MO262THE SINGLE-CELL TRANSCRIPTOMICS OF HUMAN IGA NEPHROPATHY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Yong Zhong ◽  
Xiangcheng Xiao
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Signaling Pathways ◽  
Iga Nephropathy ◽  
Molecular Mechanisms ◽  
Mesangial Cells ◽  
Therapeutic Targets ◽  
Cell Types ◽  
Receptor Crosstalk ◽  
Overt Proteinuria

Abstract Background and Aims The exact molecular mechanisms underlying IgA nephropathy (IgAN) remains incompletely defined. Therefore, it is necessary to further elucidate the mechanism of IgA nephropathy and find novel therapeutic targets. Method Single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from 4 IgAN and 1 control subjects to define the transcriptomic landscape at the single-cell resolution. Unsupervised clustering analysis of kidney specimens was used to identify distinct cell clusters. Differentially expressed genes and potential signaling pathways involved in IgAN were also identified. Results Our analysis identified 14 cell subsets in kidney biopsies from IgAN patients, and analyzed changing gene expression in distinct renal cell types. We found increased mesangial expression of several novel genes including MALAT1, GADD45B, SOX4 and EDIL3, which were related to proliferation and matrix accumulation and have not been reported in IgAN previously. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling and NOD-like receptor signaling. Moreover, the receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. Specifically, IgAN with overt proteinuria displayed elevated genes participating in several signaling pathways which may be involved in pathogenesis of progression of IgAN. Conclusion The comprehensive analysis of kidney biopsy specimen demonstrated different gene expression profile, potential pathologic ligand-receptor crosstalk, signaling pathways in human IgAN. These results offer new insight into pathogenesis and identify new therapeutic targets for patients with IgA nephropathy.

scholarly journals A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy

2021 ◽  
Vol 12 ◽  
Author(s):  
Rong Tang ◽  
Ting Meng ◽  
Wei Lin ◽  
Chanjuan Shen ◽  
Joshua D. Ooi ◽  
...  
Keyword(s):  
Single Cell ◽  
Iga Nephropathy ◽  
Molecular Mechanisms ◽  
Mesangial Cells ◽  
Receptor Signaling ◽  
Inflammatory Pathways ◽  
Number Of Patients ◽  
Tubule Cells ◽  
Overt Proteinuria ◽  
Matrix Accumulation

The molecular mechanisms underlying renal damage of IgA nephropathy (IgAN) remain incompletely defined. Here, single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from IgAN and control subjects to define the transcriptomic landscape at single-cell resolution. We presented a comprehensive scRNA-seq analysis of human renal biopsies from IgAN. We showed for the first time that IgAN mesangial cells displayed increased expression of several novel genes including MALAT1, GADD45B, SOX4, and EDIL3, which were related to cell proliferation and matrix accumulation. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. Furthermore, we compared the results of 4 IgAN patients with the published scRNA-Seq data of healthy kidney tissues of three human donors in order to further validate the findings in our study. The results also verified that the overexpressed genes in tubule cells from IgAN patients were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. The receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. IgAN patients with overt proteinuria displayed elevated genes participating in several signaling pathways compared with microproteinuria group. It needs to be mentioned that based on number of mesangial cells and other kidney cells analyzed in this study, the results of our study are preliminary and needs to be confirmed on larger number of cells from larger number of patients and controls in future studies. Therefore, these results offer new insight into pathogenesis and identify new therapeutic targets for IgAN.

scholarly journals A Single-Cell Transcriptome Atlas of the Mouse Glomerulus

2018 ◽  
Vol 29 (8) ◽  
pp. 2060-2068 ◽  
Author(s):  
Nikos Karaiskos ◽  
Mahdieh Rahmatollahi ◽  
Anastasiya Boltengagen ◽  
Haiyue Liu ◽  
Martin Hoehne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Mesangial Cells ◽  
Transcriptional Profiling ◽  
Wild Type Mouse ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Glomerular Cell

Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.

scholarly journals Single-Cell Sequencing Applications in the Inner Ear

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingxuan Wu ◽  
Mingyu Xia ◽  
Wenyan Li ◽  
Huawei Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Inner Ear ◽  
Signaling Pathways ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Heterogeneity ◽  
Single Cell Sequencing ◽  
Cell Groups ◽  
Different Cell Types

Genomics studies face specific challenges in the inner ear due to the multiple types and limited amounts of inner ear cells that are arranged in a very delicate structure. However, advances in single-cell sequencing (SCS) technology have made it possible to analyze gene expression variations across different cell types as well as within specific cell groups that were previously considered to be homogeneous. In this review, we summarize recent advances in inner ear research brought about by the use of SCS that have delineated tissue heterogeneity, identified unknown cell subtypes, discovered novel cell markers, and revealed dynamic signaling pathways during development. SCS opens up new avenues for inner ear research, and the potential of the technology is only beginning to be explored.

scholarly journals Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Jie Sheng ◽  
Daifeng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Manifold Learning ◽  
Molecular Mechanisms ◽  
Single Cells ◽  
Cell Types ◽  
Functional Enrichment ◽  
Multimodal Data ◽  
Network Analyses ◽  
Manifold Alignment

AbstractRecent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. To address this, we applied and benchmarked multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form clusters highly corresponding to transcriptomic and morphological cell types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. The aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.

scholarly journals Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement

2018 ◽  
Vol 115 (5) ◽  
pp. E1051-E1060 ◽  
Author(s):  
Brian T. Kalish ◽  
Lucas Cheadle ◽  
Sinisa Hrvatin ◽  
M. Aurel Nagy ◽  
Samuel Rivera ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Lateral Geniculate Nucleus ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Lateral Geniculate ◽  
Circuit Development ◽  
Circuit Assembly ◽  
Relay Neurons

Coordinated changes in gene expression underlie the early patterning and cell-type specification of the central nervous system. However, much less is known about how such changes contribute to later stages of circuit assembly and refinement. In this study, we employ single-cell RNA sequencing to develop a detailed, whole-transcriptome resource of gene expression across four time points in the developing dorsal lateral geniculate nucleus (LGN), a visual structure in the brain that undergoes a well-characterized program of postnatal circuit development. This approach identifies markers defining the major LGN cell types, including excitatory relay neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells. Most cell types exhibit significant transcriptional changes across development, dynamically expressing genes involved in distinct processes including retinotopic mapping, synaptogenesis, myelination, and synaptic refinement. Our data suggest that genes associated with synapse and circuit development are expressed in a larger proportion of nonneuronal cell types than previously appreciated. Furthermore, we used this single-cell expression atlas to identify the Prkcd-Cre mouse line as a tool for selective manipulation of relay neurons during a late stage of sensory-driven synaptic refinement. This transcriptomic resource provides a cellular map of gene expression across several cell types of the LGN, and offers insight into the molecular mechanisms of circuit development in the postnatal brain.

scholarly journals Single-cell RNA sequencing reveals mRNA splice isoform switching during kidney development

2019 ◽  
Author(s):  
Yishay Wineberg ◽  
Tali Hana Bar-Lev ◽  
Anna Futorian ◽  
Nissim Ben-Haim ◽  
Leah Armon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Kidney Development ◽  
Cell Types ◽  
Additional Layer ◽  
Single Cell Rna Sequencing ◽  
Splice Isoform ◽  
Isoform Switching

ABSTRACTDuring mammalian kidney development, nephron progenitors undergo a mesenchymal to epithelial transition and eventually differentiate into the various tubular segments of the nephron. Recently, the different cell types in the developing kidney were characterized using the Dropseq single cell RNA sequencing technology for measuring gene expression from thousands of individual cells. However, many genes can also be alternatively spliced and this creates an additional layer of heterogeneity. We therefore used full transcript length single-cell RNA sequencing to obtain the transcriptomes of 544 individual cells from mouse embryonic kidneys. We first used gene expression levels to identify each cell type. Then, we comprehensively characterized the splice isoform switching that occurs during the transition between mesenchymal and epithelial cellular states and identified several putative splicing regulators, including the genes Esrp1/2 and Rbfox1/2. We anticipate that these results will improve our understanding of the molecular mechanisms involved in kidney development.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Tim-3 enhances FcεRI-proximal signaling to modulate mast cell activation

2015 ◽  
Vol 212 (13) ◽  
pp. 2289-2304 ◽  
Author(s):  
Binh L. Phong ◽  
Lyndsay Avery ◽  
Tina L. Sumpter ◽  
Jacob V. Gorman ◽  
Simon C. Watkins ◽  
...  
Keyword(s):  
T Cells ◽  
Mast Cell ◽  
Signaling Pathways ◽  
Cell Activation ◽  
Molecular Mechanisms ◽  
Late Phase ◽  
Cell Types ◽  
Mast Cell Activation ◽  
Positive Role ◽  
Ample Evidence

T cell (or transmembrane) immunoglobulin and mucin domain protein 3 (Tim-3) has attracted significant attention as a novel immune checkpoint receptor (ICR) on chronically stimulated, often dysfunctional, T cells. Antibodies to Tim-3 can enhance antiviral and antitumor immune responses. Tim-3 is also constitutively expressed by mast cells, NK cells and specific subsets of macrophages and dendritic cells. There is ample evidence for a positive role for Tim-3 in these latter cell types, which is at odds with the model of Tim-3 as an inhibitory molecule on T cells. At this point, little is known about the molecular mechanisms by which Tim-3 regulates the function of T cells or other cell types. We have focused on defining the effects of Tim-3 ligation on mast cell activation, as these cells constitutively express Tim-3 and are activated through an ITAM-containing receptor for IgE (FcεRI), using signaling pathways analogous to those in T cells. Using a variety of gain- and loss-of-function approaches, we find that Tim-3 acts at a receptor-proximal point to enhance Lyn kinase-dependent signaling pathways that modulate both immediate-phase degranulation and late-phase cytokine production downstream of FcεRI ligation.

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