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Orsenigo F, Stewart A, Hammer CP, Clarke E, Simpkin D, Attia H, Rockall T, Gordon S, Martinez FO. Unifying considerations and evidence of macrophage activation mosaicism through human CSF1R and M1/M2 genes. Cell Rep 2024; 43:114352. [PMID: 38870011 DOI: 10.1016/j.celrep.2024.114352] [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: 12/04/2023] [Revised: 05/02/2024] [Accepted: 05/28/2024] [Indexed: 06/15/2024] Open
Abstract
Addressing the mononuclear phagocyte system (MPS) and macrophage M1/M2 activation is important in diagnosing hematological disorders and inflammatory pathologies and designing therapeutic tools. CSF1R is a reliable marker to identify all circulating MPS cells and tissue macrophages in humans using a single surface protein. CSF1R permits the quantification and isolation of monocyte and dendritic cell (DC) subsets in conjunction with CD14, CD16, and CD1c and is stable across the lifespan and sexes in the absence of overt pathology. Beyond cell detection, measuring M1/M2 activation in humans poses challenges due to response heterogeneity, transient signaling, and multiple regulation steps for transcripts and proteins. MPS cells respond in a conserved manner to M1/M2 pathways such as interleukin-4 (IL-4), steroids, interferon-γ (IFNγ), and lipopolysaccharide (LPS), for which we propose an ad hoc modular gene expression tool. Signature analysis highlights macrophage activation mosaicism in experimental samples, an emerging concept that points to mixed macrophage activation states in pathology.
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Affiliation(s)
- Federica Orsenigo
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK
| | - Alexander Stewart
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK; Virology Department, Animal and Plant Health Agency, APHA-Weybridge, KT15 3NB Addlestone, UK
| | - Clare P Hammer
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK; Royal Surrey County Hospital NHS Foundation Trust, GU2 7XX Guildford, UK
| | - Emma Clarke
- Royal Surrey County Hospital NHS Foundation Trust, GU2 7XX Guildford, UK
| | - Daniel Simpkin
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK
| | - Hossameldin Attia
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK; Royal Surrey County Hospital NHS Foundation Trust, GU2 7XX Guildford, UK
| | - Timothy Rockall
- Royal Surrey County Hospital NHS Foundation Trust, GU2 7XX Guildford, UK
| | - Siamon Gordon
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan City 33302, Taiwan; Sir William Dunn School of Pathology, University of Oxford, OX13RE Oxford, UK
| | - Fernando O Martinez
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK.
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2
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Lee HJ, Choi YR, Ko JH, Ryu JS, Oh JY. Defining mesenchymal stem/stromal cell-induced myeloid-derived suppressor cells using single-cell transcriptomics. Mol Ther 2024; 32:1970-1983. [PMID: 38627968 PMCID: PMC11184332 DOI: 10.1016/j.ymthe.2024.04.026] [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: 12/30/2023] [Revised: 02/27/2024] [Accepted: 04/12/2024] [Indexed: 04/29/2024] Open
Abstract
Mesenchymal stem/stromal cells (MSCs) modulate the immune response through interactions with innate immune cells. We previously demonstrated that MSCs alleviate ocular autoimmune inflammation by directing bone marrow cell differentiation from pro-inflammatory CD11bhiLy6ChiLy6Glo cells into immunosuppressive CD11bmidLy6CmidLy6Glo cells. Herein, we analyzed MSC-induced CD11bmidLy6Cmid cells using single-cell RNA sequencing and compared them with CD11bhiLy6Chi cells. Our investigation revealed seven distinct immune cell types including myeloid-derived suppressor cells (MDSCs) in the CD11bmidLy6Cmid cells, while CD11bhiLy6Chi cells included mostly monocytes/macrophages with a small cluster of neutrophils. These MSC-induced MDSCs highly expressed Retnlg, Cxcl3, Cxcl2, Mmp8, Cd14, and Csf1r as well as Arg1. Comparative analyses of CSF-1RhiCD11bmidLy6Cmid and CSF-1RloCD11bmidLy6Cmid cells demonstrated that the former had a homogeneous monocyte morphology and produced elevated levels of interleukin-10. Functionally, these CSF-1RhiCD11bmidLy6Cmid cells, compared with the CSF-1RloCD11bmidLy6Cmid cells, inhibited CD4+ T cell proliferation and promoted CD4+CD25+Foxp3+ Treg expansion in culture and in a mouse model of experimental autoimmune uveoretinitis. Resistin-like molecule (RELM)-γ encoded by Retnlg, one of the highly upregulated genes in MSC-induced MDSCs, had no direct effects on T cell proliferation, Treg expansion, or splenocyte activation. Together, our study revealed a distinct transcriptional profile of MSC-induced MDSCs and identified CSF-1R as a key cell-surface marker for detection and therapeutic enrichment of MDSCs.
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Affiliation(s)
- Hyun Ju Lee
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
| | - Yoo Rim Choi
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
| | - Jung Hwa Ko
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
| | - Jin Suk Ryu
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
| | - Joo Youn Oh
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Ophthalmology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea.
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3
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Akshay A, Katoch M, Shekarchizadeh N, Abedi M, Sharma A, Burkhard FC, Adam RM, Monastyrskaya K, Gheinani AH. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience 2024; 13:giad111. [PMID: 38206587 PMCID: PMC10783149 DOI: 10.1093/gigascience/giad111] [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: 07/04/2023] [Revised: 09/20/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.
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Affiliation(s)
- Akshay Akshay
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Mitali Katoch
- Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Navid Shekarchizadeh
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105 Leipzig, Germany
| | - Masoud Abedi
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
| | - Ankush Sharma
- KG Jebsen Centre for B-cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Fiona C Burkhard
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Rosalyn M Adam
- Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA
- Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, 02142 MA, USA
| | - Katia Monastyrskaya
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Ali Hashemi Gheinani
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
- Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA
- Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, 02142 MA, USA
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Bekhbat M, Drake J, Reed EC, Lauten TH, Natour T, Vladimirov VI, Case AJ. Repeated social defeat stress leads to immunometabolic shifts in innate immune cells of the spleen. Brain Behav Immun Health 2023; 34:100690. [PMID: 37791319 PMCID: PMC10543777 DOI: 10.1016/j.bbih.2023.100690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/23/2023] [Indexed: 10/05/2023] Open
Abstract
Psychosocial stress has been shown to prime peripheral innate immune cells, which take on hyper-inflammatory phenotypes and are implicated in depressive-like behavior in mouse models. However, the impact of stress on cellular metabolic states that are thought to fuel inflammatory phenotypes in immune cells are unknown. Using single cell RNA-sequencing, we investigated mRNA enrichment of immunometabolic pathways in innate immune cells of the spleen in mice subjected to repeated social defeat stress (RSDS) or no stress (NS). RSDS mice displayed a significant increase in the number of splenic macrophages and granulocytes (p < 0.05) compared to NS littermates. RSDS-upregulated genes in macrophages, monocytes, and granulocytes significantly enriched immunometabolic pathways thought to play a role in myeloid-driven inflammation (glycolysis, HIF-1 signaling, MTORC1 signaling) as well as pathways related to oxidative phosphorylation (OXPHOS) and oxidative stress (p < 0.05 and FDR<0.1). These results suggest that the metabolic enhancement reflected by upregulation of glycolytic and OXPHOS pathways may be important for cellular proliferation of splenic macrophages and granulocytes following repeated stress exposure. A better understanding of these intracellular metabolic mechanisms may ultimately help develop novel strategies to reverse the impact of stress and associated peripheral immune changes on the brain and behavior.
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Affiliation(s)
- Mandakh Bekhbat
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, 30322, USA
| | - John Drake
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
| | - Emily C. Reed
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
- Department of Medical Physiology, Texas A&M University, Bryan, TX, 77807, USA
| | - Tatlock H. Lauten
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
- Department of Medical Physiology, Texas A&M University, Bryan, TX, 77807, USA
| | - Tamara Natour
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
- Department of Medical Physiology, Texas A&M University, Bryan, TX, 77807, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
- Department of Psychiatry, University of Arizona, Phoenix, AZ, 85004, USA
- Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Adam J. Case
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, Bryan, TX, 77807, USA
- Department of Medical Physiology, Texas A&M University, Bryan, TX, 77807, USA
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5
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Ibrahim T, Wu P, Wang LJ, Fang-Mei C, Murillo J, Merlo J, Shein SS, Tumanov AV, Lai Z, Weldon K, Chen Y, Ruparel S. Sex-dependent differences in the genomic profile of lingual sensory neurons in naïve and tongue-tumor bearing mice. Sci Rep 2023; 13:13117. [PMID: 37573456 PMCID: PMC10423281 DOI: 10.1038/s41598-023-40380-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/09/2023] [Indexed: 08/14/2023] Open
Abstract
Mechanisms of sex-dependent orofacial pain are widely understudied. A significant gap in knowledge exists about comprehensive regulation of tissue-specific trigeminal sensory neurons in diseased state of both sexes. Using RNA sequencing of FACS sorted retro-labeled sensory neurons innervating tongue tissue, we determined changes in transcriptomic profiles in males and female mice under naïve as well as tongue-tumor bearing conditions Our data revealed the following interesting findings: (1) FACS sorting obtained higher number of neurons from female trigeminal ganglia (TG) compared to males; (2) Naïve female neurons innervating the tongue expressed immune cell markers such as Csf1R, C1qa and others, that weren't expressed in males. This was validated by Immunohistochemistry. (3) Accordingly, immune cell markers such as Csf1 exclusively sensitized TRPV1 responses in female TG neurons. (4) Male neurons were more tightly regulated than female neurons upon tumor growth and very few differentially expressed genes (DEGs) overlapped between the sexes, (5) Male DEGs contained higher number of transcription factors whereas female DEGs contained higher number of enzymes, cytokines and chemokines. Collectively, this is the first study to characterize the effect of sex as well as of tongue-tumor on global gene expression, pathways and molecular function of tongue-innervating sensory neurons.
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Affiliation(s)
- Tarek Ibrahim
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Ping Wu
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Li-Ju Wang
- Greehey Children's Cancer Institute, University of Texas Health San Antonio, San Antonio, USA
- Department of Population Health Sciences, University of Texas Health at San Antonio, San Antonio, USA
| | - Chang Fang-Mei
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Josue Murillo
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Jaclyn Merlo
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Sergey S Shein
- Department of Microbiology, Immunology and Molecular Genetics, University of Texas Health San Antonio, San Antonio, USA
| | - Alexei V Tumanov
- Department of Microbiology, Immunology and Molecular Genetics, University of Texas Health San Antonio, San Antonio, USA
| | - Zhao Lai
- Greehey Children's Cancer Institute, University of Texas Health San Antonio, San Antonio, USA
- Department of Molecular Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA
| | - Korri Weldon
- Greehey Children's Cancer Institute, University of Texas Health San Antonio, San Antonio, USA
- Department of Molecular Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA
| | - Yidong Chen
- Greehey Children's Cancer Institute, University of Texas Health San Antonio, San Antonio, USA
- Department of Population Health Sciences, University of Texas Health at San Antonio, San Antonio, USA
| | - Shivani Ruparel
- Department of Endodontics, School of Dentistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA.
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6
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Akshay A, Katoch M, Shekarchizadeh N, Abedi M, Sharma A, Burkhard FC, Adam RM, Monastyrskaya K, Gheinani AH. Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.04.546825. [PMID: 37461685 PMCID: PMC10349995 DOI: 10.1101/2023.07.04.546825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Background Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. Conclusion MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.
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Affiliation(s)
- Akshay Akshay
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Mitali Katoch
- Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Navid Shekarchizadeh
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105 Leipzig, Germany
| | - Masoud Abedi
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
| | - Ankush Sharma
- KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Fiona C. Burkhard
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Rosalyn M. Adam
- Urological Diseases Research Center, Boston Children’s Hospital, MA, USA
- Harvard Medical School, Boston, Department of Surgery MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Katia Monastyrskaya
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Ali Hashemi Gheinani
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
- Urological Diseases Research Center, Boston Children’s Hospital, MA, USA
- Harvard Medical School, Boston, Department of Surgery MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Ibrahim T, Wu P, Wang LJ, Fang-Mei C, Murillo J, Merlo J, Tumanov A, Lai Z, Weldon K, Chen Y, Ruparel S. Sex-dependent Differences in the Genomic Profile of Lingual Sensory Neurons in Naïve and Tongue-Tumor Bearing Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.14.524011. [PMID: 36711730 PMCID: PMC9882171 DOI: 10.1101/2023.01.14.524011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Mechanisms of sex-dependent orofacial pain are widely understudied. A significant gap in knowledge exists about comprehensive regulation of tissue-specific trigeminal sensory neurons in diseased state of both sexes. Using RNA sequencing of FACS sorted retro-labeled sensory neurons innervating tongue tissue, we determined changes in transcriptomic profiles in males and female mice under naïve as well as tongue-tumor bearing conditions Our data revealed the following interesting findings: 1) Tongue tissue of female mice was innervated with higher number of trigeminal neurons compared to males; 2) Naïve female neurons innervating the tongue exclusively expressed immune cell markers such as Csf1R, C1qa and others, that weren't expressed in males. This was validated by Immunohistochemistry. 4) Accordingly, immune cell markers such as Csf1 exclusively sensitized TRPV1 responses in female TG neurons. 3) Male neurons were more tightly regulated than female neurons upon tumor growth and very few differentially expressed genes (DEGs) overlapped between the sexes, 5) Male DEGs contained higher number of transcription factors whereas female DEGs contained higher number of enzymes, cytokines and chemokines. Collectively, this is the first study to characterize the effect of sex as well as of tongue-tumor on global gene expression, pathways and molecular function of tongue-innervating sensory neurons.
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Affiliation(s)
- Tarek Ibrahim
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
| | - Ping Wu
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
| | - Li-Ju Wang
- Greehey Children’s Cancer Institute, University of Texas Health San Antonio, USA
- Department of Population Health Sciences, University of Texas Health at San Antonio, USA
| | - Chang Fang-Mei
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
| | - Josue Murillo
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
| | - Jaclyn Merlo
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
| | - Alexei Tumanov
- Department of Microbiology, Immunology and Molecular Genetics, University of Texas Health San Antonio, USA
| | - Zhao Lai
- Greehey Children’s Cancer Institute, University of Texas Health San Antonio, USA
- Department of Molecular Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA
| | - Korri Weldon
- Greehey Children’s Cancer Institute, University of Texas Health San Antonio, USA
- Department of Molecular Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA
| | - Yidong Chen
- Greehey Children’s Cancer Institute, University of Texas Health San Antonio, USA
- Department of Population Health Sciences, University of Texas Health at San Antonio, USA
| | - Shivani Ruparel
- Department of Endodontics, School of Dentistry, University of Texas Health San Antonio, USA
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8
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Predicting the severity of COVID-19 patients using the CD24-CSF1R index in whole blood samples. Heliyon 2023; 9:e13945. [PMID: 36851954 PMCID: PMC9946875 DOI: 10.1016/j.heliyon.2023.e13945] [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: 02/03/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.
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Zhu K, Chen Z, Xiao Y, Lai D, Wang X, Fang X, Shu Q. Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine. EPMA J 2023; 14:1-17. [PMID: 36845281 PMCID: PMC9942629 DOI: 10.1007/s13167-023-00317-5] [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: 08/31/2022] [Accepted: 02/01/2023] [Indexed: 02/23/2023]
Abstract
Background Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM). Methods Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified. Results Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FCij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients. Conclusion The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log2fold change (log2FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00317-5.
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Affiliation(s)
- Kun Zhu
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zhonghua Chen
- Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ,Department of Anesthesiology, Shaoxing People’s Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Yi Xiao
- Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Dengming Lai
- Department of Neonatal Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiaofeng Wang
- Department of Information Center, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiangming Fang
- Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Shu
- Department of Thoracic and Cardiovascular Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
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10
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Rigamonti A, Castagna A, Viatore M, Colombo FS, Terzoli S, Peano C, Marchesi F, Locati M. Distinct responses of newly identified monocyte subsets to advanced gastrointestinal cancer and COVID-19. Front Immunol 2022; 13:967737. [PMID: 36263038 PMCID: PMC9576306 DOI: 10.3389/fimmu.2022.967737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/16/2022] [Indexed: 11/25/2022] Open
Abstract
Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.
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Affiliation(s)
- Alessandra Rigamonti
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Alessandra Castagna
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Marika Viatore
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Milan, Italy
| | | | - Sara Terzoli
- Laboratory of Clinical and Experimental Immunology, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Clelia Peano
- Genomic Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Milan, Italy
| | - Federica Marchesi
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Massimo Locati
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
- *Correspondence: Massimo Locati,
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11
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Kgatle MM, Lawal IO, Mashabela G, Boshomane TMG, Koatale PC, Mahasha PW, Ndlovu H, Vorster M, Rodrigues HG, Zeevaart JR, Gordon S, Moura-Alves P, Sathekge MM. COVID-19 Is a Multi-Organ Aggressor: Epigenetic and Clinical Marks. Front Immunol 2021; 12:752380. [PMID: 34691068 PMCID: PMC8531724 DOI: 10.3389/fimmu.2021.752380] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/21/2021] [Indexed: 12/19/2022] Open
Abstract
The progression of coronavirus disease 2019 (COVID-19), resulting from a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, may be influenced by both genetic and environmental factors. Several viruses hijack the host genome machinery for their own advantage and survival, and similar phenomena might occur upon SARS-CoV-2 infection. Severe cases of COVID-19 may be driven by metabolic and epigenetic driven mechanisms, including DNA methylation and histone/chromatin alterations. These epigenetic phenomena may respond to enhanced viral replication and mediate persistent long-term infection and clinical phenotypes associated with severe COVID-19 cases and fatalities. Understanding the epigenetic events involved, and their clinical significance, may provide novel insights valuable for the therapeutic control and management of the COVID-19 pandemic. This review highlights different epigenetic marks potentially associated with COVID-19 development, clinical manifestation, and progression.
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Affiliation(s)
- Mankgopo Magdeline Kgatle
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
| | - Ismaheel Opeyemi Lawal
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, Steve Biko Academic Hospital, Pretoria, South Africa
| | - Gabriel Mashabela
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, DSI/NRF Centre of Excellence for Biomedical TB Research, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Tebatso Moshoeu Gillian Boshomane
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, Steve Biko Academic Hospital, Pretoria, South Africa
- Nuclear and Oncology Division, AXIM Medical (Pty), Midrand
| | - Palesa Caroline Koatale
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
| | - Phetole Walter Mahasha
- Precision Medicine and SAMRC Genomic Centre, Grants, Innovation, and Product Development (GIPD) Unit, South African Medical Research Council, Pretoria, South Africa
| | - Honest Ndlovu
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
| | - Mariza Vorster
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
| | - Hosana Gomes Rodrigues
- Laboratory of Nutrients and Tissue Repair, School of Applied Sciences, University of Campinas, Campinas, Brazil
| | - Jan Rijn Zeevaart
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- South African Nuclear Energy Corporation, Radiochemistry and NuMeRI PreClinical Imaging Facility, Mahikeng, South Africa
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Siamon Gordon
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
| | - Pedro Moura-Alves
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Mike Machaba Sathekge
- Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
- Department of Nuclear Medicine, University of Pretoria & Steve Biko Academic Hospital, Pretoria, South Africa
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, DSI/NRF Centre of Excellence for Biomedical TB Research, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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