Biology subjects

Qian, B.

Publications and source records attributed to Qian, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Robust integration of weakly anchored spatial multi-omics

Spatial multi-omics holds great promise for dissecting complex biological processes, though inherent technical constraints continue to limit its widespread adoption. Currently, most studies therefore measure distinct omics features on separate tissue sections, necessitating spatial diagonal integration. An emerging practical solution is to leverage hematoxylin and eosin (H&E) images as an integration anchor, given their ubiquity, low cost, and compatibility across tissue preparations. However, this anchor is frequently compromised in real-world settings by variations in H&E staining style, absence of reliable histological landmarks, and mismatches in spatial resolutions across omics modalities. To address this, we introduce SpaWeaver, a computational framework that couples a pathology foundation model with a graph Transformer and a latent feature aligner module, providing a highly robust solution for weakly anchored spatial omics data diagonal integration. Extensive experiments demonstrate that SpaWeaver exhibits superior robustness against isolated or synergistic weak-anchoring factors. The spatial multi-omics profiles generated by SpaWeaver link molecular features originally separated on two sections, unlocking diverse downstream analyses once exclusive to co-assayed spatial multi-omics data, including niche-aware cell-cell communication inference and multi-omics resolved cell state. In this study, it unveils tumor-distance-dependent fibroblast-CD4{square}T-cell signaling in human colon adenocarcinoma and identifies a hypoxic glycolytic tumor state with pyknotic nuclei in human ovarian cancer. Overall, our approach bridges readily accessible single-omics measurements across weakly anchored tissue sections, enabling unified spatial multi-omics characterization and system-level tissue analysis.

bioinformatics↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

A lightweight, ultrafast and general embedding framework for large-scale spatial omics data

Deciphering ultra-large-scale omics data with minimal resources while maintaining high computational efficiency is a longstanding challenge in biology. Here, we present Local Pooling (LP), a lightweight, ultrafast and general framework that leverage neighbor-indexing strategy and local pooling module to generate omics embedding and compatible with variety of downstream analyses. We developed its adaptation for spatial omics, SpaLP, and evaluated it on over 20 large-scale datasets spanning 9 technology platforms. SpaLP consistently outperformed baseline methods across multiple benchmarks, including niche identification, expression reconstruction, multiple slices integration, 3D organ construction, multi-omics integration and cross-platform generalization. Notably, SpaLP processed a 1.35-million-cell mouse embryo slice in just 47 seconds, achieving up to 300-fold increase in computational efficiency compared to Graph Neural Network (GNN)-based methods. Meanwhile, SpaLP increased the average adjusted rand index (ARI) by over 30% for niche identification in simulated and realistic settings. Furthermore, we applied SpaLP to integrate 8.4 million mouse brain cells within 4 minutes on a single GPU and constructed a 3D spatial atlas. Finally, we explored SpaLPs ability of cross-platform generalization and potential for developing an omics foundation model. As a novel and general framework, we believe that LP could help more researchers develop new model on large-scale data and overcome the research barriers caused by computing resources in more fields.

bioinformatics↗

bioRxiv2025

High-Parameter Spatial Multi-Omics through Histology-Anchored Integration

Spatial omics face challenges in achieving high-parameter, multi-omics co-profiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pre-trained H&E foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without co-measured training data. Extensive validations show superior H&E-to-omics prediction, panel diagonal integration, and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding one million cells, maintain robustness with non-overlapping or heterogeneous sections, and support unlimited omics layers in principle. Our work makes multi-modal spatial profiling broadly accessible.

bioinformatics↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

The Role of ADGRA3 in Adipose Thermogenesis: A Potential Therapeutic Target for Obesity

The induction of adipose thermogenesis plays a critical role in maintaining body temperature and improving metabolic homeostasis to combat obesity. {beta}3-adrenoceptor ({beta}3-AR) is widely recognized as a canonical {beta}-adrenergic G protein-coupled receptor (GPCR) that plays a crucial role in mediating adipose thermogenesis in mice. Nonetheless, the limited expression of {beta}3-AR in human adipocytes restricts its clinical application. The objective of this study was to identify a GPCR that is highly expressed in human adipocytes and to explore its potential involvement in adipose thermogenesis. Our research findings have demonstrated that the adhesion G protein-coupled receptor A3 (ADGRA3), an orphan GPCR, plays a significant role in adipose thermogenesis through its constitutively active effects. ADGRA3 exhibited high expression levels in human adipocytes and mouse brown fat. Furthermore, the knockdown of Adgra3 resulted in an exacerbated obese phenotype and a reduction in the expression of thermogenic markers. Conversely, Adgra3 overexpression activated the adipose thermogenic program and improved metabolic homeostasis without an exogenous ligand supplementation. We found that ADGRA3 facilitates the biogenesis of beige adipocytes through the Gs-PKA-CREB axis. Moreover, hesperetin was identified as a potential agonist of ADGRA3, capable of inducing adipocyte browning and ameliorating insulin resistance. In conclusion, our study demonstrated that the overexpression of constitutively active ADGRA3 or the activation of ADGRA3 by hesperetin can induce adipocyte browning by Gs-PKA-CREB axis. These findings indicate that the utilization of hesperetin and the selectively overexpression of ADGRA3 in adipose tissue could serve as promising therapeutic strategies in the fight against obesity.

cell biology↗

bioRxiv2024

Engineering an anti-CD206-synNotch receptor: insightsinto the development of novel synthetic receptors

Immune cells play a pivotal role in the establishment, growth and progression of tumors at primary and metastatic sites. Macrophages, in particular, play a critical role in suppressing immune responses and promoting an anti-inflammatory environment through both direct and indirect cell-cell interactions. However, our understanding of the mechanisms underlying such interactions is limited due to a lack of reliable tools for studying transient interactions between cancer cells and macrophages within the tumor microenvironment. Recent advances in mammalian synthetic biology have introduced a wide range of synthetic receptors that have been used in diverse biosensing applications. One such synthetic receptor is the synNotch receptor, which can be tailored to sense specific ligands displayed on the surface of target cells. With this study, we aimed at developing a novel CD206-synNotch receptor, targeting CD206+ macrophages, a population of macrophages that play a crucial role in promoting metastatic seeding and persistent growth. Engineered in cancer cells and used in mouse metastasis models, such tool could help monitor and understand the effects cell-cell interactions between macrophages and cancer cells have on metastasis establishment. Here, we report the development of cancer landing pad cells for versatile applications, the engineering of CD206-synNotch cells, report the measurements of their activity and specificity, and discuss the unexpected caveats when considering their in vivo applications.

synthetic biology↗

bioRxiv2024

CDC5L surveils cellular stress responses and stress granule formation through transcriptional repression

Cells have evolved a variety of mechanisms to respond to stress, such as activating the PERK- eIF2 pathway and forming stress granules (SGs). It is important that these mechanisms are inducted only when necessary and exerted at appropriate levels, to prevent spontaneous or excessive activation of stress responses. However, the mechanisms by which cells keep the stress response programs in check are elusive. In this study, we discovered that downregulation of Cell Division Cycle 5 Like (CDC5L) causes spontaneous SG formation in the absence of any stress, which is independent of its known functions in the cell cycle or the PRP19 complex. Instead, we found that CDC5L binds to the PERK promoter through its DNA-binding domains and represses PERK mRNA transcription. As a result, it negatively regulates the abundance of PERK protein and the phosphorylation levels of eIF2, thereby suppressing the PERK-eIF2 signaling pathway and preventing undesirable SG assembly. Further RNA-sequencing (seq) and chromatin immunoprecipitation (ChIP)-seq analyses reveal a dual function of CDC5L in gene transcription: it acts as a transcriptional activator in cell cycle control but as a repressor in cellular stress responses. Finally, we show that the loss of CDC5L decreases cell viability and fly survival under mild stress conditions. Together, our findings demonstrate a previously unknown role and mechanism of CDC5L in the surveillance of cellular stress through transcriptional repression, which serves as a gatekeeper for the stress response programs such as the PERK-eIF2 pathway and SG formation. Significance statementCells need to respond to stress promptly for survival. Meanwhile, it is equally important to prevent spontaneous or excessive activation of stress response programs when no stress or only minor stress is present. Here, we reveal that the DNA/RNA-binding protein CDC5L represses the transcription of a cluster of stress response genes including PERK. In doing so, CDC5L suppresses the PERK-eIF2 pathway and prevents spontaneous SG assembly. Downregulation of CDC5L releases the restraint on these genes, resulting in an exaggerated response to stress and decreased viability in both cell and fly models. Taken together, this study demonstrates the existence of a gatekeeper mechanism that surveils the stress response programs and highlights the crucial role of CDC5L-mediated transcriptional repression in this regulation.

cell biology↗

bioRxiv2023

Uncertainty in crop phenology simulations is driven primarily by parameter variability

It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated sing random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.

plant biology↗

bioRxiv2022

Identification of differentially expressed pyroptosis-related genes in LUAD

BackgroundLung cancer has the highest mortality rate among malignant tumors in the world, and adenocarcinoma, the most important pathological type of lung cancer, accounts for 80%-85% of all lung cancers, causing a significant burden of disease. Pyroptosis is a newly recognized form of programmed cell death (PCD). Methods288 normal tissue data from The Genotype-Tissue Expression (GTEx) database and 501 tumor tissue data from The Cancer Genome Atlas (TCGA) database were selected. Differentially expressed genes were identified between normal and LUAD patients. Results and ConclusionsWith a consensus cluster analysis, 501 LUAD patients were divided into two clusters, and there was a significant difference in survival time between the two clusters. Ten differentially expressed genes were identified, with four up-regulated and six down-regulated. Pyroptosis is closely related to LUAD.

genetics↗

bioRxiv2022

Development and validation of glycolysis-cholesterol synthesis genes in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common subtype of lung cancer which is one of the most dangerous malignant tumors affecting human health. The pathways of glycolysis and cholesterol production play an essential role in the metabolism of cancer. The mitochondrial pyruvate carrier (MPC) consists of MPC1 and MPC2, and impaired MPC function may induce a solid capacity for tumor proliferation, migration, and invasion. MethodsGenes positively and negatively correlated with MPC1/2 expression were identified by calculating Spearman correlation, then gene ontology (GO) analysis was conducted. Univariate cox regression, lasso regression, and multivariate cox regression analyses were performed to model predictive outcome events using differentially expressed genes. Thirteen prognostic genes were selected to construct a prognostic model. Results1359 and 2026 genes were positively and negatively associated with MPC1/2, respectively. The expression of MPC1 and MPC2 was significantly different. The prognostic model had great predictive performance in the test set. ConclusionsMPC1/2 genes were involved in a cellular network associated with the malignant development of LUAD. The prognostic model can provide an essential basis for physicians to predict the clinical outcomes of LUAD patients.

genetics↗

bioRxiv2022

Identification of subgroups and development of prognostic risk models along the glycolysis-cholesterol synthesis axis in lung adenocarcinoma

BackgroundLung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. MethodsA dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas (TCGA) database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. Compared the prognosis of different subtypes and identified differentially expressed genes (DEGs) between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation (SHAP) analysis. A risk score model was built based on multivariate Cox analysis. ResultsLUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. ConclusionsPatients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients.

genetics↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗

bioRxiv2020

Contact patterns reveal a stable dynamic community structure with fission-fusion dynamics in wild house mice

Living in groups is a widely adopted strategy in gregarious species. For group-living individuals it is crucial to be capable to integrate into a social structure. While there is an intuitive understanding that the concept of a group arises through some form of cohesion between its members, the exact definition of what constitutes a group and thus tasks like the detection of the dynamics of a group over time is a challenge. One way of measuring cohesion is through direct interactions between individuals. However, there is increasing evidence that associations between individuals can be mediated by others, and thus, that the drivers for group cohesion extend beyond direct individual interactions. We use dynamic community detection, allowing to relate individuals beyond direct contacts, both structurally and temporally, to study the social structure in a long-term study of a population of free-ranging house mice in a barn in Switzerland. During the 2-year study period, mice had unlimited access to food, and population density increased by 50%. Despite strong fluctuations in individual contact behaviour, population demography and structure embed into long-lived dynamic communities that are characterised by spatial fidelity, persist over several seasons and reproduction cycles, and considerably extend the life-span of single individuals. Within these multi-male and multi-female communities, seasonal changes strongly affect their structure, leading to fission-fusion like dynamics. We identify female-female interactions as the main driver for the longevity of these communities, a finding that contrasts with prior reports of the importance of a dominant male for the stability of a group. Moreover, males have a drastically shorter presence time in the study population and more often move between communities than females. Nevertheless, interacting with other breeding males in stable communities increases the duration of male presence and thus, potentially, reproductive success. Our analysis of contact patterns in a rodent that uses shelters to rest, hide and rear offspring emphasises the importance of female-bonded communities in the structuring of the population.

ecology↗

bioRxiv2019

Stress induces cytoprotective TDP-43 nuclear bodies through lncRNA NEAT1-promoted phase separation

Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/802058v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1963bb9org.highwire.dtl.DTLVardef@2b00eforg.highwire.dtl.DTLVardef@14a7a2forg.highwire.dtl.DTLVardef@9ba973_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights(Up to four bullet points. The length of each highlight cannot exceed 85 characters, including spaces) O_LIStress induces phase-separated TDP-43 NBs to alleviate cytotoxicity C_LIO_LIThe two RRMs interact with different RNAs and act distinctly in the assembly of TDP-43 NBs C_LIO_LILncRNA NEAT1 promotes TDP-43 LLPS and is upregulated in stressed neurons C_LIO_LIThe ALS-causing D169G mutation is NB-defective and forms pTDP-43 cytoplasmic foci C_LI SummaryDespite the prominent role of TDP-43 in neurodegeneration, its physiological and pathological functions are not fully understood. Here, we report an unexpected function of TDP-43 in the formation of dynamic, reversible, liquid droplet-like nuclear bodies (NBs) in response to stress. Formation of NBs alleviates TDP-43-mediated cytotoxicity in mammalian cells and fly neurons. Super-resolution microscopy reveals a "core-shell" organization of TDP-43 NBs, antagonistically maintained by the two RRMs. TDP-43 NBs are partially colocalized with nuclear paraspeckles, whose scaffolding lncRNA NEAT1 is dramatically upregulated in stressed neurons. Moreover, increase of NEAT1 promotes TDP-43 liquid-liquid phase separation (LLPS) in vitro. Finally, we uncover that the ALS-associated mutation D169G impairs the NEAT1-mediated TDP-43 LLPS and NB assembly, causing excessive cytoplasmic translocation of TDP-43 to form stress granules that become phosphorylated TDP-43 cytoplasmic foci upon prolonged stress. Together, our findings suggest a stress-mitigating role and mechanism of TDP-43 NBs, whose dysfunction may be involved in ALS pathogenesis.

neuroscience↗