scholarly journals Transcriptional and Cellular Diversity of the Human Heart

2020 ◽  
Author(s):  
Nathan R. Tucker ◽  
Mark Chaffin ◽  
Stephen J. Fleming ◽  
Amelia W. Hall ◽  
Victoria A. Parsons ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Human Heart ◽  
Large Scale ◽  
Cell Types ◽  
Matrix Remodeling ◽  
Cellular Specificity ◽  
Normal Human ◽  
Differential Gene ◽  
Association Data

AbstractIntroductionThe human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low input RNA-sequencing have allowed definitions of cellular transcriptomes at single cell resolution at scale, here we have applied these approaches to assess the cellular and transcriptional diversity of the non-failing human heart.MethodsMicrofluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from seven human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based upon transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic dataResultsWe sequenced the transcriptomes of 287,269 single cardiac nuclei, revealing a total of 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include two distinct groups of resident macrophages, four endothelial subtypes, and two fibroblasts subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs, but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Finally, intersection of our dataset with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity.ConclusionsUsing large-scale single nuclei RNA sequencing, we have defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

scholarly journals Transcriptional and Cellular Diversity of the Human Heart

Circulation ◽  
2020 ◽  
Vol 142 (5) ◽  
pp. 466-482 ◽  
Author(s):  
Nathan R. Tucker ◽  
Mark Chaffin ◽  
Stephen J. Fleming ◽  
Amelia W. Hall ◽  
Victoria A. Parsons ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Human Heart ◽  
Large Scale ◽  
Cell Types ◽  
Matrix Remodeling ◽  
Data Set ◽  
Cellular Specificity ◽  
Normal Human ◽  
Differential Gene ◽  
Association Data

Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

scholarly journals Role of p14ARF in Replicative and Induced Senescence of Human Fibroblasts

2001 ◽  
Vol 21 (20) ◽  
pp. 6748-6757 ◽  
Author(s):  
Wenyi Wei ◽  
Ruth M. Hemmer ◽  
John M. Sedivy
Keyword(s):  
Replicative Senescence ◽  
Human Fibroblasts ◽  
Growth Arrest ◽  
Fibroblast Cell ◽  
Cell Types ◽  
Major Pathway ◽  
Telomere Shortening ◽  
Normal Human ◽  
Cell Strains

ABSTRACT Following a proliferative phase of variable duration, most normal somatic cells enter a growth arrest state known as replicative senescence. In addition to telomere shortening, a variety of environmental insults and signaling imbalances can elicit phenotypes closely resembling senescence. We used p53−/− and p21−/− human fibroblast cell strains constructed by gene targeting to investigate the involvement of the Arf-Mdm2-p53-p21 pathway in natural as well as premature senescence states. We propose that in cell types that upregulate p21 during replicative exhaustion, such as normal human fibroblasts, p53, p21, and Rb act sequentially and constitute the major pathway for establishing growth arrest and that the telomere-initiated signal enters this pathway at the level of p53. Our results also revealed a number of significant differences between human and rodent fibroblasts in the regulation of senescence pathways.

scholarly journals deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

2021 ◽  
Vol 12 ◽  
Author(s):  
Bin Zou ◽  
Tongda Zhang ◽  
Ruilong Zhou ◽  
Xiaosen Jiang ◽  
Huanming Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Batch Correction ◽  
Single Cell Rna Sequencing ◽  
Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

scholarly journals A Method for Cryopreservation and Single Nucleus RNA-sequencing of Normal Adult Human Interventricular Septum Heart Tissue Reveals Cellular Diversity and Function

2020 ◽  
Author(s):  
Amy Larson ◽  
Michael T. Chin
Keyword(s):  
Single Cell ◽  
Human Heart ◽  
Expression Patterns ◽  
Interventricular Septum ◽  
Cell Types ◽  
Heart Tissue ◽  
Gene Expression Patterns ◽  
High Quality ◽  
Normal Human ◽  
Human Heart Tissue

Abstract Background: Single cell sequencing of human heart tissue is technically challenging and methods to cryopreserve heart tissue for obtaining single cell information have not been standardized. Studies published to date have used varying methods to preserve and process human heart tissue, and have generated interesting datasets, but development of a biobanking standard has not yet been achieved. Heart transcription patterns are known to be regionally diverse, and there are few single cell datasets for normal human heart tissue. Methods: Using pig tissue, we developed a rigorous and reproducible method for tissue mincing and cryopreservation that allowed recovery of high quality single nuclei RNA. We subsequently tested this protocol on normal human heart tissue obtained from organ donors and were able to recover high quality nuclei for generation of single nuclei RNA-seq datasets, using a commercially available platform from 10x Genomics. We analyzed these datasets using standard software packages such as CellRanger and Seurat. Results: Human heart tissue preserved with our method consistently yielded nuclear RNA with RNA Integrity Numbers of greater than 8.5. We demonstrate the utility of this method for single nuclei RNA-sequencing of the normal human interventricular septum and delineating its cellular diversity. The human IVS showed unexpected diversity with detection of 23 distinct cell clusters that were subsequently categorized into different cell types. Cardiomyocytes and fibroblasts were the most commonly identified cell types and could be further subdivided into 5 different cardiomyocyte subtypes and 6 different fibroblast subtypes that differed by gene expression patterns. Ingenuity Pathway analysis of these gene expression patterns suggested functional diversity in these cell subtypes. Conclusions: Here we report a simple technical method for cryopreservation and subsequent nuclear isolation of human interventricular septum tissue that can be done with common laboratory equipment. We show how this method can be used to generate single nuclei transcriptomic datasets that rival those already published by larger groups in terms of cell diversity and complexity and suggest that this simple method can provide guidance for biobanking of human myocardial tissue for complex genomic analysis.

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Single-cell RNA sequencing of adultDrosophilaovary identifies transcriptional programs governing oogenesis

2019 ◽  
Author(s):  
Allison Jevitt ◽  
Deeptiman Chatterjee ◽  
Gengqiang Xie ◽  
Xian-Feng Wang ◽  
Taylor Otwell ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Developmental Process ◽  
Expression Profiles ◽  
Follicle Cell ◽  
Cell Types ◽  
Broad Perspective ◽  
Single Cell Rna Sequencing ◽  
Egg Shell

AbstractOogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modelled extensively using theDrosophilaovary. While different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic dataset for the adultDrosophilaovary and connected tissues. This dataset captures the entire transcriptional trajectory of the developing follicle cell population over time. Our findings provide detailed insight into processes such as cell-cycle switching, migration, symmetry breaking, nurse cell engulfment, egg-shell formation, and signaling during corpus luteum formation, marking a newly identified oogenesis-to-ovulation transition. Altogether, these findings provide a broad perspective on oogenesis at a single-cell resolution while revealing new genetic markers and fate-specific transcriptional signatures to facilitate future studies.

scholarly journals Functional Network Analysis Reveals Versatile MicroRNAs in Human Heart

2015 ◽  
Vol 36 (4) ◽  
pp. 1628-1643 ◽  
Author(s):  
Yingqi Xu ◽  
Wenliang Zhu ◽  
Yong Sun ◽  
Zhe Wang ◽  
Wei Yuan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Human Heart ◽  
Myocardial Injury ◽  
Dynamic Balance ◽  
Mirna Gene ◽  
Vital Role ◽  
Association Data ◽  
Functional Analyses

Background: Acting on many mRNAs allows the power of a single miRNA to modulate multiple pathophysiological phenotypes. One question is whether versatile miRNAs exist in the pathological scenarios of myocardial infarction (MI) and heart failure (HF). Methods: A hypergeometric analysis, in combination with network-based functional analyses, was performed on the available human protein interaction and miRNA-gene association data to highlight versatile miRNAs among the significantly dysregulated miRNAs in MI and HF. In vivo, mice models of MI and HF were then established to investigate whether dysregulated expression be undertaken by versatile miRNA identified here. Results: Systematic analyses really identified the previously validated miRNAs that have been verified of multiple important roles in MI and HF, demonstrating method effectiveness. By using this means, we innovatively revealed the vital role of miR-7 in maintaining the dynamic balance of protein interactions and its obvious overexpression in MI and HF that implies pathological involvement. Functional experiments are definitely needed for further revealing its potential influences on MI- or HF-led myocardial injury. Conclusion: Our results have implications not only for the coming miRNA-based strategy in treating MI and HF but also for further understanding on gene regulation by miRNAs in human heart.

Clock genes SNP array identifies a key role of the PER1/HES7 gene in the risk of cannabis addiction and psychiatric comorbidities

2017 ◽  
Vol 41 (S1) ◽  
pp. s867-s868
Author(s):  
G. Lafaye ◽  
A. Benyamina
Keyword(s):  
Large Scale ◽  
Clock Genes ◽  
Strong Association ◽  
Snp Array ◽  
Biological Rhythms ◽  
Cell Types ◽  
Predisposing Factor ◽  
Circadian Expression ◽  
Potential Biomarker

The existence of biological rhythms disruption in addicted subjects has been described including disturbances in their sleep-wake pattern, rest-activity rhythms, and feeding schedules. Circadian rhythms have also been related to psychiatric diseases, including mood and anxiety disorders and the regulation of dopaminergic transmission, especially in reward circuitry in substance abusers. The relationship between them remained enigmatic and no data on the role of clock genes variants on cannabis dependence have been documented. We aimed at exploring the role of clock gene genotypes as potential predisposing factor to cannabis addiction, using a high throughput mass spectrometry methodology that enables the large-scale analysis of all the known clinically-relevant polymorphisms of the core human clock genes. We have conducted a case-control study on 177 Caucasians categorizing between cannabis-addicted subjects (n = 83) and casual cannabis consumers (n = 94). We report here a strong association between the TT* genotype RS1442849 in PER1/HES7 gene and a significantly higher risk of vulnerability to be dependent to cannabis. Moreover, this SNP was overrepresented in the subsets of cannabis users with more severe characteristics like personal psychiatric history, unemployed status, and beginning of cannabis use early in lifetime as well as large weekly consumption. HES7 gene is a newly described gene with a circadian expression regulated by reactive oxygen species in many cell types including neural stem cells. The HES7 TT* genotype RS1442849 gene could intervene on the dopamine reward systems. This genotype thus represents the first potential biomarker for stratification of cannabis consumers for the risk to develop a true dependence.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Building an RNA Sequencing Transcriptome of the Central Nervous System

2016 ◽  
Vol 22 (6) ◽  
pp. 579-592 ◽  
Author(s):  
Xiaomin Dong ◽  
Yanan You ◽  
Jia Qian Wu
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Nervous System ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Cell ◽  
Rna Seq ◽  
The Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

scholarly journals EPCO-32. AN EPIGENETIC SINGLE-CELL ATLAS OF IDH-MUTANT GLIOMA REVEALS THE ROLE OF ATRX IN SHAPING TUMOR COMPOSITION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii76-ii76
Author(s):  
Husam Babikir ◽  
Lin Wang ◽  
Karin Shamardani ◽  
Sweta Sudhir ◽  
Gary Kohanbash ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Rna Sequencing ◽  
Myeloid Cell ◽  
Cell Types ◽  
Open Chromatin ◽  
Helix Loop Helix ◽  
Local Immunosuppression ◽  
Single Cell Rna Sequencing

Abstract Recent single-cell RNA-sequencing studies have identified a hierarchy of cell types that is common to all isocitrate dehydrogenase (IDH) -mutant gliomas. This finding is somewhat paradoxical since the genetic differences between IDH-mutant astrocytomas and IDH-mutant oligodendrogliomas are prognostic, predictive of therapeutic response, and correlated with differences in immune infiltrates. To integrate these disparate findings, we constructed a single-cell atlas of 28 human IDH-mutant primary untreated grade-II/III gliomas. All specimens were profiled by single-cell assay for transposase-accessible chromatin, with additional cohorts profiled via single-cell RNA-sequencing and single-cell spatial proteomics. We determined the cell-type specific differences between IDH-mutant gliomas in transcription-factor utilization, associated targeting and cis-regulatory grammars. To elucidate the role of the chromatin remodeler ATRX (inactivated in over 86% of IDH-mutant astrocytomas) in shaping observed differences in open chromatin, we knocked out ATRX in an immunocompetent model of IDH-mutant glioma and subjected murine tumors to single-cell profiling. We found: 1. ATRX-deficient, IDH-mutant human and murine gliomas both upregulate an astrocytic regulatory program driven by Nuclear Factor I genes and downregulate an oligodendrocytic program driven by basic helix-loop-helix transcription factors. 2. Both human and mouse ATRX-deficient, IDH-mutant gliomas up-regulate genes that promote myeloid-cell chemotaxis and both have significantly higher percentages of myeloid-derived immune-suppressive cells than controls; 3. A transcription-factor program is conserved between human and murine ATRX-deficient tumors that shapes glial identity and promotes local immunosuppression. These studies elucidate how IDH-mutant gliomas from different subtypes can have distinct cellular morphologies and tumor micronenvironments despite a common lineage hierarchy.

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