Biology subjects

Nie, Q.

Publications and source records attributed to Nie, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Multimodal Analyses of Stable Vitiligo Skin Identify Tissue Interactions That Control Disease Persistence

Vitiligo is an autoimmune skin disease that is characterized by the progressive destruction of melanocytes by autoreactive CD8+ T cells. Melanocyte destruction in active vitiligo is mediated by CD8+ T cells but why white patches in stable disease persist is poorly understood. The interaction between immune cells, melanocytes, and keratinocytes in situ in human skin has been difficult to study due to the lack of proper tools. Here, we combine non-invasive multiphoton microscopy (MPM) imaging and single-cell RNA sequencing (scRNA-seq) to identify distinct subpopulations of keratinocytes in lesional skin of stable vitiligo patients. We show that these keratinocytes are enriched in lesional vitiligo skin and differ in metabolism, an observation corroborated by both MPM and scRNA-seq. Systematic investigation of cell-cell communication show that CXCL is the prominent signaling change in this small population of keratinocytes, which secrete CXCL9 and CXCL10 to create local inflammatory cytokine loops with T cells to drive stable vitiligo persistence. Pseudotemporal dynamics analyses predict an alternative keratinocyte differentiation trajectory that generates this new population of keratinocytes in vitiligo skin. In summary, we couple advanced imaging with transcriptomics and bioinformatics to discover cellcell communication networks and keratinocyte cell states that perpetuate inflammation and prevent repigmentation. One Sentence SummaryCommunication between keratinocytes, immune cells, and melanocytes maintain depigmented patches in stable vitiligo.

molecular biology↗

bioRxiv2021

De novo assembly of 20 chickens reveals the undetectable phenomenon for thousands of core genes on sub-telomeric regions

The gene numbers and evolutionary rates of birds were assumed to be much lower than that of mammals, which in sharp contrast to the huge species number and morphological diversity of birds. It is very necessary to construct a complete avian genome and analyze its evolution.We constructed a chicken pan-genome from 20 de novo genome assemblies with high sequencing depth, newly identified 1,335 protein-coding genes and 3,011 long noncoding RNAs. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were accidentally measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal sub-telomeric regions, surrounded with extremely high proportions of tandem repeats, and strongly blocked DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had three-fold elevated substitution rates than known ones, updating the evolutionary rates of birds. Our study provides a framework for constructing a better chicken genome, which will contribute towards the understanding of avian evolution and improvement of poultry breeding.

genomics↗

bioRxiv2021

Pre-neoplastic stromal cells drive BRCA1-mediated breast tumorigenesis

Women with germline mutations in BRCA1 (BRCA1+/mut) have increased risk for developing hereditary breast cancer1, 2. Cancer initiation in BRCA1+/mut is associated with pre-malignant changes in the breast epithelium including altered differentiation3-5, proliferative stress6 and genomic instability7. However, the role of the epithelium- associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here, we show that the pre-malignant stromal niche promotes epithelial proliferation and BRCA1- driven cancer initiation in trans. Using single-cell RNAseq (scRNAseq) analysis of human pre-neoplastic BRCA1+/mut and control breast tissues, we show that stromal cells provide numerous pro-proliferative paracrine signals inducing epithelial proliferation. We identify a subpopulation of pre-cancer associated fibroblasts (pre-CAFs) that produces copious amounts of pro-tumorigenic factors including matrix metalloproteinase 3 (MMP3)8, 9, and promotes BRCA1-driven tumorigenesis in vivo. Our gene-signature analysis and mathematical modeling of epithelial differentiation reveals that stromal-induced proliferation leads to the accumulation of luminal progenitor cells with altered differentiation, and thus contributes to increased breast cancer risk in BRCA1+/mut. Our results demonstrate how alterations in cell-cell communication can induce imbalances in epithelial homeostasis ultimately leading to cancer initiation. We anticipate our results to form the foundation for novel disease monitoring and therapeutic strategies to improve patient management in hereditary breast cancer. For example, pre-CAF specific proteins may serve as biomarkers for pre-cancerous disease initiation to inform whether radical bilateral mastectomy is needed. In addition, MMP inhibitors could be re-indicated for primary cancer prevention treatment in women with high-risk BRCA1 mutations.

cancer biology↗

bioRxiv2021

Single-cell analysis of basal cell carcinoma reveals heat shock proteins promote tumor growth in response to WNT5A-mediated inflammatory signals

How basal cell carcinoma (BCC) interacts with its tumor microenvironment to promote growth is unclear. Here we use singe-cell RNA sequencing to define the human BCC ecosystem and discriminate between normal and malignant epithelial cells. We identify spatial biomarkers of both tumors and their surrounding stroma that reinforce the heterogeneity of each tissue type. Combining pseudotime, RNA velocity, cellular entropy, and regulon analysis in stromal cells reveal a cancer-specific rewiring of fibroblasts where STAT1, TGF-{beta}, and inflammatory signals induce a non-canonical WNT5A program that maintains the stromal inflammatory state. Cell-cell communication modeling suggests that tumors respond to the sudden burst of fibroblast-specific inflammatory signaling pathways by producing heat shock proteins, which we validated in situ. Finally, dose-dependent treatment with an HSP70 inhibitor suppresses in vitro BCC cell growth and Hedgehog signaling and in vivo tumor growth in a BCC mouse model, validating HSP70s essential role in tumor growth and reinforcing the critical nature of tumor microenvironment crosstalk in BCC progression.

cancer biology↗

bioRxiv2021

Single-cell transcriptomic analysis unveils spinal motor neuron subtype diversity underpinning the water-to-land transition in vertebrates

Spinal motor neurons (MNs) integrate sensory stimuli and brain commands to generate motor movements in vertebrates. Distinct MN populations and their diversity has long been hypothesized to co-evolve with motor circuit to provide the neural basis from undulatory to ambulatory locomotion during aquatic-to-terrestrial transition of vertebrates. However, how these subtypes are evolved remains largely enigmatic. Using single-cell transcriptomics, we investigate heterogeneity in mouse MNs and discover novel segment-specific subtypes. Among limb-innervating MNs, we reveal a diverse neuropeptide code for delineating putative motor pool identities. We further uncovered that axial MNs are subdivided by three conserved and molecularly distinct subpopulations, defined by Satb2, Nr2f2 or Bcl11b expression. Although axial MNs are conserved from cephalochordates to humans, subtype diversity becomes prominent in land animals and appears to continue evolving in humans. Overall, our study provides a unified classification system for spinal MNs and paves the way towards deciphering how neuronal subtypes are evolved.

developmental biology↗

bioRxiv2021

Multiple morphogens and rapid elongation promote segmental patterning during development

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately-sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development. Author SummaryIn segmental pattern formation, chemical gradients control gene expression in a concentration-dependent manner to specify distinct gene expression domains. Despite the stochasticity inherent to such biological processes, precise and accurate borders form between segmental gene expression domains. Previous work has revealed synergy between gene regulation and cell sorting in sharpening borders that are initially rough. However, it is still poorly understood how size and boundary sharpness of multiple segments are regulated in a tissue that changes dramatically in its morphology as the embryo develops. Here we develop a stochastic multiscale cell-base model to investigate these questions. Two novel strategies synergize to promote accurate segment formation, a combination of long- and short-range morphogens plus rapid tissue convergence, with one responsible for pattern initiation and the other enabling pattern refinement.

developmental biology↗

bioRxiv2021

Dissecting Transition Cells from Single-Cell Transcriptome Data through Multiscale Stochastic Dynamics

Advances of single-cell technologies allow scrutinizing of heterogeneous cell states, however, analyzing transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique for the underlying stochastic dynamical systems that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transition, and distinguishes meta-stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using the coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. Mathematical analysis reveals consistency of the method with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms and benchmarking with seven existing tools, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

bioinformatics↗

bioRxiv2021

Modeling of histone modifications reveals formation mechanism and function of bivalent chromatin

Bivalent chromatin is characterized by occupation of both activating histone modifications and repressive histone modifications. While bivalent chromatin is known to link with many biological processes, the mechanisms responsible for its multiple functions remain unclear. Here, we develop a mathematical model that involves antagonistic histone modifications H3K4me3 and H3K27me3 to capture the key features of bivalent chromatin. Three necessary conditions for the emergence of bivalent chromatin are identified, including advantageous methylating activity over demethylating activity, frequent noise conversions of modifications, and sufficient nonlinearity. The first condition is further confirmed by analyzing the experimental data from a recent study. Investigation of the composition of bivalent chromatin reveals that bivalent nucleosomes carrying both H3K4me3 and H3K27me3 account for no more than half of nucleosomes at the bivalent chromatin domain. We identify that bivalent chromatin not only allows transitions to multiple states but also serves as a stepping stone to facilitate a step-wise transition between repressive chromatin state and activating chromatin state, and thus elucidate crucial roles of bivalent chromatin in mediating phenotypical plasticity during cell fate determination.

systems biology↗

bioRxiv2021

SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration

MotivationSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modelling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies, and high sensitivity to confounding factors from various sources. ResultsHere we propose a new deep generative model framework, named SAILER, for analysing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: Clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. AvailabilityThe software is publicly available at https://github.com/uci-cbcl/SAILER Contactjingz31@uci.edu and xhx@uci.edu

bioinformatics↗

bioRxiv2020

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine. Author summaryReconstructing gene regulatory network (GRN) is an essential question in systems biology. The lack of temporal information in sample-based transcriptomic data leads to a major challenge for inferring GRN and its translation to precision medicine. To address the above challenge, we propose to decode the latent temporal information underlying cancer progression via ordering patient samples based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer GRNs from sample-based transcriptomic data of cancer patients. The advantages of our method include its capability to infer causal GRNs (with directed and signed edges) and its robustness to the measurement variability in the data. Performance evaluation using both simulated data and real data demonstrate that our method outperforms other existing methods in both pseudotime inference and GRN inference. Our method is then applied to reconstruct EMT regulatory networks in bladder cancer and to identify key regulators underlying progression of breast cancer. Importantly, the predicted key regulators/interactions are experimentally validated. Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanisms underlying cancer progression and to discovery key regulators which may be used as candidate drug targets.

systems biology↗

bioRxiv2020

Age-specific gene signatures underlying the transcriptomes and functional connectomes of human cerebral cortex

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method TCA (Transcriptome-connectome Correlation Analysis), which integrates the brain functional MR magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood, and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value <2.00e-3) and myelination (P-value <1.00e-3), two key brain structural features that vary in accordance with brain development. In addition to the molecular underpinning of age-related brain functions, the ASC gene signatures allow delineation of the molecular mechanisms of neuropsychiatric disorders, such as the regulation between ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets.

neuroscience↗

bioRxiv2020

Divergent resistance mechanisms to immunotherapy explains response in different skin cancers.

The advent of immune checkpoint therapy for metastatic skin cancer has greatly improved patient survival. However, most skin cancer patients are refractory to checkpoint therapy, and furthermore the differential intra-immune signaling between cancers driving the response to checkpoint therapy remains uncharacterized. When comparing the immune transcriptome in the tumor microenvironment of melanoma and basal cell carcinoma (BCC), we found that the presence of memory B cells and macrophages negatively correlate when stratifying patients by response, with memory B cells more present in responders. Inhibitory immune signaling is reduced in melanoma responders and is increased in BCC responders. We further explored the relationships between macrophages, B cells and response to checkpoint therapy by developing a stochastic differential equation model which qualitatively agrees with the data analysis. Our model predicts BCC to be more refractory to checkpoint therapy than melanoma. We show differences in tumor progression and regression that could serve as a diagnostic and predict the optimal ratio of macrophages and memory B cells for successful treatment.

cancer biology↗

bioRxiv2020

UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

MotivationFor many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. ResultsHere we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 10~30% improvement over traditional thermodynamic models and 14% improvement over other learning-based methods. It achieves an F1 score of 0.91 on base pair prediction accuracy on an RNA structure prediction benchmark dataset. UFold is also fast with an inference time about 160ms per sequence up to 1600bp length. We provide an online web server that implements UFold for RNA structure prediction and is made freely available. AvailabilityAn online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold. Contactxhx@uci.edu

bioinformatics↗

bioRxiv2020

Inference and analysis of cell-cell communication using CellChat

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

bioinformatics↗

bioRxiv2020

Predicting transcription factor binding in single cells through deep learning

Characterizing genome-wide binding profiles of transcription factor (TF) is essential for understanding many biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining binding profiles at a single cell level remains elusive. Here we report scFAN (Single Cell Factor Analysis Network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pre-trained on genome-wide bulk ATAC-seq, DNA sequence and ChIP-seq data, and utilizes single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by studying sequence motifs enriched within predicted binding peaks and investigating the effectiveness of predicted TF peaks for discovering cell types. We develop a new metric "TF activity score" to characterize each cell, and show that the activity scores can reliably capture cell identities. The method allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.

bioinformatics↗

bioRxiv2019

Feedback control of morphogen gradient scale

Gradients of the morphogen decapentaplegic (Dpp) pattern Drosophila wing imaginal discs, establishing gene expression boundaries at specific locations. As discs grow, Dpp gradients expand, keeping relative boundary positions approximately stationary. Such scaling fails in mutants for Pentagone (pent), a gene repressed by Dpp that encodes a diffusible protein that expands Dpp gradients. Although these properties fit a recent mathematical model of automatic gradient scaling, we show here that Pent lacks a property essential to that model--the ability to spread with minimal loss throughout the morphogen field. Instead, Pents actions appear confined to within a few cell diameters of its site of synthesis, and can be phenocopied by manipulating non-diffusible targets of Pent strictly within the Pent expression domain. Through genetic manipulation and mathematical modeling we develop an alternative model of scaling, driven by feedback down-regulation of Dpp receptors and co-receptors. Among the models predictions is a size limit beyond which scaling fails--something we observe directly in wing discs.

developmental biology↗

bioRxiv2019

Rapidly cycling stem cells regenerate the intestine independent of Lgr5high cells

The +4 cells in intestinal crypts are DNA damage-resistant and contribute to regeneration. However, their exact identity and the mechanism underlying +4 cell-mediated regeneration remain unclear. Using lineage tracing, we show that cells marked by an Msi1 reporter (Msi1+) are enriched at the +4 position in intestinal crypts and exhibit DNA damage resistance. Single-cell RNA sequencing reveals that the Msi1+ cells are heterogeneous with the majority being intestinal stem cells (ISCs). The DNA damage-resistant subpopulation of Msi1+ cells is characterized by low-to-negative Lgr5 expression and is more rapidly cycling than Lgr5high radio-sensitive crypt base columnar stem cells (CBCs); they enable fast repopulation of the intestinal epithelium independent of CBCs that are largely depleted after irradiation. Furthermore, relative to CBCs, Msi1+ cells preferentially produce Paneth cells during homeostasis and upon radiation repair. Together, we demonstrate that the DNA damage-resistant Msi1+ cells are rapidly cycling ISCs that maintain and regenerate the intestinal epithelium.

cell biology↗

bioRxiv2019

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Our knowledge of transcriptional heterogeneities in epithelial stem/progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem/progenitor potential, but a non-biased dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA-sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell transcriptional states in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses produce a quasi-linear differentiation hierarchy where basal cells progress from Col17a1high/Trp63high state to early response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal states. Our study provides a systematic view of epidermal cellular dynamics supporting a revised \"hierarchical-lineage\" model of homeostasis.

developmental biology↗