scholarly journals Epigenetic adaptation prolongs photoreceptor survival during retinal degeneration

2019 ◽  
Author(s):  
Rachayata Dharmat ◽  
Sangbae Kim ◽  
Hehe Liu ◽  
Shangyi Fu ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Exponential Distribution ◽  
Time Course ◽  
Chromatin State ◽  
Retinal Cell ◽  
Marker Genes ◽  
Specific Marker ◽  
Stretched Exponential ◽  
Rod Cells ◽  
Stretched Exponential Distribution

AbstractNeural degenerative diseases often display a progressive loss of cells as a stretched exponential distribution. The mechanisms underlying the survival of a subset of genetically identical cells in a population beyond what is expected by chance alone remains unknown. To gain mechanistic insights underlying prolonged cellular survival, we used Spata7 mutant mice as a model and performed single-cell transcriptomic profiling of retinal tissue along the time course of photoreceptor degeneration. Intriguingly, rod cells that survive beyond the initial rapid cell apoptosis phase progressively acquire a distinct transcriptome profile. In these rod cells, expression of photoreceptor-specific phototransduction pathway genes is downregulated while expression of other retinal cell type-specific marker genes is upregulated. These transcriptomic changes are achieved by modulation of the epigenome and changes of the chromatin state at these loci, as indicated by immunofluorescence staining and single-cell ATAC-seq. Consistent with this model, when induction of the repressive epigenetic state is blocked by in vivo histone deacetylase inhibition, all photoreceptors in the mutant retina undergo rapid degeneration, strongly curtailing the stretched exponential distribution. Our study reveals an intrinsic mechanism by which neural cells progressively adapt to genetic stress to achieve prolonged survival through epigenomic regulation and chromatin state modulation.

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 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 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-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Generation, transcriptome profiling, and functional validation of cone-rich human retinal organoids

2019 ◽  
Vol 116 (22) ◽  
pp. 10824-10833 ◽  
Author(s):  
Sangbae Kim ◽  
Albert Lowe ◽  
Rachayata Dharmat ◽  
Seunghoon Lee ◽  
Leah A. Owen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Time Course ◽  
Transcriptome Profiling ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Functional Validation ◽  
Retinal Progenitor ◽  
Retinal Differentiation

Rod and cone photoreceptors are light-sensing cells in the human retina. Rods are dominant in the peripheral retina, whereas cones are enriched in the macula, which is responsible for central vision and visual acuity. Macular degenerations affect vision the most and are currently incurable. Here we report the generation, transcriptome profiling, and functional validation of cone-rich human retinal organoids differentiated from hESCs using an improved retinal differentiation system. Induced by extracellular matrix, aggregates of hESCs formed single-lumen cysts composed of epithelial cells with anterior neuroectodermal/ectodermal fates, including retinal cell fate. Then, the cysts were en bloc-passaged, attached to culture surface, and grew, forming colonies in which retinal progenitor cell patches were found. Following gentle cell detachment, retinal progenitor cells self-assembled into retinal epithelium—retinal organoid—that differentiated into stratified cone-rich retinal tissue in agitated cultures. Electron microscopy revealed differentiating outer segments of photoreceptor cells. Bulk RNA-sequencing profiling of time-course retinal organoids demonstrated that retinal differentiation in vitro recapitulated in vivo retinogenesis in temporal expression of cell differentiation markers and retinal disease genes, as well as in mRNA alternative splicing. Single-cell RNA-sequencing profiling of 8-mo retinal organoids identified cone and rod cell clusters and confirmed the cone enrichment initially revealed by quantitative microscopy. Notably, cones from retinal organoids and human macula had similar single-cell transcriptomes, and so did rods. Cones in retinal organoids exhibited electrophysiological functions. Collectively, we have established cone-rich retinal organoids and a reference of transcriptomes that are valuable resources for retinal studies.

scholarly journals An Investigation of Stretched Exponential Function in Quantifying Long-Term Memory of Extreme Events Based on Artificial Data following Lévy Stable Distribution

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
HongGuang Sun ◽  
Lin Yuan ◽  
Yong Zhang ◽  
Nicholas Privitera
Keyword(s):  
Exponential Distribution ◽  
Exponential Function ◽  
Extreme Events ◽  
Stable Distribution ◽  
Wide Spectrum ◽  
Long Term Memory ◽  
Term Memory ◽  
Stretched Exponential ◽  
Stretched Exponential Distribution

Extreme events, which are usually characterized by generalized extreme value (GEV) models, can exhibit long-term memory, whose impact needs to be quantified. It was known that extreme recurrence intervals can better characterize the significant influence of long-term memory than using the GEV model. Our statistical analyses based on time series datasets following the Lévy stable distribution confirm that the stretched exponential distribution can describe a wide spectrum of memory behavior transition from exponentially distributed intervals (without memory) to power-law distributed ones (with strong memory or fractal scaling property), extending the previous evaluation of the stretched exponential function using Gaussian/exponential distributed random data. Further deviation and discussion of a historical paradox (i.e., the residual waiting time tends to increase with an increasing elapsed time under long-term memory) are also provided, based on the theoretical analysis of the Bayesian law and the stretched exponential distribution.

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 Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Tombor ◽  
D John ◽  
S.F Glaser ◽  
G Luxan ◽  
E Forte ◽  
...  
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Metabolic Adaptation ◽  
Marker Genes ◽  
Funding Source ◽  
Paracrine Factors ◽  
Mesenchymal Transition ◽  
Mesenchymal Marker ◽  
Baseline Levels

Abstract   After myocardial infarct (MI), followed by ischemia and scar formation, interstitial cells play key roles in the adaptation to injury. Endothelial cells (ECs), for instance, can clonally expand, migrate into the infarct area and facilitate crucial functions promoting revascularization, reestablishment of oxygen supply and secretion of paracrine factors. Moreover, ECs can transiently undergo changes towards a mesenchymal phenotype (Endothelial-to-mesenchymal transition; EndMT). Whether this process contributes to long-term cardiac fibrosis or helps to facilitate post-ischemic vessel growth remains controversial. Here, we aim to delineate kinetics and characteristics of phenotypic changes in ECs with single cell RNA-sequencing (scRNA-seq). We performed a time course (homeostasis or 0 day (d), 1d, 3d, 5d, 7d, 14d, 28d post-MI) in mice and isolated the non-cardiomyocyte fraction for scRNA-seq (n=35,312 cells). Pecam1/Cdh5 double positive ECs showed expression of apoptosis, hypoxia and inflammation markers at 3d. Bioinformatic cell cycle analysis predicted high association with proliferative capacities at 3d, indicative of EC turnover post-MI. Metabolism, recently linked to regulate EndMT, was altered. We found genes of the glycolysis and the TCA-cycle pathway upregulated at 1d to 3d, and a decrease of fatty acid signaling genes. At 3d, mesenchymal markers Fn1, Vim, S100a4, Serpine1 transiently increased compared to homeostasis (>1.6-fold, p<0.05) together with a reduction of EC genes such as Pecam1. Interestingly, mesenchymal transition was transient and returned to baseline levels at 28d after MI. Cell fate trajectory analysis confirmed these findings by identifying an EC state characterized by high proliferation and mesenchymal but low EC properties. At 3d to 7d the majority of the ECs were assigned to this state, based on their transcriptomic profile. We additionally used Cdh5-CreERT2; R26-mT/mG mice followed by scRNA-seq to trace the fate of ECs. Bioinformatic analysis of GFP-positive ECs confirmed the gain in mesenchymal marker but revealed no full transition to the mesenchymal state at later timepoints. This suggests a transient mesenchymal activation of ECs rather than a complete lineage transition. We further induced EndMT with TGF-β2 in ECs in vitro and observed reversibility of the phenotype after withdrawal of the stimulus. After treatment, ECs upregulated various mesenchymal marker genes. Withdrawal of TGF-β2 at 3d or 7d, reverted expression to baseline levels. We further determined DNA methylation of EndMT gene loci to assess if TGF-β2 leads to a true fate change but did not observe changes after TGF-β2 stimulation and withdrawal. Taken together, our data suggests that ECs undergo a transient mesenchymal activation concomitant with a metabolic adaptation early after MI but do not acquire a long-term mesenchymal fate. This activation may facilitate EC migration and clonal expansion to regenerate the vascular network. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): German Center of Cardiovascular Research (DZHK), Deutsche Forschungsgemeinschaft (DFG) CRC1366 Project B4

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