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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Front Immunol 2024; 15:1428773. [PMID: 39161769 PMCID: PMC11330812 DOI: 10.3389/fimmu.2024.1428773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024] Open
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- Institute of Computational Life Sciences, Zürich University of Applied Sciences (ZHAW), Wädenswil, Switzerland
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - María Rodríguez Martínez
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, United States
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2
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Zhou X, Pan J, Chen L, Zhang S, Chen Y. DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data. Biomolecules 2024; 14:766. [PMID: 39062480 PMCID: PMC11274664 DOI: 10.3390/biom14070766] [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: 05/11/2024] [Revised: 06/22/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.
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Affiliation(s)
- Xiguo Zhou
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Jingyi Pan
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Liang Chen
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Yong Chen
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
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Wagle MM, Long S, Chen C, Liu C, Yang P. Interpretable deep learning in single-cell omics. Bioinformatics 2024; 40:btae374. [PMID: 38889275 PMCID: PMC11211213 DOI: 10.1093/bioinformatics/btae374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/11/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. RESULTS In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.
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Affiliation(s)
- Manoj M Wagle
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Siqu Long
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Carissa Chen
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Chunlei Liu
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
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4
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Peng D, Cahan P. OneSC: A computational platform for recapitulating cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596831. [PMID: 38895453 PMCID: PMC11185539 DOI: 10.1101/2024.05.31.596831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
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Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland, 21205, USA
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Filipovic D, Kana O, Marri D, Bhattacharya S. Unique challenges and best practices for single cell transcriptomic analysis in toxicology. CURRENT OPINION IN TOXICOLOGY 2024; 38:100475. [PMID: 38645720 PMCID: PMC11027889 DOI: 10.1016/j.cotox.2024.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.
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Affiliation(s)
- David Filipovic
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
| | - Omar Kana
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. [Explainable artificial intelligence in pathology]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:133-139. [PMID: 38315198 DOI: 10.1007/s00292-024-01308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/19/2024] [Indexed: 02/07/2024]
Abstract
With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).
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Affiliation(s)
- Frederick Klauschen
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland.
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland.
| | - Jonas Dippel
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
| | - Philipp Keyl
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Philipp Jurmeister
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Michael Bockmayr
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Pädiatrische Hämatologie und Onkologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Forschungsinstitut Kinderkrebs-Zentrum Hamburg, Hamburg, Deutschland
| | - Andreas Mock
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Oliver Buchstab
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Maximilian Alber
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Aignostics GmbH, Berlin, Deutschland
| | | | - Grégoire Montavon
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
- Fachbereich Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland
| | - Klaus-Robert Müller
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland.
- Department of Artificial Intelligence, Korea University, Seoul, Südkorea.
- Max-Planck-Institut für Informatik, Saarbrücken, Deutschland.
- Machine Learning/Intelligent Data Analysis (IDA), Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Deutschland.
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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. Toward Explainable Artificial Intelligence for Precision Pathology. ANNUAL REVIEW OF PATHOLOGY 2024; 19:541-570. [PMID: 37871132 DOI: 10.1146/annurev-pathmechdis-051222-113147] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.
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Affiliation(s)
- Frederick Klauschen
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Jonas Dippel
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
| | - Philipp Keyl
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Philipp Jurmeister
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Andreas Mock
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Oliver Buchstab
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Maximilian Alber
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Aignostics, Berlin, Germany
| | | | - Grégoire Montavon
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573505. [PMID: 38234732 PMCID: PMC10793435 DOI: 10.1101/2023.12.28.573505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
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Pelissier A, Laragione T, Harris C, Martínez MR, Gulko PS. Gene Network Analyses Identify Co-regulated Transcription Factors and BACH1 as a Key Driver in Rheumatoid Arthritis Fibroblast-like Synoviocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573506. [PMID: 38234777 PMCID: PMC10793426 DOI: 10.1101/2023.12.28.573506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying Rheumatoid Arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks (GRNs) to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This is the first time that BACH1 is shown to have a central role in the regulation of FLS phenotypes, and gene expression signatures, as well as in ferroptosis and fatty acid metabolism. These new discoveries have the potential to become new targets for treatments aimed at selectively targeting the RA FLS.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Carolyn Harris
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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11
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Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci 2023; 332:122110. [PMID: 37734434 DOI: 10.1016/j.lfs.2023.122110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.
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Affiliation(s)
- Srinjan Bhattacharya
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rahul Kumar Mahato
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Satwinder Singh
- Department of Computer Science and Technology, Central University of Punjab, Bathinda 151401, Punjab, India.
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, India
| | - Sarabjit Singh Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Leicestershire, Loughborough LE11 3TU, UK.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India.
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12
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Edsjö A, Holmquist L, Geoerger B, Nowak F, Gomon G, Alix-Panabières C, Ploeger C, Lassen U, Le Tourneau C, Lehtiö J, Ott PA, von Deimling A, Fröhling S, Voest E, Klauschen F, Dienstmann R, Alshibany A, Siu LL, Stenzinger A. Precision cancer medicine: Concepts, current practice, and future developments. J Intern Med 2023; 294:455-481. [PMID: 37641393 DOI: 10.1111/joim.13709] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.
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Affiliation(s)
- Anders Edsjö
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Genomic Medicine Sweden (GMS), Kristianstad, Sweden
| | - Louise Holmquist
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Genomic Medicine Sweden (GMS), Kristianstad, Sweden
| | - Birgit Geoerger
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | | | - Georgy Gomon
- Department of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Catherine Alix-Panabières
- Laboratory of Rare Human Circulating Cells, University Medical Center of Montpellier, Montpellier, France
- CREEC, MIVEGEC, University of Montpellier, Montpellier, France
| | - Carolin Ploeger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Ulrik Lassen
- Department of Oncology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
- INSERM U900 Research Unit, Saint-Cloud, France
- Faculty of Medicine, Paris-Saclay University, Paris, France
| | - Janne Lehtiö
- Department of Oncology Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Andreas von Deimling
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan Fröhling
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Emile Voest
- Department of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frederick Klauschen
- Institute of Pathology, Charite - Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute of Pathology, Ludwig-Maximilians-University, Munich, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich Partner Site, Heidelberg, Germany
| | | | | | - Lillian L Siu
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
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13
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Mao G, Pang Z, Zuo K, Wang Q, Pei X, Chen X, Liu J. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Brief Bioinform 2023; 24:bbad414. [PMID: 37985457 PMCID: PMC10661972 DOI: 10.1093/bib/bbad414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.
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Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Qinglin Wang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xiangdong Pei
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xinhai Chen
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, deya, 410073 Changsha, China
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Kazdal D, Menzel M, Budczies J, Stenzinger A. [Molecular tumor diagnostics as the driving force behind precision oncology]. Dtsch Med Wochenschr 2023; 148:1157-1165. [PMID: 37657453 DOI: 10.1055/a-1937-0347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.
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15
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Fernández-Moya SM, Ganesh AJ, Plass M. Neural cell diversity in the light of single-cell transcriptomics. Transcription 2023; 14:158-176. [PMID: 38229529 PMCID: PMC10807474 DOI: 10.1080/21541264.2023.2295044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024] Open
Abstract
The development of highly parallel and affordable high-throughput single-cell transcriptomics technologies has revolutionized our understanding of brain complexity. These methods have been used to build cellular maps of the brain, its different regions, and catalog the diversity of cells in each of them during development, aging and even in disease. Now we know that cellular diversity is way beyond what was previously thought. Single-cell transcriptomics analyses have revealed that cell types previously considered homogeneous based on imaging techniques differ depending on several factors including sex, age and location within the brain. The expression profiles of these cells have also been exploited to understand which are the regulatory programs behind cellular diversity and decipher the transcriptional pathways driving them. In this review, we summarize how single-cell transcriptomics have changed our view on the cellular diversity in the human brain, and how it could impact the way we study neurodegenerative diseases. Moreover, we describe the new computational approaches that can be used to study cellular differentiation and gain insight into the functions of individual cell populations under different conditions and their alterations in disease.
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Affiliation(s)
- Sandra María Fernández-Moya
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Akshay Jaya Ganesh
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Mireya Plass
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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