Biology subjects

Hua, K.

Publications and source records attributed to Hua, K..

13 recordsLinked to original sources

bioRxiv2026

Cryo-EM Structures of Brain-Derived G Protein-Coupled Receptors: The First Direct Visualization from Mammalian Brain Tissue

Glutamate is the main excitatory neurotransmitter in the brain and mediates its actions by both ionotropic (e.g. NMDA and AMPA) and metabotropic glutamate receptors (mGluRs). The Groups II and III mGluRs, which pre-synaptically inhibit glutamate release, are important for synaptic plasticity, modulating neuronal excitation, learning and memory. Our current understanding of the structural organization and dynamics of these and other mGluRs, as well as most other GPCRs, relies mainly on studies using recombinant and highly engineered systems in vitro. Here, we combine CRISPR-mediated protein tagging, proteomics and a rapid immunoaffinity purification method to isolate endogenous mGluR2-containing assemblies from mouse brain and visualize them via cryo-EM. Analysis of the particle sets reveals the molecular structures of at least 11 distinct endogenous receptor assemblies that span active and inactive states, homomeric and heteromeric dimers, and G protein-coupled and uncoupled species. We find that mGluR2 homodimers and mGluR2/3 heterodimers are the major endogenous mGluR2-containing species present in the brain, with the mGluR2/3 heterodimers detected only in active state complexes, potentially reflecting basal activation of mGluR3 containing dimers by chloride. Reconstructing a comprehensive conformational equilibrium for the brain-isolated receptors in detergent reveals endogenous ternary complexes comprising mGluR2 homodimers and mGluR2/3 heterodimers with a single GoA heterotrimer which exhibit significant differences from prior studies with recombinant systems. Our work illuminates the endogenous conformational, proteomic and compositional landscape of the heterogenous mGluR2 complexes in the brain, thereby providing a structural framework for the pathophysiology of psychiatric disorders. This information has the potential to be leveraged for therapeutic targeting of endogenous glutamatergic signaling complexes.

neuroscience↗

bioRxiv2026

Uncertainty-aware synthetic lethality prediction with pretrained foundation models

Synthetic lethality (SL) offers a promising paradigm for targeted cancer therapy, yet experimental identification of SL gene pairs remains costly, context-dependent, and biased toward well-studied genes. Existing computational approaches often rely on curated protein-protein interaction (PPI) networks and Gene Ontology (GO) annotations, which limit their ability to generalize to novel genes. Here we introduce CO_SCPLOWILANTROC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWSLC_SCPLOW, a two-stage, graph-free framework that leverages pretrained biological foundation models to predict SL pairs with calibrated uncertainty. In Stage 1, we apply a pretrained single-cell foundation model to bulk RNA-seq profiles of cancer cell lines to obtain context-aware embeddings and perform in silico gene knockouts to generate delta embeddings. These perturbation signals are further conditioned on a data-driven gene prior and supervised with CRISPR viability readouts to learn knockout-aware viability embeddings. In Stage 2, we derive pairwise features from these embeddings and train a lightweight classifier to distinguish SL from non-SL pairs. To enable reliable experimental prioritization, CO_SCPLOWILANTROC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWSLC_SCPLOW incorporates conformal prediction, producing calibrated and interpretable prediction sets that highlight high-confidence SL candidates. Across two evaluation settings, including zero-shot generalization to unseen gene pairs and to unseen genes, ablation analyses show that viability pretraining and the gene prior substantially improve performance while avoiding reliance on PPI and GO features. CO_SCPLOWILANTROC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWSLC_SCPLOW therefore transforms pretrained biological representations into practical, uncertainty-aware hypotheses that support robust and scalable discovery of therapeutic targets.

bioinformatics↗

bioRxiv2025

The biosynthetic gene cluster landscape of the oral microbiome across health and dental caries

Specialized metabolites encoded by biosynthetic gene clusters (BGCs) in the oral microbiome remain largely unexplored in the context of oral health and disease. Previous genome-centric surveys have cataloged hundreds of uncharacterized BGCs in the oral cavity associated with health and disease, but these studies relied on reference genomes and did not capture strain-level variation or the native distribution of BGCs. Here, we assembled three independently sourced metagenomic datasets from healthy and dental caries samples, extracted BGCs, and quantified their abundance alongside expression in a metatranscriptomic dataset. We first identified that aryl polyene, ribosomally synthesized and post-translationally modified peptide (RiPP), and nonribosomal peptide (NRP) encoding BGCs were the most abundant BGC classes across all three metagenomic datasets. We then grouped these BGCs into homology-based families and found that homologous clusters were usually consistently associated with either health or dental caries, suggesting conserved community-level roles for BGCs. An elastic-net regression model further selected 45 BGCs out of >5000 that could distinguish healthy and dental caries samples in the metatranscriptomic dataset, which demonstrated that BGCs could be predictive markers of disease. This analysis emphasizes the importance of high-quality metagenomic and metatranscriptomic datasets to resolve BGC expression patterns and to guide discovery of metabolites relevant to oral health and disease.

bioinformatics↗

bioRxiv2023

A suite of engineered mice for interrogating psychedelic drug actions

Psychedelic drugs like lysergic acid diethylamide (LSD) and psilocybin have emerged as potentially transformative therapeutics for many neuropsychiatric diseases, including depression, anxiety, post-traumatic stress disorder, migraine, and cluster headaches. LSD and psilocybin exert their psychedelic effects via activation of the 5-hydroxytryptamine 2A receptor (HTR2A). Here we provide a suite of engineered mice useful for clarifying the role of HTR2A and HTR2A-expressing neurons in psychedelic drug actions. We first generated Htr2a-EGFP-CT-IRES-CreERT2 mice (CT:C-terminus) to independently identify both HTR2A-EGFP-CT receptors and HTR2A-containing cells thereby providing a detailed anatomical map of HTR2A and identifying cell types that express HTR2A. We also generated a humanized Htr2a mouse line and an additional constitutive Htr2A-Cre mouse line. Psychedelics induced a variety of known behavioral changes in our mice validating their utility for behavioral studies. Finally, electrophysiology studies revealed that extracellular 5-HT elicited a HTR2A-mediated robust increase in firing of genetically-identified pyramidal neurons--consistent with a plasma membrane localization and mode of action. These mouse lines represent invaluable tools for elucidating the molecular, cellular, pharmacological, physiological, behavioral, and other actions of psychedelic drugs in vivo.

neuroscience↗

bioRxiv2021

A General Framework for Representing and Annotating Multifaceted Cell Heterogeneity in Human Cell Atlas

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for future molecular and cellular studies. Studies have shown that cells in complex organs exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data argumentation. We developed UniCoord, a specially tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The original transcriptomic profiles can be regenerated from the latent dimensions. The latent dimensions can be easily reconfigured to generate transcriptomic profiles of pseudo cells with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

bioinformatics↗

bioRxiv2021

A human ensemble cell atlas (hECA) enables in data cell sorting

Single-cell omics data can characterize multifaceted features of massive cells and bring significant insights to biomedical researches. The accumulation of single-cell data provides growing resources for constructing atlases for all cells of a human organ or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We proposed a unified information framework enabling seamless cell-centric data assembly and developed a human Ensemble Cell Atlas (hECA) as an instance. hECA version 1.0 assembled scRNA-seq data across multiple studies into one orchestrated data repository. It contains 1,093,299 labeled cells and metadata from 116 published human single-cell studies, covering 38 human organs and 11 systems. We invented three methods of applications based on the cell-centric assembly: "In data" cell sorting enables targeted data retrieval in the full atlas with customizable logic expressions; The "quantitative portraiture" system provides a multi-view presentation of biological entities (organs, cell types, and genes) of multiple granularities; The customizable reference creation allows users to use the cell-centric assembly to generate references for their own cell type annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential of cell-centric atlas assembly.

bioinformatics↗

bioRxiv2021

NeuronMotif: Deciphering transcriptional cis-regulatory codes from deep neural networks

Discovering DNA regulatory sequence motifs and their relative positions are vital to understand the mechanisms of gene expression regulation. Such complicated motif grammars are difficult to be summarized from shallow models. Although Deep Convolutional Neural Network (DCNN) achieved great success in annotating cis-regulatory elements, few combinatorial motif grammars have been accurately interpreted due to the mixed signal in DCNN. To address this problem, we proposed NeuronMotif, a general backward decoupling algorithm, to reveal the homo-/hetero-typic motif combinations and arrangements embedded in convolutional neurons. We applied NeuronMotif on several widely-used DCNN models. Many uncovered motif grammars of deep convolutional neurons are supported by literature or ATAC-seq footprinting. We further diagnosed the sick neurons that are sensitive to adversarial noises, which can guide DCNN architecture optimization for better prediction performance and motif feature extraction. Overall, NeuronMotif enables decoding cis-regulatory codes from deep convolutional neurons and understanding DCNN from a novel perspective.

bioinformatics↗

bioRxiv2021

HGC: fast hierarchical clustering for large-scale single-cell data

Clustering is a key step in revealing heterogeneities in single-cell data. Cell heterogeneity can be explored at different resolutions and the resulted varying cell states are inherently nested. However, most existing single-cell clustering methods output a fixed number of clusters without the hierarchical information. Classical hierarchical clustering provides dendrogram of cells, but cannot scale to large datasets due to the high computational complexity. We present HGC, a fast Hierarchical Graph-based Clustering method to address both problems. It combines the advantages of graph-based clustering and hierarchical clustering. On the shared nearest neighbor graph of cells, HGC constructs the hierarchical tree with linear time complexity. Experiments showed that HGC enables multiresolution exploration of the biological hierarchy underlying the data, achieves state-of-the-art accuracy on benchmark data, and can scale to large datasets. HGC is freely available for academic use at https://www.github.com/XuegongLab/HGC. Contactzhangxg@tsinghua.edu.cn, stevenhuakui@gmail.com

bioinformatics↗

bioRxiv2020

SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data. We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in [~]5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use.

bioinformatics↗

bioRxiv2019

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

ObjectiveTo investigate whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). MethodsThirty-eight drug-naive patients with PI and 44 healthy controls (HC) underwent resting-state functional MR imaging. Three commonly used FC metrics were calculated for each participant. We used the MVPA framework using linear support vector machine (SVM) with the three types of metrics as features separately. Subsequently, an unbiased N-fold cross-validation strategy was used to generate a classification system and was then used to evaluate its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS). ResultsThe best classifier could reach up to an accuracy of 81.5%, with sensitivity of 84.9%, specificity of 79.1% and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right fronto-insular cortex, left precuneus and left middle frontal gyrus showed high classification weights. In addition, right fronto-insular cortex and left middle frontal gyrus were the overlapping regions between MVPA and group comparison. Correlation analysis showed that functional connectivity strength (FCS) in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS respectively. ConclusionThe current study suggests abnormal FCS might serve as a potential neuromarkers for PI. Key PointsFCS in fronto-insular cortex and middle frontal gyrus may be a neuroimaging biomarker for insomnia. FCS can be used to distinguish between patients with primary insomnia from healthy controls with high classification accuracy (81.5%; P < 0.001). FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS respectively. Abbreviations

neuroscience↗

bioRxiv2019

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods.\n\nResultsWe propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github (https://github.com/ShengquanChen/VPAC). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages.\n\nConclusionsVPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

bioinformatics↗

bioRxiv2018

A case study on the detailed reproducibility of a human cell atlas project

Reproducibility is a defining feature of a scientific discovery. Reproducibility can be at different levels for different types of study. The purpose of the Human Cell Atlas (HCA) project is to build maps of molecular signatures of all human cell types and states to serve as references for future discoveries. Constructing such a complex reference atlas must involve the assembly and aggregation of data from multiple labs, probably generated with different technologies. It has much higher requirements on reproducibility than individual research projects. To add another layer of complexity, the bioinformatics procedures involved for single-cell data have high flexibility and diversity. There are many factors in the processing and analysis of single-cell RNA-seq data that can shape the final results in different ways. To study what levels of reproducibility can be reached in current practices, we conducted a detailed reproduction study for a well-documented recent publication on the atlas of human blood dendritic cells as an example to break down the bioinformatics steps and factors that are crucial for the reproducibility at different levels. We found that the major scientific discovery can be well reproduced after some efforts, but there are also some differences in some details that may cause uncertainty in the future reference. This study provides a detailed case observation on the on-going discussions of the type of standards the HCA community should take when releasing data and publications to guarantee the reproducibility and reliability of the future atlas.

bioinformatics↗

bioRxiv2018

SIP: An Interchangeable Pipeline for scRNA-seq Data Processing

Multiple steps of bioinformatics processing are needed to convert the raw scRNA-seq data to information that can be used in downstream analyses and in building cell atlases. Dozens of software packages have been developed and different labs tend to have different preferences on choices of the workflow. Such diversity can cause difficulties in future efforts of aggregating data from multiple labs, and also difficulties for new labs to start in this field. A few pipelines have been developed to help integrating multiple steps into a whole, but the fixed software architecture makes it hard for developers to add new features or exchange parts in the pipeline.\n\nWe presented SIP, a Single-cell Interchangeable Pipeline. It is a one-stop platform for the processing of scRNA-seq data from multiple platforms, and will also support for other types of data like scATAC-seq data. SIP utilizes container technology to solve the deployment dilemma when handling multiple packages and provides an easy-to-use interface for users to conduct the complicated multi-step process from raw data to final results with a single command. It also allows advanced users to assemble different versions of the pipeline by interchanging parts or adding new modules. SIP is available at https://github.com/XuegongLab/SIP under the GPL-3.0 license.

bioinformatics↗