1
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Giangreco G, Rullan A, Naito Y, Biswas D, Liu YH, Hooper S, Nenclares P, Bhide S, Chon U Cheang M, Chakravarty P, Hirata E, Swanton C, Melcher A, Harrington K, Sahai E. Cancer cell - Fibroblast crosstalk via HB-EGF, EGFR, and MAPK signaling promotes the expression of macrophage chemo-attractants in squamous cell carcinoma. iScience 2024; 27:110635. [PMID: 39262776 PMCID: PMC11387794 DOI: 10.1016/j.isci.2024.110635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 04/09/2024] [Accepted: 07/30/2024] [Indexed: 09/13/2024] Open
Abstract
Interactions between cells in the tumor microenvironment (TME) shape cancer progression and patient prognosis. To gain insights into how the TME influences cancer outcomes, we derive gene expression signatures indicative of signaling between stromal fibroblasts and cancer cells, and demonstrate their prognostic significance in multiple and independent squamous cell carcinoma cohorts. By leveraging information within the signatures, we discover that the HB-EGF/EGFR/MAPK axis represents a hub of tumor-stroma crosstalk, promoting the expression of CSF2 and LIF and favoring the recruitment of macrophages. Together, these analyses demonstrate the utility of our approach for interrogating the extent and consequences of TME crosstalk.
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Affiliation(s)
- Giovanni Giangreco
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Antonio Rullan
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Yutaka Naito
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Dhruva Biswas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, 72 Huntley Street, London WC1E 6DD, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, 72 Huntley Street, London WC1E 6DD, UK
| | - Yun-Hsin Liu
- Bioinformatics Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Steven Hooper
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Pablo Nenclares
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Shreerang Bhide
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Maggie Chon U Cheang
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Probir Chakravarty
- Bioinformatics Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Eishu Hirata
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, 72 Huntley Street, London WC1E 6DD, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Alan Melcher
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Kevin Harrington
- Department of Radiotherapy and Imaging, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Head and Neck Unit, The Royal Marsden Hospital, 203 Fulham Road, London SW3 6JJ, UK
| | - Erik Sahai
- Tumour Cell Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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2
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Jagodinsky JC, Vera JM, Jin WJ, Shea AG, Clark PA, Sriramaneni RN, Havighurst TC, Chakravarthy I, Allawi RH, Kim K, Harari PM, Sondel PM, Newton MA, Crittenden MR, Gough MJ, Miller JR, Ong IM, Morris ZS. Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade. Sci Transl Med 2024; 16:eadk0642. [PMID: 39292804 DOI: 10.1126/scitranslmed.adk0642] [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: 07/31/2023] [Revised: 04/03/2024] [Accepted: 08/08/2024] [Indexed: 09/20/2024]
Abstract
Radiation therapy (RT) activates multiple immunologic effects in the tumor microenvironment (TME), with diverse dose-response relationships observed. We hypothesized that, in contrast with homogeneous RT, a heterogeneous RT dose would simultaneously optimize activation of multiple immunogenic effects in a single TME, resulting in a more effective antitumor immune response. Using high-dose-rate brachytherapy, we treated mice bearing syngeneic tumors with a single fraction of heterogeneous RT at a dose ranging from 2 to 30 gray. When combined with dual immune checkpoint inhibition in murine models, heterogeneous RT generated more potent antitumor responses in distant, nonirradiated tumors compared with any homogeneous dose. The antitumor effect after heterogeneous RT required CD4 and CD8 T cells and low-dose RT to a portion of the tumor. At the 3-day post-RT time point, dose heterogeneity imprinted the targeted TME with spatial differences in immune-related gene expression, antigen presentation, and susceptibility of tumor cells to immune-mediated destruction. At a later 10-day post-RT time point, high-, moderate-, or low-RT-dose regions demonstrated distinct infiltrating immune cell populations. This was associated with an increase in the expression of effector-associated cytokines in circulating CD8 T cells. Consistent with enhanced adaptive immune priming, heterogeneous RT promoted clonal expansion of effector CD8 T cells. These findings illuminate the breadth of dose-dependent effects of RT on the TME and the capacity of heterogeneous RT to promote antitumor immunity when combined with immune checkpoint inhibitors.
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Affiliation(s)
- Justin C Jagodinsky
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Jessica M Vera
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA
- Sage Bionetworks, 2901 Third Ave. Suite 330, Seattle, WA 98121, USA
| | - Won Jong Jin
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Amanda G Shea
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Paul A Clark
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Raghava N Sriramaneni
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Thomas C Havighurst
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA
| | - Ishan Chakravarthy
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Raad H Allawi
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - KyungMann Kim
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA
| | - Paul M Harari
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Paul M Sondel
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Michael A Newton
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA
| | - Marka R Crittenden
- Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, NE Glisan St., Portland, OR 97213, USA
- Oregon Clinic, Portland, OR 97232, USA
| | - Michael J Gough
- Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, NE Glisan St., Portland, OR 97213, USA
| | - Jessica R Miller
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Irene M Ong
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA
- Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Zachary S Morris
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
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3
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Shivarov V, Tsvetkova G, Micheva I, Hadjiev E, Petrova J, Ivanova A, Madjarova G, Ivanova M. Differential modulation of mutant CALR and JAK2 V617F-driven oncogenesis by HLA genotype in myeloproliferative neoplasms. Front Immunol 2024; 15:1427810. [PMID: 39351227 PMCID: PMC11439724 DOI: 10.3389/fimmu.2024.1427810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 08/26/2024] [Indexed: 10/04/2024] Open
Abstract
It has been demonstrated previously that human leukocyte antigen class I (HLA-I) and class II (HLA-II) alleles may modulate JAK2 V617F and CALR mutation (CALRmut)-associated oncogenesis in myeloproliferative neoplasms (MPNs). However, the role of immunogenetic factors in MPNs remains underexplored. We aimed to investigate the potential involvement of HLA genes in CALRmut+ MPNs. High-resolution genotyping of HLA-I and -II loci was conducted in 42 CALRmut+ and 158 JAK2 V617F+ MPN patients and 1,083 healthy controls. A global analysis of the diversity of HLA-I genotypes revealed no significant differences between CALRmut+ patients and controls. However, one HLA-I allele (C*06:02) showed an inverse correlation with presence of CALR mutation. A meta-analysis across independent cohorts and healthy individuals from the 1000 Genomes Project confirmed an inverse correlation between the presentation capabilities of the HLA-I loci for JAK2 V617F and CALRmut-derived peptides in both patients and healthy individuals. scRNA-Seq analysis revealed low expression of TAP1 and CIITA genes in CALRmut+ hematopoietic stem and progenitor cells. In conclusion, the HLA-I genotype differentially restricts JAK2 V617F and CALRmut-driven oncogenesis potentially explaining the mutual exclusivity of the two mutations and differences in their presentation latency. These findings have practical implications for the development of neoantigen-based vaccines in MPNs.
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Affiliation(s)
- Velizar Shivarov
- Department of Experimental Research, Medical University Pleven, Pleven, Bulgaria
| | - Gergana Tsvetkova
- Department of Clinical Hematology, Alexandrovska University Hospital, Medical University Sofia, Sofia, Bulgaria
| | - Ilina Micheva
- Department of Clinical Hematology, Saint Marina University Hospital, Medical University Varna, Varna, Bulgaria
| | - Evgueniy Hadjiev
- Department of Clinical Hematology, Alexandrovska University Hospital, Medical University Sofia, Sofia, Bulgaria
| | - Jasmina Petrova
- Department of Physical Chemistry, Faculty of Chemistry and Pharmacy, Sofia University “St. Kl. Ohridski”, Sofia, Bulgaria
| | - Anela Ivanova
- Department of Physical Chemistry, Faculty of Chemistry and Pharmacy, Sofia University “St. Kl. Ohridski”, Sofia, Bulgaria
| | - Galia Madjarova
- Department of Physical Chemistry, Faculty of Chemistry and Pharmacy, Sofia University “St. Kl. Ohridski”, Sofia, Bulgaria
| | - Milena Ivanova
- Department of Clinical Immunology, Alexandrovska University Hospital, Medical University Sofia, Sofia, Bulgaria
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4
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Wang RH, Thakar J. Comparative analysis of single-cell pathway scoring methods and a novel approach. NAR Genom Bioinform 2024; 6:lqae124. [PMID: 39318507 PMCID: PMC11420841 DOI: 10.1093/nargab/lqae124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/22/2024] [Accepted: 09/03/2024] [Indexed: 09/26/2024] Open
Abstract
Single-cell gene set analysis (scGSA) provides a useful approach for quantifying molecular functions and pathways in high-throughput transcriptomic data, facilitating the biological interpretation of complex human datasets. However, various factors such as gene set size, quality of the gene sets and the dropouts impact the performance of scGSA. To address these limitations, we present a single-cell Pathway Score (scPS) method to measure gene set activity at single-cell resolution. Furthermore, we benchmark our method with six other methods: AUCell, AddModuleScore, JASMINE, UCell, SCSE and ssGSEA. The comparison across all the methods using two different simulation approaches highlights the effect of cell count, gene set size, noise, condition-specific genes and zero imputation on their performance. The results of our study indicate that the scPS is comparable with other single-cell scoring methods and detects fewer false positives. Importantly, this work reveals critical variables in the scGSA.
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Affiliation(s)
- Ruoqiao H Wang
- Department of Biomedical Genetics, University of Rochester, 601 Elmwood Ave, NY 14642, USA
| | - Juilee Thakar
- Department of Biomedical Genetics, University of Rochester, 601 Elmwood Ave, NY 14642, USA
- Department of Microbiology and Immunology, University of Rochester, 601 Elmwood Ave, NY 14642, USA
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5
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Ali A, Manzoor S, Ali T, Asim M, Muhammad G, Ahmad A, Jamaludin MI, Devaraj S, Munawar N. Innovative aspects and applications of single cell technology for different diseases. Am J Cancer Res 2024; 14:4028-4048. [PMID: 39267684 PMCID: PMC11387862 DOI: 10.62347/vufu1836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 08/24/2024] [Indexed: 09/15/2024] Open
Abstract
Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.
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Affiliation(s)
- Ashiq Ali
- Department of Histology and Embryology, Shantou University Medical College Shantou 515041, Guangdong, China
| | - Saba Manzoor
- Department of Zoology, University of Sialkot Sialkot 51310, Pakistan
| | - Tayyab Ali
- Clinico-Molecular Biochemistry Laboratory, Department of Biochemistry, University of Agriculture Faisalabad 38000, Pakistan
| | - Muhammad Asim
- Clinico-Molecular Biochemistry Laboratory, Department of Biochemistry, University of Agriculture Faisalabad 38000, Pakistan
| | - Ghulam Muhammad
- Jinnah Burn and Reconstructive Surgery Centre, Jinnah Hospital, Allama Iqbal Medical College Lahore 54000, Pakistan
| | - Aftab Ahmad
- Biochemistry/Center for Advanced Studies in Agriculture and Food Security (CAS-AFS), University of Agriculture Faisalabad 38040, Pakistan
| | - Mohamad Ikhwan Jamaludin
- BioInspired Device and Tissue Engineering Research Group (BioInspira), Department of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia Johor Bahru 81310, Johor, Malaysia
| | - Sutha Devaraj
- Graduate School of Medicine, Perdana University Wisma Chase Perdana, Changkat Semantan, Damansara Heights, Kuala Lumpur 50490, Malaysia
| | - Nayla Munawar
- Department of Chemistry, College of Science, United Arab Emirates University Al-Ain 15551, United Arab Emirates
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6
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Schmidt MJ, Naghdloo A, Prabakar RK, Kamal M, Cadaneanu R, Garraway IP, Lewis M, Aparicio A, Zurita-Saavedra A, Corn P, Kuhn P, Pienta KJ, Amend SR, Hicks J. Polyploid cancer cells reveal signatures of chemotherapy resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.19.608632. [PMID: 39229204 PMCID: PMC11370377 DOI: 10.1101/2024.08.19.608632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression-free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.
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Affiliation(s)
- Michael J Schmidt
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
| | - Amin Naghdloo
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
| | - Rishvanth K Prabakar
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
- Currently at: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Mohamed Kamal
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
- Department of Zoology, Faculty of Science, Benha University, Benha, Egypt
| | - Radu Cadaneanu
- Department of Urology, Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, University of California, Los Angeles, Los Angeles, California, USA
| | - Isla P Garraway
- Department of Urology, Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, University of California, Los Angeles, Los Angeles, California, USA
| | - Michael Lewis
- VA Greater Los Angeles Medical Center, Los Angeles, CA, USA
- Departments of Medicine and Pathology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Cancer Research and Cellular Therapeutics, Clark, Atlanta, GA, USA
| | - Ana Aparicio
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amado Zurita-Saavedra
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Corn
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Kuhn
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
| | - Kenneth J Pienta
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sarah R Amend
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James Hicks
- Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
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7
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Wang JY, Michki NS, Sitaraman S, Banaschewski BJ, Lin SM, Katzen JB, Basil MC, Cantu E, Zepp JA, Frank DB, Young LR. Dysregulated alveolar epithelial cell progenitor function and identity in Hermansky-Pudlak syndrome pulmonary fibrosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.17.545390. [PMID: 38496421 PMCID: PMC10942273 DOI: 10.1101/2023.06.17.545390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Hermansky-Pudlak syndrome (HPS) is a genetic disorder of endosomal protein trafficking associated with pulmonary fibrosis in specific subtypes, including HPS-1 and HPS-2. Single mutant HPS1 and HPS2 mice display increased fibrotic sensitivity while double mutant HPS1/2 mice exhibit spontaneous fibrosis with aging, which has been attributed to HPS mutations in alveolar epithelial type II (AT2) cells. We utilized HPS mouse models and human lung tissue to investigate mechanisms of AT2 cell dysfunction driving fibrotic remodeling in HPS. Starting at 8 weeks of age, HPS mice exhibited progressive loss of AT2 cell numbers. HPS AT2 cell was impaired ex vivo and in vivo. Incorporating AT2 cell lineage tracing in HPS mice, we observed aberrant differentiation with increased AT2-derived alveolar epithelial type I cells. Transcriptomic analysis of HPS AT2 cells revealed elevated expression of genes associated with aberrant differentiation and p53 activation. Lineage tracing and modeling studies demonstrated that HPS AT2 cells were primed to persist in a Krt8+ reprogrammed transitional state, mediated by p53 activity. Intrinsic AT2 progenitor cell dysfunction and p53 pathway dysregulation are novel mechanisms of disease in HPS-related pulmonary fibrosis, with the potential for early targeted intervention before the onset of fibrotic lung disease.
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8
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Fan C, Chen F, Chen Y, Huang L, Wang M, Liu Y, Wang Y, Guo H, Zheng N, Liu Y, Wang H, Ma L. irGSEA: the integration of single-cell rank-based gene set enrichment analysis. Brief Bioinform 2024; 25:bbae243. [PMID: 38801700 PMCID: PMC11129768 DOI: 10.1093/bib/bbae243] [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: 12/08/2023] [Revised: 03/06/2024] [Indexed: 05/29/2024] Open
Abstract
irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).
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Affiliation(s)
- Chuiqin Fan
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Fuyi Chen
- Department of Obstetrics and Gynecology; Department of Pediatrics; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affi1iated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Yuanguo Chen
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Liangping Huang
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Manna Wang
- Department of Obstetrics and Gynecology; Department of Pediatrics; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affi1iated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Yulin Liu
- Department of Pediatrics, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, China
| | - Yu Wang
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Huijie Guo
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Nanpeng Zheng
- Department of Pediatrics, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, China
| | - Yanbing Liu
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
| | - Hongwu Wang
- Department of Pediatrics, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, China
| | - Lian Ma
- Department of Obstetrics and Gynecology; Department of Pediatrics; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affi1iated Hospital of Guangzhou Medical University, Guangzhou 510150, China
- Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China
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9
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Chaudhry FN, Michki NS, Shirmer DL, McGrath-Morrow S, Young LR, Frank DB, Zepp JA. Dynamic Hippo pathway activity underlies mesenchymal differentiation during lung alveolar morphogenesis. Development 2024; 151:dev202430. [PMID: 38602485 PMCID: PMC11112347 DOI: 10.1242/dev.202430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/26/2024] [Indexed: 04/12/2024]
Abstract
Alveologenesis, the final stage in lung development, substantially remodels the distal lung, expanding the alveolar surface area for efficient gas exchange. Secondary crest myofibroblasts (SCMF) exist transiently in the neonatal distal lung and are crucial for alveologenesis. However, the pathways that regulate SCMF function, proliferation and temporal identity remain poorly understood. To address this, we purified SCMFs from reporter mice, performed bulk RNA-seq and found dynamic changes in Hippo-signaling components during alveologenesis. We deleted the Hippo effectors Yap/Taz from Acta2-expressing cells at the onset of alveologenesis, causing a significant arrest in alveolar development. Using single cell RNA-seq, we identified a distinct cluster of cells in mutant lungs with altered expression of marker genes associated with proximal mesenchymal cell types, airway smooth muscle and alveolar duct myofibroblasts. In vitro studies confirmed that Yap/Taz regulates myofibroblast-associated gene signature and contractility. Together, our findings show that Yap/Taz is essential for maintaining functional myofibroblast identity during postnatal alveologenesis.
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Affiliation(s)
- Fatima N. Chaudhry
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nigel S. Michki
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Dain L. Shirmer
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Sharon McGrath-Morrow
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lisa R. Young
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - David B. Frank
- Division of Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jarod A. Zepp
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
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10
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Hong T, Xing J. Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition. Genesis 2024; 62:e23591. [PMID: 38553870 PMCID: PMC11017362 DOI: 10.1002/dvg.23591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/16/2024] [Accepted: 03/16/2024] [Indexed: 04/02/2024]
Abstract
Reversible transitions between epithelial and mesenchymal cell states are a crucial form of epithelial plasticity for development and disease progression. Recent experimental data and mechanistic models showed multiple intermediate epithelial-mesenchymal transition (EMT) states as well as trajectories of EMT underpinned by complex gene regulatory networks. In this review, we summarize recent progress in quantifying EMT and characterizing EMT paths with computational methods and quantitative experiments including omics-level measurements. We provide perspectives on how these studies can help relating fundamental cell biology to physiological and pathological outcomes of EMT.
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Affiliation(s)
- Tian Hong
- Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville TN, USA
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA
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11
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Brand J, Haro M, Lin X, Rimel B, McGregor SM, Lawrenson K, Dinh HQ. Fallopian tube single cell analysis reveals myeloid cell alterations in high-grade serous ovarian cancer. iScience 2024; 27:108990. [PMID: 38384837 PMCID: PMC10879678 DOI: 10.1016/j.isci.2024.108990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/10/2024] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
Most high-grade serous ovarian cancers (HGSCs) likely initiate from fallopian tube (FT) epithelia. While epithelial subtypes have been characterized using single-cell RNA-sequencing (scRNA-Seq), heterogeneity of other compartments and their involvement in tumor progression are poorly defined. Integrated analysis of human FT scRNA-Seq and HGSC-related tissues, including tumors, revealed greater immune and stromal transcriptional diversity than previously reported. We identified abundant monocytes in FTs across two independent cohorts. The ratio of macrophages to monocytes is similar between benign FTs, ovaries, and adjacent normal tissues but significantly greater in tumors. FT-defined monocyte and macrophage signatures, cell-cell communication, and gene set enrichment analyses identified monocyte- and macrophage-specific interactions and functional pathways in paired tumors and adjacent normal tissues. Further reanalysis of HGSC scRNA-Seq identified monocyte and macrophage subsets associated with neoadjuvant chemotherapy. Taken together, our work provides data that an altered FT myeloid cell composition could inform the discovery of early detection markers for HGSC.
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Affiliation(s)
- Joshua Brand
- McArdle Laboratory for Cancer Research, Department of Oncology, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI 53705, USA
| | - Marcela Haro
- Women’s Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Xianzhi Lin
- Women’s Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- RNA Biology Group, Division of Natural and Applied Sciences and Global Health Research Center, Duke Kunshan University, Kunshan 215316, Jiangsu Province, China
| | - B.J. Rimel
- Women’s Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Stephanie M. McGregor
- Department of Pathology and Laboratory Medicine, University of Wisconsin – Madison, Madison, WI 53705, USA
- University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Kate Lawrenson
- Women’s Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Huy Q. Dinh
- McArdle Laboratory for Cancer Research, Department of Oncology, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI 53705, USA
- University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI 53705, USA
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12
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Michki NS, Singer BD, Perez JV, Thomas AJ, Natale V, Helmin KA, Wright J, Cheng L, Young LR, Lederman HM, McGrath-Morrow SA. Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in Ataxia Telangiectasia. Orphanet J Rare Dis 2024; 19:67. [PMID: 38360726 PMCID: PMC10870445 DOI: 10.1186/s13023-024-03073-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/03/2024] [Indexed: 02/17/2024] Open
Abstract
INTRODUCTION Ataxia telangiectasia (A-T) is an autosomal recessive neurodegenerative disease with widespread systemic manifestations and marked variability in clinical phenotypes. In this study, we sought to determine whether transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) defines subsets of individuals with A-T beyond mild and classic phenotypes, enabling identification of novel features for disease classification and treatment response to therapy. METHODS Participants with classic A-T (n = 77), mild A-T (n = 13), and unaffected controls (n = 15) were recruited from two outpatient clinics. PBMCs were isolated and bulk RNAseq was performed. Plasma was also isolated in a subset of individuals. Affected individuals were designated mild or classic based on ATM mutations and clinical and laboratory features. RESULTS People with classic A-T were more likely to be younger and IgA deficient and to have higher alpha-fetoprotein levels and lower % forced vital capacity compared to individuals with mild A-T. In classic A-T, the expression of genes required for V(D)J recombination was lower, and the expression of genes required for inflammatory activity was higher. We assigned inflammatory scores to study participants and found that inflammatory scores were highly variable among people with classic A-T and that higher scores were associated with lower ATM mRNA levels. Using a cell type deconvolution approach, we inferred that CD4 + T cells and CD8 + T cells were lower in number in people with classic A-T. Finally, we showed that individuals with classic A-T exhibit higher SERPINE1 (PAI-1) mRNA and plasma protein levels, irrespective of age, and higher FLT4 (VEGFR3) and IL6ST (GP130) plasma protein levels compared with mild A-T and controls. CONCLUSION Using a transcriptomic approach, we identified novel features and developed an inflammatory score to identify subsets of individuals with different inflammatory phenotypes in A-T. Findings from this study could be used to help direct treatment and to track treatment response to therapy.
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Affiliation(s)
- Nigel S Michki
- Division of Pulmonary and Sleep Medicine, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin D Singer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Javier V Perez
- Division of Pulmonary and Sleep Medicine, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron J Thomas
- Division of Pulmonary and Sleep Medicine, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie Natale
- Forgotten Diseases Research Foundation, Santa Clara, CA, USA
| | - Kathryn A Helmin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Jennifer Wright
- Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Leon Cheng
- Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Lisa R Young
- Division of Pulmonary and Sleep Medicine, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Howard M Lederman
- Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Sharon A McGrath-Morrow
- Division of Pulmonary and Sleep Medicine, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
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13
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Kousnetsov R, Bourque J, Surnov A, Fallahee I, Hawiger D. Single-cell sequencing analysis within biologically relevant dimensions. Cell Syst 2024; 15:83-103.e11. [PMID: 38198894 DOI: 10.1016/j.cels.2023.12.005] [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: 12/09/2022] [Revised: 05/23/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.
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Affiliation(s)
- Robert Kousnetsov
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Jessica Bourque
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Alexey Surnov
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Ian Fallahee
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Daniel Hawiger
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA.
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14
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He D, Yu Q, Zeng X, Feng J, Yang R, Wan H, Zhong Y, Yang Y, Zhao R, Lu J, Zhang J. Single-Cell RNA Sequencing and Transcriptome Analysis Revealed the Immune Microenvironment and Gene Markers of Acute Respiratory Distress Syndrome. J Inflamm Res 2023; 16:3205-3217. [PMID: 37547124 PMCID: PMC10404049 DOI: 10.2147/jir.s419576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/27/2023] [Indexed: 08/08/2023] Open
Abstract
Background Acute respiratory distress syndrome (ARDS) is caused by severe pulmonary inflammation and the leading cause of death in the intensive care unit. Methods We used single-cell RNA sequencing to compare peripheral blood mononuclear cells from sepsis-induced ARDS (SEP-ARDS) and pneumonic ARDS (PNE-ARDS) patient. Then, we used the GSE152978 and GSE152979 datasets to identify molecular dysregulation mechanisms at the transcriptional level in ARDS. Results Markedly increased CD14 cells were the predominant immune cell type observed in SEP-ARDS and PNE-ARDS patients. Cytotoxic cells and natural killer (NK) T cells were exclusively identified in patients with PNE-ARDS. An enrichment analysis of differentially expressed genes (DEGs) suggested that Th1 cell differentiation and Th2 cell differentiation were enriched in cytotoxic cells, and that the IL-17 signaling pathway, NOD receptor signaling pathway, and complement and coagulation cascades were enriched in CD14 cells. Furthermore, according to GSE152978 and GSE152979, 1939 DEGs were identified in patients with ARDS and controls; they were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes pathways. RBP7 had the highest area under the curve values among the 12 hub genes and was mainly expressed in CD14 cells. Additionally, hub genes were negatively correlated with NK cells and positively correlated with neutrophils, cytotoxic cells, B cells, and macrophages. Conclusion A severe imbalance in the proportion of immune cells and immune dysfunction were observed in SEP-ARDS and PNE-ARDS patients. RBP7 may be immunologically associated with CD14 cells and serve as a potential marker of ARDS.
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Affiliation(s)
- Dan He
- Department of General Practice, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Qiao Yu
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Xiaona Zeng
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Jihua Feng
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Ruiqi Yang
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Huan Wan
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Ying Zhong
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Yanli Yang
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Ruzhi Zhao
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
| | - Junyu Lu
- Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
- Guangxi Health Commission Key Laboratory of Emergency and Critical Medicine, Nanning, 530007, People’s Republic of China
| | - Jianfeng Zhang
- Department of General Practice, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China
- Guangxi Health Commission Key Laboratory of Emergency and Critical Medicine, Nanning, 530007, People’s Republic of China
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15
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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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16
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Franchini M, Pellecchia S, Viscido G, Gambardella G. Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genom Bioinform 2023; 5:lqad024. [PMID: 36879897 PMCID: PMC9985338 DOI: 10.1093/nargab/lqad024] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/16/2023] [Accepted: 02/20/2023] [Indexed: 03/07/2023] Open
Abstract
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
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Affiliation(s)
- Melania Franchini
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy.,Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
| | - Simona Pellecchia
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy
| | - Gaetano Viscido
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy
| | - Gennaro Gambardella
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy.,Department of Chemical Materials and Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
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17
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Riojas AM, Spradling-Reeves KD, Christensen CL, Hall-Ursone S, Cox LA. Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.528258. [PMID: 36824792 PMCID: PMC9949078 DOI: 10.1101/2023.02.13.528258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Traditional bulk RNA-Seq pipelines do not assess cell-type composition within heterogeneous tissues. Therefore, it is difficult to determine whether conflicting findings among samples or datasets are the result of biological differences or technical differences due to variation in sample collections. This report provides a user-friendly, open source method to assess cell-type composition in bulk RNA-Seq datasets for heterogeneous tissues using published single cell (sc)RNA-Seq data as a reference. As an example, we apply the method to analysis of kidney cortex bulk RNA-Seq data from female (N=8) and male (N=9) baboons to assess whether observed transcriptome sex differences are biological or technical, i.e., variation due to ultrasound guided biopsy collections. We found cell-type composition was not statistically different in female versus male transcriptomes based on expression of 274 kidney cell-type specific transcripts, indicating differences in gene expression are not due to sampling differences. This method of cell-type composition analysis is recommended for providing rigor in analysis of bulk RNA-Seq datasets from complex tissues. It is clear that with reduced costs, more analyses will be done using scRNA-Seq; however, the approach described here is relevant for data mining and meta analyses of the thousands of bulk RNA-Seq data archived in the NCBI GEO public database.
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Affiliation(s)
- Angelica M. Riojas
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Kimberly D. Spradling-Reeves
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | | | - Shannan Hall-Ursone
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Laura A. Cox
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA
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18
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Noureen N, Wang X, Zheng S. Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer. STAR Protoc 2022; 3:101877. [PMID: 36595948 PMCID: PMC9706629 DOI: 10.1016/j.xpro.2022.101877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/26/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulated signatures, generating gold standard signatures for specificity and sensitivity tests, and simulating the impact of dropouts using down sampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such context. For complete details on the use and execution of this protocol, please refer to Noureen et al. (2022).1.
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Affiliation(s)
- Nighat Noureen
- Greehey Children's Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA.
| | - Xiaojing Wang
- Greehey Children's Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
| | - Siyuan Zheng
- Greehey Children's Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA; Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA.
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19
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
- Department of Immunology, Nanjing Medical University, Nanjing, 211166 China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401174 China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110 Guangdong China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW 2308 Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305 Australia
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
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20
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Bishara I, Chen J, Griffiths JI, Bild AH, Nath A. A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types. Front Genet 2022; 13:982019. [PMID: 36506328 PMCID: PMC9732024 DOI: 10.3389/fgene.2022.982019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/04/2022] [Indexed: 11/27/2022] Open
Abstract
Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.
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Affiliation(s)
- Isaac Bishara
- Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, United States,Irell & Manella Graduate School of Biological Science, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Jinfeng Chen
- Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, United States,State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Jason I. Griffiths
- Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Andrea H. Bild
- Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Aritro Nath
- Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, United States,*Correspondence: Aritro Nath,
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Subramanian A, Zakeri P, Mousa M, Alnaqbi H, Alshamsi FY, Bettoni L, Damiani E, Alsafar H, Saeys Y, Carmeliet P. Angiogenesis goes computational - The future way forward to discover new angiogenic targets? Comput Struct Biotechnol J 2022; 20:5235-5255. [PMID: 36187917 PMCID: PMC9508490 DOI: 10.1016/j.csbj.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.
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Affiliation(s)
- Abhishek Subramanian
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Pooya Zakeri
- Laboratory of Angiogenesis & Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Brain and Disease Research, Flanders Institute for Biotechnology (VIB), Leuven, Belgium
- Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Halima Alnaqbi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Fatima Yousif Alshamsi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Leo Bettoni
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ernesto Damiani
- Robotics and Intelligent Systems Institute, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Peter Carmeliet
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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