Biology subjects

Wong, W. H.

Publications and source records attributed to Wong, W. H..

At least 19 recordsLinked to original sources

bioRxiv2026

scMTG reconstructs single-cell temporal dynamics with Markov transition generators

Single-cell time-series data offer a unique opportunity to study how cell states emerge, evolve, and diversify over time. However, cells are destructively measured across time points and individual cells cannot be directly tracked, making it challenging to reconstruct cell-state transitions and uncover the dynamic regulatory programs. Existing methods are predominantly based on optimal transport and typically require predefined low-dimensional representations, which can limit scalability, flexibility, and mechanistic interpretability. Here we present scMTG, a single-cell Markov transition generative framework that jointly learns cell representations and temporal cell-state transitions from unpaired single-cell time-series data. In a series of experiments, scMTG demonstrates superior performance in interpolating held-out missing-time-point data and inferring cross-time-point transition matrices, compared with state-of-the-art methods. More importantly, the learned Markov transition generator provides an interpretable framework for identifying the molecular programs that drive cell-state changes, enabling characterization of cell fate decisions and construction of time-resolved regulatory networks. Together, scMTG provides a unified generative framework for reconstructing cellular dynamics and uncovering regulatory programs that shape development, differentiation, and disease progression. scMTG is available as open-source software at https://github.com/liuq-lab/scMTG.

bioinformatics↗

bioRxiv2025

ProVADA: Generation of Subcellular Protein Variants via Ensemble-Guided Test-Time Steering

Engineering protein variants to function in exogenous environments remains a significant challenge due to the complexity of sequence and fitness landscapes. Experimental strategies often require extensive labor and domain expertise. While recent advances in protein generative modeling offer a promising in silico alternative, many of these methods rely on differentiable fitness predictors, which limits their applicability. To this end, we introduce Protein Variant ADAptation (ProVADA), an ensemble-guided, test-time steering framework that combines implicit generative priors with fitness oracles via a composite functional objective. ProVADA leverages Mixture-Adaptation Directed Annealing (MADA), a novel sampler integrating population-annealing, adaptive mixture proposals, and directed local mutations. Furthermore, ProVADA requires no gradients or explicit likelihoods, yet efficiently concentrates sampling on high-fitness, low-divergence variants. We demonstrate its effectiveness by in silico redesigning human renin for cytosolic functionality. Our results achieve significant gains in predicted localization fitness while preserving structural integrity.

bioengineering↗

bioRxiv2024

Functional Diversity of Memory CD8 T Cells is Spatiotemporally Imprinted

Tissue-resident memory CD8 T cells (TRM) kill infected cells and recruit additional immune cells to limit pathogen invasion at barrier sites. Small intestinal (SI) TRM cells consist of distinct subpopulations with higher expression of effector molecules or greater memory potential. We hypothesized that occupancy of diverse anatomical niches imprints these distinct TRM transcriptional programs. We leveraged human samples and a murine model of acute systemic viral infection to profile the location and transcriptome of pathogen-specific TRM cell differentiation at single-transcript resolution. We developed computational approaches to capture cellular locations along three anatomical axes of the small intestine and to visualize the spatiotemporal distribution of cell types and gene expression. TRM populations were spatially segregated: with more effector- and memory-like TRM preferentially localized at the villus tip or crypt, respectively. Modeling ligand-receptor activity revealed patterns of key cellular interactions and cytokine signaling pathways that initiate and maintain TRM differentiation and functional diversity, including different TGF{beta} sources. Alterations in the cellular networks induced by loss of TGF{beta}RII expression revealed a model consistent with TGF{beta} promoting progressive TRM maturation towards the villus tip. Ultimately, we have developed a framework for the study of immune cell interactions with the spectrum of tissue cell types, revealing that T cell location and functional state are fundamentally intertwined.

immunology↗

bioRxiv2024

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimers disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

genetics↗

bioRxiv2023

EpiGePT: a Pretrained Transformer model for epigenomics

The inherent similarities between natural language and biological sequences have given rise to great interest in adapting the transformer-based large language models (LLMs) underlying recent breakthroughs in natural language processing (references), for applications in genomics. However, current LLMs for genomics suffer from several limitations such as the inability to include chromatin interactions in the training data, and the inability to make prediction in new cellular contexts not represented in the training data. To mitigate these problems, we propose EpiGePT, a transformer-based pretrained language model for predicting context-specific epigenomic signals and chromatin contacts. By taking the context-specific activities of transcription factors (TFs) and 3D genome interactions into consideration, EpiGePT offers wider applicability and deeper biological insights than models trained on DNA sequence only. In a series of experiments, EpiGePT demonstrates superior performance in a diverse set of epigenomic signals prediction tasks when compared to existing methods. In particular, our model enables cross-cell-type prediction of long-range interactions and offers insight on the functional impact of genetic variants under different cellular contexts. These new capabilities will enhance the usefulness of LLM in the study of gene regulatory mechanisms. We provide free online prediction service of EpiGePT through http://health.tsinghua.edu.cn/epigept/.

genomics↗

bioRxiv2023

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mecha-nisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

bioinformatics↗

bioRxiv2022

Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with GWAS summary statistics, identify relevant tissues, and depict shared heritability and regulations by relevance correlation. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Experiments on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared heritability and regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK-Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar.

genetics↗

bioRxiv2022

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

Non-coding sequences that are evolutionarily conserved and bio-chemically active offer clues to mechanistic interpretations of human genome-wide association studies (GWAS). However, their genetic effects have not been systematically examined across a wide range of human tissues and traits. Here we develop a simple method to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 tissues and cell types. Combining these elements with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, we identify tissue-specific enrichments of heritability and causal variants for many traits, as well as candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS. Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits, and reinforce the importance of integrating evolutionary and biochemical data to elucidate human disease genetics.

genomics↗

bioRxiv2021

The origins and functional effects of postzygotic mutations throughout the human lifespan

Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remains unclear. To study the origins and functional consequences of PZMs, we generated a multi-tissue atlas of PZMs from 948 donors using the final major release of the Genotype-Tissue Expression (GTEx) project. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, while 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we find that their type and predicted functional impact varies during prenatal development, across tissues, and the germ cell lifecycle. Remarkably, a class of prenatal mutations was predicted to be more deleterious than any other category of genetic variation investigated and under positive selection as strong as somatic mutations in cancers. In total, the data indicate that PZMs can contribute to phenotypic variation throughout the human lifespan, and, to better understand the relationship between genotype and phenotype, we must broaden the long-held assumption of one genome per individual to multiple, dynamic genomes per individual. One-Sentence SummaryThe predicted rates, functional effects and selection pressure of postzygotic mutations vary through the human lifecycle.

genomics↗

bioRxiv2021

Interpreting ruminant specific conserved non-coding elements by developmental gene regulatory network

BackgroundBiologists long recognized that the genetic information encoded in DNA leads to trait innovation via gene regulatory network (GRN) in development. ResultsHere, we generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep and revealed 1,601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we developed a Conserved Non-coding Element interpretation method by gene Regulatory network (CNEReg) to define toolkit transcription factors (TTF) and model its regulation on rumen specific gene via batteries of active-RSCNEs during development. Our developmental GRN reveals 18 TTFs and 313 active-RSCNEs regulating the functional modules of the rumen and identifies OTX1, SOX21, HOXC8, SOX2, TP63, PPARG and 16 active-RSCNEs that functionally distinguish the rumen from the esophagus. ConclusionsWe argue that CNEReg is an attractive systematic approach to integrate evo-devo concepts with omics data to understand how gene regulation evolves and shapes complex traits.

genomics↗

bioRxiv2021

Direct induction of human neurons from fibroblasts carrying the neuropsychiatric 22q11.2 microdeletion reveals transcriptome- and epigenome-wide alterations

Standard methods for the creation of neuronal cells via direct induction from primary tissue use perinatal fibroblasts, which hinders the important study of patient specific genetic lesions such as those underlying neuropsychiatric disorders. To address this we developed a novel method for the direct induction of neuronal cells (induced neuronal cells, iN cells) from adult human fibroblast cells. Reprogramming fibroblasts into iN cells via recombinant virus resulted in cells that stain for markers such as MAP2 and PSA-NCAM and exhibit electrophysiological properties such as action potentials and voltage dependent sodium- and potassium currents that reveal a neuronal phenotype. Transcriptome and chromatin analysis using RNA-Seq, microRNA-Seq and ATAC-Seq, respectively, further confirm neuronal character. 22q11.2 Deletion-Syndrome (22q11DS) is caused by a large 3 million base-pair heterozygous deletion on human chromosome 22 and is strongly associated with neurodevelopmental, neuropsychiatric phenotypes such as schizophrenia and autism. We leverage the direct-iN cell model for the study of genetic neurodevelopmental conditions by presenting gene-by-gene as well as networkwide effects of the 22q11DS deletion on gene expression in human neuronal cells, on several levels of functional genomics analysis. Some of the genes within the 22q11DS deletion boundary exhibit unexpected cell-type-specific changes in transcript levels, and genome-wide we can detect dysregulation of calcium channel subunit genes and other genes known to be involved in autism or schizophrenia, such as NRXN1, as well synaptic pathways. This genome-wide effect on gene expression can also be observed at the microRNA and chromatin levels, showing that the iN cells have indeed converted to a neuronal phenotype at several regulatory levels: chromatin, protein-coding RNAs and microRNAs, revealing relevant disease pathways and genes. We present this model of inducing neurons from fibroblasts as a useful general resource to study the genetic and molecular basis of normal and abnormal brain development and brain function.

genomics↗

bioRxiv2021

Revealing Free Energy Landscape from MD Data via Conditional Angle Partition Tree

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.

molecular biology↗

bioRxiv2021

Dynamic chromatin regulatory landscape of human CAR T cell exhaustion

Dysfunction in T cells limits the efficacy of cancer immunotherapy1-6. We profiled the epigenome, transcriptome, and enhancer connectome of exhaustion-prone GD2-targeting HA-28z chimeric antigen receptor (CAR) T cells and control CD19-targeting CAR T cells, which present less exhaustion-inducing tonic signaling, at multiple points during their ex vivo expansion7. We found widespread, dynamic changes in chromatin accessibility and 3D chromosome conformation preceding changes in gene expression, notably at loci proximal to exhaustion-associated genes such as PDCD1, CTLA4, and HAVCR2, and increased DNA motif access for AP-1 family transcription factors, which are known to promote exhaustion. Although T cell exhaustion has been studied in detail in mouse, we find that the regulatory networks of T cell exhaustion differ between the species and involve distinct loci of accessible chromatin and cis-regulated target genes in human CAR T cell exhaustion. Deletion of exhaustion-specific candidate enhancers of PDCD1 suppress the expression of PD-1 in an in vitro model of T cell dysfunction and in HA-28z CAR T cells, suggesting enhancer editing as a path forward in improving cancer immunotherapy.

genomics↗

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

scJoint: transfer learning for data integration of single-cell RNA-seq and ATAC-seq

Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.

bioinformatics↗

bioRxiv2020

Complete and haplotype-specific sequence assembly of segmental duplication-mediated genome rearrangements using targeted CRISPR-targeted ultra-long read sequencing (CTLR-Seq)

We have developed a generally applicable method based on CRISPR/Cas9-targeted ultra-long read sequencing (CTLR-Seq) to completely and haplotype-specifically resolve, at base-pair resolution, large, complex, and highly repetitive genomic regions that had been previously impenetrable to next-generation sequencing analysis such as large segmental duplication (SegDup) regions and their associated genome rearrangements that stretch hundreds of kilobases. Our method combines in vitro Cas9-mediated cutting of the genome and pulse-field gel electrophoresis to haplotype-specifically isolate intact large (200-550 kb) target regions that encompass previously unresolvable genomic sequences. These target fragments are then sequenced (amplification-free) to produce ultra-long reads at up to 40x on-target coverage using Oxford nanopore technology, allowing for the complete assembly of the complex genomic regions of interest at single base-pair resolution. We applied CTLR-Seq to resolve the exact sequence of SegDup rearrangements that constitute the boundary regions of the 22q11.2 deletion CNV and of the 16p11.2 deletion and duplication CNVs. These CNVs are among the strongest known risk factors for schizophrenia and autism. We then perform de novo assembly to resolve, for the first time, at single base-pair resolution, the sequence rearrangements of the 22q11.2 and 16p11.2 CNVs, mapping out exactly the genes and non-coding regions that are affected by the CNV for different carriers.

genomics↗

bioRxiv2020

A Non-stop identity complex (NIC) supervises enterocyte identity and protects from pre-mature aging

A hallmark of aging is the inability of differentiated cells to maintain their identity. In the aged Drosophila midgut differentiated enterocytes (ECs) lose their identity, and the integrity of the midgut tissue and its homeostasis are impaired. To discover regulators of EC identity relevant to aging we performed an RNAi screen targeting 453 ubiquitin-related genes in fully differentiated ECs. Seventeen genes were identified, including the de-ubiquitinase Non-stop (Not/dUSP22; CG4166). Acute loss of Non-stop in young ECs phenotypically resembled aged ECs. Lineage tracing experiments established that Non-stop-deficient young ECs as well as wild-type aged ECs are no longer differentiated. Aging or acute loss of Non-stop also resulted in progenitor cell hyperproliferation and mis-differentiation, loss of gut integrity, and reduced organismal survival. Proteomic analysis unveiled that Non-stop maintains identity as part of a Non-stop identity complex (NIC) that contains E(y)2, Sgf11, Cp190, (Mod) mdg4, and Nup98. Transcriptionally, Non-stop ensured chromatin accessibility at EC genes, maintained an EC-specific gene expression signature, and silenced non-EC-relevant transcriptional programs. Within the NIC, Non-stop was required for stabilizing of NIC subunits. Upon aging, the levels of Non-stop and NIC subunits declined, and the large-scale organization of the nucleus was distorted. Maintaining youthful levels of Non-stop in wildtype aged ECs safeguarded the protein level of NIC subunits, restored the large-scale organization of the differentiated nucleus, and suppressed aging phenotypes and tissue integrity. Thus, the isopeptidase Non-stop, and NIC, supervise EC identity and protects from premature aging.

cell biology↗

bioRxiv2020

Simultaneous deep generative modeling and clustering of single cell genomic data

Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we proposed scDEC, a computational tool for single cell ATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks (GANs), and is capable of learning the latent representation and inferring the cell labels, simultaneously. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In the downstream applications, we demonstrated that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations.

bioinformatics↗