scholarly journals Single-cell RNA seq analysis identifies the biomarkers and differentiation of chondrocyte in human osteoarthritis

2020 ◽  
Author(s):  
Xiaolu Zhang ◽  
Nianlai Huang ◽  
Rongfu Huang ◽  
Liangming Wang ◽  
Qingfeng Ke ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Cell Subset ◽  
Marker Genes ◽  
Rna Seq ◽  
Candidate Marker ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Hypertrophic Chondrocyte ◽  
Different Types

Abstract Background: Single-cell RNA sequencing (scRNA-seq) was recently adopted for exploring molecular programmes and lineage progression patterns of pathogenesis of important diseases. In this study, scRNA-seq was used to identify potential markers for chondrocytes in osteoarthritis (OA) and to explore the function of different types of chondrocytes in OA. Methods: Here we aimed to identify the biomarkers and differentiation of chondrocyte by Single-cell RNA seq analysis. GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to identify the function of candidate marker genes in chondrocytes. Protein-protein interaction (PPI) network was constructed to find the hub genes in 3 types of chondrocyte respectively. We also used qRT-PCR to detect the expression level of the candidate marker genes in different types of chondrocyte. Results: In this study, we characterized the single-cell expression profiling of 480 chondrocyte samples and found hypertrophic chondrocyte (HTC), homeostatic chondrocyte (HomC) and fibrocartilage chondrocyte (FC) respectively. The results of GO and KEGG analysis showed the candidate marker genes made specific function in these chondrocytes to regulate the development of OAs respectively. We further revealed the differential expression of top 10 marker genes in 3 types of chondrocyte. The marker genes of HTC and FC were mainly expressed in their cell subset respectively. The marker genes of HomC did not have obviously differential expression among different types of chondrocyte. Last, we predicted the key genes in each cell subset. CD44, JUN and FN1 were predicted tightly related to the proliferation and differentiation of chondrocytes in OAs and could be regarded as biomarkers to estimate the development of OA. Conclusion: Our results provide new insights into exploring the roles of different types of chondrocyte in OA. The biomarkers of chondrocyte were also valuable for estimating OA progression.

scholarly journals Single-cell RNA seq analysis identifies the biomarkers and differentiation of chondrocyte in human osteoarthritis

2020 ◽  
Author(s):  
Xiaolu Zhang ◽  
Nianlai Huang ◽  
Rongfu Huang ◽  
Liangming Wang ◽  
Qingfeng Ke ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Cell Subset ◽  
Marker Genes ◽  
Rna Seq ◽  
Candidate Marker ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Hypertrophic Chondrocyte ◽  
Different Types

Abstract Background： Single-cell RNA sequencing (scRNA-seq) was recently adopted for exploring molecular programmes and lineage progression patterns of pathogenesis of important diseases. In this study, scRNA-seq was used to identify potential markers for chondrocytes in osteoarthritis (OA) and to explore the function of different types of chondrocytes in OA.Methods：Here we aimed to identify the biomarkers and differentiation of chondrocyte by Single-cell RNA seq analysis. GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to identify the function of candidate marker genes in chondrocytes. Protein–protein interaction (PPI) network was constructed to find the hub genes in 3 types of chondrocyte respectively. We also used qRT-PCR to detect the expression level of the candidate marker genes in different types of chondrocyte. Results： In this study, we characterized the single-cell expression profiling of 480 chondrocyte samples and found hypertrophic chondrocyte (HTC), homeostatic chondrocyte (HomC) and fibrocartilage chondrocyte (FC) respectively. The results of GO and KEGG analysis showed the candidate marker genes made specific function in these chondrocytes to regulate the development of OAs respectively. We further revealed the differential expression of top 10 marker genes in 3 types of chondrocyte. The marker genes of HTC and FC were mainly expressed in their cell subset respectively. The marker genes of HomC did not have obviously differential expression among different types of chondrocyte. Last, we predicted the key genes in each cell subset. CD44, JUN and FN1 were predicted tightly related to the proliferation and differentiation of chondrocytes in OAs and could be regarded as biomarkers to estimate the development of OA. Conclusion： Our results provide new insights into exploring the roles of different types of chondrocyte in OA. The biomarkers of chondrocyte were also valuable for estimating OA progression.

scholarly journals Single-cell RNA seq analysis identifies the biomarkers and differentiation of chondrocyte in human osteoarthritis

2020 ◽  
Author(s):  
Xiaolu Zhang ◽  
Nianlai Huang ◽  
Rongfu Huang ◽  
Liangming Wang ◽  
Qingfeng Ke ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Cell Subset ◽  
Marker Genes ◽  
Rna Seq ◽  
Candidate Marker ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Hypertrophic Chondrocyte ◽  
Different Types

Abstract Background： Single-cell RNA sequencing (scRNA-seq) was recently adopted for exploring molecular programmes and lineage progression patterns of pathogenesis of important diseases. In this study, we use scRNA-seq to identify potential markers for chondrocytes in osteoarthritis (OA) and explore the function of different types of chondrocytes in OA. Methods：Here we aimed to identifies the biomarkers and differentiation of chondrocyte by Single-cell RNA seq analysis. GO and KEGG analysis were used to prove the function of candidate marker genes in chondrocytes. Protein–protein interaction (PPI) network was constructed to found the hub genes in 3 types of chondrocyte respectively. We also used qRT-PCR to detect the expression level of the candidate marker genes in different types of chondrocyte. Results： In this study, we characterized the single-cell expression profiling of 480 chondrocyte samples and found hypertrophic chondrocyte (HTC), homeostatic chondrocyte (HomC) and fibrocartilage chondrocyte (FC) respectively. The results of GO and KEGG analysis to the candidate marker genes of 3 types of chondrocyte. showed the candidate marker genes made specific function in these chondrocytes to regulate the development of OAs respectively. We further revealed the differential expression of top 10 marker genes of 3 types of chondrocyte in different type of chondrocytes. The marker genes of HTC and FC were mainly expressed in their respective cell. The marker genes of HomC did not have obviously differential expression among different types of chondrocyte. Last, we proved the key genes in each cell subset. CD44, JUN and FN1 were proved tightly related to the proliferation and differentiation of chondrocytes in OAs and could be regarded as biomarkers to estimate the development of OA. Conclusion： Our results provide new insights into exploring the roles of different types of chondrocyte in OA. The biomarkers of chondrocyte were also valuable for estimating OA progression.

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 DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Integrated Single Cell Atlas of Endothelial Cells of the Human Lung

Circulation ◽  
2021 ◽  
Author(s):  
Jonas C. Schupp ◽  
Taylor S. Adams ◽  
Carlos Cosme Jr. ◽  
Micha Sam Brickman Raredon ◽  
Yifan Yuan ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Pulmonary Hypertension ◽  
Single Cell ◽  
Differential Expression ◽  
Human Lung ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Lung Endothelium ◽  
Lung Endothelial Cells

Background: The cellular diversity of the lung endothelium has not been systematically characterized in humans. Here, we provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium. Methods: We reprocessed human control single cell RNA sequencing (scRNAseq) data from six datasets. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by fluorescent microscopy and in situ hybridization. scRNAseq of primary lung ECs cultured in-vitro was performed. The signaling network between different lung cell types was studied. For cross species analysis or disease relevance, we applied the same methods to scRNAseq data obtained from mouse lungs or from human lungs with pulmonary hypertension. Results: Six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs, we found previously indistinguishable subpopulations: among venous EC, we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC, we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes, are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. scRNAseq of commercially available primary lung ECs demonstrated a loss of their native lung phenotype in culture. scRNAseq revealed that the endothelial diversity is maintained in pulmonary hypertension. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com). Conclusions: Our integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

scholarly journals DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data

2019 ◽  
Vol 35 (24) ◽  
pp. 5155-5162 ◽  
Author(s):  
Chengzhong Ye ◽  
Terence P Speed ◽  
Agus Salim
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Type I Error ◽  
R Package ◽  
Supplementary Information ◽  
Type I ◽  
Common Phenomenon ◽  
Rna Seq ◽  
Capture Process ◽  
Technological Platforms

Abstract Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.

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 Two-phase differential expression analysis for single cell RNA-seq

2018 ◽  
Vol 34 (19) ◽  
pp. 3340-3348 ◽  
Author(s):  
Zhijin Wu ◽  
Yi Zhang ◽  
Michael L Stitzel ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Two Phase

scholarly journals Unveiling the heterogeneity of NKT cells in the liver through single cell RNA sequencing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hao Shen ◽  
Chan Gu ◽  
Tao Liang ◽  
Haifeng Liu ◽  
Fan Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Subset ◽  
Nkt Cells ◽  
Organ Distribution ◽  
Marker Genes ◽  
Type I ◽  
Nkt Cell ◽  
Single Cell Rna Sequencing

Abstract CD1d-dependent type I NKT cells, which are activated by lipid antigen, are known to play important roles in innate and adaptive immunity, as are a portion of type II NKT cells. However, the heterogeneity of NKT cells, especially NKT-like cells, remains largely unknown. Here, we report the profiling of NKT (NK1.1+CD3e+) cells in livers from wild type (WT), Jα18-deficient and CD1d-deficient mice by single-cell RNA sequencing. Unbiased transcriptional clustering revealed distinct cell subsets. The transcriptomic profiles identified the well-known CD1d-dependent NKT cells and defined two CD1d-independent NKT cell subsets. In addition, validation of marker genes revealed the differential organ distribution and landscape of NKT cell subsets during liver tumor progression. More importantly, we found that CD1d-independent Sca-1−CD62L+ NKT cells showed a strong ability to secrete IFN-γ after costimulation with IL-2, IL-12 and IL-18 in vitro. Collectively, our findings provide a comprehensive characterization of NKT cell heterogeneity and unveil a previously undefined functional NKT cell subset.

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