Biology subjects

Qian, L.

Publications and source records attributed to Qian, L..

At least 19 recordsLinked to original sources

bioRxiv2026

A robust and scalable image analysis framework for phenotyping human thoracic aorta

Large-scale phenotyping of the thoracic aorta requires a workflow in which every step, from segmentation to phenotype extraction, runs without manual intervention. We present a robust and scalable framework that converts contrast-enhanced CT into anatomical landmarks, a patient-specific quadrilateral mesh, a wall-stress field and regional phenotypes of the thoracic aorta. Four nnU-Net segmentation tasks delineate the aorta, its anatomical regions and two partitions of the aortic root. A surface-curve-point procedure derives the annulus, the sinotubular junction, the ostia of the supra-aortic branches, the cusp nadirs and the commissures from the interfaces between segmented regions. We fit a structured template of 24,882 quadrilateral elements to the segmented aortic surface by staged, differentiable mesh deformation and compute the transmural mean wall stress with PyTorch-FEA. The structured mesh provides node-to-node correspondence across patients, anchored to the anatomy by the landmarks, from which the framework extracts regional morphological and biomechanical phenotypes.

bioengineering↗

bioRxiv2026

Systematic evaluation of structural connectome thresholding in whole-brain network modelling

Large-scale whole-brain network models rely on structural connectomes to constrain simulated neural dynamics, yet the optimal processing of these anatomical scaffolds is not fully established. Here, we investigate the impact of structural connectome thresholding on whole-brain model performance to advance precision individual modelling. We measure model goodness-of-fit to both static functional connectivity and dynamic functional connectivity across a comprehensive spectrum of network densities, spatial parcellations, and model complexities using neuroimaging datasets of healthy individuals and a clinical cohort of post-stroke patients. We demonstrate that the optimal structural sparsity is highly resolution dependent. In coarse-grained parcellations, proportional thresholding enhances the model's fit to static functional connectivity but relies on denser connectome to maintain dynamic functional fits. Conversely, fine-grained models require stringent connectome thresholding which simultaneously optimizes both static and dynamic functional fits. Taken together, these results indicate that the uncritical use of raw structural connectomes introduces suboptimal dynamical regimes, establishing resolution-tailored thresholding as an indispensable step for constructing precision brain network models.

neuroscience↗

bioRxiv2026

Heart rate dynamics embed a shared representation of individual brain organization and cognitive function

Idiosyncratic brain functional organization shapes intricate cardiac dynamics through central-peripheral autonomic interactions, yet a comprehensive mapping between multi-scale heart rate dynamics and whole-brain functional architecture remains lacking. Here, combining highly comparative time-series analysis with multi-modal neuroimaging and intracranial electrophysiology, we establish heart rate dynamics as a physiological fingerprint that maps onto whole-brain functional architecture. Partial least squares analysis revealed a generalizable latent axis linking reduced heart rate temporal complexity and elevated micro-scale predictability to heightened resting-state functional connectivity across default mode, salience, and sensorimotor networks. This covariance aligns spatially with serotonergic, noradrenergic, and cholinergic neuromodulatory gradients, persists across physiological confound controls and cross-session validations, derives support from human intracranial electrophysiological recordings, and extends to active cognitive states. Furthermore, brain-covarying heart rate signatures underpin the predictive capacity of heart rate dynamics for individual fluid and crystallized intelligence, demonstrating a shared representational substrate. Our findings demonstrate a robust neurovisceral coupling architecture, establishing well-characterized heart rate dynamics as a scalable, neurobiologically anchored window into human brain functional organization and cognitive traits.

neuroscience↗

bioRxiv2026

Drug-Prot: A query system for statistical inference of drug effects and interactions in dynamic proteomic networks

Understanding drug effects and drug-drug interactions is essential for developing combination therapies. We present Drug-Prot, a computational framework that leverages large-scale perturbation proteomics to quantify causal drug effects, drug-drug interactions, and dynamic protein relationships. Using data from 63 single drugs and 59 drug combinations applied to 18 breast cancer cell lines at 6, 24, and 48 hours, Drug-Prot estimates drug effects on protein expression and reconstructs directed temporal protein dependency networks. The publicly available software enables targeted analyses of user-defined protein sets, substantially reducing the multiple-testing burden. Through an interactive web application, users obtain corrected p-values for single-drug and combination effects, directed temporal dependency networks, and downloadable results without requiring access to the underlying proteomic dataset. As a use case, we apply invariance-regularized Random Forests to triple-negative breast cancer cell lines to identify proteins associated with drug response. Querying these proteins in Drug-Prot reveals drug-specific and interaction effects at the protein-network level, illustrating how the framework links candidate causal protein features to actionable drug combinations.

bioinformatics↗

bioRxiv2026

A comprehensive DNA methylation atlas for the Chinese population through nanopore long-read sequencing of 106 individuals

DNA methylation constitutes the primary epigenetic language mediating organismal phenotypic plasticity. Establishing a cohort-level genomic methylation landscape featuring wide geographical diversity is fundamental for dissecting its genetic and environmental attributes. Leveraging nanopore sequencings strength in genome-methylome co-sequencing, we generated a whole-genome, haplotype-resolved methylation atlas for 106 individuals from 19 provinces across China. The atlas identified 27,609,354 CpG sites genome-wide, with notably more informed gene proximal regions and CpG islands compared to whole-genome bisulfite sequencing. Detailed analyses revealed genomic structural variants as a pervasive covariate of DNA methylation, with a remarkable 2-fold compensation effect found in genome-wide heterozygous deletions. On the other hand, habitat altitude is found to be a strong environmental determinant of DNA methylation. We established a quantitative relationship between altitude and methylation states and identified a gene set strictly responsive to altitude differences, revealing epigenetically regulated genes such as PRDM16, EPHB2 and WNT7A. The methylation atlas provides a reference resource to facilitate further explorations into human epigenetics.

genomics↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

neuroscience↗

bioRxiv2026

CCycDB: an integrative knowledgebase to fingerprint microbially mediated carbon cycling processes

Microorganisms play essential roles in mediating biogeochemical cycling of carbon across Earths ecosystems. Understanding the processes and underlying mechanisms for microbially mediated carbon cycling is therefore critical for advancing global ecology and climate change research. To comprehensively depict these complex biogeochemical processes, we developed CCycDB, a knowledge-based functional gene database, to accurately fingerprint microbially-mediated carbon cycling pathways and gene families, particularly from shotgun metagenomes. The CCycDB database comprises 4,676 gene families classified into six major functional categories, further structured into 45 level-1 and 188 level-2 sub-categories, encompassing a total of 10,991,724 high-quality reference sequences. Validation using both synthetic and real-world datasets demonstrated that CCycDB outperforms existing orthology databases in terms of accuracy, coverage and specificity. By directly targeting carbon-cycling functional gene families, CCycDB provided promising routines to reconstruct both functional gene and taxonomic profiles associated with microbially mediated carbon cycling. Application of CCycDB to shotgun metagenomes from diverse and complex ecosystems revealed pronounced habitat-specific differences in carbon cycling processes and their associated microbial taxa. Collectively, CCycDB provides a powerful and reliable tool for profiling carbon cycling processes from both functional and taxonomic perspectives in complex ecosystems. CCycDB is accessible at https://ccycdb.github.io/. Impact StatementThe microbially mediated carbon cycling processes are the most complex biogeochemical processes in the Earths biosphere, playing profound regulatory roles on global climate changes. A key bottleneck in linking microbial communities to global change is the lack of integrated tools for comprehensive carbon cycle profiling. Here, we present CCycDB, a tool that serves a dual purpose--first being a reference database that obtains functional gene and taxonomic profiles and functioning as a customized routine for efficiently aligning sequences and querying associated functional information. CCycDB enables researchers to accurately link microbial community dynamics to carbon cycling and transforming pathways, thereby advancing integrated global change studies with microbes and ecological research via complex metagenomic datasets.

microbiology↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2025

GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data

Cell type annotation is critical for interpreting single-cell transcriptomic data but remains challenging due to uncertain cellular clustering granularity and inconsistent labeling across studies. Here we present GPTAnno, an automated, ontology-tree-guided, uncertainty-aware, hierarchical cell type annotation method based on GPT models. GPTAnno directly handles gene expression matrices, integrates multi-resolution clustering with large language model reasoning constrained by the cell ontology to produce standardized, ontology-aware, and reproducible annotations with automatic resolution selection. GPTAnno selects optimal clustering resolutions based on the annotation distance on the ontology tree and quantifies annotation uncertainty to flag ambiguous clusters for expert review. Benchmarking across twelve large-scale datasets demonstrates GPTAnnos superior accuracy on annotating cell types across various species, tissues, and disease contexts against existing methods. Implemented in R and Python with Seurat and Scanpy compatibility, GPTAnno allows simple inputs to streamline the reproducible annotation, considerably reducing human efforts in repeated reclustering, assigning and examining the labels.

genomics↗

bioRxiv2025

Hierarchical taxonomic constraints shape bacterial nitrogen cycling across functional and molecular scales

Microbes are essential for global nitrogen cycling, yet the extent to which taxonomic identity constrains functional potential remains poorly quantified. Using 73,472 representative bacterial genomes, we establish a multi-scale quantitative framework revealing systematic, hierarchical relationships between taxonomic identity and nitrogen cycling functional potential. Hierarchical variance decomposition reveals that taxonomy explains 20-46% of functional variation across six nitrogen pathways, with the strongest constraints for dissimilatory nitrite reduction to ammonium (46.1%) and nitrogen fixation (44.2%), both negatively correlated with functional prevalence. K-means clustering identifies four class-level functional archetypes (Functionally Inactive, Diversified, Integrated, and Specialized) among 77 bacterial classes. 1,281 genera resolve into five ecological strategies differentiated by their nitrogen retention versus loss capabilities, exhibiting strong phylogenetic signals. Cross-scale validation demonstrates 78.7% genus-level conformity to class-level archetypes, confirming hierarchical functional organization. Molecular evolutionary analysis of 13 genes reveals sequence conservation as a dimension partially independent of pathway-level functional constraints. Paradoxically, the functionally most constrained pathway exhibits only moderate sequence conservation (nrfA: 9th/13 genes), while a moderately constrained pathway shows exceptional conservation (napA). Four constraint-conservation patterns demonstrate that gene-specific structural and ecological factors generate evolutionary rate variation independently of taxonomic associations. Our results establish a hierarchical framework in which taxonomic constraints set baseline functional potential, ecological trade-offs shape strategy diversification, and molecular evolution modulates gene-level conservation patterns across biological scales. This framework establishes quantitative baselines that enable probabilistic inference of nitrogen cycling capabilities from taxonomic composition, with potential applications in amplicon-based community analysis, targeted cultivation, and biogeochemical modeling. ImportanceA fundamental challenge in microbial ecology is inferring functional potential from taxonomic data--a relationship widely assumed but never rigorously quantified. Resolving this is critical because while amplicon sequencing provides cost-effective taxonomic profiling, functional characterization requires expensive metagenomics, limiting large-scale biogeochemical studies. We provide the first systematic quantification demonstrating that taxonomic identity explains 20-46% of nitrogen cycling functional variation, operating hierarchically from class to genus level. Crucially, we reveal that taxonomic constraints operate at both functional distribution and molecular evolution levels as partially independent dimensions, indicating distinct evolutionary mechanisms. This work establishes quantitative foundations for taxonomy-based functional prediction, enabling researchers to extract functional insights from readily available taxonomic surveys. As reference databases expand, this framework will enhance predictive capabilities for nitrogen cycling and broader biogeochemical processes.

genomics↗

bioRxiv2025

Regioselective biosynthesis of oligoamides as precursors for sequence-controlled co-polyamides

Polyamides are important natural and synthetic polymers, best exemplified by proteins and nylons respectively. Proteins demonstrate that novel polymers with emergent properties can be generated by combining diverse monomers in precisely defined sequences. However, commercial polyamides represent only a small fraction of the potential diversity in polyamide sequences, due to the synthetic challenges of sequence-controlled polymerization. Amide synthetases have been shown to synthesize a broad array of nylon-relevant diads, but the generation of novel sequenced copolyamides requires enzymes capable of acting with longer and more diverse substrates. In this study, we demonstrated that NRPS-independent siderophore (NIS) synthetases, represented by DesD, can ligate oligomeric substrates. A simultaneous enzyme cascade using DesD enabled the synthesis of an oligotriad directly from unprotected substrates. Moreover, the regioselectivity of DesD allowed the selective synthesis of a sequenced amide tetrad, the precursor to a novel sequenced-defined polyamide with properties superior to nylon 66. This study establishes a direct biocatalytic route for the facile synthesis of sequenced oligoamides from unprotected bifunctional substrates, opening new possibilities to synthesize sequenced copolyamides.

biochemistry↗

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

Charting Postnatal Heart Development Using In Vivo Single-Cell Functional Genomics

The transition at birth, marked by increased circulatory demands and rapid growth, necessitates extensive remodeling of the hearts structure, function, and metabolism. This transformation requires precise spatial and temporal coordination among diverse cardiac cell types; central to this process is cardiomyocyte maturation, yet the regulatory mechanisms driving these changes remain poorly understood. Here, we present a temporal and spatial atlas of postnatal hearts by integrating single-nucleus transcriptomics with image-based spatial transcriptomics, which uncovers the dynamic regulatory networks of cardiomyocyte maturation. To functionally interrogate candidate regulators in vivo, we developed Probe-based Indel-detectable Perturb-seq (PIP-seq), a high-throughput platform that uses probe-based chemistry to directly capture sgRNA expression, perturbation status, and transcriptomic profiles at single-nucleus resolution. Applying PIP-seq to postnatal cardiac development identified 21 novel regulators of cardiomyocyte maturation, highlighting critical nodal points in this process. Our study establishes a high-resolution framework for dissecting postnatal heart development, underscoring the integrative and highly ordered roles of microenvironment and intercellular communication in cardiomyocyte maturation. Importantly, PIP-seq enables systematic, high-throughput exploration of gene function and networks underlying complex biological processes in their native in vivo context.

developmental biology↗

bioRxiv2025

Deciphering the molecular mechanisms of FET fusion oncoprotein-DNA hollow co-condensates

Biomolecules such as nucleic acids and proteins can undergo liquid-liquid phase separation to form biomolecular condensates with diverse architectures. However, fundamental questions surrounding the biophysical principles that govern condensate architecture and their potential biotechnological applications remain unresolved. Here, we report that the FUS/EWS/TAF15 family fusion oncoprotein FUS-ERG forms hollow co-condensates with double-stranded DNA containing GGAA microsatellites. Through a combination of biochemical assays, super-resolution imaging, and mathematical modeling, we reveal that the interior surface of hollow co-condensates exhibits properties distinct from those of the external surface, a phenomenon we term nested asymmetric phase separation. Furthermore, we demonstrate that the self-organization and morphology control of FUS-ERG hollow co-condensates can be harnessed for DNA-based information manipulation, enabling targeted DNA deletion within dsDNA libraries and facilitating dynamic, hierarchical data selection. These findings provide critical insights into the biophysical mechanisms underlying multicomponent phase-separated cellular bodies and establish a foundation for leveraging condensate morphology in biotechnology.

biophysics↗

bioRxiv2025

Enzymatic synthesis of diverse oligoamide polymer precursors

Nylons, a major class of synthetic polyamides, are widely used due to their excellent mechanical strength, thermal stability, and chemical resistance. Conventional nylon production relies on the polymerization of lactams or stoichiometric nylon salts. However, applying these approaches to unconventional precursors such as bio-derived glutaric acid produces polymers with low molecular weight and limited applications. To address these challenges, we demonstrated that chemically-synthesized nylon diads enable the production of higher-molecular-weight polyamides compared with traditional salts. We then identified a biosynthetic approach using amide synthetases to convert unprotected bifunctional substrates into nylon-relevant diads. Using a cofactor regeneration system, enzymatic diad synthesis was scaled to produce sufficient material for laboratory-scale characterization and solid-state polymerization. Amide synthetases demonstrated broad substrate scope, catalyzing the regioselective assembly of diverse nylon-relevant diacids, diamines, and {omega}-amino acids. This strategy offers a novel route to synthesize challenging nylon monomers and advances production of bioderived nylons.

biochemistry↗

bioRxiv2025

gcSV: a unified framework for comprehensive structural variant detection

The characterization of structural variants (SVs) is fundamental to genomic studies and advanced computational approaches are on demand to exert the ability of the ubiquitous high-throughput sequencing data. Herein, we propose gcSV, a read-length agnostic alignment-based approach to well-handle the issues of genome repeats, SV breakpoints and read alignments/assemblies for comprehensive, cost-effective and versatile SV calling. For long reads, its yield is 20-38% higher than state-of-the-art tools in HG002 benchmark. For hybrid sequencing, it provides a cost-effective solution (2-4x long plus 30-60x short reads) to achieve even higher yield than that of state-of-the-art tools using 30x long reads. For short reads, gcSV also achieves over 8% higher precision without any loss of sensitivity. Furthermore, gcSV confidently brings over 93,000 novel SVs comparing to the official callset of 1000 Genomes Project Phase4. The results suggest that gcSV is promising to make valuable SV discoveries in many cutting-edge studies.

bioinformatics↗

bioRxiv2024

Superimposed inhibitory surrounds underlying Saliency-based Stimulus Selection in avian Midbrain isthmi pars magnocellularis

In the avian midbrain network, bottom-up spatial attention is directed by saliency-based stimulus selection. However, it remains unclear whether the isthmi pars magnocellularis (Imc), the first site in the midbrain network to represent stimulus selection, can represent stimulus salience and what is the mechanism by which the midbrain network computes salience. Here, we used two separate translational motion stimuli as the main stimulation protocols and conducted in vivo electrophysiological experiments in the pigeons Imc. By combining bio-plausible model validation, we found two types of inhibitory surrounds of the Imc neuron receptive field, homogenous inhibitory surrounds (HIS) and non-homogenous inhibitory surrounds (non-HIS), and elucidated the mechanism by which both arise. While HIS is local and depends on stimulus feature similarity, which can be used to compute stimulus saliency, non-HIS is global and doesnt depend on stimulus feature similarity, which can be used to compute stimulus selection. Moreover, the superimposition of HIS and non-HIS modulates the neural response of Imc. The two inhibitory surrounds of Imc identified in this study more clearly elucidate the full process of achieving bottom-up stimulus selection based on saliency in the midbrain network and show that Imc is a nucleus that can represent both stimulus saliency and stimulus selection.

neuroscience↗

bioRxiv2024

High-throughput two-photon volumetric brain imaging in freely moving mice

Imaging neural activities across large volumes at subcellular resolution without impeding animal behaviors remains difficult. Here, we develop a high-throughput miniature Bessel-beam two-photon microscope (miniBB2p) capable of imaging calcium dynamics from neurons and dendrites over a volume of 420 x 420 x 80 m3 during free behavior. We share a full description of miniBB2p and demonstrate its ability to perform large-scale recordings of the calcium activities of more than 1000 neurons at a time in anterior cingulate cortex and secondary motor cortex of freely moving mice in various behavioral paradigms. Our results indicate that miniBB2p opens new avenues in the design of miniature multiphoton microscopes, facilitating precise monitoring of neural activities across large populations within a three-dimensional volume in freely moving animals.

neuroscience↗