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Jin W, Pei J, Roy JR, Jayaraman S, Ahalliya RM, Kanniappan GV, Mironescu M, Palanisamy CP. Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research. Ageing Res Rev 2024; 100:102454. [PMID: 39142391 DOI: 10.1016/j.arr.2024.102454] [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: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 08/16/2024]
Abstract
Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.
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Affiliation(s)
- Wengang Jin
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - JinJin Pei
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - Jeane Rebecca Roy
- Department of Anatomy, Bhaarath Medical College and hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu 600073, India
| | - Selvaraj Jayaraman
- Centre of Molecular Medicine and Diagnostics (COMManD), Department of Biochemistry, Saveetha Dental College & Hospital, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai 600077, India
| | - Rathi Muthaiyan Ahalliya
- Department of Biochemistry and Cancer Research Centre, FASCM, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu 641021, India
| | - Gopalakrishnan Velliyur Kanniappan
- Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, Tamil Nadu 602105, India.
| | - Monica Mironescu
- Faculty of Agricultural Sciences Food Industry and Environmental Protection, Lucian Blaga University of Sibiu, Bv. Victoriei 10, Sibiu 550024, Romania.
| | - Chella Perumal Palanisamy
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
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Zhang Z, Chen M, Peng X. Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on drug response genes to predict prognosis and therapeutic response in ovarian cancer. Heliyon 2024; 10:e33367. [PMID: 39040239 PMCID: PMC11260940 DOI: 10.1016/j.heliyon.2024.e33367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.
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Affiliation(s)
- ZhenWei Zhang
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| | - MianMian Chen
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| | - XiaoLian Peng
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
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Derisoud E, Jiang H, Zhao A, Chavatte-Palmer P, Deng Q. Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies. Hum Reprod Update 2024; 30:410-441. [PMID: 38478759 PMCID: PMC11215163 DOI: 10.1093/humupd/dmae006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/12/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types. OBJECTIVE AND RATIONALE We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases. SEARCH METHODS We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types. OUTCOMES Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation. WIDER IMPLICATIONS This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.
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Affiliation(s)
- Emilie Derisoud
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Hong Jiang
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Allan Zhao
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Pascale Chavatte-Palmer
- INRAE, BREED, Université Paris-Saclay, UVSQ, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
| | - Qiaolin Deng
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Solna, Stockholm, Sweden
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Lyons A, Brown J, Davenport KM. Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities. Curr Issues Mol Biol 2024; 46:5291-5306. [PMID: 38920988 PMCID: PMC11202421 DOI: 10.3390/cimb46060316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/20/2024] [Accepted: 05/25/2024] [Indexed: 06/27/2024] Open
Abstract
Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.
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Xu Z, Qu HQ, Chan J, Kao C, Hakonarson H, Wang K. Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.590597. [PMID: 38746128 PMCID: PMC11092450 DOI: 10.1101/2024.04.29.590597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction of R10 flowcells by Oxford Nanopore, we propose that previous computational methods designed to handle high sequencing error rates are no longer relevant, and that the prevailing approach using short reads to compile "barcode space" (candidate barcode list) to de-multiplex long reads are no longer necessary. Instead, computational methods should now shift focus on harnessing the unique benefits of long reads to analyze transcriptome complexity. In this context, we introduce a comprehensive suite of computational methods named Single-Cell Omics for Transcriptome CHaracterization (SCOTCH). Our method is compatible with the single-cell library preparation platform from both 10X Genomics and Parse Biosciences, facilitating the analysis of special cell populations, such as neurons, hepatocytes and developing cardiomyocytes. We specifically re-formulated the transcript mapping problem with a compatibility matrix and addressed the multiple-mapping issue using probabilistic inference, which allows the discovery of novel isoforms as well as the detection of differential isoform usage between cell populations. We evaluated SCOTCH through analysis of real data across different combinations of single-cell libraries and sequencing technologies (10X + Illumina, Parse + Illumina, 10X + Nanopore_R9, 10X + Nanopore_R10, Parse + Nanopore_R10), and showed its ability to infer novel biological insights on cell type-specific isoform expression. These datasets enhance the availability of publicly available data for continued development of computational approaches. In summary, SCOTCH allows extraction of more biological insights from the new advancements in single-cell library construction and sequencing technologies, facilitating the examination of transcriptome complexity at the single-cell level.
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Affiliation(s)
- Zhuoran Xu
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Hui-Qi Qu
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Joe Chan
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Charlly Kao
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Li X, Li B, Gu S, Pang X, Mason P, Yuan J, Jia J, Sun J, Zhao C, Henry R. Single-cell and spatial RNA sequencing reveal the spatiotemporal trajectories of fruit senescence. Nat Commun 2024; 15:3108. [PMID: 38600080 PMCID: PMC11006883 DOI: 10.1038/s41467-024-47329-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 03/26/2024] [Indexed: 04/12/2024] Open
Abstract
The senescence of fruit is a complex physiological process, with various cell types within the pericarp, making it highly challenging to elucidate their individual roles in fruit senescence. In this study, a single-cell expression atlas of the pericarp of pitaya (Hylocereus undatus) is constructed, revealing exocarp and mesocarp cells undergoing the most significant changes during the fruit senescence process. Pseudotime analysis establishes cellular differentiation and gene expression trajectories during senescence. Early-stage oxidative stress imbalance is followed by the activation of resistance in exocarp cells, subsequently senescence-associated proteins accumulate in the mesocarp cells at late-stage senescence. The central role of the early response factor HuCMB1 is unveiled in the senescence regulatory network. This study provides a spatiotemporal perspective for a deeper understanding of the dynamic senescence process in plants.
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Affiliation(s)
- Xin Li
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
- Queensland Alliance for Agriculture & Food Innovation, Queensland Biosciences Precinct, The University of Queensland, St Lucia, QLD 4072, Australia
- National Demonstration Center for Experimental Food Processing and Safety Education, Luoyang, 471023, China
| | - Bairu Li
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Shaobin Gu
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Xinyue Pang
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Patrick Mason
- Queensland Alliance for Agriculture & Food Innovation, Queensland Biosciences Precinct, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Jiangfeng Yuan
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Jingyu Jia
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Jiaju Sun
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Chunyan Zhao
- Institute of Environment and Health, Jianghan University, Wuhan, 430056, China.
| | - Robert Henry
- Queensland Alliance for Agriculture & Food Innovation, Queensland Biosciences Precinct, The University of Queensland, St Lucia, QLD 4072, Australia.
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Banerjee M, Srivastava S, Rai SN, States JC. Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression. Toxicol Appl Pharmacol 2024; 484:116865. [PMID: 38373578 PMCID: PMC10994602 DOI: 10.1016/j.taap.2024.116865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/11/2024] [Accepted: 02/13/2024] [Indexed: 02/21/2024]
Abstract
Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.
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Affiliation(s)
- Mayukh Banerjee
- Department of Pharmacology and Toxicology, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA; Center for Integrative Environmental Health Sciences, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA
| | - Sudhir Srivastava
- Department of Bioinformatics and Biostatistics, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA
| | - Shesh N Rai
- Department of Bioinformatics and Biostatistics, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA; Biostatistics and Bioinformatics Facility, James Graham Brown Cancer Center, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA; Biostatistics and Informatics Facility Core, Center for Integrative Environmental Health Sciences, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA
| | - J Christopher States
- Department of Pharmacology and Toxicology, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA; Center for Integrative Environmental Health Sciences, University of Louisville, 505, S. Hancock Street, Louisville, KY 40202, USA.
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Wu J, Liu Y, Fu Q, Cao Z, Ma X, Li X. Characterization of tumor-associated endothelial cells and the development of a prognostic model in pancreatic ductal adenocarcinoma. Biochim Biophys Acta Gen Subj 2024; 1868:130545. [PMID: 38141886 DOI: 10.1016/j.bbagen.2023.130545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/10/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a complex tumor microenvironment. Angiogenesis is of paramount importance in the proliferation and metastasis of PDAC. However, currently, there are no well-defined biomarkers available to guide the prognosis and treatment of PDAC. In this study, we investigated the interactions between tumor-associated endothelial cells (TAECs) and tumor cells in PDAC, and identified a specific subset of TAECs characterized by high expression of COL4A1. COL4A1+ endothelial cells interact with tumor cells through the COLLAGEN signaling pathway to promote tumor cell proliferation, migration, and invasion. We also observed activation of HOXD9 in COL4A1+ endothelial cells. Based on these findings, we developed a prognostic model called TaEMS, which accurately predicts patient prognosis. TaEMS identified high-risk patients enriched in cell cycle-related pathways and low-risk patients enriched in focal adhesions, smooth muscle regulation, and immune pathways. Moreover, high-risk patients displayed a reduced level of immune cell infiltration, indicating the presence of a "cold tumor" phenotype. Overall, our study uncovered an intricate crosstalk between TAECs and tumor cells in PDAC, emphasizing the role of HOXD9 and highlighting the potential of TaEMS as a prognostic biomarker for precise therapies.
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Affiliation(s)
- Jun Wu
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, College of Basic Medical Science, China Three Gorges University, Yichang 443003, China; Key Laboratory of Brain, Cognition and Education Sciences, Institute for Brain Research and Rehabilitation, Guangdong; Key Laboratory of Mental Health and Cognitive Science, Ministry of Education, South China Normal University, Guangzhou 510631, China
| | - Yang Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Qi Fu
- Key Laboratory of Brain, Cognition and Education Sciences, Institute for Brain Research and Rehabilitation, Guangdong; Key Laboratory of Mental Health and Cognitive Science, Ministry of Education, South China Normal University, Guangzhou 510631, China
| | - Zhi Cao
- Department of Gastroenterology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen 518020, Guangdong Province, China
| | - Xiaodong Ma
- Key Laboratory of Brain, Cognition and Education Sciences, Institute for Brain Research and Rehabilitation, Guangdong; Key Laboratory of Mental Health and Cognitive Science, Ministry of Education, South China Normal University, Guangzhou 510631, China.
| | - Xun Li
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, College of Basic Medical Science, China Three Gorges University, Yichang 443003, China.
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Chan CL, Sugimura R. Unveiling the immune system aging in single-cell resolution. J Leukoc Biol 2024; 115:16-18. [PMID: 37934864 DOI: 10.1093/jleuko/qiad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/09/2023] Open
Abstract
This commentary investigates the findings presented in the article by Yang et al. in 2023, published in the Journal of Leukocyte Biology. This commentary first summarizes the spatial-temporal dynamics of regulatory T cells derived from mice (Tabula Muris Senis) of different ages (3, 18, and 24 mo) at different anatomic niches like lymph nodes and bone marrow. We also reported possible combinations of receptor-ligand interactions among T follicular regulatory cells, T follicular helper cells, and germinal center B cells, such as the calmodulin/Fas axis and PSGL-1/L-selectin axis. Then, we have elaborated on the significance of understanding aging regulatory T cells and offered some possible future research directions for Yang et al., contributing to a critical analysis of their recent study. Building on these foundations, further investigations and studies can be conducted to delve deeper into the mechanisms by which regulatory T cells influence health upon aging, potentially unveiling novel therapeutic targets to ameliorate age-related pathogenicity.
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Affiliation(s)
- Chun Lai Chan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Laboratory Block, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China
- School of Biological Sciences, Kadoorie Biological Sciences Building, The University of Hong Kong, Pok Fu Lam Road, Hong Kong, SAR, China
| | - Ryohichi Sugimura
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Laboratory Block, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China
- Centre for Translational Stem Cell Biology, Rm1105, 11/F, 17W, Hong Kong Science and Technology Park, Shatin, N.T., Hong Kong SAR, China
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Satpathy S, Thomas BE, Pilcher WJ, Bakhtiari M, Ponder LA, Pacholczyk R, Prahalad S, Bhasin SS, Munn DH, Bhasin MK. The Simple prEservatioN of Single cElls method for cryopreservation enables the generation of single-cell immune profiles from whole blood. Front Immunol 2023; 14:1271800. [PMID: 38090590 PMCID: PMC10713715 DOI: 10.3389/fimmu.2023.1271800] [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: 08/02/2023] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction Current multistep methods utilized for preparing and cryopreserving single-cell suspensions from blood samples for single-cell RNA sequencing (scRNA-seq) are time-consuming, requiring trained personnel and special equipment, so limiting their clinical adoption. We developed a method, Simple prEservatioN of Single cElls (SENSE), for single-step cryopreservation of whole blood (WB) along with granulocyte depletion during single-cell assay, to generate high quality single-cell profiles (SCP). Methods WB was cryopreserved using the SENSE method and peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved using the traditional density-gradient method (PBMC method) from the same blood sample (n=6). The SCPs obtained from both methods were processed using a similar pipeline and quality control parameters. Further, entropy calculation, differential gene expression, and cellular communication analysis were performed to compare cell types and subtypes from both methods. Results Highly viable (86.3 ± 1.51%) single-cell suspensions (22,353 cells) were obtained from the six WB samples cryopreserved using the SENSE method. In-depth characterization of the scRNA-seq datasets from the samples processed with the SENSE method yielded high-quality profiles of lymphoid and myeloid cell types which were in concordance with the profiles obtained with classical multistep PBMC method processed samples. Additionally, the SENSE method cryopreserved samples exhibited significantly higher T-cell enrichment, enabling deeper characterization of T-cell subtypes. Overall, the SENSE and PBMC methods processed samples exhibited transcriptional, and cellular communication network level similarities across cell types with no batch effect except in myeloid lineage cells. Discussion Comparative analysis of scRNA-seq datasets obtained with the two cryopreservation methods i.e., SENSE and PBMC methods, yielded similar cellular and molecular profiles, confirming the suitability of the former method's incorporation in clinics/labs for cryopreserving and obtaining high-quality single-cells for conducting critical translational research.
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Affiliation(s)
- Sarthak Satpathy
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Beena E. Thomas
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - William J. Pilcher
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Mojtaba Bakhtiari
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - Lori A. Ponder
- Division of Rheumatology, Children’s Healthcare of Atlanta, Atlanta, GA, United States
| | - Rafal Pacholczyk
- Georgia Cancer Center, Augusta University, Augusta, GA, United States
- Department of Biochemistry and Molecular Biology, Augusta University, Augusta, GA, United States
| | - Sampath Prahalad
- Department of Pediatrics, Emory University, Atlanta, GA, United States
- Division of Rheumatology, Children’s Healthcare of Atlanta, Atlanta, GA, United States
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, United States
| | - Swati S. Bhasin
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - David H. Munn
- Georgia Cancer Center, Augusta University, Augusta, GA, United States
- Department of Pediatrics, Augusta University, Augusta, GA, United States
| | - Manoj K. Bhasin
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
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11
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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12
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Hume DA, Millard SM, Pettit AR. Macrophage heterogeneity in the single-cell era: facts and artifacts. Blood 2023; 142:1339-1347. [PMID: 37595274 DOI: 10.1182/blood.2023020597] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/20/2023] Open
Abstract
In this spotlight, we review technical issues that compromise single-cell analysis of tissue macrophages, including limited and unrepresentative yields, fragmentation and generation of remnants, and activation during tissue disaggregation. These issues may lead to a misleading definition of subpopulations of macrophages and the expression of macrophage-specific transcripts by unrelated cells. Recognition of the technical limitations of single-cell approaches is required in order to map the full spectrum of tissue-resident macrophage heterogeneity and assess its biological significance.
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Affiliation(s)
- David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Susan M Millard
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Allison R Pettit
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
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13
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Lazaros K, Vlamos P, Vrahatis AG. Methods for cell-type annotation on scRNA-seq data: A recent overview. J Bioinform Comput Biol 2023; 21:2340002. [PMID: 37743364 DOI: 10.1142/s0219720023400024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has consequently brought about significant alterations in this area over the years. In our study, we spotlight all note-worthy cell type annotation techniques developed over the past four years. We provide an overview of the latest trends in this field, showcasing the most advanced methods in taxonomy. Our research underscores the demand for additional tools that incorporate a biological context and also predicts that the rising trend of graph neural network approaches will likely lead this research field in the coming years.
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Affiliation(s)
- Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
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14
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Shiino S, Tokura M, Nakayama J, Yoshida M, Suto A, Yamamoto Y. Investigation of Tumor Heterogeneity Using Integrated Single-Cell RNA Sequence Analysis to Focus on Genes Related to Breast Cancer-, EMT-, CSC-, and Metastasis-Related Markers in Patients with HER2-Positive Breast Cancer. Cells 2023; 12:2286. [PMID: 37759508 PMCID: PMC10527746 DOI: 10.3390/cells12182286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Human epidermal growth factor receptor 2 (HER2) protein, which is characterized by the amplification of ERBB2, is a molecular target for HER2-overexpressing breast cancer. Many targeted HER2 strategies have been well developed thus far. Furthermore, intratumoral heterogeneity in HER2 cases has been observed with immunohistochemical staining and has been considered one of the reasons for drug resistance. Therefore, we conducted an integrated analysis of the breast cancer single-cell gene expression data for HER2-positive breast cancer cases from both scRNA-seq data from public datasets and data from our cohort and compared them with those for luminal breast cancer datasets. In our results, heterogeneous distribution of the expression of breast cancer-related genes (ESR1, PGR, ERBB2, and MKI67) was observed. Various gene expression levels differed at the single-cell level between the ERBB2-high group and ERBB2-low group. Moreover, molecular functions and ERBB2 expression levels differed between estrogen receptor (ER)-positive and ER-negative HER2 cases. Additionally, the gene expression levels of typical breast cancer-, CSC-, EMT-, and metastasis-related markers were also different across each patient. These results suggest that diversity in gene expression could occur not only in the presence of ERBB2 expression and ER status but also in the molecular characteristics of each patient.
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Affiliation(s)
- Sho Shiino
- Department of Breast Surgery, National Cancer Center Hospital, Tokyo 104-0045, Japan;
| | - Momoko Tokura
- Laboratory of Integrative Oncology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (M.T.); (J.N.)
| | - Jun Nakayama
- Laboratory of Integrative Oncology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (M.T.); (J.N.)
| | - Masayuki Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan;
| | - Akihiko Suto
- Department of Breast Surgery, National Cancer Center Hospital, Tokyo 104-0045, Japan;
| | - Yusuke Yamamoto
- Laboratory of Integrative Oncology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (M.T.); (J.N.)
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15
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Virgolini N, Silvano M, Hagan R, Correia R, Alves PM, Clarke C, Roldão A, Isidro IA. Impact of dual-baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single-cell RNA-seq. Biotechnol Bioeng 2023; 120:2588-2600. [PMID: 36919374 DOI: 10.1002/bit.28377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/17/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023]
Abstract
The insect cell-baculovirus expression vector system (IC-BEVS) has shown to be a powerful platform to produce complex biopharmaceutical products, such as recombinant proteins and virus-like particles. More recently, IC-BEVS has also been used as an alternative to produce recombinant adeno-associated virus (rAAV). However, little is known about the variability of insect cell populations and the potential effect of heterogeneity (e.g., stochastic infection process and differences in infection kinetics) on product titer and/or quality. In this study, transcriptomics analysis of Sf9 insect cells during the production of rAAV of serotype 2 (rAAV2) using a low multiplicity of infection, dual-baculovirus system was performed via single-cell RNA-sequencing (scRNA-seq). Before infection, the principal source of variability in Sf9 insect cells was associated with the cell cycle. Over the course of infection, an increase in transcriptional heterogeneity was detected, which was linked to the expression of baculovirus genes as well as to differences in rAAV transgenes (rep, cap and gfp) expression. Noteworthy, at 24 h post-infection, only 29.4% of cells enclosed all three necessary rAAV transgenes to produce packed rAAV2 particles, indicating limitations of the dual-baculovirus system. In addition, the trajectory analysis herein performed highlighted that biological processes such as protein folding, metabolic processes, translation, and stress response have been significantly altered upon infection. Overall, this work reports the first application of scRNA-seq to the IC-BEVS and highlights significant variations in individual cells within the population, providing insight into the rational cell and process engineering toward improved rAAV2 production in IC-BEVS.
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Affiliation(s)
- Nikolaus Virgolini
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Marco Silvano
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Ryan Hagan
- National Institute for Bioprocessing Research and Training, Dublin, Ireland
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Belfield, Ireland
| | - Ricardo Correia
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Paula M Alves
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Colin Clarke
- National Institute for Bioprocessing Research and Training, Dublin, Ireland
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Belfield, Ireland
| | - António Roldão
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Inês A Isidro
- IBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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16
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Zilbauer M, James KR, Kaur M, Pott S, Li Z, Burger A, Thiagarajah JR, Burclaff J, Jahnsen FL, Perrone F, Ross AD, Matteoli G, Stakenborg N, Sujino T, Moor A, Bartolome-Casado R, Bækkevold ES, Zhou R, Xie B, Lau KS, Din S, Magness ST, Yao Q, Beyaz S, Arends M, Denadai-Souza A, Coburn LA, Gaublomme JT, Baldock R, Papatheodorou I, Ordovas-Montanes J, Boeckxstaens G, Hupalowska A, Teichmann SA, Regev A, Xavier RJ, Simmons A, Snyder MP, Wilson KT. A Roadmap for the Human Gut Cell Atlas. Nat Rev Gastroenterol Hepatol 2023; 20:597-614. [PMID: 37258747 PMCID: PMC10527367 DOI: 10.1038/s41575-023-00784-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2023] [Indexed: 06/02/2023]
Abstract
The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.
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Affiliation(s)
- Matthias Zilbauer
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- University Department of Paediatrics, University of Cambridge, Cambridge, UK.
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals, Cambridge, UK.
| | - Kylie R James
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mandeep Kaur
- School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zhixin Li
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Albert Burger
- Department of Computer Science, Heriot-watt University, Edinburgh, UK
| | - Jay R Thiagarajah
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Burclaff
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Frode L Jahnsen
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Francesca Perrone
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Alexander D Ross
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
- University Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Gianluca Matteoli
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Nathalie Stakenborg
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Tomohisa Sujino
- Center for the Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Raquel Bartolome-Casado
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, UK
| | - Espen S Bækkevold
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ran Zhou
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Bingqing Xie
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shahida Din
- Edinburgh IBD Unit, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Scott T Magness
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qiuming Yao
- Department of Computer Science and Engineering, University of Nebraska Lincoln, Lincoln, NE, USA
| | - Semir Beyaz
- Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA
| | - Mark Arends
- Division of Pathology, Centre for Comparative Pathology, Cancer Research UK Edinburgh Centre, Institute of Cancer and Genetics, University of Edinburgh, Edinburgh, UK
| | - Alexandre Denadai-Souza
- Laboratory of Mucosal Biology, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Lori A Coburn
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
| | | | | | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Guy Boeckxstaens
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Hinxton, UK
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK
| | - Aviv Regev
- Genentech, San Francisco, CA, USA
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ramnik J Xavier
- Broad Institute and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alison Simmons
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - Keith T Wilson
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
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17
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Huang D, Ma N, Li X, Gou Y, Duan Y, Liu B, Xia J, Zhao X, Wang X, Li Q, Rao J, Zhang X. Advances in single-cell RNA sequencing and its applications in cancer research. J Hematol Oncol 2023; 16:98. [PMID: 37612741 PMCID: PMC10463514 DOI: 10.1186/s13045-023-01494-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/09/2023] [Indexed: 08/25/2023] Open
Abstract
Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.
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Affiliation(s)
- Dezhi Huang
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Naya Ma
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Xinlei Li
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Yang Gou
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Yishuo Duan
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Bangdong Liu
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Jing Xia
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Xianlan Zhao
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Xiaoqi Wang
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China
- Jinfeng Laboratory, Chongqing, 401329, China
| | - Qiong Li
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China.
- Jinfeng Laboratory, Chongqing, 401329, China.
| | - Jun Rao
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China.
- Jinfeng Laboratory, Chongqing, 401329, China.
- National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
| | - Xi Zhang
- Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 400037, China.
- Jinfeng Laboratory, Chongqing, 401329, China.
- National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
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18
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Lin J, Feng D, Liu J, Yang Y, Wei X, Lin W, Lin Q. Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer. Aging (Albany NY) 2023; 15:8185-8203. [PMID: 37602872 PMCID: PMC10496995 DOI: 10.18632/aging.204963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023]
Abstract
Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.
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Affiliation(s)
- Jun Lin
- Department of Anesthesiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Anesthesiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
- Anesthesiology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Deyi Feng
- Xiamen University, Xiamen 361100, China
| | - Jie Liu
- Department of Endoscopy, Shengli Clinical Medical College of Fujian Medical University, Fuzhou 350001, China
| | - Ye Yang
- The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
| | - Xujin Wei
- The Graduate School of Fujian Medical University, Fuzhou 350001, China
| | - Wenqian Lin
- Department of Anesthesiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Anesthesiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
- Anesthesiology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Qun Lin
- Department of Anesthesiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Anesthesiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
- Anesthesiology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
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19
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Pasqualetti F, Barberis A, Zanotti S, Montemurro N, De Salvo GL, Soffietti R, Mazzanti CM, Ius T, Caffo M, Paiar F, Bocci G, Lombardi G, Harris AL, Buffa FM. The impact of survivorship bias in glioblastoma research. Crit Rev Oncol Hematol 2023; 188:104065. [PMID: 37392899 DOI: 10.1016/j.critrevonc.2023.104065] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/26/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radio-chemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.
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Affiliation(s)
- Francesco Pasqualetti
- Department of Oncology, University of Oxford, Oxford, UK; Radiation Oncology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.
| | | | - Sofia Zanotti
- Anatomic Pathology Unit, IRCCS Humanitas University Research Hospital, Milan, Italy
| | - Nicola Montemurro
- Department of Neurosurgery, Azienda Ospedaliero Universitaria Pisana (AOUP), Pisa, Italy
| | | | - Riccardo Soffietti
- Division of Neuro-Oncology, Department of Neuroscience, University and City of Health and Science University Hospital, Turin, Italy
| | | | - Tamara Ius
- Neurosurgery Unit, Head-Neck and NeuroScience Department University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Maria Caffo
- Unit of Neurosurgery, Department of Biomorphology and Dental Sciences and Morfophunctional Imaging, University Hospital "G. Martino", Messina, Italy
| | - Fabiola Paiar
- Radiation Oncology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Guido Bocci
- Department of Clinical and Experimental Medicine, University of Pisa, I-56126 Pisa, Italy
| | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | | | - Francesca M Buffa
- Department of Oncology, University of Oxford, Oxford, UK; Department of Computing Sciences, Bocconi University, Milan, Italy; Institute for Data Science and Analytics, Bocconi University, Milano, Italy
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20
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Xia Y, Wang Y, Hao Y, Shan M, Liu H, Liang Z, Kuang X. Deciphering the single-cell transcriptome network in keloids with intra-lesional injection of triamcinolone acetonide combined with 5-fluorouracil. Front Immunol 2023; 14:1106289. [PMID: 37275903 PMCID: PMC10235510 DOI: 10.3389/fimmu.2023.1106289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/08/2023] [Indexed: 06/07/2023] Open
Abstract
Objectives Keloid is a highly aggressive fibrotic disease resulting from excessive extracellular matrix deposition after dermal injury. Intra-lesional injection of triamcinolone acetonide (TAC) in combination with 5-fluorouracil (5-FU) is a commonly used pharmacological regimen and long-term repeated injections can achieve sustained inhibition of keloid proliferation. However, the molecular mechanisms underlying the inhibitory effect on keloids remain insufficiently investigated. Methods and materials This study performed single-cell RNA sequencing analysis of keloids treated with TAC+5-FU injections, keloids, and skins to explore patterns of gene expression regulation and cellular reprogramming. Results The results revealed that TAC+5-FU interrupted the differentiation trajectory of fibroblasts toward pro-fibrotic subtypes and induced keloid atrophy possibly by inhibiting the FGF signaling pathway in intercellular communication. It also stimulated partial fibroblasts to develop the potential for self-replication and multidirectional differentiation, which may be a possible cellular source of keloid recurrence. T cell dynamics demonstrated elevated expression of secretory globulin family members, which may be possible immunotherapeutic targets. Schwann cell populations achieved functional changes by increasing the proportion of apoptotic or senescence-associated cell populations and reducing cell clusters that promote epidermal development and fibroblast proliferation. Conclusions Our findings elucidated the molecular and cellular reprogramming of keloids by intra-lesional injection of TAC+5-FU, which will provide new insights to understand the mechanism of action and therapeutic targets.
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Affiliation(s)
- Yijun Xia
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Youbin Wang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing, China
| | - Yan Hao
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Mengjie Shan
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Hao Liu
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing, China
| | - Zhengyun Liang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xinwen Kuang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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21
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Komatsu J, Cico A, Poncin R, Le Bohec M, Morf J, Lipin S, Graindorge A, Eckert H, Saffarian A, Cathaly L, Guérin F, Majello S, Ulveling D, Vayaboury A, Fernandez N, Dimitrova D, Bussell X, Fourne Y, Chaumat P, André B, Baldivia E, Godet U, Guinin M, Moretto V, Ismail J, Caille O, Roblot N, Beaupère C, Liboz A, Guillemain G, Blondeau B, Walrafen P, Edelstein S. RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding. Sci Rep 2023; 13:4866. [PMID: 36964177 PMCID: PMC10039079 DOI: 10.1038/s41598-023-31915-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.
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Affiliation(s)
| | | | | | | | - Jörg Morf
- Scipio Bioscience, Paris, France
- Skyhawk Therapeutics, Basel, Switzerland
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Natacha Roblot
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Carine Beaupère
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Alexandrine Liboz
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Ghislaine Guillemain
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Bertrand Blondeau
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
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22
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY (MOSCOW) 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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