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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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Zhu L, Yang S, Zhang K, Wang H, Fang X, Wang J. Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics. Proc Natl Acad Sci U S A 2024; 121:e2401540121. [PMID: 39150785 PMCID: PMC11348339 DOI: 10.1073/pnas.2401540121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/28/2024] [Indexed: 08/18/2024] Open
Abstract
Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.
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Affiliation(s)
- Ligang Zhu
- College of Physics, Jilin University, Changchun130021, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Songlin Yang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Kun Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Hong Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Xiaona Fang
- College of Chemistry, Northeast Normal University, Changchun130024, China
| | - Jin Wang
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou325001, China
- Department of Chemistry, Physics and Astronomy, Stony Brook University, Stony Brook, NY11794
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3
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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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4
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Teschendorff AE. Computational single-cell methods for predicting cancer risk. Biochem Soc Trans 2024; 52:1503-1514. [PMID: 38856037 DOI: 10.1042/bst20231488] [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: 01/29/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024]
Abstract
Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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Samanta P, Cooke SF, McNulty R, Hormoz S, Rosenthal A. ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets. Nat Protoc 2024:10.1038/s41596-024-01002-1. [PMID: 38769144 DOI: 10.1038/s41596-024-01002-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/12/2024] [Indexed: 05/22/2024]
Abstract
Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.
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Affiliation(s)
- Prosenjit Samanta
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
| | - Samuel F Cooke
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
| | - Ryan McNulty
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Sahand Hormoz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adam Rosenthal
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA.
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Lin W, Li Q, Liu L, Wang Q, Zhang D, Wang F, Xu R, Fan Y, Xing M, Zhou C, Yuan Q. Early infiltrating NKT lymphocytes attenuate bone regeneration through secretion of CXCL2. SCIENCE ADVANCES 2024; 10:eadl6343. [PMID: 38758783 PMCID: PMC11100573 DOI: 10.1126/sciadv.adl6343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/15/2024] [Indexed: 05/19/2024]
Abstract
Trauma rapidly mobilizes the immune response of surrounding tissues and activates regeneration program. Manipulating immune response to promote tissue regeneration shows a broad application prospect. However, the understanding of bone healing dynamics at cellular level remains limited. Here, we characterize the landscape of immune cells after alveolar bone injury and reveal a pivotal role of infiltrating natural killer T (NKT) cells. We observe a rapid increase in NKT cells after injury, which inhibit osteogenic differentiation of mesenchymal stem cells (MSCs) and impair alveolar bone healing. Cxcl2 is up-regulated in NKT cells after injury. Systemic administration of CXCL2-neutralizing antibody or genetic deletion of Cxcl2 improves the bone healing process. In addition, we fabricate a gelatin-based porous hydrogel to deliver NK1.1 depletion antibody, which successfully promotes alveolar bone healing. In summary, our study highlights the importance of NKT cells in the early stage of bone healing and provides a potential therapeutic strategy for accelerating bone regeneration.
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Affiliation(s)
- Weimin Lin
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Qiwen Li
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Linfeng Liu
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Qian Wang
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Danting Zhang
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Feiyu Wang
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Ruoshi Xu
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Yi Fan
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
- Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Malcolm Xing
- Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 2N2, Canada
| | - Chenchen Zhou
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
- Department of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
| | - Quan Yuan
- State Key Laboratory of Oral Diseases and National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, 610041 Chengdu, Sichuan, China
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8
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Zhu L, Wang J. Quantifying Landscape-Flux via Single-Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308879. [PMID: 38353329 DOI: 10.1002/advs.202308879] [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: 11/18/2023] [Revised: 01/23/2024] [Indexed: 04/25/2024]
Abstract
Recent developments in single-cell sequencing technology enable the acquisition of entire transcriptome data. Understanding the underlying mechanism and identifying the driving force of transcriptional regulation governing cell function directly from these data remains challenging. This study reconstructs a continuous vector field of the cell cycle based on discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium dynamic landscape-flux. It reveals that large fluctuations disrupt the global landscape and genetic perturbations alter landscape-flux, thus identifying key genes in maintaining cell cycle dynamics and predicting associated functional effects. Additionally, it quantifies the fundamental energy cost of the cell cycle initiation and unveils that sustaining the cell cycle requires curl flux and dissipation to maintain the oscillatory phase coherence. This study enables the inference of the cell cycle gene regulatory networks directly from the single-cell transcriptomic data, including the feedback mechanisms and interaction intensity. This provides a golden opportunity to experimentally verify the landscape-flux theory and also obtain its associated quantifications. It also offers a unique framework for combining the landscape-flux theory and single-cell high-through sequencing experiments for understanding the underlying mechanisms of the cell cycle and can be extended to other nonequilibrium biological processes, such as differentiation development and disease pathogenesis.
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Affiliation(s)
- Ligang Zhu
- College of Physics, Jilin University, Changchun, 130021, P. R. China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Jin Wang
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, P. R. China
- Department of Chemistry, Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, USA
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Duanis-Assaf T, Reches M. Factors influencing initial bacterial adhesion to antifouling surfaces studied by single-cell force spectroscopy. iScience 2024; 27:108803. [PMID: 38303698 PMCID: PMC10831873 DOI: 10.1016/j.isci.2024.108803] [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: 07/10/2023] [Revised: 10/29/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024] Open
Abstract
Biofilm formation, a major concern for healthcare systems, is initiated when bacteria adhere to surfaces. Escherichia coli adhesion is mediated by appendages, including type-1 fimbriae and curli amyloid fibers. Antifouling surfaces prevent the adhesion of bacteria to combat biofilm formation. Here, we used single-cell force-spectroscopy to study the interaction between E. coli and glass or two antifouling surfaces: the tripeptide DOPA-Phe(4F)-Phe(4F)-OMe and poly(ethylene glycol) polymer-brush. Our results indicate that both antifoulants significantly deter E. coli initial adhesion. By using two mutant strains expressing no type-1 fimbriae or curli amyloids, we studied the adhesion mechanism. Our results suggest that the bacteria adhere to different antifoulants via separate mechanisms. Finally, we show that some bacteria adhere much better than others, illustrating how the variability of bacterial cultures affects biofilm formation. Our results emphasize how additional study at the single-cell level can enhance our understanding of bacterial adhesion, thus leading to novel antifouling technologies.
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Affiliation(s)
- Tal Duanis-Assaf
- Institute of Chemistry and The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Meital Reches
- Institute of Chemistry and The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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10
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Rocque B, Guion K, Singh P, Bangerth S, Pickard L, Bhattacharjee J, Eguizabal S, Weaver C, Chopra S, Zhou S, Kohli R, Sher L, Akbari O, Ekser B, Emamaullee JA. Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease. Sci Rep 2024; 14:3612. [PMID: 38351241 PMCID: PMC10864257 DOI: 10.1038/s41598-024-53993-2] [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: 08/29/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024] Open
Abstract
Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.
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Affiliation(s)
- Brittany Rocque
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Kate Guion
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Pranay Singh
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Sarah Bangerth
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Lauren Pickard
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Jashdeep Bhattacharjee
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Sofia Eguizabal
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Carly Weaver
- Division of Abdominal Organ Transplantation, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Shefali Chopra
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shengmei Zhou
- Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California Los Angeles, Los Angeles, CA, USA
| | - Rohit Kohli
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Linda Sher
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA
| | - Omid Akbari
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcin Ekser
- Division of Transplant Surgery, Department of Surgery, Indiana University School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Juliet A Emamaullee
- Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA.
- Division of Abdominal Organ Transplantation, Children's Hospital Los Angeles, Los Angeles, CA, USA.
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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11
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Corrêa MSL, Silva EN, Dos Santos TCF, Simielli Fonseca LF, Magalhães AFB, Verardo LL, de Albuquerque LG, Silva DBDS. A network-based approach to understanding gene-biological processes affecting economically important traits of Nelore cattle. Anim Genet 2024; 55:55-65. [PMID: 38112158 DOI: 10.1111/age.13389] [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: 10/07/2023] [Revised: 10/07/2023] [Accepted: 11/29/2023] [Indexed: 12/20/2023]
Abstract
This study aimed to build gene-biological process networks with differentially expressed genes associated with economically important traits of Nelore cattle from 17 previous studies. The genes were clustered into three groups by evaluated traits: group 1, production traits; group 2, carcass traits; and group 3, meat quality traits. For each group, a gene-biological process network analysis was performed with the differentially expressed genes in common. For production traits, 37 genes were found in common, of which 13 genes were enriched for six Gene Ontology (GO) terms; these terms were not functionally grouped. However, the enriched GO terms were related to homeostasis, the development of muscles and the immune system. For carcass traits, four genes were found in common. Thus, it was not possible to functionally group these genes into a network. For meat quality traits, the analysis revealed 222 genes in common. CSRP3 was the only gene differentially expressed in all three groups. Non-redundant biological terms for clusters of genes were functionally grouped networks, reflecting the cross-talk between all biological processes and genes involved. Many biological processes and pathways related to muscles, the immune system and lipid metabolism were enriched, such as striated muscle cell development and triglyceride metabolic processes. This study provides insights into the genetic mechanisms of production, carcass and meat quality traits of Nelore cattle. This information is fundamental for a better understanding of the complex traits and could help in planning strategies for the production and selection systems of Nelore cattle.
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Affiliation(s)
| | - Evandro Neves Silva
- Professor Edson Antônio Velano University (UNIFENAS), Alfenas, Minas Gerais, Brazil
- Federal University of Alfenas (UNIFAL), Alfenas, Minas Gerais, Brazil
| | - Thaís Cristina Ferreira Dos Santos
- Professor Edson Antônio Velano University (UNIFENAS), Alfenas, Minas Gerais, Brazil
- National Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil
| | | | - Ana Fabrícia Braga Magalhães
- Department of Animal Science, Federal University of Vales do Jequitinhonha e Mucuri (UFVJM), Diamantina, Minas Gerais, Brazil
| | - Lucas Lima Verardo
- Department of Animal Science, Federal University of Vales do Jequitinhonha e Mucuri (UFVJM), Diamantina, Minas Gerais, Brazil
| | - Lucia Galvão de Albuquerque
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - Danielly Beraldo Dos Santos Silva
- Professor Edson Antônio Velano University (UNIFENAS), Alfenas, Minas Gerais, Brazil
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
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12
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Xu F, Hu H, Lin H, Lu J, Cheng F, Zhang J, Li X, Shuai J. scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks. Brief Bioinform 2024; 25:bbae091. [PMID: 38487851 PMCID: PMC10940817 DOI: 10.1093/bib/bbae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.
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Affiliation(s)
- Fei Xu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Hai Lin
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jun Lu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- School of Medical Imageology, Wannan Medical College, Wuhu 241002, China
| | - Feng Cheng
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jiqian Zhang
- Department of Physics, Anhui Normal University, Wuhu 241002, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China
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13
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Zang Y, Pei Y, Cong X, Ran F, Liu L, Wang C, Wang D, Min Y. Single-cell RNA-sequencing profiles reveal the developmental landscape of the Manihot esculenta Crantz leaves. PLANT PHYSIOLOGY 2023; 194:456-474. [PMID: 37706525 PMCID: PMC10756766 DOI: 10.1093/plphys/kiad500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 09/15/2023]
Abstract
Cassava (Manihot esculenta Crantz) is an important crop with a high photosynthetic rate and high yield. It is classified as a C3-C4 plant based on its photosynthetic and structural characteristics. To investigate the structural and photosynthetic characteristics of cassava leaves at the cellular level, we created a single-cell transcriptome atlas of cassava leaves. A total of 11,177 high-quality leaf cells were divided into 15 cell clusters. Based on leaf cell marker genes, we identified 3 major tissues of cassava leaves, which were mesophyll, epidermis, and vascular tissue, and analyzed their distinctive properties and metabolic activity. To supplement the genes for identifying the types of leaf cells, we screened 120 candidate marker genes. We constructed a leaf cell development trajectory map and discovered 6 genes related to cell differentiation fate. The structural and photosynthetic properties of cassava leaves analyzed at the single cellular level provide a theoretical foundation for further enhancing cassava yield and nutrition.
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Affiliation(s)
- Yuwei Zang
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Yechun Pei
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
- Laboratory of Biopharmaceuticals and Molecular Pharmacology, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Xinli Cong
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
- Laboratory of Biopharmaceuticals and Molecular Pharmacology, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Fangfang Ran
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Liangwang Liu
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Changyi Wang
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Dayong Wang
- Laboratory of Biopharmaceuticals and Molecular Pharmacology, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Yi Min
- Department of Biotechnology, School of Life Sciences, Hainan University, Haikou, Hainan 570228, China
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14
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Ding Y, Gao W, Qin Y, Li X, Zhang Z, Lai W, Yang Y, Guo K, Li P, Zhou S, Hu H. Single-cell RNA landscape of the special fiber initiation process in Bombax ceiba. PLANT COMMUNICATIONS 2023; 4:100554. [PMID: 36772797 PMCID: PMC10518721 DOI: 10.1016/j.xplc.2023.100554] [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: 08/07/2022] [Revised: 12/19/2022] [Accepted: 01/20/2023] [Indexed: 06/03/2023]
Abstract
As a new source of natural fibers, the Bombax ceiba tree can provide thin, light, extremely soft and warm fiber material for the textile industry. Natural fibers are an ideal model system for studying cell growth and differentiation, but the molecular mechanisms that regulate fiber initiation are not fully understood. In B. ceiba, we found that fiber cells differentiate from the epidermis of the inner ovary wall. Each initiated cell then divides into a cluster of fiber cells that eventually develop into mature fibers, a process very different from the classical fiber initiation process of cotton. We used high-throughput single-cell RNA sequencing (scRNA-seq) to examine the special characteristics of fiber initiation in B. ceiba. A total of 15 567 high-quality cells were identified from the inner wall of the B. ceiba ovary, and 347 potential marker genes for fiber initiation cell types were identified. Two major cell types, initiated fiber cells and epidermal cells, were identified and verified by RNA in situ hybridization. A developmental trajectory analysis was used to reconstruct the process of fiber cell differentiation in B. ceiba. Comparative analysis of scRNA-seq data from B. ceiba and cotton (Gossypium hirsutum) confirmed that the additional cell division process in B. ceiba is a novel species-specific mechanism for fiber cell development. Candidate genes and key regulators that may contribute to fiber cell differentiation and division in B. ceiba were identified. This work reveals gene expression signatures during B. ceiba fiber initiation at a single-cell resolution, providing a new strategy and viewpoint for investigation of natural fiber cell differentiation and development.
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Affiliation(s)
- Yuanhao Ding
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572000, China
| | - Wei Gao
- State Key Laboratory of Cotton Biology, School of Life Science, Henan University, Kaifeng, Henan, P.R. China
| | - Yuan Qin
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou 434025, China
| | - Xinping Li
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China
| | - Zhennan Zhang
- State Key Laboratory of Cotton Biology, School of Life Science, Henan University, Kaifeng, Henan, P.R. China
| | - Wenjie Lai
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China
| | - Yong Yang
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China
| | - Kai Guo
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400716, China
| | - Ping Li
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China
| | - Shihan Zhou
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China
| | - Haiyan Hu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou 570228, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572000, China.
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15
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Rocque B, Guion K, Singh P, Bangerth S, Pickard L, Bhattacharjee J, Eguizabal S, Weaver C, Chopra S, Zhou S, Kohli R, Sher L, Ekser B, Emamaullee JA. Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease. RESEARCH SQUARE 2023:rs.3.rs-3307940. [PMID: 37720049 PMCID: PMC10503835 DOI: 10.21203/rs.3.rs-3307940/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNASeq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Deconvolution of the spatial transcriptome using paired snRNASeq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNASeq data poorly correlated with celullar relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Shengmei Zhou
- Children's Hospital Los Angeles, University of Southern California Los Angeles
| | | | | | - Burcin Ekser
- Indiana University School of Medicine, Indiana University
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16
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Hegarty C, Neto N, Cahill P, Floudas A. Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions. Comput Struct Biotechnol J 2023; 21:4009-4020. [PMID: 37649712 PMCID: PMC10462794 DOI: 10.1016/j.csbj.2023.08.005] [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: 04/04/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.
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Affiliation(s)
- Ciara Hegarty
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Nuno Neto
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland
| | - Paul Cahill
- Vascular Biology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Achilleas Floudas
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
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17
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Zhang Z, Shi Q, Zhu X, Jin L, Lang L, Lyu S, Xin X, Huang Y, Yu X, Li Z, Chen S, Xu Z, Zhang W, Wang E. Identification and Functional Analysis of Transcriptome Profiles, Long Non-Coding RNAs, Single-Nucleotide Polymorphisms, and Alternative Splicing from the Oocyte to the Preimplantation Stage of Sheep by Single-Cell RNA Sequencing. Genes (Basel) 2023; 14:1145. [PMID: 37372325 DOI: 10.3390/genes14061145] [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: 04/03/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/29/2023] Open
Abstract
Numerous dynamic and complicated processes characterize development from the oocyte to the embryo. However, given the importance of functional transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms, and alternative splicing during embryonic development, the effect that these features have on the blastomeres of 2-, 4-, 8-, 16-cell, and morula stages of development has not been studied. Here, we carried out experiments to identify and functionally analyze the transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms (SNPs), and alternative splicing (AS) of cells from sheep from the oocyte to the blastocyst developmental stages. We found between the oocyte and zygote groups significantly down-regulated genes and the second-largest change in gene expression occurred between the 8- and 16-cell stages. We used various methods to construct a profile to characterize cellular and molecular features and systematically analyze the related GO and KEGG profile of cells of all stages from the oocyte to the blastocyst. This large-scale, single-cell atlas provides key cellular information and will likely assist clinical studies in improving preimplantation genetic diagnosis.
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Affiliation(s)
- Zijing Zhang
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Qiaoting Shi
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Xiaoting Zhu
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Lei Jin
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Limin Lang
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Shijie Lyu
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Xiaoling Xin
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Yongzhen Huang
- College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Xiang Yu
- Henan Animal Health Supervision Institute, Zhengzhou 450003, China
| | - Zhiming Li
- Henan Provincial Animal Husbandry General Station, Zhengzhou 450008, China
| | - Sujuan Chen
- Synthetic Biology Engineering Lab of Henan Province, School of Sciences and Technology, Xinxiang Medical University, Xinxiang 453003, China
| | - Zhaoxue Xu
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
| | - Wei Zhang
- College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Eryao Wang
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, No. 116 Hua Yuan Road, Zhengzhou 450002, China
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18
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Al-Sharif M, Abdo M, Shabrawy OE, El-Naga EMA, Fericean L, Banatean-Dunea I, Ateya A. Investigating Polymorphisms and Expression Profile of Immune, Antioxidant, and Erythritol-Related Genes for Limiting Postparturient Endometritis in Holstein Cattle. Vet Sci 2023; 10:370. [PMID: 37368756 DOI: 10.3390/vetsci10060370] [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: 04/16/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
This study looked at genetic polymorphisms and transcript levels of immune, antioxidant, and erythritol-related markers for postparturient endometritis prediction and tracking in Holstein dairy cows. One hundred and thirty female dairy cows (65 endometritis affected and 65 apparently healthy) were used. Nucleotide sequence variations between healthy and endometritis-affected cows were revealed using PCR-DNA sequencing for immune (TLR4, TLR7, TNF-α, IL10, NCF4, and LITAF), antioxidant (ATOX1, GST, and OXSR1), and erythritol-related (TKT, RPIA, and AMPD1) genes. Chi-square investigation exposed a noteworthy variance amongst cow groups with and without endometritis in likelihood of dispersal of all distinguished nucleotide variants (p < 0.05). The IL10, ATOX1, and GST genes were expressed at substantially lower levels in endometritis-affected cows. Gene expression levels were considerably higher in endometritis-affected cows than in resistant ones for the genes TLR4, TLR7, TNF-α, NCF4, LITAF, OXSR1, TKT, RPIA, and AMPD1. The sort of marker and vulnerability or resistance to endometritis had a significant impact on the transcript levels of the studied indicators. The outcomes might confirm the importance of nucleotide variants along with gene expression patterns as markers of postparturient endometritis susceptibility/resistance and provide a workable control plan for Holstein dairy cows.
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Affiliation(s)
- Mona Al-Sharif
- Department of Biology, College of Science, University of Jeddah, Jeddah 21589, Saudi Arabia
| | - Mohamed Abdo
- Department of Animal Histology and Anatomy, School of Veterinary Medicine, Badr University in Cairo (BUC), Cairo 11829, Egypt
- Department of Anatomy and Embryology, Faculty of Veterinary Medicine, University of Sadat City, Sadat City 32897, Egypt
| | - Omnia El Shabrawy
- Department of Biochemistry and Chemistry of Nutrition, Faculty of Veterinary Medicine, Menofia University, Menofia 32951, Egypt
| | - Eman M Abu El-Naga
- Department of Theriogenology, Faculty of Veterinary Medicine, Aswan University, Aswan 81528, Egypt
| | - Liana Fericean
- Department of Biology and Plant Protection, Faculty of Agricultural Sciences, University of Life Sciences King Michael I, 300645 Timisoara, Romania
| | - Ioan Banatean-Dunea
- Department of Biology and Plant Protection, Faculty of Agricultural Sciences, University of Life Sciences King Michael I, 300645 Timisoara, Romania
| | - Ahmed Ateya
- Department of Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Mansoura 35516, Egypt
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19
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Huo D, Hao Y, Zou J, Qin L, Wang C, Du D. Integrated transcriptome and metabonomic analysis of key metabolic pathways in response to cadmium stress in novel buckwheat and cultivated species. FRONTIERS IN PLANT SCIENCE 2023; 14:1142814. [PMID: 37008482 PMCID: PMC10064074 DOI: 10.3389/fpls.2023.1142814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/16/2023] [Indexed: 06/19/2023]
Abstract
INTRODUCTION Buckwheat (Fagopyrum tataricum), an important food crop, also has medicinal uses. It is widely planted in Southwest China, overlapping with planting areas remarkably polluted by cadmium (Cd). Therefore, it is of great significance to study the response mechanism of buckwheat under Cd stress and further develop varieties with excellent Cd tolerance. METHODS In this study, two critical periods of Cd stress treatment (days 7 and 14 after Cd treatment) of cultivated buckwheat (Pinku-1, named K33) and perennial species (F. tatari-cymosum Q.F. Chen) (duoku, named DK19) were analyzed using transcriptome and metabolomics. RESULTS The results showed that Cd stress led to changes in reactive oxygen species (ROS) and the chlorophyll system. Moreover, Cd-response genes related to stress response, amino acid metabolism, and ROS scavenging were enriched or activated in DK19. Transcriptome and metabolomic analyses highlighted the important role of galactose, lipid (glycerophosphatide metabolism and glycerophosphatide metabolism), and glutathione metabolism in response to Cd stress in buckwheat, which are significantly enriched at the gene and metabolic levels in DK19. DISCUSSION The results of the present study provide valuable information for a better understanding of the molecular mechanisms underlying Cd tolerance in buckwheat and useful clues for the genetic improvement of drought tolerance in buckwheat.
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Affiliation(s)
- Dongao Huo
- Guizhou Normal University, Guiyang, China
- College of Biological Sciences and Technology, Taiyuan Normal University, Taiyuan, China
| | - Ying Hao
- Guizhou Normal University, Guiyang, China
| | - Juan Zou
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, China
| | - Lixia Qin
- College of Agriculture, Shanxi Agricultural University, Taiyuan, China
| | - Chuangyun Wang
- College of Agriculture, Shanxi Agricultural University, Taiyuan, China
| | - Dengxiang Du
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, China
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20
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Dong X, Chowdhury S, Victor U, Li X, Qian L. Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1492-1505. [PMID: 35536811 DOI: 10.1109/tcbb.2022.3173587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model "SemiRNet" to use unlabeled scRNAseq cells and a limited amount of labeled scRNAseq cells to implement cell identification. The proposed model is based on recurrent convolutional neural networks (RCNN), which includes a shared network, a supervised network and an unsupervised network. The proposed model is evaluated on two large scale single-cell transcriptomic datasets. It is observed that the proposed model is able to achieve encouraging performance by learning on the very limited amount of labeled scRNAseq cells together with a large number of unlabeled scRNAseq cells.
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21
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Dong F, Ping P, Ma Y, Chen XF. Application of single-cell RNA sequencing on human testicular samples: a comprehensive review. Int J Biol Sci 2023; 19:2167-2197. [PMID: 37151874 PMCID: PMC10158017 DOI: 10.7150/ijbs.82191] [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: 12/27/2022] [Accepted: 03/25/2023] [Indexed: 05/09/2023] Open
Abstract
So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.
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Affiliation(s)
- Fan Dong
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
| | - Ping Ping
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
| | - Yi Ma
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- ✉ Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: . Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address:
| | - Xiang-Feng Chen
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- Shanghai Human Sperm Bank, Shanghai, China
- ✉ Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: . Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address:
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22
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A Device for Isolation of Selected Single Adherent Cells. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4303586. [PMID: 36567910 PMCID: PMC9780011 DOI: 10.1155/2022/4303586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 10/29/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022]
Abstract
In recent years, extensive research has been focused on the field of single cell analysis. The isolation of single cells is the first step in this type of research. However, the techniques used for direct isolation and acquisition of single adherent cells are limited. Here, we present a method of obtaining selected single adherent cells using a separation device. Compared with other single cell isolation methods, this method has the advantages of simple operation, low cost, minimal cell damage, and preservation of cell morphology. Our methodology is, therefore, suitable for the collection of selected single adherent cells.
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23
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Jeon HJ, Lim HG, Shung KK, Lee OJ, Kim MG. Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing. Sci Rep 2022; 12:19873. [PMID: 36400803 PMCID: PMC9674693 DOI: 10.1038/s41598-022-22075-6] [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: 05/17/2022] [Accepted: 10/10/2022] [Indexed: 11/19/2022] Open
Abstract
This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.
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Affiliation(s)
- Hyeon-Ju Jeon
- grid.482520.90000 0004 0578 4668Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems, Seoul, 07071 Republic of Korea
| | - Hae Gyun Lim
- grid.412576.30000 0001 0719 8994Department of Biomedical Engineering, Pukyong National University, Busan, 48513 Republic of Korea
| | - K. Kirk Shung
- grid.42505.360000 0001 2156 6853Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - O-Joun Lee
- grid.411947.e0000 0004 0470 4224Department of Artificial Intelligence, The Catholic University of Korea, Bucheon, 14662 Republic of Korea
| | - Min Gon Kim
- grid.42505.360000 0001 2156 6853Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 USA
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24
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Ragia G, Manolopoulos VG. The revolution of pharmaco-omics: ready to open new avenues in materializing precision medicine? Pharmacogenomics 2022; 23:869-872. [DOI: 10.2217/pgs-2022-0145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Tweetable abstract The pharmaco-omics revolution has started and, as a wild stream, sooner or later, will expand and dramatically improve drug discovery and individual response to pharmacotherapy. Hopefully, we will all be ready to follow the stream.
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Affiliation(s)
- Georgia Ragia
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupolis, 68100, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, 68100, Greece
| | - Vangelis G Manolopoulos
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupolis, 68100, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, 68100, Greece
- Clinical Pharmacology Unit, Academic General Hospital of Alexandroupolis, Alexandroupolis, 68100, Greece
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25
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Geraldes I, Fernandes M, Fraga AG, Osório NS. The impact of single-cell genomics on the field of mycobacterial infection. Front Microbiol 2022; 13:989464. [PMID: 36246265 PMCID: PMC9562642 DOI: 10.3389/fmicb.2022.989464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.
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Affiliation(s)
- Inês Geraldes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Mónica Fernandes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Alexandra G. Fraga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Nuno S. Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- *Correspondence: Nuno S. Osório
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26
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Understanding Mammalian Hair Follicle Ecosystems by Single-Cell RNA Sequencing. Animals (Basel) 2022; 12:ani12182409. [PMID: 36139270 PMCID: PMC9495062 DOI: 10.3390/ani12182409] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/28/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Single-cell sequencing technology can reflect cell population heterogeneity at the single-cell level, leading to a better understanding of the role of individual cells in the microenvironment. Over the past few years, single-cell sequencing technology has not only made more new discoveries in the study of cellular heterogeneity of other rare cells such as stem cells, but has also become the most powerful research method for embryonic development, organ differentiation, cancer occurrence, and cell mapping. In this review, we outline the use of scRNA-seq in hair follicles. In particular, by focusing on landmark studies and the recent discovery of novel subpopulations of hair follicles, we summarize the phenotypic diversity of hair follicle cells and their links to hair follicle morphogenesis. Enhancing our understanding of the progress of hair follicle research will help to elucidate the regulatory mechanisms that determine the fate of different types of cells in the hair follicle, thereby guiding hair loss treatment and hair-producing economic animal breeding research. Abstract Single-cell sequencing technology can fully reflect the heterogeneity of cell populations at the single cell level, making it possible for us to re-recognize various tissues and organs. At present, the sequencing study of hair follicles is transiting from the traditional ordinary transcriptome level to the single cell level, which will provide diverse insights into the function of hair follicle cells. This review focuses on research advances in the hair follicle microenvironment obtained from scRNA-seq studies of major cell types in hair follicle development, with a special emphasis on the discovery of new subpopulations of hair follicles by single-cell techniques. We also discuss the problems and current solutions in scRNA-seq observation and look forward to its prospects.
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27
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Zhang Y, Xu S, Wen Z, Gao J, Li S, Weissman SM, Pan X. Sample-multiplexing approaches for single-cell sequencing. Cell Mol Life Sci 2022; 79:466. [PMID: 35927335 PMCID: PMC11073057 DOI: 10.1007/s00018-022-04482-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/25/2022] [Accepted: 07/11/2022] [Indexed: 12/12/2022]
Abstract
Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.
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Affiliation(s)
- Yulong Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Siwen Xu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
- SequMed BioTechnology, Inc., Guangzhou, Guangdong, China
| | - Zebin Wen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jinyu Gao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Shuang Li
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Sherman M Weissman
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06520-8005, USA
| | - Xinghua Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
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28
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Song W, Lu F, Ding Z, Huang L, Hu K, Chen J, Wei L. Identification of Heparan Sulfate in Dilated Cardiomyopathy by Integrated Bioinformatics Analysis. Front Cardiovasc Med 2022; 9:900428. [PMID: 35711374 PMCID: PMC9197211 DOI: 10.3389/fcvm.2022.900428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 04/27/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives Heparan sulfate (HS) forms heparan sulfate proteoglycans (HSPGs), such as syndecans (SDCs) and glypicans (GPCs), to perform biological processes in the mammals. This study aimed to explore the role of HS in dilated cardiomyopathy (DCM). Methods Two high throughput RNA sequencing, two microarrays, and one single-cell RNA sequencing dataset of DCM hearts were downloaded from the Gene Expression Omnibus (GEO) database and integrated for bioinformatics analyses. Differential analysis, pathway enrichment, immunocytes infiltration, subtype identification, and single-cell RNA sequencing analysis were used in this study. Results The expression level of most HSPGs was significantly upregulated in DCM and was closely associated with immune activation, cardiac fibrosis, and heart failure. Syndecan2 (SDC2) was highly associated with collagen I and collagen III in cardiac fibroblasts of DCM hearts. HS biosynthetic pathway was activated, while the only enzyme to hydrolyze HS was downregulated. Based on the expression of HSPGs, patients with DCM were classified into three molecular subtypes, i.e., C1, C2, and C3. Cardiac fibrosis and heart failure were more severe in the C1 subtype. Conclusion Heparan sulfate is closely associated with immune activation, cardiac fibrosis, and heart failure in DCM. A novel molecular classification of patients with DCM is established based on HSPGs.
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Affiliation(s)
- Wenyu Song
- Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fujian Lu
- Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Zequan Ding
- Department of Pediatric Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Liqi Huang
- Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kui Hu
- Department of Cardiovascular Surgery, Guizhou Provincial People's Hospital, Guiyang, China
- Kui Hu
| | - Jinmiao Chen
- Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Jinmiao Chen
| | - Lai Wei
- Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Lai Wei
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29
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Liu T, Zhao X, Lin Y, Luo Q, Zhang S, Xi Y, Chen Y, Lin L, Fan W, Yang J, Ma Y, Maity AK, Huang Y, Wang J, Chang J, Lin D, Teschendorff AE, Wu C. Computational identification of preneoplastic cells displaying high stemness and risk of cancer progression. Cancer Res 2022; 82:2520-2537. [PMID: 35536873 DOI: 10.1158/0008-5472.can-22-0668] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/05/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Evidence points towards the differentiation state of cells as a marker of cancer risk and progression. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Recent maps of somatic mutagenesis in normal tissues from young healthy individuals have revealed cancer driver mutations, indicating that these do not correlate well with differentiation state and that other molecular events also contribute to cancer development. We hypothesized that the differentiation state of single cells can be measured by estimating the regulatory activity of the transcription factors (TFs) that control differentiation within that cell lineage. To this end, we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA-Seq data. CancerStemID is validated in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. Spatial transcriptomics and whole-genome bisulfite sequencing demonstrated that differentiation activity of tissue-specific TFs was decreased in cancer cells compared to the basal cell-of-origin layer and established that differentiation state correlated with differential DNA methylation at the promoters of these TFs, independently of underlying NOTCH1 and TP53 mutations. The findings were replicated in a mouse model of ESCC development, and the broad applicability of CancerStemID to other cancer-types was demonstrated. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy to identify stem-like preneoplastic cells that undergo positive selection.
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Affiliation(s)
- Tianyuan Liu
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuan Zhao
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Lin
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University (PKU), Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Shaosen Zhang
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yiyi Xi
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yamei Chen
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyi Fan
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Yang
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuling Ma
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Alok K Maity
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University (PKU), Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Jianbin Wang
- School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Jiang Chang
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, Hubei, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, United Kingdom
| | - Chen Wu
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- CAMS Oxford Institute (COI), Chinese Academy of Medical Sciences, Beijing, China
- CAMS key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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30
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Chen T, Huang C, Wang Y, Wu J. Microfluidic methods for cell separation and subsequent analysis. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2021.07.067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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31
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Cherbonneau F, Li G, Gokulnath P, Sahu P, Prunevieille A, Kitchen R, Benichou G, Larghero J, Domian I, Das S. TRACE-seq: A transgenic system for unbiased and non-invasive transcriptome profiling of living cells. iScience 2022; 25:103806. [PMID: 35198871 PMCID: PMC8844816 DOI: 10.1016/j.isci.2022.103806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 11/11/2021] [Accepted: 01/20/2022] [Indexed: 11/16/2022] Open
Abstract
Dynamic profiling of changes in gene expression in response to stressors in specific microenvironments without requiring cellular destruction remains challenging. Current methodologies that seek to interrogate gene expression at a molecular level require sampling of cellular transcriptome and therefore lysis of the cell, preventing serial analysis of cellular transcriptome. To address this area of unmet need, we have recently developed a technology allowing transcriptomic analysis over time without cellular destruction. Our method, TRACE-seq (TRanscriptomic Analysis Captured in Extracellular vesicles using sequencing), is characterized by a cell-type specific transgene expression. It provides data on the transcriptome inside extracellular vesicles that provides an accurate representation of stress-responsive cellular transcriptomic changes. Thus, the transcriptome of cells expressing TRACE can be followed over time without destroying the source cell, which is a powerful tool for many fields of fundamental and translational biology research. TRanscriptomic Analysis Captured in Extracellular vesicles using RNA-sequencing Excellent correlation of cellular and EV transcriptome using this method Accurate profiling of gene expression changes in response to stress signals
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Affiliation(s)
- François Cherbonneau
- Université de Paris, AP-HP, Hôpital Saint-Louis, Unité de Thérapie Cellulaire, U976, CICBT CBT501, INSERM, Paris, France.,Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Guoping Li
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Priyanka Gokulnath
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Parul Sahu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Aurore Prunevieille
- Université de Paris, AP-HP, Hôpital Saint-Louis, Human Immunology and Immunopathology, UMR976, INSERM, Paris, France.,Transplant Research Center, Department of Surgery, Center for Transplantation Sciences, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Robert Kitchen
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Gilles Benichou
- Transplant Research Center, Department of Surgery, Center for Transplantation Sciences, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Jérôme Larghero
- Université de Paris, AP-HP, Hôpital Saint-Louis, Unité de Thérapie Cellulaire, U976, CICBT CBT501, INSERM, Paris, France
| | - Ibrahim Domian
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
| | - Saumya Das
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street, Simches 3(rd.) Floor, Boston, MA 02114, USA
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32
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Gao L, Zhang Y, Sterling K, Song W. Brain-derived neurotrophic factor in Alzheimer's disease and its pharmaceutical potential. Transl Neurodegener 2022; 11:4. [PMID: 35090576 PMCID: PMC8796548 DOI: 10.1186/s40035-022-00279-0] [Citation(s) in RCA: 145] [Impact Index Per Article: 72.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/01/2022] [Indexed: 12/14/2022] Open
Abstract
Synaptic abnormalities are a cardinal feature of Alzheimer's disease (AD) that are known to arise as the disease progresses. A growing body of evidence suggests that pathological alterations to neuronal circuits and synapses may provide a mechanistic link between amyloid β (Aβ) and tau pathology and thus may serve as an obligatory relay of the cognitive impairment in AD. Brain-derived neurotrophic factors (BDNFs) play an important role in maintaining synaptic plasticity in learning and memory. Considering AD as a synaptic disorder, BDNF has attracted increasing attention as a potential diagnostic biomarker and a therapeutical molecule for AD. Although depletion of BDNF has been linked with Aβ accumulation, tau phosphorylation, neuroinflammation and neuronal apoptosis, the exact mechanisms underlying the effect of impaired BDNF signaling on AD are still unknown. Here, we present an overview of how BDNF genomic structure is connected to factors that regulate BDNF signaling. We then discuss the role of BDNF in AD and the potential of BDNF-targeting therapeutics for AD.
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Affiliation(s)
- Lina Gao
- Shandong Collaborative Innovation Center for Diagnosis, Treatment and Behavioral Interventions of Mental Disorders, Institute of Mental Health, College of Pharmacy, Jining Medical University, Jining, 272067, Shandong, China
- Townsend Family Laboratories, Department of Psychiatry, The University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Yun Zhang
- National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Keenan Sterling
- Townsend Family Laboratories, Department of Psychiatry, The University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Weihong Song
- Shandong Collaborative Innovation Center for Diagnosis, Treatment and Behavioral Interventions of Mental Disorders, Institute of Mental Health, College of Pharmacy, Jining Medical University, Jining, 272067, Shandong, China.
- Townsend Family Laboratories, Department of Psychiatry, The University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
- National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, School of Mental Health and The Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, Zhejiang, China.
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33
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Fu H, Sun H, Kong H, Lou B, Chen H, Zhou Y, Huang C, Qin L, Shan Y, Dai S. Discoveries in Pancreatic Physiology and Disease Biology Using Single-Cell RNA Sequencing. Front Cell Dev Biol 2022; 9:732776. [PMID: 35141228 PMCID: PMC8819087 DOI: 10.3389/fcell.2021.732776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022] Open
Abstract
Transcriptome analysis is used to study gene expression in human tissues. It can promote the discovery of new therapeutic targets for related diseases by characterizing the endocrine function of pancreatic physiology and pathology, as well as the gene expression of pancreatic tumors. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) can detect transcriptional activity within a single cell. The scRNA-seq had an invaluable contribution to discovering previously unknown cell subtypes in normal and diseased pancreases, studying the functional role of rare islet cells, and studying various types of cells in diabetes as well as cancer. Here, we review the recent in vitro and in vivo advances in understanding the pancreatic physiology and pathology associated with single-cell sequencing technology, which may provide new insights into treatment strategy optimization for diabetes and pancreatic cancer.
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Affiliation(s)
- Haotian Fu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Hongwei Sun
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou, China
| | - Hongru Kong
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bin Lou
- Department of Surgery, The Third People’s Hospital of Yuhang District, Hangzhou, China
| | - Hao Chen
- Department of Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yilin Zhou
- Department of Biology, Boston University, Boston, MA, United States
| | - Chaohao Huang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lei Qin
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Lei Qin, ; Yunfeng Shan, ; Shengjie Dai,
| | - Yunfeng Shan
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou, China
- *Correspondence: Lei Qin, ; Yunfeng Shan, ; Shengjie Dai,
| | - Shengjie Dai
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Lei Qin, ; Yunfeng Shan, ; Shengjie Dai,
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34
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Pandey P, Sesena-Rubfiaro A, Khatri S, He J. Development of multifunctional nanopipettes for controlled intracellular delivery and single-entity detection. Faraday Discuss 2021; 233:315-335. [PMID: 34889345 DOI: 10.1039/d1fd00057h] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The intracellular delivery of biomolecules and nanoscale materials to individual cells has gained remarkable attention in recent years owing to its wide applications in drug delivery, clinical diagnostics, bio-imaging and single-cell analysis. It remains a challenge to control and measure the delivered amount in one cell. In this work, we developed a multifunctional nanopipette - containing both a nanopore and nanoelectrode (pyrolytic carbon) at the apex - as a facile, minimally invasive and effective platform for both controllable single-cell intracellular delivery and single-entity counting. While controlled by a micromanipulator, the baseline changes of the nanopore ionic current (I) and nanoelectrode open circuit potential (V) help to guide the nanopipette tip insertion and positioning processes. The delivery from the nanopore barrel can be facilely controlled by the applied nanopore bias. To optimize the intracellular single-entity detection during delivery, we studied the effects of the nanopipette tip geometry and solution salt concentration in controlled experiments. We have successfully delivered gold nanoparticles and biomolecules into the cell, as confirmed by the increased scattering and fluorescence signals, respectively. The delivered entities have also been detected at the single-entity level using either one or both transient I and V signals. We found that the sensitivity of the single-entity electrochemical measurement was greatly affected by the local environment of the cell and varied between cell lines.
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Affiliation(s)
- Popular Pandey
- Physics Department, Florida International University, Miami, Florida, 33199, USA.
| | | | - Santosh Khatri
- Physics Department, Florida International University, Miami, Florida, 33199, USA.
| | - Jin He
- Physics Department, Florida International University, Miami, Florida, 33199, USA. .,Biomolecular Sciences Institute, Florida International University, Miami, Florida, 33199, USA
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35
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Seo B, Lin L, Li J. Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.1982724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Beomseok Seo
- Department of Statistics, The Pennsylvania State University, University Park, PA
| | - Lin Lin
- Department of Statistics, The Pennsylvania State University, University Park, PA
| | - Jia Li
- Department of Statistics, The Pennsylvania State University, University Park, PA
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36
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Schobers G, Koeck R, Pellaers D, Stevens SJC, Macville MVE, Paulussen ADC, Coonen E, van den Wijngaard A, de Die-Smulders C, de Wert G, Brunner HG, Zamani Esteki M. Liquid biopsy: state of reproductive medicine and beyond. Hum Reprod 2021; 36:2824-2839. [PMID: 34562078 PMCID: PMC8523207 DOI: 10.1093/humrep/deab206] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 08/06/2021] [Indexed: 01/23/2023] Open
Abstract
Liquid biopsy is the process of sampling and analyzing body fluids, which enables non-invasive monitoring of complex biological systems in vivo. Liquid biopsy has myriad applications in health and disease as a wide variety of components, ranging from circulating cells to cell-free nucleic acid molecules, can be analyzed. Here, we review different components of liquid biopsy, survey state-of-the-art, non-invasive methods for detecting those components, demonstrate their clinical applications and discuss ethical considerations. Furthermore, we emphasize the importance of artificial intelligence in analyzing liquid biopsy data with the aim of developing ethically-responsible non-invasive technologies that can enhance individualized healthcare. While previous reviews have mainly focused on cancer, this review primarily highlights applications of liquid biopsy in reproductive medicine.
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Affiliation(s)
- Gaby Schobers
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rebekka Koeck
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Dominique Pellaers
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Servi J C Stevens
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Merryn V E Macville
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Aimée D C Paulussen
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Edith Coonen
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Center for Reproductive Medicine, Maastricht University Medical Centre+, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Arthur van den Wijngaard
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Christine de Die-Smulders
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Guido de Wert
- Faculty of Health, Medicine and Life Sciences, Department of Health, Ethics and Society, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Han G Brunner
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Masoud Zamani Esteki
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Genetics and Cell Biology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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37
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Wu L, Xu S, Wang J, Paguirigan AL, Radich JP, Qin Y, Chiu DT. Capillary-Mediated Single-Cell Dispenser. Anal Chem 2021; 93:10750-10755. [PMID: 34319086 DOI: 10.1021/acs.analchem.1c01879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Single-cell manipulation, sorting, and dispensing into multiwell plates is useful for single-cell multiomics studies. Here, we develop a single-cell dispenser inspired by electrohydrodynamic jet printing that achieves accurate droplet generation and single-cell sorting and dispensing using fused silica capillary tubing as both the optical detection window and nozzle for droplet dispensing. Parameters that affect droplet dispensing performance-capillary inner and outer diameter, flow rate, applied voltage, and solution properties-were optimized systematically with COMSOL simulations and experimentation. Small (5-10 nL) droplets were obtained by using 100-μm inner diameter and 160-μm outer diameter capillary tubing and allowed efficient encapsulation and dispensing of single cells. We demonstrate an application of this easy-to-assemble single-cell dispenser by sorting and dispensing cells into multiwell plates for single-cell PCR analysis.
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Affiliation(s)
- Li Wu
- Department of Chemistry and Bioengineering, University of Washington, Seattle, Washington 98195, United States.,School of Public Health, Nantong University, Nantong, Jiangsu 226019, P. R. China
| | - Shihan Xu
- Department of Chemistry and Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Jingang Wang
- Department of Chemistry and Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Amy L Paguirigan
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - Jerald P Radich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - Yuling Qin
- Department of Chemistry and Bioengineering, University of Washington, Seattle, Washington 98195, United States.,School of Public Health, Nantong University, Nantong, Jiangsu 226019, P. R. China
| | - Daniel T Chiu
- Department of Chemistry and Bioengineering, University of Washington, Seattle, Washington 98195, United States
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38
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Xin Z, Zhang W, Gong S, Zhu J, Li Y, Zhang Z, Fang X. Mapping Human Pluripotent Stem Cell-derived Erythroid Differentiation by Single-cell Transcriptome Analysis. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:358-376. [PMID: 34284135 PMCID: PMC8864192 DOI: 10.1016/j.gpb.2021.03.009] [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: 01/22/2021] [Revised: 01/22/2021] [Accepted: 03/06/2021] [Indexed: 10/28/2022]
Abstract
There is an imbalance between the supply and demand of functional red blood cells (RBCs) in clinical applications. This imbalance can be addressed by regenerating RBCs using several in vitro methods. Induced pluripotent stem cells (iPSCs) can handle the low supply of cord blood and the ethical issues in embryonic stem cell research and provide a promising strategy to eliminate immune rejection. However, no complete single-cell level differentiation pathway exists for the iPSC-derived RBC differentiation system. In this study, we used iPSC line BC1 to establish a RBCs regeneration system. The 10× genomics single-cell transcriptome platform was used to map the cell lineage and differentiation trajectories on day 14 of the regeneration system. We observed that iPSCs differentiation was not synchronized during embryoid body (EB) culture. The cells (day 14) mainly consisted of mesodermal and various blood cells, similar to the yolk sac hematopoiesis. We identified six cell classifications and characterized the regulatory transcription factors (TFs) networks and cell-cell contacts underlying the system. iPSCs undergo two transformations during the differentiation trajectory, accompanied by the dynamic expression of cell adhesion molecules and estrogen-responsive genes. We identified different stages of erythroid cells, such as burst-forming unit erythroid (BFU-E) and orthochromatic erythroblasts (ortho-E), and found that the regulation of TFs (e.g., TFDP1 and FOXO3) is erythroid-stage specific. Immune erythroid cells were identified in our system. This study provides systematic theoretical guidance for optimizing the iPSCs-derived RBCs differentiation system, and this system is a useful model for simulating in vivo hematopoietic development and differentiation.
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Affiliation(s)
- Zijuan Xin
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhang
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shangjin Gong
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junwei Zhu
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, 100101, China
| | - Yanming Li
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China
| | - Zhaojun Zhang
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, 100101, China.
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Science and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center of Bioinformation, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, 100101, China.
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39
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Single-Cell RNA-Seq Reveals Transcriptomic Heterogeneity and Post-Traumatic Osteoarthritis-Associated Early Molecular Changes in Mouse Articular Chondrocytes. Cells 2021; 10:cells10061462. [PMID: 34200880 PMCID: PMC8230441 DOI: 10.3390/cells10061462] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/02/2021] [Accepted: 06/07/2021] [Indexed: 12/25/2022] Open
Abstract
Articular cartilage is a connective tissue lining the surfaces of synovial joints. When the cartilage severely wears down, it leads to osteoarthritis (OA), a debilitating disease that affects millions of people globally. The articular cartilage is composed of a dense extracellular matrix (ECM) with a sparse distribution of chondrocytes with varying morphology and potentially different functions. Elucidating the molecular and functional profiles of various chondrocyte subtypes and understanding the interplay between these chondrocyte subtypes and other cell types in the joint will greatly expand our understanding of joint biology and OA pathology. Although recent advances in high-throughput OMICS technologies have enabled molecular-level characterization of tissues and organs at an unprecedented resolution, thorough molecular profiling of articular chondrocytes has not yet been undertaken, which may be in part due to the technical difficulties in isolating chondrocytes from dense cartilage ECM. In this study, we profiled articular cartilage from healthy and injured mouse knee joints at a single-cell resolution and identified nine chondrocyte subtypes with distinct molecular profiles and injury-induced early molecular changes in these chondrocytes. We also compared mouse chondrocyte subpopulations to human chondrocytes and evaluated the extent of molecular similarity between mice and humans. This work expands our view of chondrocyte heterogeneity and rapid molecular changes in chondrocyte populations in response to joint trauma and highlights potential mechanisms that trigger cartilage degeneration.
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40
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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41
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Vallejo AF, Davies J, Grover A, Tsai CH, Jepras R, Polak ME, West J. Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering. iScience 2021; 24:102147. [PMID: 33665566 PMCID: PMC7900351 DOI: 10.1016/j.isci.2021.102147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 12/09/2020] [Accepted: 01/31/2021] [Indexed: 12/24/2022] Open
Abstract
Single-cell transcriptomics suffer from sensitivity limits that restrict low abundance transcript identification, affects clustering and can hamper downstream analyses. Here, we describe Constellation sequencing (Constellation-Seq), a molecular transcriptome filter that delivers two orders of magnitude sensitivity gains by maximizing read utility while reducing the data sparsity and sequencing costs. The technique reliably measures changes in gene expression and was demonstrated by resolving rare dendritic cell populations from a peripheral blood mononuclear cell sample sample and exploring their biology with extreme resolution. The simple and powerful method is fully compatible with standard scRNA-Seq library preparation protocols and can be used for hypothesis testing, marker validation or investigating pathways.
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Affiliation(s)
- Andres F. Vallejo
- Clinical and Experimental Sciences, Sir Henry Wellcome Laboratories, Faculty of Medicine, University of Southampton, SO16 6YD Southampton, UK
| | - James Davies
- Clinical and Experimental Sciences, Sir Henry Wellcome Laboratories, Faculty of Medicine, University of Southampton, SO16 6YD Southampton, UK
| | - Amit Grover
- GlaxoSmithKline, Gunnels Wood Road, Stevenage SG1 2NY, UK
| | - Ching-Hsuan Tsai
- GlaxoSmithKline, 200 Cambridge Park Drive, Cambridge, MA 02140, USA
| | - Robert Jepras
- GlaxoSmithKline, Gunnels Wood Road, Stevenage SG1 2NY, UK
| | - Marta E. Polak
- Clinical and Experimental Sciences, Sir Henry Wellcome Laboratories, Faculty of Medicine, University of Southampton, SO16 6YD Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Jonathan West
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
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42
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Liu Q, Liang Z, Feng D, Jiang S, Wang Y, Du Z, Li R, Hu G, Zhang P, Ma Y, Lohmann JU, Gu X. Transcriptional landscape of rice roots at the single-cell resolution. MOLECULAR PLANT 2021; 14:384-394. [PMID: 33352304 DOI: 10.1016/j.molp.2020.12.014] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/30/2020] [Accepted: 12/16/2020] [Indexed: 05/22/2023]
Abstract
There are two main types of root systems in flowering plants, namely taproot systems of dicots and fibrous root systems found in monocots. Despite this fundamental split, our current knowledge of cellular and molecular mechanism driving root development is mainly based on studies of the dicot model Arabidopsis. However, the world major crops are monocots and little is known about the transcriptional programs underlying cell-type specification in this clade. Here, we report the transcriptomes of more than 20 000 single cells derived from root tips of two agronomically important rice cultivars. Using combined computational and experimental analyses we were able to robustly identify most of the major cell types and define novel cell-type-specific marker genes for both cultivars. Importantly, we found divergent cell types associated with specific regulatory programs, including phytohormone biosynthesis, signaling, and response, which were well conserved between the two rice cultivars. In addition, we detected substantial differences between the cell-type transcript profiles of Arabidopsis and rice. These species-specific features emphasize the importance of analyzing tissues across diverse model species, including rice. Taken together, our study provides insight into the transcriptomic landscape of major cell types of rice root tip at single-cell resolution and opens new avenues to study cell-type specification, function, and evolution in plants.
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Affiliation(s)
- Qing Liu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhe Liang
- Centre for Organismal Studies, Heidelberg University, Heidelberg 69120, Germany
| | - Dan Feng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | | | - Yifan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhuoying Du
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ruoxi Li
- Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA
| | - Guihua Hu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Pingxian Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yanfei Ma
- Centre for Organismal Studies, Heidelberg University, Heidelberg 69120, Germany
| | - Jan U Lohmann
- Centre for Organismal Studies, Heidelberg University, Heidelberg 69120, Germany.
| | - Xiaofeng Gu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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43
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Nayak R, Hasija Y. A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines. Genomics 2021; 113:606-619. [PMID: 33485955 DOI: 10.1016/j.ygeno.2021.01.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/30/2020] [Accepted: 01/18/2021] [Indexed: 12/20/2022]
Abstract
Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.
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Affiliation(s)
- Richa Nayak
- Department of Biotechnology, Delhi Technological University, Delhi 110042, India
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Delhi 110042, India.
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44
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Deng Y, Chen H, Zeng Y, Wang K, Zhang H, Hu H. Leaving no one behind: tracing every human thymocyte by single-cell RNA-sequencing. Semin Immunopathol 2021; 43:29-43. [PMID: 33449155 DOI: 10.1007/s00281-020-00834-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 12/22/2020] [Indexed: 02/05/2023]
Abstract
The thymus is the primary organ for T-cell development, providing an essential microenvironment consisting of the appropriate cytokine milieu and specialized stromal cells. Thymus-seeding progenitors from circulation immigrate into the thymus and undergo the stepwise T-cell specification, commitment, and selection processes. The transcriptional factors, epigenetic regulators, and signaling pathways involved in the T-cell development have been intensively studied using mouse models. Despite our growing knowledge of T-cell development, major questions remain unanswered regarding the ontogeny and early events of T-cell development at the fetal stage, especially in humans. The recently developed single-cell RNA-sequencing technique provides an ideal tool to investigate the heterogeneity of T-cell precursors and the molecular mechanisms underlying the divergent fates of certain T-cell precursors at the single-cell level. In this review, we aim to summarize the current progress of the study on human thymus organogenesis and thymocyte and thymic epithelial cell development, which is to shed new lights on developing novel strategies for in vitro T-cell regeneration and thymus rejuvenation.
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Affiliation(s)
- Yujun Deng
- Department of Rheumatology and Immunology and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Hong Chen
- Department of Rheumatology and Immunology and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Yang Zeng
- State Key Laboratory of Experimental Hematology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China.,State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing, 100071, China
| | - Keyue Wang
- Department of Rheumatology and Immunology and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Huiyuan Zhang
- Department of Rheumatology and Immunology and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China.
| | - Hongbo Hu
- Department of Rheumatology and Immunology and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China.
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45
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Liu L, Dong X, Tu Y, Miao G, Zhang Z, Zhang L, Wei Z, Yu D, Qiu X. Methods and platforms for analysis of nucleic acids from single-cell based on microfluidics. MICROFLUIDICS AND NANOFLUIDICS 2021; 25:87. [PMID: 34580578 PMCID: PMC8457033 DOI: 10.1007/s10404-021-02485-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 08/30/2021] [Indexed: 05/14/2023]
Abstract
Single-cell nucleic acid analysis aims at discovering the genetic differences between individual cells which is well known as the cellular heterogeneity. This technology facilitates cancer diagnosis, stem cell research, immune system analysis, and other life science applications. The conventional platforms for single-cell nucleic acid analysis more rely on manual operation or bulky devices. Recently, the emerging microfluidic technology has provided a perfect platform for single-cell nucleic acid analysis with the characteristic of accurate and automatic single-cell manipulation. In this review, we briefly summarized the procedure of single-cell nucleic acid analysis including single-cell isolation, single-cell lysis, nucleic acid amplification, and genetic analysis. And then, three representative microfluidic platforms for single-cell nucleic acid analysis are concluded as valve-, microwell-, and droplet-based platforms. Furthermore, we described the state-of-the-art integrated single-cell nucleic acid analysis systems based on the three platforms. Finally, the future development and challenges of microfluidics-based single-cell nucleic acid analysis are discussed as well.
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Affiliation(s)
- Luyao Liu
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Xiaobin Dong
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Yunping Tu
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Guijun Miao
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Zhongping Zhang
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Lulu Zhang
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Zewen Wei
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081 China
| | - Duli Yu
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing, 100029 China
| | - Xianbo Qiu
- Institute of Microfluidic Chip Development in Biomedical Engineering, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China
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46
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Jeong H, Liu Z. PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing. Bioinformatics 2020; 36:4021-4029. [PMID: 32348450 DOI: 10.1093/bioinformatics/btaa278] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/03/2020] [Accepted: 04/22/2020] [Indexed: 01/01/2023] Open
Abstract
SUMMARY Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. AVAILABILITY AND IMPLEMENTATION The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hyundoo Jeong
- Department of Mechatronics Engineering, Incheon National University, Incheon, Korea
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital.,Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
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47
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Wang C, Gao X, Liu J. Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data. BMC Bioinformatics 2020; 21:440. [PMID: 33028196 PMCID: PMC7541255 DOI: 10.1186/s12859-020-03797-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 10/01/2020] [Indexed: 01/08/2023] Open
Abstract
Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.
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Affiliation(s)
- Chunxiang Wang
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai, 264209, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai, 264209, China.
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48
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Du J, Su Y, Qian C, Yuan D, Miao K, Lee D, Ng AHC, Wijker RS, Ribas A, Levine RD, Heath JR, Wei L. Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nat Commun 2020; 11:4830. [PMID: 32973134 PMCID: PMC7518429 DOI: 10.1038/s41467-020-18376-x] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 08/14/2020] [Indexed: 02/06/2023] Open
Abstract
Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies. Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.
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Affiliation(s)
- Jiajun Du
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Yapeng Su
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.,Institute for Systems Biology, Seattle, WA, USA
| | - Chenxi Qian
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Dan Yuan
- Institute for Systems Biology, Seattle, WA, USA
| | - Kun Miao
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Dongkwan Lee
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | - Reto S Wijker
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Antoni Ribas
- Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Raphael D Levine
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Lu Wei
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
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49
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Sun H, Chen W, Wang J, Zhang L, Rossiter SJ, Mao X. Echolocation call frequency variation in horseshoe bats: molecular basis revealed by comparative transcriptomics. Proc Biol Sci 2020; 287:20200875. [PMID: 32900318 DOI: 10.1098/rspb.2020.0875] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Recently diverged taxa with contrasting phenotypes offer opportunities for unravelling the genetic basis of phenotypic variation in nature. Horseshoe bats are a speciose group that exhibit a derived form of high-duty cycle echolocation in which the inner ear is finely tuned to echoes of the narrowband call frequency. Here, by focusing on three recently diverged subspecies of the intermediate horseshoe bat (Rhinolophus affinis) that display divergent echolocation call frequencies, we aim to identify candidate loci putatively involved in hearing frequency variation. We used de novo transcriptome sequencing of two mainland taxa (himalayanus and macrurus) and one island taxon (hainanus) to compare expression profiles of thousands of genes. By comparing taxa with divergent call frequencies (around 15 kHz difference), we identified 252 differentially expressed genes, of which six have been shown to be involved in hearing or deafness in human/mouse. To obtain further validation of these results, we applied quantitative reverse transcription-PCR to the candidate gene FBXL15 and found a broad association between the level of expression and call frequency across taxa. The genes identified here represent strong candidate loci associated with hearing frequency variation in bats.
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Affiliation(s)
- Haijian Sun
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200062, People's Republic of China
| | - Wenli Chen
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200062, People's Republic of China
| | - Jiaying Wang
- Institute of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, People's Republic of China
| | - Libiao Zhang
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Guangdong Institute of Applied Biological Resources, Guangzhou 510260, People's Republic of China
| | - Stephen J Rossiter
- School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Xiuguang Mao
- School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200062, People's Republic of China.,Institute of Eco-Chongming (IEC), East China Normal University, Shanghai 200062, People's Republic of China
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50
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Snyder MP, Gingeras TR, Moore JE, Weng Z, Gerstein MB, Ren B, Hardison RC, Stamatoyannopoulos JA, Graveley BR, Feingold EA, Pazin MJ, Pagan M, Gilchrist DA, Hitz BC, Cherry JM, Bernstein BE, Mendenhall EM, Zerbino DR, Frankish A, Flicek P, Myers RM. Perspectives on ENCODE. Nature 2020; 583:693-698. [PMID: 32728248 PMCID: PMC7410827 DOI: 10.1038/s41586-020-2449-8] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 05/05/2020] [Indexed: 12/25/2022]
Abstract
The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate cis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression1. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.
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Affiliation(s)
- Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA.
- Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA.
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jill E Moore
- University of Massachusetts Medical School, Program in Bioinformatics and Integrative Biology, Worcester, MA, USA
| | - Zhiping Weng
- University of Massachusetts Medical School, Program in Bioinformatics and Integrative Biology, Worcester, MA, USA
- Department of Thoracic Surgery, Clinical Translational Research Center, Shanghai Pulmonary Hospital, The School of Life Sciences and Technology, Tongji University, Shanghai, China
- Bioinformatics Program, Boston University, Boston, MA, USA
| | | | - Bing Ren
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Ross C Hardison
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - John A Stamatoyannopoulos
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brenton R Graveley
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, UConn Health, Farmington, CT, USA
| | - Elise A Feingold
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael J Pazin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael Pagan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A Gilchrist
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Benjamin C Hitz
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - J Michael Cherry
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Bradley E Bernstein
- Broad Institute and Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric M Mendenhall
- Biological Sciences, University of Alabama in Huntsville, Huntsville, AL, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Daniel R Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
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