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Liu X, Wang H, Gao J. scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error. Comput Struct Biotechnol J 2024; 23:549-558. [PMID: 38274995 PMCID: PMC10809077 DOI: 10.1016/j.csbj.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.
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Affiliation(s)
- Xiaohong Liu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Han Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jingyang Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
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Wang C, Huyan T, Guo W, Shu Q, Li Q, Shi J. NTCdb: Single-cell transcriptome database of human inflammatory-associated diseases. Comput Struct Biotechnol J 2024; 23:1978-1989. [PMID: 38765608 PMCID: PMC11098674 DOI: 10.1016/j.csbj.2024.04.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
With both the advancement of technology and the decline in costs, single-cell transcriptomics sequencing has become widespread in the biomedical area in recent years. It can facilitate the pathogenic characteristics at the single-cell level, which will assist clinical researchers in exploring the mechanism of diseases. As a result, single-cell transcriptome data based on clinical samples grew exponentially. However, there is still a lack of a comprehensive database about immunocytes in inflammatory-associated diseases. To address this deficiency, we propose a human inflammatory-associated disease-based single-cell transcriptome database, NTCdb (www.ntcdb.org.cn). NTCdb integrates the open-source data of 1,023,166 cells derived from 11 tissues of 17 inflammatory-associated diseases in a uniform pipeline. It provides a set of analyzing results, including cell communication analysis, enrichment analysis, and Pseudo-Time analysis, to obtain various characteristics of immune cells in inflammatory-associated disease. Taking COVID-19 as a case study, NTCdb displays important information including potentially significant functions of certain cells, genes, and signaling pathways, as well as the commonalities of specific immunocytes between different inflammatory-associated disease.
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Affiliation(s)
| | | | - Wuli Guo
- School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
| | - Qi Shu
- School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
| | | | - Jianyu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
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3
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Abdank K, Cetin SZ, Abedini A, Susztak K, Eckardt KU, Balzer MS. A comparative scRNAseq data analysis to match mouse models with human kidney disease at the molecular level. Nephrol Dial Transplant 2024; 39:1044-1047. [PMID: 38323426 DOI: 10.1093/ndt/gfae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Indexed: 02/08/2024] Open
Affiliation(s)
- Kathrien Abdank
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sena Zeynep Cetin
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael S Balzer
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Clinician Scientist Program, Berlin, Germany
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4
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Lin P, Gan YB, He J, Lin SE, Xu JK, Chang L, Zhao LM, Zhu J, Zhang L, Huang S, Hu O, Wang YB, Jin HJ, Li YY, Yan PL, Chen L, Jiang JX, Liu P. Advancing skeletal health and disease research with single-cell RNA sequencing. Mil Med Res 2024; 11:33. [PMID: 38816888 PMCID: PMC11138034 DOI: 10.1186/s40779-024-00538-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.
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Grants
- 2022YFA1103202 National Key Research and Development Program of China
- 82272507 National Natural Science Foundation of China
- 32270887 National Natural Science Foundation of China
- 32200654 National Natural Science Foundation of China
- CSTB2023NSCQ-ZDJO008 Natural Science Foundation of Chongqing
- BX20220397 Postdoctoral Innovative Talent Support Program
- SFLKF202201 Independent Research Project of State Key Laboratory of Trauma and Chemical Poisoning
- 2021-XZYG-B10 General Hospital of Western Theater Command Research Project
- 14113723 University Grants Committee, Research Grants Council of Hong Kong, China
- N_CUHK472/22 University Grants Committee, Research Grants Council of Hong Kong, China
- C7030-18G University Grants Committee, Research Grants Council of Hong Kong, China
- T13-402/17-N University Grants Committee, Research Grants Council of Hong Kong, China
- AoE/M-402/20 University Grants Committee, Research Grants Council of Hong Kong, China
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Affiliation(s)
- Peng Lin
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yi-Bo Gan
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Jian He
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
- Pancreatic Injury and Repair Key Laboratory of Sichuan Province, the General Hospital of Western Theater Command, Chengdu, 610031, China
| | - Si-En Lin
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Jian-Kun Xu
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Liang Chang
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Li-Ming Zhao
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Sacramento, CA, 94305, USA
| | - Jun Zhu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Liang Zhang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Sha Huang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Ou Hu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Ying-Bo Wang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Huai-Jian Jin
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yang-Yang Li
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Pu-Lin Yan
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Lin Chen
- Center of Bone Metabolism and Repair, State Key Laboratory of Trauma and Chemical Poisoning, Trauma Center, Research Institute of Surgery, Laboratory for the Prevention and Rehabilitation of Military Training Related Injuries, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Jian-Xin Jiang
- Wound Trauma Medical Center, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
| | - Peng Liu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
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Long Q, Zhang P, Ou Y, Li W, Yan Q, Yuan X. Single-cell sequencing advances in research on mesenchymal stem/stromal cells. Hum Cell 2024:10.1007/s13577-024-01076-9. [PMID: 38743204 DOI: 10.1007/s13577-024-01076-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/04/2024] [Indexed: 05/16/2024]
Abstract
Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.
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Affiliation(s)
- Qingxi Long
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Pingshu Zhang
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Ya Ou
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Wen Li
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Qi Yan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Xiaodong Yuan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China.
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China.
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6
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Yang B, Hu S, Jiang Y, Xu L, Shu S, Zhang H. Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases. Mol Neurobiol 2024:10.1007/s12035-024-04126-3. [PMID: 38564138 DOI: 10.1007/s12035-024-04126-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.
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Affiliation(s)
- Bingjie Yang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Shuqi Hu
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yiru Jiang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Lei Xu
- Department of Neurology, Zhejiang Rongjun Hospital, Jiaxing, Zhejiang, China
| | - Song Shu
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Hao Zhang
- Department of Neurology, The Fourth Clinical School of Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
- Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China.
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7
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Xie J, Wu D, Zhang P, Zhao S, Qi M. Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms. Exp Dermatol 2024; 33:e15070. [PMID: 38570935 DOI: 10.1111/exd.15070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Dan Wu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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8
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Huang X, Liu R, Yang S, Chen X, Li H. scAnnoX: an R package integrating multiple public tools for single-cell annotation. PeerJ 2024; 12:e17184. [PMID: 38560451 PMCID: PMC10981883 DOI: 10.7717/peerj.17184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Background Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.
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Affiliation(s)
- Xiaoqian Huang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Ruiqi Liu
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Shiwei Yang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Xiaozhou Chen
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Huamei Li
- Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, Jiangsu Province, China
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Tukker AM, Bowman AB. Application of Single Cell Gene Expression Technologies to Neurotoxicology. CURRENT OPINION IN TOXICOLOGY 2024; 37:100458. [PMID: 38617035 PMCID: PMC11008280 DOI: 10.1016/j.cotox.2023.100458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Neurotoxicological research faces the challenge of linking biological changes resulting from exposures to neuronal function. An additional challenge is understanding cell-type specific differences and selective vulnerabilities of distinct neuronal populations to toxic insults. Single cell RNA-sequencing (scRNA-seq) allows for measurement of the transcriptome of individual cells. This makes it a valuable tool for validating and characterizing cell types present in multicell type samples in complex tissue or cell culture models, but also for understanding how different cell types respond to toxic insults. Pathway analysis of differentially expressed genes can provide in depth insights into underlying cell type-specific mechanisms of neurotoxicity. Toxicological data often has to be translated to outcomes for human health which requires an understanding of inter-species differences. Transcriptomic data aids in understanding these differences, including understanding developmental timelines of different species. We believe that scRNA-seq holds exciting promises for future neurotoxicological research.
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Affiliation(s)
- Anke M Tukker
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - Aaron B Bowman
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
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10
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Chang JT, Liu LB, Wang PG, An J. Single-cell RNA sequencing to understand host-virus interactions. Virol Sin 2024; 39:1-8. [PMID: 38008383 PMCID: PMC10877424 DOI: 10.1016/j.virs.2023.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/23/2023] [Indexed: 11/28/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.
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Affiliation(s)
- Jia-Tong Chang
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Li-Bo Liu
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Pei-Gang Wang
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China.
| | - Jing An
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China.
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11
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Ye Q, Xu G, Xue C, Pang S, Xie B, Huang G, Li H, Chen X, Yang R, Li W. Urinary SPP1 has potential as a non-invasive diagnostic marker for focal segmental glomerulosclerosis. FEBS Open Bio 2023; 13:2061-2080. [PMID: 37696527 PMCID: PMC10626280 DOI: 10.1002/2211-5463.13704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/26/2023] [Accepted: 09/08/2023] [Indexed: 09/13/2023] Open
Abstract
Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non-invasive diagnostic tools. We downloaded scRNA-seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP-Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP-Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP-Genes might be involved in the immune response and extracellular components. Six key EP-Genes were identified and correlated with renal function. IMC showed that key EP-Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non-invasive diagnosis of FSGS.
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Affiliation(s)
- Qinglin Ye
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Guiling Xu
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Chao Xue
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Shuting Pang
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Boji Xie
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Guanwen Huang
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Haoyu Li
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Xuesong Chen
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
| | - Rirong Yang
- Centre for Genomic and Personalized MedicineDepartment of ImmunologySchool of Basic Medical SciencesGuangxi Medical UniversityNanning530021China
| | - Wei Li
- Department of NephrologyThe Second Affiliated Hospital of Guangxi Medical UniversityNanningChina
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12
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Zhou J, Abedini A, Balzer MS, Shrestha R, Dhillon P, Liu H, Hu H, Susztak K. Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States. J Am Soc Nephrol 2023; 34:1843-1862. [PMID: 37639336 PMCID: PMC10631616 DOI: 10.1681/asn.0000000000000217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/09/2023] [Indexed: 08/31/2023] Open
Abstract
SIGNIFICANCE STATEMENT Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. The authors characterized 18 different mouse kidney disease models at both bulk and single-cell gene expression levels and compared single-cell gene expression data from diabetic kidney disease (DKD) mice and from patients with DKD. Although single cell-level gene expression changes were mostly model-specific, different disease models showed similar changes when compared at a pathway level. The authors also found that changes in fractions of cell types are major drivers of bulk gene expression differences. Although the authors found only a small overlap of single cell-level gene expression changes between the mouse DKD model and patients, they observed consistent pathway-level changes. BACKGROUND Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. METHODS We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls. RESULTS We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples. CONCLUSIONS This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes.
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Affiliation(s)
- Jianfu Zhou
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael S. Balzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rojesh Shrestha
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Poonam Dhillon
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hailong Hu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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13
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Wang L, Li W, Xie W, Wang R, Yu K. Dual-GCN-based deep clustering with triplet contrast for ScRNA-seq data analysis. Comput Biol Chem 2023; 106:107924. [PMID: 37487251 DOI: 10.1016/j.compbiolchem.2023.107924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/08/2023] [Accepted: 07/12/2023] [Indexed: 07/26/2023]
Abstract
Single-cell RNA sequencing (ScRNA-seq) technology reveals gene expression information at the cellular level. The critical tasks in ScRNA-seq data analysis are clustering and dimensionality reduction. Recent deep clustering algorithms are used to optimize the two tasks jointly, and their variations, graph-based deep clustering algorithms, are used to capture and preserve topological information in the process. However, the existing graph-based deep clustering algorithms ignore the distribution information of nodes when constructing cell graphs which leads to incomplete information in the embedding representation; and graph convolutional networks (GCN), which are most commonly used, often suffer from over-smoothing that leads to high sample similarity in the embedding representation and then poor clustering performance. Here, the dual-GCN-based deep clustering with Triplet contrast (scDGDC) is proposed for dimensionality reduction and clustering of scRNA-seq data. Two critical components are dual-GCN-based encoder for capturing more comprehensive topological information and triplet contrast for reducing GCN over-smoothing. The two components improve the dimensionality reduction and clustering performance of scDGDC in terms of information acquisition and model optimization, respectively. The experiments on eight real ScRNA-seq datasets showed that scDGDC achieves excellent performance for both clustering and dimensionality reduction tasks and is high robustness to parameters.
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Affiliation(s)
- LinJie Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Wei Li
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Ministry of Education, Shenyang 110000, China.
| | - WeiDong Xie
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Rui Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
| | - Kun Yu
- College of Medicine and Bioinformation Engineering, Northeastern University, Shenyang 110819, China.
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14
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Danev N, Harman RM, Oliveira L, Huntimer L, Van de Walle GR. Bovine milk-derived cells express transcriptome markers of pluripotency and secrete bioactive factors with regenerative and antimicrobial activity. Sci Rep 2023; 13:12600. [PMID: 37537239 PMCID: PMC10400535 DOI: 10.1038/s41598-023-39833-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
The bovine mammary stem/progenitor cell secretome stimulates regeneration in vitro and contains proteins associated with antimicrobial defense. This has led to the exploration of the secretome as a biologic treatment for mastitis, a costly inflammation of the udder commonly caused by bacteria. This study reports on a population of bovine mammary stem/progenitor cells isolated non-invasively from milk (MiDCs). MiDCs were characterized by immunophenotyping, mammosphere formation assays, and single cell RNA sequencing. They displayed epithelial morphology, exhibited markers of mammary stem/progenitor cells, and formed mammospheres, like mammary gland tissue-isolated stem/progenitor cells. Single cell RNA sequencing revealed two sub-populations of MiDCs: epithelial cells and macrophages. Functionally, the MiDC secretome increased fibroblast migration, promoted angiogenesis of endothelial cells, and inhibited the growth of mastitis-associated bacteria, including antibiotic-resistant strains, in vitro. These qualities of MiDCs render them a source of stem cells and stem cell products that may be used to treat diseases affecting the dairy industry, including mastitis.
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Affiliation(s)
- Nikola Danev
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, 235 Hungerford Hill Road, Ithaca, NY, 14853, USA
| | - Rebecca M Harman
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, 235 Hungerford Hill Road, Ithaca, NY, 14853, USA
| | - Leane Oliveira
- Elanco Animal Health, 2500 Innovation Way, Indianapolis, IN, 46241, USA
| | - Lucas Huntimer
- Elanco Animal Health, 2500 Innovation Way, Indianapolis, IN, 46241, USA
| | - Gerlinde R Van de Walle
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, 235 Hungerford Hill Road, Ithaca, NY, 14853, USA.
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15
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Kuai Z, Hu Y. Integration single-cell and bulk RNA-sequencing data to reveal senescence gene expression profiles in heart failure. Heliyon 2023; 9:e16214. [PMID: 37332931 PMCID: PMC10275773 DOI: 10.1016/j.heliyon.2023.e16214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/06/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023] Open
Abstract
Background Heart failure (HF) represents one of healthcare's biggest challenges. Although rarely noticed, aging is a crucial risk factor for cardiovascular disease. Our study aims to reveal aging's role in HF by integrating single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing databases. Methods We collected HF heart sample data from the Gene Expression Omnibus database and senescence gene data from CellAge. The FindCluster () package was used for cell cluster analysis. Differentially expressed genes (DEG) were identified operating the FindMarkers function. Cell activity score calculation was performed using the AUCell package. UpSetR plotted the intersection between DEGs of active cell types, bulk data DEGs, and genes associated with aging. Using the DGIdb database gene-drug interaction data, we search for potential targeted therapeutics based on common senescence genes. Results The scRNA-seq data revealed myocardial heterogeneity in HF tissues. A series of crucial common senescence genes were found. The senescence gene expression profile hints at an intriguing connection between monocytes and HF. After analyzing the DEGs in the bulk dataset, the DEGs in scRNA-seq, the DEGs in each active cell type, and senescence genes, we identified ten genes as common senescence genes present in HF. Correlation analysis of transcriptomics, proteomics, and ceRNA was performed to provide ideas for future studies individually. Moreover, we discovered that common senescence genes and potential therapeutic drugs interact among different cell types. Further research is needed on the expression pattern of senescence genes and molecular regulation in HF. Conclusions In summary, we identified the functional significance of the senescence gene in HF using integrated data. It is possible that this more profound understanding of how senescence contributes to the development of HF will aid in unraveling the mechanisms that promote the disease and provide hints for developing therapeutics.
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Affiliation(s)
- Zheng Kuai
- Department of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Geriatrics, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
| | - Yu Hu
- Department of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Geriatrics, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
- Center for Evidence Based Medicine and Clinical Epidemiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
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16
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Xu X, Hua X, Mo H, Hu S, Song J. Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside. Basic Res Cardiol 2023; 118:7. [PMID: 36750503 DOI: 10.1007/s00395-022-00972-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 02/09/2023]
Abstract
The mechanisms of cardiovascular diseases (CVDs) remain incompletely elucidated. Single-cell RNA sequencing (scRNA-seq) has enabled the profiling of single-cell transcriptomes at unprecedented resolution and throughput, which is critical for deciphering cardiovascular cellular heterogeneity and underlying disease mechanisms, thereby facilitating the development of therapeutic strategies. In this review, we summarize cellular heterogeneity in cardiovascular homeostasis and diseases as well as the discovery of potential disease targets based on scRNA-seq, and yield new insights into the promise of scRNA-seq technology in precision medicine and clinical application.
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Affiliation(s)
- Xinjie Xu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
| | - Xiumeng Hua
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
| | - Han Mo
- Shenzhen Key Laboratory of Cardiovascular Disease, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, 518057, China
| | - Shengshou Hu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
| | - Jiangping Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
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17
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Zhang Y, Zhao X, Li C, Yang Y, Li L, Chen Y, Shi Q, Li Z, Wu Y, Zhang L, Li R, Si M, Liang X, Chen Y. Aberrant NAD synthetic flux in podocytes under diabetic conditions and effects of indoleamine 2,3-dioxygenase on promoting de novo NAD synthesis. Biochem Biophys Res Commun 2023; 643:61-68. [PMID: 36586160 DOI: 10.1016/j.bbrc.2022.12.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/10/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Nicotinamide adenine dinucleotide (NAD) is an essential coenzyme in the kidney. The first step in de novo NAD synthesis is regulated by indoleamine 2,3-dioxygenase (IDO), a tryptophan-catabolizing enzyme. Here, we investigated NAD synthetic flux and NAD levels in podocytes under diabetic conditions. We also studied the effects of IDO overexpression on NAD synthetic flux and high glucose (HG)-induced podocyte injury. NAD synthetases in the de novo, Preiss-Handler and salvage pathways were analyzed using in vivo single-nucleus RNA sequencing datasets (GSE131882) of control and diabetic kidney disease (DKD). The mRNA levels of these NAD synthetases were measured in vitro in HG-treated podocytes. The effects of IDO on NAD synthesis were examined by transducing cultured podocytes with an adenovirus encoding IDO, and apoptosis, podocyte markers and mobility were investigated. Cellular transcriptome analysis revealed that control podocytes had relatively low levels of NAD synthetases. In DKD podocytes, de novo NAD synthetase levels were further downregulated. IDO levels were virtually undetectable and did not increase in DKD. In vitro experiments confirmed aberrant de novo NAD synthetic flux and decreased IDO levels in HG-treated podocytes. Overexpression of IDO promoted NAD de novo synthesis, reduced NAD-bypass metabolic enzyme, increased NAD content and recovered podocyte injury markers under diabetic conditions. Taken together, our findings suggest that the de novo NAD synthetic flux is aberrant in DKD, and IDO promotes de novo NAD synthesis and NAD levels, as well as alleviates injury in HG-treated podocytes.
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Affiliation(s)
- Yuhua Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xingchen Zhao
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Cuili Li
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Yan Yang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Luan Li
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Yingwen Chen
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Qingying Shi
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhilian Li
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Yanhua Wu
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Li Zhang
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Ruizhao Li
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Meijun Si
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xinling Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China.
| | - Yuanhan Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Department of Nephrology, School of Medicine, South China University of Technology, Guangzhou, 510006, China.
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18
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Balzer MS, Doke T, Yang YW, Aldridge DL, Hu H, Mai H, Mukhi D, Ma Z, Shrestha R, Palmer MB, Hunter CA, Susztak K. Single-cell analysis highlights differences in druggable pathways underlying adaptive or fibrotic kidney regeneration. Nat Commun 2022; 13:4018. [PMID: 35821371 PMCID: PMC9276703 DOI: 10.1038/s41467-022-31772-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 07/01/2022] [Indexed: 01/14/2023] Open
Abstract
The kidney has tremendous capacity to repair after acute injury, however, pathways guiding adaptive and fibrotic repair are poorly understood. We developed a model of adaptive and fibrotic kidney regeneration by titrating ischemic injury dose. We performed detailed biochemical and histological analysis and profiled transcriptomic changes at bulk and single-cell level (> 110,000 cells) over time. Our analysis highlights kidney proximal tubule cells as key susceptible cells to injury. Adaptive proximal tubule repair correlated with fatty acid oxidation and oxidative phosphorylation. We identify a specific maladaptive/profibrotic proximal tubule cluster after long ischemia, which expresses proinflammatory and profibrotic cytokines and myeloid cell chemotactic factors. Druggability analysis highlights pyroptosis/ferroptosis as vulnerable pathways in these profibrotic cells. Pharmacological targeting of pyroptosis/ferroptosis in vivo pushed cells towards adaptive repair and ameliorates fibrosis. In summary, our single-cell analysis defines key differences in adaptive and fibrotic repair and identifies druggable pathways for pharmacological intervention to prevent kidney fibrosis.
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Affiliation(s)
- Michael S Balzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tomohito Doke
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ya-Wen Yang
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel L Aldridge
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hailong Hu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hung Mai
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dhanunjay Mukhi
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ziyuan Ma
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rojesh Shrestha
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew B Palmer
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christopher A Hunter
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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19
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Wang X, Xu Y, Sun Q, Zhou X, Ma W, Wu J, Zhuang J, Sun C. New insights from the single-cell level: Tumor associated macrophages heterogeneity and personalized therapy. Biomed Pharmacother 2022; 153:113343. [PMID: 35785706 DOI: 10.1016/j.biopha.2022.113343] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/02/2022] Open
Abstract
Tumor-associated macrophages (TAMs) are important immune cells in the tumor microenvironment, and their invasion in tumors is closely related to poor prognosis. Although TAMs are recognized as therapeutic targets, their heterogeneity makes studying tumor mechanism and developing drugs targeting TAMs difficult. The study of TAMs heterogeneity can be used to analyze the mechanism of tumor progression and drug resistance, and may provide possible treatment strategies for cancer patients. Single-cell RNA sequencing (scRNA-seq) can reveal the RNA expression profile for each TAM to distinguish heterogeneity, thereby providing a more efficient detection method and more accurate information for TAM-related studies. In this review, by summarizing the research progress in macrophage heterogeneity and other aspects of scRNA-seq over the past five years, we introduced the development of scRNA-seq technology and its application status in solid tumors, analyzed the advantages and selections of scRNA-seq in TAMs, and summarized the detailed specific research fields. To explore the mechanism of tumor progression and drug intervention from single cell level will provide new perspective for personalized treatment strategies targeting macrophages.
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Affiliation(s)
- Xiaomin Wang
- Special Medicine Department, School of Basic Medicine, Qingdao University, Qingdao, China
| | - Yiwei Xu
- Institute of Integrated Medicine, School of Medicine, Qingdao University, Qingdao, China
| | - Qi Sun
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xintong Zhou
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wenzhe Ma
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - JiBiao Wu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jing Zhuang
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China.
| | - Changgang Sun
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China; College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China.
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20
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Pan Y, Cao W, Mu Y, Zhu Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. BIOSENSORS 2022; 12:bios12070450. [PMID: 35884253 PMCID: PMC9312765 DOI: 10.3390/bios12070450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.
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Affiliation(s)
- Yating Pan
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
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21
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Abstract
Altered lipid metabolism is a characteristic feature and potential driving factor of acute kidney injury (AKI). Of the lipids that accumulate in injured renal tissues, ceramides are potent regulators of metabolism and cell fate. Up-regulation of ceramide synthesis is a common feature shared across several AKI etiologies in vitro and in vivo. Furthermore, ceramide accumulation is an early event in the natural history of AKI that precedes cell death and organ dysfunction. Emerging evidence suggests that inhibition of ceramide accumulation may improve renal outcomes in several models of AKI. This review examines the landscape of ceramide metabolism and regulation in the healthy and injured kidney. Furthermore, we discuss the body of literature regarding ceramides as therapeutic targets for AKI and consider potential mechanisms by which ceramides drive kidney pathogenesis.
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Affiliation(s)
- Rebekah J Nicholson
- Department of Nutrition and Integrative Physiology, Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, UT
| | - William L Holland
- Department of Nutrition and Integrative Physiology, Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, UT
| | - Scott A Summers
- Department of Nutrition and Integrative Physiology, Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, UT.
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22
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Miao Z, Balzer MS, Ma Z, Liu H, Wu J, Shrestha R, Aranyi T, Kwan A, Kondo A, Pontoglio M, Kim J, Li M, Kaestner KH, Susztak K. Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets. Nat Commun 2021; 12:2277. [PMID: 33859189 PMCID: PMC8050063 DOI: 10.1038/s41467-021-22266-1] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 02/28/2021] [Indexed: 12/19/2022] Open
Abstract
Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development.
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Affiliation(s)
- Zhen Miao
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Michael S Balzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Ziyuan Ma
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Junnan Wu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Rojesh Shrestha
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Tamas Aranyi
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Amy Kwan
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Ayano Kondo
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Marco Pontoglio
- Epigenetics and Development Laboratory, Université de Paris Inserm U1151/CNRS UMR 8253, Institut Necker Enfants Malades, Paris, France
| | - Junhyong Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mingyao Li
- Department of Epidemiology and Biostatistics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Klaus H Kaestner
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
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