA damage on a genome-wide scale poses a challenge. In this study, we propose an interpretable and scalable inference framework, DeepTX, that leverages deep learning methods to connect mechanistic models and scRNA-seq data, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments, we observed that fluctuations in transcriptional bursting induced by different drugs was associated with distinct fate decisions: IdU treatment was associated with differentiation in mouse embryonic stem cells by increasing the burst size of gene expression, while low- and high-dose 5FU treatments in human colon cancer cells were associated with changes in burst frequency that corresponded to apoptosis- and survival-related fate, respectively. Together, these results show that DeepTX enables genome-wide inference of transcriptional bursting from single-cell transcriptomics data and can generate hypotheses about how bursting dynamics relate to cell fate decisions.

systems biology↗

bioRxiv2024

Polyunsaturated Fatty Acid - mediated Cellular Rejuvenation for Reversing Age-related Vision Decline

The retina is uniquely enriched in polyunsaturated fatty acids (PUFAs), which are primarily localized in cell membranes, where they govern membrane biophysical properties such as diffusion, permeability, domain formation, and curvature generation. During aging, alterations in lipid metabolism lead to reduced content of very long-chain PUFAs (VLC-PUFAs) in the retina, and this decline is associated with normal age-related visual decline and pathological age-related macular degeneration (AMD). ELOVL2 (Elongation of very-long-chain fatty acids-like 2) encodes a transmembrane protein that produces precursors to docosahexaenoic acid (DHA) and VLC-PUFAs, and methylation level of its promoter is currently the best predictor of chronological age. Here, we show that mice lacking ELOVL2-specific enzymatic activity (Elovl2C234W) have impaired contrast sensitivity and slower rod response recovery following bright light exposure. Intravitreal supplementation with the direct product of ELOVL2, 24:5n-3, in aged animals significantly improved visual function and reduced accumulation of ApoE, HTRA1 and complement proteins in sub-RPE deposits. At the molecular level, the gene expression pattern observed in retinas supplemented with 24:5n-3 exhibited a partial rejuvenation profile, including decreased expression of aging-related genes and a transcriptomic signature of younger retina. Finally, we present the first human genetic data showing significant association of several variants in the human ELOVL2 locus with the onset of intermediate AMD, underlying the translational significance of our findings. In sum, our study identifies novel therapeutic opportunities and defines ELOVL2 as a promising target for interventions aimed at preventing age-related vision loss.

molecular biology↗