scholarly journals Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data

2016 ◽  
Author(s):  
B. Ogan Mancarci ◽  
Lilah Toker ◽  
Shreejoy J Tripathy ◽  
Brenna Li ◽  
Brad Rocco ◽  
...  
Keyword(s):  
Nervous System ◽  
Cell Types ◽  
Brain Cell ◽  
Marker Genes ◽  
Specific Gene ◽  
Published Data ◽  
Specific Marker ◽  
Cell Type ◽  
Cell Type Specific ◽  
Bulk Tissue

AbstractEstablishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single cell RNA-sequencing studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at Neuroexpresso.org.Significance StatementCell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability

2018 ◽  
Author(s):  
Regina H Reynolds ◽  
Juan Botía ◽  
Mike A Nalls ◽  
John Hardy ◽  
Sarah A Gagliano ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cell Types ◽  
Brain Regions ◽  
Brain Cell ◽  
Specific Gene ◽  
Specific Cell ◽  
Cell Type ◽  
Related Cell ◽  
Cell Type Specific

AbstractParkinson’s disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set specifically expressed in astrocytic and microglial subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes to which cell types may have varying vulnerability.

scholarly journals scSorter: assigning cells to known cell types according to marker genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hongyu Guo ◽  
Jun Li
Keyword(s):  
Real Data ◽  
Cell Types ◽  
Exact Expression ◽  
Marker Genes ◽  
Specific Marker ◽  
Sequencing Data ◽  
Reference Dataset ◽  
Over Expression ◽  
Higher Power ◽  
Cell Type Specific

AbstractOn single-cell RNA-sequencing data, we consider the problem of assigning cells to known cell types, assuming that the identities of cell-type-specific marker genes are given but their exact expression levels are unavailable, that is, without using a reference dataset. Based on an observation that the expected over-expression of marker genes is often absent in a nonnegligible proportion of cells, we develop a method called scSorter. scSorter allows marker genes to express at a low level and borrows information from the expression of non-marker genes. On both simulated and real data, scSorter shows much higher power compared to existing methods.

scholarly journals An analytical method for the identification of cell type-specific disease gene modules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Yang Wang ◽  
Junchao Gao ◽  
Guoli Ji
Keyword(s):  
Disease Gene ◽  
Gene Interaction ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Disease ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

Cytoplasmic, nuclear, membrane-bound and secreted [35S]methionine-labelled polypeptide pattern in differentiating fibroblast stem cells in vitro

1989 ◽  
Vol 92 (2) ◽  
pp. 231-239
Author(s):  
P.I. Francz ◽  
K. Bayreuther ◽  
H.P. Rodemann
Keyword(s):  
Protein Fraction ◽  
Fibroblast Cell ◽  
Cell Types ◽  
Specific Marker ◽  
Human Skin Fibroblast ◽  
Nuclear Fraction ◽  
Cell Type ◽  
Marker Proteins ◽  
Cell Type Specific ◽  
Membrane Bound

Methods for the selective enrichment of various subpopulations of the human skin fibroblast cell line HH-8 have been developed. These methods permit the selection of homogeneous populations of the three mitotic fibroblast cell types MF I, II and III, and the four postmitotic cell types PMF IV, V, VI and VII. These seven cell types exhibit differentiation-dependent and cell-type-specific patterns of [35S]methionine-labelled polypeptides in total soluble cytoplasmic and nuclear proteins, also in membrane-bound proteins, and in secreted proteins. In the differentiation sequence MF II-MF III-PMF IV - PMF V - PMF VI 14 cell-type-specific marker proteins have been found in the cytoplasmic and nuclear fraction, also 24 cell-type-specific marker proteins have been found in the membrane-bound protein fraction, and 11 cell-type-specific marker proteins in the secreted protein fraction. Markers in spontaneously arising and experimentally selected or induced populations of a single fibroblast cell type were found to be identical.

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 ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

2018 ◽  
Author(s):  
Wennan Chang ◽  
Changlin Wan ◽  
Xiaoyu Lu ◽  
Szu-wei Tu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Training Data ◽  
Marker Genes ◽  
Cell Detection ◽  
Omics Data ◽  
Deconvolution Method ◽  
Cell Type ◽  
Data Set ◽  
Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

scholarly journals Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ana J. Chucair-Elliott ◽  
Sarah R. Ocañas ◽  
David R. Stanford ◽  
Victor A. Ansere ◽  
Kyla B. Buettner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Tissue Level ◽  
Cell Phenotype ◽  
Specific Gene ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

scholarly journals Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Natalie M. Clark ◽  
Eli Buckner ◽  
Adam P. Fisher ◽  
Emily C. Nelson ◽  
Thomas T. Nguyen ◽  
...  
Keyword(s):  
Stem Cell ◽  
Gene Networks ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Renewal ◽  
Cell Type ◽  
Stem Cell Maintenance ◽  
Differentiated Cells ◽  
Cell Type Specific ◽  
Stem Cell Type

AbstractStem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together.

scholarly journals A regulatory element is characterized by purine-mediated and cell-type-specific gene transcription.

1990 ◽  
Vol 10 (8) ◽  
pp. 4356-4364 ◽  
Author(s):  
M J Walsh ◽  
A Sanchez-Pozo ◽  
N S Leleiko
Keyword(s):  
De Novo ◽  
Cell Types ◽  
High Rate ◽  
Specific Gene ◽  
Intestinal Epithelial ◽  
Purine Nucleotide ◽  
Cell Type ◽  
Hprt Gene ◽  
Purine Nucleotides ◽  
Cell Type Specific

Purines and purine nucleotides were found to affect transcription of the hypoxanthine-guanine phosphoribosyltransferase (HPRT) gene in whole nuclei isolated from intestinal mucosa of adult rats fed a purine- and purine nucleotide-free diet. Nuclear run-on transcription assays, performed on whole nuclei from different tissues and cell types, identified an intestine-specific decrease in the overall incorporation of [alpha-32P]UTP in HPRT transcripts from intestinal epithelial cell nuclei when exogenous purines or purine nucleotides were omitted from either the diet or culture medium. Using a 990-base-pair genomic fragment that contains the 5'-flanking region from the HPRT gene, we generated plasmid constructs with deletions, transfected the DNA into various cell types, and assayed for chloramphenicol acetyltransferase (CAT) reporter activity in vitro. We determined that an element upstream from the putative transcriptional start site is necessary to maintain the regulatory response to purine and nucleotide levels in cultured intestinal epithelial cells. These results were tissue and cell type specific and suggest that in the absence of exogenous purines, the presence of specific factors influences transcriptional initiation of HPRT. This information provides evidence for a mechanism by which the intestinal epithelium, which has been reported to lack constitutive levels of de novo purine nucleotide biosynthetic activity, could maintain and regulate the salvage of purines and nucleotides necessary for its high rate of cell and protein turnover during fluctuating nutritional and physiological conditions. Furthermore, this information may provide more insight into regulation of the broad class of genes recognized by their lack of TATA and CCAAT box consensus sequences within the region proximal to the promoter.

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