Biology subjects

Miao, Z.

Publications and source records attributed to Miao, Z..

70 records · Page 4Linked to original sources

bioRxiv2019

Integrative single-cell and bulk RNA-seq analysis in human retina identified cell type-specific composition and gene expression changes for age-related macular degeneration

Age-related macular degeneration (AMD) preferentially affects distinct cell types and topographic regions in retina. To characterize the impact of AMD on gene expression changes across retinal cell types and regions, we generated both single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data from macular and peripheral retina in postmortem human donors with and without AMD. The scRNA-seq data revealed 11 major cell types with many previously reported AMD risk genes showing substantial cell type and region specificity. Cell type proportional changes with advancing AMD stage were significant for Muller glia, rods, astrocytes, microglia and endothelium.

genomics↗

bioRxiv2019

Single cell transcriptomics identifies a unique adipocyte population that regulates bone marrow environment

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells (MSCs), the true identity of MSCs and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing single cell transcriptome analysis, we identified MSCs and delineated their bi-lineage differentiation paths in young, adult and aging mice. Among several newly discovered mesenchymal subpopulations, one is a distinct population of adipose-lineage cells that we named marrow environment regulating adipose cells (MERAs). MERAs are non-proliferative, post-progenitor cells that express many mature adipocyte markers but are devoid of lipid droplets. They are abundant in the bone marrow of young mice, acting as pericytes and stromal cells that form numerous connections among themselves and with other cells inside bone, including endothelial cells. Genetic ablation of MERAs disrupts marrow vessel structure, promotes de novo bone formation. Taken together, MERAs represent a unique population of adipose lineage cells that exist only in the bone marrow with critical roles in regulating bone and vessel homeostasis.

cell biology↗

bioRxiv2019

Rora regulates activated T helper cells during inflammation

The transcription factor Rora has been shown to be important for the development of ILC2 and the regulation of ILC3, macrophages and Treg cells. Here we investigate the role of Rora across CD4+ T cells, both in vitro as well as in the context of several in vivo type 2 infection models. We dissect the function of Rora using overexpression and a CD4-conditional Rora-knockout mouse, as well as a RORA-reporter mouse. We establish the importance of Rora in CD4+ T cells for controlling lung inflammation induced by Nippostrongylus brasiliensis infection, and have measured the effect on downstream genes using RNA-seq. Using a systematic stimulation screen of CD4+ T cells, coupled with RNA-seq, we identify upstream regulators of Rora, most importantly IL-33 and CCL7. Our data suggest that Rora is a negative regulator of the immune system, possibly through several downstream pathways, and is under control of the local microenvironment.

immunology↗

bioRxiv2019

The role of lipoprotein subfractions in coronary artery disease: A Mendelian randomization study

Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. In this study, we first used the LD score regression to estimate the genetic correlation of 77 subfraction traits with traditional lipid profile and identified 27 traits that may involve distinct genetic mechanisms. We then used Mendelian randomization (MR) to estimate the causal effect of these traits on the risk of CAD. In univariable MR, the concentration and content of medium high-density lipoprotein (HDL) particles showed a protective effect against coronary artery disease. The effect was not attenuated in multivariable MR that adjusted for traditional lipid profile. The multivariable MR analyses also found that small HDL particles and smaller mean HDL particle diameter may have a protective effect. We identified four genetic markers for HDL particle size and CAD.

genetics↗

bioRxiv2019

Computational analysis of transcriptome signature repurposes low dose trifluoperazine for the treatment of fragile X syndrome in mouse model

Fragile X syndrome (FXS), caused by mutations in fragile X mental retardation 1 gene (FMR1), is a prevailing genetic disorder of intellectual disability and autism. Currently, there is no efficacious medication for FXS. Here, we use transcriptome landscape as a holistic molecular phenotype/endpoint to identify potential therapeutic intervention. Through in silico screening with public gene signature database, computational analysis of transcriptome profile in Fmr1 knockout (KO) neurons predicts therapeutic value of an FDA-approved drug trifluoperazine. Through experimental validation, we find that systemic administration of low dose trifluoperazine at 0.05 mg/kg attenuates multiple FXS- and autism-related behavioral symptoms. Moreover, computational analysis of transcriptome alteration caused by trifluoperazine suggests a new mechanism of action against PI3K (Phosphatidylinositol-4,5-bisphosphate 3-kinase) activity. Consistently, trifluoperazine suppresses PI3K activity and its down-stream targets Akt (protein kinase B) and S6K1 (S6 kinase 1) in neurons. Further, trifluoperazine normalizes the aberrantly elevated activity of Akt and S6K1 and enhanced protein synthesis in FXS mouse. In conclusion, our data demonstrate promising value of gene signature-based computation in identification of therapeutic strategy and repurposing drugs for neurological disorders, and suggest trifluoperazine as a potential practical treatment for FXS.

neuroscience↗

bioRxiv2019

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and single-nucleus RNA-seq (snRNA-seq) data, Bisque was able to replicate previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. Bisque requires a single-cell reference dataset that reflects physiological cell type composition and can further leverage datasets that includes both bulk and single cell measurements over the same samples for improved accuracy. We further propose an additional mode of operation that merely requires a set of known marker genes. Bisque is available as an R package at: https://github.com/cozygene/bisque.

bioinformatics↗

bioRxiv2019

scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputation

High-throughput single-cell RNA-seq (scRNA-seq) data contains excess zero values, including those of genes not expressed in the cell, and those produced due to dropout events. Existing imputation methods do not distinguish these two types of zeros. We present a modest imputation method scRecover to only impute the dropout zeros. It estimates the zero dropout probability of each gene in each cell, and predicts the number of truly expressed genes in the cell. scRecover is combined with other imputation methods like scImpute, SAVER and MAGIC to fulfil the imputation. Down-sampling experiments show that it recovers dropout zeros with higher accuracy and avoids over-imputing true zero values. Experiments on real data illustrate scRecover improves downstream analysis and visualization.

bioinformatics↗

bioRxiv2019

Decoding the development of the blood and immune systems during human fetal liver haematopoiesis

Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells/multipotent progenitors (HSC/MPPs), yet remains poorly defined in humans. Using single cell transcriptome profiling of ~133,000 fetal liver and ~65,000 fetal skin and kidney cells, we identify the repertoire of blood and immune cells in first and early second trimesters of development. From this data, we infer differentiation trajectories from HSC/MPPs, and evaluate the impact of tissue microenvironment on blood and immune cell development. We predict coupling of mast cell differentiation with erythro-megakaryopoiesis and identify physiological erythropoiesis in fetal skin. We demonstrate a shift in fetal liver haematopoietic composition during gestation away from being erythroid-predominant, accompanied by a parallel change in HSC/MPP differentiation potential, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a valuable reference for understanding and harnessing the therapeutic potential of HSC/MPPs.

developmental biology↗

bioRxiv2019

Comprehensive circular RNA expression profiles in intermuscular fat from lean and obese pigs

Circular RNA (circRNA) plays an important regulatory role in development and differentiation. Intermuscular fat in pork affects the tenderness and juiciness of the meat. In this study, we investigated the performances of Landrace (lean) and Jinhua (obese) pigs at the fattening period and explored the expression profile of circRNAs in intermuscular fat from the two breeds by Illumina high-throughput sequencing. we identified 5 548 circRNAs, specifically 2 651 (47.78%) in the Jinhua pigs, and 2 897 (52.22%) in the Landrace pigs. A totale of 809 differentially expressed circRNAs were observed between the the Jinhua and Landrace pigs, but only 29 of these circRNAs showed significant difference (19 upregulated and 10 downregulated). All 1 306 unigenes and 27 differentially expressed unigenesinvolved in lipid transport and metabolism; replication, recombination and repair; and signaling pathway were annotated. A total of 550 target miRNAs were perfect seed matches and 20 522 target genes were foundBy RNAhybrid and miRanda software prediction. Results from real-time quantitative PCR also confirmed the differential expression of 13 mRNAs between the two pig breeds. This study provides comprehensive expression profiles of circRNAs in Sus scrofa adipose metabolism and development, which can be used to clarify their functions. Summary statementThe paper explored the expression profile of circRNAs in intermuscular fat from Landrace and Jinhua pigs at the fattening period, to provide insights into circRNAregulation in animal adipose metabolism.

molecular biology↗

bioRxiv2018

Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

All single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

genomics↗

bioRxiv2018

A comparison of visual features used by humans and machines to classify wildlife

In our quest to develop more intelligent machines, knowledge of the visual features used by machines to classify objects shall be helpful. The current state of the art in training machines to classify wildlife species from camera-trap data is to employ convolutional neural networks (CNN) encoded within deep learning algorithms. Here we report on results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We then used a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that are in most, but not all, cases similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species, and we then interpret the features in the image where the strongest responses occur. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated how images that were not part of the training set fell within our dendrogram when these images were one of the 20 species \"known\" to our CNN in contrast to where they fell when these images were \"unknown\" to our CNN.

ecology↗

bioRxiv2018

SNP Variable Selection by Generalized Graph Domination

High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants. The resulting datasets share a number of common characteristics that might lead to poor generalization capacity. Concerns include noise accumulated due to the large number of predictors, sparse information regarding the p >> n problem, and overfitting and model mis-identification resulting from spurious collinearity. Additionally, complex correlation patterns are present among variables. As a consequence, reliable variable selection techniques play a pivotal role in predictive analysis, generalization capability, and robustness in clustering, as well as interpretability of the derived models.\n\nK-dominating set, a parameterized graph-theoretic generalization model, was used to model SNP (single nucleotide polymorphism) data as a similarity network and searched for representative SNP variables. In particular, each SNP was represented as a vertex in the graph, (dis)similarity measures such as correlation coefficients or pairwise linkage disequilibrium were estimated to describe the relationship between each pair of SNPs; a pair of vertices are adjacent, i.e. joined by an edge, if the pairwise similarity measure exceeds a user-specified threshold. A minimum K-dominating set in the SNP graph was then made as the smallest subset such that every SNP that is excluded from the subset has at least k neighbors in the selected ones. The strength of\n\nk-dominating set selection in identifying independent variables, and in culling representative variables that are highly correlated with others, was demonstrated by a simulated dataset. The advantages of k-dominating set variable selection were also illustrated in two applications: pedigree reconstruction using SNP profiles of 1,372 Douglas-fir trees, and species delineation for 226 grasshopper mouse samples. A C++ source code that implements SNP-SELECT and uses Gurobi optimization solver for the k-dominating set variable selection is available (https://github.com/transgenomicsosu/SNP-SELECT).

genetics↗

bioRxiv2017

The IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation

The IRE1a-XBP1 pathway, a conserved adaptive mediator of the unfolded protein response, is indispensable for the development of secretory cells. It maintains endoplasmic reticulum homeostasis by facilitating protein folding and enhancing secretory capacity of the cells. Its role in immune cells is emerging. It is involved in dendritic cell, plasma cell and eosinophil development and differentiation. Using genome-wide approaches, integrating ChIPmentation and mRNA-sequencing data, we have elucidated the regulatory circuitry governed by the IRE1a-XBP1 pathway in type-2 T helper cells (Th2). We show that the XBP1 transcription factor is activated by splicing in vivo in T helper cell lineages. We report a comprehensive repertoire of XBP1 target genes in Th2 lymphocytes. We found that the pathway is conserved across cell types in terms of resolving secretory stress, and has T helper cell-specific functions in controlling activation-dependent Th2 cell proliferation and regulating cytokine expression in addition to secretion. These results provide a detailed picture of the regulatory map governed by the XBP1 transcription factor during Th2 lymphocyte activation.

cell biology↗

bioRxiv2017

Assessment of batch-correction methods for scRNA-seq data with a new test metric

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension-reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.\n\n[Before final publication, we will upload the R package to Bioconductor]

bioinformatics↗

bioRxiv2017

The Parasite Extinction Assessment & Red List: an open-source, online biodiversity database for neglected symbionts

Parasite conservation is a rapidly growing field at the intersection of ecology, epidemiology, parasitology, and public health. The overwhelming diversity of parasitic life on earth, and recent work showing that parasites and other symbionts face severe extinction risk, necessitates infrastructure for parasite conservation assessments. Here, we describe the release of the Parasite Extinction Assessment & Red List (PEARL) version 1.0, an open-access database of conservation assessments and distributional data for almost 500 macroparasitic invertebrates. The current approach to vulnerability assessment is based on range shifts and loss from climate change, and will be expanded as additional data (e.g., host-parasite associations and coextinction risk) is consolidated in PEARL. The web architecture is also open-source, scalable, and extensible, making PEARL a template for more eZcient red listing for other high-diversity, data-de1cient groups. Future iterations will also include new functionality, including a user-friendly open data pository and automated assessment and re-listing.

ecology↗

bioRxiv2017

DEsingle: A new method for single-cell differentially expressed genes detection and classification

SummaryThe excessive amount of zeros in single-cell RNA-seq data include \"real\" zeros due to the on-off nature of gene transcription in single cells and \"dropout\" zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.\n\nAvailability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductors consideration now.\n\nContactzhangxg@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗