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Zhang L, Nishi H, Kinoshita K. Multi-Omics Profiling Reveals Phenotypic and Functional Heterogeneity of Neutrophils in COVID-19. Int J Mol Sci 2024; 25:3841. [PMID: 38612651 PMCID: PMC11011481 DOI: 10.3390/ijms25073841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed. This study aimed to identify and validate the heterogeneity of neutrophils in COVID-19 and evaluate the functions of each subpopulation. We analyzed public single-cell RNA-seq, bulk RNA-seq, and proteome data from healthy donors and patients with COVID-19 to investigate neutrophil subpopulations and their response to disease pathogenesis. We identified eight neutrophil subtypes: pro-neutrophil, pre-neutrophil, immature neutrophil, and five mature neutrophil subpopulations. The subtypes exhibited distinct features, including diverse activation signatures and multiple enriched pathways. The pro-neutrophil subtype was associated with severe and fatal disease, while the pre-neutrophil subtype was particularly abundant in mild/moderate disease. One of the mature neutrophil subtypes showed consistently large fractions in patients with different disease severity. Bulk RNA-seq dataset analyses using a cellular deconvolution approach validated the relative abundances of neutrophil subtypes and the expansion of pro-neutrophils in severe COVID-19 patients. Cell-cell communication analysis revealed representative ligand-receptor interactions among the identified neutrophil subtypes. Further investigation into transcription factors and differential protein abundance revealed the regulatory network differences between healthy donors and patients with severe COVID-19. Overall, we demonstrated the complex interactions among heterogeneous neutrophil subtypes and other blood cell types during COVID-19 disease. Our work has great value in terms of both clinical and public health as it furthers our understanding of the phenotypic and functional heterogeneity of neutrophils and other cell populations in multiple diseases.
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Affiliation(s)
- Lin Zhang
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Miyagi, Japan
- Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Miyagi, Japan
| | - Hafumi Nishi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Miyagi, Japan
- Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Miyagi, Japan
- Faculty of Core Research, Ochanomizu University, Tokyo 112-8610, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Miyagi, Japan
- Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Miyagi, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai 980-8573, Miyagi, Japan
- Department of In Silico Analyses, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Miyagi, Japan
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2
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Han DJ, Kim S, Lee SY, Kang SJ, Moon Y, Kim HS, Kim M, Kim TM. Cellular abundance-based prognostic model associated with deregulated gene expression of leukemic stem cells in acute myeloid leukemia. Front Cell Dev Biol 2024; 12:1345660. [PMID: 38523628 PMCID: PMC10958127 DOI: 10.3389/fcell.2024.1345660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/06/2024] [Indexed: 03/26/2024] Open
Abstract
Background: Previous studies have reported that genes highly expressed in leukemic stem cells (LSC) may dictate the survival probability of patients and expression-based cellular deconvolution may be informative in forecasting prognosis. However, whether the prognosis of acute myeloid leukemia (AML) can be predicted using gene expression and deconvoluted cellular abundances is debatable. Methods: Nine different cell-type abundances of a training set composed of the AML samples of 422 patients, were used to build a model for predicting prognosis by least absolute shrinkage and selection operator Cox regression. This model was validated in two different validation sets, TCGA-LAML and Beat AML (n = 179 and 451, respectively). Results: We introduce a new prognosis predicting model for AML called the LSC activity (LSCA) score, which incorporates the abundance of 5 cell types, granulocyte-monocyte progenitors, common myeloid progenitors, CD45RA + cells, megakaryocyte-erythrocyte progenitors, and multipotent progenitors. Overall survival probabilities between the high and low LSCA score groups were significantly different in TCGA-LAML and Beat AML cohorts (log-rank p-value = 3.3 × 10 - 4 and 4.3 × 10 - 3 , respectively). Also, multivariate Cox regression analysis on these two validation sets shows that LSCA score is independent prognostic factor when considering age, sex, and cytogenetic risk (hazard ratio, HR = 2.17; 95% CI 1.40-3.34; p < 0.001 and HR = 1.20; 95% CI 1.02-1.43; p < 0.03, respectively). The performance of the LSCA score was comparable to other prognostic models, LSC17, APS, and CTC scores, as indicated by the area under the curve. Gene set variation analysis with six LSC-related functional gene sets indicated that high and low LSCA scores are associated with upregulated and downregulated genes in LSCs. Conclusion: We have developed a new prognosis prediction scoring system for AML patients, the LSCA score, which uses deconvoluted cell-type abundance only.
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Affiliation(s)
- Dong-Jin Han
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunmin Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seo-Young Lee
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Su Jung Kang
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Youngbeen Moon
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hoon Seok Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Genetic Laboratory Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Myungshin Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Genetic Laboratory Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Tae-Min Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
- CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, Republic of Korea
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3
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Bell-Glenn S, Salas LA, Molinaro AM, Butler RA, Christensen BC, Kelsey KT, Wiencke JK, Koestler DC. Calculating detection limits and uncertainty of reference-based deconvolution of whole-blood DNA methylation data. Epigenomics 2023; 15:435-451. [PMID: 37337720 PMCID: PMC10308256 DOI: 10.2217/epi-2023-0006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 05/16/2023] [Indexed: 06/21/2023] Open
Abstract
DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.
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Affiliation(s)
- Shelby Bell-Glenn
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH 03756, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Rondi A Butler
- Departments of Epidemiology & Pathology & Laboratory Medicine, Brown University, Providence, RI 02912, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH 03756, USA
- Department of Molecular & Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA
- Department of Community & Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA
| | - Karl T Kelsey
- Departments of Epidemiology & Pathology & Laboratory Medicine, Brown University, Providence, RI 02912, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA
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Trink Y, Urbach A, Dekel B, Hohenstein P, Goldberger J, Kalisky T. Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms' Tumors Using Unsupervised Machine Learning. Int J Mol Sci 2023; 24:ijms24043532. [PMID: 36834944 PMCID: PMC9965420 DOI: 10.3390/ijms24043532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Wilms' tumors are pediatric malignancies that are thought to arise from faulty kidney development. They contain a wide range of poorly differentiated cell states resembling various distorted developmental stages of the fetal kidney, and as a result, differ between patients in a continuous manner that is not well understood. Here, we used three computational approaches to characterize this continuous heterogeneity in high-risk blastemal-type Wilms' tumors. Using Pareto task inference, we show that the tumors form a triangle-shaped continuum in latent space that is bounded by three tumor archetypes with "stromal", "blastemal", and "epithelial" characteristics, which resemble the un-induced mesenchyme, the cap mesenchyme, and early epithelial structures of the fetal kidney. By fitting a generative probabilistic "grade of membership" model, we show that each tumor can be represented as a unique mixture of three hidden "topics" with blastemal, stromal, and epithelial characteristics. Likewise, cellular deconvolution allows us to represent each tumor in the continuum as a unique combination of fetal kidney-like cell states. These results highlight the relationship between Wilms' tumors and kidney development, and we anticipate that they will pave the way for more quantitative strategies for tumor stratification and classification.
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Affiliation(s)
- Yaron Trink
- Faculty of Engineering and Bar-Ilan Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Achia Urbach
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Benjamin Dekel
- Pediatric Stem Cell Research Institute and Division of Pediatric Nephrology, Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Tel-Hashomer 5262000, Israel
| | - Peter Hohenstein
- Department of Human Genetics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Jacob Goldberger
- Faculty of Engineering and Bar-Ilan Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Tomer Kalisky
- Faculty of Engineering and Bar-Ilan Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat Gan 5290002, Israel
- Correspondence: ; Tel.: +972-3-738-4656
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5
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Rathnayake SNH, Ditz B, van Nijnatten J, Sadaf T, Hansbro PM, Brandsma CA, Timens W, van Schadewijk A, Hiemstra PS, ten Hacken NHT, Oliver B, Kerstjens HAM, van den Berge M, Faiz A. Smoking induces shifts in cellular composition and transcriptome within the bronchial mucus barrier. Respirology 2023; 28:132-142. [PMID: 36414410 PMCID: PMC10947540 DOI: 10.1111/resp.14401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND AND OBJECTIVE Smoking disturbs the bronchial-mucus-barrier. This study assesses the cellular composition and gene expression shifts of the bronchial-mucus-barrier with smoking to understand the mechanism of mucosal damage by cigarette smoke exposure. We explore whether single-cell-RNA-sequencing (scRNA-seq) based cellular deconvolution (CD) can predict cell-type composition in RNA-seq data. METHODS RNA-seq data of bronchial biopsies from three cohorts were analysed using CD. The cohorts included 56 participants with chronic obstructive pulmonary disease [COPD] (38 smokers; 18 ex-smokers), 77 participants without COPD (40 never-smokers; 37 smokers) and 16 participants who stopped smoking for 1 year (11 COPD and 5 non-COPD-smokers). Differential gene expression was used to investigate gene expression shifts. The CD-derived goblet cell ratios were validated by correlating with staining-derived goblet cell ratios from the COPD cohort. Statistics were done in the R software (false discovery rate p-value < 0.05). RESULTS Both CD methods indicate a shift in bronchial-mucus-barrier cell composition towards goblet cells in COPD and non-COPD-smokers compared to ex- and never-smokers. It shows that the effect was reversible within a year of smoking cessation. A reduction of ciliated and basal cells was observed with current smoking, which resolved following smoking cessation. The expression of mucin and sodium channel (ENaC) genes, but not chloride channel genes, were altered in COPD and current smokers compared to never smokers or ex-smokers. The goblet cell-derived staining scores correlate with CD-derived goblet cell ratios. CONCLUSION Smoking alters bronchial-mucus-barrier cell composition, transcriptome and increases mucus production. This effect is partly reversible within a year of smoking cessation. CD methodology can predict goblet-cell percentages from RNA-seq.
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Affiliation(s)
- Senani N. H. Rathnayake
- University of Technology Sydney, Respiratory Bioinformatics and Molecular Biology (RBMB), School of Life SciencesSydneyNew South WalesAustralia
- The University of Sydney, Respiratory Cellular and Molecular Biology (RCMB), Woolcock Institute of Medical ResearchSydneyNew South WalesAustralia
| | - Benedikt Ditz
- Department of Pulmonary DiseasesUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
| | - Jos van Nijnatten
- University of Technology Sydney, Respiratory Bioinformatics and Molecular Biology (RBMB), School of Life SciencesSydneyNew South WalesAustralia
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
- Department of Pathology & Medical BiologyUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
| | - Tayyaba Sadaf
- University of Technology Sydney, Respiratory Bioinformatics and Molecular Biology (RBMB), School of Life SciencesSydneyNew South WalesAustralia
- Centre for InflammationCentenary Institute, and the University of Technology Sydney, Faculty of ScienceSydneyNew South WalesAustralia
| | - Philip M. Hansbro
- Centre for InflammationCentenary Institute, and the University of Technology Sydney, Faculty of ScienceSydneyNew South WalesAustralia
| | - Corry A. Brandsma
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
- Department of Pathology & Medical BiologyUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
| | - Wim Timens
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
- Department of Pathology & Medical BiologyUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
| | | | - Peter S. Hiemstra
- Department of PulmonologyLeiden University Medical CenterLeidenthe Netherlands
| | - Nick H. T. ten Hacken
- Department of Pulmonary DiseasesUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
| | - Brian Oliver
- The University of Sydney, Respiratory Cellular and Molecular Biology (RCMB), Woolcock Institute of Medical ResearchSydneyNew South WalesAustralia
| | - Huib A. M. Kerstjens
- Department of Pulmonary DiseasesUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
| | - Maarten van den Berge
- Department of Pulmonary DiseasesUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPDGroningenthe Netherlands
| | - Alen Faiz
- University of Technology Sydney, Respiratory Bioinformatics and Molecular Biology (RBMB), School of Life SciencesSydneyNew South WalesAustralia
- The University of Sydney, Respiratory Cellular and Molecular Biology (RCMB), Woolcock Institute of Medical ResearchSydneyNew South WalesAustralia
- Department of Pulmonary DiseasesUniversity of Groningen, University Medical Center GroningenGroningenthe Netherlands
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Eyres M, Bell JA, Davies ER, Fabre A, Alzetani A, Jogai S, Marshall BG, Johnston DA, Xu Z, Fletcher SV, Wang Y, Marshall G, Davies DE, Offer E, Jones MG. Spatially resolved deconvolution of the fibrotic niche in lung fibrosis. Cell Rep 2022; 40:111230. [PMID: 35977489 PMCID: PMC10073410 DOI: 10.1016/j.celrep.2022.111230] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/07/2022] [Accepted: 07/26/2022] [Indexed: 11/03/2022] Open
Abstract
A defining pathological feature of human lung fibrosis is localized tissue heterogeneity, which challenges the interpretation of transcriptomic studies that typically lose spatial information. Here we investigate spatial gene expression in diagnostic tissue using digital profiling technology. We identify distinct, region-specific gene expression signatures as well as shared gene signatures. By integration with single-cell data, we spatially map the cellular composition within and distant from the fibrotic niche, demonstrating discrete changes in homeostatic and pathologic cell populations even in morphologically preserved lung, while through ligand-receptor analysis, we investigate cellular cross-talk within the fibrotic niche. We confirm findings through bioinformatic, tissue, and in vitro analyses, identifying that loss of NFKB inhibitor zeta in alveolar epithelial cells dysregulates the TGFβ/IL-6 signaling axis, which may impair homeostatic responses to environmental stress. Thus, spatially resolved deconvolution advances understanding of cell composition and microenvironment in human lung fibrogenesis.
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Affiliation(s)
- Michael Eyres
- Medicines Discovery Catapult, Alderley Park, Cheshire, UK
| | - Joseph A Bell
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Elizabeth R Davies
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Aurelie Fabre
- Department of Histopathology, St. Vincent's University Hospital & UCD School of Medicine, University College Dublin, Dublin, Ireland
| | - Aiman Alzetani
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; University Hospital Southampton, Southampton, UK
| | - Sanjay Jogai
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; University Hospital Southampton, Southampton, UK
| | - Ben G Marshall
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; University Hospital Southampton, Southampton, UK
| | - David A Johnston
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Zijian Xu
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Sophie V Fletcher
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; University Hospital Southampton, Southampton, UK
| | - Yihua Wang
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Gayle Marshall
- Medicines Discovery Catapult, Alderley Park, Cheshire, UK
| | - Donna E Davies
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Emily Offer
- Medicines Discovery Catapult, Alderley Park, Cheshire, UK
| | - Mark G Jones
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK; Institute for Life Sciences, University of Southampton, Southampton, UK.
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7
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Bordone MC, Barbosa-Morais NL. Unraveling Targetable Systemic and Cell-Type-Specific Molecular Phenotypes of Alzheimer's and Parkinson's Brains With Digital Cytometry. Front Neurosci 2020; 14:607215. [PMID: 33362460 PMCID: PMC7756021 DOI: 10.3389/fnins.2020.607215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/17/2020] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most common neurodegenerative disorders worldwide, with age being their major risk factor. The increasing worldwide life expectancy, together with the scarcity of available treatment choices, makes it thus pressing to find the molecular basis of AD and PD so that the causing mechanisms can be targeted. To study these mechanisms, gene expression profiles have been compared between diseased and control brain tissues. However, this approach is limited by mRNA expression profiles derived for brain tissues highly reflecting their degeneration in cellular composition but not necessarily disease-related molecular states. We therefore propose to account for cell type composition when comparing transcriptomes of healthy and diseased brain samples, so that the loss of neurons can be decoupled from pathology-associated molecular effects. This approach allowed us to identify genes and pathways putatively altered systemically and in a cell-type-dependent manner in AD and PD brains. Moreover, using chemical perturbagen data, we computationally identified candidate small molecules for specifically targeting the profiled AD/PD-associated molecular alterations. Our approach therefore not only brings new insights into the disease-specific and common molecular etiologies of AD and PD but also, in these realms, foster the discovery of more specific targets for functional and therapeutic exploration.
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Affiliation(s)
- Marie C Bordone
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Nuno L Barbosa-Morais
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
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