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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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Konecny T, Nikoghosyan M, Binder H. Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of Vitis vinifera. FRONTIERS IN PLANT SCIENCE 2023; 14:1303542. [PMID: 38126012 PMCID: PMC10731266 DOI: 10.3389/fpls.2023.1303542] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/14/2023] [Indexed: 12/23/2023]
Abstract
Introduction The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Vitis vinifera. Temperature variations can affect the growth and overall health of vine. Methods We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five Vitis vinifera cultivars each treated by four different temperature conditions. The algorithm generated sample-specific "portraits" of the normalized gene expression data, revealing distinct patterns related to the temperature conditions applied. Results Our analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines. Discussion Application of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants.
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Affiliation(s)
- Tomas Konecny
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Maria Nikoghosyan
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Bioinformatics Group, Institute of Molecular Biology Institute of National Academy of Sciences RA, Yerevan, Armenia
| | - Hans Binder
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
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3
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Ashekyan O, Shahbazyan N, Bareghamyan Y, Kudryavzeva A, Mandel D, Schmidt M, Loeffler-Wirth H, Uduman M, Chand D, Underwood D, Armen G, Arakelyan A, Nersisyan L, Binder H. Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine. Cancers (Basel) 2023; 15:3835. [PMID: 37568651 PMCID: PMC10417131 DOI: 10.3390/cancers15153835] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.
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Affiliation(s)
- Ohanes Ashekyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Nerses Shahbazyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Yeva Bareghamyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Anna Kudryavzeva
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Daria Mandel
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Maria Schmidt
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Henry Loeffler-Wirth
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Mohamed Uduman
- Agenus Inc., 3 Forbes Road, Lexington, MA 7305, USA; (M.U.); (D.C.); (D.U.); (G.A.)
| | - Dhan Chand
- Agenus Inc., 3 Forbes Road, Lexington, MA 7305, USA; (M.U.); (D.C.); (D.U.); (G.A.)
| | - Dennis Underwood
- Agenus Inc., 3 Forbes Road, Lexington, MA 7305, USA; (M.U.); (D.C.); (D.U.); (G.A.)
| | - Garo Armen
- Agenus Inc., 3 Forbes Road, Lexington, MA 7305, USA; (M.U.); (D.C.); (D.U.); (G.A.)
| | - Arsen Arakelyan
- Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Has-Ratyan Str., Yerevan 0014, Armenia;
| | - Lilit Nersisyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
| | - Hans Binder
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia; (O.A.); (N.S.); (Y.B.); (A.K.); (D.M.); (L.N.)
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
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Kirsten T, Meineke FA, Loeffler-Wirth H, Beger C, Uciteli A, Stäubert S, Löbe M, Hänsel R, Rauscher FG, Schuster J, Peschel T, Herre H, Wagner J, Zachariae S, Engel C, Scholz M, Rahm E, Binder H, Loeffler M. The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online. Methods Inf Med 2022; 61:e103-e115. [PMID: 35915977 PMCID: PMC9788914 DOI: 10.1055/a-1914-1985] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. OBJECTIVE The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. METHODS Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. RESULTS The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.
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Affiliation(s)
- Toralf Kirsten
- Department of Medical Data Science, Leipzig University Medical Center, Leipzig, Germany,Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany,Address for correspondence Toralf Kirsten Department of Medical Data Science, Leipzig UniversityHärtelstraße 16-18, 04107 LeipzigGermany
| | - Frank A. Meineke
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Henry Loeffler-Wirth
- LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Christoph Beger
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Alexandr Uciteli
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Sebastian Stäubert
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Matthias Löbe
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
| | - René Hänsel
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Franziska G. Rauscher
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Judith Schuster
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Thomas Peschel
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Heinrich Herre
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Jonas Wagner
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Silke Zachariae
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Christoph Engel
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
| | - Erhard Rahm
- Department of Computer Sciences, Leipzig University, Leipzig, Germany
| | - Hans Binder
- LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany,Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany,LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany
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Loeffler-Wirth H, Rade M, Arakelyan A, Kreuz M, Loeffler M, Koehl U, Reiche K, Binder H. Transcriptional states of CAR-T infusion relate to neurotoxicity – lessons from high-resolution single-cell SOM expression portraying. Front Immunol 2022; 13:994885. [PMID: 36248848 PMCID: PMC9558919 DOI: 10.3389/fimmu.2022.994885] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022] Open
Abstract
Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.
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Affiliation(s)
- Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- *Correspondence: Henry Loeffler-Wirth,
| | - Michael Rade
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Arsen Arakelyan
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, Yerevan, Armenia
| | - Markus Kreuz
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Markus Loeffler
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
| | - Ulrike Koehl
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Kristin Reiche
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
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6
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Loeffler-Wirth H, Kreuz M, Schmidt M, Ott G, Siebert R, Binder H. Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape. Cancers (Basel) 2022; 14:3434. [PMID: 35884496 PMCID: PMC9321060 DOI: 10.3390/cancers14143434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022] Open
Abstract
Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.
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Affiliation(s)
- Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
| | - Markus Kreuz
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), 04103 Leipzig, Germany;
| | - Maria Schmidt
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
| | - German Ott
- Department of Clinical Pathology, Robert-Bosch-Krankenhaus, Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376 Stuttgart, Germany;
| | - Reiner Siebert
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89073 Ulm, Germany;
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
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Integrated Multi-Omics Maps of Lower-Grade Gliomas. Cancers (Basel) 2022; 14:cancers14112797. [PMID: 35681780 PMCID: PMC9179546 DOI: 10.3390/cancers14112797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 02/01/2023] Open
Abstract
Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It "portrays" the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.
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The Transcriptome and Methylome of the Developing and Aging Brain and Their Relations to Gliomas and Psychological Disorders. Cells 2022; 11:cells11030362. [PMID: 35159171 PMCID: PMC8834030 DOI: 10.3390/cells11030362] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/15/2022] [Accepted: 01/18/2022] [Indexed: 02/01/2023] Open
Abstract
Mutually linked expression and methylation dynamics in the brain govern genome regulation over the whole lifetime with an impact on cognition, psychological disorders, and cancer. We performed a joint study of gene expression and DNA methylation of brain tissue originating from the human prefrontal cortex of individuals across the lifespan to describe changes in cellular programs and their regulation by epigenetic mechanisms. The analysis considers previous knowledge in terms of functional gene signatures and chromatin states derived from independent studies, aging profiles of a battery of chromatin modifying enzymes, and data of gliomas and neuropsychological disorders for a holistic view on the development and aging of the brain. Expression and methylation changes from babies to elderly adults decompose into different modes associated with the serial activation of (brain) developmental, learning, metabolic and inflammatory functions, where methylation in gene promoters mostly represses transcription. Expression of genes encoding methylome modifying enzymes is very diverse reflecting complex regulations during lifetime which also associates with the marked remodeling of chromatin between permissive and restrictive states. Data of brain cancer and psychotic disorders reveal footprints of pathophysiologies related to brain development and aging. Comparison of aging brains with gliomas supports the view that glioblastoma-like and astrocytoma-like tumors exhibit higher cellular plasticity activated in the developing healthy brain while oligodendrogliomas have a more stable differentiation hierarchy more resembling the aged brain. The balance and specific shifts between volatile and stable and between more irreversible and more plastic epigenomic networks govern the development and aging of healthy and diseased brain.
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9
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Schmidt M, Arshad M, Bernhart SH, Hakobyan S, Arakelyan A, Loeffler-Wirth H, Binder H. The Evolving Faces of the SARS-CoV-2 Genome. Viruses 2021; 13:1764. [PMID: 34578345 PMCID: PMC8472651 DOI: 10.3390/v13091764] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 02/07/2023] Open
Abstract
Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for 'bioinformatics surveillance' of the pandemic, with strong odds regarding visualization, intuitive perception and 'personalization' of the mutational patterns of the virus genomes.
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Affiliation(s)
- Maria Schmidt
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.A.); (S.H.B.); (H.L.-W.)
| | - Mamoona Arshad
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.A.); (S.H.B.); (H.L.-W.)
| | - Stephan H. Bernhart
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.A.); (S.H.B.); (H.L.-W.)
| | - Siras Hakobyan
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (S.H.); (A.A.)
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Hasratyan Str., Yerevan 0014, Armenia
| | - Arsen Arakelyan
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (S.H.); (A.A.)
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Hasratyan Str., Yerevan 0014, Armenia
| | - Henry Loeffler-Wirth
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.A.); (S.H.B.); (H.L.-W.)
| | - Hans Binder
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (M.A.); (S.H.B.); (H.L.-W.)
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (S.H.); (A.A.)
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10
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Willscher E, Hopp L, Kreuz M, Schmidt M, Hakobyan S, Arakelyan A, Hentschel B, Jones DTW, Pfister SM, Loeffler M, Loeffler-Wirth H, Binder H. High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas. Cancers (Basel) 2021; 13:3198. [PMID: 34206856 PMCID: PMC8268631 DOI: 10.3390/cancers13133198] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 02/01/2023] Open
Abstract
Molecular mechanisms of lower-grade (II-III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context.
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Affiliation(s)
- Edith Willscher
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (E.W.); (L.H.); (M.S.)
| | - Lydia Hopp
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (E.W.); (L.H.); (M.S.)
| | - Markus Kreuz
- IMISE, Institute for Medical Informatics, Statistics and Epidemiology, Universität of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.K.); (B.H.); (M.L.)
| | - Maria Schmidt
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (E.W.); (L.H.); (M.S.)
| | - Siras Hakobyan
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Hasratyan Str., Yerevan 0014, Armenia; (S.H.); (A.A.)
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (D.T.W.J.); (S.M.P.)
| | - Arsen Arakelyan
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Hasratyan Str., Yerevan 0014, Armenia; (S.H.); (A.A.)
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (D.T.W.J.); (S.M.P.)
| | - Bettina Hentschel
- IMISE, Institute for Medical Informatics, Statistics and Epidemiology, Universität of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.K.); (B.H.); (M.L.)
| | - David T. W. Jones
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (D.T.W.J.); (S.M.P.)
- Hopp Children’s Cancer Center Heidelberg (KiTZ), Im Neuenheimer Feld 430, 69120 Heidelberg, Germany
| | - Stefan M. Pfister
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (D.T.W.J.); (S.M.P.)
- Hopp Children’s Cancer Center Heidelberg (KiTZ), Im Neuenheimer Feld 430, 69120 Heidelberg, Germany
| | - Markus Loeffler
- IMISE, Institute for Medical Informatics, Statistics and Epidemiology, Universität of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.K.); (B.H.); (M.L.)
| | - Henry Loeffler-Wirth
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (E.W.); (L.H.); (M.S.)
| | - Hans Binder
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany; (E.W.); (L.H.); (M.S.)
- Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia; (D.T.W.J.); (S.M.P.)
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11
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Guo G, Gou Y, Jiang X, Wang S, Wang R, Liang C, Yang G, Wang T, Yu A, Zhu G. Long Non-coding RNAs in Traumatic Brain Injury Accelerated Fracture Healing. Front Surg 2021; 8:663377. [PMID: 34150839 PMCID: PMC8211774 DOI: 10.3389/fsurg.2021.663377] [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: 02/02/2021] [Accepted: 04/28/2021] [Indexed: 11/13/2022] Open
Abstract
It is commonly observed that patients with bone fracture concomitant with traumatic brain injury (TBI) had significantly increased fracture healing, but the underlying mechanisms were not fully revealed. Long non-coding RNAs (lncRNAs) are known to play complicated roles in bone homeostasis, but their role in TBI accelerated fracture was rarely reported. The present study was designed to determine the role of lncRNAs in TBI accelerated fracture via transcriptome sequencing and further bioinformatics analyses. Blood samples from three fracture-only patients, three fracture concomitant with TBI patients, and three healthy controls were harvested and were subsequently subjected to transcriptome lncRNA sequencing. Differentially expressed genes were identified, and pathway enrichment was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. High-dimensional data visualization by self-organizing map (SOM) machine learning was applied to further interpret the data. An xCell method was then used to predict cellular behavior in all samples based on gene expression profiles, and an lncRNA-cell interaction network was generated. A total of 874 differentially expressed genes were identified, of which about 26% were lncRNAs. Those identified lncRNAs were mainly enriched on TBI-related and damage repair-related pathways. SOM analyses revealed that those differentially expressed lncRNAs could be divided into three major module implications and were mainly enriched on transcriptional regulation and immune-related signal pathways, which promote us to further explore cellular behaviors based on differentially expressed lncRNAs. We have predicted that basophils, CD8+ T effector memory cells, B cells, and naïve B cells were significantly downregulated, while microvascular endothelial cells were predicted to be significantly upregulated in the Fr/TBI group, was the lowest and highest, respectively. ENSG00000278905, ENSG00000240980, ENSG00000255670, and ENSG00000196634 were the most differentially expressed lncRNAs related to all changes of cellular behavior. The present study has revealed for the first time that several critical lncRNAs may participate in TBI accelerated fracture potentially via regulating cellular behaviors of basophils, cytotoxic T cells, B cells, and endothelial cells.
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Affiliation(s)
- Guoning Guo
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Yajun Gou
- Department of Orthopedic, Shapingba District Hospital, Chongqing, China
| | - Xingyu Jiang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Shuhong Wang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Ruilie Wang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Changqiang Liang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Guang Yang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Tinggang Wang
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Anyong Yu
- Department of Emergency, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Guoyan Zhu
- Department of Health Management, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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12
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Wolf J, Willscher E, Loeffler-Wirth H, Schmidt M, Flemming G, Zurek M, Uhlig HH, Händel N, Binder H. Deciphering the Transcriptomic Heterogeneity of Duodenal Coeliac Disease Biopsies. Int J Mol Sci 2021; 22:ijms22052551. [PMID: 33806322 PMCID: PMC7961974 DOI: 10.3390/ijms22052551] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/11/2022] Open
Abstract
Coeliac disease (CD) is a clinically heterogeneous autoimmune disease with variable presentation and progression triggered by gluten intake. Molecular or genetic factors contribute to disease heterogeneity, but the reasons for different outcomes are poorly understood. Transcriptome studies of tissue biopsies from CD patients are scarce. Here, we present a high-resolution analysis of the transcriptomes extracted from duodenal biopsies of 24 children and adolescents with active CD and 21 individuals without CD but with intestinal afflictions as controls. The transcriptomes of CD patients divide into three groups-a mixed group presenting the control cases, and CD-low and CD-high groups referring to lower and higher levels of CD severity. Persistence of symptoms was weakly associated with subgroup, but the highest marsh stages were present in subgroup CD-high, together with the highest cell cycle rates as an indicator of virtually complete villous atrophy. Considerable variation in inflammation-level between subgroups was further deciphered into immune cell types using cell type de-convolution. Self-organizing maps portrayal was applied to provide high-resolution landscapes of the CD-transcriptome. We find asymmetric patterns of miRNA and long non-coding RNA and discuss the effect of epigenetic regulation. Expression of genes involved in interferon gamma signaling represent suitable markers to distinguish CD from non-CD cases. Multiple pathways overlay in CD biopsies in different ways, giving rise to heterogeneous transcriptional patterns, which potentially provide information about etiology and the course of the disease.
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Affiliation(s)
- Johannes Wolf
- Department of Laboratory Medicine at Hospital “St. Georg” Leipzig, 04129 Leipzig, Germany;
- Immuno Deficiency Centre Leipzig (IDCL) at Hospital St. Georg Leipzig, Jeffrey Modell Diagnostic and Research Centre for Primary Immunodeficiency Diseases, 04129 Leipzig, Germany
| | - Edith Willscher
- IZBI, Interdisciplinary Centre for Bioinformatics, University Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (E.W.); (H.L.-W.); (M.S.)
| | - Henry Loeffler-Wirth
- IZBI, Interdisciplinary Centre for Bioinformatics, University Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (E.W.); (H.L.-W.); (M.S.)
| | - Maria Schmidt
- IZBI, Interdisciplinary Centre for Bioinformatics, University Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (E.W.); (H.L.-W.); (M.S.)
| | - Gunter Flemming
- Paediatric Gastroenterology Unit, University Hospital for Children and Adolescents, 04103 Leipzig, Germany;
| | - Marlen Zurek
- Children’s Hospital of the Clinical Centre “Sankt Georg”, 04129 Leipzig, Germany; (M.Z.); (N.H.)
| | - Holm H. Uhlig
- Translational Gastroenterology Unit, Oxford NIHR Biomedical Research Centre, Experimental Medicine, Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Oxford OX4 2PG, UK;
| | - Norman Händel
- Children’s Hospital of the Clinical Centre “Sankt Georg”, 04129 Leipzig, Germany; (M.Z.); (N.H.)
| | - Hans Binder
- IZBI, Interdisciplinary Centre for Bioinformatics, University Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (E.W.); (H.L.-W.); (M.S.)
- Correspondence:
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