Single Cell Transcriptome Helps Better Understanding Crosstalk in Diabetic Kidney Disease

Single-cell RNA sequencing in diabetic kidney disease: a literature review

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Crosstalk among podocytes, glomerular endothelial cells and mesangial cells in diabetic kidney disease: an updated review

Diabetic kidney disease (DKD) is a long-term and serious complication of diabetes that affects millions of people worldwide. It is characterized by proteinuria, glomerular damage, and renal fibrosis, leading to end-stage renal disease, and the pathogenesis is complex and involves multiple cellular and molecular mechanisms. Among three kinds of intraglomerular cells including podocytes, glomerular endothelial cells (GECs) and mesangial cells (MCs), the alterations in one cell type can produce changes in the others. The cell-to-cell crosstalk plays a crucial role in maintaining the glomerular filtration barrier (GFB) and homeostasis. In this review, we summarized the recent advances in understanding the pathological changes and interactions of these three types of cells in DKD and then focused on the signaling pathways and factors that mediate the crosstalk, such as angiopoietins, vascular endothelial growth factors, transforming growth factor-β, Krüppel-like factors, retinoic acid receptor response protein 1 and exosomes, etc. Furthermore, we also simply introduce the application of the latest technologies in studying cell interactions within glomerular cells and new promising mediators for cell crosstalk in DKD. In conclusion, this review provides a comprehensive and updated overview of the glomerular crosstalk in DKD and highlights its importance for the development of novel intervention approaches.

Single-cell transcriptomic profiles in the pathophysiology within the microenvironment of early diabetic kidney disease

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease, resulting in a huge socio-economic impact. Kidney is a highly complex organ and the pathogenesis underlying kidney organization involves complex cell-to-cell interaction within the heterogeneous kidney milieu. Advanced single-cell RNA sequencing (scRNA-seq) could reveal the complex architecture and interaction with the microenvironment in early DKD. We used scRNA-seq to investigate early changes in the kidney of db/m mice and db/db mice at the 14th week. Uniform Manifold Approximation and Projection were applied to classify cells into different clusters at a proper resolution. Weighted gene co-expression network analysis was used to identify the key molecules specifically expressed in kidney tubules. Information of cell-cell communication within the kidney was obtained using receptor-ligand pairing resources. In vitro model, human subjects, and co-detection by indexing staining were used to identify the pathophysiologic role of the hub genes in DKD. Among four distinct subsets of the proximal tubule (PT), lower percentages of proliferative PT and PT containing AQP4 expression (PT^AQP4+) in db/db mice induced impaired cell repair activity and dysfunction of renin-angiotensin system modulation in early DKD. We found that ferroptosis was involved in DKD progression, and ceruloplasmin acted as a central regulator of the induction of ferroptosis in PT^AQP4+. In addition, lower percentages of thick ascending limbs and collecting ducts with impaired metabolism function were also critical pathogenic features in the kidney of db/db mice. Secreted phosphoprotein 1 (SPP1) mediated pathogenic cross-talk in the tubular microenvironment, as validated by a correlation between urinary SPP1/Cr level and tubular injury. Finally, mesangial cell-derived semaphorin 3C (SEMA3C) further promoted endothelium-mesenchymal transition in glomerular endothelial cells through NRP1 and NRP2, and urinary SEMA3C/Cr level was positively correlated with glomerular injury. These data identified the hub genes involved in pathophysiologic changes within the microenvironment of early DKD.

The biological significance of cuproptosis-key gene MTF1 in pan-cancer and its inhibitory effects on ROS-mediated cell death of liver hepatocellular carcinoma

Metal regulatory transcription factor 1 (MTF1) has been reported to be correlated with several human diseases, especially like cancers. Exploring the underlying mechanisms and biological functions of MTF1 could provide novel strategies for clinical diagnosis and therapy of cancers. In this study, we conducted the comprehensive analysis to evaluate the profiles of MTF1 in pan-cancer. For example, TIMER2.0, TNMplot and GEPIA2.0 were employed to analyze the expression values of MTF1 in pan-cancer. The methylation levels of MTF1 were evaluated via UALCAN and DiseaseMeth version 2.0 databases. The mutation profiles of MTF1 in pan-cancers were analyzed using cBioPortal. GEPIA2.0, Kaplan-Meier plotter and cBioPortal were also used to explore the roles of MTF1 in cancer prognosis. We found that high MTF1 expression was related to poor prognosis of liver hepatocellular carcinoma (LIHC) and brain lower grade glioma (LGG). Also, high expression level of MTF1 was associated with good prognosis of kidney renal clear cell carcinoma (KIRC), lung cancer, ovarian cancer and breast cancer. We investigated the genetic alteration and methylation levels of MTF1 between the primary tumor and normal tissues. The relationship between MTF1 expression and several immune cells was analyzed, including T cell CD8 + and dendritic cells (DC). Mechanically, MTF1-interacted molecules might participate in the regulation of metabolism-related pathways, such as peptidyl-serine phosphorylation, negative regulation of cellular amide metabolic process and peptidyl-threonine phosphorylation. Single cell sequencing indicated that MTF1 was associated with angiogenesis, DNA repair and cell invasion. In addition, in vitro experiment indicated knockdown of MTF1 resulted in the suppressed cell proliferation, increased reactive oxygen species (ROS) and promoted cell death in LIHC cells HepG2 and Huh7. Taken together, this pan-cancer analysis of MTF1 has implicated that MTF1 could play an essential role in the progression of various human cancers.

A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics.