scholarly journals Analysis of single cell data as it relates to aging and longevity

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 677-678
Author(s):  
Tanya Karagiannis ◽  
Todd Dowrey ◽  
Carlos Villacorta-Martin ◽  
George Murphy ◽  
Stefano Monti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune System ◽  
Single Cell ◽  
Peripheral Blood ◽  
Age Groups ◽  
Cell Types ◽  
Cell Type ◽  
Younger Age ◽  
Cell Type Specific ◽  
Extreme Longevity

Abstract Age-related disability and diseases are known to be delayed in people living to 100 years or more. Changes in the immune system with age are known, including in cell type composition and gene expression differences. To further explore changes in extreme longevity subjects, we investigated peripheral blood immune system cell subpopulations across age and extreme longevity at a single cell resolution. We performed an integrative analysis of public scRNA-seq datasets to define consensus cell types of longevity and age, and classified cell types in our novel New England Centenarian Study dataset. We integrated these datasets together to investigate cell type specific differences at a composition and gene expression level. Our findings identified higher cell type diversity in extreme longevity subjects compared to younger age groups, but no significant difference among younger age groups demonstrating that overall composition differences are unique to longevity. We identified novel differences in myeloid and lymphocyte populations; Extreme longevity subjects have higher composition of CD14+ Monocytes, Natural Killer cells, and T gamma delta populations and lower composition of CD16+ Monocytes and dendritic populations. We characterized gene expression differences between extreme longevity and younger age groups and differences in aging across younger age groups. We found that extreme longevity cell type specific signatures overlapped with the aging signatures by at least 50%. We identified unique genes to extreme longevity that are enriched for pathways specific to immune activation and inflammation, suggesting a protective mechanism for centenarians through efficient activation and regulation of immune subpopulations in peripheral blood.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Patterns ◽  
Cell Types ◽  
Gene Expression Patterns ◽  
Cell Type ◽  
Web Resource ◽  
Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

scholarly journals Single-cell RNA-sequencing reveals thoracolumbar vertebra heterogeneity and rib-genesis in pigs

2021 ◽  
Author(s):  
Jianbo Li ◽  
Ligang Wang ◽  
Dawei Yu ◽  
Junfeng Hao ◽  
Longchao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Lumbar Vertebra ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Coupled Process ◽  
Gene Expression Dynamics ◽  
Cell Type Specific ◽  
Thoracolumbar Vertebra

Thoracolumbar vertebra (TLV) and rib primordium (RP) development is a common evolutionary feature across vertebrates although whole-organism analysis of TLV and RP gene expression dynamics has been lacking. Here we investigated the single-cell transcriptomic landscape of thoracic vertebra (TV), lumbar vertebra (LV), and RP cells from a pig embryo at 27 days post-fertilization (dpf) and identified six cell types with distinct gene-expression signatures. In-depth dissection of the gene-expression dynamics and RNA velocity revealed a coupled process of osteogenesis and angiogenesis during TLV and rib development. Further analysis of cell-type-specific and strand-specific expression uncovered the extremely high levels of HOXA10 3'-UTR sequence specific to osteoblast of LV cells, which may function as anti-HOXA10-antisense by counteracting the HOXA10-antisense effect to determine TLV transition. Thus, this work provides a valuable resource for understanding embryonic osteogenesis and angiogenesis underlying vertebrate TLV and RP development at the cell-type-specific resolution, which serves as a comprehensive view on the transcriptional profile of animal embryo development.

scholarly journals Dynamics of gene expression in single root cells ofA. thaliana

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Root Cells ◽  
Cell Lineages ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

scholarly journals Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

2020 ◽  
Author(s):  
Jiebiao Wang ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Cell ◽  
Expression Data ◽  
Cell Type ◽  
Disease Etiology ◽  
Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

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 Cell-type–specific transcriptome and histone modification dynamics during cellular reprogramming in the Arabidopsis stomatal lineage

2019 ◽  
Vol 116 (43) ◽  
pp. 21914-21924 ◽  
Author(s):  
Laura R. Lee ◽  
Diego L. Wengier ◽  
Dominique C. Bergmann
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Histone Modifications ◽  
Developmental Plasticity ◽  
Plant Cells ◽  
Cell Types ◽  
Cellular Reprogramming ◽  
Cell Type ◽  
Chip Sequencing ◽  
Cell Type Specific

Plant cells maintain remarkable developmental plasticity, allowing them to clonally reproduce and to repair tissues following wounding; yet plant cells normally stably maintain consistent identities. Although this capacity was recognized long ago, our mechanistic understanding of the establishment, maintenance, and erasure of cellular identities in plants remains limited. Here, we develop a cell-type–specific reprogramming system that can be probed at the genome-wide scale for alterations in gene expression and histone modifications. We show that relationships among H3K27me3, H3K4me3, and gene expression in single cell types mirror trends from complex tissue, and that H3K27me3 dynamics regulate guard cell identity. Further, upon initiation of reprogramming, guard cells induce H3K27me3-mediated repression of a regulator of wound-induced callus formation, suggesting that cells in intact tissues may have mechanisms to sense and resist inappropriate dedifferentiation. The matched ChIP-sequencing (seq) and RNA-seq datasets created for this analysis also serve as a resource enabling inquiries into the dynamic and global-scale distribution of histone modifications in single cell types in plants.

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 Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

2016 ◽  
Vol 113 (17) ◽  
pp. E2393-E2402 ◽  
Author(s):  
Alexis Vandenbon ◽  
Viet H. Dinh ◽  
Norihisa Mikami ◽  
Yohko Kitagawa ◽  
Shunsuke Teraguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune System ◽  
Gene Expression Data ◽  
Cell Types ◽  
Treg Cells ◽  
Integrated Analysis ◽  
Expression Data ◽  
Batch Effects ◽  
Cell Type ◽  
Cell Type Specific

High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are study-specific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin β8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Treg-specific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells.

scholarly journals Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

2020 ◽  
Author(s):  
Mengjie Chen ◽  
Qi Zhan ◽  
Zepeng Mu ◽  
Lili Wang ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kernel Density ◽  
Cell Types ◽  
Clinical Samples ◽  
Cell Type ◽  
Inflammatory Monocytes ◽  
A Cell ◽  
Cell Type Specific

AbstractSingle-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

scholarly journals Single-cell dissection of live human hearts in ischemic heart disease and heart failure reveals cell-type-specific driver genes and pathways

2021 ◽  
Author(s):  
Suvi Linna-Kuosmanen ◽  
Eloi Schmauch ◽  
Kyriakitsa Galani ◽  
Carles A. Boix ◽  
Lei Hou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Single Cell ◽  
Human Heart ◽  
Cell Types ◽  
Ischemic Heart ◽  
Cell Type ◽  
Cell Type Specific

Ischemic heart disease is the single most common cause of death worldwide with an annual death rate of over 9 million people. Genome-wide association studies have uncovered over 200 genetic loci underlying the disease, providing a deeper understanding of the causal mechanisms leading to it. However, in order to understand ischemic heart disease at the cellular and molecular level, it is necessary to identify the cell-type-specific circuits enabling dissection of driver variants, genes, and signaling pathways in normal and diseased tissues. Here, we provide the first detailed single-cell dissection of the cell types and disease-associated gene expression changes in the living human heart, using cardiac biopsies collected during open-heart surgery from control, ischemic heart disease, and ischemic and non-ischemic heart failure patients. We identify 84 cell types/states, grouped in 12 major cell types. We define markers for each cell type, providing the first extensive reference set for the live human heart. These major cell types include cardiovascular cells (cardiomyocytes, endothelial cells, fibroblasts), rarer cell types (B lymphocytes, neurons, Schwann cells), and rich populations of previously understudied layer-specific epicardial and endocardial cells. In addition, we reveal substantial differences in disease-associated gene expression at the cell subtype level, revealing arterial pericytes as having a central role in the pathogenesis of ischemic heart disease and heart failure. Our results demonstrate the importance of high-resolution cellular subtype mapping in gaining mechanistic insight into human cardiovascular disease.

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