scholarly journals A robust semi-supervised NMF model for single cell RNA-seq data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10091
Author(s):  
Peng Wu ◽  
Mo An ◽  
Hai-Ren Zou ◽  
Cai-Ying Zhong ◽  
Wei Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Prior Knowledge ◽  
Clustering Analysis ◽  
Prior Information ◽  
Expression Patterns ◽  
Biological Significance ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Gene ◽  
Advanced Analysis

Background Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis. Methods We propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data. Results Twelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.

scholarly journals Prediction of single-cell mechanisms for disease progression in hypertrophic remodelling by a trans-omics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Momoko Hamano ◽  
Seitaro Nomura ◽  
Midori Iida ◽  
Issei Komuro ◽  
Yoshihiro Yamanishi
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Marker Genes ◽  
Specific Gene ◽  
Rna Seq ◽  
Aortic Constriction ◽  
Multiple Risk Factors

AbstractHeart failure is a heterogeneous disease with multiple risk factors and various pathophysiological types, which makes it difficult to understand the molecular mechanisms involved. In this study, we proposed a trans-omics approach for predicting molecular pathological mechanisms of heart failure and identifying marker genes to distinguish heterogeneous phenotypes, by integrating multiple omics data including single-cell RNA-seq, ChIP-seq, and gene interactome data. We detected a significant increase in the expression level of natriuretic peptide A (Nppa), after stress loading with transverse aortic constriction (TAC), and showed that cardiomyocytes with high Nppa expression displayed specific gene expression patterns. Multiple NADH ubiquinone complex family, which are associated with the mitochondrial electron transport system, were negatively correlated with Nppa expression during the early stages of cardiac hypertrophy. Large-scale ChIP-seq data analysis showed that Nkx2-5 and Gtf2b were transcription factors characteristic of high-Nppa-expressing cardiomyocytes. Nppa expression levels may, therefore, represent a useful diagnostic marker for heart failure.

scholarly journals DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

2021 ◽  
Author(s):  
Yanhui Hu ◽  
Sudhir Gopal Tattikota ◽  
Yifang Liu ◽  
Aram Comjean ◽  
Yue Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Expression ◽  
Web Based ◽  
Manual Curation ◽  
Original Analysis ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractWith the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, called DRscDB (https://www.flyrnai.org/tools/single_cell/web/) to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and the relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. DRscDB serves as a web-based user interface that allows users to mine, utilize and compare gene expression data pertaining to scRNA-seq datasets from the published literature.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

2021 ◽  
Author(s):  
Yixuan Qiu ◽  
Jiebiao Wang ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Correlation Pattern ◽  
Tissue Samples ◽  
Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254194
Author(s):  
Hong-Tae Park ◽  
Woo Bin Park ◽  
Suji Kim ◽  
Jong-Sung Lim ◽  
Gyoungju Nah ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Single Cell ◽  
Mycobacterium Avium ◽  
Expression Patterns ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

scholarly journals Single-cell genomic atlas of great ape cerebral organoids uncovers human-specific features of brain development

2019 ◽  
Author(s):  
Sabina Kanton ◽  
Michael James Boyle ◽  
Zhisong He ◽  
Malgorzata Santel ◽  
Anne Weigert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Adult Brain ◽  
The Other ◽  
Specific Gene ◽  
Great Ape ◽  
Specific Gene Expression ◽  
Gene Regulatory ◽  
Human Specific

ABSTRACTThe human brain has changed dramatically since humans diverged from our closest living relatives, chimpanzees and the other great apes1–5. However, the genetic and developmental programs underlying this divergence are not fully understood6–8. Here, we have analyzed stem cell-derived cerebral organoids using single-cell transcriptomics (scRNA-seq) and accessible chromatin profiling (scATAC-seq) to explore gene regulatory changes that are specific to humans. We first analyze cell composition and reconstruct differentiation trajectories over the entire course of human cerebral organoid development from pluripotency, through neuroectoderm and neuroepithelial stages, followed by divergence into neuronal fates within the dorsal and ventral forebrain, midbrain and hindbrain regions. We find that brain region composition varies in organoids from different iPSC lines, yet regional gene expression patterns are largely reproducible across individuals. We then analyze chimpanzee and macaque cerebral organoids and find that human neuronal development proceeds at a delayed pace relative to the other two primates. Through pseudotemporal alignment of differentiation paths, we identify human-specific gene expression resolved to distinct cell states along progenitor to neuron lineages in the cortex. We find that chromatin accessibility is dynamic during cortex development, and identify instances of accessibility divergence between human and chimpanzee that correlate with human-specific gene expression and genetic change. Finally, we map human-specific expression in adult prefrontal cortex using single-nucleus RNA-seq and find developmental differences that persist into adulthood, as well as cell state-specific changes that occur exclusively in the adult brain. Our data provide a temporal cell atlas of great ape forebrain development, and illuminate dynamic gene regulatory features that are unique to humans.

scholarly journals Single cell transcriptomic analysis of bloodstream form Trypanosoma brucei reconstructs cell cycle progression and differentiation via quorum sensing

2020 ◽  
Author(s):  
Emma Marie Briggs ◽  
Richard McCulloch ◽  
Keith Roland Matthews ◽  
Thomas Dan Otto
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Trypanosoma Brucei ◽  
Expression Patterns ◽  
Life Cycles ◽  
Bloodstream Form ◽  
Cycle Phase ◽  
Mammalian Host ◽  
Marker Genes ◽  
African Trypanosomes

The life cycles of African trypanosomes are dependent on several differentiation steps, where parasites transition between replicative and non-replicative forms specialised for infectivity and survival in mammal and tsetse fly hosts. Here, we use single cell transcriptomics (scRNA-seq) to dissect the asynchronous differentiation of replicative slender to transmissible stumpy bloodstream form Trypanosoma brucei. Using oligopeptide-induced differentiation, we accurately modelled stumpy development in vitro and captured the transcriptomes of 9,344 slender and stumpy stage parasites, as well as parasites transitioning between these extremes. Using this framework, we detail the relative order of biological events during development, profile dynamic gene expression patterns and identify putative novel regulators. Using marker genes to deduce the cell cycle phase of each parasite, we additionally map the cell cycle of proliferating parasites and position stumpy cell cycle exit at early G1, with subsequent progression to a distinct G0 state. We also explored the role of one gene, ZC3H20, with transient elevated expression at the key slender to stumpy transition point. By scRNA-seq analysis of ZC3H20 null parasites exposed to oligopeptides and mapping the resulting transcriptome to our atlas of differentiation, we identified the point of action for this key regulator. Using a developmental transition relevant for both virulence in the mammalian host and disease transmission, our data provide a paradigm for the temporal mapping of differentiation events and regulators in the trypanosome life cycle.

scholarly journals A network regularized linear model to infer spatial expression pattern for single cells

2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Capture Rate ◽  
Marker Genes ◽  
Spatial Expression ◽  
Cellular Expression

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

scholarly journals Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

2020 ◽  
Author(s):  
Yixuan Qiu ◽  
Jiebiao Wang ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Correlation Pattern ◽  
Tissue Samples ◽  
Bulk Data ◽  
Tissue Marker

AbstractMotivationMarker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern.ResultsTo capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list.Availability and implementationWe implement this method as an R package markerpen, hosted on https://github.com/yixuan/[email protected]

scholarly journals Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1014
Author(s):  
Maryam Zand ◽  
Jianhua Ruan
Keyword(s):  
Single Cell ◽  
Gold Standard ◽  
Gene Selection ◽  
Single Cells ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Weighted Averaging ◽  
Marker Genes ◽  
Spatial Mapping ◽  
Successful Recovery

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

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