Single-Cell Transcriptomics Reveal Immune Mechanisms of the Onset and Progression of IgA Nephropathy

Zheng, Ying; Lü, Ping; Deng, Yiyao; Wen, Lu; Wang, Yong; Ma, Xin; Wang, Zhongxin; Wu, Lingling; Hong, Quan; Duan, Shuwei; Yin, Zhimin; Fu, Bo; Cai, Guangyan; Chen, Xiangmei; Tang, Fuchou

doi:10.1016/j.celrep.2020.108525

Cited by 58 publications

(67 citation statements)

References 78 publications

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“…Nevertheless, we acknowledge that the pooled analysis of the CRC dataset largely limits the interpretation of results and warrants the future analysis of the same dataset on a per-patient/phenotype basis. Collectively, these results suggest that the common practice to highlight the most actively communicating cell clusters based on the CCC inference 23,46 should be considered with caution.…”

Section: Impact Of Methods and Resourcesmentioning

confidence: 88%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

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Section: Impact Of Methods and Resourcesmentioning

confidence: 88%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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show abstract

“…However, we only found one differentially expressed gene due to the small number of podocytes captured in the disease group as well as healthy control group. A further increase in throughput of the next generation of single-cell sequencing techniques or extracting the glomerular from kidney tissue before dissociation into single cells may prove efficient to capture enough podocytes for subsequent analysis (70). Third, as no other kidney disease control group was included in this study, the changes of DEGs are not specific for anti-PLA2R positive IMN and the changes may be generic to being proteinuria or glomerular inflammation.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Profiling Reveals Transcriptional Signatures and Cell-Cell Crosstalk in Anti-PLA2R Positive Idiopathic Membranous Nephropathy Patients

Shen

Lin

et al. 2021

Front. Immunol.

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Idiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease of the kidney glomerulus. It may gradually progress to end-stage renal disease (ESRD) characterized by increased proteinuria, which leads to serious consequences. Although substantial advances have been made in the understanding of the molecular bases of IMN in the last 10 years, certain questions remain largely unanswered. To define the transcriptomic landscape at single-cell resolution, we analyzed kidney samples from 6 patients with anti-PLA2R positive IMN and 2 healthy control subjects using single-cell RNA sequencing. We then identified distinct cell clusters through unsupervised clustering analysis of kidney specimens. Identification of the differentially expressed genes (DEGs) and enrichment analysis as well as the interaction between cells were also performed. Based on transcriptional expression patterns, we identified all previously described cell types in the kidney. The DEGs in most kidney parenchymal cells were primarily enriched in genes involved in the regulation of inflammation and immune response including IL-17 signaling, TNF signaling, NOD-like receptor signaling, and MAPK signaling. Moreover, cell-cell crosstalk highlighted the extensive communication of mesangial cells, which infers great importance in IMN. IMN with massive proteinuria displayed elevated expression of genes participating in inflammatory signaling pathways that may be involved in the pathogenesis of the progression of IMN. Overall, we applied single-cell RNA sequencing to IMN to uncover intercellular interactions, elucidate key pathways underlying the pathogenesis, and identify novel therapeutic targets of anti-PLA2R positive IMN.

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“…scRNA-seq has allowed, for example, to dissect the key cells involved in scar formation in the kidney [16], understand the distribution of distinct immune cell populations in the kidney [17], and identify protective mechanisms mediated by nuclear receptors [18]. A first study applied scRNA-seq to kidney cells and monocytes from peripheral blood of 13 IgAN patients and compared these to 6 controls [19]. The analysis found upregulation of JCHAIN, a gene involved in the dimerization of IgA in mesangial cells, and altered expression profiles of macrophages and CD8+ Tcells that could lead to a deregulation of inflammation.…”

Section: Omics Datamentioning

confidence: 99%

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

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Single-Cell Transcriptomics Reveal Immune Mechanisms of the Onset and Progression of IgA Nephropathy

Cited by 58 publications

References 78 publications

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Single-Cell Profiling Reveals Transcriptional Signatures and Cell-Cell Crosstalk in Anti-PLA2R Positive Idiopathic Membranous Nephropathy Patients

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

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