scholarly journals Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

2020 ◽  
Author(s):  
Pawan K. Jha ◽  
Utham K. Valekunja ◽  
Sandipan Ray ◽  
Mathieu Nollet ◽  
Akhilesh B. Reddy
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Protein ◽  
Cell Type ◽  
Transcriptional Responses ◽  
A Cell ◽  
Cell Type Specific ◽  
Different Cell Types

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single cell RNA sequencing and cell-type specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

scholarly journals The complement of desmosomal plaque proteins in different cell types.

1985 ◽  
Vol 101 (4) ◽  
pp. 1442-1454 ◽  
Author(s):  
P Cowin ◽  
H P Kapprell ◽  
W W Franke
Keyword(s):  
Guinea Pig ◽  
Cell Types ◽  
Cardiac Cells ◽  
Myocardial Tissue ◽  
Cell Type ◽  
Wide Range ◽  
Specific Manner ◽  
A Cell ◽  
Cell Type Specific ◽  
Different Cell Types

Desmosomal plaque proteins have been identified in immunoblotting and immunolocalization experiments on a wide range of cell types from several species, using a panel of monoclonal murine antibodies to desmoplakins I and II and a guinea pig antiserum to desmosomal band 5 protein. Specifically, we have taken advantage of the fact that certain antibodies react with both desmoplakins I and II, whereas others react only with desmoplakin I, indicating that desmoplakin I contains unique regions not present on the closely related desmoplakin II. While some of these antibodies recognize epitopes conserved between chick and man, others display a narrow species specificity. The results show that proteins whose size, charge, and biochemical behavior are very similar to those of desmoplakin I and band 5 protein of cow snout epidermis are present in all desmosomes examined. These include examples of simple and pseudostratified epithelia and myocardial tissue, in addition to those of stratified epithelia. In contrast, in immunoblotting experiments, we have detected desmoplakin II only among cells of stratified and pseudostratified epithelial tissues. This suggests that the desmosomal plaque structure varies in its complement of polypeptides in a cell-type specific manner. We conclude that the obligatory desmosomal plaque proteins, desmoplakin I and band 5 protein, are expressed in a coordinate fashion but independently from other differentiation programs of expression such as those specific for either epithelial or cardiac cells.

scholarly journals Profiling of Primary and Mature miRNA Expression in Atherosclerosis Associated Cell Types

2021 ◽  
Author(s):  
Pierre R. Moreau ◽  
Vanesa Tomas Bosch ◽  
Maria Bouvy-Liivrand ◽  
Kadri Õunap ◽  
Tiit Örd ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Smooth Muscle ◽  
Molecular Mechanisms ◽  
Umbilical Vein ◽  
Cell Types ◽  
Integrative Approach ◽  
Dependent Manner ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

Objective: Atherosclerosis is the underlying cause of most cardiovascular diseases. The main cell types associated with disease progression in the vascular wall are endothelial cells, smooth muscle cells, and macrophages. Although their role in atherogenesis has been extensively described, molecular mechanisms underlying gene expression changes remain unknown. The objective of this study was to characterize microRNA (miRNA)-related regulatory mechanisms taking place in the aorta during atherosclerosis: Approach and Results: We analyzed the changes in primary human aortic endothelial cells and human umbilical vein endothelial cell, human aortic smooth muscle cell, and macrophages (CD14+) under various proatherogenic stimuli by integrating GRO-seq, miRNA-seq, and RNA-seq data. Despite the highly cell-type-specific expression of multi-variant pri-miRNAs, the majority of mature miRNAs were found to be common to all cell types and dominated by 2 to 5 abundant miRNA species. We demonstrate that transcription contributes significantly to the mature miRNA levels although this is dependent on miRNA stability. An analysis of miRNA effects in relation to target mRNA pools highlighted pathways and targets through which miRNAs could affect atherogenesis in a cell-type-dependent manner. Finally, we validate miR-100-5p as a cell-type specific regulator of inflammatory and HIPPO-YAP/TAZ-pathways. Conclusions: This integrative approach allowed us to characterize miRNA dynamics in response to a proatherogenic stimulus and identify potential mechanisms by which miRNAs affect atherogenesis in a cell-type-specific manner.

scholarly journals Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

2020 ◽  
Author(s):  
Jiaxin Fan ◽  
Xuran Wang ◽  
Rui Xiao ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Allelic Expression ◽  
Rna Seq ◽  
Allelic Expression Imbalance ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractAllelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.Author SummaryDetection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI use bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCET’s ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.

scholarly journals Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

PLoS Genetics ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. e1009080
Author(s):  
Jiaxin Fan ◽  
Xuran Wang ◽  
Rui Xiao ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Allelic Expression ◽  
Rna Seq ◽  
Allelic Expression Imbalance ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Different Cell Types

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

scholarly journals Single-Cell Regulatory Network Inference and Clustering Identifies Cell-Type Specific Expression Pattern of Transcription Factors in Mouse Sciatic Nerve

2021 ◽  
Vol 15 ◽  
Author(s):  
Mingchao Li ◽  
Qing Min ◽  
Matthew C. Banton ◽  
Xinpeng Dun
Keyword(s):  
Transcription Factors ◽  
Sciatic Nerve ◽  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Different Cell Types

Advances in single-cell RNA sequencing technologies and bioinformatics methods allow for both the identification of cell types in a complex tissue and the large-scale gene expression profiling of various cell types in a mixture. In this report, we analyzed a single-cell RNA sequencing (scRNA-seq) dataset for the intact adult mouse sciatic nerve and examined cell-type specific transcription factor expression and activity during peripheral nerve homeostasis. In total, we identified 238 transcription factors expressed in nine different cell types of intact mouse sciatic nerve. Vascular smooth muscle cells have the lowest number of transcription factors expressed with 17 transcription factors identified. Myelinating Schwann cells (mSCs) have the highest number of transcription factors expressed, with 61 transcription factors identified. We created a cell-type specific expression map for the identified 238 transcription factors. Our results not only provide valuable information about the expression pattern of transcription factors in different cell types of adult peripheral nerves but also facilitate future studies to understand the function of key transcription factors in the peripheral nerve homeostasis and disease.

scholarly journals Complex transcriptional regulation and independent evolution of fungal-like traits in a relative of animals

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Alex de Mendoza ◽  
Hiroshi Suga ◽  
Jon Permanyer ◽  
Manuel Irimia ◽  
Iñaki Ruiz-Trillo
Keyword(s):  
Cell Types ◽  
Cell Type ◽  
Dynamic Regulation ◽  
Transcriptional Dynamics ◽  
Genome Regulation ◽  
A Cell ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Multicellular Organisms ◽  
Different Cell Types

Cell-type specification through differential genome regulation is a hallmark of complex multicellularity. However, it remains unclear how this process evolved during the transition from unicellular to multicellular organisms. To address this question, we investigated transcriptional dynamics in the ichthyosporean Creolimax fragrantissima, a relative of animals that undergoes coenocytic development. We find that Creolimax utilizes dynamic regulation of alternative splicing, long inter-genic non-coding RNAs and co-regulated gene modules associated with animal multicellularity in a cell-type specific manner. Moreover, our study suggests that the different cell types of the three closest animal relatives (ichthyosporeans, filastereans and choanoflagellates) are the product of lineage-specific innovations. Additionally, a proteomic survey of the secretome reveals adaptations to a fungal-like lifestyle. In summary, the diversity of cell types among protistan relatives of animals and their complex genome regulation demonstrates that the last unicellular ancestor of animals was already capable of elaborate specification of cell types.

scholarly journals Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

2021 ◽  
Author(s):  
Yongjin Park ◽  
Liang He ◽  
Jose Davila-Velderrain ◽  
Lei Hou ◽  
Shahin Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genetic Variants ◽  
Cell Types ◽  
Brain Regions ◽  
Post Mortem ◽  
Rna Seq ◽  
Cell Type ◽  
Post Mortem Brain ◽  
Cell Type Specific

AbstractThousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

scholarly journals SC2disease: a manually curated database of single-cell transcriptome for human diseases

2020 ◽  
Vol 49 (D1) ◽  
pp. D1413-D1419 ◽  
Author(s):  
Tianyi Zhao ◽  
Shuxuan Lyu ◽  
Guilin Lu ◽  
Liran Juan ◽  
Xi Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cellular Level ◽  
Human Diseases ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

scholarly journals Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Sun ◽  
Qichao Yu ◽  
Lei Li ◽  
Zhanlong Mei ◽  
Biaofeng Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Specific Protein ◽  
Embryonic Cells ◽  
Cell Type ◽  
Gene Expressions ◽  
Protein Coding ◽  
C Elegans ◽  
Protein Coding Genes ◽  
Cell Type Specific

Abstract Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

scholarly journals How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

2021 ◽  
Author(s):  
Maria Mircea ◽  
Stefan Semrau
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Lineage Tracing ◽  
Molecular Profile ◽  
Cell Type ◽  
Molecular Changes ◽  
Starting Point ◽  
A Cell ◽  
Type Specification

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.

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