Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.     

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing(sc RNA-seq)analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in sc RNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model(sc LM), a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, sc LM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. sc LM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that sc LM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of sc LM, we apply it to our in-house and public experimental sc RNA-seq datasets. sc LM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the sc LM method is available at https://github.com/QSong-github/sc LM.     

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.     

Resveratrol and 4-hydroxynonenal act in concert to increase glutamate cysteine ligase expression and glutathione in human bronchial epithelial cells

Resveratrol has been shown to protect against oxidative stress through modulating antioxidant capacity. In this study, we investigated resveratrol-mediated induction of glutathione (GSH) and glutamate cysteine ligase (GCL), and the combined effect of resveratrol and 4-hydroxynonenal (HNE) on GSH synthesis in cultured HBE1 human bronchial epithelial cells. Resveratrol increased GSH and the mRNA contents of both the catalytic (GCLC) and modulatory subunit (GCLM) of GCL. Combined HNE and resveratrol treatment increased GSH content and GCL mRNAs to a greater extent than either compound did alone. Compared to individual agent, combining exposure to HNE and resveratrol also showed more protection against cell death caused by oxidative stress. These effects of combined exposure were additive rather than synergistic. In addition, Nrf2 silencing significantly decreased the combined effect of HNE and resveratrol on GCL induction. Our data suggest that resveratrol increases GSH and GCL gene expression and that there is an additive effect on GSH synthesis between resveratrol and HNE. The results also reveal that Nrf2-EpRE signaling was involved in the combined effects.     

Separation and characterization of basal and secretory cells from the rat trachea by flow cytometry

Basal and secretory cells have been separated as highly enriched viable populations from single-cell suspensions of rat tracheal epithelial cells. Isolation of the populations was achieved by preparation of a cell suspension and separation by flow cytometry using contour maps generated from 2 degrees and 90 degrees light scatter signals. Flow cytometric analysis of cells showed 10% of the whole preparation were cells in SG2M phase of the cell cycle. The secretory cells accounted for 86% of these cycling cells; the remainder were accounted for by the basal cells. Culture of sorted populations of basal and secretory cells in serum free defined medium showed that basal cells had a lower (0.6%) colony-forming efficiency than secretory cells (3.4%). Significant differences in blue auto-fluorescence, Hoechst 33342 uptake, and lectin staining were apparent between basal and secretory cells. These results suggest that the secretory cell rather than the basal cell is primarily the cell type involved in maintenance of the normal tracheal epithelium. Secretory cells are greater in number, have a higher proliferative potential, and greater metabolic capability. Because of these traits they may be a critical cell at risk from damage by environmental agents.     

Approximate distance correlation for selecting highly interrelated genes across datasets

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.     

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or"tipping point"of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regu-latory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the"dark genes"that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes. The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.     

单细胞转录组技术及其在人类细胞图谱构建中的应用

单细胞转录组技术在单细胞水平上进行转录组测序,提供了单个细胞的基因表达差异信息,使在单细胞尺度下研究个体细胞、相关环境细胞及其相互作用的机理成为可能.近年来,单细胞转录组技术在c DNA扩增原理上经历了从末端加尾、体外逆转录到模板置换的方法发展,大大提高了基因检测的数量、基因表达的准确性等.同时,在单细胞选取方式上进行了从96/384孔板到油包水液滴以及纳米微孔的创新,在提高通量和重复性的同时降低了整体实验成本.单细胞转录组技术广泛应用于细胞群体分类和异质性研究,推动了从发育生物学到正常、病态组织细胞图谱的构建.本文对单细胞转录组技术近年的技术进展以及在人类细胞图谱构建中的应用进行了综述.     

PRDM5 基因甲基化及在HTB-182、A549 中的表达

目的:本实验检测PRDM5在HTB-182、A549、HBE正常支气管上皮细胞系中甲基化状态及去甲基化处理对其表达的影响。方法:运用MSP甲基化特异性PCR检测PRDM5在A549、HTB-182和HBE正常支气管上皮细胞系的甲基化状态,再加入不同浓度的去甲基化药物检测PRDM5在A549、HTB-182和HBE正常支气管上皮细胞的甲基化状态,用RT-PCR检测PRDM5在加药前后的mRNA表达水平;用Western-Blot检测PRDM5在加药前后的蛋白表达水平。结果:在肺肿瘤细胞系中,PRDM5存在不同程度的高甲基化,加入不同浓度去甲基化药物后,甲基化表达水平逐渐减弱(P<0.05),mRNA表达水平逐渐增强(P<0.05),蛋白表达水平也逐渐增强(P<0.05)。结论:在肺癌细胞系中PRDM5启动子高甲基化是导致PRDM5表达水平降低的主要原因,PRD-M5启动子去甲基化可能成为肺癌治疗的新靶点。     

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method,reveals non-genetic gene-expression heterogeneity

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.     

Culturing of Human Nasal Epithelial Cells at the Air Liquid Interface

In vitro models using human primary epithelial cells are essential in understanding key functions of the respiratory epithelium in the context of microbial infections or inhaled agents. Direct comparisons of cells obtained from diseased populations allow us to characterize different phenotypes and dissect the underlying mechanisms mediating changes in epithelial cell function. Culturing epithelial cells from the human tracheobronchial region has been well documented, but is limited by the availability of human lung tissue or invasiveness associated with obtaining the bronchial brushes biopsies. Nasal epithelial cells are obtained through much less invasive superficial nasal scrape biopsies and subjects can be biopsied multiple times with no significant side effects. Additionally, the nose is the entry point to the respiratory system and therefore one of the first sites to be exposed to any kind of air-borne stressor, such as microbial agents, pollutants, or allergens. Briefly, nasal epithelial cells obtained from human volunteers are expanded on coated tissue culture plates, and then transferred onto cell culture inserts. Upon reaching confluency, cells continue to be cultured at the air-liquid interface (ALI), for several weeks, which creates more physiologically relevant conditions. The ALI culture condition uses defined media leading to a differentiated epithelium that exhibits morphological and functional characteristics similar to the human nasal epithelium, with both ciliated and mucus producing cells. Tissue culture inserts with differentiated nasal epithelial cells can be manipulated in a variety of ways depending on the research questions (treatment with pharmacological agents, transduction with lentiviral vectors, exposure to gases, or infection with microbial agents) and analyzed for numerous different endpoints ranging from cellular and molecular pathways, functional changes, morphology, etc. In vitro models of differentiated human nasal epithelial cells will enable investigators to address novel and important research questions by using organotypic experimental models that largely mimic the nasal epithelium in vivo.     

Methods for predicting single-cell miRNA in breast cancer

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.     

Secretion of lactoferrin and lysozyme by cultures of human airway epithelium

Lactoferrin and lysozyme are important antimicrobial compounds of airway surface liquid, derived predominantly from serous cells of submucosal glands but also from surface epithelium. Here we compared release of these compounds from the following human cell cultures: primary cultures of tracheal epithelium (HTE), Calu-3 cells (a lung adenocarcinoma cell line frequently used as a model of serous gland cells), 16HBE14o- cells (an SV40 transformed line from airway surface epithelium), T84 cells (a colon carcinoma cell line), and human foreskin fibroblasts (HFF). For lysozyme, baseline secretory rates were in the order Calu-3 > 16HBE14o- > HTE T84 > HFF = 0; for lactoferrin, the only cell type showing measurable release was HTE; for mucus, HTE > Calu-3 > 16HBE14o- T84 > HFF = 0. A wide variety of neurohumoral agents and inflammatory stimuli was without effect on lactoferrin and lysozyme release from HTE or Calu-3 cells, although forskolin did stimulate secretion of water and lysozyme from Calu-3 cells. However, the concentration of lysozyme in the forskolin-induced secretions was much less than in airway gland secretions. Thus our data cast doubt on the utility of Calu-3 cells as a model of airway serous gland cells but do suggest that HTE could prove highly suitable for studies of mucin synthesis and release.     

CellDepot: A Unified Repository for scRNA-seq Data and Visual Exploration

CellDepot containing over 270 datasets from 8 species and many tissues serves as an integrated web application to empower scientists in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical capabilities. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the graphical multi-logic, multi-condition query builder and convenient filtering tool backed by MySQL database system, allows users to quickly find the datasets of interest and compare the expression of gene(s) across these. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research. In summary, the web portal available at http://celldepot.bxgenomics.com, prompts large scale single cell data sharing, facilitates meta-analysis and visualization, and encourages scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software under MIT license to motivate crowd contribution, broad adoption, and local deployment for private datasets.     

Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders

Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.     

Molecular mechanisms controlling CFTR gene expression in the airway

The low levels of CFTR gene expression and paucity of CFTR protein in human airway epithelial cells are not easily reconciled with the pivotal role of the lung in cystic fibrosis pathology. Previous data suggested that the regulatory mechanisms controlling CFTR gene expression might be different in airway epithelium in comparison to intestinal epithelium where CFTR mRNA and protein is much more abundant. Here we examine chromatin structure and modification across the CFTR locus in primary human tracheal (HTE) and bronchial (NHBE) epithelial cells and airway cell lines including 16HBE14o- and Calu3. We identify regions of open chromatin that appear selective for primary airway epithelial cells and show that several of these are enriched for a histone modification (H3K4me1) that is characteristic of enhancers. Consistent with these observations, three of these sites encompass elements that have cooperative enhancer function in reporter gene assays in 16HBE14o- cells. Finally, we use chromosome conformation capture (3C) to examine the three-dimensional structure of nearly 800 kb of chromosome 7 encompassing CFTR and observe long-range interactions between the CFTR promoter and regions far outside the locus in cell types that express high levels of CFTR.