151
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Li Z, Brittan M, Mills NL. A Multimodal Omics Framework to Empower Target Discovery for Cardiovascular Regeneration. Cardiovasc Drugs Ther 2024; 38:223-236. [PMID: 37421484 PMCID: PMC10959818 DOI: 10.1007/s10557-023-07484-7] [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] [Accepted: 06/19/2023] [Indexed: 07/10/2023]
Abstract
Ischaemic heart disease is a global healthcare challenge with high morbidity and mortality. Early revascularisation in acute myocardial infarction has improved survival; however, limited regenerative capacity and microvascular dysfunction often lead to impaired function and the development of heart failure. New mechanistic insights are required to identify robust targets for the development of novel strategies to promote regeneration. Single-cell RNA sequencing (scRNA-seq) has enabled profiling and analysis of the transcriptomes of individual cells at high resolution. Applications of scRNA-seq have generated single-cell atlases for multiple species, revealed distinct cellular compositions for different regions of the heart, and defined multiple mechanisms involved in myocardial injury-induced regeneration. In this review, we summarise findings from studies of healthy and injured hearts in multiple species and spanning different developmental stages. Based on this transformative technology, we propose a multi-species, multi-omics, meta-analysis framework to drive the discovery of new targets to promote cardiovascular regeneration.
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Affiliation(s)
- Ziwen Li
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
| | - Mairi Brittan
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Nicholas L Mills
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
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152
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Wu X, Zhang H. Omics Approaches Unveiling the Biology of Human Atherosclerotic Plaques. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:482-498. [PMID: 38280419 PMCID: PMC10988765 DOI: 10.1016/j.ajpath.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 12/16/2023] [Accepted: 12/20/2023] [Indexed: 01/29/2024]
Abstract
Atherosclerosis is a chronic inflammatory disease of the arterial wall, characterized by the buildup of plaques with the accumulation and transformation of lipids, immune cells, vascular smooth muscle cells, and necrotic cell debris. Plaques with collagen-poor thin fibrous caps infiltrated by macrophages and lymphocytes are considered unstable because they are at the greatest risk of rupture and clinical events. However, the current histologic definition of plaque types may not fully capture the complex molecular nature of atherosclerotic plaque biology and the underlying mechanisms contributing to plaque progression, rupture, and erosion. The advances in omics technologies have changed the understanding of atherosclerosis plaque biology, offering new possibilities to improve risk prediction and discover novel therapeutic targets. Genomic studies have shed light on the genetic predisposition to atherosclerosis, and integrative genomic analyses expedite the translation of genomic discoveries. Transcriptomic, proteomic, metabolomic, and lipidomic studies have refined the understanding of the molecular signature of atherosclerotic plaques, aiding in data-driven hypothesis generation for mechanistic studies and offering new prospects for biomarker discovery. Furthermore, advancements in single-cell technologies and emerging spatial analysis techniques have unveiled the heterogeneity and plasticity of plaque cells. This review discusses key omics-based discoveries that have advanced the understanding of human atherosclerotic plaque biology, focusing on insights derived from omics profiling of human atherosclerotic vascular specimens.
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Affiliation(s)
- Xun Wu
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Hanrui Zhang
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York.
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153
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Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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154
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Wang X, Zhai Y, Zheng H. Deciphering the cellular heterogeneity of the insect brain with single-cell RNA sequencing. INSECT SCIENCE 2024; 31:314-327. [PMID: 37702319 DOI: 10.1111/1744-7917.13270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 09/14/2023]
Abstract
Insects show highly complicated adaptive and sophisticated behaviors, including spatial orientation skills, learning ability, and social interaction. These behaviors are controlled by the insect brain, the central part of the nervous system. The tiny insect brain consists of millions of highly differentiated and interconnected cells forming a complex network. Decades of research has gone into an understanding of which parts of the insect brain possess particular behaviors, but exactly how they modulate these functional consequences needs to be clarified. Detailed description of the brain and behavior is required to decipher the complexity of cell types, as well as their connectivity and function. Single-cell RNA-sequencing (scRNA-seq) has emerged recently as a breakthrough technology to understand the transcriptome at cellular resolution. With scRNA-seq, it is possible to uncover the cellular heterogeneity of brain cells and elucidate their specific functions and state. In this review, we first review the basic structure of insect brains and the links to insect behaviors mainly focusing on learning and memory. Then the scRNA applications on insect brains are introduced by representative studies. Single-cell RNA-seq has allowed researchers to classify cell subpopulations within different insect brain regions, pinpoint single-cell developmental trajectories, and identify gene regulatory networks. These developments empower the advances in neuroscience and shed light on the intricate problems in understanding insect brain functions and behaviors.
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Affiliation(s)
- Xiaofei Wang
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Yifan Zhai
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
- Shandong Provincial Engineering Technology Research Center on Biocontrol of Crops Diseases and In-sect Pests, Jinan, China
| | - Hao Zheng
- Institute of Plant Protection, Shandong Academy of Agricultural Sciences, Jinan, China
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
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155
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Cheng Y, Zhang M, Xu R, Fu L, Xue M, Xu C, Tang C, Fang T, Liu X, Sun B, Chen L. p53 accelerates endothelial cell senescence in diabetic retinopathy by enhancing FoxO3a ubiquitylation and degradation via UBE2L6. Exp Gerontol 2024; 188:112391. [PMID: 38437929 DOI: 10.1016/j.exger.2024.112391] [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: 04/22/2023] [Revised: 02/25/2024] [Accepted: 03/01/2024] [Indexed: 03/06/2024]
Abstract
Diabetic retinopathy (DR) is the most common ocular fundus disease in diabetic patients. Chronic hyperglycemia not only promotes the development of diabetes and its complications, but also aggravates the occurrence of senescence. Previous studies have shown that DR is associated with senescence, but the specific mechanism has not been fully elucidated. Here, we first detected the differentially expressed genes (DEGs) and cellular senescence level of db/db mouse retinas by bulk RNA sequencing. Then, we used single-cell sequencing (scRNA-seq) to identify the main cell types in the retina and analyzed the DEGs in each cluster. We demonstrated that p53 expression was significantly increased in retinal endothelial cell cluster of db/db mice. Inhibition of p53 can reduce the expression of SA-β-Gal and the senescence-associated secretory phenotype (SASP) in HRMECs. Finally, we found that p53 can promote FoxO3a ubiquitination and degradation by increasing the expression of the ubiquitin-conjugating enzyme UBE2L6. Overall, our results demonstrate that p53 can accelerate the senescence process of endothelial cells and aggravate the development of DR. These data reveal new targets and insights that may be used to treat DR.
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Affiliation(s)
- Ying Cheng
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Man Zhang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Rong Xu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Lingli Fu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Mei Xue
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Chaofei Xu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Chao Tang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Ting Fang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Xiaohuan Liu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China
| | - Bei Sun
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China.
| | - Liming Chen
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China.
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156
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Wang L, Xue Z, Tian Y, Zeng W, Zhang T, Lu H. A single-cell transcriptome atlas of Lueyang black-bone chicken skin. Poult Sci 2024; 103:103513. [PMID: 38350389 PMCID: PMC10875617 DOI: 10.1016/j.psj.2024.103513] [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: 12/18/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Abstract
As the largest organ of the body, the skin participates in various physiological activities, such as barrier function, sensory function, and temperature regulation, thereby maintaining the balance between the body and the natural environment. To date, compositional and transcriptional profiles in chicken skin cells have not been reported. Here, we report detailed transcriptome analyses of cell populations present in the skin of a black-feather chicken and a white-feather chicken using single-cell RNA sequencing (scRNA-seq). By analyzing cluster-specific gene expression profiles, we identified 12 cell clusters, and their corresponding cell types were also characterized. Subsequently, we characterized the subpopulations of keratinocytes, myocytes, mesenchymal cells, fibroblasts, and melanocytes. It is worth noting that we have identified a subpopulation of keratinocytes involved in pigment granule capture and a subpopulation of melanocytes involved in pigment granule deposition, both of which have a higher cell abundance in black-feather chicken compared to white-feather chicken. Meanwhile, we also compared the cellular heterogeneity features of Lueyang black-bone chicken skin with different feather colors. In addition, we also screened out 12 genes those could be potential markers of melanocytes. Finally, we validated the specific expression of SGK1, WNT5A, CTSC, TYR, and LAPTM5 in black-feather chicken, which may be the key candidate genes determining the feather color differentiation of Lueyang black-bone chicken. In summary, this study first revealed the transcriptome characteristics of chicken skin cells via scRNA-seq technology. These datasets provide valuable information for the study of avian skin characteristics and have important implications for future poultry breeding.
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Affiliation(s)
- Ling Wang
- School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 723001 Hanzhong, China
- Engineering Research Center of Quality Improvement and Safety Control of Qinba Special Meat Products, Universities of Shaanxi Province, 723001 Hanzhong, China
- QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C., Shaanxi University of Technology, 723001 Hanzhong, China
| | - Zhen Xue
- School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China
| | - Yingmin Tian
- School of Mathematics and Computer Science, Shaanxi University of Technology, 723001 Hanzhong, China
| | - Wenxian Zeng
- School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 723001 Hanzhong, China
- Engineering Research Center of Quality Improvement and Safety Control of Qinba Special Meat Products, Universities of Shaanxi Province, 723001 Hanzhong, China
| | - Tao Zhang
- School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 723001 Hanzhong, China
- Engineering Research Center of Quality Improvement and Safety Control of Qinba Special Meat Products, Universities of Shaanxi Province, 723001 Hanzhong, China
- QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C., Shaanxi University of Technology, 723001 Hanzhong, China
| | - Hongzhao Lu
- School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 723001 Hanzhong, China
- Engineering Research Center of Quality Improvement and Safety Control of Qinba Special Meat Products, Universities of Shaanxi Province, 723001 Hanzhong, China
- QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C., Shaanxi University of Technology, 723001 Hanzhong, China
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157
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Perez KA, Deppe DW, Filas A, Singh SA, Aikawa E. Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:539-550. [PMID: 37517686 PMCID: PMC10988764 DOI: 10.1016/j.ajpath.2023.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 06/14/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023]
Abstract
This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.
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Affiliation(s)
- Katelyn A Perez
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel W Deppe
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Aidan Filas
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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158
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Gibson Hughes TA, Dona MSI, Sobey CG, Pinto AR, Drummond GR, Vinh A, Jelinic M. Aortic Cellular Heterogeneity in Health and Disease: Novel Insights Into Aortic Diseases From Single-Cell RNA Transcriptomic Data Sets. Hypertension 2024; 81:738-751. [PMID: 38318714 DOI: 10.1161/hypertensionaha.123.20597] [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] [Indexed: 02/07/2024]
Abstract
Aortic diseases such as atherosclerosis, aortic aneurysms, and aortic stiffening are significant complications that can have significant impact on end-stage cardiovascular disease. With limited pharmacological therapeutic strategies that target the structural changes in the aorta, surgical intervention remains the only option for some patients with these diseases. Although there have been significant contributions to our understanding of the cellular architecture of the diseased aorta, particularly in the context of atherosclerosis, furthering our insight into the cellular drivers of disease is required. The major cell types of the aorta are well defined; however, the advent of single-cell RNA sequencing provides unrivaled insights into the cellular heterogeneity of each aortic cell type and the inferred biological processes associated with each cell in health and disease. This review discusses previous concepts that have now been enhanced with recent advances made by single-cell RNA sequencing with a focus on aortic cellular heterogeneity.
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Affiliation(s)
- Tayla A Gibson Hughes
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
| | - Malathi S I Dona
- Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia (M.S.I.D., A.R.P.)
| | - Christopher G Sobey
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
| | - Alexander R Pinto
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
- Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia (M.S.I.D., A.R.P.)
| | - Grant R Drummond
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
| | - Antony Vinh
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
| | - Maria Jelinic
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia (T.A.G.H., C.G.S., A.R.P., G.R.D., A.V., M.J.)
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159
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Zhao R, Ding Y, Han R, Ding R, Liu J, Zhu C, Ding D, Bao M. Prognostic correlation between specialized capillary endothelial cells and lung adenocarcinoma. Heliyon 2024; 10:e28236. [PMID: 38533005 PMCID: PMC10963648 DOI: 10.1016/j.heliyon.2024.e28236] [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: 09/30/2023] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Background In-depth analysis of the functional changes occurring in endothelial cells (ECs) involved in capillary formation can help to elucidate the mechanism of tumour vascular growth. Methods Appropriate datasets were retrieved from the GEO database to obtain single-cell data on LUAD samples and adjacent normal tissue samples. ECs were selected by an automatic annotation program in R and further subdivided based on reported EC marker genes. Functional changes in different types of capillary ECs were then visualized, and the concrete expression was classified by genetic data in the TCGA. Finally, a prognostic model was constructed to predict immunoinfiltration status, survival and drug therapy effects. Results The LUAD data contained in the GSE183219 dataset were suitable for our analysis. After dimensionality reduction analysis and cell annotation, EC general capillary and EC aerocyte subsets as capillary specialized phenotypes showed a series of functional changes in tumour samples, with a total of 108 genes found to undergo functional changes. Use of CellPhoneDB revealed a close interaction of activity between ECs. After integration of TCGA, GSE68465 and GSE11969 datasets, the genes obtained were analysed by cluster analysis and risk model construction, identifying 8 genes. Drug sensitivity, immune cell and molecular differences can be accurately predicted. Conclusions EC general capillary and EC aerocyte subsets are recognized capillary ECs in the tumour microenvironment, and the functional changes between them are relevant to the prognosis and treatment of LUAD patients and have the potential to be used in target therapy.
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Affiliation(s)
- Rongchang Zhao
- Department of Oncology, Taixing People's Hospital, Taixing, China
| | - Yan Ding
- Department of Oncology, Taixing People's Hospital, Taixing, China
| | - Rongbo Han
- Department of Oncology, The Fourth Affiliated Hospital Of Nanjing Medical University, Nanjing, China
| | - Rongjie Ding
- Department of Oncology, Taixing People's Hospital, Taixing, China
| | - Jun Liu
- Department of Oncology, Taixing People's Hospital, Taixing, China
| | - Chunrong Zhu
- Department of Oncology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Dan Ding
- Department of Oncology, Taixing People's Hospital, Taixing, China
| | - Minhui Bao
- Department of Oncology, Taixing People's Hospital, Taixing, China
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160
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Alfattah MA, Correia CN, Browne JA, McGettigan PA, Pluta K, Carrington SD, MacHugh DE, Irwin JA. Transcriptomics analysis of the bovine endometrium during the perioestrus period. PLoS One 2024; 19:e0301005. [PMID: 38547106 PMCID: PMC10977793 DOI: 10.1371/journal.pone.0301005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/10/2024] [Indexed: 04/02/2024] Open
Abstract
During the oestrous cycle, the bovine endometrium undergoes morphological and functional changes, which are regulated by alterations in the levels of oestrogen and progesterone and consequent changes in gene expression. To clarify these changes before and after oestrus, RNA-seq was used to profile the transcriptome of oestrus-synchronized beef heifers. Endometrial samples were collected from 29 animals, which were slaughtered in six groups beginning 12 h after the withdrawal of intravaginal progesterone releasing devices until seven days post-oestrus onset (luteal phase). The groups represented proestrus, early oestrus, metoestrus and early dioestrus (luteal phase). Changes in gene expression were estimated relative to gene expression at oestrus. Ingenuity Pathway Analysis (IPA) was used to identify canonical pathways and functional processes of biological importance. A total of 5,845 differentially expressed genes (DEGs) were identified. The lowest number of DEGs was observed at the 12 h post-oestrus time point, whereas the greatest number was observed at Day 7 post-oestrus onset (luteal phase). A total of 2,748 DEGs at this time point did not overlap with any other time points. Prior to oestrus, Neurological disease and Organismal injury and abnormalities appeared among the top IPA diseases and functions categories, with upregulation of genes involved in neurogenesis. Lipid metabolism was upregulated before oestrus and downregulated at 48h post-oestrus, at which point an upregulation of immune-related pathways was observed. In contrast, in the luteal phase the Lipid metabolism and Small molecule biochemistry pathways were upregulated.
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Affiliation(s)
- Mohammed A. Alfattah
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
- King Faisal University, Al-Ahsa, Saudi Arabia
| | - Carolina N. Correia
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - John A. Browne
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - Paul A. McGettigan
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - Katarzyna Pluta
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - Stephen D. Carrington
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - David E. MacHugh
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
| | - Jane A. Irwin
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, Ireland
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161
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Qiu Y, Guo D, Zhao P, Zou Q. scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization. Brief Bioinform 2024; 25:bbae228. [PMID: 38754408 PMCID: PMC11097994 DOI: 10.1093/bib/bbae228] [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: 01/31/2024] [Revised: 04/02/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements. AVAILABILITY AND IMPLEMENTATION scMNMF code can be found at https://github.com/yushanqiu/scMNMF.
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Affiliation(s)
- Yushan Qiu
- School of Mathematical Sciences, Shenzhen University, 518000, Guangdong, China
| | - Dong Guo
- School of Mathematical Sciences, Shenzhen University, 518000, Guangdong, China
| | - Pu Zhao
- College of Life and Health Sciences, Northeastern University, Shenyang, 110169, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610056, China
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162
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Ye F, Zhang S, Fu Y, Yang L, Zhang G, Wu Y, Pan J, Chen H, Wang X, Ma L, Niu H, Jiang M, Zhang T, Jia D, Wang J, Wang Y, Han X, Guo G. Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3. Cell Discov 2024; 10:33. [PMID: 38531851 DOI: 10.1038/s41421-023-00642-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/21/2023] [Indexed: 03/28/2024] Open
Abstract
Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yijun Wu
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Pan
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinru Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haofu Niu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingyue Zhang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Danmei Jia
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yongcheng Wang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, China.
- Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, China.
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163
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Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [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: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
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Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
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164
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Jones EF, Haldar A, Oza VH, Lasseigne BN. Quantifying transcriptome diversity: a review. Brief Funct Genomics 2024; 23:83-94. [PMID: 37225889 DOI: 10.1093/bfgp/elad019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/14/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.
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Affiliation(s)
- Emma F Jones
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vishal H Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Brittany N Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
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165
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Ding J, Liu R, Wen H, Tang W, Li Z, Venegas J, Su R, Molho D, Jin W, Wang Y, Lu Q, Li L, Zuo W, Chang Y, Xie Y, Tang J. DANCE: a deep learning library and benchmark platform for single-cell analysis. Genome Biol 2024; 25:72. [PMID: 38504331 PMCID: PMC10949782 DOI: 10.1186/s13059-024-03211-z] [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: 02/07/2023] [Accepted: 03/05/2024] [Indexed: 03/21/2024] Open
Abstract
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
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Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Runze Su
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Dylan Molho
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Lingxiao Li
- Department of Computer Science, Boston University, Boston, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Zhejiang, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, USA.
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
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166
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Nguyen AD, Haines C, Price MJ, Dalton TE, Baëta CD, Hockenberry HA, Goodwin CR. Single-cell RNA sequencing comparison of the human metastatic prostate spine tumor microenvironment. STAR Protoc 2024; 5:102805. [PMID: 38341849 PMCID: PMC10867439 DOI: 10.1016/j.xpro.2023.102805] [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: 09/25/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 02/13/2024] Open
Abstract
Spinal column tumors can be difficult to process for single-cell omic studies, given the heterogeneity in tissue. Here, we present a protocol for operating room-to-benchtop single-cell processing of clinical specimens from a prostate cancer patient. We describe steps for sample homogenization, red blood cell lysis, cryopreservation, and single-cell sequencing analysis. This protocol can be used to identify prognostic markers and therapeutic targets for patients with osseous spine metastases and better inform eligibility for clinical trials.
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Affiliation(s)
- Annee D Nguyen
- Department of Neurosurgery, Duke University Medical Center, 200 Trent Dr, Durham, NC 27710, USA
| | - Corinne Haines
- Department of Molecular Genetics, Ohio State University, 1060 Carmack Road, Columbus, OH 43210, USA
| | - Meghan J Price
- Department of Medicine, John Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287, USA
| | - Tara E Dalton
- Department of Neurosurgery, Duke University Medical Center, 200 Trent Dr, Durham, NC 27710, USA
| | - César D Baëta
- Center for Population Health Sciences, Stanford University, 1701 Page Mill Road, Palo Alto, CA 94304, USA
| | - Harrison A Hockenberry
- Department of Neurosurgery, Duke University Medical Center, 200 Trent Dr, Durham, NC 27710, USA
| | - C Rory Goodwin
- Department of Neurosurgery, Duke University Medical Center, 200 Trent Dr, Durham, NC 27710, USA.
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167
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Fisher AG. Cell and developmental biology: grand challenges. Front Cell Dev Biol 2024; 12:1377073. [PMID: 38559812 PMCID: PMC10978741 DOI: 10.3389/fcell.2024.1377073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Amanda G. Fisher
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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168
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Li R, Du Y, Li K, Xiong X, Zhang L, Guo C, Gao S, Yao Y, Xu Y, Yang J. Single-cell transcriptome profiling implicates the psychological stress-induced disruption of spermatogenesis. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102158. [PMID: 38439912 PMCID: PMC10910125 DOI: 10.1016/j.omtn.2024.102158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/15/2024] [Indexed: 03/06/2024]
Abstract
Male infertility has emerged as a global issue, partly attributed to psychological stress. However, the cellular and molecular mechanisms underlying the adverse effects of psychological stress on male reproductive function remain elusive. We created a psychologically stressed model using terrified-sound and profiled the testes from stressed and control rats using single-cell RNA sequencing. Comparative and comprehensive transcriptome analyses of 11,744 testicular cells depicted the cellular landscape of spermatogenesis and revealed significant molecular alterations of spermatogenesis suffering from psychological stress. At the cellular level, stressed rats exhibited delayed spermatogenesis at the spermatogonia and pachytene phases, resulting in reduced sperm production. Additionally, psychological stress rewired cellular interactions among germ cells, negatively impacting reproductive development. Molecularly, we observed the down-regulation of anti-oxidation-related genes and up-regulation of genes promoting reactive oxygen species (ROS) generation in the stress group. These alterations led to elevated ROS levels in testes, affecting the expression of key regulators such as ATF2 and STAR, which caused reproductive damage through apoptosis or inhibition of testosterone synthesis. Overall, our study aimed to uncover the cellular and molecular mechanisms by which psychological stress disrupts spermatogenesis, offering insights into the mechanisms of psychological stress-induced male infertility in other species and promises in potential therapeutic targets.
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Affiliation(s)
- Rufeng Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Yuefeng Du
- Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, P.R. China
| | - Kang Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Xiaofan Xiong
- Center for Tumor and Immunology, the Precision Medical Institute, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, P.R. China
| | - Lingyu Zhang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Chen Guo
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Shanfeng Gao
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Yufei Yao
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Yungang Xu
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
| | - Juan Yang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, P.R. China
- Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education of China, Xi’an 710061, P.R. China
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169
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Preedy MK, White MRH, Tergaonkar V. Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells. Cell Death Dis 2024; 15:202. [PMID: 38467621 PMCID: PMC10928192 DOI: 10.1038/s41419-024-06559-z] [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: 09/29/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 03/13/2024]
Abstract
Cellular responses to TNF are inherently heterogeneous within an isogenic cell population and across different cell types. TNF promotes cell survival by activating pro-inflammatory NF-κB and MAPK signalling pathways but may also trigger apoptosis and necroptosis. Following TNF stimulation, the fate of individual cells is governed by the balance of pro-survival and pro-apoptotic signalling pathways. To elucidate the molecular mechanisms driving heterogenous responses to TNF, quantifying TNF/TNFR1 signalling at the single-cell level is crucial. Fluorescence live-cell imaging techniques offer real-time, dynamic insights into molecular processes in single cells, allowing for detection of rapid and transient changes, as well as identification of subpopulations, that are likely to be missed with traditional endpoint assays. Whilst fluorescence live-cell imaging has been employed extensively to investigate TNF-induced inflammation and TNF-induced cell death, it has been underutilised in studying the role of TNF/TNFR1 signalling pathway crosstalk in guiding cell-fate decisions in single cells. Here, we outline the various opportunities for pathway crosstalk during TNF/TNFR1 signalling and how these interactions may govern heterogenous responses to TNF. We also advocate for the use of live-cell imaging techniques to elucidate the molecular processes driving cell-to-cell variability in single cells. Understanding and overcoming cellular heterogeneity in response to TNF and modulators of the TNF/TNFR1 signalling pathway could lead to the development of targeted therapies for various diseases associated with aberrant TNF/TNFR1 signalling, such as rheumatoid arthritis, metabolic syndrome, and cancer.
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Affiliation(s)
- Marcus K Preedy
- Laboratory of NF-κB Signalling, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, D3308, Dover Street, Manchester, M13 9PT, England, UK
| | - Michael R H White
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, D3308, Dover Street, Manchester, M13 9PT, England, UK.
| | - Vinay Tergaonkar
- Laboratory of NF-κB Signalling, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 8 Medical Drive, MD7, Singapore, 117596, Singapore.
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170
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Zorc M, Dolinar M, Dovč P. A Single-Cell Transcriptome of Bovine Milk Somatic Cells. Genes (Basel) 2024; 15:349. [PMID: 38540408 PMCID: PMC10970057 DOI: 10.3390/genes15030349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 06/14/2024] Open
Abstract
The production of milk by dairy cows far exceeds the nutritional needs of the calf and is vital for the economical use of dairy cattle. High milk yield is a unique production trait that can be effectively enhanced through traditional selection methods. The process of lactation in cows serves as an excellent model for studying the biological aspects of lactation with the aim of exploring the mechanistic base of this complex trait at the cellular level. In this study, we analyzed the milk transcriptome at the single-cell level by conducting scRNA-seq analysis on milk samples from two Holstein Friesian cows at mid-lactation (75 and 93 days) using the 10× Chromium platform. Cells were pelleted and fat was removed from milk by centrifugation. The cell suspension from each cow was loaded on separate channels, resulting in the recovery of 9313 and 14,544 cells. Library samples were loaded onto two lanes of the NovaSeq 6000 (Illumina) instrument. After filtering at the cell and gene levels, a total of 7988 and 13,973 cells remained, respectively. We were able to reconstruct different cell types (milk-producing cells, progenitor cells, macrophages, monocytes, dendritic cells, T cells, B cells, mast cells, and neutrophils) in bovine milk. Our findings provide a valuable resource for identifying regulatory elements associated with various functions of the mammary gland such as lactation, tissue renewal, native immunity, protein and fat synthesis, and hormonal response.
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Affiliation(s)
| | | | - Peter Dovč
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia; (M.Z.); (M.D.)
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171
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Gong L, Cui X, Liu Y, Lin C, Gao Z. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares. Comput Biol Med 2024; 171:108225. [PMID: 38442556 DOI: 10.1016/j.compbiomed.2024.108225] [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: 10/28/2023] [Revised: 01/28/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVES Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. METHODS Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. RESULTS A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. CONCLUSIONS SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.
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Affiliation(s)
- Lejun Gong
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
| | - Xiong Cui
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yang Liu
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Cai Lin
- Department of Burn, Wound Repair and Regenerative Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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172
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Yan M, Wu R, Fu H, Hu C, Hao Y, Zeng J, Chen T, Wang Y, Wang Y, Hu J, Jin A. Integrated analysis of single-cell and bulk RNA sequencing data reveals the association between hypoxic tumor cells and exhausted T cells in predicting immune therapy response. Comput Biol Med 2024; 171:108179. [PMID: 38394803 DOI: 10.1016/j.compbiomed.2024.108179] [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: 11/19/2023] [Revised: 01/30/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
Continuous stimulation of tumor neoantigens and various cytokines in the tumor microenvironment leads to T cell dysfunction, but the specific mechanisms by which these key factors are distributed among different cell subpopulations and how they affect patient outcomes and treatment response are incompletely characterized. By integrating single-cell and bulk sequencing data of non-small cell lung cancer patients, we constructed a clinical outcome-associated T cell exhaustion signature. We discovered a significant association between the T cell exhaustion state and tumor cell hypoxia. Hypoxic malignant cells were significantly correlated with the proportion of exhausted T cells, and they co-occurred in patients at advanced stage. By analyzing the ligand-receptor interactions between these two cell states, we observed that T cells were recruited towards tumor cells through production of chemokines such as CXCL16-CXCR6 axis and CCL3/CCL4/CCL5-CCR5 axis. Based on 15 immune checkpoint blockade (ICB)-treatment cohorts, we constructed an interaction signature that can be used to predict the response to immune checkpoint blockade therapy. Among genes composed of the signature, CXCR6 alone has similarly high prediction efficacy (Area Under Curve (AUC) = 1, 0.89 and 0.73 for GSE126044, GSE135222 and GSE93157, respectively) with the signature and thus could serve as a potential biomarker for predicting immunotherapy response. Together, we have discovered and validated a significant association between exhausted T cells and hypoxic malignant cells, elucidating key interaction factors that significantly associated with response to immunotherapy.
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Affiliation(s)
- Min Yan
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China; Chongqing Key Laboratory of Basic and Translational Research of Tumor Immunology, Chongqing Medical University, Chongqing, 400010, China
| | - Ruixin Wu
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Han Fu
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Chao Hu
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Yanan Hao
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Jie Zeng
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Tong Chen
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Yingming Wang
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Yingying Wang
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China
| | - Jing Hu
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Aishun Jin
- Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China; Chongqing Key Laboratory of Basic and Translational Research of Tumor Immunology, Chongqing Medical University, Chongqing, 400010, China.
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173
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Bai X, Duren Z, Wan L, Xia LC. Joint inference of clonal structure using single-cell genome and transcriptome sequencing data. NAR Genom Bioinform 2024; 6:lqae017. [PMID: 38486887 PMCID: PMC10939367 DOI: 10.1093/nargab/lqae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/19/2023] [Accepted: 01/29/2024] [Indexed: 03/17/2024] Open
Abstract
Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.
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Affiliation(s)
- Xiangqi Bai
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA
| | - Lin Wan
- NCMIS, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li C Xia
- Department of Statistics and Financial Mathematics, School of Mathematics, South China University of Technology, Guangzhou, 510006, China
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174
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Lin A, Ramaswamy Y, Misra A. Developmental heterogeneity of vascular cells: Insights into cellular plasticity in atherosclerosis? Semin Cell Dev Biol 2024; 155:3-15. [PMID: 37316416 DOI: 10.1016/j.semcdb.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
Smooth muscle cells, endothelial cells and macrophages display remarkable heterogeneity within the healthy vasculature and under pathological conditions. During development, these cells arise from numerous embryological origins, which confound with different microenvironments to generate postnatal vascular cell diversity. In the atherosclerotic plaque milieu, all these cell types exhibit astonishing plasticity, generating a variety of plaque burdening or plaque stabilizing phenotypes. And yet how developmental origin influences intraplaque cell plasticity remains largely unexplored despite evidence suggesting this may be the case. Uncovering the diversity and plasticity of vascular cells is being revolutionized by unbiased single cell whole transcriptome analysis techniques that will likely continue to pave the way for therapeutic research. Cellular plasticity is only just emerging as a target for future therapeutics, and uncovering how intraplaque plasticity differs across vascular beds may provide key insights into why different plaques behave differently and may confer different risks of subsequent cardiovascular events.
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Affiliation(s)
- Alexander Lin
- Atherosclerosis and Vascular Remodeling Group, Heart Research Institute, Sydney, NSW, Australia; School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Yogambha Ramaswamy
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Ashish Misra
- Atherosclerosis and Vascular Remodeling Group, Heart Research Institute, Sydney, NSW, Australia; Heart Research Institute, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
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175
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Andresen AMS, Taylor RS, Grimholt U, Daniels RR, Sun J, Dobie R, Henderson NC, Martin SAM, Macqueen DJ, Fosse JH. Mapping the cellular landscape of Atlantic salmon head kidney by single cell and single nucleus transcriptomics. FISH & SHELLFISH IMMUNOLOGY 2024; 146:109357. [PMID: 38181891 DOI: 10.1016/j.fsi.2024.109357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024]
Abstract
Single-cell transcriptomics is the current gold standard for global gene expression profiling, not only in mammals and model species, but also in non-model fish species. This is a rapidly expanding field, creating a deeper understanding of tissue heterogeneity and the distinct functions of individual cells, making it possible to explore the complexities of immunology and gene expression on a highly resolved level. In this study, we compared two single cell transcriptomic approaches to investigate cellular heterogeneity within the head kidney of healthy farmed Atlantic salmon (Salmo salar). We compared 14,149 cell transcriptomes assayed by single cell RNA-seq (scRNA-seq) with 18,067 nuclei transcriptomes captured by single nucleus RNA-Seq (snRNA-seq). Both approaches detected eight major cell populations in common: granulocytes, heamatopoietic stem cells, erythrocytes, mononuclear phagocytes, thrombocytes, B cells, NK-like cells, and T cells. Four additional cell types, endothelial, epithelial, interrenal, and mesenchymal cells, were detected in the snRNA-seq dataset, but appeared to be lost during preparation of the single cell suspension submitted for scRNA-seq library generation. We identified additional heterogeneity and subpopulations within the B cells, T cells, and endothelial cells, and revealed developmental trajectories of heamatopoietic stem cells into differentiated granulocyte and mononuclear phagocyte populations. Gene expression profiles of B cell subtypes revealed distinct IgM and IgT-skewed resting B cell lineages and provided insights into the regulation of B cell lymphopoiesis. The analysis revealed eleven T cell sub-populations, displaying a level of T cell heterogeneity in salmon head kidney comparable to that observed in mammals, including distinct subsets of cd4/cd8-negative T cells, such as tcrγ positive, progenitor-like, and cytotoxic cells. Although snRNA-seq and scRNA-seq were both useful to resolve cell type-specific expression in the Atlantic salmon head kidney, the snRNA-seq pipeline was overall more robust in identifying several cell types and subpopulations. While scRNA-seq displayed higher levels of ribosomal and mitochondrial genes, snRNA-seq captured more transcription factor genes. However, only scRNA-seq-generated data was useful for cell trajectory inference within the myeloid lineage. In conclusion, this study systematically outlines the relative merits of scRNA-seq and snRNA-seq in Atlantic salmon, enhances understanding of teleost immune cell lineages, and provides a comprehensive list of markers for identifying major cell populations in the head kidney with significant immune relevance.
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Affiliation(s)
| | - Richard S Taylor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Rose Ruiz Daniels
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Jianxuan Sun
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Ross Dobie
- Centre for Inflammation Research, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, United Kingdom
| | - Neil C Henderson
- Centre for Inflammation Research, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, United Kingdom; MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel A M Martin
- Scottish Fish Immunology Research Centre, School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Daniel J Macqueen
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
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176
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Puvogel S, Alsema A, North HF, Webster MJ, Weickert CS, Eggen BJL. Single-Nucleus RNA-Seq Characterizes the Cell Types Along the Neuronal Lineage in the Adult Human Subependymal Zone and Reveals Reduced Oligodendrocyte Progenitor Abundance with Age. eNeuro 2024; 11:ENEURO.0246-23.2024. [PMID: 38351133 PMCID: PMC10913050 DOI: 10.1523/eneuro.0246-23.2024] [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: 07/12/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 03/06/2024] Open
Abstract
The subependymal zone (SEZ), also known as the subventricular zone (SVZ), constitutes a neurogenic niche that persists during postnatal life. In humans, the neurogenic potential of the SEZ declines after the first year of life. However, studies discovering markers of stem and progenitor cells highlight the neurogenic capacity of progenitors in the adult human SEZ, with increased neurogenic activity occurring under pathological conditions. In the present study, the complete cellular niche of the adult human SEZ was characterized by single-nucleus RNA sequencing, and compared between four youth (age 16-22) and four middle-aged adults (age 44-53). We identified 11 cellular clusters including clusters expressing marker genes for neural stem cells (NSCs), neuroblasts, immature neurons, and oligodendrocyte progenitor cells. The relative abundance of NSC and neuroblast clusters did not differ between the two age groups, indicating that the pool of SEZ NSCs does not decline in this age range. The relative abundance of oligodendrocyte progenitors and microglia decreased in middle-age, indicating that the cellular composition of human SEZ is remodeled between youth and adulthood. The expression of genes related to nervous system development was higher across different cell types, including NSCs, in youth as compared with middle-age. These transcriptional changes suggest ongoing central nervous system plasticity in the SEZ in youth, which declined in middle-age.
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Affiliation(s)
- Sofía Puvogel
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen 9700 AD, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen 6500 HB, The Netherlands
| | - Astrid Alsema
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen 9700 AD, The Netherlands
| | - Hayley F North
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Sydney, New South Wales 2031, Australia
- School of Psychiatry, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Maree J Webster
- Laboratory of Brain Research, Stanley Medical Research Institute, Rockville 20850, Maryland
| | - Cynthia Shannon Weickert
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Sydney, New South Wales 2031, Australia
- School of Psychiatry, University of New South Wales, Sydney, New South Wales 2052, Australia
- Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, New York 13201
| | - Bart J L Eggen
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen 9700 AD, The Netherlands
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177
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Ren L, Ma W, Wang Y. SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation. Comput Biol Med 2024; 171:108182. [PMID: 38422958 DOI: 10.1016/j.compbiomed.2024.108182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/18/2024] [Accepted: 02/18/2024] [Indexed: 03/02/2024]
Abstract
Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.
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Affiliation(s)
- Lixin Ren
- Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, 100083, Beijing, China.
| | - Wanbiao Ma
- Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, 100083, Beijing, China.
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 330106, China.
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178
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Jogi HR, Smaraki N, Nayak SS, Rajawat D, Kamothi DJ, Panigrahi M. Single cell RNA-seq: a novel tool to unravel virus-host interplay. Virusdisease 2024; 35:41-54. [PMID: 38817399 PMCID: PMC11133279 DOI: 10.1007/s13337-024-00859-w] [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: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 06/01/2024] Open
Abstract
Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.
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Affiliation(s)
- Harsh Rajeshbhai Jogi
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Nabaneeta Smaraki
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Dhaval J. Kamothi
- Division of Pharmacology and Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
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179
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Adkins-Threats M, Huang YZ, Mills JC. Highlights of how single-cell analyses are illuminating differentiation and disease in the gastric corpus. Am J Physiol Gastrointest Liver Physiol 2024; 326:G205-G215. [PMID: 38193187 PMCID: PMC11211037 DOI: 10.1152/ajpgi.00164.2023] [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: 08/02/2023] [Revised: 12/18/2023] [Accepted: 12/23/2023] [Indexed: 01/10/2024]
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique to identify novel cell markers, developmental trajectories, and transcriptional changes during cell differentiation and disease onset and progression. In this review, we highlight recent scRNA-seq studies of the gastric corpus in both human and murine systems that have provided insight into gastric organogenesis, identified novel markers for the various gastric lineages during development and in adults, and revealed transcriptional changes during regeneration and tumorigenesis. Overall, by elucidating transcriptional states and fluctuations at the cellular level in healthy and disease contexts, scRNA-seq may lead to better, more personalized clinical treatments for disease progression.
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Affiliation(s)
- Mahliyah Adkins-Threats
- Section of Gastroenterology, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
| | - Yang-Zhe Huang
- Section of Gastroenterology, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
- Graduate School of Biomedical Sciences, Cancer and Cell Biology Program, Baylor College of Medicine, Houston, Texas, United States
| | - Jason C Mills
- Section of Gastroenterology, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, United States
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180
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Kaur H, Jha P, Ochatt SJ, Kumar V. Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities. Crit Rev Biotechnol 2024; 44:202-217. [PMID: 36775666 DOI: 10.1080/07388551.2023.2165900] [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: 08/07/2022] [Revised: 11/04/2022] [Accepted: 12/08/2022] [Indexed: 02/14/2023]
Abstract
Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.
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Affiliation(s)
- Harmeet Kaur
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
| | - Priyanka Jha
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
- Department of Research Facilitation, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Sergio J Ochatt
- Agroécologie, InstitutAgro Dijon, INRAE, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Vijay Kumar
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
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181
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You Y, Chen Z, Hu WW. The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets? Pharmacol Ther 2024; 255:108606. [PMID: 38346477 DOI: 10.1016/j.pharmthera.2024.108606] [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: 10/31/2023] [Revised: 01/05/2024] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.
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Affiliation(s)
- Yi You
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhong Chen
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Wei-Wei Hu
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China.
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182
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Wang J, Zhang Y, Zhang T, Tan WT, Lambert F, Darmawan J, Huber R, Wan Y. RNA structure profiling at single-cell resolution reveals new determinants of cell identity. Nat Methods 2024; 21:411-422. [PMID: 38177506 PMCID: PMC10927541 DOI: 10.1038/s41592-023-02128-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
RNA structure is critical for multiple steps in gene regulation. However, how the structures of transcripts differ both within and between individual cells is unknown. Here we develop a SHAPE-inspired method called single-cell structure probing of RNA transcripts that enables simultaneous determination of transcript secondary structure and abundance at single-cell resolution. We apply single-cell structure probing of RNA transcripts to human embryonic stem cells and differentiating neurons. Remarkably, RNA structure is more homogeneous in human embryonic stem cells compared with neurons, with the greatest homogeneity found in coding regions. More extensive heterogeneity is found within 3' untranslated regions and is determined by specific RNA-binding proteins. Overall RNA structure profiles better discriminate cell type identity and differentiation stage than gene expression profiles alone. We further discover a cell-type variable region of 18S ribosomal RNA that is associated with cell cycle and translation control. Our method opens the door to the systematic characterization of RNA structure-function relationships at single-cell resolution.
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Affiliation(s)
- Jiaxu Wang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
| | - Yu Zhang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Tong Zhang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Wen Ting Tan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Finnlay Lambert
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jefferson Darmawan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Roland Huber
- Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
- Department of Biochemistry, National University of Singapore, Singapore, Singapore.
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183
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Kehl A, Aupperle-Lellbach H, de Brot S, van der Weyden L. Review of Molecular Technologies for Investigating Canine Cancer. Animals (Basel) 2024; 14:769. [PMID: 38473154 DOI: 10.3390/ani14050769] [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: 12/21/2023] [Revised: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.
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Affiliation(s)
- Alexandra Kehl
- Laboklin GmbH & Co. KG, Steubenstr. 4, 97688 Bad Kissingen, Germany
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 81675 München, Germany
| | - Heike Aupperle-Lellbach
- Laboklin GmbH & Co. KG, Steubenstr. 4, 97688 Bad Kissingen, Germany
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 81675 München, Germany
| | - Simone de Brot
- Institute of Animal Pathology, COMPATH, University of Bern, 3012 Bern, Switzerland
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184
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Rezaie N, Rebboah E, Williams BA, Liang HY, Reese F, Balderrama-Gutierrez G, Dionne LA, Reinholdt L, Trout D, Wold BJ, Mortazavi A. Identification of robust cellular programs using reproducible LDA that impact sex-specific disease progression in different genotypes of a mouse model of AD. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582178. [PMID: 38464087 PMCID: PMC10925135 DOI: 10.1101/2024.02.26.582178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The gene expression profiles of distinct cell types reflect complex genomic interactions among multiple simultaneous biological processes within each cell that can be altered by disease progression as well as genetic background. The identification of these active cellular programs is an open challenge in the analysis of single-cell RNA-seq data. Latent Dirichlet Allocation (LDA) is a generative method used to identify recurring patterns in counts data, commonly referred to as topics that can be used to interpret the state of each cell. However, LDA's interpretability is hindered by several key factors including the hyperparameter selection of the number of topics as well as the variability in topic definitions due to random initialization. We developed Topyfic, a Reproducible LDA (rLDA) package, to accurately infer the identity and activity of cellular programs in single-cell data, providing insights into the relative contributions of each program in individual cells. We apply Topyfic to brain single-cell and single-nucleus datasets of two 5xFAD mouse models of Alzheimer's disease crossed with C57BL6/J or CAST/EiJ mice to identify distinct cell types and states in different cell types such as microglia. We find that 8-month 5xFAD/Cast F1 males show higher level of microglial activation than matching 5xFAD/BL6 F1 males, whereas female mice show similar levels of microglial activation. We show that regulatory genes such as TFs, microRNA host genes, and chromatin regulatory genes alone capture cell types and cell states. Our study highlights how topic modeling with a limited vocabulary of regulatory genes can identify gene expression programs in single-cell data in order to quantify similar and divergent cell states in distinct genotypes.
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Affiliation(s)
- Narges Rezaie
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Elisabeth Rebboah
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Brian A Williams
- Division of Biology, California Institute of Technology, Pasadena, CA, USA
| | - Heidi Yahan Liang
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Fairlie Reese
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Gabriela Balderrama-Gutierrez
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | | | | | - Diane Trout
- Division of Biology, California Institute of Technology, Pasadena, CA, USA
| | - Barbara J Wold
- Division of Biology, California Institute of Technology, Pasadena, CA, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
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185
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Kuppe C, Kramann R. A spatially resolved atlas of healthy and injured kidney cell states. Nephrol Dial Transplant 2024; 39:379-381. [PMID: 37708037 DOI: 10.1093/ndt/gfad203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Indexed: 09/16/2023] Open
Affiliation(s)
- Christoph Kuppe
- Department of Nephrology and Clinical Immunology, RWTH Aachen University, Medical Faculty, Aachen, Germany
| | - Rafael Kramann
- Department of Nephrology and Clinical Immunology, RWTH Aachen University, Medical Faculty, Aachen, Germany
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186
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Wang Y, Yang X, Zhang Y, Hong L, Xie Z, Jiang W, Chen L, Xiong K, Yang S, Lin M, Guo X, Li Q, Deng X, Lin Y, Cao M, Yi G, Fu M. Single-cell RNA sequencing reveals roles of unique retinal microglia types in early diabetic retinopathy. Diabetol Metab Syndr 2024; 16:49. [PMID: 38409074 PMCID: PMC10895757 DOI: 10.1186/s13098-024-01282-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/02/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND The pathophysiological mechanisms of diabetic retinopathy (DR), a blinding disease, are intricate. DR was thought to be a microvascular disease previously. However, growing studies have indicated that the retinal microglia-induced inflammation precedes microangiopathy. The binary concept of microglial M1/M2 polarization paradigms during inflammatory activation has been debated. In this study, we confirmed microglia had the most significant changes in early DR using single-cell RNA sequencing. METHODS A total of five retinal specimens were collected from donor SD rats. Changes in various cells of the retina at the early stage of DR were analyzed using single-cell sequencing technology. RESULTS We defined three new microglial subtypes at cellular level, including two M1 types (Egr2+ M1 and Egr2- M1) and one M2 type. We also revealed the anatomical location between these subtypes, the dynamic changes of polarization phenotypes, and the possible activation sequence and mutual activation regulatory mechanism of different cells. Furthermore, we constructed an inflammatory network involving microglia, blood-derived macrophages and other retinal nonneuronal cells. The targeted study of new disease-specific microglial subtypes can shorten the time for drug screening and clinical application, which provided insight for the early control and reversal of DR. CONCLUSIONS We found that microglia show the most obvious differential expression changes in early DR and reveal the changes in microglia in a high-glucose microenvironment at the single-cell level. Our comprehensive analysis will help achieve early reversal and control the occurrence and progression of DR.
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Affiliation(s)
- Yan Wang
- Department of Ophthalmology, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, People's Republic of China
| | - Xiongyi Yang
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Yuxi Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Libing Hong
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Zhuohang Xie
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Wenmin Jiang
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, People's Republic of China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, People's Republic of China
| | - Lin Chen
- Department of Anesthesiology, Shenzhen Hospital, Southern Medical University, 1333 Xinhu Road, Shenzhen, 518100, Guangdong, People's Republic of China
| | - Ke Xiong
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Siyu Yang
- Department of Ophthalmology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, People's Republic of China
| | - Meiping Lin
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Xi Guo
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Qiumo Li
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Xiaoqing Deng
- The Second Clinical School, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Yanhui Lin
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Mingzhe Cao
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China.
| | - Guoguo Yi
- Department of Ophthalmology, The Sixth Affiliated Hospital, Sun Yat-Sen University, No. 26, Erheng Road, Yuancun, Tianhe, Guangzhou, Guangdong, People's Republic of China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
| | - Min Fu
- Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
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187
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Zhang W, Huckaby B, Talburt J, Weissman S, Yang MQ. cnnImpute: missing value recovery for single cell RNA sequencing data. Sci Rep 2024; 14:3946. [PMID: 38365936 PMCID: PMC10873334 DOI: 10.1038/s41598-024-53998-x] [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: 11/06/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis.
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Affiliation(s)
- Wenjuan Zhang
- MidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock, University of Arkansas for Medical Sciences, Little Rock, 72204, AR, USA
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - Brandon Huckaby
- Department of Computer Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - John Talburt
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - Sherman Weissman
- Department of Genetics, Yale School of Medicine, New Haven, 06520, CT, USA
| | - Mary Qu Yang
- MidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock, University of Arkansas for Medical Sciences, Little Rock, 72204, AR, USA.
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA.
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188
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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189
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Wang Z, Huang AS, Tang L, Wang J, Wang G. Microfluidic-assisted single-cell RNA sequencing facilitates the development of neutralizing monoclonal antibodies against SARS-CoV-2. LAB ON A CHIP 2024; 24:642-657. [PMID: 38165771 DOI: 10.1039/d3lc00749a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
As a class of antibodies that specifically bind to a virus and block its entry, neutralizing monoclonal antibodies (neutralizing mAbs) have been recognized as a top choice for combating COVID-19 due to their high specificity and efficacy in treating serious infections. Although conventional approaches for neutralizing mAb development have been optimized for decades, there is an urgent need for workflows with higher efficiency due to time-sensitive concerns, including the high mutation rate of SARS-CoV-2. One promising approach is the identification of neutralizing mAb candidates via single-cell RNA sequencing (RNA-seq), as each B cell has a unique transcript sequence corresponding to its secreted antibody. The state-of-the-art high-throughput single-cell sequencing technologies, which have been greatly facilitated by advances in microfluidics, have greatly accelerated the process of neutralizing mAb development. Here, we provide an overview of the general procedures for high-throughput single-cell RNA-seq enabled by breakthroughs in droplet microfluidics, introduce revolutionary approaches that combine single-cell RNA-seq to facilitate the development of neutralizing mAbs against SARS-CoV-2, and outline future steps that need to be taken to further improve development strategies for effective treatments against infectious diseases.
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Affiliation(s)
- Ziwei Wang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Amelia Siqi Huang
- Dalton Academy, The Affiliated High School of Peking University, Beijing, 100190, China
| | - Lingfang Tang
- Dalton Academy, The Affiliated High School of Peking University, Beijing, 100190, China
| | - Jianbin Wang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Guanbo Wang
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
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190
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Zhao L, Xing E, Bai T, Cao T, Wang G, Banie L, Lin G, Tang Y, Lue T. Age-Related Changes in Urethral Structure and Responds to Injury: Single-Cell Atlas of a Rat Model of Vaginal Birth Injury induced Stress Urinary Incontinence. RESEARCH SQUARE 2024:rs.3.rs-3901406. [PMID: 38410468 PMCID: PMC10896383 DOI: 10.21203/rs.3.rs-3901406/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Stress urinary incontinence (SUI) greatly affects the daily life of numerous women and is closely related to a history of vaginal delivery and aging. We used vaginal balloon dilation to simulate vaginal birth injury in young and middle-aged rats to produce a SUI animal model, and found that young rats restored urethral structure and function well, but not the middle-aged rats. To identify the characteristics of cellular and molecular changes in the urethral microenvironment during the repair process of SUI. We profiled 51,690 individual female rat urethra cells from 24 and 48 weeks old, with or without simulated vaginal birth injury. Cell interaction analysis showed that signal networks during repair process changed from resting to active, and aging altered the distribution but not the overall level of cell interaction in the repair process. Similarity analysis showed that muscle, fibroblasts, and immune cells underwent large transcriptional changes during aging and repair. In middle-aged rats, cell senescence occurs mainly in the superficial and middle urothelium due to cellular death and shedding, and the basal urothelium expressed many Senescence-Associated Secretory Phenotype (SASP) genes. In conclusion, we established the aging and vaginal balloon dilation (VBD) model of female urethral cell anatomy and the signal network landscape, which provides an insight into the normal or disordered urethra repair process and the scientific basis for developing novel SUI therapies.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Tom Lue
- University of California San Francisco
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191
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Bai D, Zhang X, Xiang H, Guo Z, Zhu C, Yi C. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat Biotechnol 2024:10.1038/s41587-024-02148-9. [PMID: 38336903 DOI: 10.1038/s41587-024-02148-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Dynamic 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications to DNA regulate gene expression in a cell-type-specific manner and are associated with various biological processes, but the two modalities have not yet been measured simultaneously from the same genome at the single-cell level. Here we present SIMPLE-seq, a scalable, base resolution method for joint analysis of 5mC and 5hmC from thousands of single cells. Based on orthogonal labeling and recording of 'C-to-T' mutational signals from 5mC and 5hmC sites, SIMPLE-seq detects these two modifications from the same molecules in single cells and enables unbiased DNA methylation dynamics analysis of heterogeneous biological samples. We applied this method to mouse embryonic stem cells, human peripheral blood mononuclear cells and mouse brain to give joint epigenome maps at single-cell and single-molecule resolution. Integrated analysis of these two cytosine modifications reveals distinct epigenetic patterns associated with divergent regulatory programs in different cell types as well as cell states.
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Affiliation(s)
- Dongsheng Bai
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Xiaoting Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Huifen Xiang
- Department of Obstetrics and Gynecology, First Affiliated Hospital of Anhui Medical University, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Anhui, China
| | - Zijian Guo
- State Key Laboratory of Coordination Chemistry, Coordination Chemistry Institute, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China
| | - Chenxu Zhu
- New York Genome Center, New York, NY, USA.
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
| | - Chengqi Yi
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
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192
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Zhou J, Chen S, Wu Y, Li H, Zhang B, Zhou L, Hu Y, Xiang Z, Li Z, Chen N, Han W, Xu C, Wang D, Gao X. PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data. SCIENCE ADVANCES 2024; 10:eadh8601. [PMID: 38295178 PMCID: PMC10830108 DOI: 10.1126/sciadv.adh8601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 12/29/2023] [Indexed: 02/02/2024]
Abstract
Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.
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Affiliation(s)
- Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Siyuan Chen
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Yulian Wu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Haoyang Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Bin Zhang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Longxi Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Yan Hu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Zihang Xiang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ningning Chen
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Chencheng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Di Wang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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193
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Khan SU, Huang Y, Ali H, Ali I, Ahmad S, Khan SU, Hussain T, Ullah M, Lu K. Single-cell RNA Sequencing (scRNA-seq): Advances and Challenges for Cardiovascular Diseases (CVDs). Curr Probl Cardiol 2024; 49:102202. [PMID: 37967800 DOI: 10.1016/j.cpcardiol.2023.102202] [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: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 11/17/2023]
Abstract
Implementing Single-cell RNA sequencing (scRNA-seq) has significantly enhanced our comprehension of cardiovascular diseases (CVDs), providing new opportunities to strengthen the prevention of CVDs progression. Cardiovascular diseases continue to be the primary cause of death worldwide. Improving treatment strategies and patient risk assessment requires a deeper understanding of the fundamental mechanisms underlying these disorders. The advanced and widespread use of Single-cell RNA sequencing enables a comprehensive investigation of the complex cellular makeup of the heart, surpassing essential descriptive aspects. This enhances our understanding of disease causes and directs functional research. The significant advancement in understanding cellular phenotypes has enhanced the study of fundamental cardiovascular science. scRNA-seq enables the identification of discrete cellular subgroups, unveiling previously unknown cell types in the heart and vascular systems that may have relevance to different disease pathologies. Moreover, scRNA-seq has revealed significant heterogeneity in phenotypes among distinct cell subtypes. Finally, we will examine current and upcoming scRNA-seq studies about various aspects of the cardiovascular system, assessing their potential impact on our understanding of the cardiovascular system and offering insight into how these technologies may revolutionise the diagnosis and treatment of cardiac conditions.
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Affiliation(s)
- Shahid Ullah Khan
- Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China; Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, 400715, China; Women Medical and Dental College, Khyber Medical University, Peshawar, KPK, 22020, Pakistan
| | - Yuqing Huang
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China; Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hamid Ali
- Department of Biosciences, COMSATS University Islamabad, Park Road Tarlai Kalan, Islamabad-44000
| | - Ijaz Ali
- Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait
| | - Saleem Ahmad
- Cardiovascular Center of Excellence, Louisiana State University Health Sciences Center, New Orleans 70112 LA, USA
| | - Safir Ullah Khan
- Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, University of Science and Technology of China, Hefei 230027, People's Republic of China
| | - Talib Hussain
- Women Dental College Abbottabad, KPK, 22020, Pakistan
| | - Muneeb Ullah
- Department of Pharmacy, Kohat University of Science and Technology, Kohat, KPK, Pakistan
| | - Kun Lu
- Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China; Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, 400715, China.
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194
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Liu Y, Huang K, Chen W. Resolving cellular dynamics using single-cell temporal transcriptomics. Curr Opin Biotechnol 2024; 85:103060. [PMID: 38194753 DOI: 10.1016/j.copbio.2023.103060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024]
Abstract
Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.
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Affiliation(s)
- Yifei Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kai Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wanze Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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195
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Karunarathne SI, Spokevicius AV, Bossinger G, Golz JF. Trees need closure too: Wound-induced secondary vascular tissue regeneration. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2024; 339:111950. [PMID: 38070652 DOI: 10.1016/j.plantsci.2023.111950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/03/2023] [Accepted: 12/05/2023] [Indexed: 01/13/2024]
Abstract
Trees play a pivotal role in terrestrial ecosystems as well as being an important natural resource. These attributes are primarily associated with the capacity of trees to continuously produce woody tissue from the vascular cambium, a ring of stem cells located just beneath the bark. Long-lived trees are exposed to a myriad of biological and environmental stresses that may result in wounding, leading to a loss of bark and the underlying vascular cambium. This affects both wood formation and the quality of timber arising from the tree. In addition, the exposed wound site is a potential entry point for pathogens that cause disease. In response to wounding, trees have the capacity to regenerate lost or damaged tissues at this site. Investigating gene expression changes associated with different stages of wound healing reveals complex and dynamic changes in the activity of transcription factors, signalling pathways and hormone responses. In this review we summarise these data and discuss how they relate to our current understanding of vascular cambium formation and xylem differentiation during secondary growth. Based on this analysis, a model for wound healing that provides the conceptual foundations for future studies aimed at understanding this intriguing process is proposed.
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Affiliation(s)
- Sachinthani I Karunarathne
- School of Agriculture, Food and Ecosystem Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Antanas V Spokevicius
- School of Agriculture, Food and Ecosystem Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Gerd Bossinger
- School of Agriculture, Food and Ecosystem Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - John F Golz
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia.
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196
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Wang X, Chai Z, Li S, Liu Y, Li C, Jiang Y, Liu Q. CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data. Bioinformatics 2024; 40:btae063. [PMID: 38317054 PMCID: PMC10873586 DOI: 10.1093/bioinformatics/btae063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/07/2024] Open
Abstract
MOTIVATION Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets. RESULTS We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.
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Affiliation(s)
- Xiao Wang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Ziyi Chai
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Shaohua Li
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Yan Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Chen Li
- Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Yu Jiang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Quanzhong Liu
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
- Shaanxi Engineering Research Center of Agricultural Information Intelligent Perception and Analysis, Northwest A&F University, Yangling 712100, China
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197
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Hosokawa M, Nishikawa Y. Tools for microbial single-cell genomics for obtaining uncultured microbial genomes. Biophys Rev 2024; 16:69-77. [PMID: 38495448 PMCID: PMC10937852 DOI: 10.1007/s12551-023-01124-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/23/2023] [Indexed: 03/19/2024] Open
Abstract
The advent of next-generation sequencing technologies has facilitated the acquisition of large amounts of DNA sequence data at a relatively low cost, leading to numerous breakthroughs in decoding microbial genomes. Among the various genome sequencing activities, metagenomic analysis, which entails the direct analysis of uncultured microbial DNA, has had a profound impact on microbiome research and has emerged as an indispensable technology in this field. Despite its valuable contributions, metagenomic analysis is a "bulk analysis" technique that analyzes samples containing a wide diversity of microbes, such as bacteria, yielding information that is averaged across the entire microbial population. In order to gain a deeper understanding of the heterogeneous nature of the microbial world, there is a growing need for single-cell analysis, similar to its use in human cell biology. With this paradigm shift in mind, comprehensive single-cell genomics technology has become a much-anticipated innovation that is now poised to revolutionize microbiome research. It has the potential to enable the discovery of differences at the strain level and to facilitate a more comprehensive examination of microbial ecosystems. In this review, we summarize the current state-of-the-art in microbial single-cell genomics, highlighting the potential impact of this technology on our understanding of the microbial world. The successful implementation of this technology is expected to have a profound impact in the field, leading to new discoveries and insights into the diversity and evolution of microbes.
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Affiliation(s)
- Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-Cho, Shinjuku-Ku, Tokyo, 162-8480 Japan
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- bitBiome, Inc., 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
| | - Yohei Nishikawa
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
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198
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He Z, Chen Q, Wang K, Lin J, Peng Y, Zhang J, Yan X, Jie Y. Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. Eur J Neurosci 2024; 59:333-357. [PMID: 38221677 DOI: 10.1111/ejn.16242] [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: 05/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/16/2024]
Abstract
Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.
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Affiliation(s)
- Ziping He
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Qianqian Chen
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Kaiyue Wang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiang Lin
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Yilin Peng
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Jinlong Zhang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
| | - Xisheng Yan
- Department of Cardiovascular Medicine, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan, China
| | - Yan Jie
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
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199
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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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200
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Song D, Wang Q, Yan G, Liu T, Sun T, Li JJ. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat Biotechnol 2024; 42:247-252. [PMID: 37169966 PMCID: PMC11182337 DOI: 10.1038/s41587-023-01772-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/30/2023] [Indexed: 05/13/2023]
Abstract
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
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Affiliation(s)
- Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA
| | - Qingyang Wang
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Tianyang Liu
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Tianyi Sun
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA.
- Department of Statistics, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, CA, USA.
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA, USA.
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