1
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Zhang Y, Yang Y, Ren L, Zhan M, Sun T, Zou Q, Zhang Y. Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb. BMC Biol 2024; 22:152. [PMID: 38978014 PMCID: PMC11232326 DOI: 10.1186/s12915-024-01950-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.
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Affiliation(s)
- Yuncong Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yu Yang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Taoping Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
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2
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Feng J, Goedegebuure SP, Zeng A, Bi Y, Wang T, Payne P, Ding L, DeNardo D, Hawkins W, Fields RC, Li F. sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data. PLoS Comput Biol 2024; 20:e1011785. [PMID: 38181047 PMCID: PMC10796047 DOI: 10.1371/journal.pcbi.1011785] [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: 03/07/2023] [Revised: 01/18/2024] [Accepted: 12/23/2023] [Indexed: 01/07/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.
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Affiliation(s)
- Jiarui Feng
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - S. Peter Goedegebuure
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Amanda Zeng
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Ye Bi
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Ting Wang
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Philip Payne
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Li Ding
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - David DeNardo
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - William Hawkins
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Ryan C. Fields
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Fuhai Li
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
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3
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Massenet-Regad L, Poirot J, Jackson M, Hoffmann C, Amblard E, Onodi F, Bouhidel F, Djouadou M, Ouzaid I, Xylinas E, Medvedovic J, Soumelis V. Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma. iScience 2023; 26:108367. [PMID: 38025776 PMCID: PMC10663819 DOI: 10.1016/j.isci.2023.108367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/25/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.
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Affiliation(s)
- Lucile Massenet-Regad
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
- Université Paris-Saclay, F-91190 Saint Aubin, France
| | - Justine Poirot
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
- Université Paris-Saclay, F-91190 Saint Aubin, France
| | | | - Caroline Hoffmann
- INSERM U932, Department of Surgical Oncology, PSL University, Institut Curie, 75005 Paris, France
- Owkin France, 75010 Paris, France
| | - Elise Amblard
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
- CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, Grenoble Alpes University, 38000 Grenoble, France
| | - Fanny Onodi
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
| | - Fatiha Bouhidel
- Department of Pathology, Saint-Louis Hospital, AP-HP.Nord, Université Paris Cité, 75010 Paris, France
| | - Malika Djouadou
- Department of Urology, Saint-Louis Hospital, AP-HP.Nord, Université Paris Cité, 75010 Paris, France
| | - Idir Ouzaid
- Department of Urology, Bichat-Claude Bernard Hospital, AP-HP.Nord, Université Paris Cité, 75018 Paris, France
| | - Evanguelos Xylinas
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
- Department of Urology, Bichat-Claude Bernard Hospital, AP-HP.Nord, Université Paris Cité, 75018 Paris, France
| | | | - Vassili Soumelis
- Université Paris Cité, INSERM, U976 HIPI, F-75010 Paris, France
- Owkin France, 75010 Paris, France
- Department of Immunology-Histocompatibility, Saint-Louis Hospital, AP-HP.Nord, Université Paris Cité, 75010 Paris, France
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4
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Kfoury YS, Ji F, Jain E, Mazzola M, Schiroli G, Papazian A, Mercier F, Sykes DB, Kiem A, Randolph M, Calvi LM, Abdel-Wahab O, Sadreyev RI, Scadden DT. The bone marrow stroma in human myelodysplastic syndrome reveals alterations that regulate disease progression. Blood Adv 2023; 7:6608-6623. [PMID: 37450380 PMCID: PMC10628805 DOI: 10.1182/bloodadvances.2022008268] [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: 09/06/2022] [Revised: 06/14/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023] Open
Abstract
Myelodysplastic syndromes (MDSs) are a heterogenous group of diseases affecting the hematopoietic stem cell that are curable only by stem cell transplantation. Both hematopoietic cell intrinsic changes and extrinsic signals from the bone marrow (BM) niche seem to ultimately lead to MDS. Animal models of MDS indicate that alterations in specific mesenchymal progenitor subsets in the BM microenvironment can induce or select for abnormal hematopoietic cells. Here, we identify a subset of human BM mesenchymal cells marked by the expression of CD271, CD146, and CD106. This subset of human mesenchymal cells is comparable with mouse mesenchymal cells that, when perturbed, result in an MDS-like syndrome. Its transcriptional analysis identified Osteopontin (SPP1) as the most overexpressed gene. Selective depletion of Spp1 in the microenvironment of the mouse MDS model, Vav-driven Nup98-HoxD13, resulted in an accelerated progression as demonstrated by increased chimerism, higher mutant myeloid cell burden, and a more pronounced anemia when compared with that in wild-type microenvironment controls. These data indicate that molecular perturbations can occur in specific BM mesenchymal subsets of patients with MDS. However, the niche adaptations to dysplastic clones include Spp1 overexpression that can constrain disease fitness and potentially progression. Therefore, niche changes with malignant disease can also serve to protect the host.
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Affiliation(s)
- Youmna S. Kfoury
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Fei Ji
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Esha Jain
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Michael Mazzola
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Giulia Schiroli
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Ani Papazian
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Francois Mercier
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - David B. Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Anna Kiem
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
| | - Mark Randolph
- Division of Plastic and Reconstructive surgery, Massachusetts General Hospital, Boston, MA
| | - Laura M. Calvi
- Department of Medicine, University of Rochester School of Medicine, Rochester, NY
| | - Omar Abdel-Wahab
- Department of Medicine, Leukemia Service, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Medicine, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ruslan I. Sadreyev
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - David T. Scadden
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Stem Cell Institute, Cambridge, MA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA
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5
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Tsuyuzaki K, Ishii M, Nikaido I. Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data. BMC Bioinformatics 2023; 24:420. [PMID: 37936079 PMCID: PMC10631077 DOI: 10.1186/s12859-023-05490-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. RESULTS In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. CONCLUSIONS Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.
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Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo, 102-0076, Japan.
| | - Manabu Ishii
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Department of Functional Genome Informatics, Division of Biological Data Science, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
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6
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Vijayakumar S, Sala R, Kang G, Chen A, Pablo MA, Adebayo AI, Cipriano A, Fowler JL, Gomes DL, Ang LT, Loh KM, Sebastiano V. Monolayer platform to generate and purify primordial germ-like cells in vitro provides insights into human germline specification. Nat Commun 2023; 14:5690. [PMID: 37709760 PMCID: PMC10502105 DOI: 10.1038/s41467-023-41302-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/30/2023] [Indexed: 09/16/2023] Open
Abstract
Generating primordial germ cell-like cells (PGCLCs) from human pluripotent stem cells (hPSCs) advances studies of human reproduction and development of infertility treatments, but often entails complex 3D aggregates. Here we develop a simplified, monolayer method to differentiate hPSCs into PGCs within 3.5 days. We use our simplified differentiation platform and single-cell RNA-sequencing to achieve further insights into PGCLC specification. Transient WNT activation for 12 h followed by WNT inhibition specified PGCLCs; by contrast, sustained WNT induced primitive streak. Thus, somatic cells (primitive streak) and PGCLCs are related-yet distinct-lineages segregated by temporally-dynamic signaling. Pluripotency factors including NANOG are continuously expressed during the transition from pluripotency to posterior epiblast to PGCs, thus bridging pluripotent and germline states. Finally, hPSC-derived PGCLCs can be easily purified by virtue of their CXCR4+PDGFRA-GARP- surface-marker profile and single-cell RNA-sequencing reveals that they harbor transcriptional similarities with fetal PGCs.
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Affiliation(s)
- Sivakamasundari Vijayakumar
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Roberta Sala
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Gugene Kang
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Angela Chen
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Michelle Ann Pablo
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Abidemi Ismail Adebayo
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Andrea Cipriano
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Jonas L Fowler
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Danielle L Gomes
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lay Teng Ang
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Kyle M Loh
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Vittorio Sebastiano
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Obstetrics & Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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7
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Tang Z, Li Z, Hou T, Zhang T, Yang B, Su J, Song Q. SiGra: single-cell spatial elucidation through an image-augmented graph transformer. Nat Commun 2023; 14:5618. [PMID: 37699885 PMCID: PMC10497630 DOI: 10.1038/s41467-023-41437-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tieying Hou
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA.
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA.
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA.
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA.
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8
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Palmiero M, Cantarosso I, di Blasio L, Monica V, Peracino B, Primo L, Puliafito A. Collective directional migration drives the formation of heteroclonal cancer cell clusters. Mol Oncol 2023; 17:1699-1725. [PMID: 36587372 PMCID: PMC10483614 DOI: 10.1002/1878-0261.13369] [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: 08/09/2022] [Revised: 11/11/2022] [Accepted: 12/30/2022] [Indexed: 01/02/2023] Open
Abstract
Metastasisation occurs through the acquisition of invasive and survival capabilities that allow tumour cells to colonise distant sites. While the role of multicellular aggregates in cancer dissemination is acknowledged, the mechanisms that drive the formation of multiclonal cell aggregates are not fully elucidated. Here, we show that cancer cells of different tissue of origins can perform collective directional migration and can actively form heteroclonal aggregates in 3D, through a proliferation-independent mechanism. Coalescence of distant cell clusters is mediated by subcellular actin-rich protrusions and multicellular outgrowths that extend towards neighbouring aggregates. Coherently, perturbation of cytoskeletal dynamics impairs collective migration while myosin II activation is necessary for multicellular movements. We put forward the hypothesis that cluster attraction is mediated by secreted soluble factors. Such a hypothesis is consistent with the abrogation of aggregation by inhibition of PI3K/AKT/mTOR and MEK/ERK, the chemoattracting activity of conditioned culture media and with a wide screening of secreted proteins. Our results present a novel collective migration model and shed light on the mechanisms of formation of heteroclonal aggregates in cancer.
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Affiliation(s)
- Miriam Palmiero
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
| | - Isabel Cantarosso
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
| | - Laura di Blasio
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
| | - Valentina Monica
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
| | - Barbara Peracino
- Department of Clinical and Biological SciencesSan Luigi Hospital, University of TurinOrbassanoItaly
| | - Luca Primo
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
| | - Alberto Puliafito
- Candiolo Cancer Institute, FPO – IRCCSCandioloItaly
- Department of OncologyUniversity of TurinCandioloItaly
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9
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [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: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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10
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Kiryluk K, Sanchez-Rodriguez E, Zhou XJ, Zanoni F, Liu L, Mladkova N, Khan A, Marasa M, Zhang JY, Balderes O, Sanna-Cherchi S, Bomback AS, Canetta PA, Appel GB, Radhakrishnan J, Trimarchi H, Sprangers B, Cattran DC, Reich H, Pei Y, Ravani P, Galesic K, Maixnerova D, Tesar V, Stengel B, Metzger M, Canaud G, Maillard N, Berthoux F, Berthelot L, Pillebout E, Monteiro R, Nelson R, Wyatt RJ, Smoyer W, Mahan J, Samhar AA, Hidalgo G, Quiroga A, Weng P, Sreedharan R, Selewski D, Davis K, Kallash M, Vasylyeva TL, Rheault M, Chishti A, Ranch D, Wenderfer SE, Samsonov D, Claes DJ, Akchurin O, Goumenos D, Stangou M, Nagy J, Kovacs T, Fiaccadori E, Amoroso A, Barlassina C, Cusi D, Del Vecchio L, Battaglia GG, Bodria M, Boer E, Bono L, Boscutti G, Caridi G, Lugani F, Ghiggeri G, Coppo R, Peruzzi L, Esposito V, Esposito C, Feriozzi S, Polci R, Frasca G, Galliani M, Garozzo M, Mitrotti A, Gesualdo L, Granata S, Zaza G, Londrino F, Magistroni R, Pisani I, Magnano A, Marcantoni C, Messa P, Mignani R, Pani A, Ponticelli C, Roccatello D, Salvadori M, Salvi E, Santoro D, Gembillo G, Savoldi S, Spotti D, Zamboli P, Izzi C, Alberici F, Delbarba E, Florczak M, Krata N, Mucha K, Pączek L, Niemczyk S, Moszczuk B, Pańczyk-Tomaszewska M, Mizerska-Wasiak M, Perkowska-Ptasińska A, Bączkowska T, Durlik M, Pawlaczyk K, Sikora P, Zaniew M, Kaminska D, Krajewska M, Kuzmiuk-Glembin I, Heleniak Z, Bullo-Piontecka B, Liberek T, Dębska-Slizien A, Hryszko T, Materna-Kiryluk A, Miklaszewska M, Szczepańska M, Dyga K, Machura E, Siniewicz-Luzeńczyk K, Pawlak-Bratkowska M, Tkaczyk M, Runowski D, Kwella N, Drożdż D, Habura I, Kronenberg F, Prikhodina L, van Heel D, Fontaine B, Cotsapas C, Wijmenga C, Franke A, Annese V, Gregersen PK, Parameswaran S, Weirauch M, Kottyan L, Harley JB, Suzuki H, Narita I, Goto S, Lee H, Kim DK, Kim YS, Park JH, Cho B, Choi M, Van Wijk A, Huerta A, Ars E, Ballarin J, Lundberg S, Vogt B, Mani LY, Caliskan Y, Barratt J, Abeygunaratne T, Kalra PA, Gale DP, Panzer U, Rauen T, Floege J, Schlosser P, Ekici AB, Eckardt KU, Chen N, Xie J, Lifton RP, Loos RJF, Kenny EE, Ionita-Laza I, Köttgen A, Julian BA, Novak J, Scolari F, Zhang H, Gharavi AG. Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy. Nat Genet 2023; 55:1091-1105. [PMID: 37337107 DOI: 10.1038/s41588-023-01422-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/05/2023] [Indexed: 06/21/2023]
Abstract
IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. Here we performed a genome-wide association study of 10,146 kidney-biopsy-diagnosed IgAN cases and 28,751 controls across 17 international cohorts. We defined 30 genome-wide significant risk loci explaining 11% of disease risk. A total of 16 loci were new, including TNFSF4/TNFSF18, REL, CD28, PF4V1, LY86, LYN, ANXA3, TNFSF8/TNFSF15, REEP3, ZMIZ1, OVOL1/RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR. The risk loci were enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. We also observed a positive genetic correlation between IgAN and serum IgA levels. High polygenic score for IgAN was associated with earlier onset of kidney failure. In a comprehensive functional annotation analysis of candidate causal genes, we observed convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.
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Affiliation(s)
- Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA.
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA.
| | - Elena Sanchez-Rodriguez
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Francesca Zanoni
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Lili Liu
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Nikol Mladkova
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Maddalena Marasa
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Jun Y Zhang
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Olivia Balderes
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Simone Sanna-Cherchi
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA
| | - Andrew S Bomback
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Pietro A Canetta
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Gerald B Appel
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Jai Radhakrishnan
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Hernan Trimarchi
- Nephrology Service, Hospital Británico de Buenos Aires, Buenos Aires, Argentina
| | - Ben Sprangers
- Department of Microbiology and Immunology, Laboratory of Molecular Immunology, KU Leuven, Leuven, Belgium
- Division of Nephrology, University Hospitals Leuven, Leuven, Belgium
| | - Daniel C Cattran
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - Heather Reich
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - York Pei
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - Pietro Ravani
- Division of Nephrology, Department of Internal Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | - Dita Maixnerova
- 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Vladimir Tesar
- 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Benedicte Stengel
- Centre for Research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint Quentin University, INSERM Clinical Epidemiology Team, Villejuif, France
| | - Marie Metzger
- Centre for Research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint Quentin University, INSERM Clinical Epidemiology Team, Villejuif, France
| | - Guillaume Canaud
- Université de Paris, Hôpital Necker-Enfants Malades, Paris, France
| | - Nicolas Maillard
- Nephrology, Dialysis, and Renal Transplantation Department, University North Hospital, Saint Etienne, France
| | - Francois Berthoux
- Nephrology, Dialysis, and Renal Transplantation Department, University North Hospital, Saint Etienne, France
| | | | - Evangeline Pillebout
- Center for Research on Inflammation, University of Paris, INSERM and CNRS, Paris, France
| | - Renato Monteiro
- Center for Research on Inflammation, University of Paris, INSERM and CNRS, Paris, France
| | - Raoul Nelson
- Division of Pediatric Nephrology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Robert J Wyatt
- Division of Pediatric Nephrology, University of Tennessee Health Sciences Center, Memphis, TN, USA
- Children's Foundation Research Center, Le Bonheur Children's Hospital, Memphis, TN, USA
| | - William Smoyer
- Division of Pediatric Nephrology, Nationwide Children's Hospital, Columbus, OH, USA
| | - John Mahan
- Division of Pediatric Nephrology, Nationwide Children's Hospital, Columbus, OH, USA
| | - Al-Akash Samhar
- Division of Pediatric Nephrology, Driscoll Children's Hospital, Corpus Christi, TX, USA
| | - Guillermo Hidalgo
- Division of Pediatric Nephrology, Department of Pediatrics, HMH Hackensack University Medical Center, Hackensack, NJ, USA
| | - Alejandro Quiroga
- Division of Pediatric Nephrology, Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Patricia Weng
- Division of Pediatric Nephrology, Mattel Children's Hospital, Los Angeles, CA, USA
| | - Raji Sreedharan
- Division of Pediatric Nephrology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - David Selewski
- Division of Pediatric Nephrology, Mott Children's Hospital, Ann Arbor, MI, USA
| | - Keefe Davis
- Division of Pediatric Nephrology, Department of Pediatrics, The Medical University of South Carolina (MUSC), Charleston, SC, USA
| | - Mahmoud Kallash
- Division of Pediatric Nephrology, SUNY Buffalo, Buffalo, NY, USA
| | - Tetyana L Vasylyeva
- Division of Pediatric Nephrology, Department of Pediatrics, Nationwide Children's Hospital, Columbus, OH, USA
| | - Michelle Rheault
- Division of Pediatric Nephrology, University of Minnesota, Minneapolis, MN, USA
| | - Aftab Chishti
- Division of Pediatric Nephrology, University of Kentucky, Lexington, KY, USA
| | - Daniel Ranch
- Division of Pediatric Nephrology, Department of Pediatrics, University of Kentucky, Lexington, KY, USA
| | - Scott E Wenderfer
- Division of Pediatric Nephrology, Baylor College of Medicine/Texas Children's Hospital, Houston, TX, USA
| | - Dmitry Samsonov
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
| | - Donna J Claes
- Division of Pediatric Nephrology, Department of Pediatrics, New York Medical College, New York City, NY, USA
| | - Oleh Akchurin
- Division of Pediatric Nephrology, Department of Pediatrics, Weill Cornell Medical College, New York City, NY, USA
| | | | - Maria Stangou
- The Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Judit Nagy
- 2nd Department of Internal Medicine, Nephrological and Diabetological Center, University of Pécs, Pécs, Hungary
| | - Tibor Kovacs
- 2nd Department of Internal Medicine, Nephrological and Diabetological Center, University of Pécs, Pécs, Hungary
| | - Enrico Fiaccadori
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Antonio Amoroso
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Cristina Barlassina
- Renal Division, Dipartimento di Medicina, Chirurgia e Odontoiatria, San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | - Daniele Cusi
- Renal Division, Dipartimento di Medicina, Chirurgia e Odontoiatria, San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | | | | | | | - Emanuela Boer
- Division of Nephrology and Dialysis, Gorizia Hospital, Gorizia, Italy
| | - Luisa Bono
- Nephrology and Dialysis, A.R.N.A.S. Civico and Benfratelli, Palermo, Italy
| | - Giuliano Boscutti
- Nephrology, Dialysis and Renal Transplant Unit, S. Maria della Misericordia Hospital, ASUFC, Udine, Italy
| | - Gianluca Caridi
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - Francesca Lugani
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - GianMarco Ghiggeri
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - Rosanna Coppo
- Regina Margherita Children's Hospital, Torino, Italy
| | - Licia Peruzzi
- Regina Margherita Children's Hospital, Torino, Italy
| | | | | | | | | | - Giovanni Frasca
- Division of Nephrology, Dialysis and Renal Transplantation, Riuniti Hospital, Ancona, Italy
| | | | - Maurizio Garozzo
- Unità Operativa di Nefrologia e Dialisi, Ospedale di Acireale, Acireale, Italy
| | - Adele Mitrotti
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Loreto Gesualdo
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Simona Granata
- Renal Unit, Department of Medicine, University of Verona, Verona, Italy
| | - Gianluigi Zaza
- Renal Unit, Department of Medicine, University of Verona, Verona, Italy
| | | | - Riccardo Magistroni
- Department of Surgical, Medical, Dental, Oncologic and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Isabella Pisani
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Andrea Magnano
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | - Piergiorgio Messa
- Nephrology Dialysis and Kidney Transplant Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy
| | - Renzo Mignani
- Azienda Unità Sanitaria Locale Rimini, Rimini, Italy
| | - Antonello Pani
- Department of Nephrology and Dialysis, G. Brotzu Hospital, Cagliari, Italy
| | | | - Dario Roccatello
- Nephrology and Dialysis Unit, G. Bosco Hub Hospital (ERK-net Member) and University of Torino, Torino, Italy
| | - Maurizio Salvadori
- Division of Nephrology and Renal Transplantation, Carreggi Hospital, Florence, Italy
| | - Erica Salvi
- Renal Division, DMCO (Dipartimento di Medicina, Chirurgia e Odontoiatria), San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | - Domenico Santoro
- Unit of Nephrology and Dialysis, AOU G Martino, University of Messina, Messina, Italy
| | - Guido Gembillo
- Unit of Nephrology and Dialysis, AOU G Martino, University of Messina, Messina, Italy
| | - Silvana Savoldi
- Unit of Nephrology and Dialysis, ASL TO4-Consultorio Cirié, Turin, Italy
| | | | | | - Claudia Izzi
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Federico Alberici
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Elisa Delbarba
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Michał Florczak
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Natalia Krata
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Krzysztof Mucha
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | - Leszek Pączek
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | - Stanisław Niemczyk
- Department of Internal Disease, Nephrology and Dialysotherapy, Military Institute of Medicine, Warsaw, Poland
| | - Barbara Moszczuk
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Department of Clinical Immunology, Medical University of Warsaw, Warsaw, Poland
| | | | | | | | - Teresa Bączkowska
- Department of Transplantation Medicine, Nephrology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Magdalena Durlik
- Department of Transplantation Medicine, Nephrology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Krzysztof Pawlaczyk
- Department of Nephrology, Transplantology and Internal Medicine, Poznan Medical University, Poznan, Poland
| | - Przemyslaw Sikora
- Department of Pediatric Nephrology, Medical University of Lublin, Lublin, Poland
| | - Marcin Zaniew
- Department of Pediatrics, University of Zielona Góra, Zielona Góra, Poland
| | - Dorota Kaminska
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Magdalena Krajewska
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Izabella Kuzmiuk-Glembin
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Zbigniew Heleniak
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Barbara Bullo-Piontecka
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Tomasz Liberek
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Alicja Dębska-Slizien
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Tomasz Hryszko
- 2nd Department of Nephrology and Hypertension with Dialysis Unit, Medical University of Bialystok, Bialystok, Poland
| | | | - Monika Miklaszewska
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Maria Szczepańska
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Katarzyna Dyga
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Edyta Machura
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Katarzyna Siniewicz-Luzeńczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Monika Pawlak-Bratkowska
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Marcin Tkaczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Dariusz Runowski
- Department of Nephrology, Kidney Transplantation and Hypertension, Children's Memorial Health Institute, Warsaw, Poland
| | - Norbert Kwella
- Department of Nephrology, Hypertension and Internal Medicine, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Dorota Drożdż
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Ireneusz Habura
- Department of Nephrology, Karol Marcinkowski Hospital, Zielona Góra, Poland
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Larisa Prikhodina
- Division of Inherited and Acquired Kidney Diseases, Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Moscow, Russia
| | - David van Heel
- Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Bertrand Fontaine
- Sorbonne University, INSERM, Center of Research in Myology, Institute of Myology, University Hospital Pitie-Salpetriere, Paris, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Service of Neuro-Myology, University Hospital Pitie-Salpetriere, Paris, France
| | - Chris Cotsapas
- Departments of Neurology and Genetics, Yale University, New Haven, CT, USA
| | | | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Vito Annese
- CBP American Hospital, Dubai, United Arab Emirates
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institutes for Medical Research, North Shore LIJ Health System, New York City, NY, USA
| | | | - Matthew Weirauch
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Leah Kottyan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - John B Harley
- US Department of Veterans Affairs Medical Center and Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, USA
| | - Hitoshi Suzuki
- Department of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Ichiei Narita
- Division of Clinical Nephrology and Rheumatology, Kidney Research Center, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shin Goto
- Division of Clinical Nephrology and Rheumatology, Kidney Research Center, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hajeong Lee
- Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Ki Kim
- Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yon Su Kim
- Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jin-Ho Park
- Department of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - BeLong Cho
- Department of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- Institute on Aging, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Murim Choi
- Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ans Van Wijk
- Amsterdam University Medical Centre, VU University Medical Center (VUMC), Amsterdam, the Netherlands
| | - Ana Huerta
- Hospital Universitario Puerta del Hierro Majadahonda, REDINREN, IISCIII, Madrid, Spain
| | - Elisabet Ars
- Molecular Biology Laboratory and Nephrology Department, Fundació Puigvert, Instituto de Investigaciones Biomédicas Sant Pau, Universitat Autònoma de Barcelona, REDINREN, IISCIII, Barcelona, Spain
| | - Jose Ballarin
- Molecular Biology Laboratory and Nephrology Department, Fundació Puigvert, Instituto de Investigaciones Biomédicas Sant Pau, Universitat Autònoma de Barcelona, REDINREN, IISCIII, Barcelona, Spain
| | - Sigrid Lundberg
- Department of Nephrology, Danderyd University Hospital, and Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Bruno Vogt
- Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laila-Yasmin Mani
- Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Yasar Caliskan
- Division of Nephrology, Saint Louis University, Saint Louis, MO, USA
| | - Jonathan Barratt
- John Walls Renal Unit, University Hospitals of Leicester, Leicester, UK
| | | | | | - Daniel P Gale
- Department of Renal Medicine, University College London, London, UK
| | | | - Thomas Rauen
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Jürgen Floege
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nan Chen
- Department of Nephrology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jingyuan Xie
- Department of Nephrology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Richard P Lifton
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York City, NY, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Eimear E Kenny
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, NY, USA
- Center for Population Genomic Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Bruce A Julian
- Departments of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jan Novak
- Departments of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Francesco Scolari
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Ali G Gharavi
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA.
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA.
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11
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Jain S, Rick JW, Joshi RS, Beniwal A, Spatz J, Gill S, Chang ACC, Choudhary N, Nguyen AT, Sudhir S, Chalif EJ, Chen JS, Chandra A, Haddad AF, Wadhwa H, Shah SS, Choi S, Hayes JL, Wang L, Yagnik G, Costello JF, Diaz A, Heiland DH, Aghi MK. Single-cell RNA sequencing and spatial transcriptomics reveal cancer-associated fibroblasts in glioblastoma with protumoral effects. J Clin Invest 2023; 133:e147087. [PMID: 36856115 PMCID: PMC9974099 DOI: 10.1172/jci147087] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 01/12/2023] [Indexed: 03/02/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) were presumed absent in glioblastoma given the lack of brain fibroblasts. Serial trypsinization of glioblastoma specimens yielded cells with CAF morphology and single-cell transcriptomic profiles based on their lack of copy number variations (CNVs) and elevated individual cell CAF probability scores derived from the expression of 9 CAF markers and absence of 5 markers from non-CAF stromal cells sharing features with CAFs. Cells without CNVs and with high CAF probability scores were identified in single-cell RNA-Seq of 12 patient glioblastomas. Pseudotime reconstruction revealed that immature CAFs evolved into subtypes, with mature CAFs expressing actin alpha 2, smooth muscle (ACTA2). Spatial transcriptomics from 16 patient glioblastomas confirmed CAF proximity to mesenchymal glioblastoma stem cells (GSCs), endothelial cells, and M2 macrophages. CAFs were chemotactically attracted to GSCs, and CAFs enriched GSCs. We created a resource of inferred crosstalk by mapping expression of receptors to their cognate ligands, identifying PDGF and TGF-β as mediators of GSC effects on CAFs and osteopontin and HGF as mediators of CAF-induced GSC enrichment. CAFs induced M2 macrophage polarization by producing the extra domain A (EDA) fibronectin variant that binds macrophage TLR4. Supplementing GSC-derived xenografts with CAFs enhanced in vivo tumor growth. These findings are among the first to identify glioblastoma CAFs and their GSC interactions, making them an intriguing target.
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Affiliation(s)
- Saket Jain
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Jonathan W. Rick
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | | | - Angad Beniwal
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Jordan Spatz
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Sabraj Gill
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | | | - Nikita Choudhary
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Alan T. Nguyen
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Sweta Sudhir
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Eric J. Chalif
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Jia-Shu Chen
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Ankush Chandra
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | | | - Harsh Wadhwa
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Sumedh S. Shah
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Serah Choi
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Josie L. Hayes
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Lin Wang
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | - Garima Yagnik
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | | | - Aaron Diaz
- Department of Neurosurgery, UCSF, San Francisco, California, USA
| | | | - Manish K. Aghi
- Department of Neurosurgery, UCSF, San Francisco, California, USA
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12
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Tang Z, Zhang T, Yang B, Su J, Song Q. spaCI: deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 2023; 24:bbac563. [PMID: 36545790 PMCID: PMC9851335 DOI: 10.1093/bib/bbac563] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/26/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Atrium Health Wake Forest Baptist, Winston Salem, NC, USA
- Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA
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13
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Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues. Nat Commun 2022; 13:7389. [PMID: 36450731 PMCID: PMC9712610 DOI: 10.1038/s41467-022-35095-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Macrophages are involved in tissue homeostasis and are critical for innate immune responses, yet distinct macrophage populations in different tissues exhibit diverse gene expression patterns and biological processes. While tissue-specific macrophage epigenomic and transcriptomic profiles have been reported, proteomes of different macrophage populations remain poorly characterized. Here we use mass spectrometry and bulk RNA sequencing to assess the proteomic and transcriptomic patterns, respectively, of 10 primary macrophage populations from seven mouse tissues, bone marrow-derived macrophages and the cell line RAW264.7. The results show distinct proteomic landscape and protein copy numbers between tissue-resident and recruited macrophages. Construction of a hierarchical regulatory network finds cell-type-specific transcription factors of macrophages serving as hubs for denoting tissue and functional identity of individual macrophage subsets. Finally, Il18 is validated to be essential in distinguishing molecular signatures and cellular function features between tissue-resident and recruited macrophages in the lung and liver. In summary, these deposited datasets and our open proteome server ( http://macrophage.mouseprotein.cn ) integrating all information will provide a valuable resource for future functional and mechanistic studies of mouse macrophages.
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14
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Armingol E, Ghaddar A, Joshi CJ, Baghdassarian H, Shamie I, Chan J, Her HL, Berhanu S, Dar A, Rodriguez-Armstrong F, Yang O, O’Rourke EJ, Lewis NE. Inferring a spatial code of cell-cell interactions across a whole animal body. PLoS Comput Biol 2022; 18:e1010715. [PMID: 36395331 PMCID: PMC9714814 DOI: 10.1371/journal.pcbi.1010715] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 12/01/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022] Open
Abstract
Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.
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Affiliation(s)
- Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Abbas Ghaddar
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Chintan J. Joshi
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Hratch Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Isaac Shamie
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Jason Chan
- Poway High School, Poway, California, United States of America
| | - Hsuan-Lin Her
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Samuel Berhanu
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Anushka Dar
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | | | - Olivia Yang
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Eyleen J. O’Rourke
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Cell Biology, School of Medicine of University of Virginia, Charlottesville, Virginia, United States of America
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
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15
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Bridges K, Miller-Jensen K. Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment. Front Immunol 2022; 13:885267. [PMID: 35572582 PMCID: PMC9096838 DOI: 10.3389/fimmu.2022.885267] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/25/2022] [Indexed: 01/25/2023] Open
Abstract
Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.
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Affiliation(s)
- Kate Bridges
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States
- Systems Biology Institute, Yale University, New Haven, CT, United States
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16
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The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets. NATURE CANCER 2021; 2:1406-1422. [PMID: 35121907 PMCID: PMC9940627 DOI: 10.1038/s43018-021-00282-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 10/01/2021] [Indexed: 01/01/2023]
Abstract
Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor-ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor-ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas ( http://fcgportal.org/TCSA ) within the Functional Cancer Genome data portal.
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17
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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18
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Landry AP, Samuel N, Spears J, Zador Z. Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma. Sci Rep 2021; 11:20694. [PMID: 34667228 PMCID: PMC8526813 DOI: 10.1038/s41598-021-00244-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 10/07/2021] [Indexed: 12/16/2022] Open
Abstract
Medulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from "stroma-active" to "stroma-suppressed" cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.
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Affiliation(s)
- Alexander P Landry
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
| | - Nardin Samuel
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Julian Spears
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Zsolt Zador
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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19
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Lu M, Sha Y, Silva TC, Colaprico A, Sun X, Ban Y, Wang L, Lehmann BD, Chen XS. LR Hunting: A Random Forest Based Cell-Cell Interaction Discovery Method for Single-Cell Gene Expression Data. Front Genet 2021; 12:708835. [PMID: 34497635 PMCID: PMC8420858 DOI: 10.3389/fgene.2021.708835] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/14/2021] [Indexed: 12/26/2022] Open
Abstract
Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.
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Affiliation(s)
- Min Lu
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Yifan Sha
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Tiago C Silva
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Antonio Colaprico
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Xiaodian Sun
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Yuguang Ban
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Lily Wang
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States.,Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, United States.,John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Brian D Lehmann
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, United States
| | - X Steven Chen
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
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20
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Ma F, Zhang S, Song L, Wang B, Wei L, Zhang F. Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level. Cell Biosci 2021; 11:121. [PMID: 34217372 PMCID: PMC8254218 DOI: 10.1186/s13578-021-00635-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/22/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases. MAIN BODY This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out. CONCLUSION The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.
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Affiliation(s)
- Fen Ma
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
| | - Siwei Zhang
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
| | - Lianhao Song
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
| | - Bozhi Wang
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
| | - Lanlan Wei
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
- Shenzhen Third People‘s Hospital, Second Hospital, Affiliated to Southern University of Science and Technology, Shenzhen, 518112 China
| | - Fengmin Zhang
- Department of Microbiology, Harbin Medical University, Harbin, 150081 China
- Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081 China
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21
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Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 2021; 22:71-88. [PMID: 33168968 PMCID: PMC7649713 DOI: 10.1038/s41576-020-00292-x] [Citation(s) in RCA: 488] [Impact Index Per Article: 162.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.
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Affiliation(s)
- Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Harismendy
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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22
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Zhang Y, Liu T, Wang J, Zou B, Li L, Yao L, Chen K, Ning L, Wu B, Zhao X, Wang D. Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis. Bioinformatics 2021; 37:btab036. [PMID: 33471060 PMCID: PMC7929259 DOI: 10.1093/bioinformatics/btab036] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/10/2020] [Accepted: 01/14/2020] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value. RESULTS In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. AVAILABILITY Cellinker is available at http://www.rna-society.org/cellinker/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yang Zhang
- Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan 528308, China
| | - Tianyuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jing Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Bohao Zou
- Department of Statistics, University of California Davis, Davis, CA 95616, USA
| | - Le Li
- Department of Pathology, Harbin Medical University, Harbin 150081, China
| | - Linhui Yao
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Kechen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Bingyi Wu
- Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan 528308, China
| | - Xiaoyang Zhao
- Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan 528308, China
- State Key Laboratory of Organ Failure Research, Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan 528308, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
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23
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Liu Q, Zaba LC, Satpathy AT, Longmire M, Zhang W, Li K, Granja J, Guo C, Lin J, Li R, Tolentino K, Kania G, Distler O, Fiorentino D, Chung L, Qu K, Chang HY. Chromatin accessibility landscapes of skin cells in systemic sclerosis nominate dendritic cells in disease pathogenesis. Nat Commun 2020; 11:5843. [PMID: 33203843 PMCID: PMC7672105 DOI: 10.1038/s41467-020-19702-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 10/27/2020] [Indexed: 12/23/2022] Open
Abstract
Systemic sclerosis (SSc) is a disease at the intersection of autoimmunity and fibrosis. However, the epigenetic regulation and the contributions of diverse cell types to SSc remain unclear. Here we survey, using ATAC-seq, the active DNA regulatory elements of eight types of primary cells in normal skin from healthy controls, as well as clinically affected and unaffected skin from SSc patients. We find that accessible DNA elements in skin-resident dendritic cells (DCs) exhibit the highest enrichment of SSc-associated single-nucleotide polymorphisms (SNPs) and predict the degrees of skin fibrosis in patients. DCs also have the greatest disease-associated changes in chromatin accessibility and the strongest alteration of cell-cell interactions in SSc lesions. Lastly, data from an independent cohort of patients with SSc confirm a significant increase of DCs in lesioned skin. Thus, the DCs epigenome links inherited susceptibility and clinically apparent fibrosis in SSc skin, and can be an important driver of SSc pathogenesis.
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Affiliation(s)
- Qian Liu
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China
| | - Lisa C Zaba
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ansuman T Satpathy
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
| | - Michelle Longmire
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Wen Zhang
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China
| | - Kun Li
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China
| | - Jeffrey Granja
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Chuang Guo
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China
| | - Jun Lin
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China
| | - Rui Li
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Karen Tolentino
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Gabriela Kania
- Department of Rheumatology, University Hospital Zurich, Zurich, Switzerland
| | - Oliver Distler
- Department of Rheumatology, University Hospital Zurich, Zurich, Switzerland
| | - David Fiorentino
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lorinda Chung
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Division of Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Kun Qu
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, China.
- CAS Center for Excellence in Molecular Cell Sciences, University of Science and Technology of China, Hefei, 230027, China.
- School of Data Sciences, University of Science and Technology of China, Hefei, 230027, China.
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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24
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Somekh J. A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose. Sci Rep 2020; 10:19535. [PMID: 33177567 PMCID: PMC7659321 DOI: 10.1038/s41598-020-73214-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/11/2020] [Indexed: 01/18/2023] Open
Abstract
The human biological system uses 'inter-organ' communication to achieve a state of homeostasis. This communication occurs through the response of receptors, located on target organs, to the binding of secreted ligands from source organs. Albeit years of research, the roles these receptors play in tissues is only partially understood. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression. These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. To facilitate supervised learning, a list of known metabolic and non-metabolic receptors was constructed using a semi-supervised approach following literature-based verification. Our approach confirms that pathway enrichment scores are good signatures for correctly classifying the metabolic receptors in adipose. We also show that the k-NN method outperforms the SVM method in classifying metabolic receptors. Finally, we predict novel metabolic roles of receptors. These predictions can enhance biological understanding and the development of new receptor-targeting metabolic drugs.
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Affiliation(s)
- Judith Somekh
- Department of Information Systems, University of Haifa, Haifa, Israel.
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25
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Shao X, Liao J, Li C, Lu X, Cheng J, Fan X. CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice. Brief Bioinform 2020; 22:5955941. [PMID: 33147626 DOI: 10.1093/bib/bbaa269] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/02/2020] [Accepted: 09/17/2020] [Indexed: 12/13/2022] Open
Abstract
Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.
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Affiliation(s)
- Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, China
| | - Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, China
| | - Junyun Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences at Zhejiang University, China
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26
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Reading the heart at single-cell resolution. J Mol Cell Cardiol 2020; 148:34-45. [PMID: 32871159 DOI: 10.1016/j.yjmcc.2020.08.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/04/2020] [Accepted: 08/20/2020] [Indexed: 12/21/2022]
Abstract
The burgeoning field of single-cell transcriptomics augments our ability to scrutinize organ systems at unprecedented resolutions. Single-cell RNA sequencing (scRNA-seq) and analytical techniques have shed light on the cellular heterogeneity, developmental trajectories, intercellular communications of the cardiac system, and thus contributed much to the understanding of cardiac development, homeostasis and disorders. Although generalized protocols are well established for scRNA-seq pipelines, customized sample preparation, quality control, and data interpretation are still needed in cardiac research. In this article, we highlight major steps that impact data quality in scRNA-seq experiments, with particular focus on sample and data processing of cardiomyocytes. We also summarize popular applications of scRNA-seq, outlining general tools, caveats and examples in cardiac research.
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27
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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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28
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Wei K, Korsunsky I, Marshall JL, Gao A, Watts GFM, Major T, Croft AP, Watts J, Blazar PE, Lange JK, Thornhill TS, Filer A, Raza K, Donlin LT, Siebel CW, Buckley CD, Raychaudhuri S, Brenner MB. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 2020; 582:259-264. [PMID: 32499639 PMCID: PMC7841716 DOI: 10.1038/s41586-020-2222-z] [Citation(s) in RCA: 258] [Impact Index Per Article: 64.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 02/26/2020] [Indexed: 12/26/2022]
Abstract
The synovium is a mesenchymal tissue composed mainly of fibroblasts with a lining and sublining that surrounds the joints. In rheumatoid arthritis (RA), the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive and destroys the joint1,2. Recently, we and others found that a subset of fibroblasts located in the sublining undergoes major expansion in RA and is linked to disease activity3,4,5. However, the molecular mechanism by which these fibroblasts differentiate and expand in RA remains unknown. Here, we identified a critical role for NOTCH3 signaling in the differentiation of perivascular and sublining CD90(THY1)+ fibroblasts. Using single cell RNA-sequencing and synovial tissue organoids, we found that NOTCH3 signaling drives both transcriptional and spatial gradients in fibroblasts emanating from vascular endothelial cells outward. In active RA, NOTCH3 and NOTCH target genes are markedly upregulated in synovial fibroblasts. Importantly, genetic deletion of Notch3 or monoclonal antibody-blockade of NOTCH3 signaling attenuates inflammation and prevents joint damage in inflammatory arthritis. Our results indicate that synovial fibroblasts exhibit positional identity regulated by endothelium-derived Notch signaling and that this stromal crosstalk pathway underlies inflammation and pathology in inflammatory arthritis.
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Affiliation(s)
- Kevin Wei
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ilya Korsunsky
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jennifer L Marshall
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Anqi Gao
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Triin Major
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Adam P Croft
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Jordan Watts
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip E Blazar
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Jeffrey K Lange
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Thomas S Thornhill
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew Filer
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Karim Raza
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Laura T Donlin
- Arthritis and Tissue Degeneration, Hospital for Special Surgery, New York, NY, USA
| | | | - Christian W Siebel
- Department of Discovery Oncology, Genentech, South San Francisco, CA, USA
| | - Christopher D Buckley
- Rheumatology Research Group, Institute for Inflammation and Ageing, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK.,The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. .,Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA. .,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. .,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
| | - Michael B Brenner
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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29
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Yeung TL, Sheng J, Leung CS, Li F, Kim J, Ho SY, Matzuk MM, Lu KH, Wong STC, Mok SC. Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer. J Natl Cancer Inst 2020; 111:272-282. [PMID: 29860390 PMCID: PMC6410941 DOI: 10.1093/jnci/djy097] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 04/04/2018] [Accepted: 04/27/2018] [Indexed: 12/28/2022] Open
Abstract
Background Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma–cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. Methods Transcriptome profiles from microdissected ovarian cancer–associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor–bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration–approved agent that suppresses the Smad signaling cascade, or vehicle control (9–11 mice per group). All statistical tests were two-sided. Results Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer–bearing mice from 36 to 48 weeks (P = .04). Conclusions Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer–stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.
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Affiliation(s)
- Tsz-Lun Yeung
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jianting Sheng
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Weill Cornell Medicine, Houston, TX.,Center for Modeling Cancer Development, Houston Methodist Cancer Center, Houston, TX
| | - Cecilia S Leung
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Fuhai Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH
| | - Jaeyeon Kim
- Department of Biochemistry and Molecular Biology, Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, IN
| | - Samuel Y Ho
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Martin M Matzuk
- Department of Pathology and Immunology and Center for Drug Discovery, Baylor College of Medicine, Houston, TX
| | - Karen H Lu
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Weill Cornell Medicine, Houston, TX.,Center for Modeling Cancer Development, Houston Methodist Cancer Center, Houston, TX
| | - Samuel C Mok
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.,The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX
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30
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Prospective isolation of nonhematopoietic cells of the niche and their differential molecular interactions with HSCs. Blood 2020; 134:1214-1226. [PMID: 31366622 DOI: 10.1182/blood.2019000176] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 07/15/2019] [Indexed: 12/14/2022] Open
Abstract
A major limitation preventing in vivo modulation of hematopoietic stem cells (HSCs) is the incomplete understanding of the cellular and molecular support of the microenvironment in regulating HSC fate decisions. Consequently, murine HSCs cannot be generated, maintained, or expanded in culture over extended periods of time. A significantly improved understanding of the bone marrow niche environment and its molecular interactions with HSCs is pivotal to overcoming this challenge. We here prospectively isolated all major nonhematopoietic cellular niche components and cross-correlate them in detail with niche cells defined by lineage marking or tracing. Compiling an extensive database of soluble and membrane-bound ligand-receptor interactions, we developed a computational method to infer potential cell-to-cell interactions based on transcriptome data of sorter-purified niche cells and hematopoietic stem and progenitor cell subpopulations. Thus, we establish a compendium of the molecular communication between defined niche components and HSCs. Our analysis suggests an important role for cytokine antagonists in the regulation of HSC functions.
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31
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Quorum Regulation via Nested Antagonistic Feedback Circuits Mediated by the Receptors CD28 and CTLA-4 Confers Robustness to T Cell Population Dynamics. Immunity 2020; 52:313-327.e7. [DOI: 10.1016/j.immuni.2020.01.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 09/22/2019] [Accepted: 01/24/2020] [Indexed: 01/03/2023]
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32
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Yu G, Yin C, Wu F, Jiang L, Zheng Z, Xu D, Zhou J, Jiang X, Liu Q, Meng F. Gene mutation profile and risk stratification in AML1‑ETO‑positive acute myeloid leukemia based on next‑generation sequencing. Oncol Rep 2019; 42:2333-2344. [PMID: 31638252 PMCID: PMC6826310 DOI: 10.3892/or.2019.7375] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 09/09/2019] [Indexed: 12/31/2022] Open
Abstract
Gene mutations play an important role in the development and progression of AML1-ETO-positive acute myeloid leukemia (AE-AML). Nevertheless, the gene mutation profile in this subtype of leukemia remains unclear. In addition, the clinical and prognostic effects of different mutant genes may be underestimated. In the present study, gene sequencing was conducted at diagnosis and relapse with next-generation sequencing (NGS) in 64 patients with newly diagnosed AE-AML, and 44/64 (68.8%) patients were found to present with a median of 2 (1–10) recurrent mutations at diagnosis and 6/11 (54.5%) cases were found to present with genetic alterations at relapse. c-KIT mutation was the most common in this cohort, with an incidence of 27/64 (42.2%) at diagnosis, followed by ASXL1 (n=10, 15.6%), MET (n=8, 12.5%), MLH1 (n=6, 9.4%), TET2 (n=5, 7.8%), and FBXW7, TP53 and DNMT3A (n=5, 7.8%). Survival analysis showed that c-KIT (exon 8, 17) but not exon 10 adversely affected survival. In addition, ASXL1 and TP53 were poor impact factors for recurrence-free survival (RFS) (P<0.05), and ASXL1, MET, FBXW7 and TP53 had a negative impact on overall survival (OS) (P<0.05). Multivariate analysis showed that c-KIT (exon 8, 17) [RFS: hazard ratio (HR) 3.36, 95% confidence interval (CI) 1.54–7.34, P=0.002; OS: HR 2.84, 95% CI 1.20–6.71, P=0.018] and ASXL1 mutations (RFS: HR 3.13, 95% CI 1.34–7.32, P=0.009; OS: HR 3.94, 95% CI 1.62–9.61, P=0.003) were independent adverse factors for survival. Further, co-mutation of these two genes showed even worse effect on disease outcome. Collectively, additional gene mutations play critical role in AE-AML. C-KIT and ASXL1 mutations are the two most common mutations in this subtype of leukemia. C-KIT (exon 8, 17) but not exon 10, and also the ASXL1 mutation poorly affect the disease outcome of this disease.
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Affiliation(s)
- Guopan Yu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Changxin Yin
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Fuqun Wu
- Hematopathy Diagnosis and Therapy Center, Kanghua Hospital, Dongguan, Guangdong 523000, P.R. China
| | - Ling Jiang
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Zhongxin Zheng
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Dan Xu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Jiaheng Zhou
- Department of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Xuejie Jiang
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Qifa Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
| | - Fanyi Meng
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China
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33
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Johnson KE, Howard G, Mo W, Strasser MK, Lima EABF, Huang S, Brock A. Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect. PLoS Biol 2019; 17:e3000399. [PMID: 31381560 PMCID: PMC6695196 DOI: 10.1371/journal.pbio.3000399] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/15/2019] [Accepted: 07/08/2019] [Indexed: 12/30/2022] Open
Abstract
Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.
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Affiliation(s)
- Kaitlyn E. Johnson
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Grant Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - William Mo
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Michael K. Strasser
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Ernesto A. B. F. Lima
- Institute for Computation Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Sui Huang
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Oncology, Livestrong Cancer Institute, Dell Medical School, The University of Texas at Austin, Austin, Texas, United States of America
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34
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Halpern KB, Shenhav R, Massalha H, Toth B, Egozi A, Massasa EE, Medgalia C, David E, Giladi A, Moor AE, Porat Z, Amit I, Itzkovitz S. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat Biotechnol 2018; 36:962-970. [PMID: 30222169 PMCID: PMC6546596 DOI: 10.1038/nbt.4231] [Citation(s) in RCA: 201] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 07/19/2018] [Indexed: 02/08/2023]
Abstract
Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.
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Affiliation(s)
- Keren Bahar Halpern
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rom Shenhav
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Hassan Massalha
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Beata Toth
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Adi Egozi
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Efi E. Massasa
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Chiara Medgalia
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Eyal David
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Amir Giladi
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Andreas E. Moor
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Porat
- The Flow Cytometry Unit, Life Sciences Faculty, Weizmann Institute of Science, Rehovot, Israel
| | - Ido Amit
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
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35
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Mikheev AM, Mikheeva SA, Severs LJ, Funk CC, Huang L, McFaline-Figueroa JL, Schwensen J, Trapnell C, Price ND, Wong S, Rostomily RC. Targeting TWIST1 through loss of function inhibits tumorigenicity of human glioblastoma. Mol Oncol 2018; 12:1188-1202. [PMID: 29754406 PMCID: PMC6026950 DOI: 10.1002/1878-0261.12320] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 04/16/2018] [Accepted: 04/25/2018] [Indexed: 12/30/2022] Open
Abstract
TWIST1 (TW) is a bHLH transcription factor (TF) and master regulator of the epithelial-to-mesenchymal transition (EMT). In vitro, TW promotes mesenchymal change, invasion, and self-renewal in glioblastoma (GBM) cells. However, the potential therapeutic relevance of TW has not been established through loss-of-function studies in human GBM cell xenograft models. The effects of TW loss of function (gene editing and knockdown) on inhibition of tumorigenicity of U87MG and GBM4 glioma stem cells were tested in orthotopic xenograft models and conditional knockdown in established flank xenograft tumors. RNAseq and the analysis of tumors investigated putative TW-associated mechanisms. Multiple bioinformatic tools revealed significant alteration of ECM, membrane receptors, signaling transduction kinases, and cytoskeleton dynamics leading to identification of PI3K/AKT signaling. We experimentally show alteration of AKT activity and periostin (POSTN) expression in vivo and/or in vitro. For the first time, we show that effect of TW knockout inhibits AKT activity in U87MG cells in vivo independent of PTEN mutation. The clinical relevance of TW and candidate mechanisms was established by analysis of the TCGA and ENCODE databases. TW expression was associated with decreased patient survival and LASSO regression analysis identified POSTN as one of top targets of TW in human GBM. While we previously demonstrated the role of TW in promoting EMT and invasion of glioma cells, these studies provide direct experimental evidence supporting protumorigenic role of TW independent of invasion in vivo and the therapeutic relevance of targeting TW in human GBM. Further, the role of TW driving POSTN expression and AKT signaling suggests actionable targets, which could be leveraged to mitigate the oncogenic effects of TW in GBM.
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Affiliation(s)
- Andrei M Mikheev
- Department of Neurosurgery, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Department of Neurosurgery and Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Svetlana A Mikheeva
- Department of Neurosurgery, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Department of Neurosurgery and Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Liza J Severs
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA, USA
| | - Lei Huang
- Department of Systems Medicine& Bioengineering, Houston Methodist Hospital and Research Institute, Weil Cornell Medical College, Houston, TX, USA
| | | | - Jeanette Schwensen
- Department of Neurosurgery and Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Stephen Wong
- Department of Systems Medicine& Bioengineering, Houston Methodist Hospital and Research Institute, Weil Cornell Medical College, Houston, TX, USA
| | - Robert C Rostomily
- Department of Neurosurgery, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Department of Neurosurgery and Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
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36
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Zhou JX, Taramelli R, Pedrini E, Knijnenburg T, Huang S. Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes. Sci Rep 2017; 7:8815. [PMID: 28821810 PMCID: PMC5562796 DOI: 10.1038/s41598-017-09307-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 07/25/2017] [Indexed: 12/18/2022] Open
Abstract
Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of the cell community which forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on intercellular communication between cancer cells themselves and between cancer-stromal cells by secreted signaling molecules (ligands) and their cognate receptors. We analyzed public cancer transcriptome database for changes in cell-cell interactions as the characteristic of malignancy. We curated a list (>2,500 ligand-receptor pairs) and identified their joint enrichment in tumors from TCGA pan-cancer data. From single-cell RNA-Seq data for a case of melanoma and the specificity of the ligand-receptor interactions and their gene expression measured in individual cells, we constructed a map of a cell-cell communication network that indicates what signal is exchanged between which cell types in the tumor. Such networks establish a new formal phenotype of cancer which captures the cell-cell communication structure - it may open new opportunities for identifying molecular signatures of coordinated behaviors of cancer cells as a population - in turn may become a determinant of cancer progression potential and prognosis.
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Affiliation(s)
| | - Roberto Taramelli
- Department of Biotechnology and Molecular Science, University of Insubria, Varese, Italy
| | - Edoardo Pedrini
- Department of Biotechnology and Molecular Science, University of Insubria, Varese, Italy
| | | | - Sui Huang
- Institute for Systems Biology, Seattle, WA, USA.
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37
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Ding C, Li Y, Guo F, Jiang Y, Ying W, Li D, Yang D, Xia X, Liu W, Zhao Y, He Y, Li X, Sun W, Liu Q, Song L, Zhen B, Zhang P, Qian X, Qin J, He F. A Cell-type-resolved Liver Proteome. Mol Cell Proteomics 2016; 15:3190-3202. [PMID: 27562671 PMCID: PMC5054343 DOI: 10.1074/mcp.m116.060145] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Indexed: 01/16/2023] Open
Abstract
Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level.
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Affiliation(s)
- Chen Ding
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Yanyan Li
- ¶School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Feifei Guo
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Ying Jiang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wantao Ying
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Dong Li
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Dong Yang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Xia Xia
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wanlin Liu
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Yan Zhao
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Yangzhige He
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; ¶School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xianyu Li
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wei Sun
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Qiongming Liu
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Lei Song
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Bei Zhen
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Pumin Zhang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Xiaohong Qian
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China;
| | - Jun Qin
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; ‖Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Fuchu He
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
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38
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Epithelial derived CTGF promotes breast tumor progression via inducing EMT and collagen I fibers deposition. Oncotarget 2016; 6:25320-38. [PMID: 26318291 PMCID: PMC4694834 DOI: 10.18632/oncotarget.4659] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 07/17/2015] [Indexed: 12/21/2022] Open
Abstract
Interactions among tumor cells, stromal cells, and extracellular matrix compositions are mediated through cytokines during tumor progression. Our analysis of 132 known cytokines and growth factors in published clinical breast cohorts and our 84 patient-derived xenograft models revealed that the elevated connective tissue growth factor (CTGF) in tumor epithelial cells significantly correlated with poor clinical prognosis and outcomes. CTGF was able to induce tumor cell epithelial-mesenchymal transition (EMT), and promote stroma deposition of collagen I fibers to stimulate tumor growth and metastasis. This process was mediated through CTGF-tumor necrosis factor receptor I (TNFR1)-IκB autocrine signaling. Drug treatments targeting CTGF, TNFR1, and IκB signaling each prohibited the EMT and tumor progression.
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39
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He L, Wang D, Wei N, Guo Z. Integrated Bioinformatics Approach Reveals Crosstalk Between Tumor Stroma and Peripheral Blood Mononuclear Cells in Breast Cancer. Asian Pac J Cancer Prev 2016; 17:1003-8. [PMID: 27039717 DOI: 10.7314/apjcp.2016.17.3.1003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.
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Affiliation(s)
- Lang He
- Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China E-mail :
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40
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Puliafito A, De Simone A, Seano G, Gagliardi PA, Di Blasio L, Chianale F, Gamba A, Primo L, Celani A. Three-dimensional chemotaxis-driven aggregation of tumor cells. Sci Rep 2015; 5:15205. [PMID: 26471876 PMCID: PMC4607978 DOI: 10.1038/srep15205] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 09/21/2015] [Indexed: 12/11/2022] Open
Abstract
One of the most important steps in tumor progression involves the transformation from a differentiated epithelial phenotype to an aggressive, highly motile phenotype, where tumor cells invade neighboring tissues. Invasion can occur either by isolated mesenchymal cells or by aggregates that migrate collectively and do not lose completely the epithelial phenotype. Here, we show that, in a three-dimensional cancer cell culture, collective migration of cells eventually leads to aggregation in large clusters. We present quantitative measurements of cluster velocity, coalescence rates, and proliferation rates. These results cannot be explained in terms of random aggregation. Instead, a model of chemotaxis-driven aggregation - mediated by a diffusible attractant - is able to capture several quantitative aspects of our results. Experimental assays of chemotaxis towards culture conditioned media confirm this hypothesis. Theoretical and numerical results further suggest an important role for chemotactic-driven aggregation in spreading and survival of tumor cells.
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Affiliation(s)
| | - Alessandro De Simone
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Giorgio Seano
- Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy.,Edwin L. Steele Laboratory for Tumor Biology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Armando Gagliardi
- Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy.,Department of Oncology, University of Turin, Turin 10060, Italy
| | - Laura Di Blasio
- Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy.,Department of Oncology, University of Turin, Turin 10060, Italy
| | | | - Andrea Gamba
- Institute of Condensed Matter Physics and Complex Systems, Department of Applied Science and Technology, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.,Human Genetics Foundation (HuGeF), Via Nizza 52, Torino, Italy.,Istituto Nazionale di Fisica Nucleare (INFN), Torino, Via Giuria 1, 10125 Torino, Italy
| | - Luca Primo
- Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy.,Department of Oncology, University of Turin, Turin 10060, Italy
| | - Antonio Celani
- Quantitative Life Sciences Unit, The Abdus Salam Center for Theoretical Physics (ICTP), Strada Costiera 11, I-34151 Trieste, Italy
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41
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Ramilowski JA, Goldberg T, Harshbarger J, Kloppman E, Lizio M, Satagopam VP, Itoh M, Kawaji H, Carninci P, Rost B, Forrest ARR. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun 2015; 6:7866. [PMID: 26198319 PMCID: PMC4525178 DOI: 10.1038/ncomms8866] [Citation(s) in RCA: 522] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 06/19/2015] [Indexed: 02/07/2023] Open
Abstract
Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand-receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1-CSF1R interacting pair.
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Affiliation(s)
- Jordan A. Ramilowski
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
| | - Tatyana Goldberg
- Department for Bioinformatics and Computational Biology-I12, Technische Universität München (TUM), Boltzmannstrasse 3, 85748 Garching, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstrasse 11, 85748 Garching, Germany
| | - Jayson Harshbarger
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
| | - Edda Kloppman
- Department for Bioinformatics and Computational Biology-I12, Technische Universität München (TUM), Boltzmannstrasse 3, 85748 Garching, Germany
| | - Marina Lizio
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine, Campus Belval, 7 Avenue des Hauts Fourneaux, L-4362 Belval, Luxembourg
| | - Masayoshi Itoh
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Hideya Kawaji
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Piero Carninci
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology-I12, Technische Universität München (TUM), Boltzmannstrasse 3, 85748 Garching, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstrasse 11, 85748 Garching, Germany
| | - Alistair R. R. Forrest
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia 6008, Australia
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Choi H, Sheng J, Gao D, Li F, Durrans A, Ryu S, Lee SB, Narula N, Rafii S, Elemento O, Altorki NK, Wong STC, Mittal V. Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep 2015; 10:1187-201. [PMID: 25704820 DOI: 10.1016/j.celrep.2015.01.040] [Citation(s) in RCA: 111] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 12/07/2014] [Accepted: 01/16/2015] [Indexed: 02/04/2023] Open
Abstract
Emerging studies have begun to demonstrate that reprogrammed stromal cells play pivotal roles in tumor growth, metastasis, and resistance to therapy. However, the contribution of stromal cells to non-small-cell lung cancer (NSCLC) has remained underexplored. We used an orthotopic model of Kras-driven NSCLC to systematically dissect the contribution of specific hematopoietic stromal cells in lung cancer. RNA deep-sequencing analysis of individually sorted myeloid lineage and tumor epithelial cells revealed cell-type-specific differentially regulated genes, indicative of activated stroma. We developed a computational model for crosstalk signaling discovery based on ligand-receptor interactions and downstream signaling networks and identified known and novel tumor-stroma paracrine and tumor autocrine crosstalk-signaling pathways in NSCLC. We provide cellular and molecular insights into components of the lung cancer microenvironment that contribute to carcinogenesis. This study has the potential for development of therapeutic strategies that target tumor-stroma interactions and may complement conventional anti-cancer treatments.
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Affiliation(s)
- Hyejin Choi
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Jianting Sheng
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, USA
| | - Dingcheng Gao
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Fuhai Li
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, USA
| | - Anna Durrans
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Seongho Ryu
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Sharrell B Lee
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Navneet Narula
- Department of Pathology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Shahin Rafii
- Ansary Stem Cell Institute and Department of Genetic Medicine, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Olivier Elemento
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Nasser K Altorki
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA
| | - Stephen T C Wong
- Department of Pathology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, USA; Methodist Cancer Center, Houston Methodist Hospital, 6650 Fannin Street, Houston, TX 77030, USA.
| | - Vivek Mittal
- Department of Cardiothoracic Surgery, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA; Neuberger Berman Lung Cancer Center, Weill Cornell Medical College of Cornell University, 1300 York Avenue, 525 East 68(th) Street, New York, NY 10065, USA.
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43
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Shiah YJ, Tharmapalan P, Casey AE, Joshi PA, McKee TD, Jackson HW, Beristain AG, Chan-Seng-Yue MA, Bader GD, Lydon JP, Waterhouse PD, Boutros PC, Khokha R. A Progesterone-CXCR4 Axis Controls Mammary Progenitor Cell Fate in the Adult Gland. Stem Cell Reports 2015; 4:313-322. [PMID: 28447939 PMCID: PMC4376056 DOI: 10.1016/j.stemcr.2015.01.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 01/12/2015] [Accepted: 01/13/2015] [Indexed: 11/18/2022] Open
Abstract
Progesterone drives mammary stem and progenitor cell dynamics through paracrine mechanisms that are currently not well understood. Here, we demonstrate that CXCR4, the receptor for stromal-derived factor 1 (SDF-1; CXC12), is a crucial instructor of hormone-induced mammary stem and progenitor cell function. Progesterone elicits specific changes in the transcriptome of basal and luminal mammary epithelial populations, where CXCL12 and CXCR4 represent a putative ligand-receptor pair. In situ, CXCL12 localizes to progesterone-receptor-positive luminal cells, whereas CXCR4 is induced in both basal and luminal compartments in a progesterone-dependent manner. Pharmacological inhibition of CXCR4 signaling abrogates progesterone-directed expansion of basal (CD24+CD49fhi) and luminal (CD24+CD49flo) subsets. This is accompanied by a marked reduction in CD49b+SCA-1− luminal progenitors, their functional capacity, and lobuloalveologenesis. These findings uncover CXCL12 and CXCR4 as novel paracrine effectors of hormone signaling in the adult mammary gland, and present a new avenue for potentially targeting progenitor cell growth and malignant transformation in breast cancer. Progesterone induces distinct molecular programs in mammary cell compartments CXCR4 induction occurs in lobuloalveoli and is progesterone dependent CXCR4 inhibition abrogates luminal progenitor expansion and mammopoiesis Targeting of the CXCL12-CXCR4 axis may limit mammary progenitor cell transformation
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Affiliation(s)
- Yu-Jia Shiah
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada
| | | | - Alison E Casey
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Purna A Joshi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Trevor D McKee
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; STTARR Innovation Centre, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON M5G 1L7, Canada
| | - Hartland W Jackson
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada
| | - Alexander G Beristain
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Michelle A Chan-Seng-Yue
- Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
| | - Gary D Bader
- Department of Molecular Genetics, Medical Science Building, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - John P Lydon
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Paul D Waterhouse
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
| | - Rama Khokha
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada.
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44
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Ruggeri C, Eng KH. Inferring active and prognostic ligand-receptor pairs with interactions in survival regression models. Cancer Inform 2015; 13:67-75. [PMID: 25657571 PMCID: PMC4310509 DOI: 10.4137/cin.s16351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Revised: 10/20/2014] [Accepted: 10/23/2014] [Indexed: 01/29/2023] Open
Abstract
Modeling signal transduction in cancer cells has implications for targeting new therapies and inferring the mechanisms that improve or threaten a patient’s treatment response. For transcriptome-wide studies, it has been proposed that simple correlation between a ligand and receptor pair implies a relationship to the disease process. Statistically, a differential correlation (DC) analysis across groups stratified by prognosis can link the pair to clinical outcomes. While the prognostic effect and the apparent change in correlation are both biological consequences of activation of the signaling mechanism, a correlation-driven analysis does not clearly capture this assumption and makes inefficient use of continuous survival phenotypes. To augment the correlation hypothesis, we propose that a regression framework assuming a patient-specific, latent level of signaling activation exists and generates both prognosis and correlation. Data from these systems can be inferred via interaction terms in survival regression models allowing signal transduction models beyond one pair at a time and adjusting for other factors. We illustrate the use of this model on ovarian cancer data from the Cancer Genome Atlas (TCGA) and discuss how the finding may be used to develop markers to guide targeted molecular therapies.
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Affiliation(s)
- Christina Ruggeri
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Kevin H Eng
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA
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45
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Abstract
Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.
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46
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McGee SF, Kornblau SM, Qiu Y, Look AT, Zhang N, Yoo SY, Coombes KR, Kentsis A. Biological properties of ligand-dependent activation of the MET receptor kinase in acute myeloid leukemia. Leukemia 2014; 29:1218-21. [PMID: 25541150 DOI: 10.1038/leu.2014.348] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- S F McGee
- 1] Department of Medicine, Mount Sinai St Luke's-Roosevelt Hospital Center, New York, NY, USA [2] Molecular Pharmacology and Chemistry Program, Sloan Kettering Institute, New York, NY, USA
| | - S M Kornblau
- Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Y Qiu
- Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - A T Look
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - N Zhang
- Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - S-Y Yoo
- Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - K R Coombes
- Departmentt of Biomedical Informatics, Ohio State University College of Medicine, Columbus, OH, USA
| | - A Kentsis
- 1] Molecular Pharmacology and Chemistry Program, Sloan Kettering Institute, New York, NY, USA [2] Department of Pediatrics, Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, Cornell University, New York, NY, USA
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47
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Gene differential coexpression analysis based on biweight correlation and maximum clique. BMC Bioinformatics 2014; 15 Suppl 15:S3. [PMID: 25474074 PMCID: PMC4271563 DOI: 10.1186/1471-2105-15-s15-s3] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.
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48
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Schwarzer A, Holtmann H, Brugman M, Meyer J, Schauerte C, Zuber J, Steinemann D, Schlegelberger B, Li Z, Baum C. Hyperactivation of mTORC1 and mTORC2 by multiple oncogenic events causes addiction to eIF4E-dependent mRNA translation in T-cell leukemia. Oncogene 2014; 34:3593-604. [PMID: 25241901 DOI: 10.1038/onc.2014.290] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2014] [Revised: 07/17/2014] [Accepted: 08/06/2014] [Indexed: 12/21/2022]
Abstract
High activation of the PI3K-AKT-mTOR pathway is characteristic for T-cell acute lymphoblastic leukemia (T-ALL). The activity of the master regulator of this pathway, PTEN, is often impaired in T-ALL. However, experimental evidence suggests that input from receptor tyrosine kinases (RTKs) is required for sustained mTOR activation, even in the absence of PTEN. We previously reported the expression of Neurotrophin receptor tyrosine kinases (TRKs) and their respective ligands in primary human leukemia samples. In the present study we aimed to dissect the downstream signaling cascades of TRK-induced T-ALL in a murine model and show that T-ALLs induced by deregulated receptor tyrosine kinase signaling acquire activating mutations in Notch1 and lose PTEN during clonal evolution. Some clones additionally lost one allele of the homeodomain transcription factor Cux1. All events independently led to a gradual hyperactivation of both mTORC1 and mTORC2 signaling. We dissected the role of the individual mTOR complexes by shRNA knockdown and found that the separate depletion of mTORC1 or mTORC2 reduced the growth of T-ALL blasts, but was not sufficient to induce apoptosis. In contrast, knockdown of the mTOR downstream effector eIF4E caused a striking cytotoxic effect, demonstrating a critical addiction to cap-dependent mRNA-translation. Although high mTORC2-AKT activation is commonly associated with drug-resistance, we demonstrate that T-ALL displaying a strong mTORC2-AKT activation were specifically susceptible to 4EGI-1, an inhibitor of the eIF4E-eIF4G interaction. To decipher the mechanism of 4EGI-1, we performed a genome-wide analysis of mRNAs that are translationally regulated by 4EGI-1 in T-ALL. 4EGI-1 effectively reduced the ribosomal occupancy of mRNAs that were strongly upregulated in T-ALL blasts compared with normal thymocytes including transcripts important for translation, mitochondria and cell cycle progression, such as cyclins and ribosomal proteins. These data suggest that disrupting the eIF4E-eIF4G interaction constitutes a promising therapy strategy in mTOR-deregulated T-cell leukemia.
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Affiliation(s)
- A Schwarzer
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
| | - H Holtmann
- Institute of Biochemistry, Hannover Medical School, Hannover, Germany
| | - M Brugman
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
| | - J Meyer
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
| | - C Schauerte
- Institute of Biochemistry, Hannover Medical School, Hannover, Germany
| | - J Zuber
- Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, Vienna, Austria
| | - D Steinemann
- Institute of Cell and Molecular Pathology, Hannover Medical School, Hannover, Germany
| | - B Schlegelberger
- Institute of Cell and Molecular Pathology, Hannover Medical School, Hannover, Germany
| | - Z Li
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
| | - C Baum
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
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49
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Eng KH, Ruggeri C. Connecting prognostic ligand receptor signaling loops in advanced ovarian cancer. PLoS One 2014; 9:e107193. [PMID: 25244152 PMCID: PMC4171104 DOI: 10.1371/journal.pone.0107193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Accepted: 08/06/2014] [Indexed: 11/19/2022] Open
Abstract
Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFA[Formula: see text]PDGFRA and COL1A1[Formula: see text]CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer.
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Affiliation(s)
- Kevin H. Eng
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - Christina Ruggeri
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, United States of America
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50
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Platelet-derived growth factor alpha mediates the proliferation of peripheral T-cell lymphoma cells via an autocrine regulatory pathway. Leukemia 2014; 28:1687-97. [PMID: 24480986 DOI: 10.1038/leu.2014.50] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 01/16/2014] [Accepted: 01/17/2014] [Indexed: 12/14/2022]
Abstract
Peripheral T-cell lymphomas not otherwise specified (PTCL/NOS) are very aggressive tumors characterized by consistent aberrant expression of platelet-derived growth factor receptor alpha (PDGFRA). In this study, we aimed to identify the determinants of PDGFRA activity in PTCL/NOS and to elucidate the biological consequences of its activation. We observed overexpression of the PDGFRA gene by gene expression profiling in most of the tested PTCLs and confirmed the expression of PDGFRA and phospho-PDGFRA using immunohistochemistry. The integrity of the PDFGRA locus was demonstrated using several different approaches, including massive parallel sequencing and Sanger sequencing. PDGF-AA was found to be expressed and secreted by PTCL/NOS cells and to be necessary and sufficient for PDGFRA phosphorylation ex vivo by sustaining an autocrine stimulation. We documented consistently high PDGF-A expression in primary biopsies and patients' plasma and tracked PDGFRA signaling in primary tumors, achieving evidence of its activation. Indeed, we found that STAT1 and STAT5 are implicated in PDGFRA signaling transduction. Finally, we demonstrated that PDGFRA activation supported tumor cell proliferation and provided the first evidence of the anti-lymphoma activity of PDGRA inhibition in a PTCL/NOS patient. Altogether, our data demonstrated that PDGFRA activity fosters PTCL/NOS proliferation via an autocrine loop.
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