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Miao J, Zhang K, Yang Y, Xu S, Du J, Wu T, Tao C, Wang Y, Yang S. Single-nucleus transcriptomics reveal cardiac cell type-specific diversification in metabolic disease transgenic pigs. iScience 2024; 27:110015. [PMID: 38868189 PMCID: PMC11166884 DOI: 10.1016/j.isci.2024.110015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/28/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
Cardiac damage is widely present in patients with metabolic diseases, but the exact pathophysiological mechanisms involved remain unclear. The porcine heart is an ideal material for cardiovascular research due to its similarities to the human heart. This study evaluated pathological features and performed single-nucleus RNA sequencing (snRNA-seq) on myocardial samples from both wild-type and metabolic disease-susceptible transgenic pigs (previously established). We found that transgenic pigs exhibited lipid metabolism disturbances and myocardial injury after a high-fat high-sucrose diet intervention. snRNA-seq reveals the cellular landscape of healthy and metabolically disturbed pig hearts and identifies the major cardiac cell populations affected by metabolic diseases. Within metabolic disorder hearts, metabolically active cardiomyocytes exhibited impaired function and reduced abundance. Moreover, massive numbers of reparative LYVE1+ macrophages were lost. Additionally, proinflammatory endothelial cells were activated with high expression of multiple proinflammatory cytokines. Our findings provide insights into the cellular mechanisms of metabolic disease-induced myocardial injury.
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Affiliation(s)
- Jiakun Miao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Kaiyi Zhang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yu Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Shuang Xu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Juan Du
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Tianwen Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Cong Tao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Yanfang Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Shulin Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
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2
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Yang C, Wang Z, Qian L, Fu J, Sun H. Deciphering the molecular landscape: evolutionary progression from gynecomastia to aggressive male breast cancer. Cell Oncol (Dordr) 2024:10.1007/s13402-024-00964-4. [PMID: 38888848 DOI: 10.1007/s13402-024-00964-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Gynecomastia denotes the benign proliferation of glandular breast tissue and stands as a recognized risk factor for male breast cancer. Nonetheless, the underlying carcinogenic mechanisms orchestrating the progression from gynecomastia to cancer remain poorly understood. METHODS This study employed single-cell RNA sequencing (scRNA-seq) to meticulously dissect the cellular landscape of gynecomastia and unravel potential associations with male breast cancer at a single-cell resolution. Pseudotime and evolutionary analyses were executed to delineate the distinct features characterizing gynecomastia and male breast cancer. The TCGA database, along with cell-cell communication analysis and immunohistochemistry staining, was harnessed to validate differential gene expression, specifically focusing on CD13. RESULT From the copy number variation profiles and evolutionary tree, we inferred shared mutation characteristics (18p+ and 18q+) underpinning both conditions. The developmental trajectory unveiled an intriguing overlap between gynecomastia and malignant epithelial cells. Moreover, the differential gene CD13 emerged as a common denominator in both gynecomastia and male breast cancer when compared with normal mammary tissue. Cell-cell interaction analysis and communication dynamics within the tumor microenvironment spotlighted distinctions between CD13+ and CD13- subsets, with the former exhibiting elevated expression of FGFR1-FGF7. CONCLUSIONS Our investigation provides novel insights into the evolutionary progression from gynecomastia to male breast cancer, shedding light on the pivotal role of CD13 in driving this transition. The identification of CD13 as a potential therapeutic target suggests the feasibility of CD13-targeted interventions, specifically tailored for male breast cancer treatment.
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Affiliation(s)
- Chuang Yang
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029, China.
| | - Zhonglin Wang
- The Second People's Hospital of Lianyungang, Lianyungang, 222006, China
| | - Lijun Qian
- The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029, China
| | - Jingyue Fu
- The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029, China
| | - Handong Sun
- Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing, 210004, China.
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029, China.
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Bukhari H, Nithianandam V, Battaglia RA, Cicalo A, Sarkar S, Comjean A, Hu Y, Leventhal MJ, Dong X, Feany MB. Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model. Genome Res 2024; 34:590-605. [PMID: 38599684 PMCID: PMC11146598 DOI: 10.1101/gr.278576.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
Missense mutations in the gene encoding the microtubule-associated protein TAU (current and approved symbol is MAPT) cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human TAU in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas-mediated gene editing to model frontotemporal dementia caused by the TAU P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for Tau P251L display age-dependent neurodegeneration, display metabolic defects, and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies, we performed single-cell RNA sequencing on approximately 130,000 cells from brains of Tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified. Gene expression was also altered in glial cells, suggestive of non-cell-autonomous regulation. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell type-specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy that faithfully recapitulates the genetic context and phenotypic features of the human disease, and use the results of comprehensive single-cell sequencing analysis to outline pathways of neurotoxicity and highlight the potential role of non-cell-autonomous changes in glia.
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Affiliation(s)
- Hassan Bukhari
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
| | - Vanitha Nithianandam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
| | - Rachel A Battaglia
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
| | - Anthony Cicalo
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
- Genomics and Bioinformatics Hub, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Souvarish Sarkar
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Aram Comjean
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Matthew J Leventhal
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- MIT Ph.D. Program in Computational and Systems Biology, Cambridge, Massachusetts 02139, USA
| | - Xianjun Dong
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
- Genomics and Bioinformatics Hub, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA;
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, USA
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Ji G, Yang Q, Wang S, Yan X, Ou Q, Gong L, Zhao J, Zhou Y, Tian F, Lei J, Mu X, Wang J, Wang T, Wang X, Sun J, Zhang J, Jia C, Jiang T, Zhao MG, Lu Q. Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma. Genome Med 2024; 16:49. [PMID: 38566201 PMCID: PMC10985969 DOI: 10.1186/s13073-024-01320-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landscape of ESCC in the context of NAT could shed light on heterogeneity and optimize therapeutic strategies for patients. METHODS We analyzed single cells from 22 baseline and 24 post-NAT treatment samples of stage II/III ESCC patients to explore the association between the immune landscape and pathological response to neoadjuvant anti-PD-1 combination therapy, including pathological complete response (pCR), major pathological response (MPR), and incomplete pathological response (IPR). RESULTS Single-cell profiling identified 14 major cell subsets of cancer, immune, and stromal cells. Trajectory analysis unveiled an interesting link between cancer cell differentiation and pathological response to NAT. ESCC tumors enriched with less differentiated cancer cells exhibited a potentially favorable pathological response to NAT, while tumors enriched with clusters of more differentiated cancer cells may resist treatment. Deconvolution of transcriptomes in pre-treatment tumors identified gene signatures in response to NAT contributed by specific immune cell populations. Upregulated genes associated with better pathological responses in CD8 + effector T cells primarily involved interferon-gamma (IFNγ) signaling, neutrophil degranulation, and negative regulation of the T cell apoptotic process, whereas downregulated genes were dominated by those in the immune response-activating cell surface receptor signaling pathway. Natural killer cells in pre-treatment tumors from pCR patients showed a similar upregulation of gene expression in response to IFNγ but a downregulation of genes in the neutrophil-mediated immunity pathways. A decreased cellular contexture of regulatory T cells in ESCC TME indicated a potentially favorable pathological response to NAT. Cell-cell communication analysis revealed extensive interactions between CCL5 and its receptor CCR5 in various immune cells of baseline pCR tumors. Immune checkpoint interaction pairs, including CTLA4-CD86, TIGIT-PVR, LGALS9-HAVCR2, and TNFSF4-TNFRSF4, might serve as additional therapeutic targets for ICI therapy in ESCC. CONCLUSIONS This pioneering study unveiled an intriguing association between cancer cell differentiation and pathological response in esophageal cancer patients, revealing distinct subgroups of tumors for which neoadjuvant chemo-immunotherapy might be effective. We also delineated the immune landscape of ESCC tumors in the context of clinical response to NAT, which provides clinical insights for better understanding how patients respond to the treatment and further identifying novel therapeutic targets for ESCC patients in the future.
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Affiliation(s)
- Gang Ji
- Department of Digestive Surgery, Xijing Hospital, Air Force Medical University, No. 169 Changle West Road, Xi'an, 710032, China
| | - Qi Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Song Wang
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc, Nanjing, 210000, Jiangsu, China
| | - Xiaolong Yan
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Qiuxiang Ou
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc, Nanjing, 210000, Jiangsu, China
| | - Li Gong
- Department of Pathology, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jinbo Zhao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Yongan Zhou
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Feng Tian
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jie Lei
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Xiaorong Mu
- Department of Pathology, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jian Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Tao Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Xiaoping Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jianyong Sun
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jipeng Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Chenghui Jia
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Medical College, Xi'an, 710000, China
| | - Tao Jiang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
| | - Ming-Gao Zhao
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
| | - Qiang Lu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
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Kang M, Armenteros JJA, Gulati GS, Gleyzer R, Avagyan S, Brown EL, Zhang W, Usmani A, Earland N, Wu Z, Zou J, Fields RC, Chen DY, Chaudhuri AA, Newman AM. Mapping single-cell developmental potential in health and disease with interpretable deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585637. [PMID: 38562882 PMCID: PMC10983880 DOI: 10.1101/2024.03.19.585637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.
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Bukhari H, Nithianandam V, Battaglia RA, Cicalo A, Sarkar S, Comjean A, Hu Y, Leventhal MJ, Dong X, Feany MB. Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578624. [PMID: 38352559 PMCID: PMC10862891 DOI: 10.1101/2024.02.02.578624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Missense mutations in the gene encoding the microtubule-associated protein tau cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human tau in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease, but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas mediated gene editing to model frontotemporal dementia caused by the tau P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for tau P251L display age-dependent neurodegeneration, metabolic defects and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies we performed single-cell RNA sequencing on approximately 130,000 cells from brains of tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified and non-cell autonomously in glial cells. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell-type specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy, which faithfully recapitulates the genetic context and phenotypic features of the human disease and use the results of comprehensive single cell sequencing analysis to outline pathways of neurotoxicity and highlight the role of non-cell autonomous changes in glia.
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Affiliation(s)
- Hassan Bukhari
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
| | - Vanitha Nithianandam
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
| | - Rachel A. Battaglia
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
| | - Anthony Cicalo
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
- Genomics and Bioinformatics Hub, Brigham and Women’s Hospital, Boston, MA 02115
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115
| | - Souvarish Sarkar
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Aram Comjean
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115
| | - Matthew J. Leventhal
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- MIT Ph.D. Program in Computational and Systems Biology, Cambridge, MA 02139
| | - Xianjun Dong
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
- Genomics and Bioinformatics Hub, Brigham and Women’s Hospital, Boston, MA 02115
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115
| | - Mel B. Feany
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815
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Wu D, Bi X, Chow KHM. Identification of female-enriched and disease-associated microglia (FDAMic) contributes to sexual dimorphism in late-onset Alzheimer's disease. J Neuroinflammation 2024; 21:1. [PMID: 38178204 PMCID: PMC10765928 DOI: 10.1186/s12974-023-02987-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/06/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Late-onset Alzheimer's disease (LOAD) is the most common form of dementia; it disproportionally affects women in terms of both incidence rates and severity of progression. The cellular and molecular mechanisms underlying this clinical phenomenon remain elusive and ill-defined. METHODS In-depth analyses were performed with multiple human LOAD single-nucleus transcriptome datasets to thoroughly characterize cell populations in the cerebral cortex. ROSMAP bulk human brain tissue transcriptome and DNA methylome datasets were also included for validation. Detailed assessments of microglial cell subpopulations and their relevance to sex-biased changes at the tissue level were performed. Clinical trait associations, cell evolutionary trajectories, and transcription regulon analyses were conducted. RESULTS The relative numbers of functionally defective microglia were aberrantly increased uniquely among affected females. Substratification of the microglia into different subtypes according to their transcriptomic signatures identified a group of female-enriched and disease-associated microglia (FDAMic), the numbers of which were positively associated with disease severity. Phenotypically, these cells exhibit transcriptomic signatures that support active proliferation, MHC class II autoantigen presentation and amyloid-β binding, but they are also likely defective in phagocytosis. FDAMic are likely evolved from female activated response microglia (ARMic) with an APOE4 background and compromised estrogen receptor (ER) signaling that is deemed to be active among most subtypes of microglia. CONCLUSION This study offered important insights at both the cellular and molecular levels into how ER signaling affects microglial heterogeneity and function. FDAMic are associated with more advanced pathologies and severe trends of cognitive decline. Their emergence could, at least in part, explain the phenomenon of greater penetrance of the APOE4 genotype found in females. The biases of FDAMic emergence toward female sex and APOE4 status may also explain why hormone replacement therapy is more effective in APOE4 carriers. The pathologic nature of FDAMic suggests that selective modulations of these cells may help to regain brain neuroimmune homeostasis, serving as a new target for future drug development.
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Affiliation(s)
- Deng Wu
- School of Life Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China
| | - Xiaoman Bi
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China
| | - Kim Hei-Man Chow
- School of Life Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
- Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
- Nexus of Rare Neurodegenerative Diseases, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
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KOÇHAN N, OKTAY Y, KARAKÜLAH G. StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution. Turk J Biol 2023; 47:383-392. [PMID: 38681778 PMCID: PMC11045207 DOI: 10.55730/1300-0152.2672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/28/2023] [Accepted: 12/15/2023] [Indexed: 05/01/2024] Open
Abstract
Background/aim Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time. Materials and methods We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency. Results Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells. Conclusion scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.
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Affiliation(s)
- Necla KOÇHAN
- İzmir Biomedicine and Genome Center, İzmir,
Turkiye
| | - Yavuz OKTAY
- İzmir Biomedicine and Genome Center, İzmir,
Turkiye
- İzmir International Biomedicine and Genome Institute, Dokuz Eylül University, İzmir,
Turkiye
| | - Gökhan KARAKÜLAH
- İzmir Biomedicine and Genome Center, İzmir,
Turkiye
- Department of Medical Biology, Faculty of Medicine, Dokuz Eylül University, İzmir,
Turkiye
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9
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Long C, Li H, Liang P, Chao L, Hong Y, Zhang J, Xi Q, Zuo Y. Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 34:102044. [PMID: 37869261 PMCID: PMC10585637 DOI: 10.1016/j.omtn.2023.102044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023]
Abstract
Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of cell fates transition. Only partial reprogramming intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher the underlying decisive factors driving cell fate, we identified induced pluripotent stem cells or stromal-like cells (iPSCs/SLCs) and iPSCs or trophoblast-like cells (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition and the activation of pluripotency networks are the main molecular series in successful reprogramming. Correspondingly, intermediates diverge into SLCs accompanied by the inhibition of cell cycle genes and the activation of extracellular matrix genes, whereas the TLCs fate is characterized by the up-regulation of placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) and three key factors (Cdc5l, Klf4, and Nanog) were individually identified as drivers of the successful reprogramming by triggering downstream pluripotent networks during iPSCs/SLCs and iPSCs/TLCs fate bifurcation. Conversely, 11 factors (Cebpb, Sox4, Junb, etc.) and four factors (Gata2, Jund, Ctnnb1, etc.) drive SLCs fate and TLCs fate, respectively. Our study sheds new light on the understanding of decisive factors driving cell fate, which is helpful for improving reprogramming efficiency through manipulating cell fates to avoid alternative fates.
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Affiliation(s)
- Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lemuge Chao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Junping Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
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10
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Truong DD, Weistuch C, Murgas KA, Deasy JO, Mikos AG, Tannenbaum A, Ludwig J. Mapping the Single-cell Differentiation Landscape of Osteosarcoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.555156. [PMID: 37745374 PMCID: PMC10515803 DOI: 10.1101/2023.09.13.555156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The genetic and intratumoral heterogeneity observed in human osteosarcomas (OS) poses challenges for drug development and the study of cell fate, plasticity, and differentiation, processes linked to tumor grade, cell metastasis, and survival. To pinpoint errors in OS differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs MSCs toward adipogenic and osteoblastic fates. Incorporating pre-existing chondrocyte data, we applied trajectory analysis and non-negative matrix factorization (NMF) to generate the first human mesenchymal differentiation atlas. This 'roadmap' served as a reference to delineate the cellular composition of morphologically complex OS tumors and quantify each cell's lineage commitment. Projecting these signatures onto a bulk RNA-seq OS dataset unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes. Our study takes the critical first step in accurately quantifying OS differentiation and lineage, a prerequisite to better understanding global differentiation bottlenecks that might someday be targeted therapeutically.
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Affiliation(s)
- Danh D. Truong
- Department of Sarcoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Corey Weistuch
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kevin A. Murgas
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Allen Tannenbaum
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
- Department of Computer Science, Stony Brook University, Stony Brook, NY
| | - Joseph Ludwig
- Department of Sarcoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
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11
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Groves SM, Quaranta V. Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1225736. [PMID: 37731743 PMCID: PMC10507267 DOI: 10.3389/fnetp.2023.1225736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023]
Abstract
Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.
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Affiliation(s)
- Sarah M. Groves
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
| | - Vito Quaranta
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
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12
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Rosales-Alvarez RE, Rettkowski J, Herman JS, Dumbović G, Cabezas-Wallscheid N, Grün D. VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells. Genome Biol 2023; 24:148. [PMID: 37353813 PMCID: PMC10290360 DOI: 10.1186/s13059-023-02974-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/18/2023] [Indexed: 06/25/2023] Open
Abstract
Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.
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Affiliation(s)
- Reyna Edith Rosales-Alvarez
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- International Max Planck Research School for Immunobiology, Epigenetics, and Metabolism (IMPRS-IEM), Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jasmin Rettkowski
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), Freiburg, Germany
| | - Josip Stefan Herman
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Gabrijela Dumbović
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Nina Cabezas-Wallscheid
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- CIBSS-Centre for Integrative Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- Helmholtz Institute for RNA-Based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany.
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13
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Zhao HC, Chen CZ, Tian YZ, Song HQ, Wang XX, Li YJ, He JF, Zhao HL. CD168+ macrophages promote hepatocellular carcinoma tumor stemness and progression through TOP2A/β-catenin/ YAP1 axis. iScience 2023; 26:106862. [PMID: 37275516 PMCID: PMC10238939 DOI: 10.1016/j.isci.2023.106862] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/20/2023] [Accepted: 05/08/2023] [Indexed: 06/07/2023] Open
Abstract
Liver cancer stem-like cells (LCSCs) are the main cause of heterogeneity and poor prognosis in hepatocellular carcinoma (HCC). In this study, we aimed to explore the origin of LCSCs and the role of the TOP2A/β-catenin/YAP1 axis in tumor stemness and progression. Using single-cell RNA-seq analysis, we identified TOP2A+CENPF+ LCSCs, which were mainly regulated by CD168+ M2-like macrophages. Furthermore, spatial location analysis and fluorescent staining confirmed that LCSCs were enriched at tumor margins, constituting the spatial heterogeneity of HCC. Mechanistically, TOP2A competitively binds to β-catenin, leading to disassociation of β-catenin from YAP1, promoting HCC stemness and overgrowth. Our study provides valuable insights into the spatial transcriptome heterogeneity of the HCC microenvironment and the critical role of TOP2A/β-catenin/YAP1 axis in HCC stemness and progression.
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Affiliation(s)
- Hai-Chao Zhao
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chang-Zhou Chen
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yan-Zhang Tian
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Huang-Qin Song
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Xiao-Xiao Wang
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Yan-Jun Li
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Jie-Feng He
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Hao-Liang Zhao
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
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14
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Dave A, Charytonowicz D, Francoeur NJ, Beaumont M, Beaumont K, Schmidt H, Zeleke T, Silva J, Sebra R. The Breast Cancer Single-Cell Atlas: Defining cellular heterogeneity within model cell lines and primary tumors to inform disease subtype, stemness, and treatment options. Cell Oncol (Dordr) 2023; 46:603-628. [PMID: 36598637 PMCID: PMC10205851 DOI: 10.1007/s13402-022-00765-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Abstract
PURPOSE Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions. METHODS We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors. RESULTS Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies. CONCLUSION The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.
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Affiliation(s)
- Arpit Dave
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
| | - Daniel Charytonowicz
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
| | - Nancy J. Francoeur
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
- Pacific Biosciences, CA Menlo Park, USA
| | - Michael Beaumont
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Kristin Beaumont
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | | | - Tizita Zeleke
- Department of Pathology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY 10029 USA
| | - Jose Silva
- Department of Pathology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY 10029 USA
| | - Robert Sebra
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY 10029 USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
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15
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Xu Q, Zhu J, Luo Y, Li W. Cell Features Reconstruction from Gene Association Network of Single Cell. Interdiscip Sci 2023; 15:202-216. [PMID: 36977959 DOI: 10.1007/s12539-023-00553-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 01/19/2023] [Accepted: 01/27/2023] [Indexed: 03/30/2023]
Abstract
Gene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusing the global position and neighborhood influence of genes. Next, we propose a computational method of gene gravitation based on CNFM to quantify the extent of gene-gene interaction, and we can construct a gene gravitation network for single cells. Finally, we further design a novel index of gene gravitation entropy to quantitatively evaluate the level of single-cell differentiation. The experiments on eight different scRNA-seq datasets show the effectiveness and broad application prospects of our method.
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Affiliation(s)
- Qingguo Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiajie Zhu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yin Luo
- School of Life Sciences, East China Normal University, Shanghai, China.
| | - Weimin Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
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16
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Zou Y, Ye F, Kong Y, Hu X, Deng X, Xie J, Song C, Ou X, Wu S, Wu L, Xie Y, Tian W, Tang Y, Wong C, Chen Z, Xie X, Tang H. The Single-Cell Landscape of Intratumoral Heterogeneity and The Immunosuppressive Microenvironment in Liver and Brain Metastases of Breast Cancer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2203699. [PMID: 36529697 PMCID: PMC9929130 DOI: 10.1002/advs.202203699] [Citation(s) in RCA: 90] [Impact Index Per Article: 90.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/11/2022] [Indexed: 05/07/2023]
Abstract
Distant metastasis remains the major cause of morbidity for breast cancer. Individuals with liver or brain metastasis have an extremely poor prognosis and low response rates to anti-PD-1/L1 immune checkpoint therapy compared to those with metastasis at other sites. Therefore, it is urgent to investigate the underlying mechanism of anti-PD-1/L1 resistance and develop more effective immunotherapy strategies for these patients. Using single-cell RNA sequencing, a high-resolution map of the entire tumor ecosystem based on 44 473 cells from breast cancer liver and brain metastases is depicted. Identified by canonical markers and confirmed by multiplex immunofluorescent staining, the metastatic ecosystem features remarkable reprogramming of immunosuppressive cells such as FOXP3+ regulatory T cells, LAMP3+ tolerogenic dendritic cells, CCL18+ M2-like macrophages, RGS5+ cancer-associated fibroblasts, and LGALS1+ microglial cells. In addition, PD-1 and PD-L1/2 are barely expressed in CD8+ T cells and cancer/immune/stromal cells, respectively. Interactions of the immune checkpoint molecules LAG3-LGALS3 and TIGIT-NECTIN2 between CD8+ T cells and cancer/immune/stromal cells are found to play dominant roles in the immune escape. In summary, this study dissects the intratumoral heterogeneity and immunosuppressive microenvironment in liver and brain metastases of breast cancer for the first time, providing insights into the most appropriate immunotherapy strategies for these patients.
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Affiliation(s)
- Yutian Zou
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Feng Ye
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Yanan Kong
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Xiaoqian Hu
- School of Biomedical SciencesFaculty of MedicineThe University of Hong Kong21 Sassoon RoadHong Kong999077China
| | - Xinpei Deng
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Jindong Xie
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Cailu Song
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Xueqi Ou
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Song Wu
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Linyu Wu
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Yi Xie
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Wenwen Tian
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Yuhui Tang
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Chau‐Wei Wong
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Zhe‐Sheng Chen
- College of Pharmacy and Health SciencesSt. John's UniversityQueensNYUSA
| | - Xinhua Xie
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
| | - Hailin Tang
- Sun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine651 East Dongfeng RoadGuangzhou510060China
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17
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Ogata N, Konishi S, Yokoyama T. In vivo-like Culture of Monophagous Animal Organ using Dietary Components. JOURNAL OF BIOTECHNOLOGY AND BIOMEDICINE 2023; 6:42-48. [PMID: 36874218 PMCID: PMC9983661 DOI: 10.26502/jbb.2642-91280070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Animals depend on other species to live, with monophagy being an extreme mode. Monophagous animals depend on their diet not only for nutritients but also for developmental and reproductive controls. Thus, dietary components may be useful in culturing tissues from monophagous animals. We hypothesized that a dedifferentiated tissue from the monophagous silkworm, Bombyx mori, would re-differentiate when cultured in a medium containing an extract of mulberry (Morus alba) leaves, the only food of B. mori. Over 40 fat-body transcriptomes were sequenced, and we concluded that it is possible to establish in vivo-like silkworm tissue cultures using their diet.
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Affiliation(s)
- Norichika Ogata
- Nihon BioData Corporation, 3-2-1 Sakado, Takatsu-ku, Kawasaki, Kanagawa 213-0012, Japan
| | - Shogo Konishi
- Nihon BioData Corporation, 3-2-1 Sakado, Takatsu-ku, Kawasaki, Kanagawa 213-0012, Japan
| | - Takeshi Yokoyama
- Laboratory of Sericultural Science, Faculty of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8, Saiwai-cho, Fuchu, Tokyo, 183-8501, Japan
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18
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Ye F, Zhang G, E. W, Chen H, Yu C, Yang L, Fu Y, Li J, Fu S, Sun Z, Fei L, Guo Q, Wang J, Xiao Y, Wang X, Zhang P, Ma L, Ge D, Xu S, Caballero-Pérez J, Cruz-Ramírez A, Zhou Y, Chen M, Fei JF, Han X, Guo G. Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution. Nat Commun 2022; 13:4228. [PMID: 35869072 PMCID: PMC9307617 DOI: 10.1038/s41467-022-31879-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 07/08/2022] [Indexed: 01/01/2023] Open
Abstract
The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis. The Mexican axolotl is a well-established tetrapod model for regeneration and development. Here the authors report a scRNA-seq method to profile neotenic, metamorphic and limb development stages, highlighting unique perturbation patterns of cell type-related gene expression throughout metamorphosis.
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19
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Shen R, Li P, Zhang B, Feng L, Cheng S. Decoding the colorectal cancer ecosystem emphasizes the cooperative role of cancer cells, TAMs and CAFsin tumor progression. J Transl Med 2022; 20:462. [PMID: 36209225 PMCID: PMC9548187 DOI: 10.1186/s12967-022-03661-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/22/2022] [Indexed: 11/10/2022] Open
Abstract
Background Single-cell transcription data provided unprecedented molecular information, enabling us to directly encode the ecosystem of colorectal cancer (CRC). Characterization of the diversity of epithelial cells and how they cooperate with tumor microenvironment cells (TME) to endow CRC with aggressive characteristics at single-cell resolution is critical for the understanding of tumor progression mechanism. Methods In this study, we comprehensively analyzed the single-cell transcription data, bulk-RNA sequencing data and pathological tissue data. In detail, cellular heterogeneity of TME and epithelial cells were analyzed by unsupervised classification and consensus nonnegative matrix factorization analysis, respectively. Functional status of epithelial clusters was annotated by CancerSEA and its crosstalk with TME cells was investigated using CellPhoneDB and correlation analysis. Findings from single-cell transcription data were further validated in bulk-RNA sequencing data and pathological tissue data. Results A distinct cellular composition was observed between tumor and normal tissues, and tumors exhibited immunosuppressive phenotypes. Regarding epithelial cells, we identified one highly invasiveQuery cluster, C4, that correlated closely with tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs). Further analysis emphasized the TAMs subclass TAM1 and CAFs subclass S5 are closely related with C4. Conclusions In summary, our study elaborates on the cellular heterogeneity of CRC, revealing that TAMs and CAFs were critical for crosstalk network epithelial cells and TME cells. This in-depth understanding of cancer cell-TME network provided theoretical basis for the development of new drugs targeting this sophisticated network in CRC. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03661-8.
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Affiliation(s)
- Rongfang Shen
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, NO. 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Ping Li
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing, 100045, China
| | - Botao Zhang
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Feng
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, NO. 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
| | - Shujun Cheng
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, NO. 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
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20
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Ogata N. Transcriptome Dedifferentiation Observed in Animal Primary Cultures is Essential to Plant Reprogramming. JOURNAL OF BIOINFORMATICS AND SYSTEMS BIOLOGY : OPEN ACCESS 2022; 5:116-118. [PMID: 36397740 PMCID: PMC9668052 DOI: 10.26502/jbsb.5107039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Tissue culture environment liberate cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as growth, dedifferentiation, acquisition of pluripotency, immortalization and reprogramming. Each phenomenon is relating to each other and hardly to determine. Recently, dedifferentiation of animal cell was quantified as increasing liberality which is information entropy of transcriptome. The increasing liberality induced by tissue culture may reappear in plant cells too. Here we corroborated it. Measuring liberality during reprogramming of plant cells suggested that reprogramming is a combined phenomenon of dedifferentiation and re-differentiation.
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21
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Ren H, Wen Q, Zhao Q, Wang N, Zhao Y. Atlas of human dental pulp cells at multiple spatial and temporal levels based on single-cell sequencing analysis. Front Physiol 2022; 13:993478. [PMID: 36267574 PMCID: PMC9578252 DOI: 10.3389/fphys.2022.993478] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
The dental pulp plays a crucial role in the long-term maintenance of tooth function. The progress of endodontic treatment and pulp tissue regeneration engineering has made pulp-regeneration therapy promising in clinical practice. However, the mechanisms of pulp regeneration and the role of dental stem cells in development and regeneration have not been fully elucidated. Bridging the gaps between clinical operation and basic research is urgently needed. With the application of single-cell sequencing technology in dental research, the landscapes of human dental pulp cells have begun being outlined. However, the specific cellular heterogeneity of dental pulp cells, especially that of dental stem cells, at different spatial and temporal levels, is still unclear. In this study, we used single-cell RNA sequencing analysis of pulp samples at four different developmental stages and combined the findings with immunohistochemical staining to explore the development of dental pulp and stem cells. The results revealed temporal changes in the proportion of pulp cells during development. For example, mononuclear phagocytes accounted for a higher proportion in early samples. Odontoblasts identified by DMP1 had a higher expression of ion channel-related and neurodevelopment-related genes. Subpopulations were identified in fibroblasts, odontoblasts, and mesenchymal stem cells. We identified a subclass of odontoblasts that expresses DGKI and RRBP1 present in early developmental samples. A population of earlier mesenchymal stem cells expressed the SEPTIN gene, which may have greater proliferative and differentiation potential. Furthermore, dental pulp stem cells can differentiate into two directions: mineralization and myogenesis. In summary, the specific cellular heterogeneity of dental pulp cells was revealed at different spatial and temporal levels. These findings may shed light on the mechanism of tooth development. The gene expression profile of developing pulp cells may help to select cells for regenerative engineering and improve the success of dental pulp regeneration.
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Affiliation(s)
- Huihui Ren
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology and National Center of Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Laboratory for Digital and Material Technology of Stomatology and Beijing Key Laboratory of Digital Stomatology and Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health and NMPK Key Laboratory for Dental Materials, Beijing, China
| | - Quan Wen
- First Clinical Division, Peking University School and Hospital of Stomatology, Beijing, China
| | - Qingxuan Zhao
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology and National Center of Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Laboratory for Digital and Material Technology of Stomatology and Beijing Key Laboratory of Digital Stomatology and Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health and NMPK Key Laboratory for Dental Materials, Beijing, China
| | - Nan Wang
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology and National Center of Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Laboratory for Digital and Material Technology of Stomatology and Beijing Key Laboratory of Digital Stomatology and Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health and NMPK Key Laboratory for Dental Materials, Beijing, China
| | - Yuming Zhao
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology and National Center of Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Laboratory for Digital and Material Technology of Stomatology and Beijing Key Laboratory of Digital Stomatology and Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health and NMPK Key Laboratory for Dental Materials, Beijing, China
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22
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Brown MS, Abdollahi B, Wilkins OM, Lu H, Chakraborty P, Ognjenovic NB, Muller KE, Jolly MK, Christensen BC, Hassanpour S, Pattabiraman DR. Phenotypic heterogeneity driven by plasticity of the intermediate EMT state governs disease progression and metastasis in breast cancer. SCIENCE ADVANCES 2022; 8:eabj8002. [PMID: 35921406 PMCID: PMC9348802 DOI: 10.1126/sciadv.abj8002] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/16/2022] [Indexed: 05/04/2023]
Abstract
The epithelial-to-mesenchymal transition (EMT) is frequently co-opted by cancer cells to enhance migratory and invasive cell traits. It is a key contributor to heterogeneity, chemoresistance, and metastasis in many carcinoma types, where the intermediate EMT state plays a critical tumor-initiating role. We isolate multiple distinct single-cell clones from the SUM149PT human breast cell line spanning the EMT spectrum having diverse migratory, tumor-initiating, and metastatic qualities, including three unique intermediates. Using a multiomics approach, we identify CBFβ as a key regulator of metastatic ability in the intermediate state. To quantify epithelial-mesenchymal heterogeneity within tumors, we develop an advanced multiplexed immunostaining approach using SUM149-derived orthotopic tumors and find that the EMT state and epithelial-mesenchymal heterogeneity are predictive of overall survival in a cohort of stage III breast cancer. Our model reveals previously unidentified insights into the complex EMT spectrum and its regulatory networks, as well as the contributions of epithelial-mesenchymal plasticity (EMP) in tumor heterogeneity in breast cancer.
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Affiliation(s)
- Meredith S. Brown
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Behnaz Abdollahi
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Owen M. Wilkins
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Norris Cotton Cancer Center, Geisel School of Medicine, Lebanon, NH 03756, USA
| | - Hanxu Lu
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Priyanka Chakraborty
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru 560012, India
| | - Nevena B. Ognjenovic
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Kristen E. Muller
- Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru 560012, India
| | - Brock C. Christensen
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Norris Cotton Cancer Center, Geisel School of Medicine, Lebanon, NH 03756, USA
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Saeed Hassanpour
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Norris Cotton Cancer Center, Geisel School of Medicine, Lebanon, NH 03756, USA
| | - Diwakar R. Pattabiraman
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Norris Cotton Cancer Center, Geisel School of Medicine, Lebanon, NH 03756, USA
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23
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He Y, Yu F, Tian Y, Hu Q, Wang B, Wang L, Hu Y, Tao Y, Chen X, Peng M. Single-Cell RNA Sequencing Unravels Distinct Tumor Microenvironment of Different Components of Lung Adenocarcinoma Featured as Mixed Ground-Glass Opacity. Front Immunol 2022; 13:903513. [PMID: 35874770 PMCID: PMC9299373 DOI: 10.3389/fimmu.2022.903513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/01/2022] [Indexed: 11/21/2022] Open
Abstract
Lung adenocarcinoma featured as mixed ground-glass opacity (mGGO) doubled its volume half of the time in comparison with that featured as pure ground-glass opacity (pGGO). The mechanisms underlying the heterogeneous appearance of mGGO remain elusive. In this study, we macro-dissected the solid (S) components and ground-glass (GG) components of mGGO and performed single-cell sequencing analyses of six paired components from three mGGO patients. A total of 19,391 single-cell profiles were taken into analysis, and the data of each patient were analyzed independently to obtain a common alteration. Cancer cells and macrophages were the dominant cell types in the S and GG components, respectively. Cancer cells in the S components, which showed relatively malignant phenotypes, were likely to originate from both the GG and S components and monitor the surrounding tumor microenvironment (TME) through an intricate cell interaction network. SPP1hi macrophages were enriched in the S components and showed increased activity of chemoattraction, while macrophages in the GG components displayed an active antimicrobial process with a higher stress-induced state. In addition, the CD47–SIRPA axis was demonstrated to be critical in the maintenance of the GG components. Taken together, our study unraveled the alterations of cell components and transcriptomic features between different components in mGGOs.
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Affiliation(s)
- Yu He
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Fenglei Yu
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yi Tian
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Thoracic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Qikang Hu
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Bin Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Li Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yan Hu
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yongguang Tao
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaofeng Chen
- Department of Anaesthesia, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Muyun Peng
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
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24
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Yu H, Tang D, Wu H, Li C, Lu Y, He F, Zhang X, Yang Y, Shi W, Hu W, Zeng Z, Dai W, Ou M, Dai Y. Integrated single-cell analyses decode the developmental landscape of the human fetal spine. iScience 2022; 25:104679. [PMID: 35832888 PMCID: PMC9272381 DOI: 10.1016/j.isci.2022.104679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/18/2022] [Accepted: 06/23/2022] [Indexed: 11/30/2022] Open
Abstract
The spine has essential roles in supporting body weight, and passaging the neural elements between the body and the brain. In this study, we used integrated single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing analyses to reveal the cellular heterogeneity, lineage, and transcriptional regulatory network of the developing human spine. We found that EPYC + HAPLN1+ fibroblasts with stem cell characteristics could differentiate into chondrocytes by highly expressing the chondrogenic markers SOX9 and MATN4. Neurons could originate from neuroendocrine cells, and MEIS2 may be an essential transcription factor that promotes spinal neural progenitor cells to selectively differentiate into neurons during early gestation. Furthermore, the interaction of NRP2_SEMA3C and CD74_APP between macrophages and neurons may be essential for spinal cord development. Our integrated map provides a blueprint for understanding human spine development in the early and midgestational stages at single-cell resolution and offers a tool for investigating related diseases. scRNA-seq and scATAC-seq analyses reveal the developmental landscape of the fetal spine Chondrocytes may originate from EPYC + HAPLN1+ fibroblasts with stem cell characteristics Neurons may originate from neuroendocrine cells with regulation by MEIS2
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Affiliation(s)
- Haiyan Yu
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China.,Department of Pharmacy, Shenzhen Pingshan District People's Hospital, Pingshan General Hospital of Southern Medical University, Shenzhen, Guangdong 518118, P.R. China
| | - Donge Tang
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Hongwei Wu
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Chunhong Li
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Yongping Lu
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China.,Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510632, China
| | - Fang He
- Singleron Biotechnologies, Yaogu Avenue 11, Nanjing, Jiangsu, China
| | - Xiaogang Zhang
- Singleron Biotechnologies, Yaogu Avenue 11, Nanjing, Jiangsu, China
| | - Yane Yang
- Shenzhen Far East Women & Children Hospital, Shenzhen 518000, Guangdong, China
| | - Wei Shi
- Department of Obstetrics and Gynecology, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Wenlong Hu
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Zhipeng Zeng
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
| | - Weier Dai
- College of Natural Science, University of Texas at Austin, Austin, TX 78721, USA
| | - Minglin Ou
- Central Laboratory, The Second Affiliated Hospital of Guilin Medical University, No. 212, Renmin Road, Lingui District, Guilin 541000, China
| | - Yong Dai
- Clinical Medical Research Center, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, P.R. China
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25
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Qi G, Xu C, Wang J, Tian Y, Wang B, Zhang Y, Ma K, Diao X, Jin Y. Optoplasmonic Modulation of Cell Metabolic State Promotes Rapid Cell Differentiation. Anal Chem 2022; 94:8354-8364. [PMID: 35622722 DOI: 10.1021/acs.analchem.2c00837] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cell differentiation plays a vital role in mediating organ formation and tissue repair and regeneration. Although rapid and effective methods to stimulate cell differentiation for clinical purposes are highly desired, it remains a great challenge in the medical fields. Herein, a highly effective and conceptual optical method was developed based on a plasmonic chip platform (made of 2D AuNPs nanomembranes). through effective light-augmented plasmonic regulation of cellular bioenergetics (CBE) and an entropy effect at bionano interfaces, to promote rapid cell differentiation. Compared with traditional methods, the developed optoplasmonic method greatly shortens cell differentiation time from usually more than 10 days to only about 3 days. Upon the optoplasmonic treatment of cells, the conformational and vibration entropy changes of cell membranes were clearly revealed through theoretical simulation and fingerprint spectra of cell membranes. Meanwhile, during the treatment process, bioenergetics levels of cells were elevated with increasing mitochondrial membrane potential (Δψm), which accelerates cell differentiation and proliferation. The developed optoplasmonic method is highly efficient and easy to implement, provides a new perspective and avenue for cell differentiation and proliferation, and has potential application prospects in accelerating tissue repair and regeneration.
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Affiliation(s)
- Guohua Qi
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Chen Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Jiafeng Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,Department of Endodontics, School and Hospital of Stomatology, Jilin University, Changchun 130021, Jilin, P.R. China
| | - Yu Tian
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Bo Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Ying Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Kongshuo Ma
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Xingkang Diao
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Yongdong Jin
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
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26
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Gonçalves AN, Correia-Pinto J, Nogueira-Silva C. Distinct Epithelial Cell Profiles in Normal Versus Induced-Congenital Diaphragmatic Hernia Fetal Lungs. Front Pediatr 2022; 10:836591. [PMID: 35601428 PMCID: PMC9120630 DOI: 10.3389/fped.2022.836591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Recent studies identified a great diversity of cell types in precise number and position to create the architectural features of the lung that ventilation and respiration at birth depend on. With damaged respiratory function at birth, congenital diaphragmatic hernia (CDH) is one of the more severe causes of fetal lung hypoplasia with unspecified cellular dynamics. OBJECTIVES To characterize the epithelial cell tissue in hypoplastic lungs, a careful analysis regarding pulmonary morphology and epithelial cell profile was conducted from pseudoglandular-to-saccular phases in normal versus nitrofen-induced CDH rat lungs. DESIGN Our analysis comprises three experimental groups, control, nitrofen (NF) and CDH, in which the relative expression levels (western blot) by group and developmental stage were analyzed in whole lung. Spatiotemporal distribution (immunohistochemistry) was revealed by pulmonary structure during normal and hypoplastic fetal lung development. Surfactant protein-C (SP-C), calcitonin gene-related peptide (CGRP), clara cell secretory protein (CCSP), and forkhead box J1 (FOXJ1) were the used molecular markers for alveolar epithelial cell type 2 (AEC2), pulmonary neuroendocrine, clara, and ciliated cell profiles, respectively. RESULTS Generally, we identified an aberrant expression of SP-C, CGRP, CCSP, and FOXJ1 in nitrofen-exposed lungs. For instance, the overexpression of FOXJ1 and CGRP in primordia of bronchiole defined the pseudoglandular stage in CDH lungs, whereas the increased expression of CGRP in bronchi; FOXJ1 and CGRP in terminal bronchiole; and SP-C in BADJ classified the canalicular and saccular stages in hypoplastic lungs. We also described higher expression levels in NF than CDH or control groups for both FOXJ1 in bronchi, terminal bronchiole and BADJ at canalicular stage, and SP-C in bronchi and terminal bronchiole at canalicular and saccular stages. Finally, we report an unexpected expression of FOXJ1 in BADJ at canalicular and saccular stages, whereas the multi cilia observed in bronchi were notably absent at embryonic day 21.5 in induced-CDH lungs. CONCLUSION The recognized alterations in the epithelial cell profile contribute to a better understanding of neonatal respiratory insufficiency in induced-CDH lungs and indicate a problem in the epithelial cell differentiation in hypoplastic lungs.
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Affiliation(s)
- Ana N Gonçalves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Guimarães, Portugal
| | - Jorge Correia-Pinto
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Guimarães, Portugal.,Department of Pediatric Surgery, Hospital de Braga, Braga, Portugal
| | - Cristina Nogueira-Silva
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Guimarães, Portugal.,Department of Obstetrics and Gynecology, Hospital de Braga, Braga, Portugal
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27
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Ali M, Ribeiro MM, Del Sol A. Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research. Methods Mol Biol 2022; 2471:83-109. [PMID: 35175592 DOI: 10.1007/978-1-0716-2193-6_4] [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: 06/14/2023]
Abstract
The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.
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Affiliation(s)
- Muhammad Ali
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg
| | - Mariana Messias Ribeiro
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg
| | - Antonio Del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
- CIC bioGUNE, Bizkaia Technology Park, Derio, Spain.
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
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28
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Capdevila C, Trifas M, Miller J, Anderson T, Sims PA, Yan KS. Cellular origins and lineage relationships of the intestinal epithelium. Am J Physiol Gastrointest Liver Physiol 2021; 321:G413-G425. [PMID: 34431400 PMCID: PMC8560372 DOI: 10.1152/ajpgi.00188.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/17/2021] [Accepted: 08/19/2021] [Indexed: 01/31/2023]
Abstract
Knowledge of the development and hierarchical organization of tissues is key to understanding how they are perturbed in injury and disease, as well as how they may be therapeutically manipulated to restore homeostasis. The rapidly regenerating intestinal epithelium harbors diverse cell types and their lineage relationships have been studied using numerous approaches, from classical label-retaining and genetic lineage tracing methods to novel transcriptome-based annotations. Here, we describe the developmental trajectories that dictate differentiation and lineage specification in the intestinal epithelium. We focus on the most recent single-cell RNA-sequencing (scRNA-seq)-based strategies for understanding intestinal epithelial cell lineage relationships, underscoring how they have refined our view of the development of this tissue and highlighting their advantages and limitations. We emphasize how these technologies have been applied to understand the dynamics of intestinal epithelial cells in homeostatic and injury-induced regeneration models.
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Affiliation(s)
- Claudia Capdevila
- Columbia Stem Cell Initiative, Division of Digestive and Liver Diseases, Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, New York
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, New York
| | - Maria Trifas
- Columbia Stem Cell Initiative, Division of Digestive and Liver Diseases, Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, New York
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, New York
| | - Jonathan Miller
- Columbia Stem Cell Initiative, Division of Digestive and Liver Diseases, Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, New York
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, New York
| | - Troy Anderson
- Columbia Stem Cell Initiative, Division of Digestive and Liver Diseases, Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, New York
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, New York
| | - Peter A Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, New York
| | - Kelley S Yan
- Columbia Stem Cell Initiative, Division of Digestive and Liver Diseases, Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, New York
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, New York
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29
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Kannan S, Farid M, Lin BL, Miyamoto M, Kwon C. Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level. PLoS Comput Biol 2021; 17:e1009305. [PMID: 34534204 PMCID: PMC8448341 DOI: 10.1371/journal.pcbi.1009305] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/26/2021] [Indexed: 01/06/2023] Open
Abstract
The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.
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Affiliation(s)
- Suraj Kannan
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
| | - Michael Farid
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
| | - Brian L. Lin
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
| | - Matthew Miyamoto
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
| | - Chulan Kwon
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
- Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, Maryland, United States of America
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30
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Teschendorff AE, Maity AK, Hu X, Weiyan C, Lechner M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data. Bioinformatics 2021; 37:1528-1534. [PMID: 33244588 PMCID: PMC8275983 DOI: 10.1093/bioinformatics/btaa987] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/26/2020] [Accepted: 11/13/2020] [Indexed: 01/16/2023] Open
Abstract
Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,UCL Cancer Institute, University College London, London WC1E 6BT, UK
| | - Alok K Maity
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xue Hu
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Weiyan
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Matthias Lechner
- UCL Cancer Institute, University College London, London WC1E 6BT, UK.,Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Palo Alto, CA 94305-5739, USA
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31
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Jo K, Sung I, Lee D, Jang H, Kim S. Inferring transcriptomic cell states and transitions only from time series transcriptome data. Sci Rep 2021; 11:12566. [PMID: 34131182 PMCID: PMC8206345 DOI: 10.1038/s41598-021-91752-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/31/2021] [Indexed: 02/05/2023] Open
Abstract
Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .
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Affiliation(s)
- Kyuri Jo
- grid.254229.a0000 0000 9611 0917Department of Computer Engineering, Chungbuk National University, Cheongju, 28644 Korea
| | - Inyoung Sung
- grid.31501.360000 0004 0470 5905Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 Korea
| | - Dohoon Lee
- grid.31501.360000 0004 0470 5905Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 Korea
| | - Hyuksoon Jang
- grid.254229.a0000 0000 9611 0917Department of Computer Engineering, Chungbuk National University, Cheongju, 28644 Korea
| | - Sun Kim
- grid.31501.360000 0004 0470 5905Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 Korea ,grid.31501.360000 0004 0470 5905Department of Computer Science and Engineering, Seoul National University, Seoul, 08826 Korea ,grid.31501.360000 0004 0470 5905Institute of Engineering Research, Seoul National University, Seoul, 08826 Korea ,grid.31501.360000 0004 0470 5905Bioinformatics Institute, Seoul National University, Seoul, 08826 Korea
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32
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Li L, Xiong F, Wang Y, Zhang S, Gong Z, Li X, He Y, Shi L, Wang F, Liao Q, Xiang B, Zhou M, Li X, Li Y, Li G, Zeng Z, Xiong W, Guo C. What are the applications of single-cell RNA sequencing in cancer research: a systematic review. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:163. [PMID: 33975628 PMCID: PMC8111731 DOI: 10.1186/s13046-021-01955-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 12/18/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.
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Affiliation(s)
- Lvyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Fang Xiong
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Yumin Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.,Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Shanshan Zhang
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiayu Li
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yi He
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lei Shi
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Bo Xiang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Ming Zhou
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Xiaoling Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Guiyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.
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33
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Basil MC, Katzen J, Engler AE, Guo M, Herriges MJ, Kathiriya JJ, Windmueller R, Ysasi AB, Zacharias WJ, Chapman HA, Kotton DN, Rock JR, Snoeck HW, Vunjak-Novakovic G, Whitsett JA, Morrisey EE. The Cellular and Physiological Basis for Lung Repair and Regeneration: Past, Present, and Future. Cell Stem Cell 2021; 26:482-502. [PMID: 32243808 PMCID: PMC7128675 DOI: 10.1016/j.stem.2020.03.009] [Citation(s) in RCA: 199] [Impact Index Per Article: 66.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The respiratory system, which includes the trachea, airways, and distal alveoli, is a complex multi-cellular organ that intimately links with the cardiovascular system to accomplish gas exchange. In this review and as members of the NIH/NHLBI-supported Progenitor Cell Translational Consortium, we discuss key aspects of lung repair and regeneration. We focus on the cellular compositions within functional niches, cell-cell signaling in homeostatic health, the responses to injury, and new methods to study lung repair and regeneration. We also provide future directions for an improved understanding of the cell biology of the respiratory system, as well as new therapeutic avenues.
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Affiliation(s)
- Maria C Basil
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeremy Katzen
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anna E Engler
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Minzhe Guo
- Division of Pulmonary Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Michael J Herriges
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Jaymin J Kathiriya
- Division of Pulmonary Medicine, Department of Medicine, University of California-San Francisco, San Francisco, CA 94143, USA
| | - Rebecca Windmueller
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra B Ysasi
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - William J Zacharias
- Division of Pulmonary Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Hal A Chapman
- Division of Pulmonary Medicine, Department of Medicine, University of California-San Francisco, San Francisco, CA 94143, USA
| | - Darrell N Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Jason R Rock
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Hans-Willem Snoeck
- Center for Human Development, Department of Medicine, Columbia University, New York, NY 10027, USA
| | - Gordana Vunjak-Novakovic
- Departments of Biomedical Engineering and Medicine, Columbia University, New York, NY 10027, USA
| | - Jeffrey A Whitsett
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Edward E Morrisey
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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34
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Wang S, Lee MP, Jones S, Liu J, Waldhaus J. Mapping the regulatory landscape of auditory hair cells from single-cell multi-omics data. Genome Res 2021; 31:1885-1899. [PMID: 33837132 DOI: 10.1101/gr.271080.120] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/23/2021] [Indexed: 11/25/2022]
Abstract
Auditory hair cells transduce sound to the brain and in mammals these cells reside together with supporting cells in the sensory epithelium of the cochlea, called the organ of Corti. To establish the organ's delicate function during development and differentiation, spatiotemporal gene expression is strictly controlled by chromatin accessibility and cell type-specific transcription factors, jointly representing the regulatory landscape. Bulk-sequencing technology and cellular heterogeneity obscured investigations on the interplay between transcription factors and chromatin accessibility in inner ear development. To study the formation of the regulatory landscape in hair cells, we collected single-cell chromatin accessibility profiles accompanied by single-cell RNA data from genetically labeled murine hair cells and supporting cells after birth. Using an integrative approach, we predicted cell type-specific activating and repressing functions of developmental transcription factors. Furthermore, by integrating gene expression and chromatin accessibility datasets, we reconstructed gene regulatory networks. Then, using a comparative approach, 20 hair cell-specific activators and repressors, including putative downstream target genes, were identified. Clustering of target genes resolved groups of related transcription factors and was utilized to infer their developmental functions. Finally, the heterogeneity in the single-cell data allowed us to spatially reconstruct transcriptional as well as chromatin accessibility trajectories, indicating that gradual changes in the chromatin accessibility landscape were lagging behind the transcriptional identity of hair cells along the organ's longitudinal axis. Overall, this study provides a strategy to spatially reconstruct the formation of a lineage specific regulatory landscape using a single-cell multi-omics approach.
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Affiliation(s)
- Shuze Wang
- University of Michigan, Kresge Hearing Research Institute
| | - Mary P Lee
- University of Michigan, Kresge Hearing Research Institute
| | - Scott Jones
- University of Michigan, Kresge Hearing Research Institute
| | | | - Joerg Waldhaus
- University of Michigan, Kresge Hearing Research Institute;
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35
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Reyna MA, Chitra U, Elyanow R, Raphael BJ. NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks. J Comput Biol 2021; 28:469-484. [PMID: 33400606 DOI: 10.1089/cmb.2020.0435] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.
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Affiliation(s)
- Matthew A Reyna
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
| | - Rebecca Elyanow
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
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36
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Sha Y, Wang S, Zhou P, Nie Q. Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data. Nucleic Acids Res 2020; 48:9505-9520. [PMID: 32870263 PMCID: PMC7515733 DOI: 10.1093/nar/gkaa725] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/19/2020] [Accepted: 08/20/2020] [Indexed: 12/17/2022] Open
Abstract
Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.
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Affiliation(s)
- Yutong Sha
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA.,The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA
| | - Shuxiong Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA.,The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA.,Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA
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37
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Gan Y, Liang S, Wei Q, Zou G. Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy. Front Cell Dev Biol 2020; 8:588041. [PMID: 33195248 PMCID: PMC7649823 DOI: 10.3389/fcell.2020.588041] [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: 07/28/2020] [Accepted: 09/14/2020] [Indexed: 11/13/2022] Open
Abstract
A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Shanshan Liang
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Qingting Wei
- School of Software, Nanchang University, Nanchang, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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38
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Riccetti M, Gokey JJ, Aronow B, Perl AKT. The elephant in the lung: Integrating lineage-tracing, molecular markers, and single cell sequencing data to identify distinct fibroblast populations during lung development and regeneration. Matrix Biol 2020; 91-92:51-74. [PMID: 32442602 PMCID: PMC7434667 DOI: 10.1016/j.matbio.2020.05.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 05/08/2020] [Accepted: 05/08/2020] [Indexed: 12/26/2022]
Abstract
During lung development, the mesenchyme and epithelium are dependent on each other for instructive morphogenic cues that direct proliferation, cellular differentiation and organogenesis. Specification of epithelial and mesenchymal cell lineages occurs in parallel, forming cellular subtypes that guide the formation of both transitional developmental structures and the permanent architecture of the adult lung. While epithelial cell types and lineages have been relatively well-defined in recent years, the definition of mesenchymal cell types and lineage relationships has been more challenging. Transgenic mouse lines with permanent and inducible lineage tracers have been instrumental in identifying lineage relationships among epithelial progenitor cells and their differentiation into distinct airway and alveolar epithelial cells. Lineage tracing experiments with reporter mice used to identify fibroblast progenitors and their lineage trajectories have been limited by the number of cell specific genes and the developmental timepoint when the lineage trace was activated. In this review, we discuss major developmental mesenchymal lineages, focusing on time of origin, major cell type, and other lineage derivatives, as well as the transgenic tools used to find and define them. We describe lung fibroblasts using function, location, and molecular markers in order to compare and contrast cells with similar functions. The temporal and cell-type specific expression of fourteen "fibroblast lineage" genes were identified in single-cell RNA-sequencing data from LungMAP in the LGEA database. Using these lineage signature genes as guides, we clustered murine lung fibroblast populations from embryonic day 16.5 to postnatal day 28 (E16.5-PN28) and generated heatmaps to illustrate expression of transcription factors, signaling receptors and ligands in a temporal and population specific manner.
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Affiliation(s)
- Matthew Riccetti
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Molecular and Developmental Biology Graduate Program, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jason J Gokey
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Bruce Aronow
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, United States
| | - Anne-Karina T Perl
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Molecular and Developmental Biology Graduate Program, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, United States.
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39
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Panina Y, Karagiannis P, Kurtz A, Stacey GN, Fujibuchi W. Human Cell Atlas and cell-type authentication for regenerative medicine. Exp Mol Med 2020; 52:1443-1451. [PMID: 32929224 PMCID: PMC8080834 DOI: 10.1038/s12276-020-0421-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/05/2020] [Accepted: 03/09/2020] [Indexed: 12/22/2022] Open
Abstract
In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.
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Affiliation(s)
- Yulia Panina
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Peter Karagiannis
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Andreas Kurtz
- BIH Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Glyn N Stacey
- International Stem Cell Banking Initiative, 2 High Street, Barley, Herts, SG88HZ, UK
- National Stem Cell Resource Centre, Institute of Zoology, Chinese Academy of Sciences, 100190, Beijing, China
- Innovation Academy for Stem Cell and Regeneration, Chinese Academy of Sciences, 100101, Beijing, China
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
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40
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Thermal Resonance and Cell Behavior. ENTROPY 2020; 22:e22070774. [PMID: 33286546 PMCID: PMC7517324 DOI: 10.3390/e22070774] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/27/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022]
Abstract
From a thermodynamic point of view, living cell life is no more than a cyclic process. It starts with the newly separated daughter cells and restarts when the next generations grow as free entities. During this cycle, the cell changes its entropy. In cancer, the growth control is damaged. In this paper, we analyze the role of the volume–area ratio in the cell in relation to the heat exchange between cell and its environment in order to point out its effect on cancer growth. The result holds to a possible control of the cancer growth based on the heat exchanged by the cancer toward its environment and the membrane potential variation, with the consequence of controlling the ions fluxes and the related biochemical reactions. This second law approach could represent a starting point for a possible future support for the anticancer therapies, in order to improve their effectiveness for the untreatable cancers.
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41
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Salzer E, Zoghi S, Kiss MG, Kage F, Rashkova C, Stahnke S, Haimel M, Platzer R, Caldera M, Ardy RC, Hoeger B, Block J, Medgyesi D, Sin C, Shahkarami S, Kain R, Ziaee V, Hammerl P, Bock C, Menche J, Dupré L, Huppa JB, Sixt M, Lomakin A, Rottner K, Binder CJ, Stradal TEB, Rezaei N, Boztug K. The cytoskeletal regulator HEM1 governs B cell development and prevents autoimmunity. Sci Immunol 2020; 5:5/49/eabc3979. [PMID: 32646852 DOI: 10.1126/sciimmunol.abc3979] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 06/04/2020] [Indexed: 12/12/2022]
Abstract
The WAVE regulatory complex (WRC) is crucial for assembly of the peripheral branched actin network constituting one of the main drivers of eukaryotic cell migration. Here, we uncover an essential role of the hematopoietic-specific WRC component HEM1 for immune cell development. Germline-encoded HEM1 deficiency underlies an inborn error of immunity with systemic autoimmunity, at cellular level marked by WRC destabilization, reduced filamentous actin, and failure to assemble lamellipodia. Hem1-/- mice display systemic autoimmunity, phenocopying the human disease. In the absence of Hem1, B cells become deprived of extracellular stimuli necessary to maintain the strength of B cell receptor signaling at a level permissive for survival of non-autoreactive B cells. This shifts the balance of B cell fate choices toward autoreactive B cells and thus autoimmunity.
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Affiliation(s)
- Elisabeth Salzer
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,St. Anna Children's Hospital, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Samaneh Zoghi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Máté G Kiss
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Frieda Kage
- Division of Molecular Cell Biology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany.,Department of Cell Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Christina Rashkova
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Stephanie Stahnke
- Department of Cell Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - René Platzer
- Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Michael Caldera
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Rico Chandra Ardy
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Birgit Hoeger
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Jana Block
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - David Medgyesi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
| | - Celine Sin
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Sepideh Shahkarami
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Medical Genetics Network (MeGeNe), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Renate Kain
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Vahid Ziaee
- Pediatric Rheumatology Research Group, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Pediatrics, Tehran University of Medical Sciences, Tehran, Iran
| | - Peter Hammerl
- Department of Molecular Biology, University of Salzburg, Salzburg, Austria
| | - Christoph Bock
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Loïc Dupré
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,Center for Pathophysiology of Toulouse Purpan, INSERM UMR1043, CNRS UMR5282, Paul Sabatier University, Toulouse, France
| | - Johannes B Huppa
- Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Michael Sixt
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Alexis Lomakin
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Klemens Rottner
- Division of Molecular Cell Biology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany.,Department of Cell Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Christoph J Binder
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Theresia E B Stradal
- Department of Cell Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Kaan Boztug
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria. .,Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria.,CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,St. Anna Children's Hospital, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
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42
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Bridges JP, Sudha P, Lipps D, Wagner A, Guo M, Du Y, Brown K, Filuta A, Kitzmiller J, Stockman C, Chen X, Weirauch MT, Jobe AH, Whitsett JA, Xu Y. Glucocorticoid regulates mesenchymal cell differentiation required for perinatal lung morphogenesis and function. Am J Physiol Lung Cell Mol Physiol 2020; 319:L239-L255. [PMID: 32460513 DOI: 10.1152/ajplung.00459.2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
While antenatal glucocorticoids are widely used to enhance lung function in preterm infants, cellular and molecular mechanisms by which glucocorticoid receptor (GR) signaling influences lung maturation remain poorly understood. Deletion of the glucocorticoid receptor gene (Nr3c1) from fetal pulmonary mesenchymal cells phenocopied defects caused by global Nr3c1 deletion, while lung epithelial- or endothelial-specific Nr3c1 deletion did not impair lung function at birth. We integrated genome-wide gene expression profiling, ATAC-seq, and single cell RNA-seq data in mice in which GR was deleted or activated to identify the cellular and molecular mechanisms by which glucocorticoids control prenatal lung maturation. GR enhanced differentiation of a newly defined proliferative mesenchymal progenitor cell (PMP) into matrix fibroblasts (MFBs), in part by directly activating extracellular matrix-associated target genes, including Fn1, Col16a4, and Eln and by modulating VEGF, JAK-STAT, and WNT signaling. Loss of mesenchymal GR signaling blocked fibroblast progenitor differentiation into mature MFBs, which in turn increased proliferation of SOX9+ alveolar epithelial progenitor cells and inhibited differentiation of mature alveolar type II (AT2) and AT1 cells. GR signaling controls genes required for differentiation of a subset of proliferative mesenchymal progenitors into matrix fibroblasts, in turn, regulating signals controlling AT2/AT1 progenitor cell proliferation and differentiation and identifying cells and processes by which glucocorticoid signaling regulates fetal lung maturation.
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Affiliation(s)
- James P Bridges
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Parvathi Sudha
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Dakota Lipps
- College of Engineering and Applied Science, University of Cincinnati, Cincinnati, Ohio
| | - Andrew Wagner
- College of Engineering and Applied Science, University of Cincinnati, Cincinnati, Ohio
| | - Minzhe Guo
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Yina Du
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Kari Brown
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Alyssa Filuta
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Joseph Kitzmiller
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Courtney Stockman
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Matthew T Weirauch
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Alan H Jobe
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Jeffrey A Whitsett
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Yan Xu
- Perinatal Institute, Section of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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43
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Seweryn MT, Pietrzak M, Ma Q. Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics. Comput Struct Biotechnol J 2020; 18:1830-1837. [PMID: 32728406 PMCID: PMC7371753 DOI: 10.1016/j.csbj.2020.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/24/2020] [Accepted: 05/06/2020] [Indexed: 02/09/2023] Open
Abstract
Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data.
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Affiliation(s)
- Michal T. Seweryn
- Center for Medical Genomics, Jagiellonian University, Cracow, Poland
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus OH, United States
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus OH, United States
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44
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Zheng X, Jin S, Nie Q, Zou X. scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes. IEEE Trans Biomed Eng 2020; 67:1418-1428. [PMID: 31449003 PMCID: PMC7250043 DOI: 10.1109/tbme.2019.2937228] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliability and stability of estimating latent substructures. To quantify the transition capability of each cell, we propose two new measures: single-cell transition entropy (scEntropy) and transition probability (scTP). When applied to two simulated and three published scRNA-seq datasets, scRCMF not only successfully captures multiple subpopulations and transition processes in large-scale data, but also identifies transition states and some known marker genes associated with cell state transitions and subpopulations. Furthermore, the quantity scEntropy is found to be significantly higher for transition cells than other cellular states during the global differentiation, and the scTP predicts the "fate decisions" of transition cells within the transition. The present study provides new insights into transition events during differentiation and development.
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45
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Yang KD, Damodaran K, Venkatachalapathy S, Soylemezoglu AC, Shivashankar GV, Uhler C. Predicting cell lineages using autoencoders and optimal transport. PLoS Comput Biol 2020; 16:e1007828. [PMID: 32343706 PMCID: PMC7209334 DOI: 10.1371/journal.pcbi.1007828] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 05/08/2020] [Accepted: 03/27/2020] [Indexed: 12/17/2022] Open
Abstract
Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used. Many key biological processes, such as development and disease progression, require analyzing lineages of cells backwards as well as forwards in time. However, current single-cell experiments tend to be destructive to cells, so that a single lineage can only be measured at one point in time. In this work, we introduce a computational framework for predicting the lineage of cells from a single snapshot in time based on measurements of other cells at other time points. The method generates these lineages by computing the most plausible path for a population of cells to transition from one time point to the next, assuming that a cell is more likely to transition to similar cells compared to dissimilar cells. We validate our computational method on imaging data of fibroblasts and cancer cells, though our method could also be applied to other modalities of single-cell data such as genomics and transcriptomics as well as multi-modal single-cell datasets.
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Affiliation(s)
- Karren Dai Yang
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Karthik Damodaran
- Mechanobiology Institute, National University of Singapore, Singapore
| | | | - Ali C. Soylemezoglu
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - G. V. Shivashankar
- Mechanobiology Institute, National University of Singapore, Singapore
- FIRC Institute of Molecular Oncology (IFOM), Milan, Italy
- Department of Health Sciences and Technology, ETH Zurich and Paul Scherrer Institute, Villigen, Switzerland
| | - Caroline Uhler
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- * E-mail:
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46
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Gulati GS, Sikandar SS, Wesche DJ, Manjunath A, Bharadwaj A, Berger MJ, Ilagan F, Kuo AH, Hsieh RW, Cai S, Zabala M, Scheeren FA, Lobo NA, Qian D, Yu FB, Dirbas FM, Clarke MF, Newman AM. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 2020; 367:405-411. [PMID: 31974247 PMCID: PMC7694873 DOI: 10.1126/science.aax0249] [Citation(s) in RCA: 429] [Impact Index Per Article: 107.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 08/03/2019] [Accepted: 12/18/2019] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.
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Affiliation(s)
- Gunsagar S Gulati
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Shaheen S Sikandar
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Daniel J Wesche
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Anoop Manjunath
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Anjan Bharadwaj
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Mark J Berger
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Francisco Ilagan
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Angera H Kuo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Robert W Hsieh
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Shang Cai
- School of Life Sciences, Westlake University, Zhejiang Province, China
| | - Maider Zabala
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Ferenc A Scheeren
- Department of Medical Oncology, Leiden University Medical Center, 2333 ZA Leiden, Netherlands
| | - Neethan A Lobo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Dalong Qian
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
| | - Feiqiao B Yu
- Chan Zuckerberg Biohub, San Francisco, CA 94305, USA
| | - Frederick M Dirbas
- Department of Surgery, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Michael F Clarke
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA.,Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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47
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Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, Chen H, Wang J, Tang H, Ge W, Zhou Y, Ye F, Jiang M, Wu J, Xiao Y, Jia X, Zhang T, Ma X, Zhang Q, Bai X, Lai S, Yu C, Zhu L, Lin R, Gao Y, Wang M, Wu Y, Zhang J, Zhan R, Zhu S, Hu H, Wang C, Chen M, Huang H, Liang T, Chen J, Wang W, Zhang D, Guo G. Construction of a human cell landscape at single-cell level. Nature 2020; 581:303-309. [PMID: 32214235 DOI: 10.1038/s41586-020-2157-4] [Citation(s) in RCA: 521] [Impact Index Per Article: 130.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 03/12/2020] [Indexed: 02/08/2023]
Abstract
Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.
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Affiliation(s)
- Xiaoping Han
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China. .,Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Ziming Zhou
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijiang Fei
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Huiyu Sun
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Renying Wang
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Chen
- Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haide Chen
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, China
| | - Jingjing Wang
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, China
| | - Huanna Tang
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenhao Ge
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yincong Zhou
- College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Fang Ye
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Mengmeng Jiang
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Junqing Wu
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanyu Xiao
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoning Jia
- Center for Neuroscience, Zhejiang University School of Medicine, Hangzhou, China
| | - Tingyue Zhang
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojie Ma
- Life Sciences Institute, Zhejiang University, Hangzhou, China
| | - Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueli Bai
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shujing Lai
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengxuan Yu
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijun Zhu
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Rui Lin
- Hangzhou Repugene Technology, Hangzhou, China
| | - Yuchi Gao
- Annoroad Gene Technology, Beijing, China
| | - Min Wang
- Veritas Genetics Asia, Hangzhou, China
| | - Yiqing Wu
- Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianming Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Renya Zhan
- Department of Neurosurgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Saiyong Zhu
- Life Sciences Institute, Zhejiang University, Hangzhou, China
| | - Hailan Hu
- Center for Neuroscience, Zhejiang University School of Medicine, Hangzhou, China
| | - Changchun Wang
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Ming Chen
- College of Life Sciences, Zhejiang University, Hangzhou, China
| | - He Huang
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Stem Cell Institute, Zhejiang University, Hangzhou, China
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weilin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Zhang
- Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guoji Guo
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China. .,Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. .,Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, China. .,Institute of Hematology, Zhejiang University, Hangzhou, China. .,Stem Cell Institute, Zhejiang University, Hangzhou, China.
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48
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Abstract
Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during de- velopment, regeneration, homeostasis, and disease is a central goal of mod- ern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality re- duction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single- cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.
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Affiliation(s)
- Sagar
- Max Planck Institute of Immunobiology and Epigenetics, D-79108 Freiburg, Germany
| | - Dominic Grün
- Max Planck Institute of Immunobiology and Epigenetics, D-79108 Freiburg, Germany.,CIBSS (Centre for Integrative Biological Signaling Studies), University of Freiburg, D-79104 Freiburg, Germany
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49
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Using single-cell RNA sequencing to unravel cell lineage relationships in the respiratory tract. Biochem Soc Trans 2020; 48:327-336. [DOI: 10.1042/bst20191010] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 01/07/2023]
Abstract
The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, ‘hybrid’ mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.
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50
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Wang S, Karikomi M, MacLean AL, Nie Q. Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res 2019; 47:e66. [PMID: 30923815 PMCID: PMC6582411 DOI: 10.1093/nar/gkz204] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 03/04/2019] [Accepted: 03/27/2019] [Indexed: 12/20/2022] Open
Abstract
The use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell–cell communication, a major remaining challenge. Here, we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell–cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.
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Affiliation(s)
- Shuxiong Wang
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Matthew Karikomi
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Adam L MacLean
- Department of Mathematics, University of California, Irvine, CA 92697, USA.,Department of Biological Sciences, University of Southern California, Irvine, CA 90089, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA 92697, USA.,Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
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