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1
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Li Y, Zhang R, Li J, Wang L, Zhou G. Dysfunction of Endothelial Cell-Mediated Intercellular Communication and Metabolic Pathways in Age-Related Macular Degeneration. Curr Eye Res 2025; 50:169-181. [PMID: 39329213 DOI: 10.1080/02713683.2024.2407361] [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: 03/19/2024] [Accepted: 08/28/2024] [Indexed: 09/28/2024]
Abstract
PURPOSE Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, but the therapies are not satisfactory. This study aimed to find AMD specific features through the analysis of high-throughput sequencing. METHODS In this study, we integrated six projects containing single-cell RNA sequencing (scRNA-seq) data to perform a comprehensive analysis for AMD samples in the tissues of retina and retinal pigment epithelium/choroid, and in the positions of macula and periphery. Differentially expressed genes (DEGs) were analyzed and crucial signaling pathways were identified across cell types and between the macula and periphery. The intercellular signaling transduction among cell types were inferred by "CellChat" to build cell-cell communication network under normal and AMD conditions, and verified at the transcriptional level. The CD31+ endothelial cells were obtained to evaluate the enrichment of KEGG pathways in atrophic and neovascular AMD, and GSVA was adopted to discover differential metabolic signals in each AMD type. RESULTS Thirteen major cell types were identified in the integrated scRNA-seq data. Although no disease-specific cell type or differential cell proportion was found, DEGs and enriched pathways were shown in cell-type- and position-dependent manners. Severe impairment of endothelial cell-mediated cell interactions was found in the signaling transduction network of the macula, and compromised cell interactions were observed in the periphery. Furthermore, distinct signaling pathways and metabolic states were uncovered in atrophic and neovascular AMD. Striking reduction in energy metabolism, lipid metabolism, and oxidative stress was indicated in the atrophic AMD. CONCLUSION Conclusively, we discover aberrant signals and metabolic pathways in AMD samples, providing insight into mechanisms and potential therapeutic targets for the AMD treatment.
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Affiliation(s)
- Yang Li
- Department of Ophthalmology, Yuncheng Central Hospital Affiliated to Shanxi Medical University, Yuncheng, China
| | - Rong Zhang
- Department of Ophthalmology, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jing Li
- Department of Ophthalmology, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Lin Wang
- Department of Ophthalmology, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Guohong Zhou
- Department of Ophthalmology, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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2
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Smith L, Quelch-Cliffe R, Liu F, Aguilar AH, Przyborski S. Evaluating Strategies to Assess the Differentiation Potential of Human Pluripotent Stem Cells: A Review, Analysis and Call for Innovation. Stem Cell Rev Rep 2025; 21:107-125. [PMID: 39340737 PMCID: PMC11762643 DOI: 10.1007/s12015-024-10793-5] [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] [Accepted: 09/23/2024] [Indexed: 09/30/2024]
Abstract
Pluripotent stem cells have the ability to differentiate into all cells and tissues within the human body, and as a result they are attractive resources for use in basic research, drug discovery and regenerative medicine. In order to successfully achieve this application, starting cell sources ideally require in-depth characterisation to confirm their pluripotent status and their ability to differentiate into tissues representative of the three developmental germ layers. Many different methods to assess potency are employed, each having its own distinct advantages and limitations. Some aspects of this characterisation process are not always well standardised, particularly techniques used to assess pluripotency as a function. In this article, we consider the methods used to establish cellular pluripotency and subsequently analyse characterisation data for over 1590 human pluripotent cell lines from publicly available repositories in the UK and USA. In particular, we focus on the teratoma xenograft assay, its use and protocols, demonstrating the level of variation and the frequency with which it is used. Finally, we reflect on the implications of the findings, and suggest in vitro alternatives using modern innovative technology as a way forward.
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Affiliation(s)
- Lucy Smith
- Department of Biosciences, Durham University, Durham, England
| | | | - Felicity Liu
- Department of Biosciences, Durham University, Durham, England
| | | | - Stefan Przyborski
- Department of Biosciences, Durham University, Durham, England.
- Reprocell Europe Ltd, NETPark, Sedgefield, England.
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3
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Yao S, Nguyen TD, Lan Y, Yang W, Chen D, Shao Y, Yang Z. MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells. Anal Chem 2024; 96:19238-19247. [PMID: 39570119 DOI: 10.1021/acs.analchem.4c02038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2024]
Abstract
Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.
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Affiliation(s)
- Songyuan Yao
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Tra D Nguyen
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Yunpeng Lan
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Wen Yang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Dan Chen
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
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Zaragoza MV, Bui TA, Widyastuti HP, Mehrabi M, Cang Z, Sha Y, Grosberg A, Nie Q. LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-Derived iPSC Differentiation Support Cell Type and Lineage-Specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency. Cells 2024; 13:1479. [PMID: 39273049 PMCID: PMC11394257 DOI: 10.3390/cells13171479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 08/14/2024] [Accepted: 08/30/2024] [Indexed: 09/15/2024] Open
Abstract
LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.
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Affiliation(s)
- Michael V. Zaragoza
- UCI Cardiogenomics Program, Pediatrics and Biological Chemistry, UC Irvine School of Medicine, Irvine, CA 92697, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA 92697, USA
| | - Thuy-Anh Bui
- UCI Cardiogenomics Program, Pediatrics and Biological Chemistry, UC Irvine School of Medicine, Irvine, CA 92697, USA
| | - Halida P. Widyastuti
- UCI Cardiogenomics Program, Pediatrics and Biological Chemistry, UC Irvine School of Medicine, Irvine, CA 92697, USA
| | - Mehrsa Mehrabi
- Biomedical Engineering and Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, Irvine, CA 92697, USA
| | - Zixuan Cang
- Mathematics and NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA
| | - Yutong Sha
- Mathematics and NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA
| | - Anna Grosberg
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA 92697, USA
- Biomedical Engineering and Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Mathematics and NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA
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5
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Zaragoza MV, Bui TA, Widyastuti HP, Mehrabi M, Cang Z, Sha Y, Grosberg A, Nie Q. LMNA -Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598335. [PMID: 38915555 PMCID: PMC11195187 DOI: 10.1101/2024.06.12.598335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
LMNA -Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -Related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10 ZNF genes and RNA polymerase II transcription in Pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CM; LMNA and epigenetic regulation and DDIT4 and mTORC1 signaling in EPDC. Top DEG also included: XIST and other X-linked genes, six imprinted genes: SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 , and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.
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6
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Filipovic D, Kana O, Marri D, Bhattacharya S. Unique challenges and best practices for single cell transcriptomic analysis in toxicology. CURRENT OPINION IN TOXICOLOGY 2024; 38:100475. [PMID: 38645720 PMCID: PMC11027889 DOI: 10.1016/j.cotox.2024.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.
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Affiliation(s)
- David Filipovic
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
| | - Omar Kana
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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7
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Vo QD, Saito Y, Ida T, Nakamura K, Yuasa S. The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. PLoS One 2024; 19:e0302537. [PMID: 38771829 PMCID: PMC11108174 DOI: 10.1371/journal.pone.0302537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/09/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research. METHODS In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing. RESULTS This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies. CONCLUSIONS Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.
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Affiliation(s)
- Quan Duy Vo
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
- Faculty of Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Yukihiro Saito
- Department of Cardiovascular Medicine, Okayama University Hospital, Okayama, Japan
| | - Toshihiro Ida
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Kazufumi Nakamura
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Shinsuke Yuasa
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
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Li Z, Napolitano A, Fedele M, Gao X, Napolitano F. AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data. Brief Bioinform 2024; 25:bbae207. [PMID: 38701411 PMCID: PMC11066897 DOI: 10.1093/bib/bbae207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024] Open
Abstract
Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.
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Affiliation(s)
- Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
| | - Antonella Napolitano
- Institute of Experimental Endocrinology and Oncology “G. Salvatore” (IEOS), National Research Council (CNR), Via De Amicis, 95 - 80131 Napoli, Italy
| | - Monica Fedele
- Institute of Experimental Endocrinology and Oncology “G. Salvatore” (IEOS), National Research Council (CNR), Via De Amicis, 95 - 80131 Napoli, Italy
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
| | - Francesco Napolitano
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
- Department of Science and Technology, University of Sannio, Via dei Mulini 74, 82100 Benevento, Italy
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Jain N, Goyal Y, Dunagin MC, Cote CJ, Mellis IA, Emert B, Jiang CL, Dardani IP, Reffsin S, Arnett M, Yang W, Raj A. Retrospective identification of cell-intrinsic factors that mark pluripotency potential in rare somatic cells. Cell Syst 2024; 15:109-133.e10. [PMID: 38335955 PMCID: PMC10940218 DOI: 10.1016/j.cels.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/31/2023] [Accepted: 01/12/2024] [Indexed: 02/12/2024]
Abstract
Pluripotency can be induced in somatic cells by the expression of OCT4, KLF4, SOX2, and MYC. Usually only a rare subset of cells reprogram, and the molecular characteristics of this subset remain unknown. We apply retrospective clone tracing to identify and characterize the rare human fibroblasts primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis increased the reprogramming efficiency. We provide evidence for a unified model in which cells can move into and out of the primed state over time, explaining how reprogramming appears deterministic at short timescales and stochastic at long timescales. Furthermore, inhibiting the activity of LSD1 enlarged the pool of cells that were primed for reprogramming. Thus, even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.
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Affiliation(s)
- Naveen Jain
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Margaret C Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher J Cote
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ian A Mellis
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Connie L Jiang
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ian P Dardani
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Reffsin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Miles Arnett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wenli Yang
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Martins B, Bister A, Dohmen RGJ, Gouveia MA, Hueber R, Melzener L, Messmer T, Papadopoulos J, Pimenta J, Raina D, Schaeken L, Shirley S, Bouchet BP, Flack JE. Advances and Challenges in Cell Biology for Cultured Meat. Annu Rev Anim Biosci 2024; 12:345-368. [PMID: 37963400 DOI: 10.1146/annurev-animal-021022-055132] [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: 11/16/2023]
Abstract
Cultured meat is an emerging biotechnology that aims to produce meat from animal cell culture, rather than from the raising and slaughtering of livestock, on environmental and animal welfare grounds. The detailed understanding and accurate manipulation of cell biology are critical to the design of cultured meat bioprocesses. Recent years have seen significant interest in this field, with numerous scientific and commercial breakthroughs. Nevertheless, these technologies remain at a nascent stage, and myriad challenges remain, spanning the entire bioprocess. From a cell biological perspective, these include the identification of suitable starting cell types, tuning of proliferation and differentiation conditions, and optimization of cell-biomaterial interactions to create nutritious, enticing foods. Here, we discuss the key advances and outstanding challenges in cultured meat, with a particular focus on cell biology, and argue that solving the remaining bottlenecks in a cost-effective, scalable fashion will require coordinated, concerted scientific efforts. Success will also require solutions to nonscientific challenges, including regulatory approval, consumer acceptance, and market feasibility. However, if these can be overcome, cultured meat technologies can revolutionize our approach to food.
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Affiliation(s)
- Beatriz Martins
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Arthur Bister
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Richard G J Dohmen
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Maria Ana Gouveia
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Rui Hueber
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Lea Melzener
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Tobias Messmer
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Joanna Papadopoulos
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Joana Pimenta
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Dhruv Raina
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Lieke Schaeken
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Sara Shirley
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
| | - Benjamin P Bouchet
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
- Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, The Netherlands;
| | - Joshua E Flack
- Mosa Meat B.V., Maastricht, The Netherlands; , , , , , , , , , , , ,
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Chehelgerdi M, Behdarvand Dehkordi F, Chehelgerdi M, Kabiri H, Salehian-Dehkordi H, Abdolvand M, Salmanizadeh S, Rashidi M, Niazmand A, Ahmadi S, Feizbakhshan S, Kabiri S, Vatandoost N, Ranjbarnejad T. Exploring the promising potential of induced pluripotent stem cells in cancer research and therapy. Mol Cancer 2023; 22:189. [PMID: 38017433 PMCID: PMC10683363 DOI: 10.1186/s12943-023-01873-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/27/2023] [Indexed: 11/30/2023] Open
Abstract
The advent of iPSCs has brought about a significant transformation in stem cell research, opening up promising avenues for advancing cancer treatment. The formation of cancer is a multifaceted process influenced by genetic, epigenetic, and environmental factors. iPSCs offer a distinctive platform for investigating the origin of cancer, paving the way for novel approaches to cancer treatment, drug testing, and tailored medical interventions. This review article will provide an overview of the science behind iPSCs, the current limitations and challenges in iPSC-based cancer therapy, the ethical and social implications, and the comparative analysis with other stem cell types for cancer treatment. The article will also discuss the applications of iPSCs in tumorigenesis, the future of iPSCs in tumorigenesis research, and highlight successful case studies utilizing iPSCs in tumorigenesis research. The conclusion will summarize the advancements made in iPSC-based tumorigenesis research and the importance of continued investment in iPSC research to unlock the full potential of these cells.
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Affiliation(s)
- Matin Chehelgerdi
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Fereshteh Behdarvand Dehkordi
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Mohammad Chehelgerdi
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran.
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
| | - Hamidreza Kabiri
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | | | - Mohammad Abdolvand
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
| | - Sharareh Salmanizadeh
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Hezar-Jereeb Street, Isfahan, 81746-73441, Iran
| | - Mohsen Rashidi
- Department Pharmacology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
- The Health of Plant and Livestock Products Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Anoosha Niazmand
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
| | - Saba Ahmadi
- Department of Molecular and Medical Genetics, Tbilisi State Medical University, Tbilisi, Georgia
| | - Sara Feizbakhshan
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
| | - Saber Kabiri
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Nasimeh Vatandoost
- Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Tayebeh Ranjbarnejad
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
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12
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Walker BL, Nie Q. NeST: nested hierarchical structure identification in spatial transcriptomic data. Nat Commun 2023; 14:6554. [PMID: 37848426 PMCID: PMC10582109 DOI: 10.1038/s41467-023-42343-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/06/2023] [Indexed: 10/19/2023] Open
Abstract
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
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Affiliation(s)
- Benjamin L Walker
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92627, USA.
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13
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Barker RA, Carpenter M, Jamieson CHM, Murry CE, Pellegrini G, Rao RC, Song J. Lessons learnt, and still to learn, in first in human stem cell trials. Stem Cell Reports 2023; 18:1599-1609. [PMID: 36563687 PMCID: PMC10444539 DOI: 10.1016/j.stemcr.2022.11.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/15/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
Developing cellular therapies is not straightforward. This Perspective summarizes the experience of a group of academic stem cell investigators working in different clinical areas and aims to share insight into what we wished we knew before starting. These include (1) choosing the stem cell line and assessing the genome of both the starting and final product, (2) familiarity with GMP manufacturing, reagent validation, and supply chain management, (3) product delivery issues and the additional regulatory challenges, (4) the relationship between clinical trial design and preclinical studies, and (5) the market approval requirements, pathways, and partnerships needed.
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Affiliation(s)
- Roger A Barker
- Department of Clinical Neuroscience and Wellcome-MRC Cambridge Stem Institute, John van Geest Centre for Brain Repair, Forvie Site, Robinson Way, Cambridge CB2 0QQ, UK.
| | | | - Catriona H M Jamieson
- Division of Regenerative Medicine, Department of Medicine, Sanford Stem Cell Clinical Center, University of California San Diego, Sanford Consortium for Regenerative Medicine, 2880 Torrey Pines Scenic Drive #0695, La Jolla, CA 92037-0695, USA
| | - Charles E Murry
- Institute for Stem Cell and Regenerative Medicine, Center for Cardiovascular Biology; Departments of Laboratory Medicine & Pathology, Bioengineering, and Medicine/Cardiology, University of Washington, Seattle, WA 98109, USA; Sana Biotechnology, Seattle, WA 98102, USA
| | - Graziella Pellegrini
- Centre for Regenerative Medicine, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Rajesh C Rao
- Departments of Ophthalmology & Visual Sciences, Pathology, and Human Genetics, University of Michigan, Surgery Service, VA Ann Arbor Health System, Ann Arbor, MI 48105, USA
| | - Jihwan Song
- Jihwan Song, Department of Biomedical Science, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea; iPS Bio, Inc., 16 Daewangpangyo-ro 712 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea
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14
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Jain N, Goyal Y, Dunagin MC, Cote CJ, Mellis IA, Emert B, Jiang CL, Dardani IP, Reffsin S, Raj A. Retrospective identification of intrinsic factors that mark pluripotency potential in rare somatic cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.527870. [PMID: 36798299 PMCID: PMC9934612 DOI: 10.1101/2023.02.10.527870] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Abstract
Pluripotency can be induced in somatic cells by the expression of the four "Yamanaka" factors OCT4, KLF4, SOX2, and MYC. However, even in homogeneous conditions, usually only a rare subset of cells admit reprogramming, and the molecular characteristics of this subset remain unknown. Here, we apply retrospective clone tracing to identify and characterize the individual human fibroblast cells that are primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis led to increased reprogramming efficiency, identifying it as a barrier to reprogramming. Changing the frequency of reprogramming by inhibiting the activity of LSD1 led to an enlarging of the pool of cells that were primed for reprogramming. Our results show that even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.
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Affiliation(s)
- Naveen Jain
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret C Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher J Cote
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian A Mellis
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Connie L Jiang
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian P Dardani
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Sam Reffsin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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15
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Ainciburu M, Ezponda T, Berastegui N, Alfonso-Pierola A, Vilas-Zornoza A, San Martin-Uriz P, Alignani D, Lamo-Espinosa J, San-Julian M, Jiménez-Solas T, Lopez F, Muntion S, Sanchez-Guijo F, Molero A, Montoro J, Serrano G, Diaz-Mazkiaran A, Lasaga M, Gomez-Cabrero D, Diez-Campelo M, Valcarcel D, Hernaez M, Romero JP, Prosper F. Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution. eLife 2023; 12:79363. [PMID: 36629404 PMCID: PMC9904760 DOI: 10.7554/elife.79363] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals. Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease. In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.
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Affiliation(s)
- Marina Ainciburu
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
| | - Teresa Ezponda
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
| | - Nerea Berastegui
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
| | - Ana Alfonso-Pierola
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
- Clinica Universidad de NavarraPamplonaSpain
| | - Amaia Vilas-Zornoza
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
| | - Patxi San Martin-Uriz
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
| | - Diego Alignani
- Flow Cytometry Core, Universidad de NavarraPamplonaSpain
| | | | | | | | - Felix Lopez
- Hospital Universitario de SalamancaSalamancaSpain
| | - Sandra Muntion
- Hospital Universitario de SalamancaSalamancaSpain
- Red de Investigación Cooperativa en Terapia Celular TerCel, ISCIII.MadridSpain
| | - Fermin Sanchez-Guijo
- Hospital Universitario de SalamancaSalamancaSpain
- Red de Investigación Cooperativa en Terapia Celular TerCel, ISCIII.MadridSpain
| | - Antonieta Molero
- Department of Hematology, Vall d'Hebron Hospital UniversitariBarcelonaSpain
| | - Julia Montoro
- Department of Hematology, Vall d'Hebron Hospital UniversitariBarcelonaSpain
| | | | - Aintzane Diaz-Mazkiaran
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
- Computational Biology Program, Universidad de NavarraPamplonaSpain
| | - Miren Lasaga
- Translational Bioinformatics Unit, NavarraBiomedPamplonaSpain
| | - David Gomez-Cabrero
- Translational Bioinformatics Unit, NavarraBiomedPamplonaSpain
- Biological & Environmental Sciences & Engineering Division, King Abdullah University of Science and TechnologyThuwalSaudi Arabia
| | | | - David Valcarcel
- Department of Hematology, Vall d'Hebron Hospital UniversitariBarcelonaSpain
| | - Mikel Hernaez
- Computational Biology Program, Universidad de NavarraPamplonaSpain
| | - Juan P Romero
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
| | - Felipe Prosper
- Area de Hemato-Oncología, Centro de Investigación Médica Aplicada, Universidad de Navarra, Instituto de investigación sanitaria de Navarra (IDISNA)PamplonaSpain
- Centro de Investigación Biomédica en Red de CáncerMadridSpain
- Clinica Universidad de NavarraPamplonaSpain
- Red de Investigación Cooperativa en Terapia Celular TerCel, ISCIII.MadridSpain
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16
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Wang J, Morgan W, Saini A, Liu T, Lough J, Han L. Single-cell transcriptomic profiling reveals specific maturation signatures in human cardiomyocytes derived from LMNB2-inactivated induced pluripotent stem cells. Front Cell Dev Biol 2022; 10:895162. [PMID: 36518540 PMCID: PMC9742441 DOI: 10.3389/fcell.2022.895162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2023] Open
Abstract
Mammalian cardiomyocyte maturation entails phenotypic and functional optimization during the late fetal and postnatal phases of heart development, both processes driven and coordinated by complex gene regulatory networks. Cardiomyocytes derived from human induced pluripotent stem cells (iPSCs) are heterogenous and immature, barely resembling their adult in vivo counterparts. To characterize relevant developmental programs and maturation states during human iPSC-cardiomyocyte differentiation, we performed single-cell transcriptomic sequencing, which revealed six cardiomyocyte subpopulations, whose heterogeneity was defined by cell cycle and maturation states. Two of those subpopulations were characterized by a mature, non-proliferative transcriptional profile. To further investigate the proliferation-maturation transition in cardiomyocytes, we induced loss-of-function of LMNB2, which represses cell cycle progression in primary cardiomyocytes in vivo. This resulted in increased maturation in LMNB2-inactivated cardiomyocytes, characterized by transcriptional profiles related to myofibril structure and energy metabolism. Furthermore, we identified maturation signatures and maturational trajectories unique for control and LMNB2-inactivated cardiomyocytes. By comparing these datasets with single-cell transcriptomes of human fetal hearts, we were able to define spatiotemporal maturation states in human iPSC-cardiomyocytes. Our results provide an integrated approach for comparing in vitro-differentiated cardiomyocytes with their in vivo counterparts and suggest a strategy to promote cardiomyocyte maturation.
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Affiliation(s)
- Jie Wang
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - William Morgan
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
- Division of Pediatric Cardiology, Herma Heart Institute, Children’s Hospital of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Ankur Saini
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
- Division of Pediatric Cardiology, Herma Heart Institute, Children’s Hospital of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tao Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - John Lough
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Lu Han
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
- Division of Pediatric Cardiology, Herma Heart Institute, Children’s Hospital of Wisconsin, Milwaukee, WI, United States
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
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17
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Xu L, Fan Y, Wang J, Shi R. Dysfunctional intercellular communication and metabolic signaling pathways in thin endometrium. Front Physiol 2022; 13:1050690. [PMID: 36505055 PMCID: PMC9729336 DOI: 10.3389/fphys.2022.1050690] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022] Open
Abstract
Background: The endometrial thickness is a key factor for successful implantation. Thin endometrium is associated with lower implantation rate and pregnancy rate. Lacking of a better understanding for the cellular and molecular mechanisms of thin endometrium, managing patients with thin endometrium still represents a major challenge for clinicians. Methods: In this study, we combined four single-cell RNA sequencing (scRNA-seq) and one bulk sequencing (bulk-seq) data for thin endometrium to perform an integrated analysis for endometrial cells in proliferating phase. Cell proportion and differentially expressed genes (DEGs) were analyzed to determine the disease-specific cell type and signaling pathways. The cell-cell communication among cell types were inferred by "CellChat" to illustrate the differential intercellular communication under normal and thin endometrium conditions. GSEA and GSVA were applied to identify dysfunctional signals and metabolic pathways before and after thin endometrium. Results: Integration of scRNA-seq identified eight cell types. The proportion of stromal cells showed a significant difference between normal and thin endometrial tissue. The DEGs in diverse cell types revealed enriched pathways in a cell-specific manner. Aberrant cell-cell signaling transduction was found in almost all cell types, especially in immune cells and epithelial cells. Furthermore, dysfunctional metabolic signaling pathways were induced in a cell-type dependent way. The down-regulation of carbohydrate metabolism and nucleotide metabolism was observed and the energy metabolism switch was indicated. Conclusion: Conclusively, we discover dysfunctional signals and metabolic pathways in thin endometrium, providing insight into mechanisms and therapeutic strategies for the atrophic endometrium.
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Affiliation(s)
- Liang Xu
- Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yingying Fan
- Department of Obstetrics and Gynecology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jianjun Wang
- Department of Obstetrics and Gynecology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China,*Correspondence: Jianjun Wang, ; Rui Shi,
| | - Rui Shi
- Department of Obstetrics and Gynecology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China,*Correspondence: Jianjun Wang, ; Rui Shi,
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18
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Watson ER, Mora A, Taherian Fard A, Mar JC. How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data. Brief Bioinform 2022; 23:bbac387. [PMID: 36151725 PMCID: PMC9677483 DOI: 10.1093/bib/bbac387] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/26/2022] [Accepted: 08/11/2022] [Indexed: 12/14/2022] Open
Abstract
Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.
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Affiliation(s)
- Ebony Rose Watson
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Ariane Mora
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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19
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Watanabe M, Buth JE, Haney JR, Vishlaghi N, Turcios F, Elahi LS, Gu W, Pearson CA, Kurdian A, Baliaouri NV, Collier AJ, Miranda OA, Dunn N, Chen D, Sabri S, Torre-Ubieta LDL, Clark AT, Plath K, Christofk HR, Kornblum HI, Gandal MJ, Novitch BG. TGFβ superfamily signaling regulates the state of human stem cell pluripotency and capacity to create well-structured telencephalic organoids. Stem Cell Reports 2022; 17:2220-2238. [PMID: 36179695 PMCID: PMC9561534 DOI: 10.1016/j.stemcr.2022.08.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 12/25/2022] Open
Abstract
Telencephalic organoids generated from human pluripotent stem cells (hPSCs) are a promising system for studying the distinct features of the developing human brain and the underlying causes of many neurological disorders. While organoid technology is steadily advancing, many challenges remain, including potential batch-to-batch and cell-line-to-cell-line variability, and structural inconsistency. Here, we demonstrate that a major contributor to cortical organoid quality is the way hPSCs are maintained prior to differentiation. Optimal results were achieved using particular fibroblast-feeder-supported hPSCs rather than feeder-independent cells, differences that were reflected in their transcriptomic states at the outset. Feeder-supported hPSCs displayed activation of diverse transforming growth factor β (TGFβ) superfamily signaling pathways and increased expression of genes connected to naive pluripotency. We further identified combinations of TGFβ-related growth factors that are necessary and together sufficient to impart broad telencephalic organoid competency to feeder-free hPSCs and enhance the formation of well-structured brain tissues suitable for disease modeling.
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Affiliation(s)
- Momoko Watanabe
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Jessie E Buth
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jillian R Haney
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Neda Vishlaghi
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Felix Turcios
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lubayna S Elahi
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Wen Gu
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Caroline A Pearson
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Arinnae Kurdian
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Natella V Baliaouri
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Amanda J Collier
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Osvaldo A Miranda
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Natassia Dunn
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Di Chen
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Shan Sabri
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Luis de la Torre-Ubieta
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Amander T Clark
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kathrin Plath
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Heather R Christofk
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harley I Kornblum
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael J Gandal
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Bennett G Novitch
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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20
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Mahalanabis A, Turinsky A, Husic M, Christensen E, Luo P, Naidas A, Brudno M, Pugh T, Ramani A, Shooshtari P. Evaluation of Single-cell RNA-seq Clustering Algorithms on Cancer Tumor Datasets. Comput Struct Biotechnol J 2022; 20:6375-6387. [DOI: 10.1016/j.csbj.2022.10.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022] Open
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21
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Wang Z, Yang S, Koga Y, Corbett SE, Shea C, Johnson W, Yajima M, Campbell JD. Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data. NAR Genom Bioinform 2022; 4:lqac066. [PMID: 36110899 PMCID: PMC9469931 DOI: 10.1093/nargab/lqac066] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 08/09/2022] [Accepted: 08/25/2022] [Indexed: 11/26/2022] Open
Abstract
Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.
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Affiliation(s)
- Zhe Wang
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Shiyi Yang
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Yusuke Koga
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Sean E Corbett
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Conor V Shea
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - W Evan Johnson
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Masanao Yajima
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Joshua D Campbell
- Bioinformatics Program, Boston University, Boston, MA, USA
- Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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22
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Jiang C, Richardson E, Farr J, Hill AP, Ullah R, Kroncke BM, Harrison SM, Thomson KL, Ingles J, Vandenberg JI, Ng CA. A calibrated functional patch-clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome. Am J Hum Genet 2022; 109:1199-1207. [PMID: 35688147 PMCID: PMC9300752 DOI: 10.1016/j.ajhg.2022.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/03/2022] [Indexed: 01/09/2023] Open
Abstract
Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.
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Affiliation(s)
- Connie Jiang
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, Australia
| | - Ebony Richardson
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Jessica Farr
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, Australia
| | - Adam P Hill
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia
| | - Rizwan Ullah
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brett M Kroncke
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Kate L Thomson
- Oxford Medical Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Jodie Ingles
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Jamie I Vandenberg
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
| | - Chai-Ann Ng
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia.
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23
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Liang Z, Zheng R, Chen S, Yan X, Li M. A deep matrix factorization based approach for single-cell RNA-seq data clustering. Methods 2022; 205:114-122. [PMID: 35777719 DOI: 10.1016/j.ymeth.2022.06.010] [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: 03/20/2022] [Revised: 05/28/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022] Open
Abstract
The rapid development of single-cell sequencing technologies makes it possible to analyze cellular heterogeneity at the single-cell level. Cell clustering is one of the most fundamental and common steps in the heterogeneity analysis. However, due to the high noise level, high dimensionality and high sparsity, accurate cell clustering is still challengeable. Here, we present DeepCI, a new clustering approach for scRNA-seq data. Using two autoencoders to obtain cell embedding and gene embedding, DeepCI can simultaneously learn cell low-dimensional representation and clustering. In addition, the recovered gene expression matrix can be obtained by the matrix multiplication of cell and gene embedding. To evaluate the performance of DeepCI, we performed it on several real scRNA-seq datasets for clustering and visualization analysis. The experimental results show that DeepCI obtains the overall better performance than several popular single cell analysis methods. We also evaluated the imputation performance of DeepCI by a dedicated experiment. The corresponding results show that the imputed gene expression of known specific marker gene can greatly improve the accuracy of cell type classification.
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Affiliation(s)
- Zhenlan Liang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Siqi Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xuhua Yan
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
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24
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Baranovsky A, Ivanov T, Granovskaya M, Papatsenko D, Pervouchine DD. Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs. PLoS One 2022; 17:e0268626. [PMID: 35587924 PMCID: PMC9119523 DOI: 10.1371/journal.pone.0268626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 05/03/2022] [Indexed: 12/01/2022] Open
Abstract
Significant alterations in signaling pathways and transcriptional regulatory programs together represent major hallmarks of many cancers. These, among all, include the reactivation of stemness, which is registered by the expression of pathways that are active in the embryonic stem cells (ESCs). Here, we assembled gene sets that reflect the stemness and proliferation signatures and used them to analyze a large panel of RNA-seq data from The Cancer Genome Atlas (TCGA) Consortium in order to specifically assess the expression of stemness-related and proliferation-related genes across a collection of different tumor types. We introduced a metric that captures the collective similarity of the expression profile of a tumor to that of ESCs, which showed that stemness and proliferation signatures vary greatly between different tumor types. We also observed a high degree of intertumoral heterogeneity in the expression of stemness- and proliferation-related genes, which was associated with increased hazard ratios in a fraction of tumors and mirrored by high intratumoral heterogeneity and a remarkable stemness capacity in metastatic lesions across cancer cells in single cell RNA-seq datasets. Taken together, these results indicate that the expression of stemness signatures is highly heterogeneous and cannot be used as a universal determinant of cancer. This calls into question the universal validity of diagnostic tests that are based on stem cell markers.
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Affiliation(s)
- Artem Baranovsky
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Timofei Ivanov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Dmitri Papatsenko
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Dmitri D. Pervouchine
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- * E-mail:
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25
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Jagnandan N, Morachis J. Microfluidic cell sorter sample preparation for genomic assays. BIOMICROFLUIDICS 2022; 16:034106. [PMID: 35698630 PMCID: PMC9188458 DOI: 10.1063/5.0092358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Single-cell RNA-Sequencing has led to many novel discoveries such as the detection of rare cell populations, microbial populations, and cancer mutations. The quality of single-cell transcriptomics relies heavily on sample preparation and cell sorting techniques that best preserve RNA quality while removing dead cells or debris prior to cDNA generation and library preparation. Magnetic bead cell enrichment is a simple process of cleaning up a sample but can only separate on a single-criterion. Droplet-based cell sorters, on the other hand, allows for higher purity of sorted cells gated on several fluorescent and scatter properties. The downside of traditional droplet-based sorters is their operational complexity, accessibility, and potential stress on cells due to their high-pressure pumps. The WOLF® Cell Sorter, and WOLF G2®, developed by NanoCellect Biomedical, are novel microfluidic-based cell sorters that use gentle sorting technology compatible with several RNA-sequencing platforms. The experiments highlighted here demonstrate how microfluidic sorting can be successfully used to remove debris and unwanted cells prior to genomic sample preparation resulting in more data per cell and improved library complexity.
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Affiliation(s)
- Nicole Jagnandan
- Applications, NanoCellect Biomedical Inc., San Diego, California 92121, USA
| | - Jose Morachis
- Applications, NanoCellect Biomedical Inc., San Diego, California 92121, USA
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26
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scTEM-seq: Single-cell analysis of transposable element methylation to link global epigenetic heterogeneity with transcriptional programs. Sci Rep 2022; 12:5776. [PMID: 35388081 PMCID: PMC8986802 DOI: 10.1038/s41598-022-09765-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/28/2022] [Indexed: 12/11/2022] Open
Abstract
Global changes in DNA methylation are observed in development and disease, and single-cell analyses are highlighting the heterogeneous regulation of these processes. However, technical challenges associated with single-cell analysis of DNA methylation limit these studies. We present single-cell transposable element methylation sequencing (scTEM-seq) for cost-effective estimation of average DNA methylation levels. By targeting high-copy SINE Alu elements, we achieve amplicon bisulphite sequencing with thousands of loci covered in each scTEM-seq library. Parallel transcriptome analysis is also performed to link global DNA methylation estimates with gene expression. We apply scTEM-seq to KG1a acute myeloid leukaemia (AML) cells, and primary AML cells. Our method reveals global DNA methylation heterogeneity induced by decitabine treatment of KG1a cells associated with altered expression of immune process genes. We also compare global DNA methylation estimates to expression of transposable elements and find a predominance of negative correlations. Finally, we observe co-ordinated upregulation of many transposable elements in a sub-set of decitabine treated cells. By linking global DNA methylation heterogeneity with transcription, scTEM-seq will refine our understanding of epigenetic regulation in cancer and beyond.
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27
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Molugu K, Battistini GA, Heaster TM, Rouw J, Guzman EC, Skala MC, Saha K. Label-Free Imaging to Track Reprogramming of Human Somatic Cells. GEN BIOTECHNOLOGY 2022; 1:176-191. [PMID: 35586336 PMCID: PMC9092522 DOI: 10.1089/genbio.2022.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/28/2022] [Indexed: 11/12/2022]
Abstract
The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.
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Affiliation(s)
- Kaivalya Molugu
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA
| | - Giovanni A. Battistini
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA
| | - Tiffany M. Heaster
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA; and Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Jacob Rouw
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA; and Madison, Wisconsin, USA
| | | | - Melissa C. Skala
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA; and Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Krishanu Saha
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA; and Madison, Wisconsin, USA
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28
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Oliveira NA, Sevim H. Dendritic cell differentiation from human induced pluripotent stem cells: challenges and progress. Stem Cells Dev 2022; 31:207-220. [PMID: 35316109 DOI: 10.1089/scd.2021.0305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Dendritic cells (DCs) are the major antigen-presenting cells of the immune system responsible for initiating and coordinating immune responses. These abilities provide potential for several clinical applications, such as the development of immunogenic vaccines. However, difficulty in obtaining DCs from conventional sources, such as bone marrow (BM), peripheral blood (PBMC), and cord blood (CB), is a significantly hinders routine application. The use of human induced pluripotent stem cells (hiPSCs) is a valuable alternative for generating sufficient numbers of DCs to be used in basic and pre-clinical studies. Despite the many challenges that must be overcome to achieve an efficient protocol for obtaining the major DC types from hiPSCs, recent progress has been made. Here we review the current state of developing DCs from hiPSCs, as well as the key elements required to enable the routine use of hiPSC-derived DCs in pre-clinical and clinical assays.
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Affiliation(s)
- Nelio Aj Oliveira
- Jackson Laboratory - Farmington, 481263, Cell Engineering , Farmington, Connecticut, United States, 06032-2374;
| | - Handan Sevim
- Hacettepe Universitesi, 37515, Faculty of Science Department of Biology, Ankara, Ankara, Turkey;
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29
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The spatial self-organization within pluripotent stem cell colonies is continued in detaching aggregates. Biomaterials 2022; 282:121389. [PMID: 35121357 DOI: 10.1016/j.biomaterials.2022.121389] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/13/2021] [Accepted: 01/23/2022] [Indexed: 12/13/2022]
Abstract
Colonies of induced pluripotent stem cells (iPSCs) reveal aspects of self-organization even under culture conditions that maintain pluripotency. To investigate the dynamics of this process under spatial confinement, we used either polydimethylsiloxane (PDMS) pillars or micro-contact printing of vitronectin. There was a progressive upregulation of OCT4, E-cadherin, and NANOG within 70 μm from the outer rim of iPSC colonies. Single-cell RNA-sequencing and spatial reconstruction of gene expression demonstrated that OCT4high subsets, residing at the edge of the colony, have pronounced up-regulation of the TGF-β pathway, particularly of NODAL and its inhibitor LEFTY. Interestingly, after 5-7 days, iPSC colonies detached spontaneously from micro-contact printed substrates to form 3D aggregates. This new method allowed generation of embryoid bodies (EBs) of controlled size without enzymatic or mechanical treatment. Within the early 3D aggregates, radial organization and differential gene expression continued in analogy to the changes observed during self-organization of iPSC colonies. Early self-detached aggregates revealed up-regulated germline-specific gene expression patterns as compared to conventional EBs. However, there were no marked differences after further directed differentiation toward hematopoietic, mesenchymal, and neuronal lineages. Our results provide further insight into the gradual self-organization within iPSC colonies and at their transition into EBs.
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30
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Zhao X, Shi Y, Pan T, Lu D, Xiong J, Li B, Xin H. In Situ Single-Cell Surgery and Intracellular Organelle Manipulation Via Thermoplasmonics Combined Optical Trapping. NANO LETTERS 2022; 22:402-410. [PMID: 34968073 DOI: 10.1021/acs.nanolett.1c04075] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Microsurgery and biopsies on individual cells in a cellular microenvironment are of great importance to better understand the fundamental cellular processes at subcellular and even single-molecular levels. However, it is still a big challenge for in situ surgery without interfering with neighboring living cells. Here, we report a thermoplasmonics combined optical trapping (TOT) technique for in situ single-cell surgery and intracellular organelle manipulation, without interfering with neighboring cells. A selective single-cell perforation was demonstrated via a localized thermoplasmonic effect, which facilitated further targeted gene delivery. Such a perforation was reversible, and the damaged membrane was capable of being repaired. Remarkably, a targeted extraction and precise manipulation of intracellular organelles were realized via the optical trapping. This TOT technique represents a new way for single-cell microsurgery, gene delivery, and intracellular organelle manipulation, and it provides a new insight for a deeper understanding of cellular processes as well as to reveal underlying causes of diseases associated with organelle malfunctions at a subcellular level.
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Affiliation(s)
- Xiaoting Zhao
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Yang Shi
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Ting Pan
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Dengyun Lu
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Jianyun Xiong
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Baojun Li
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Hongbao Xin
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
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31
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Tian Y, Zhan Y, Jiang Q, Lu W, Li X. Expression and function of PDGF-C in development and stem cells. Open Biol 2021; 11:210268. [PMID: 34847773 PMCID: PMC8633783 DOI: 10.1098/rsob.210268] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Platelet-derived growth factor C (PDGF-C) is a relatively new member of the PDGF family, discovered nearly 20 years after the finding of platelet-derived growth factor A (PDGF-A) and platelet-derived growth factor B (PDGF-B). PDGF-C is generally expressed in most organs and cell types. Studies from the past 20 years have demonstrated critical roles of PDGF-C in numerous biological, physiological and pathological processes, such as development, angiogenesis, tumour growth, tissue remodelling, wound healing, atherosclerosis, fibrosis, stem/progenitor cell regulation and metabolism. Understanding PDGF-C expression and activities thus will be of great importance to various research disciplines. In this review, however, we mainly discuss the expression and functions of PDGF-C and its receptors in development and stem cells.
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Affiliation(s)
- Yi Tian
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, People’s Republic of China
| | - Ying Zhan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, People’s Republic of China
| | - Qin Jiang
- Ophthalmic Department, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Weisi Lu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, People’s Republic of China
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, People’s Republic of China
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32
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Angius A, Scanu AM, Arru C, Muroni MR, Carru C, Porcu A, Cossu-Rocca P, De Miglio MR. A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer. MEDICINA (KAUNAS, LITHUANIA) 2021; 57:1257. [PMID: 34833475 PMCID: PMC8624593 DOI: 10.3390/medicina57111257] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/14/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022]
Abstract
In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.
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Affiliation(s)
- Andrea Angius
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Cittadella Universitaria di Cagliari, 09042 Monserrato, Italy
| | - Antonio Mario Scanu
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Via P. Manzella, 4, 07100 Sassari, Italy; (A.M.S.); (M.R.M.); (A.P.); (P.C.-R.)
| | - Caterina Arru
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy; (C.A.); (C.C.)
| | - Maria Rosaria Muroni
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Via P. Manzella, 4, 07100 Sassari, Italy; (A.M.S.); (M.R.M.); (A.P.); (P.C.-R.)
| | - Ciriaco Carru
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy; (C.A.); (C.C.)
| | - Alberto Porcu
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Via P. Manzella, 4, 07100 Sassari, Italy; (A.M.S.); (M.R.M.); (A.P.); (P.C.-R.)
| | - Paolo Cossu-Rocca
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Via P. Manzella, 4, 07100 Sassari, Italy; (A.M.S.); (M.R.M.); (A.P.); (P.C.-R.)
| | - Maria Rosaria De Miglio
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Via P. Manzella, 4, 07100 Sassari, Italy; (A.M.S.); (M.R.M.); (A.P.); (P.C.-R.)
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33
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Shen S, Sun Y, Matsumoto M, Shim WJ, Sinniah E, Wilson SB, Werner T, Wu Z, Bradford ST, Hudson J, Little MH, Powell J, Nguyen Q, Palpant NJ. Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation. Trends Mol Med 2021; 27:1135-1158. [PMID: 34657800 DOI: 10.1016/j.molmed.2021.09.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/19/2021] [Accepted: 09/22/2021] [Indexed: 12/12/2022]
Abstract
Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.
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Affiliation(s)
- Sophie Shen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Yuliangzi Sun
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Maika Matsumoto
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Woo Jun Shim
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Enakshi Sinniah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Sean B Wilson
- Murdoch Children's Research Institute, Melbourne, Australia
| | - Tessa Werner
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Zhixuan Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | | | - James Hudson
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Melissa H Little
- Murdoch Children's Research Institute, Melbourne, Australia; Department of Pediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Joseph Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, Australia; UNSW Cellular Genomics Futures Institute, UNSW, Sydney, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
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34
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Del Olmo B, Merkurjev D, Yao L, Pinsach-Abuin ML, Garcia-Bassets I, Almenar-Queralt A. Analysis of Clonal Composition in Human iPSC and ESC and Derived 2D and 3D Differentiated Cultures. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2454:31-47. [PMID: 34505265 DOI: 10.1007/7651_2021_414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Human induced pluripotent and embryonic stem cell cultures (hiPSC/hESC) are phenotypically heterogeneous and prone to clonal deviations during subculturing and differentiation. Clonal deviations often emerge unnoticed, but they can change the biology of the cell culture with a negative impact on experimental reproducibility. Here, we describe a computational workflow to profile the bulk clonal composition in a hiPSC/hESC culture that can also be used to infer clonal deviations. This workflow processes data obtained with two versions of the same method. The two versions-epigenetic and transcriptomic-rely on a mechanism of stochastic H3K4me3 deposition during hiPSC/hESC derivation. This mechanism generates a signature of ten or more H3K4me3-enriched clustered protocadherin (PCDH) promoters distinct in every single cell. The aggregate of single-cell signatures provides an identificatory feature in every hiPSC/hESC line. This feature is stably transmitted to the cell progeny of the culture even after differentiation unless there is a clonal deviation event that changes the internal balance of single-cell signatures. H3K4me3 signatures can be profiled by chromatin immunoprecipitation and next-generation sequencing (ChIP-seq). Alternatively, an equivalent PCDH-expression version can be profiled by RNA-seq in PCDH-expressing hiPSC/hESC-derived cells (such as neurons, astrocytes, and cardiomyocytes; and, in long-term cultures, such as cerebral organoids). Notably, our workflow can also distinguish genetically identical hiPSC/hESC lines derived from the same patient or generated in the same editing process. Together, we propose a method to improve data sharing and reproducibility in the hiPSC and hESC fields.
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Affiliation(s)
- Bernat Del Olmo
- Visiting Scholar Program, Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Daria Merkurjev
- Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Likun Yao
- Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Mel Lina Pinsach-Abuin
- Visiting Scholar Program, Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Ivan Garcia-Bassets
- Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA.
| | - Angels Almenar-Queralt
- Department of Cellular and Molecular Medicine, Sanford Consortium for Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA.
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35
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Babarinde IA, Ma G, Li Y, Deng B, Luo Z, Liu H, Abdul MM, Ward C, Chen M, Fu X, Shi L, Duttlinger M, He J, Sun L, Li W, Zhuang Q, Tong G, Frampton J, Cazier JB, Chen J, Jauch R, Esteban MA, Hutchins AP. Transposable element sequence fragments incorporated into coding and noncoding transcripts modulate the transcriptome of human pluripotent stem cells. Nucleic Acids Res 2021; 49:9132-9153. [PMID: 34390351 PMCID: PMC8450112 DOI: 10.1093/nar/gkab710] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Transposable elements (TEs) occupy nearly 40% of mammalian genomes and, whilst most are fragmentary and no longer capable of transposition, they can nevertheless contribute to cell function. TEs within genes transcribed by RNA polymerase II can be copied as parts of primary transcripts; however, their full contribution to mature transcript sequences remains unresolved. Here, using long and short read (LR and SR) RNA sequencing data, we show that 26% of coding and 65% of noncoding transcripts in human pluripotent stem cells (hPSCs) contain TE-derived sequences. Different TE families are incorporated into RNAs in unique patterns, with consequences to transcript structure and function. The presence of TE sequences within a transcript is correlated with TE-type specific changes in its subcellular distribution, alterations in steady-state levels and half-life, and differential association with RNA Binding Proteins (RBPs). We identify hPSC-specific incorporation of endogenous retroviruses (ERVs) and LINE:L1 into protein-coding mRNAs, which generate TE sequence-derived peptides. Finally, single cell RNA-seq reveals that hPSCs express ERV-containing transcripts, whilst differentiating subpopulations lack ERVs and express SINE and LINE-containing transcripts. Overall, our comprehensive analysis demonstrates that the incorporation of TE sequences into the RNAs of hPSCs is more widespread and has a greater impact than previously appreciated.
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Affiliation(s)
- Isaac A Babarinde
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Gang Ma
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yuhao Li
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Boping Deng
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Zhiwei Luo
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Hao Liu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Mazid Md Abdul
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Carl Ward
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Minchun Chen
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiuling Fu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liyang Shi
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Martha Duttlinger
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jiangping He
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Li Sun
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wenjuan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Qiang Zhuang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guoqing Tong
- Center for Reproductive Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China
| | - Jon Frampton
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Jean-Baptiste Cazier
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK.,Centre for Computational Biology, University of Birmingham, Birmingham, UK
| | - Jiekai Chen
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ralf Jauch
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Miguel A Esteban
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Andrew P Hutchins
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
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36
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Gupta K, Yadav P, Maryam S, Ahuja G, Sengupta D. Quantification of Age-Related Decline in Transcriptional Homeostasis. J Mol Biol 2021; 433:167179. [PMID: 34339725 DOI: 10.1016/j.jmb.2021.167179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 10/20/2022]
Abstract
Age-dependent dysregulation of transcription regulatory machinery triggers modulations in the gene expression levels leading to the decline in cellular fitness. Tracking of these transcripts along the temporal axis in multiple species revealed a spectrum of evolutionarily conserved pathways, such as electron transport chain, translation regulation, DNA repair, etc. Recent shreds of evidence suggest that aging deteriorates the transcription machinery itself, indicating the hidden complexity of the aging transcriptomes. This reinforces the need for devising novel computational methods to view aging through the lens of transcriptomics. Here, we present Homeostatic Divergence Score (HDS) to quantify the extent of messenger RNA (mRNA) homeostasis by assessing the balance between spliced and unspliced mRNA repertoire in single cells. We validated its utility in two independent aging datasets, and identified sets of genes undergoing age-related breakdown of transcriptional homeostasis. Moreover, testing of our method on a subpopulation of human embryonic stem cells revealed a set of differentially processed transcripts segregating these subpopulations. Our preliminary analyses in this direction suggest that mRNA processing level information offered by single-cell RNA sequencing (scRNA-seq) data is a superior determinant of chronological age as compared to transcriptional noise.
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Affiliation(s)
- Krishan Gupta
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India
| | - Princey Yadav
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India
| | - Sidrah Maryam
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India.
| | - Debarka Sengupta
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Centre for Artificial Intelligence, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia.
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37
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Chen CXQ, Abdian N, Maussion G, Thomas RA, Demirova I, Cai E, Tabatabaei M, Beitel LK, Karamchandani J, Fon EA, Durcan TM. A Multistep Workflow to Evaluate Newly Generated iPSCs and Their Ability to Generate Different Cell Types. Methods Protoc 2021; 4:mps4030050. [PMID: 34287353 PMCID: PMC8293472 DOI: 10.3390/mps4030050] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 07/10/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
Abstract
Induced pluripotent stem cells (iPSCs) derived from human somatic cells have created new opportunities to generate disease-relevant cells. Thus, as the use of patient-derived stem cells has become more widespread, having a workflow to monitor each line is critical. This ensures iPSCs pass a suite of quality-control measures, promoting reproducibility across experiments and between labs. With this in mind, we established a multistep workflow to assess our newly generated iPSCs. Our workflow tests four benchmarks: cell growth, genomic stability, pluripotency, and the ability to form the three germline layers. We also outline a simple test for assessing cell growth and highlight the need to compare different growth media. Genomic integrity in the human iPSCs is analyzed by G-band karyotyping and a qPCR-based test for the detection of common karyotypic abnormalities. Finally, we confirm that the iPSC lines can differentiate into a given cell type, using a trilineage assay, and later confirm that each iPSC can be differentiated into one cell type of interest, with a focus on the generation of cortical neurons. Taken together, we present a multistep quality-control workflow to evaluate newly generated iPSCs and detail the findings on these lines as they are tested within the workflow.
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Affiliation(s)
- Carol X.-Q. Chen
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Narges Abdian
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Gilles Maussion
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Rhalena A. Thomas
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Iveta Demirova
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Eddie Cai
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Mahdieh Tabatabaei
- The Neuro’s Clinical Biological Imaging and Genetic Repository (C-BIG), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (M.T.); (J.K.)
| | - Lenore K. Beitel
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Jason Karamchandani
- The Neuro’s Clinical Biological Imaging and Genetic Repository (C-BIG), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (M.T.); (J.K.)
| | - Edward A. Fon
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
| | - Thomas M. Durcan
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; (C.X.-Q.C.); (N.A.); (G.M.); (R.A.T.); (I.D.); (E.C.); (L.K.B.); (E.A.F.)
- Correspondence: ; Tel.: +1-514-398-6933
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38
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Thompson M, Matsumoto M, Ma T, Senabouth A, Palpant NJ, Powell JE, Nguyen Q. scGPS: Determining Cell States and Global Fate Potential of Subpopulations. Front Genet 2021; 12:666771. [PMID: 34349778 PMCID: PMC8326972 DOI: 10.3389/fgene.2021.666771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/04/2021] [Indexed: 12/20/2022] Open
Abstract
Finding cell states and their transcriptional relatedness is a main outcome from analysing single-cell data. In developmental biology, determining whether cells are related in a differentiation lineage remains a major challenge. A seamless analysis pipeline from cell clustering to estimating the probability of transitions between cell clusters is lacking. Here, we present Single Cell Global fate Potential of Subpopulations (scGPS) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more samples into clusters (SCORE algorithm) and estimates pairwise transitioning potential (scGPS algorithm) of any pair of clusters. SCORE allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. scGPS implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. scGPS also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied scGPS in diverse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. scGPS is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in R with optimised functions using C++ and is publicly available in Bioconductor.
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Affiliation(s)
- Michael Thompson
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Maika Matsumoto
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Tianqi Ma
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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39
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Kim HK, Ha TW, Lee MR. Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming. Int J Mol Sci 2021; 22:ijms22115988. [PMID: 34206025 PMCID: PMC8198005 DOI: 10.3390/ijms22115988] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/26/2021] [Accepted: 05/31/2021] [Indexed: 12/15/2022] Open
Abstract
Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.
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40
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Epigenetic plasticity, selection, and tumorigenesis. Biochem Soc Trans 2021; 48:1609-1621. [PMID: 32794546 DOI: 10.1042/bst20191215] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 12/11/2022]
Abstract
Epigenetic processes converge on chromatin in order to direct a cell's gene expression profile. This includes both maintaining a stable cell identity, but also priming the cell for specific controlled transitions, such as differentiation or response to stimuli. In cancer, this normally tight control is often disrupted, leading to a wide scale hyper-plasticity of the epigenome and allowing stochastic gene activation and silencing, cell state transition, and potentiation of the effects of genetic lesions. Many of these epigenetic disruptions will confer a proliferative advantage to cells, allowing for a selection process to occur and leading to tumorigenesis even in the case of reversible or unstable epigenetic states. This review seeks to highlight how the fundamental epigenetic shifts in cancer contribute to tumorigenesis, and how understanding an integrated view of cancer genetics and epigenetics may more effectively guide research and treatment.
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41
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Abstract
Single-cell sequencing-based methods for profiling gene transcript levels have revealed substantial heterogeneity in expression levels among morphologically indistinguishable cells. This variability has important functional implications for tissue biology and disease states such as cancer. Mapping of epigenomic information such as chromatin accessibility, nucleosome positioning, histone tail modifications and enhancer-promoter interactions in both bulk-cell and single-cell samples has shown that these characteristics of chromatin state contribute to expression or repression of associated genes. Advances in single-cell epigenomic profiling methods are enabling high-resolution mapping of chromatin states in individual cells. Recent studies using these techniques provide evidence that variations in different aspects of chromatin organization collectively define gene expression heterogeneity among otherwise highly similar cells.
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Affiliation(s)
- Benjamin Carter
- Laboratory of Epigenome Biology, Systems Biology Center, NHLBI, NIH, Bethesda, MD, USA.
| | - Keji Zhao
- Laboratory of Epigenome Biology, Systems Biology Center, NHLBI, NIH, Bethesda, MD, USA.
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42
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Neavin D, Nguyen Q, Daniszewski MS, Liang HH, Chiu HS, Wee YK, Senabouth A, Lukowski SW, Crombie DE, Lidgerwood GE, Hernández D, Vickers JC, Cook AL, Palpant NJ, Pébay A, Hewitt AW, Powell JE. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol 2021; 22:76. [PMID: 33673841 PMCID: PMC7934233 DOI: 10.1186/s13059-021-02293-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 02/10/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. RESULTS Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. CONCLUSIONS This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.
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Affiliation(s)
- Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Maciej S Daniszewski
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Helena H Liang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Han Sheng Chiu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yong Kiat Wee
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Samuel W Lukowski
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Duncan E Crombie
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Grace E Lidgerwood
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Damián Hernández
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - James C Vickers
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Anthony L Cook
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Alice Pébay
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia.
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, Sydney, Australia.
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Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J, Steer J, Patel M, Pearce D, Andersson M, Bonder MJ, Mountjoy E, Ghoussaini M, Lancaster MA, Marioni JC, Merkle FT, Gaffney DJ, Stegle O. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat Genet 2021; 53:304-312. [PMID: 33664506 PMCID: PMC7610897 DOI: 10.1038/s41588-021-00801-6] [Citation(s) in RCA: 156] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.
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Affiliation(s)
- Julie Jerber
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel D Seaton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Anna S E Cuomo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Natsuhiko Kumasaka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - James Haldane
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Juliette Steer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Minal Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel Pearce
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Malin Andersson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ed Mountjoy
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Maya Ghoussaini
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
| | - Florian T Merkle
- Metabolic Research Laboratories and Medical Research Council Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
| | - Daniel J Gaffney
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Oliver Stegle
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany.
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Ghannoum S, Leoncio Netto W, Fantini D, Ragan-Kelley B, Parizadeh A, Jonasson E, Ståhlberg A, Farhan H, Köhn-Luque A. DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics. Int J Mol Sci 2021; 22:ijms22031399. [PMID: 33573289 PMCID: PMC7866810 DOI: 10.3390/ijms22031399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/08/2021] [Accepted: 01/28/2021] [Indexed: 02/08/2023] Open
Abstract
The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.
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Affiliation(s)
- Salim Ghannoum
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway; (A.P.); (H.F.)
- Correspondence: (S.G.); (A.K.-L.); Tel.: +46-76-5770129 (S.G.)
| | - Waldir Leoncio Netto
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway;
| | - Damiano Fantini
- Department of Urology, Northwestern University, Chicago, IL 60611, USA;
| | | | - Amirabbas Parizadeh
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway; (A.P.); (H.F.)
| | - Emma Jonasson
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, SE-41390 Gothenburg, Sweden; (E.J.); (A.S.)
| | - Anders Ståhlberg
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, SE-41390 Gothenburg, Sweden; (E.J.); (A.S.)
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, SE-41390 Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, SE-41390 Gothenburg, Sweden
| | - Hesso Farhan
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway; (A.P.); (H.F.)
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway;
- Correspondence: (S.G.); (A.K.-L.); Tel.: +46-76-5770129 (S.G.)
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45
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Afeworki Y, Wollenzien H, Kareta MS. Transcriptional Profiling During Neural Conversion. Methods Mol Biol 2021; 2352:171-181. [PMID: 34324187 PMCID: PMC9131516 DOI: 10.1007/978-1-0716-1601-7_12] [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] [Indexed: 06/13/2023]
Abstract
The processes that underlie neuronal conversion ultimately involve a reorganization of transcriptional networks to establish a neuronal cell fate. As such, transcriptional profiling is a key component toward understanding this process. In this chapter, we will discuss methods of elucidating transcriptional networks during neuronal reprogramming and considerations that should be incorporated in experimental design.
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Affiliation(s)
- Yohannes Afeworki
- Functional Genomics and Bioinformatics Core, Sanford Research, Sioux Falls, SD, USA
| | - Hannah Wollenzien
- Division of Basic Biomedical Sciences, University of South Dakota, Vermillion, SD, USA
- Genetics and Genomics Group, Sanford Research, Sioux Falls, SD, USA
| | - Michael S Kareta
- Functional Genomics and Bioinformatics Core, Sanford Research, Sioux Falls, SD, USA.
- Division of Basic Biomedical Sciences, University of South Dakota, Vermillion, SD, USA.
- Genetics and Genomics Group, Sanford Research, Sioux Falls, SD, USA.
- Cellular Therapies and Stem Cell Biology Group, Sanford Research, Sioux Falls, SD, USA.
- Department of Pediatrics, Sanford School of Medicine, Sioux Falls, SD, USA.
- Department of Chemistry and Biochemistry, South Dakota State University, Brookings, SD, USA.
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46
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Pinto AR, Bobik A. Mapping human pluripotent stem cell-endothelial cell differentiation using scRNA-seq: a step towards therapeutic angiogenesis. Eur Heart J 2020; 41:1037-1039. [PMID: 31263875 DOI: 10.1093/eurheartj/ehz464] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Alexander R Pinto
- Baker Heart and Diabetes Institute, Melbourne, Australia.,Centre for Cardiovascular Biology and Disease Research, La Trobe University, Melbourne, Australia
| | - Alex Bobik
- Baker Heart and Diabetes Institute, Melbourne, Australia.,Department of Immunology, Monash University, Melbourne, Australia
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47
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Luo L, Zhou Y, Zhang C, Huang J, Du J, Liao J, Bergholt NL, Bünger C, Xu F, Lin L, Tong G, Zhou G, Luo Y. Feeder-free generation and transcriptome characterization of functional mesenchymal stromal cells from human pluripotent stem cells. Stem Cell Res 2020; 48:101990. [PMID: 32950887 DOI: 10.1016/j.scr.2020.101990] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/23/2020] [Accepted: 09/05/2020] [Indexed: 01/18/2023] Open
Abstract
Induced mesenchymal stromal cells (iMSCs) derived from human pluripotent stem cells (PSCs) are attractive cells for regenerative medicine. However, the transcriptome of iMSCs and signature genes that can distinguish MSCs from fibroblasts and other cell types are rarely explored. In this study, we reported an optimized feeder-free method for the generation of iMSCs from human pluripotent stem cells. These iMSCs display a typical MSC morphology, express classic MSC markers (CD29, CD44, CD73, CD90, CD105, CD166), are negative for lymphocyte markers (CD11b, CD14, CD31, CD34, CD45, HLA-DR), and are potent for osteogenic and chondrogenic differentiation. Using genome-wide transcriptome profiling, we created an easily accessible transcriptome reference for the process of differentiating PSCs into iMSCs. The iMSC transcriptome reference revealed clear patterns in the silencing of pluripotency genes, activation of lineage commitment genes, and activation of mesenchymal genes during iMSC generation. All previously known positive and negative markers for MSCs were confirmed by our iMSC transcriptomic reference, and most importantly, gene classification and time course analysis identified 52 genes including FN1, TGFB1, TAGLN and SERPINE1, which showed significantly higher expression in MSCs (over 3 folds) than fibroblasts and other cell types. Taken together, these results provide a useful method and important resources for developing and understanding iMSCs in regenerative medicine.
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Affiliation(s)
- Lidan Luo
- Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark.
| | - Yan Zhou
- Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark; Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; Lungene Technologies Co., Ltd, Shenzhen, China.
| | - Chenxi Zhang
- Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, BGI-Shenzhen, Shenzhen 518083, China.
| | - Jinrong Huang
- Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, BGI-Shenzhen, Shenzhen 518083, China; Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark.
| | - Jie Du
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; Lungene Technologies Co., Ltd, Shenzhen, China.
| | - Jinqi Liao
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; Lungene Technologies Co., Ltd, Shenzhen, China.
| | | | - Cody Bünger
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark.
| | - Fengping Xu
- Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, BGI-Shenzhen, Shenzhen 518083, China.
| | - Lin Lin
- Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark.
| | - Guangdong Tong
- Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518033, China.
| | - Guangqian Zhou
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China.
| | - Yonglun Luo
- Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, BGI-Shenzhen, Shenzhen 518083, China; Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark.
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48
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Capp JP, Thomas F. A Similar Speciation Process Relying on Cellular Stochasticity in Microbial and Cancer Cell Populations. iScience 2020; 23:101531. [PMID: 33083761 PMCID: PMC7502340 DOI: 10.1016/j.isci.2020.101531] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Similarities between microbial and cancer cells were noticed in recent years and serve as a basis for an atavism theory of cancer. Cancer cells would rely on the reactivation of an ancestral "genetic program" that would have been repressed in metazoan cells. Here we argue that cancer cells resemble unicellular organisms mainly in their similar way to exploit cellular stochasticity to produce cell specialization and maximize proliferation. Indeed, the relationship between low stochasticity, specialization, and quiescence found in normal differentiated metazoan cells is lost in cancer. On the contrary, low stochasticity and specialization are associated with high proliferation among cancer cells, as it is observed for the "specialist" cells in microbial populations that fully exploit nutritional resources to maximize proliferation. Thus, we propose a model where the appearance of cancer phenotypes can be solely due to an adaptation and a speciation process based on initial increase in cellular stochasticity.
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Affiliation(s)
- Jean-Pascal Capp
- Toulouse Biotechnology Institute, University of Toulouse, INSA, CNRS, INRAE, 31077 Toulouse, France
| | - Frédéric Thomas
- CREEC, UMR IRD 224, CNRS 5290, University of Montpellier, 34394 Montpellier, France
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49
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Januszyk M, Chen K, Henn D, Foster DS, Borrelli MR, Bonham CA, Sivaraj D, Wagh D, Longaker MT, Wan DC, Gurtner GC. Characterization of Diabetic and Non-Diabetic Foot Ulcers Using Single-Cell RNA-Sequencing. MICROMACHINES 2020; 11:mi11090815. [PMID: 32872278 PMCID: PMC7570277 DOI: 10.3390/mi11090815] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/25/2020] [Accepted: 08/27/2020] [Indexed: 12/19/2022]
Abstract
Background: Recent advances in high-throughput single-cell sequencing technologies have led to their increasingly widespread adoption for clinical applications. However, challenges associated with tissue viability, cell yield, and delayed time-to-capture have created unique obstacles for data processing. Chronic wounds, in particular, represent some of the most difficult target specimens, due to the significant amount of fibrinous debris, extracellular matrix components, and non-viable cells inherent in tissue routinely obtained from debridement. Methods: Here, we examined the feasibility of single cell RNA sequencing (scRNA-seq) analysis to evaluate human chronic wound samples acquired in the clinic, subjected to prolonged cold ischemia time, and processed without FACS sorting. Wound tissue from human diabetic and non-diabetic plantar foot ulcers were evaluated using an optimized 10X Genomics scRNA-seq platform and analyzed using a modified data pipeline designed for low-yield specimens. Cell subtypes were identified informatically and their distributions and transcriptional programs were compared between diabetic and non-diabetic tissue. Results: 139,000 diabetic and non-diabetic wound cells were delivered for 10X capture after either 90 or 180 min of cold ischemia time. cDNA library concentrations were 858.7 and 364.7 pg/µL, respectively, prior to sequencing. Among all barcoded fragments, we found that 83.5% successfully aligned to the human transcriptome and 68% met the minimum cell viability threshold. The average mitochondrial mRNA fraction was 8.5% for diabetic cells and 6.6% for non-diabetic cells, correlating with differences in cold ischemia time. A total of 384 individual cells were of sufficient quality for subsequent analyses; from this cell pool, we identified transcriptionally-distinct cell clusters whose gene expression profiles corresponded to fibroblasts, keratinocytes, neutrophils, monocytes, and endothelial cells. Fibroblast subpopulations with differing fibrotic potentials were identified, and their distributions were found to be altered in diabetic vs. non-diabetic cells. Conclusions: scRNA-seq of clinical wound samples can be achieved using minor modifications to standard processing protocols and data analysis methods. This simple approach can capture widespread transcriptional differences between diabetic and non-diabetic tissue obtained from matched wound locations.
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Affiliation(s)
- Michael Januszyk
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Kellen Chen
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Dominic Henn
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Deshka S. Foster
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Mimi R. Borrelli
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Clark A. Bonham
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Dharshan Sivaraj
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Dhananjay Wagh
- Stanford Functional Genomics Facility, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Michael T. Longaker
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Derrick C. Wan
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
| | - Geoffrey C. Gurtner
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (M.J.); (K.C.); (D.H.); (D.S.F.); (M.R.B.); (C.A.B.); (D.S.); (M.T.L.); (D.C.W.)
- Correspondence: ; Tel.: +1-650-736-2776
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Paik DT, Cho S, Tian L, Chang HY, Wu JC. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol 2020; 17:457-473. [PMID: 32231331 PMCID: PMC7528042 DOI: 10.1038/s41569-020-0359-y] [Citation(s) in RCA: 196] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/24/2020] [Indexed: 02/08/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.
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Affiliation(s)
- David T Paik
- Stanford Cardiovascular Institute, Stanford, CA, USA.
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Sangkyun Cho
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei Tian
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford, CA, USA.
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
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