Biology subjects

Zhang, M. Q.

Publications and source records attributed to Zhang, M. Q..

At least 19 recordsLinked to original sources

bioRxiv2026

CHACAM: a cell-cell interaction-guided hierarchical attention model for high-precision cell identity annotation of scRNA-seq data in early C. elegans embryogenesis

Accurate cell identity annotation is essential for reconstructing cell lineages and gene-regulatory networks in embryogenesis. However, in Caenorhabditis elegans (C. elegans) single-cell RNA-sequencing (scRNA-seq) atlases, closely related sub-lineage cells often have near-identical transcriptomes, leading to merged labels and limiting downstream analyses. We present CHACAM (Cell-Cell Interaction-guided Hierarchical Attention-based Cell Allocation Model), a supervised machine learning framework that integrates gene expression, a curated C. elegans ligand-receptor (L-R) interaction database, and a high-resolution cell contact map to refine cell annotations. CHACAM learns interpretable "interaction signatures" that distinguish contacting vs. non-contacting neighbors and applies a triangulation strategy using reference cells to resolve ambiguous sister identities. Applied to the Tintori and Cole-Yanai embryo atlases, CHACAM achieves near-perfect annotation accuracy, resolving longstanding ambiguities of the AB lineage and other lineages. The model also identifies new discriminative marker genes and provides mechanistic hypotheses linking contact-specific signaling to fate specification.

bioinformatics↗

bioRxiv2024

Bridging the Dimensional Gap from Planar Spatial Transcriptomics to 3D Cell Atlases

Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation using real 3D ST and independent serial sectioning datasets demonstrates that SpatialZ accurately reconstructs virtual slices while preserving cell identities, gene expression patterns, and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells, a scale not feasible experimentally. This dense atlas enables unprecedented capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, and 3D mapping of query tissue sections. While currently validated for spatial transcriptomics, the underlying principles of SpatialZ could potentially be adapted for spatial proteomics, spatial metabolomics, and even spatial multi-omics. Validated through internal and external testing, our computationally generated atlas maintains biological accuracy, providing unprecedented resolution of spatial molecular landscapes and demonstrating the potential of computational approaches in advancing 3D ST.

bioinformatics↗

bioRxiv2024

Sequential-GAM constructs the single-cell geometric 3D genome structure

Three-dimensional structure of the chromatin is crucial for cell identity and gene regulation. However, how genome-wide hierarchical geometric structure is organized in single cells remains elusive. Here we developed Sequential-GAM (Sequential Genome Architecture Mapping) to construct the hierarchical geometric structure and estimated the radial position of chromosomes, compartments, subcompartments, and genes in single cells by capturing contiguous thin sections of the nucleus. We found that several epigenomic features distributed gradually from the nuclear center to the periphery, including histone modifications and subcompartments. Besides, we estimated the variance of hierarchical structure and revealed the dynamic radial position of subcompartments B1 and B2. Furthermore, we defined the quasi-stable TADs set (q-stable TADs), in which TADs maintain a relatively stable distance to each other. Interestingly, q-stable TADs revealed the correlation between the stability of chromatin structure and chromatin activity. Finally, we discovered that the radial distance and the stability of radial positions for genes in single cells were negatively correlated with their expressions. Taken together, Sequential-GAM is able to estimate 3D genomes geometric structure in single cell, and to reveal the structural stability and inter-cell heterogeneity.

genomics↗

bioRxiv2023

Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer

KRASG12C inhibitors including adagrasib and sortorasib have shown clinical promise in targeting KRASG12C-mutated lung cancers, however, most patients eventually develop drug resistance. In lung adenocarcinoma patients with co-occurring KRASG12C and STK11/LKB1 mutations, we found a high squamous gene signature at baseline significantly correlated with poor adagrasib response. Through integrative studies of Lkb1-deficient KRASG12Cand KrasG12D lung cancer mouse models and/or organoids treated with KRAS inhibitors, we found tumor cells invoked a lineage plasticity program: adeno-to-squamous transition (AST) that mediated resistance to KRAS inhibition. Transcriptomic and epigenomic analyses revealed {Delta}Np63 drives AST and modulates response to KRAS inhibition. We identified an intermediate high-plasticity cell state with distinct gene expression program marked by Krt6a upregulation. Notably, higher KRT6A expression at baseline correlated with shorter overall survival in KRAS-mutant patients receiving adagrasib. These data support the role of AST in KRAS inhibitor resistance and provide predictive biomarker for KRAS-targeted therapies in lung cancer.

cancer biology↗

bioRxiv2023

Chromatin-associated RNA Dictates the ecDNA Interactome in the Nucleus

Extrachromosomal DNA (ecDNA) promotes cancer by driving copy number heterogeneity and amplifying oncogenes along with functional enhancers. More recent studies suggest two additional mechanisms for further enhancing their oncogenic potential, one via forming ecDNA hubs to augment oncogene expression 1 and the other through acting as portable enhancers to trans- activate target genes 2. However, it has remained entirely elusive about how ecDNA explores the three-dimensional space of the nucleus and whether different ecDNA have distinct interacting mechanisms. Here, by profiling the DNA-DNA and DNA-RNA interactomes in tumor cells harboring different types of ecDNAs in comparison with similarly amplified homogenously staining regions (HSRs) in the chromosome, we show that specific ecDNA interactome is dictated by ecDNA-borne nascent RNA. We demonstrate that the ecDNA co-amplifying PVT1 and MYC utilize nascent noncoding PVT1 transcripts to mediate specific trans-activation of both ecDNA and chromosomal genes. In contrast, the ecDNA amplifying EGFR is weak in this property because of more efficient splicing to remove chromatin-associated nascent RNA. These findings reveal a noncoding RNA-orchestrated program hijacked by cancer cells to enhance the functional impact of amplified oncogenes and associated regulatory elements.

genomics↗

bioRxiv2022

Accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features

The three-dimensional (3D) genome organization influences diverse nuclear processes. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and Hi-C are powerful methods to study the 3D genome organization. However, ChIA-PET and Hi-C experiments are expensive, time-consuming, require tens to hundreds of millions of cells, and are challenging to optimize and analyze. Predicting ChIA-PET/Hi-C data using cheaper ChIP-Seq data and other easily obtainable features could be a useful alternative. It is well-established that the cohesin protein complex is a key determinant of 3D genome organization. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks (DNN), random forest, and gradient boosting, respectively, to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. Comprehensive tests on four cell lines show that the predictions of ChIPr correlate well with the original ChIA-PET data at the peak-level resolution and bin sizes of 25 and 5 Kbp. In addition, ChIPr can accurately capture most of the cell-type-dependent loops identified by ChIA-PET and Hi-C data. Rigorous feature testing indicated that genomic distance and RAD21 (a cohesin component) ChIP-Seq signals are the most important inputs for ChIPr in determining chromatin interaction strength. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac (enhancer/active chromatin mark) and H3K27me3 (inactive chromatin mark). The minimal ChIPr model performs comparably and requires a single experimental input: ChIP-Seq signals for RAD21. Integrative analysis revealed novel insights into the role of CTCF motif, its orientation, and CTCF binding on the prevalence and strength of cohesin-mediated chromatin interactions. These studies outline the general features of genome folding and open new avenues to analyze spatial genome organization in specimens with limited cell numbers.

genomics↗

bioRxiv2021

A data-driven method to learn a jump diffusion process from aggregate biological gene expression data

Dynamic models of gene expression are urgently required. Different from trajectory inference and RNA velocity, our method reveals gene dynamics by learning a jump diffusion process for modeling the biological process directly. The algorithm needs aggregate gene expression data as input and outputs the parameters of the jump diffusion process. The learned jump diffusion process can predict population distributions of gene expression at any developmental stage, achieve long-time trajectories for individual cells, and offer a novel approach to computing RNA velocity. Moreover, it studies biological systems from a stochastic dynamics perspective. Gene expression data at a time point, which is a snapshot of a cellular process, is treated as an empirical marginal distribution of a stochastic process. The Wasserstein distance between the empirical distribution and predicted distribution by the jump diffusion process is minimized to learn the dynamics. For the learned jump diffusion equation, its trajectories correspond to the development process of cells and stochasticity determines the heterogeneity of cells. Its instantaneous rate of state change can be taken as "RNA velocity", and the changes in scales and orientations of clusters can be noticed too. We demonstrate that our method can recover the underlying nonlinear dynamics better compared to parametric models and diffusion processes driven by Brownian motion for both synthetic and real world datasets. Our method is also robust to perturbations of data because it only involves population expectations.

bioinformatics↗

bioRxiv2020

Quantifying liquid-liquid phase separation property of chromatin under physiological conditions using Hi-MS and Hi-C

BackgroundLiquid-liquid phase separation (LLPS) is an important organizing principle for biomolecular condensation and chromosome compartmentalization. However, while many proteins have been reported to undergo LLPS, quantitative and global analysis of chromatin LLPS property remains absent. ResultsHere, by combing chromatin associated protein pull-down, quantitative proteomics and 1,6-hexanediol treatment, we developed Hi-MS and defined anti-1,6-HD index of chromatin-associated proteins (AICAP) to quantitative measurement of LLPS property of chromatin-associated proteins in their endogenous state and physiological abundance. The AICAP values were verified by previously reported experiments and were reproducible across different MS platforms. Moreover, the AICAP values were highly correlate with protein functions. Proteins act in active/regulatory biological process often exhibit low AICAP values, while proteins act in structural and repressed biological process often exhibit high AICAP values. We further revealed that chromatin organization changes more in compartment A than B, and the changes in chromatin organization at various levels, including compartments, TADs and loops are highly correlated to the LLPS properties of their neighbor nuclear condensates. ConclusionsOur work provided the first global quantitative measurement of LLPS properties of chromatin-associated proteins and higher-order chromatin structure, and demonstrate that the active/regulatory chromatin components, both protein (trans) and DNA (cis), exhibit more hydrophobicity-dependent LLPS properties than the repressed/structural chromatin components.

cell biology↗

bioRxiv2020

MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data

Distinguishing cell types and cell states is one of the fundamental questions in single-cell studies. Meanwhile, exploring the lineage relations among cells and finding the path and critical points in the cell fate transition are also of great importance. Existing unsupervised clustering methods and lineage trajectory reconstruction methods often face several challenges such as clustering data of arbitrary shapes, tracking precise trajectories and identifying critical points. Certain adaptive landscape approach1-3, which constructs a pseudo-energy landscape of the dynamical system, may be used to explore such problems. Thus, we propose Markov hierarchical clustering algorithm (MarkovHC), which reconstructs multi-scale pseudo-energy landscape by exploiting underlying metastability structure in an exponentially perturbed Markov chain4. A Markov process describes the random walk of a hypothetically traveling cell in the corresponding pseudo-energy landscape over possible gene expression states. Technically, MarkovHC integrates the tasks of cell classification, trajectory reconstruction, and critical point identification in a single theoretical framework consistent with topological data analysis (TDA)5. In addition to the algorithm development and simulation tests, we also applied MarkovHC to diverse types of real biological data: single-cell RNA-Seq data, cytometry data, and single-cell ATAC-Seq data. Remarkably, when applying to single-cell RNA-Seq data of human ESC derived progenitor cells6, MarkovHC not only could successfully identify known cell types, but also discover new cell types and stages. In addition, when using MarkovHC to analyze single-cell RNA-Seq data of human preimplantation embryos in early development7, the hierarchical structure of the lineage trajectories was faithfully reconstituted. Furthermore, the critical points representing important stage transitions had also been identified by MarkovHC from early gastric cancer data8. In summary, these results demonstrate that MarkovHC is a powerful tool based on rigorous metastability theory to explore hierarchical structures of biological data, to identify a cell sub-population (basin) and a critical point (stage transition), and to track a lineage trajectory (differentiation path). HighlightsO_LIMarkovHC explores the topology hierarchy in high-dimensional data. C_LIO_LIMarkovHC can find clusters (basins) and cores (attractors) of clusters in different scales. C_LIO_LIThe trajectory of state transition (transition paths) and critical points in the process of state transition (critical points) among clusters can be tracked. C_LIO_LIMarkovHC can be applied on diverse types of single-cell omics data. C_LI

bioinformatics↗

bioRxiv2020

MyoD is a structure organizer for emergent property of 3D genome architecture in muscle cells

The genome is not a linear molecule of DNA randomly folded in the nucleus, but exists as an organized, three-dimensional (3D) dynamic architecture. Intriguingly, it is now clear that each cell type has a unique and characteristic 3D genome organization that functions in determining cell identity during development. A currently challenging basic question is how cell-type specific 3D genome structures are established during development. Herein, we analyzed 3D genome structures in primary myoblasts and myocytes from MyoD knockout (MKO) and wild type (WT) mice and discovered that MyoD, a pioneer transcription factor (TF), can function as a "genome organizer" that specifies the proper 3D genome architecture unique to muscle cell development. Importantly, we genetically demonstrate that H3K27ac is insufficient for establishing MyoD-induced chromatin loops in muscle cells. The establishment of MyoDs "architectural role" should have profound impacts on advancing understanding of other pioneer transcription factors in orchestrating lineage specific 3D genome organization during development in a potentially very large number of cell types in diverse organisms.

genomics↗

bioRxiv2020

3D Genome Analysis Identifies Enhancer Hijacking Mechanism for High-Risk Factors in Human T-Lineage Acute Lymphoblastic Leukemia

Recent studies have demonstrated that 3D genome alterations play important roles in tumorigenesis1-3, including the development of hematological malignancies4-7. However, how such alterations may provide key insights into T-lineage acute lymphoblastic leukemia (T-ALL) patients is largely unknown. Here, we report integrated analyses of 3D genome alterations and differentially expressed genes (DEGs) in 18 newly diagnosed T-ALL patients and 4 healthy T cell controls. We found that 3D genome organization at the compartment, topologically associated domains (TAD) and loop levels as well as the gene expression profiles could hierarchically classify different subtypes of T-ALL according to the T cell differentiation trajectory. Alterations in the 3D genome were associated with nearly 45% of the upregulated genes in T-ALL. We also identified 34 previously unrecognized translocations in the noncoding regions of the genome and 44 new loops formed between translocated chromosomes, including translocation-mediated enhancer hijacking of the HOXA cluster. Our analysis demonstrated that T-ALLs with HOXA cluster overexpression were heterogeneous clinical entities, and ectopic expressions of the HOXA11-A13 genes, but not other genes in the HOXA cluster, were associated with immature phenotypes and poor outcomes. Our findings highlight the potentially important roles of 3D genome alterations in the etiology and prognosis of T-ALL.

genomics↗

bioRxiv2019

Widespread transcriptional responses to the thermal stresses are prewired in human 3D genome

Temperature changes is one of the most common environmental stress that consequences with massive phenotypic responses for almost all the life forms. The dysregulation of heat shock (HS) response genes had been found associated with various severe diseases, including cancer. Although the HS response has been well studied in animal cells, it remains elusive whether or not the cells response to cold shock (CS) similarly. Here, we comprehensively compared the changes of gene expression, epigenetic marks (H3K4me3 and H3K27ac), binding of genome architecture proteins (CTCF, SMC3 and Pol II) and chromatin conformation after HS and CS in human cells. Widespread expression change was observed after both HS and CS. Remarkably, we identified distinguished characters in those thermal stress responded genes at nearly all levels of chromatin architecture, i.e, the compartment, topological associated domain, chromatin loops and transcription elongation regulators, in the normal condition. However, the global chromatin architecture remains largely stable after both CS and HS. Interestingly, the thermal stresses responded genes are prone to spatial clustering even before the temperature changes. Our data suggested that the transcriptional response to the thermal stresses maybe independent to the changes of the high-level chromatin architecture, e.g., compartments and TAD, while it may be more dependent on the precondition of the chromatin and epigenetic settings at the normal condition.

genomics↗

bioRxiv2019

Imaging chromatin interactions at sub-kilobase resolution Via Tn5-FISH

There is increasing interest in understanding how the three-dimensional organization of the genome is regulated. Different strategies have been employed to identify chromatin interactions genome wide. However, due to the current limitations in resolving genomic contacts, visualization and validation of these genomic loci with sub-kilobase resolution remain the bottleneck for many years. Here, we describe Tn5 transposase-based Fluorescence in situ Hybridization (Tn5-FISH), a Polymerase Chain Reaction (PCR)-based, cost-effective imaging method, which achieved the co-localization of genomic loci with sub-kilobase resolution, to fine dissect genome architecture at sub-kilobase resolution and to verify chromatin interactions detected by Chromatin Configuration Capture (3C)-derivative methods. Especially, Tn5-FISH is very useful to verify short-range chromatin interactions inside of contact domain and Topologically Associated Domain (TAD). It also offers one powerful molecular diagnosis tool for clinical detection of cytogenetic changes in cancers.

cell biology↗

bioRxiv2019

Lipid rafts increase to facilitate ectoderm lineage specification of differentiating embryonic stem cells

Lipid rafts are packed nanoscopic domains on plasma membrane and essential signalling platforms for transducing extracellular stimuli into cellular responses. Although depletion of raft component glycoshpingolipids causes abnormality particularly in ectoderm layer formation, it remains unclear whether rafts play a role in lineage determination, a critical but less-known stage in lineage commitment. Here, inducing mouse embryonic stem cell (mESC) differentiation with retinoic acid (RA), we observed lipid rafts increased since early stage, especially in ectoderm-like cells. Stochastic optical reconstruction microscopy characterized at super-resolution the distinct raft features in mESCs and the derived differentiated cells. Furthermore, RA-induced commitment of ectoderm-like cells was significantly diminished not only by genetic ablation of rafts but by applying inhibitor for glycosphingolipids or cholesterol at early differentiation stages. Meanwhile, raft inhibition delayed RA-induced pluripotency exit, an early step required for differentiation. Therefore, lipid rafts increase and facilitate ectoderm lineage specification as well as pluripotency exit during mESC differentiation.

cell biology↗

bioRxiv2018

Integrating Hi-C and FISH data for modeling 3D organizations of chromosomes

The new advances in various experimental techniques that provide complementary in-formation about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. In this paper, we propose GEM-FISH, a first method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes for which both Hi-C and FISH data were available have demonstrated that GEM-FISH can reconstruct the 3D models of chromosomes with more accurate spatial organizations of TADs and compartments than using only Hi-C data. In addition, GEM-FISH can accurately capture the spatial proximity of loop loci and the colocalization of loci from the same sub-compartments. Moreover, our reconstructed 3D models of chromosomes revealed novel patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation. All these results demonstrated that, through integrating both Hi-C and FISH data into a unified framework, GEM-FISH can provide a better tool for modeling the 3D organizations of chromosomes than using the Hi-C data alone.

bioinformatics↗

bioRxiv2018

Germline genetics encode the resistance, risk, and lymphatic metastasis of triple-negative breast cancer in the southern Chinese population

Early identification of the risk for triple-negative breast cancer (TNBC) at the asymptomatic phase could lead to better prognosis. Here we developed a machine learning method to quantify systematic impact of all rare germline mutations on each pathway. We collected 106 TNBC patients and 287 elder healthy women controls. The spectra of activity profiles in multiple pathways were mapped and most pathway activities exhibited globally suppressed by the portfolio of individual germline mutations in TNBC patients. Accordingly, all individuals were delineated into two types: A and B. Type A patients could be differentiated from controls (AUC = 0.89) and sensitive to BRCA1/2 damages; Type B patients can be also differentiated from controls (AUC = 0.69) but probably being protected from BRCA1/2 damages. Further we found that Individuals with the lowest activity of selected pathways had extreme high relative risk (up to 21.67 in type A) and increased lymph node metastasis in these patients. Our study showed that genomic DNA contains information of unimaginable pathogenic factors. And this information is in a distributed form that could be applied to risk assessment for more cancer types. SignificanceWe identified individuals who are more susceptible to triple negative breast cancer. Our method performs much better than previous assessments based on BRCA1/2 damages, even polygenic risk scores. We disclosed previously unimaginable pathogens in a distributed form on genome and extended risk prediction to scenarios for other cancers.

cancer biology↗

bioRxiv2018

Regulation by competition: a hidden layer of gene regulatory network

Molecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

systems biology↗

bioRxiv2017

Evolutionary outcomes of plasmid-CRISPR conflicts in an opportunistic pathogen

Enterococcus faecalis is a Gram-positive bacterium that natively colonizes the human gastrointestinal tract and opportunistically causes life-threatening infections. Multidrug-resistant (MDR) E. faecalis strains have emerged that are replete with mobile genetic elements (MGEs). Non-MDR E. faecalis strains frequently possess CRISPR-Cas systems, which reduce the frequency of mobile genetic element (MGE) acquisition. We demonstrated in previous studies that E. faecalis populations can transiently maintain both a functional CRISPR-Cas system and a CRISPR-Cas target. In this study, we used serial passage and deep sequencing to analyze these populations. In the presence of antibiotic selection for the plasmid, mutants with compromised CRISPR-Cas defense and enhanced ability to acquire a second antibiotic resistance plasmid emerged. Conversely, in the absence of selection, the plasmid was lost from wild-type E. faecalis populations, but not E. faecalis populations that lacked the cas9 gene. Our results indicate that E. faecalis CRISPR-Cas can become compromised under antibiotic selection, generating populations with enhanced abilities to undergo horizontal gene transfer. ImportanceEnterococcus faecalis is a leading cause of hospital-acquired infections and disseminator of antibiotic resistance plasmids among Gram-positive bacteria. We have previously shown that E. faecalis strains with an active CRISPR-Cas system can prevent plasmid acquisition and thus limit the transmission of antibiotic resistance determinants. Yet, CRISPR-Cas was not a perfect barrier. In this study, we observed populations of E. faecalis with transient co-existence of CRISPR-Cas and one of its plasmid targets. Our experimental data demonstrate that antibiotic selection results in compromised E. faecalis CRISPR-Cas function, thereby facilitating the acquisition of additional resistance plasmids by E. faecalis.

microbiology↗