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Zheng Q, Lin R, Zheng C. Transcriptomics in the Study of Antiviral Innate Immunity. Methods Mol Biol 2025; 2854:83-91. [PMID: 39192121 DOI: 10.1007/978-1-0716-4108-8_10] [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: 08/29/2024]
Abstract
Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.
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Affiliation(s)
- Qingcong Zheng
- Department of Spinal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Rongjie Lin
- Department of Orthopedic Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chunfu Zheng
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada
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2
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Liu X, Wang H, Gao J. scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error. Comput Struct Biotechnol J 2024; 23:549-558. [PMID: 38274995 PMCID: PMC10809077 DOI: 10.1016/j.csbj.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.
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Affiliation(s)
- Xiaohong Liu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Han Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jingyang Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
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3
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Zhao J, Wang Y, Feng C, Yin M, Gao Y, Wei L, Song C, Ai B, Wang Q, Zhang J, Zhu J, Li C. SCInter: A comprehensive single-cell transcriptome integration database for human and mouse. Comput Struct Biotechnol J 2024; 23:77-86. [PMID: 38125297 PMCID: PMC10731004 DOI: 10.1016/j.csbj.2023.11.024] [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: 09/27/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.
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Affiliation(s)
- Jun Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Yuezhu Wang
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Gao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Ling Wei
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
- Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Chunquan Li
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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Han S, Xu Q, Du Y, Tang C, Cui H, Xia X, Zheng R, Sun Y, Shang H. Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine. Genes Dis 2024; 11:101163. [PMID: 39224111 PMCID: PMC11367031 DOI: 10.1016/j.gendis.2023.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/17/2023] [Accepted: 10/29/2023] [Indexed: 09/04/2024] Open
Abstract
Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.
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Affiliation(s)
- Songjie Han
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Qianqian Xu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yawen Du
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chuwei Tang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Herong Cui
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xiaofeng Xia
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
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5
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Leite-Aguiar R, Bello-Santos VG, Castro NG, Coutinho-Silva R, Savio LEB. Techniques for evaluating the ATP-gated ion channel P2X7 receptor function in macrophages and microglial cells. J Immunol Methods 2024; 532:113727. [PMID: 38997100 DOI: 10.1016/j.jim.2024.113727] [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: 02/19/2024] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024]
Abstract
Resident macrophages are tissue-specific innate immune cells acting as sentinels, constantly patrolling their assigned tissue to maintain homeostasis, and quickly responding to pathogenic invaders or molecular danger signals molecules when necessary. Adenosine triphosphate (ATP), when released to the extracellular medium, acts as a danger signal through specific purinergic receptors. Interaction of ATP with the purinergic receptor P2X7 activates macrophages and microglial cells in different pathological conditions, triggering inflammation. The highly expressed P2X7 receptor in these cells induces cell membrane permeabilization, inflammasome activation, cell death, and the production of inflammatory mediators, including cytokines and nitrogen and oxygen-reactive species. This review explores the techniques to evaluate the functional and molecular aspects of the P2X7 receptor, particularly in macrophages and microglial cells. Polymerase chain reaction (PCR), Western blotting, and immunocytochemistry or immunohistochemistry are essential for assessing gene and protein expression in these cell types. Evaluation of P2X7 receptor function involves the use of ATP and selective agonists and antagonists and diverse techniques, including electrophysiology, intracellular calcium measurements, ethidium bromide uptake, and propidium iodide cell viability assays. These techniques are crucial for studying the role of P2X7 receptors in immune responses, neuroinflammation, and various pathological conditions. Therefore, a comprehensive understanding of the functional and molecular aspects of the P2X7 receptor in macrophages and microglia is vital for unraveling its involvement in immune modulation and its potential as a therapeutic target. The methodologies presented and discussed herein offer valuable tools for researchers investigating the complexities of P2X7 receptor signaling in innate immune cells in health and disease.
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Affiliation(s)
- Raíssa Leite-Aguiar
- Laboratório de Imunofisiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | - Robson Coutinho-Silva
- Laboratório de Imunofisiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Luiz Eduardo Baggio Savio
- Laboratório de Imunofisiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil..
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Wang N, Hu L, Walsh AJ. Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images. Comput Biol Med 2024; 179:108846. [PMID: 38976959 DOI: 10.1016/j.compbiomed.2024.108846] [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: 11/27/2023] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters. METHOD Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells. RESULTS Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells. CONCLUSION These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.
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Affiliation(s)
- Nianchao Wang
- Texas A&M University, 3120 TAMU, College Station, 77840, United States
| | - Linghao Hu
- Texas A&M University, 3120 TAMU, College Station, 77840, United States
| | - Alex J Walsh
- Texas A&M University, 3120 TAMU, College Station, 77840, United States.
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7
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Sumida TS, Lincoln MR, He L, Park Y, Ota M, Oguchi A, Son R, Yi A, Stillwell HA, Leissa GA, Fujio K, Murakawa Y, Kulminski AM, Epstein CB, Bernstein BE, Kellis M, Hafler DA. An autoimmune transcriptional circuit drives FOXP3 + regulatory T cell dysfunction. Sci Transl Med 2024; 16:eadp1720. [PMID: 39196959 DOI: 10.1126/scitranslmed.adp1720] [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: 03/11/2024] [Accepted: 08/02/2024] [Indexed: 08/30/2024]
Abstract
Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. Although CD4+FOXP3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify critical transcriptional programs regulating human autoimmunity. We found that up-regulation of a primate-specific short isoform of PR domain zinc finger protein 1 (PRDM1-S) induces expression of serum and glucocorticoid-regulated kinase 1 (SGK1) independent from the evolutionarily conserved long PRDM1, which led to destabilization of forkhead box P3 (FOXP3) and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, the chromatin landscape profiling in Tregs from individuals with MS revealed enriched activating protein-1 (AP-1)/interferon regulatory factor (IRF) transcription factor binding as candidate upstream regulators of PRDM1-S expression and Treg dysfunction. Our study uncovers a mechanistic model where the evolutionary emergence of PRDM1-S and epigenetic priming of AP-1/IRF may be key drivers of dysfunctional Tregs in autoimmune diseases.
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Affiliation(s)
- Tomokazu S Sumida
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew R Lincoln
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON M6R 1B5, Canada
- Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Toronto, ON M6R 1B5, Canada
| | - Liang He
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27705, USA
| | - Yongjin Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, Tokyo 113-8655, Japan
| | - Akiko Oguchi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8303, Japan
| | - Raku Son
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8303, Japan
| | - Alice Yi
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Helen A Stillwell
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Greta A Leissa
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, Tokyo 113-8655, Japan
| | - Yasuhiro Murakawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8303, Japan
| | - Alexander M Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27705, USA
| | | | - Bradley E Bernstein
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Manolis Kellis
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - David A Hafler
- Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Seeler S, Arnarsson K, Dreßen M, Krane M, Doppler SA. Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research. Curr Cardiol Rep 2024:10.1007/s11886-024-02117-3. [PMID: 39158785 DOI: 10.1007/s11886-024-02117-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2024] [Indexed: 08/20/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues. RECENT FINDINGS Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.
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Affiliation(s)
- Sabine Seeler
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Kristjan Arnarsson
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Martina Dreßen
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Markus Krane
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Division of Cardiac Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Stefanie A Doppler
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany.
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany.
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9
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Xu J, Yang S, Fan R, Wu H, Mo H. MRI and single-cell RNA sequence results reveal the influence of anterior talofibular ligament injury on osteochondral lesions of the talus. J Orthop Surg Res 2024; 19:474. [PMID: 39127696 DOI: 10.1186/s13018-024-04826-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/30/2024] [Indexed: 08/12/2024] Open
Abstract
Anterior talofibular ligament injuries and osteochondral lesions of the talus present unique challenges to orthopedic surgeons. This study aimed to investigate the relevant relationship between them by analyzing the Magnetic resonance imaging (MRI) results of clinical patients and single-cell RNA sequence (scRNA seq) results of healthy talus cartilage to discuss the risk factors. Data from 164 patients from 2018 to 2023 was retrospectively analyzed. The correlation analysis between ATFL injury grade and the Hepple stage of OLT determined by MRI was performed. Publicly available single-cell RNA datasets were collected. Single-cell RNA datasets from five volunteers of healthy talus cartilage were analyzed. ATFL injury grade was relevant with the Hepple stage of OLT (P < 0.05). The results of multivariate logistic regression analysis showed that injured area was the independent influencing factor of the incidence rate and the severity of OLT (P < 0.05). The Hepple stage of OLT was relevant with AOFAS and VAS (P < 0.05). Single-cell RNA sequence results showed that among the 9 subtypes of chondrocytes, the interaction strength between HTC-A and HTC-B is the highest. Their physical interactions are mainly achieved through the CD99 signaling pathway, and factor interactions are mainly achieved through the ANGPTL signaling pathway. Anterior talofibular ligament injury may lead to osteochondral lesions of the talus. Early medical intervention should be carried out for ligament injuries to restore joint stability and avoid cartilage damage.
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Affiliation(s)
- Jie Xu
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, 999078, China
- Macau University of Science and Technology, Macau, 999078, China
| | - Siyi Yang
- School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, 100000, China
| | - Ruiqi Fan
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, 999078, China
| | - Hongbo Wu
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Hui Mo
- Macau University of Science and Technology, Macau, 999078, China.
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10
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Cao X, Huang YA, You ZH, Shang X, Hu L, Hu PW, Huang ZA. scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data. Genome Biol 2024; 25:207. [PMID: 39103856 DOI: 10.1186/s13059-024-03357-w] [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: 10/26/2023] [Accepted: 07/29/2024] [Indexed: 08/07/2024] Open
Abstract
Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.
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Affiliation(s)
- Xiyue Cao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China
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11
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Yun S, Noh M, Yu J, Kim HJ, Hui CC, Lee H, Son JE. Unlocking biological mechanisms with integrative functional genomics approaches. Mol Cells 2024; 47:100092. [PMID: 39019219 PMCID: PMC11345568 DOI: 10.1016/j.mocell.2024.100092] [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: 06/08/2024] [Revised: 07/01/2024] [Accepted: 07/08/2024] [Indexed: 07/19/2024] Open
Abstract
Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.
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Affiliation(s)
- Sehee Yun
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Minsoo Noh
- Department of Life Sciences, Korea University, Seoul 02841, Korea; Department of Internal Medicine and Laboratory of Genomics and Translational Medicine, Gachon University College of Medicine, Incheon 21565, Korea
| | - Jivin Yu
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Hyeon-Jai Kim
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Chi-Chung Hui
- Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Hunsang Lee
- Department of Life Sciences, Korea University, Seoul 02841, Korea.
| | - Joe Eun Son
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Korea.
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12
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Nisar S, Dastgeer G, Shazad ZM, Zulfiqar MW, Rasheed A, Iqbal MZ, Hussain K, Rabani I, Kim D, Irfan A, Chaudhry AR. 2D Materials in Advanced Electronic Biosensors for Point-of-Care Devices. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401386. [PMID: 38894575 PMCID: PMC11336981 DOI: 10.1002/advs.202401386] [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: 02/06/2024] [Revised: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Since two-dimensionalal (2D) materials have distinct chemical and physical properties, they are widely used in various sectors of modern technologies. In the domain of diagnostic biodevices, particularly for point-of-care (PoC) biomedical diagnostics, 2D-based field-effect transistor biosensors (bio-FETs) demonstrate substantial potential. Here, in this review article, the operational mechanisms and detection capabilities of biosensing devices utilizing graphene, transition metal dichalcogenides (TMDCs), black phosphorus, and other 2D materials are addressed in detail. The incorporation of these materials into FET-based biosensors offers significant advantages, including low detection limits (LOD), real-time monitoring, label-free diagnosis, and exceptional selectivity. The review also highlights the diverse applications of these biosensors, ranging from conventional to wearable devices, underscoring the versatility of 2D material-based FET devices. Additionally, the review provides a comprehensive assessment of the limitations and challenges faced by these devices, along with insights into future prospects and advancements. Notably, a detailed comparison of FET-based biosensors is tabulated along with various other biosensing platforms and their working mechanisms. Ultimately, this review aims to stimulate further research and innovation in this field while educating the scientific community about the latest advancements in 2D materials-based biosensors.
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Affiliation(s)
- Sobia Nisar
- Department of Electrical EngineeringSejong UniversitySeoul05006Republic of Korea
- Department of Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Ghulam Dastgeer
- Department of Physics & AstronomySejong UniversitySeoul05006Republic of Korea
| | - Zafar Muhammad Shazad
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon16419Republic of Korea
- Department of Chemical Polymer and Composite EngineeringUniversity of Engineering & TechnologyFaisalabad CampusLahore38000Pakistan
| | - Muhammad Wajid Zulfiqar
- Department of Electrical EngineeringSejong UniversitySeoul05006Republic of Korea
- Department of Semiconductor EngineeringSejong UniversitySeoul05006Republic of Korea
| | - Amir Rasheed
- School of Materials Science and EngineeringAnhui UniversityHefeiAnhui230601China
| | - Muhammad Zahir Iqbal
- Renewable Energy Research LaboratoryFaculty of Engineering SciencesGhulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopiKhyber Pakhtunkhwa23640Pakistan
| | - Kashif Hussain
- THz Technical Research Center; Shenzhen Key Laboratory of Micro‐Nano Photonic Information Technology; Key Laboratory of Optoelectronic Devices and Systems, College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhenGuangdong Province518060China
- School of Materials Science and EngineeringCAPTPeking UniversityBeijing100871China
| | - Iqra Rabani
- Department of Nanotechnology and Advanced Materials EngineeringSejong UniversitySeoul05006Republic of Korea
| | - Deok‐kee Kim
- Department of Electrical EngineeringSejong UniversitySeoul05006Republic of Korea
- Department of Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
- Department of Semiconductor EngineeringSejong UniversitySeoul05006Republic of Korea
| | - Ahmad Irfan
- Department of ChemistryCollege of ScienceKing Khalid UniversityP. O. Box 9004Abha61413Saudi Arabia
| | - Aijaz Rasool Chaudhry
- Department of PhysicsCollege of ScienceUniversity of BishaP.O. Box 551Bisha61922Saudi Arabia
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13
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Weng W, Fu J, Cheng F, Wang Y, Zhang J. Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease. Mol Neurobiol 2024; 61:6013-6030. [PMID: 38265551 DOI: 10.1007/s12035-023-03907-6] [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: 03/14/2023] [Accepted: 12/22/2023] [Indexed: 01/25/2024]
Abstract
Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.
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Affiliation(s)
- Weipin Weng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Neurology, Center for Cognitive Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianhan Fu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fan Cheng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Yixuan Wang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Jie Zhang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China.
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China.
- Department of Neurology, Turpan City People's Hospital, Tulufan, China.
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14
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Jia H, Wang W, Zhou Z, Chen Z, Lan Z, Bo H, Fan L. Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives. Mol Cell Biochem 2024; 479:2017-2033. [PMID: 37659974 DOI: 10.1007/s11010-023-04840-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: 06/09/2023] [Accepted: 08/14/2023] [Indexed: 09/04/2023]
Abstract
Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.
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Affiliation(s)
- Hanbo Jia
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Wei Wang
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zhaowen Zhou
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zhiyi Chen
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zijun Lan
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Hao Bo
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, Hunan, China.
| | - Liqing Fan
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, Hunan, China.
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15
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Wu Y, Shi Z, Zhou X, Zhang P, Yang X, Ding J, Wu H. scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information. Commun Biol 2024; 7:923. [PMID: 39085477 PMCID: PMC11291681 DOI: 10.1038/s42003-024-06626-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 07/24/2024] [Indexed: 08/02/2024] Open
Abstract
The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predicting cell cycle phases based on scHi-C data remains a formidable challenge. Here, we present scHiCyclePred, a prediction model that integrates multiple feature sets to leverage scHi-C data for predicting cell cycle phases. scHiCyclePred extracts 3D chromatin structure features by incorporating multi-scale interaction information. The comparative analysis illustrates that scHiCyclePred surpasses existing methods such as Nagano_method and CIRCLET across various metrics including accuracy (ACC), F1 score, Precision, Recall, and balanced accuracy (BACC). In addition, we evaluate scHiCyclePred against the previously published CIRCLET using the dataset of complex tissues (Liu_dataset). Experimental results reveal significant improvements with scHiCyclePred exhibiting improvements of 0.39, 0.52, 0.52, and 0.39 over the CIRCLET in terms of ACC, F1 score, Precision, and Recall metrics, respectively. Furthermore, we conduct analyses on three-dimensional chromatin dynamics and gene features during the cell cycle, providing a more comprehensive understanding of cell cycle dynamics through chromatin structure. scHiCyclePred not only offers insights into cell biology but also holds promise for catalyzing breakthroughs in disease research. Access scHiCyclePred on GitHub at https:// github.com/HaoWuLab-Bioinformatics/ scHiCyclePred .
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Affiliation(s)
- Yingfu Wu
- School of Software, Shandong University, Jinan, Shandong, China
- Shenzhen Research Institute of Shandong University, Shenzhen, Guangdong, China
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Zhenqi Shi
- School of Software, Shandong University, Jinan, Shandong, China
| | - Xiangfei Zhou
- School of Software, Shandong University, Jinan, Shandong, China
| | - Pengyu Zhang
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Xiuhui Yang
- School of Software, Shandong University, Jinan, Shandong, China
| | - Jun Ding
- Department of Medicine, Meakins-Christie Laboratories, McGill University, Montreal, QC, Canada.
| | - Hao Wu
- School of Software, Shandong University, Jinan, Shandong, China.
- Shenzhen Research Institute of Shandong University, Shenzhen, Guangdong, China.
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16
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Salisbury SJ, Daniels RR, Monaghan SJ, Bron JE, Villamayor PR, Gervais O, Fast MD, Sveen L, Houston RD, Robinson N, Robledo D. Keratinocytes drive the epithelial hyperplasia key to sea lice resistance in coho salmon. BMC Biol 2024; 22:160. [PMID: 39075472 PMCID: PMC11287951 DOI: 10.1186/s12915-024-01952-8] [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: 11/15/2023] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Salmonid species have followed markedly divergent evolutionary trajectories in their interactions with sea lice. While sea lice parasitism poses significant economic, environmental, and animal welfare challenges for Atlantic salmon (Salmo salar) aquaculture, coho salmon (Oncorhynchus kisutch) exhibit near-complete resistance to sea lice, achieved through a potent epithelial hyperplasia response leading to rapid louse detachment. The molecular mechanisms underlying these divergent responses to sea lice are unknown. RESULTS We characterized the cellular and molecular responses of Atlantic salmon and coho salmon to sea lice using single-nuclei RNA sequencing. Juvenile fish were exposed to copepodid sea lice (Lepeophtheirus salmonis), and lice-attached pelvic fin and skin samples were collected 12 h, 24 h, 36 h, 48 h, and 60 h after exposure, along with control samples. Comparative analysis of control and treatment samples revealed an immune and wound-healing response that was common to both species, but attenuated in Atlantic salmon, potentially reflecting greater sea louse immunomodulation. Our results revealed unique but complementary roles of three layers of keratinocytes in the epithelial hyperplasia response leading to rapid sea lice rejection in coho salmon. Our results suggest that basal keratinocytes direct the expansion and mobility of intermediate and, especially, superficial keratinocytes, which eventually encapsulate the parasite. CONCLUSIONS Our results highlight the key role of keratinocytes in coho salmon's sea lice resistance and the diverged biological response of the two salmonid host species when interacting with this parasite. This study has identified key pathways and candidate genes that could be manipulated using various biotechnological solutions to improve Atlantic salmon sea lice resistance.
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Affiliation(s)
- S J Salisbury
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.
| | - R Ruiz Daniels
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | - S J Monaghan
- Institute of Aquaculture, University of Stirling, Stirling, UK
| | - J E Bron
- Institute of Aquaculture, University of Stirling, Stirling, UK
| | - P R Villamayor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
- Department of Genetics, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - O Gervais
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | - M D Fast
- Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Canada
| | | | - R D Houston
- Benchmark Genetics, 1 Pioneer BuildingMilton Bridge, Edinburgh TechnopolePenicuik, UK
| | - N Robinson
- Nofima AS, Tromsø, Norway.
- Sustainable Aquaculture Laboratory - Temperate and Tropical (SALTT), Deakin University, Melbourne, VIC, 3225, Australia.
| | - D Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.
- Department of Genetics, University of Santiago de Compostela, Santiago de Compostela, Spain.
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17
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Yang J, Chen S, Ma F, Ding N, Mi S, Zhao Q, Xing Y, Yang T, Xing K, Yu Y, Wang C. Pathogen stimulations and immune cells synergistically affect the gene expression profile characteristics of porcine peripheral blood mononuclear cells. BMC Genomics 2024; 25:719. [PMID: 39054472 PMCID: PMC11270792 DOI: 10.1186/s12864-024-10603-9] [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: 02/07/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Pigs serve as a crucial source of protein in the human diet and play a fundamental role in ensuring food security. However, infectious diseases caused by bacteria or viruses are a major threat to effective global pig farming, jeopardizing human health. Peripheral blood mononuclear cells (PBMCs) are a mixture of immune cells that play crucial roles in immunity and disease resistance in pigs. Previous studies on the gene expression regulation patterns of PBMCs have concentrated on a single immune stimulus or immune cell subpopulation, which has limited our comprehensive understanding of the mechanisms of the pig immune response. RESULTS Here, we integrated and re-analyzed RNA-seq data published online for porcine PBMC stimulated by lipopolysaccharide (LPS), polyinosinic acid (PolyI:C), and various unknown microorganisms (EM). The results revealed that gene expression and its functional characterization are highly specific to the pathogen, identifying 603, 254, and 882 pathogen-specific genes and 38 shared genes, respectively. Notably, LPS and PolyI:C stimulation directly triggered inflammatory and immune-response pathways, while exposure to mixed microbes (EM) enhanced metabolic processes. These pathogen-specific genes were enriched in immune trait-associated quantitative trait loci (QTL) and eGenes in porcine immune tissues and were implicated in specific cell types. Furthermore, we discussed the roles of eQTLs rs3473322705 and rs1109431654 in regulating pathogen- and cell-specific genes CD300A and CD93, using cellular experiments. Additionally, by integrating genome-wide association studies datasets from 33 complex traits and diseases in humans, we found that pathogen-specific genes were significantly enriched for immune traits and metabolic diseases. CONCLUSIONS We systematically analyzed the gene expression profiles of the three stimulations and demonstrated pathogen-specific and cell-specific gene regulation across different stimulations in porcine PBMCs. These findings enhance our understanding of shared and distinct regulatory mechanisms of genetic variants in pig immune traits.
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Affiliation(s)
- Jinyan Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Siqian Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Fuping Ma
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ning Ding
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Qingyao Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Yue Xing
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ting Yang
- Dabei-Nong Science and Technology Group Co., Ltd, Beijing, 100080, China
| | - Kai Xing
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China.
| | - Chuduan Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China.
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18
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Asaad W, Utkina M, Shcherbakova A, Popov S, Melnichenko G, Mokrysheva N. scRNA sequencing technology for PitNET studies. Front Endocrinol (Lausanne) 2024; 15:1414223. [PMID: 39114291 PMCID: PMC11303145 DOI: 10.3389/fendo.2024.1414223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
Pituitary neuroendocrine tumors (PitNETs) are common, most likely benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNET types are classified according to their expression of specific transcriptional factors (TFs) and hormone secretion levels. Some types show aggressive, invasive, and reoccurrence behavior. Current research is being conducted to understand the molecular mechanisms regulating these high-heterogeneous neoplasms originating from adenohypophysis, and single-cell RNA sequencing (scRNA-seq) technology is now playing an essential role in these studies due to its remarkable resolution at the single-cell level. This review describes recent studies on human PitNETs performed with scRNA-seq technology, highlighting the potential of this approach in revealing these tumor pathologies, behavior, and regulatory mechanisms.
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Affiliation(s)
| | - Marina Utkina
- Department of General, Molecular and Population Genetics, Endocrinology Research Centre, Moscow, Russia
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19
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Yeo S, Jang J, Jung HJ, Lee H, Lee S, Choe Y. A Zwitterionic Detergent and Catalyst-Based Single-Cell Proteomics Using a Loss-Free Microhole-Collection Disc. Anal Chem 2024; 96:11690-11698. [PMID: 38991018 DOI: 10.1021/acs.analchem.4c00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Recent advances in single-cell proteomics have solved many bottlenecks, such as throughput, sample recovery, and scalability via nanoscale sample handling. In this study, we aimed for a sensitive mass spectrometry (MS) analysis capable of handling single cells with a conventional mass spectrometry workflow without additional equipment. We achieved seamless cell lysis and TMT labeling in a micro-HOLe Disc (microHOLD) by developing a mass-compatible single solution based on a zwitterionic detergent and a catalyst for single-cell lysis and tandem mass tag labeling without a heat incubation step. This method was developed to avoid peptide loss by surface adsorption and buffer or tube changes by collecting tandem mass tag-labeled peptide through microholes placed in the liquid chromatography injection vials in a single solution. We successfully applied the microHOLD single-cell proteomics method for the analysis of proteome reprogramming in hormone-sensitive prostate cells to develop castration-resistant prostate cancer cells. This novel single-cell proteomics method is not limited by cutting-edge nanovolume handling equipment and achieves high throughput and ultrasensitive proteomics analysis of limited samples, such as single cells.
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Affiliation(s)
- Seungeun Yeo
- Developmental disorders & rare diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaemyung Jang
- Developmental disorders & rare diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Hyun Jin Jung
- Developmental disorders & rare diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Hyeyoung Lee
- Division of Applied Bioengineering, Dong-Eui University, Busan 47340, Republic of Korea
| | - Sangkyu Lee
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Youngshik Choe
- Developmental disorders & rare diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
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20
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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21
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Fortner A, Bucur O. Multiplexed spatial transcriptomics methods and the application of expansion microscopy. Front Cell Dev Biol 2024; 12:1378875. [PMID: 39105173 PMCID: PMC11298486 DOI: 10.3389/fcell.2024.1378875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/10/2024] [Indexed: 08/07/2024] Open
Abstract
While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.
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Affiliation(s)
- Andra Fortner
- Medical School, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- Victor Babes National Institute of Pathology, Bucharest, Romania
| | - Octavian Bucur
- Victor Babes National Institute of Pathology, Bucharest, Romania
- Genomics Research and Development Institute, Bucharest, Romania
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22
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Zheng X, Wu W, Zhao Z, Zhang X, Yu S. Single-cell transcriptomic insights into chemotherapy-induced remodeling of the osteosarcoma tumor microenvironment. J Cancer Res Clin Oncol 2024; 150:356. [PMID: 39033089 PMCID: PMC11271355 DOI: 10.1007/s00432-024-05787-2] [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: 03/22/2024] [Accepted: 05/07/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE Neoadjuvant chemotherapy serves as an effective strategy for treating osteosarcoma (OS) not only by targeting cancerous cells but also by influencing the tumor's immune and stromal elements. Gaining insights into how chemotherapy reshapes the tumor's local environment is crucial for advancing OS treatment protocols. METHODS Using single-cell RNA sequencing, this study analyzed tumor samples from patients with advanced osteosarcoma collected both before and after chemotherapy. RESULTS The results revealed that chemotherapy caused the remaining OS cells to express higher levels of genes associated with stemness. Additionally, this process enhances the presence of cancer-associated fibroblasts, increasing their ability to modify the extracellular matrix (ECM). Chemotherapy also increases the number of endothelial cells, albeit with compromised differentiation capabilities. Importantly, the treatment reduced the immune cell population, including myeloid and T/NK cells, particularly impacting the subpopulations with tumor-fighting capabilities. CONCLUSION These findings highlight the complex reaction of the tumor environment to chemotherapy, providing valuable insights into how chemotherapy influences OS cells and the tumor microenvironment (TME). This knowledge is essential for understanding OS resistance mechanisms to treatments, potentially guiding the development of novel therapies for managing advanced OS.
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Affiliation(s)
- Xuejing Zheng
- Departments of Orthopedics, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Nanli, Panjiayuan, Chaoyang District, Beijing, 100021, China
| | - Wence Wu
- Department of Orthopedics, Peking University First Hospital, Beijing, 100021, China
| | - Zhenguo Zhao
- Departments of Orthopedics, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Nanli, Panjiayuan, Chaoyang District, Beijing, 100021, China
| | - Xinxin Zhang
- Departments of Orthopedics, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Nanli, Panjiayuan, Chaoyang District, Beijing, 100021, China
| | - Shengji Yu
- Departments of Orthopedics, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Nanli, Panjiayuan, Chaoyang District, Beijing, 100021, China.
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23
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Wang Z, Xie J, Nie F, Wang R, Jia Y, Liu S. T-distributed Stochastic Neighbor Network for unsupervised representation learning. Neural Netw 2024; 179:106520. [PMID: 39024709 DOI: 10.1016/j.neunet.2024.106520] [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: 09/17/2023] [Revised: 07/02/2024] [Accepted: 07/06/2024] [Indexed: 07/20/2024]
Abstract
Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple end-to-end T-distributed Stochastic Neighbor Network (TsNet) for URL with clustering downstream task. Concretely, our TsNet model has three major components: (1) an adaptive connectivity distribution learning module is presented to construct a pairwise graph for preserving the local structure of generic data; (2) a T-distributed stochastic neighbor embedding based loss function is designed to learn a transformation between embeddings and original data, which improves the discrimination of representations; (3) a nonlinear parametric mapping is learned via our TsNet on an unsupervised generalized manner, which can address the "out-of-sample" issue. By combining these components, our method is able to considerably outperform previous related unsupervised learning approaches on visualization and clustering of generic data. A simple deep neural network equipped on our model respectively achieves 74.90%, 76.56% ACC and NMI, which is 8% relative improvement over previous state-of-the-art on real single-cell RNA-sequencing (scRNA-seq) datasets clustering.
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Affiliation(s)
- Zheng Wang
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.
| | - Jiaxi Xie
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.
| | - Feiping Nie
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.
| | - Rong Wang
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.
| | - Yanyan Jia
- Department of Pharmacy, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, PR China.
| | - Shichang Liu
- Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710032, Shaanxi, PR China.
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24
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Sun D, Zhang X, Chen R, Sang T, Li Y, Wang Q, Xie L, Zhou Q, Dou S. Decoding cellular plasticity and niche regulation of limbal stem cells during corneal wound healing. Stem Cell Res Ther 2024; 15:201. [PMID: 38971839 PMCID: PMC11227725 DOI: 10.1186/s13287-024-03816-y] [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] [Accepted: 06/25/2024] [Indexed: 07/08/2024] Open
Abstract
BACKGROUND Dysfunction or deficiency of corneal epithelium results in vision impairment or blindness in severe cases. The rapid and effective regeneration of corneal epithelial cells relies on the limbal stem cells (LSCs). However, the molecular and functional responses of LSCs and their niche cells to injury remain elusive. METHODS Single-cell RNA sequencing was performed on corneal tissues from normal mice and corneal epithelium defect models. Bioinformatics analysis was performed to confirm the distinct characteristics and cell fates of LSCs. Knockdown of Creb5 and OSM treatment experiment were performed to determine their roles of in corneal epithelial wound healing. RESULTS Our data defined the molecular signatures of LSCs and reconstructed the pseudotime trajectory of corneal epithelial cells. Gene network analyses characterized transcriptional landmarks that potentially regulate LSC dynamics, and identified a transcription factor Creb5, that was expressed in LSCs and significantly upregulated after injury. Loss-of-function experiments revealed that silencing Creb5 delayed the corneal epithelial healing and LSC mobilization. Through cell-cell communication analysis, we identified 609 candidate regeneration-associated ligand-receptor interaction pairs between LSCs and distinct niche cells, and discovered a unique subset of Arg1+ macrophages infiltrated after injury, which were present as the source of Oncostatin M (OSM), an IL-6 family cytokine, that were demonstrated to effectively accelerate the corneal epithelial wound healing. CONCLUSIONS This research provides a valuable single-cell resource and reference for the discovery of mechanisms and potential clinical interventions aimed at ocular surface reconstruction.
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Affiliation(s)
- Di Sun
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Xiaowen Zhang
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Rong Chen
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Tian Sang
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Ya Li
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Qun Wang
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Lixin Xie
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Qingjun Zhou
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China.
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China.
| | - Shengqian Dou
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China.
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China.
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25
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Yang GN, Sun YBY, Roberts PK, Moka H, Sung MK, Gardner-Russell J, El Wazan L, Toussaint B, Kumar S, Machin H, Dusting GJ, Parfitt GJ, Davidson K, Chong EW, Brown KD, Polo JM, Daniell M. Exploring single-cell RNA sequencing as a decision-making tool in the clinical management of Fuchs' endothelial corneal dystrophy. Prog Retin Eye Res 2024; 102:101286. [PMID: 38969166 DOI: 10.1016/j.preteyeres.2024.101286] [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: 01/17/2024] [Revised: 06/14/2024] [Accepted: 07/02/2024] [Indexed: 07/07/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has enabled the identification of novel gene signatures and cell heterogeneity in numerous tissues and diseases. Here we review the use of this technology for Fuchs' Endothelial Corneal Dystrophy (FECD). FECD is the most common indication for corneal endothelial transplantation worldwide. FECD is challenging to manage because it is genetically heterogenous, can be autosomal dominant or sporadic, and progress at different rates. Single-cell RNA sequencing has enabled the discovery of several FECD subtypes, each with associated gene signatures, and cell heterogeneity. Current FECD treatments are mainly surgical, with various Rho kinase (ROCK) inhibitors used to promote endothelial cell metabolism and proliferation following surgery. A range of emerging therapies for FECD including cell therapies, gene therapies, tissue engineered scaffolds, and pharmaceuticals are in preclinical and clinical trials. Unlike conventional disease management methods based on clinical presentations and family history, targeting FECD using scRNA-seq based precision-medicine has the potential to pinpoint the disease subtypes, mechanisms, stages, severities, and help clinicians in making the best decision for surgeries and the applications of therapeutics. In this review, we first discuss the feasibility and potential of using scRNA-seq in clinical diagnostics for FECD, highlight advances from the latest clinical treatments and emerging therapies for FECD, integrate scRNA-seq results and clinical notes from our FECD patients and discuss the potential of applying alternative therapies to manage these cases clinically.
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Affiliation(s)
- Gink N Yang
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Yu B Y Sun
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Philip Ke Roberts
- Department of Ophthalmology, Medical University Vienna, 18-20 Währinger Gürtel, Vienna, Austria
| | - Hothri Moka
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Min K Sung
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Jesse Gardner-Russell
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Layal El Wazan
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Bridget Toussaint
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Satheesh Kumar
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Heather Machin
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Lions Eye Donation Service, Level 7, Smorgon Family Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia
| | - Gregory J Dusting
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Geraint J Parfitt
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Kathryn Davidson
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Elaine W Chong
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Department of Ophthalmology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Karl D Brown
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Jose M Polo
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Mark Daniell
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Lions Eye Donation Service, Level 7, Smorgon Family Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia.
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26
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [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: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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27
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Maimaiti Y, Su T, Zhang Z, Ma L, Zhang Y, Xu H. NOX4-mediated astrocyte ferroptosis in Alzheimer's disease. Cell Biosci 2024; 14:88. [PMID: 38956702 PMCID: PMC11218381 DOI: 10.1186/s13578-024-01266-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
This study investigates NADPH oxidase 4 (NOX4) involvement in iron-mediated astrocyte cell death in Alzheimer's Disease (AD) using single-cell sequencing data and transcriptomes. We analyzed AD single-cell RNA sequencing data, identified astrocyte marker genes, and explored biological processes in astrocytes. We integrated AD-related chip data with ferroptosis-related genes, highlighting NOX4. We validated NOX4's role in ferroptosis and AD in vitro and in vivo. Astrocyte marker genes were enriched in AD, emphasizing their role. NOX4 emerged as a crucial player in astrocytic ferroptosis in AD. Silencing NOX4 mitigated ferroptosis, improved cognition, reduced Aβ and p-Tau levels, and alleviated mitochondrial abnormalities. NOX4 promotes astrocytic ferroptosis, underscoring its significance in AD progression.
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Affiliation(s)
- Yasenjiang Maimaiti
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China.
| | - Ting Su
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China
| | - Zhanying Zhang
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China
| | - Lingling Ma
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China
| | - Yuan Zhang
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China
| | - Hong Xu
- Gerontology Center, People's Hospital of Xinjiang Uygur Autonomous Region, No.91 Tianchi Road, Urumqi, Xinjiang, China.
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28
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Zeng Y, Ma Q, Chen J, Kong X, Chen Z, Liu H, Liu L, Qian Y, Wang X, Lu S. Single-cell sequencing: Current applications in various tuberculosis specimen types. Cell Prolif 2024:e13698. [PMID: 38956399 DOI: 10.1111/cpr.13698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/21/2024] [Accepted: 06/07/2024] [Indexed: 07/04/2024] Open
Abstract
Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.
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Affiliation(s)
- Yuqin Zeng
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Quan Ma
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Jinyun Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xingxing Kong
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Zhanpeng Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Huazhen Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Lanlan Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Yan Qian
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xiaomin Wang
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Shuihua Lu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
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Long Q, Zhang P, Ou Y, Li W, Yan Q, Yuan X. Single-cell sequencing advances in research on mesenchymal stem/stromal cells. Hum Cell 2024; 37:904-916. [PMID: 38743204 DOI: 10.1007/s13577-024-01076-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/04/2024] [Indexed: 05/16/2024]
Abstract
Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.
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Affiliation(s)
- Qingxi Long
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Pingshu Zhang
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Ya Ou
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Wen Li
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Qi Yan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Xiaodong Yuan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China.
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China.
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Si Y, Zou J, Gao Y, Chuai G, Liu Q, Chen L. Foundation models in molecular biology. BIOPHYSICS REPORTS 2024; 10:135-151. [PMID: 39027316 PMCID: PMC11252241 DOI: 10.52601/bpr.2024.240006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 07/20/2024] Open
Abstract
Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.
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Affiliation(s)
- Yunda Si
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Jiawei Zou
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yicheng Gao
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Guohui Chuai
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
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Moriel N, Memet E, Nitzan M. Optimal sequencing budget allocation for trajectory reconstruction of single cells. Bioinformatics 2024; 40:i446-i452. [PMID: 38940162 PMCID: PMC11211845 DOI: 10.1093/bioinformatics/btae258] [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] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. RESULTS Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.
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Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Edvin Memet
- Department of Physics, Harvard University, Cambridge, MA 02138, United States
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
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Nishide M, Shimagami H, Kumanogoh A. Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice. Nat Rev Immunol 2024:10.1038/s41577-024-01043-3. [PMID: 38914790 DOI: 10.1038/s41577-024-01043-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/26/2024]
Abstract
Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.
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Affiliation(s)
- Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
| | - Hiroshi Shimagami
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan.
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Osaka, Japan.
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Osaka, Japan.
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Jackson DJ, Cerveau N, Posnien N. De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide. Front Zool 2024; 21:17. [PMID: 38902827 PMCID: PMC11188175 DOI: 10.1186/s12983-024-00538-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.
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Affiliation(s)
- Daniel J Jackson
- University of Göttingen, Department of Geobiology, Goldschmidtstr.3, Göttingen, 37077, Germany.
| | - Nicolas Cerveau
- University of Göttingen, Department of Geobiology, Goldschmidtstr.3, Göttingen, 37077, Germany
| | - Nico Posnien
- University of Göttingen, Department of Developmental Biology, GZMB, Justus-Von-Liebig-Weg 11, Göttingen, 37077, Germany.
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Raja KKB, Yeung K, Li Y, Chen R, Mardon G. A single cell RNA sequence atlas of the early Drosophila larval eye. BMC Genomics 2024; 25:616. [PMID: 38890587 PMCID: PMC11186242 DOI: 10.1186/s12864-024-10423-x] [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: 04/03/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024] Open
Abstract
The Drosophila eye has been an important model to understand principles of differentiation, proliferation, apoptosis and tissue morphogenesis. However, a single cell RNA sequence resource that captures gene expression dynamics from the initiation of differentiation to the specification of different cell types in the larval eye disc is lacking. Here, we report transcriptomic data from 13,000 cells that cover six developmental stages of the larval eye. Our data show cell clusters that correspond to all major cell types present in the eye disc ranging from the initiation of the morphogenetic furrow to the differentiation of each photoreceptor cell type as well as early cone cells. We identify dozens of cell type-specific genes whose function in different aspects of eye development have not been reported. These single cell data will greatly aid research groups studying different aspects of early eye development and will facilitate a deeper understanding of the larval eye as a model system.
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Affiliation(s)
- Komal Kumar Bollepogu Raja
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Kelvin Yeung
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Graeme Mardon
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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Lv Z, Jiang S, Kong S, Zhang X, Yue J, Zhao W, Li L, Lin S. Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants. PLANTS (BASEL, SWITZERLAND) 2024; 13:1679. [PMID: 38931111 PMCID: PMC11207393 DOI: 10.3390/plants13121679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/31/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.
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Affiliation(s)
- Zhuo Lv
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Shuaijun Jiang
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Shuxin Kong
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Xu Zhang
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Jiahui Yue
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Wanqi Zhao
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Long Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
| | - Shuyan Lin
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
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Hong D, Kim HK, Yang W, Yoon C, Kim M, Yang CS, Yoon S. Integrative analysis of single-cell RNA-seq and gut microbiome metabarcoding data elucidates macrophage dysfunction in mice with DSS-induced ulcerative colitis. Commun Biol 2024; 7:731. [PMID: 38879692 PMCID: PMC11180211 DOI: 10.1038/s42003-024-06409-w] [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: 12/08/2023] [Accepted: 06/03/2024] [Indexed: 06/19/2024] Open
Abstract
Ulcerative colitis (UC) is a significant inflammatory bowel disease caused by an abnormal immune response to gut microbes. However, there are still gaps in our understanding of how immune and metabolic changes specifically contribute to this disease. Our research aims to address this gap by examining mouse colons after inducing ulcerative colitis-like symptoms. Employing single-cell RNA-seq and 16 s rRNA amplicon sequencing to analyze distinct cell clusters and microbiomes in the mouse colon at different time points after induction with dextran sodium sulfate. We observe a significant reduction in epithelial populations during acute colitis, indicating tissue damage, with a partial recovery observed in chronic inflammation. Analyses of cell-cell interactions demonstrate shifts in networking patterns among different cell types during disease progression. Notably, macrophage phenotypes exhibit diversity, with a pronounced polarization towards the pro-inflammatory M1 phenotype in chronic conditions, suggesting the role of macrophage heterogeneity in disease severity. Increased expression of Nampt and NOX2 complex subunits in chronic UC macrophages contributes to the inflammatory processes. The chronic UC microbiome exhibits reduced taxonomic diversity compared to healthy conditions and acute UC. The study also highlights the role of T cell differentiation in the context of dysbiosis and its implications in colitis progression, emphasizing the need for targeted interventions to modulate the inflammatory response and immune balance in colitis.
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Affiliation(s)
- Dawon Hong
- RNA Cell Biology Laboratory, Graduate Department of Bioconvergence Engineering, Dankook University, Yongin, Republic of Korea
| | - Hyo Keun Kim
- Dept of Molecular and Life Science and Center for Bionano Intelligence Education and Research, Hanyang University, Ansan-si, Korea
| | - Wonhee Yang
- Department of AI-based Convergence, Dankook University, Yongin, Republic of Korea
| | - Chanjin Yoon
- Dept of Molecular and Life Science and Institute of Natural Science and Technology, Hanyang University, Ansan-si, Korea
| | - Minsoo Kim
- Department of Computer Science, College of SW Convergence, Dankook University, Yongin, Republic of Korea
| | - Chul-Su Yang
- Dept of Medicinal and Life Science and Center for Bionano Intelligence Education and Research, Hanyang University, Ansan-si, Korea.
| | - Seokhyun Yoon
- Department of Electronics & Electrical Engineering, College of Engineering, Dankook University, Yongin, Republic of Korea.
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Schiebout C, Frost HR. CAraCAl: CAMML with the integration of chromatin accessibility. BMC Bioinformatics 2024; 25:212. [PMID: 38872103 PMCID: PMC11170880 DOI: 10.1186/s12859-024-05833-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
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Affiliation(s)
- Courtney Schiebout
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA.
| | - H Robert Frost
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA
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Lee CY, Clatworthy MR, Withers DR. Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies. Immunol Cell Biol 2024. [PMID: 38853634 DOI: 10.1111/imcb.12787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.
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Affiliation(s)
- Colin Yc Lee
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - David R Withers
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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Guo Y, Yu L, Guo L, Xu L, Li Q. A Regularized Bayesian Dirichlet-multinomial Regression Model for Integrating Single-cell-level Omics and Patient-level Clinical Study Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597391. [PMID: 38895417 PMCID: PMC11185671 DOI: 10.1101/2024.06.04.597391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.
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Affiliation(s)
- Yanghong Guo
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
| | - Lei Yu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Lei Guo
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
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40
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Yang X, Chen Y, Yang Y, Li S, Mi P, Jing N. The molecular and cellular choreography of early mammalian lung development. MEDICAL REVIEW (2021) 2024; 4:192-206. [PMID: 38919401 PMCID: PMC11195428 DOI: 10.1515/mr-2023-0064] [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: 12/04/2023] [Accepted: 03/08/2024] [Indexed: 06/27/2024]
Abstract
Mammalian lung development starts from a specific cluster of endodermal cells situated within the ventral foregut region. With the orchestrating of delicate choreography of transcription factors, signaling pathways, and cell-cell communications, the endodermal diverticulum extends into the surrounding mesenchyme, and builds the cellular and structural basis of the complex respiratory system. This review provides a comprehensive overview of the current molecular insights of mammalian lung development, with a particular focus on the early stage of lung cell fate differentiation and spatial patterning. Furthermore, we explore the implications of several congenital respiratory diseases and the relevance to early organogenesis. Finally, we summarize the unprecedented knowledge concerning lung cell compositions, regulatory networks as well as the promising prospect for gaining an unbiased understanding of lung development and lung malformations through state-of-the-art single-cell omics.
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Affiliation(s)
- Xianfa Yang
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
| | - Yingying Chen
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
| | - Yun Yang
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Shiting Li
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan Province, China
| | - Panpan Mi
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Naihe Jing
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
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41
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Ding Y, Peng YY, Li S, Tang C, Gao J, Wang HY, Long ZY, Lu XM, Wang YT. Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases. Cell Biochem Biophys 2024; 82:329-342. [PMID: 38133792 DOI: 10.1007/s12013-023-01207-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.
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Affiliation(s)
- Yang Ding
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yu-Yuan Peng
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Sen Li
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Can Tang
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Jie Gao
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Hai-Yan Wang
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Zai-Yun Long
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Xiu-Min Lu
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
| | - Yong-Tang Wang
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
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Liu C, Tang J, Liang K, Liu P, Li Z. Ready for renascence in mosquito: The regulation of gene expression in Plasmodium sexual development. Acta Trop 2024; 254:107191. [PMID: 38554994 DOI: 10.1016/j.actatropica.2024.107191] [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: 12/21/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 04/02/2024]
Abstract
Malaria remains one of the most perilous vector-borne infectious diseases for humans globally. Sexual gametocyte represents the exclusive stage at which malaria parasites are transmitted from the vertebrate to the Anopheles host. The feasible and effective approach to prevent malaria transmission is by addressing the sexual developmental processes, that is, gametocytogenesis and gametogenesis. Thus, this review will comprehensively cover advances in the regulation of gene expression surrounding the transmissible stages, including epigenetic, transcriptional, and post-transcriptional control.
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Affiliation(s)
- Cong Liu
- Institute of Pathogenic Biology and Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China; School of Public Health, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jingjing Tang
- Institute of Pathogenic Biology and Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Kejia Liang
- Institute of Pathogenic Biology and Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Peng Liu
- Institute of Pathogenic Biology and Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Zhenkui Li
- Institute of Pathogenic Biology and Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
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43
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Zhang T, Ren J, Li L, Wu Z, Zhang Z, Dong G, Wang G. scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering. Int J Mol Sci 2024; 25:5976. [PMID: 38892162 PMCID: PMC11172799 DOI: 10.3390/ijms25115976] [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: 03/24/2024] [Revised: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.
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Affiliation(s)
| | | | | | | | | | | | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China; (T.Z.); (J.R.); (L.L.); (Z.W.); (Z.Z.); (G.D.)
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44
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Wan R, Zhang Y, Peng Y, Tian F, Gao G, Tang F, Jia J, Ge H. Unveiling gene regulatory networks during cellular state transitions without linkage across time points. Sci Rep 2024; 14:12355. [PMID: 38811747 PMCID: PMC11137113 DOI: 10.1038/s41598-024-62850-1] [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: 01/24/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024] Open
Abstract
Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR's perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.
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Affiliation(s)
- Ruosi Wan
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yuhao Zhang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Yongli Peng
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Feng Tian
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Ge Gao
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Jinzhu Jia
- School of Public Health and Center for Statistical Science, Peking University, Beijing, China.
| | - Hao Ge
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China.
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45
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Feng J, Song H, Province M, Li G, Payne PRO, Chen Y, Li F. PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications. Front Cell Neurosci 2024; 18:1369242. [PMID: 38846640 PMCID: PMC11155453 DOI: 10.3389/fncel.2024.1369242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/30/2024] [Indexed: 06/09/2024] Open
Abstract
Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.
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Affiliation(s)
- Jiarui Feng
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Haoran Song
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Michael Province
- Division of Statistical Genomics, Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
| | - Guangfu Li
- Department of Surgery, University of Missouri-Columbia, Columbia, MO, United States
- Department of Molecular Microbiology and Immunology, University of Missouri-Columbia, Columbia, MO, United States
- NextGen Precision Health Institute, University of Missouri-Columbia, Columbia, MO, United States
| | - Philip R. O. Payne
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Fuhai Li
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
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46
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Chen H, Lu Y, Dai Z, Yang Y, Li Q, Rao Y. Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge. Brief Bioinform 2024; 25:bbae314. [PMID: 38960404 PMCID: PMC11221887 DOI: 10.1093/bib/bbae314] [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: 10/13/2023] [Revised: 12/13/2023] [Accepted: 06/20/2024] [Indexed: 07/05/2024] Open
Abstract
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.
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Affiliation(s)
- Hegang Chen
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Yuyin Lu
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Qing Li
- Department of Computing, The Hong Kong Polytechnic University, PQ806, Mong Man Wai Building, 999077, Hong Kong SAR
| | - Yanghui Rao
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
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47
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Klimek A, Mondal D, Block S, Sharma P, Netz RR. Data-driven classification of individual cells by their non-Markovian motion. Biophys J 2024; 123:1173-1183. [PMID: 38515300 PMCID: PMC11140416 DOI: 10.1016/j.bpj.2024.03.023] [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: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
We present a method to differentiate organisms solely by their motion based on the generalized Langevin equation (GLE) and use it to distinguish two different swimming modes of strongly confined unicellular microalgae Chlamydomonas reinhardtii. The GLE is a general model for active or passive motion of organisms and particles that can be derived from a time-dependent general many-body Hamiltonian and in particular includes non-Markovian effects (i.e., the trajectory memory of its past). We extract all GLE parameters from individual cell trajectories and perform an unbiased cluster analysis to group them into different classes. For the specific cell population employed in the experiments, the GLE-based assignment into the two different swimming modes works perfectly, as checked by control experiments. The classification and sorting of single cells and organisms is important in different areas; our method, which is based on motion trajectories, offers wide-ranging applications in biology and medicine.
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Affiliation(s)
- Anton Klimek
- Fachbereich Physik, Freie Universität Berlin, Berlin, Germany
| | - Debasmita Mondal
- Department of Physics, Indian Institute of Science, Bangalore, India; James Franck Institute, University of Chicago, Chicago, Illinois
| | - Stephan Block
- Institut für Chemie und Biochemie, Freie Universität Berlin, Berlin, Germany
| | - Prerna Sharma
- Department of Physics, Indian Institute of Science, Bangalore, India; Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Roland R Netz
- Fachbereich Physik, Freie Universität Berlin, Berlin, Germany.
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48
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Chen S, Liang B, Xu J. Unveiling heterogeneity in MSCs: exploring marker-based strategies for defining MSC subpopulations. J Transl Med 2024; 22:459. [PMID: 38750573 PMCID: PMC11094970 DOI: 10.1186/s12967-024-05294-5] [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: 11/28/2023] [Accepted: 05/11/2024] [Indexed: 05/19/2024] Open
Abstract
Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.
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Affiliation(s)
- Si Chen
- Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, People's Republic of China
| | - Bowei Liang
- Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, People's Republic of China
| | - Jianyong Xu
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Guangdong Engineering Technology Research Center of Reproductive Immunology for Peri-Implantation, Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), Fuqiang Avenue 1001, Shenzhen, 518060, Guangdong, People's Republic of China.
- Guangdong Engineering Technology Research Center of Reproductive Immunology for Peri-Implantation, Shenzhen, 518000, People's Republic of China.
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49
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Ran R, Muñoz Briones J, Jena S, Anderson NL, Olson MR, Green LN, Brubaker DK. Detailed survey of an in vitro intestinal epithelium model by single-cell transcriptomics. iScience 2024; 27:109383. [PMID: 38523788 PMCID: PMC10959667 DOI: 10.1016/j.isci.2024.109383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/01/2023] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
The co-culture of two adult human colorectal cancer cell lines, Caco-2 and HT29, on Transwell is commonly used as an in vitro gut mimic, yet the translatability of insights from such a system to adult human physiological contexts is not fully characterized. Here, we used single-cell RNA sequencing on the co-culture to obtain a detailed survey of cell type heterogeneity in the system and conducted a holistic comparison with human physiology. We identified the intestinal stem cell-, transit amplifying-, enterocyte-, goblet cell-, and enteroendocrine-like cells in the system. In general, the co-culture was fetal intestine-like, with less variety of gene expression compared to the adult human gut. Transporters for major types of nutrients were found in the majority of the enterocytes-like cells in the system. TLR 4 was not expressed in the sample, indicating that the co-culture model is incapable of mimicking the innate immune aspect of the human epithelium.
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Affiliation(s)
- Ran Ran
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH, USA
| | - Javier Muñoz Briones
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Interdisciplinary Life Science Program, West Lafayette, IN, USA
| | - Smrutiti Jena
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Nicole L. Anderson
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Matthew R. Olson
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Leopold N. Green
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Douglas K. Brubaker
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH, USA
- The Blood, Heart, Lung, and Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, OH, USA
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50
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Pan L, Parini P, Tremmel R, Loscalzo J, Lauschke VM, Maron BA, Paci P, Ernberg I, Tan NS, Liao Z, Yin W, Rengarajan S, Li X. Single Cell Atlas: a single-cell multi-omics human cell encyclopedia. Genome Biol 2024; 25:104. [PMID: 38641842 PMCID: PMC11027364 DOI: 10.1186/s13059-024-03246-2] [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: 11/16/2022] [Accepted: 04/12/2024] [Indexed: 04/21/2024] Open
Abstract
Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.
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Affiliation(s)
- Lu Pan
- Institute of Environmental Medicine, Karolinska Institutet, 171 65, Solna, Sweden
| | - Paolo Parini
- Cardio Metabolic Unit, Department of Medicine, and, Department of Laboratory Medicine , Karolinska Institutet, 141 86, Stockholm, Sweden
- Theme Inflammation and Ageing, Medicine Unit, Karolinska University Hospital, 141 86, Stockholm, Sweden
| | - Roman Tremmel
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376, Stuttgart, Germany
- University of Tuebingen, 72076, Tuebingen, Germany
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Volker M Lauschke
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376, Stuttgart, Germany
- University of Tuebingen, 72076, Tuebingen, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 65, Solna, Sweden
| | - Bradley A Maron
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy
| | - Ingemar Ernberg
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 171 65, Solna, Sweden
| | - Nguan Soon Tan
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, 308232, Singapore
| | - Zehuan Liao
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 171 65, Solna, Sweden
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Weiyao Yin
- Institute of Environmental Medicine, Karolinska Institutet, 171 65, Solna, Sweden
| | - Sundararaman Rengarajan
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Xuexin Li
- Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang, 110032, China.
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 65, Solna, Sweden.
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