1
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Stassen SV, Kobashi M, Lam EY, Huang Y, Ho JWK, Tsia KK. StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases. Genome Biol 2024; 25:224. [PMID: 39152459 PMCID: PMC11328412 DOI: 10.1186/s13059-024-03347-y] [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: 10/24/2023] [Accepted: 07/23/2024] [Indexed: 08/19/2024] Open
Abstract
Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.
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Affiliation(s)
- Shobana V Stassen
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong.
| | - Minato Kobashi
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
- AI Chip Center for Emerging Smart Systems, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Yuanhua Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong.
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2
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Schott M, León-Periñán D, Splendiani E, Strenger L, Licha JR, Pentimalli TM, Schallenberg S, Alles J, Samut Tagliaferro S, Boltengagen A, Ehrig S, Abbiati S, Dommerich S, Pagani M, Ferretti E, Macino G, Karaiskos N, Rajewsky N. Open-ST: High-resolution spatial transcriptomics in 3D. Cell 2024; 187:3953-3972.e26. [PMID: 38917789 DOI: 10.1016/j.cell.2024.05.055] [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: 01/04/2024] [Revised: 04/05/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
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Affiliation(s)
- Marie Schott
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Daniel León-Periñán
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Elena Splendiani
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Leon Strenger
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Jan Robin Licha
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Tancredi Massimo Pentimalli
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Jonathan Alles
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sarah Samut Tagliaferro
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Anastasiya Boltengagen
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sebastian Ehrig
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Stefano Abbiati
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Steffen Dommerich
- Department of Otorhinolaryngology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin 13353, Germany
| | - Massimiliano Pagani
- IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | | | - Giuseppe Macino
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Cellular Biotechnologies and Hematology, La Sapienza University of Rome, 00161 Rome, Italy.
| | - Nikos Karaiskos
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany.
| | - Nikolaus Rajewsky
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Charité - Universitätsmedizin, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Site Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Berlin, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Site Berlin, Berlin, Germany.
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3
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Coulombe B, Chapleau A, Macintosh J, Durcan TM, Poitras C, Moursli YA, Faubert D, Pinard M, Bernard G. Towards a Treatment for Leukodystrophy Using Cell-Based Interception and Precision Medicine. Biomolecules 2024; 14:857. [PMID: 39062571 PMCID: PMC11274857 DOI: 10.3390/biom14070857] [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: 05/23/2024] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Cell-based interception and precision medicine is a novel approach aimed at improving healthcare through the early detection and treatment of diseased cells. Here, we describe our recent progress towards developing cell-based interception and precision medicine to detect, understand, and advance the development of novel therapeutic approaches through a single-cell omics and drug screening platform, as part of a multi-laboratory collaborative effort, for a group of neurodegenerative disorders named leukodystrophies. Our strategy aims at the identification of diseased cells as early as possible to intercept progression of the disease prior to severe clinical impairment and irreversible tissue damage.
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Affiliation(s)
- Benoit Coulombe
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC H3T 1A8, Canada
| | - Alexandra Chapleau
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Julia Macintosh
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
| | - Thomas M. Durcan
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Christian Poitras
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Yena A. Moursli
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Denis Faubert
- Mass Spectrometry and Proteomics Platform, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada;
| | - Maxime Pinard
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- Department Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, QC H4A 3J1, Canada
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4
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Curion F, Rich-Griffin C, Agarwal D, Ouologuem S, Rue-Albrecht K, May L, Garcia GEL, Heumos L, Thomas T, Lason W, Sims D, Theis FJ, Dendrou CA. Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis. Genome Biol 2024; 25:181. [PMID: 38978088 PMCID: PMC11229213 DOI: 10.1186/s13059-024-03322-7] [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: 03/14/2023] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
Abstract
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
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Affiliation(s)
- Fabiola Curion
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Devika Agarwal
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Sarah Ouologuem
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Kevin Rue-Albrecht
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Lilly May
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Giulia E L Garcia
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Doctoral Training Centre, University of Oxford, Oxford, UK
| | - Lukas Heumos
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Comprehensive Pneumology Center With the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Tom Thomas
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Wojciech Lason
- Nuffield Department of Medicine, Respiratory Medicine Unit, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - David Sims
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
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5
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Nitsch L, Lareau CA, Ludwig LS. Mitochondrial genetics through the lens of single-cell multi-omics. Nat Genet 2024; 56:1355-1365. [PMID: 38951641 PMCID: PMC11260401 DOI: 10.1038/s41588-024-01794-8] [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: 09/17/2023] [Accepted: 05/09/2024] [Indexed: 07/03/2024]
Abstract
Mitochondria carry their own genetic information encoding for a subset of protein-coding genes and translational machinery essential for cellular respiration and metabolism. Despite its small size, the mitochondrial genome, its natural genetic variation and molecular phenotypes have been challenging to study using bulk sequencing approaches, due to its variation in cellular copy number, non-Mendelian modes of inheritance and propensity for mutations. Here we highlight emerging strategies designed to capture mitochondrial genetic variation across individual cells for lineage tracing and studying mitochondrial genetics in primary human cells and clinical specimens. We review recent advances surrounding single-cell mitochondrial genome sequencing and its integration with functional genomic readouts, including leveraging somatic mitochondrial DNA mutations as clonal markers that can resolve cellular population dynamics in complex human tissues. Finally, we discuss how single-cell whole mitochondrial genome sequencing approaches can be utilized to investigate mitochondrial genetics and its contribution to cellular heterogeneity and disease.
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Affiliation(s)
- Lena Nitsch
- Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Caleb A Lareau
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Leif S Ludwig
- Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
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6
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Rahimi A, Vale-Silva LA, Fälth Savitski M, Tanevski J, Saez-Rodriguez J. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nat Commun 2024; 15:4994. [PMID: 38862466 PMCID: PMC11167014 DOI: 10.1038/s41467-024-48868-z] [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: 08/07/2023] [Accepted: 05/14/2024] [Indexed: 06/13/2024] Open
Abstract
Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.
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Affiliation(s)
- Arezou Rahimi
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany
- Cellzome GmbH, GlaxoSmithKline, Heidelberg, Germany
| | | | | | - Jovan Tanevski
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany.
- Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany.
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7
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Coulombe B, Durcan TM, Bernard G, Moursli A, Poitras C, Faubert D, Pinard M. The 37TrillionCells initiative for improving global healthcare via cell-based interception and precision medicine: focus on neurodegenerative diseases. Mol Brain 2024; 17:18. [PMID: 38605409 PMCID: PMC11007934 DOI: 10.1186/s13041-024-01088-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
One of the main burdens in the treatment of diseases is imputable to the delay between the appearance of molecular dysfunctions in the first affected disease cells and their presence in sufficient number for detection in specific tissues or organs. This delay obviously plays in favor of disease progression to an extent that makes efficient treatments difficult, as they arrive too late. The development of a novel medical strategy, termed cell-based interception and precision medicine, seeks to identify dysfunctional cells early, when tissue damages are not apparent and symptoms not yet present, and develop therapies to treat diseases early. Central to this strategy is the use of single-cell technologies that allow detection of molecular changes in cells at the time of phenotypical bifurcation from health to disease. In this article we describe a general procedure to support such an approach applied to neurodegenerative disorders. This procedure combines four components directed towards highly complementary objectives: 1) a high-performance single-cell proteomics (SCP) method (Detect), 2) the development of disease experimental cell models and predictive computational models of cell trajectories (Understand), 3) the discovery of specific targets and personalized therapies (Cure), and 4) the creation of a community of collaborating laboratories to accelerate the development of this novel medical paradigm (Collaborate). A global initiative named 37TrillionCells (37TC) was launched to advance the development of cell-based interception and precision medicine.
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Affiliation(s)
- Benoit Coulombe
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada.
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada.
| | - Thomas M Durcan
- The Neuro's Early Drug Discovery Unit (EDDU), McGill University, Montreal, Canada
| | - Geneviève Bernard
- Departments of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montreal, Canada
- Department Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montreal, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Asmae Moursli
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
| | - Christian Poitras
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
| | - Denis Faubert
- Mass Spectrometry and Proteomics Platform, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W1R7, Canada
| | - Maxime Pinard
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
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8
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Busarello E, Biancon G, Lauria F, Ibnat Z, Ramirez C, Tomè G, Aass KR, VanOudenhove J, Standal T, Viero G, Halene S, Tebaldi T. Interpreting single-cell messages in normal and aberrant hematopoiesis with the Cell Marker Accordion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584053. [PMID: 38559181 PMCID: PMC10979856 DOI: 10.1101/2024.03.08.584053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in hematopoiesis, reveal malignant hematopoietic cells with clinically significant features and measure gene signatures linked to pathological pathways. However, reliable identification of cell types is a crucial bottleneck in single-cell analysis. Available databases contain dissimilar nomenclature and non-concurrent marker sets, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, lacking extensive applicability in disease. We developed the Cell Marker Accordion, a user-friendly platform for the automatic annotation and biological interpretation of single-cell populations based on consistency weighted markers. We validated our approach on peripheral blood and bone marrow single-cell datasets, using surface markers and expert-based annotation as the ground truth. In all cases, we significantly improved the accuracy in identifying cell types with respect to any single source database. Moreover, the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of contexts in human and murine single-cell datasets. It characterizes leukemia stem cell subtypes, including therapy-resistant cells in acute myeloid leukemia patients; it identifies malignant plasma cells in multiple myeloma samples; it dissects cell type alterations in splicing factor-mutant cells from myelodysplastic syndrome patients; it discovers activation of innate immunity pathways in bone marrow from mice treated with METTL3 inhibitors. The breadth of these applications elevates the Cell Marker Accordion as a flexible, faithful and standardized tool to annotate and interpret hematopoietic populations in single-cell datasets focused on the study of hematopoietic development and disease.
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Affiliation(s)
- Emma Busarello
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Giulia Biancon
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Fabio Lauria
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Zuhairia Ibnat
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Christian Ramirez
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Gabriele Tomè
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Kristin R Aass
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Jennifer VanOudenhove
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Therese Standal
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Stephanie Halene
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Toma Tebaldi
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
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9
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Vierdag WMAM, Saka SK. A perspective on FAIR quality control in multiplexed imaging data processing. FRONTIERS IN BIOINFORMATICS 2024; 4:1336257. [PMID: 38405548 PMCID: PMC10885342 DOI: 10.3389/fbinf.2024.1336257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
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Affiliation(s)
| | - Sinem K. Saka
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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10
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Balachandran S, Prada-Medina CA, Mensah MA, Kakar N, Nagel I, Pozojevic J, Audain E, Hitz MP, Kircher M, Sreenivasan VKA, Spielmann M. STIGMA: Single-cell tissue-specific gene prioritization using machine learning. Am J Hum Genet 2024; 111:338-349. [PMID: 38228144 PMCID: PMC10870135 DOI: 10.1016/j.ajhg.2023.12.011] [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: 08/04/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/18/2024] Open
Abstract
Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.
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Affiliation(s)
- Saranya Balachandran
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Cesar A Prada-Medina
- Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin A Mensah
- Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; BIH Charité Digital Clinician Scientist Program, BIH Biomedical Innovation Academy, Anna-Louisa-Karsch-Strasse 2, 10178 Berlin, Germany; RG Development & Disease, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Naseebullah Kakar
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany; Department of Biotechnology, BUITEMS, Quetta, Pakistan
| | - Inga Nagel
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Jelena Pozojevic
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Enrique Audain
- Institute of Medical Genetics, Carl von Ossietzky University, 26129 Oldenburg, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck; Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein, 24105 Kiel, Germany
| | - Marc-Phillip Hitz
- Institute of Medical Genetics, Carl von Ossietzky University, 26129 Oldenburg, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck; Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein, 24105 Kiel, Germany
| | - Martin Kircher
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany.
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany; Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck.
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11
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Baumgart DC. An intriguing vision for transatlantic collaborative health data use and artificial intelligence development. NPJ Digit Med 2024; 7:19. [PMID: 38263436 PMCID: PMC10806986 DOI: 10.1038/s41746-024-01005-y] [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: 11/14/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
Our traditional approach to diagnosis, prognosis, and treatment, can no longer process and transform the enormous volume of information into therapeutic success, innovative discovery, and health economic performance. Precision health, i.e., the right treatment, for the right person, at the right time in the right place, is enabled through a learning health system, in which medicine and multidisciplinary science, economic viability, diverse culture, and empowered patient's preferences are digitally integrated and conceptually aligned for continuous improvement and maintenance of health, wellbeing, and equity. Artificial intelligence (AI) has been successfully evaluated in risk stratification, accurate diagnosis, and treatment allocation, and to prevent health disparities. There is one caveat though: dependable AI models need to be trained on population-representative, large and deep data sets by multidisciplinary and multinational teams to avoid developer, statistical and social bias. Such applications and models can neither be created nor validated with data at the country, let alone institutional level and require a new dimension of collaboration, a cultural change with the establishment of trust in a precompetitive space. The Data for Health (#DFH23) conference in Berlin and the Follow-Up Workshop at Harvard University in Boston hosted a representative group of stakeholders in society, academia, industry, and government. With the momentum #DFH23 created, the European Health Data Space (EHDS) as a solid and safe foundation for consented collaborative health data use and the G7 Hiroshima AI process in place, we call on citizens and their governments to fully support digital transformation of medicine, research and innovation including AI.
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Affiliation(s)
- Daniel C Baumgart
- Precision Health Signature Area, College of Health Sciences, College of Natural and Applied Sciences all at University of Alberta, Edmonton, Alberta, Canada.
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12
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Abstract
Brain development in humans is achieved through precise spatiotemporal genetic control, the mechanisms of which remain largely elusive. Recently, integration of technological advances in human stem cell-based modelling with genome editing has emerged as a powerful platform to establish causative links between genotypes and phenotypes directly in the human system. Here, we review our current knowledge of complex genetic regulation of each key step of human brain development through the lens of evolutionary specialization and neurodevelopmental disorders and highlight the use of human stem cell-derived 2D cultures and 3D brain organoids to investigate human-enriched features and disease mechanisms. We also discuss opportunities and challenges of integrating new technologies to reveal the genetic architecture of human brain development and disorders.
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Affiliation(s)
- Yi Zhou
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongjun Song
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA
- The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guo-Li Ming
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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13
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Schultze JL. Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle. JOURNAL OF CME 2023; 12:2162202. [PMID: 36969482 PMCID: PMC10031775 DOI: 10.1080/28338073.2022.2162202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient's data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as "Open Science", "Open Data", "Open Access" principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.
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Affiliation(s)
- Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and University of Bonn, Bonn, Germany
- Genomics & Immunoregulation, sLife and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
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14
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Huizing GJ, Deutschmann IM, Peyré G, Cantini L. Paired single-cell multi-omics data integration with Mowgli. Nat Commun 2023; 14:7711. [PMID: 38001063 PMCID: PMC10673889 DOI: 10.1038/s41467-023-43019-2] [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: 02/02/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the integration of paired multi-omics data with any type and number of omics. Of note, Mowgli combines integrative Nonnegative Matrix Factorization and Optimal Transport, enhancing at the same time the clustering performance and interpretability of integrative Nonnegative Matrix Factorization. We apply Mowgli to multiple paired single-cell multi-omics data profiled with 10X Multiome, CITE-seq, and TEA-seq. Our in-depth benchmark demonstrates that Mowgli's performance is competitive with the state-of-the-art in cell clustering and superior to the state-of-the-art once considering biological interpretability. Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem and it is available at http://github.com/cantinilab/mowgli .
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Affiliation(s)
- Geert-Jan Huizing
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015, Paris, France.
- Institut de Biologie de l'Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005, Paris, France.
| | - Ina Maria Deutschmann
- Institut de Biologie de l'Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005, Paris, France
| | - Gabriel Peyré
- CNRS and DMA de l'Ecole Normale Supérieure, CNRS, Ecole Normale Supérieure, Université PSL, 75005, Paris, France
| | - Laura Cantini
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015, Paris, France.
- Institut de Biologie de l'Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005, Paris, France.
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15
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Knoll R, Bonaguro L, dos Santos JC, Warnat-Herresthal S, Jacobs-Cleophas MCP, Blümel E, Reusch N, Horne A, Herbert M, Nuesch-Germano M, Otten T, van der Heijden WA, van de Wijer L, Shalek AK, Händler K, Becker M, Beyer MD, Netea MG, Joosten LAB, van der Ven AJAM, Schultze JL, Aschenbrenner AC. Identification of drug candidates targeting monocyte reprogramming in people living with HIV. Front Immunol 2023; 14:1275136. [PMID: 38077315 PMCID: PMC10703486 DOI: 10.3389/fimmu.2023.1275136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/18/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.
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Affiliation(s)
- Rainer Knoll
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Jéssica C. dos Santos
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Stefanie Warnat-Herresthal
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Maartje C. P. Jacobs-Cleophas
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Edda Blümel
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Nico Reusch
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Arik Horne
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Systems Hematology, Stem Cells & Precision Medicine, Max Delbrück Center - Berlin Institute for Medical Systems Biology (MDCBIMSB), Berlin, Germany
| | - Miriam Herbert
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- In Vivo Cell Biology of Infection, Max Planck Institute for Infection Biology (MPIIB), Berlin, Germany
| | - Melanie Nuesch-Germano
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Twan Otten
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Wouter A. van der Heijden
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Lisa van de Wijer
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Alex K. Shalek
- Broad Institute at Massachusetts Institute of Technology (MIT) and Harvard, Boston, MA, United States
- Ragon Institute of Mass General Hospital (MGH), MIT, and Harvard, Cambridge, MA, United States
- Department of Chemistry, Institute for Medical Engineering and Science, Koch Institute, Cambridge, MA, United States
| | - Kristian Händler
- Platform for Single Cell Genomics and Epigenomics (PRECISE), DZNE and University of Bonn, Bonn, Germany
- Institute for Human Genetics, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Matthias Becker
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Platform for Single Cell Genomics and Epigenomics (PRECISE), DZNE and University of Bonn, Bonn, Germany
| | - Mihai G. Netea
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
- Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Leo A. B. Joosten
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Andre J. A. M. van der Ven
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
- Platform for Single Cell Genomics and Epigenomics (PRECISE), DZNE and University of Bonn, Bonn, Germany
| | - Anna C. Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
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16
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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17
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Easter QT, Matuck BF, Warner BM, Byrd KM. Biogeographical Impacts of Dental, Oral, and Craniofacial Microbial Reservoirs. J Dent Res 2023; 102:1303-1314. [PMID: 37731320 DOI: 10.1177/00220345231191115] [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] [Indexed: 09/22/2023] Open
Abstract
The human mouth, or oral cavity, is at the crossroads of our external and internal environments, and it is increasingly evident that local colonization of dental, oral, and craniofacial (DOC) tissues and cells by bacteria and viruses may also have systemic effects across myriad diseases and disorders. Better understanding of this phenomenon will require a holistic understanding of host-microbial interactions in both spatiotemporal and biogeographical contexts while also considering person-, organ-, tissue-, cell-, and molecular-level variation. After the acute phase interaction with microbes, the establishment of site-specific reservoirs constitutes an important relationship to understand within the human body; however, despite a preliminary understanding of how viral reservoirs originate and persist across the human body, the landscape of single-cell and spatial multiomic tools has challenged our current understanding of what cells and niches can support microbial reservoirs. The lack of complete understanding impacts research into these relevant topics and implementing precision care for microbial-induced or microbial-influenced diseases. Here, via the lens of acute and chronic microbial infections of the DOC tissues, the goal of this review is to highlight and link the emerging spatiotemporal biogeography of host-viral interactomics at 3 levels: (1) DOC cell types in distinct tissues, (2) DOC-associated microbes, and (3) niche-specific DOC pathologies. Further, we will focus on the impact of postacute infectious syndromes such as long COVID, neurodegenerative disorders, and other underappreciated postviral conditions. We will provide hypotheses about how DOC tissues may play roles systemically in these conditions. Throughout, we will underscore how COVID-19 has catalyzed a new understanding of these biological questions, discuss future directions to study these phenomena, and highlight the utility of noninvasive oral biofluids in screening, monitoring, and intervening to prevent and/or ameliorate human infectious diseases.
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Affiliation(s)
- Q T Easter
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - B Fernandes Matuck
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - B M Warner
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - K M Byrd
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Division of Oral & Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, NC, USA
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18
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Graeber SY, Mall MA. The future of cystic fibrosis treatment: from disease mechanisms to novel therapeutic approaches. Lancet 2023; 402:1185-1198. [PMID: 37699417 DOI: 10.1016/s0140-6736(23)01608-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/23/2023] [Accepted: 07/31/2023] [Indexed: 09/14/2023]
Abstract
With the 2019 breakthrough in the development of highly effective modulator therapy providing unprecedented clinical benefits for over 90% of patients with cystic fibrosis who are genetically eligible for treatment, this rare disease has become a front runner of transformative molecular therapy. This success is based on fundamental research, which led to the identification of the disease-causing CFTR gene and our subsequent understanding of the disease mechanisms underlying the pathogenesis of cystic fibrosis, working together with a continuously evolving clinical research and drug development pipeline. In this Series paper, we focus on advances since 2018, and remaining knowledge gaps in our understanding of the molecular mechanisms of CFTR dysfunction in the airway epithelium and their links to mucus dysfunction, impaired host defences, airway infection, and chronic inflammation of the lungs of people with cystic fibrosis. We review progress in (and the remaining obstacles to) pharmacological approaches to rescue CFTR function, and novel strategies for improved symptomatic therapies for cystic fibrosis, including how these might be applicable to common lung diseases, such as bronchiectasis and chronic obstructive pulmonary disease. Finally, we discuss the promise of genetic therapies and gene editing approaches to restore CFTR function in the lungs of all patients with cystic fibrosis independent of their CFTR genotype, and the unprecedented opportunities to transform cystic fibrosis from a fatal disease to a treatable and potentially curable one.
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Affiliation(s)
- Simon Y Graeber
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Cystic Fibrosis Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Center for Lung Research, associated partner site, Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus A Mall
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Cystic Fibrosis Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Center for Lung Research, associated partner site, Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
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19
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Latapiat V, Saez M, Pedroso I, Martin AJM. Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective. Front Genet 2023; 14:1209416. [PMID: 37636264 PMCID: PMC10449456 DOI: 10.3389/fgene.2023.1209416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.
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Affiliation(s)
- Verónica Latapiat
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
| | - Mauricio Saez
- Centro de Oncología de Precisión, Facultad de Medicina y Ciencias de la Salud, Universidad Mayor, Santiago, Chile
- Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco, Chile
| | - Inti Pedroso
- Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
| | - Alberto J. M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
- Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile
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20
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Müller S, Schultze JL. Systems analysis of human innate immunity in COVID-19. Semin Immunol 2023; 68:101778. [PMID: 37267758 PMCID: PMC10201327 DOI: 10.1016/j.smim.2023.101778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/13/2023] [Accepted: 05/13/2023] [Indexed: 06/04/2023]
Abstract
Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.
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Affiliation(s)
- Sophie Müller
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; Genomics & Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany; Genomics & Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany; PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE and University of Bonn, Bonn, Germany.
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21
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Piwecka M, Rajewsky N, Rybak-Wolf A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol 2023:10.1038/s41582-023-00809-y. [PMID: 37198436 DOI: 10.1038/s41582-023-00809-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2023] [Indexed: 05/19/2023]
Abstract
In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.
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Affiliation(s)
- Monika Piwecka
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Nikolaus Rajewsky
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Agnieszka Rybak-Wolf
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany.
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22
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Husain SS, Ong EJ, Minskiy D, Bober-Irizar M, Irizar A, Bober M. Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures. Commun Biol 2023; 6:489. [PMID: 37147530 PMCID: PMC10163260 DOI: 10.1038/s42003-023-04840-z] [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: 10/21/2022] [Accepted: 04/12/2023] [Indexed: 05/07/2023] Open
Abstract
Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our AI-trains-AI approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL is best performing in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived.
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Affiliation(s)
| | - Eng-Jon Ong
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
| | - Dmitry Minskiy
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
| | - Mikel Bober-Irizar
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
- ForecomAI, London, W1W 5PF, UK
| | | | - Miroslaw Bober
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
- ForecomAI, London, W1W 5PF, UK
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23
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Chu WY, Tsia KK. EuniceScope: Low-Cost Imaging Platform for Studying Microgravity Cell Biology. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:204-211. [PMID: 38274779 PMCID: PMC10810312 DOI: 10.1109/ojemb.2023.3257991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/13/2023] [Accepted: 03/13/2023] [Indexed: 01/27/2024] Open
Abstract
Microgravity is proven to impact a wide range of human physiology, from stimulating stem cell differentiation to confounding cell health in bones, skeletal muscles, and blood cells. The research in this arena is progressively intensified by the increasing promises of human spaceflights. Considering the limited access to spaceflight, ground-based microgravity-simulating platforms have been indispensable for microgravity-biology research. However, they are generally complex, costly, hard to replicate and reconfigure - hampering the broad adoption of microgravity biology and astrobiology. To address these limitations, we developed a low-cost reconfigurable 3D-printed microscope coined EuniceScope to allow the democratization of astrobiology, especially for educational use. EuniceScope is a compact 2D clinostat system integrated with a modularized brightfield microscope, built upon 3D-printed toolbox. We demonstrated that this compact system offers plausible imaging quality and microgravity-simulating performance. Its high degree of reconfigurability thus holds great promise in the wide dissemination of microgravity-cell-biology research in the broader community, including Science, technology, engineering, and mathematics (STEM) educational and scientific community in the future.
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Affiliation(s)
- Wing Yan Chu
- University of Hong KongHong Kong
- University of TorontoTorontoONM5SCanada
| | - Kevin K. Tsia
- Department of Electrical and Electronic Engineering, Faculty of EngineeringUniversity of Hong KongHong Kong
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24
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Mulinacci G, Cristoferi L, Palermo A, Luca M, Gerussi A, Invernizzi P, Carbone M. Risk stratification in primary sclerosing cholangitis. Minerva Gastroenterol (Torino) 2023; 69:84-94. [PMID: 33300753 DOI: 10.23736/s2724-5985.20.02821-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Primary sclerosing cholangitis (PSC) is a chronic liver disorder commonly affecting young patients and associated with uncertain prognosis and elevated risk of end-stage liver disease and hepatobiliary cancer. Rate of progression in PSC is heterogeneous and accurately predicting the disease course is of paramount importance to clinical practice and interventional trial design. So far, efforts have brought to the development of models looking at short-to-middle-term outcome using composite models including clinical, laboratory, radiological and histological parameters with limited performance. In the era of whole genome sequencing and digital innovation, the time is ripe for the development of stratified medicine in PSC. Efforts should be directed toward developing well-phenotyped cohorts of patients with longitudinal follow-up across sustained periods of time, application of novel image-processing technology, and biomarker discovery using multiomics platforms.
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Affiliation(s)
- Giacomo Mulinacci
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Andrea Palermo
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Martina Luca
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Alessio Gerussi
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Marco Carbone
- Division of Gastroenterology, Department of Medicine and Surgery, Center for Autoimmune Liver Diseases, University of Milan Bicocca, Milan, Italy - .,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
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25
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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26
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Nomura S, Ono M. Precision and genomic medicine for dilated and hypertrophic cardiomyopathy. Front Cardiovasc Med 2023; 10:1137498. [PMID: 36950287 PMCID: PMC10025380 DOI: 10.3389/fcvm.2023.1137498] [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: 01/04/2023] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
Cardiomyopathy develops through an interaction of genetic and environmental factors. The clinical manifestations of both dilated cardiomyopathy and hypertrophic cardiomyopathy are diverse, but genetic testing defines the causative genes in about half of cases and can predict clinical prognosis. It has become clear that cardiomyopathy is caused not only by single rare variants but also by combinations of multiple common variants, and genome-wide genetic research is important for accurate disease risk assessment. Single-cell analysis research aimed at understanding the pathophysiology of cardiomyopathy is progressing rapidly, and it is expected that genomic analysis and single-cell molecular profiling will be combined to contribute to more detailed stratification of cardiomyopathy.
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Affiliation(s)
- Seitaro Nomura
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Correspondence: Seitaro Nomura
| | - Minoru Ono
- Department of Cardiac Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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27
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Neurodevelopmental disorders-high-resolution rethinking of disease modeling. Mol Psychiatry 2023; 28:34-43. [PMID: 36434058 PMCID: PMC9812768 DOI: 10.1038/s41380-022-01876-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022]
Abstract
Neurodevelopmental disorders arise due to various risk factors that can perturb different stages of brain development, and a combinatorial impact of these risk factors programs the phenotype in adulthood. While modeling the complete phenotype of a neurodevelopmental disorder is challenging, individual developmental perturbations can be successfully modeled in vivo in animals and in vitro in human cellular models. Nevertheless, our limited knowledge of human brain development restricts modeling strategies and has raised questions of how well a model corresponds to human in vivo brain development. Recent progress in high-resolution analysis of human tissue with single-cell and spatial omics techniques has enhanced our understanding of the complex events that govern the development of the human brain in health and disease. This new knowledge can be utilized to improve modeling of neurodevelopmental disorders and pave the way to more accurately portraying the relevant developmental perturbations in disease models.
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28
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Pramanik SK, Sreedharan S, Tiwari R, Dutta S, Kandoth N, Barman S, Aderinto SO, Chattopadhyay S, Das A, Thomas JA. Nanoparticles for super-resolution microscopy: intracellular delivery and molecular targeting. Chem Soc Rev 2022; 51:9882-9916. [PMID: 36420611 DOI: 10.1039/d1cs00605c] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Following an overview of the approaches and techniques used to acheive super-resolution microscopy, this review presents the advantages supplied by nanoparticle based probes for these applications. The various clases of nanoparticles that have been developed toward these goals are then critically described and these discussions are illustrated with a variety of examples from the recent literature.
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Affiliation(s)
- Sumit Kumar Pramanik
- CSIR - Central Salt and Marine Chemicals Research Institute, Gijubhai Badheka Marg, Bhavnagar, Gujarat 364002, India.
| | - Sreejesh Sreedharan
- Human Science Research Centre, University of Derby, Kedleston road, DE22 1GB, UK
| | - Rajeshwari Tiwari
- CSIR - Central Salt and Marine Chemicals Research Institute, Gijubhai Badheka Marg, Bhavnagar, Gujarat 364002, India.
| | - Sourav Dutta
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research, Kolkata, West Bengal, India.
| | - Noufal Kandoth
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research, Kolkata, West Bengal, India.
| | - Surajit Barman
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research, Kolkata, West Bengal, India.
| | - Stephen O Aderinto
- Department of Chemistry, University of Sheffield, Western Bank, Sheffield, S3 7HF, UK.
| | - Samit Chattopadhyay
- Department of Biological Sciences, BITS-Pilani, K K Birla Goa Campus, NH 17B, Zuarinagar, Goa 403726, India.
| | - Amitava Das
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research, Kolkata, West Bengal, India.
| | - Jim A Thomas
- Department of Chemistry, University of Sheffield, Western Bank, Sheffield, S3 7HF, UK.
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29
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Sreenivasan VKA, Henck J, Spielmann M. Single-cell sequencing: promises and challenges for human genetics. MED GENET-BERLIN 2022; 34:261-273. [PMID: 38836091 PMCID: PMC11006387 DOI: 10.1515/medgen-2022-2156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Over the last decade, single-cell sequencing has transformed many fields. It has enabled the unbiased molecular phenotyping of even whole organisms with unprecedented cellular resolution. In the field of human genetics, where the phenotypic consequences of genetic and epigenetic alterations are of central concern, this transformative technology promises to functionally annotate every region in the human genome and all possible variants within them at a massive scale. In this review aimed at the clinicians in human genetics, we describe the current status of the field of single-cell sequencing and its role for human genetics, including how the technology works as well as how it is being applied to characterize and monitor diseases, to develop human cell atlases, and to annotate the genome.
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Affiliation(s)
- Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
| | - Jana Henck
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
- Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
- Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany
- DZHK e. V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, 23538 Lübeck, Germany
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30
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Bonaguro L, Schulte-Schrepping J, Carraro C, Sun LL, Reiz B, Gemünd I, Saglam A, Rahmouni S, Georges M, Arts P, Hoischen A, Joosten LA, van de Veerdonk FL, Netea MG, Händler K, Mukherjee S, Ulas T, Schultze JL, Aschenbrenner AC. Human variation in population-wide gene expression data predicts gene perturbation phenotype. iScience 2022; 25:105328. [PMID: 36310583 PMCID: PMC9614568 DOI: 10.1016/j.isci.2022.105328] [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: 03/02/2022] [Revised: 07/13/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.
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Affiliation(s)
- Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Laura L. Sun
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | | | - Ioanna Gemünd
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, 3010 VIC, Australia
| | - Adem Saglam
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Peer Arts
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, 5000 SA, Australia
| | - Alexander Hoischen
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Leo A.B. Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Medical Genetics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Frank L. van de Veerdonk
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Immunology and Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Kristian Händler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Anna C. Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
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31
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Gerussi A, Scaravaglio M, Cristoferi L, Verda D, Milani C, De Bernardi E, Ippolito D, Asselta R, Invernizzi P, Kather JN, Carbone M. Artificial intelligence for precision medicine in autoimmune liver disease. Front Immunol 2022; 13:966329. [PMID: 36439097 PMCID: PMC9691668 DOI: 10.3389/fimmu.2022.966329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/13/2022] [Indexed: 09/10/2023] Open
Abstract
Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Miki Scaravaglio
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | | | - Chiara Milani
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Elisabetta De Bernardi
- Department of Medicine and Surgery and Tecnomed Foundation, University of Milano - Bicocca, Monza, Italy
| | | | - Rosanna Asselta
- Humanitas Clinical and Research Center, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
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Su Y, Zhou Y, Bennett ML, Li S, Carceles-Cordon M, Lu L, Huh S, Jimenez-Cyrus D, Kennedy BC, Kessler SK, Viaene AN, Helbig I, Gu X, Kleinman JE, Hyde TM, Weinberger DR, Nauen DW, Song H, Ming GL. A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan. Cell Stem Cell 2022; 29:1594-1610.e8. [PMID: 36332572 PMCID: PMC9844262 DOI: 10.1016/j.stem.2022.09.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/26/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022]
Abstract
The molecular diversity of glia in the human hippocampus and their temporal dynamics over the lifespan remain largely unknown. Here, we performed single-nucleus RNA sequencing to generate a transcriptome atlas of the human hippocampus across the postnatal lifespan. Detailed analyses of astrocytes, oligodendrocyte lineages, and microglia identified subpopulations with distinct molecular signatures and revealed their association with specific physiological functions, age-dependent changes in abundance, and disease relevance. We further characterized spatiotemporal heterogeneity of GFAP-enriched astrocyte subpopulations in the hippocampal formation using immunohistology. Leveraging glial subpopulation classifications as a reference map, we revealed the diversity of glia differentiated from human pluripotent stem cells and identified dysregulated genes and pathological processes in specific glial subpopulations in Alzheimer's disease (AD). Together, our study significantly extends our understanding of human glial diversity, population dynamics across the postnatal lifespan, and dysregulation in AD and provides a reference atlas for stem-cell-based glial differentiation.
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Affiliation(s)
- Yijing Su
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Yi Zhou
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mariko L Bennett
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pediatrics, Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Shiying Li
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu 226001, China
| | - Marc Carceles-Cordon
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lu Lu
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sooyoung Huh
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dennisse Jimenez-Cyrus
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin C Kennedy
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sudha K Kessler
- Department of Pediatrics, Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ingo Helbig
- Department of Pediatrics, Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Xiaosong Gu
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu 226001, China
| | - Joel E Kleinman
- Lieber Institute for Brain Development, The Solomon H. Snyder Department of Neuroscience, Department of Neurology, and Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, The Solomon H. Snyder Department of Neuroscience, Department of Neurology, and Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, The Solomon H. Snyder Department of Neuroscience, Department of Neurology, and Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - David W Nauen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hongjun Song
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Guo-Li Ming
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Solary E, Blanc P, Boutros M, Girvalaki C, Locatelli F, Medema RH, Nagy P, Tabernero J. UNCAN.eu, a European Initiative to UNderstand CANcer. Cancer Discov 2022; 12:2504-2508. [PMID: 36074491 DOI: 10.1158/2159-8290.cd-22-0970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
"UNCAN.eu" refers to a collective European effort seeking to enable a leap forward in our understanding of cancer. This initiative, which includes the creation of a European cancer research data hub, will pave the way to new advances in cancer care. Starting on September 1, 2022, a 15-month coordination and support action will generate a blueprint for UNCAN.eu. Here, we summarize the cancer research issues that the blueprint will propose to tackle at the European level.
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Affiliation(s)
- Eric Solary
- Université Paris-Saclay and INSERM, Gustave Roussy Cancer Center, Villejuif, France
| | | | - Michael Boutros
- German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany
| | | | - Franco Locatelli
- Bambino Gesù Children's Hospital, University of the Sacred Heart, Rome, Italy
| | - Rene H Medema
- Oncode Institute and The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Péter Nagy
- National Institute of Oncology and National Tumor Biology Laboratory, Budapest, Hungary
| | - Josep Tabernero
- Vall d'Hebron Hospital Campus and Institute of Oncology (VHIO), Barcelona, Spain
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Tansey MG, Wallings RL, Houser MC, Herrick MK, Keating CE, Joers V. Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol 2022; 22:657-673. [PMID: 35246670 PMCID: PMC8895080 DOI: 10.1038/s41577-022-00684-6] [Citation(s) in RCA: 404] [Impact Index Per Article: 202.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2022] [Indexed: 01/18/2023]
Abstract
Parkinson disease (PD) is a progressive neurodegenerative disease that affects peripheral organs as well as the central nervous system and involves a fundamental role of neuroinflammation in its pathophysiology. Neurohistological and neuroimaging studies support the presence of ongoing and end-stage neuroinflammatory processes in PD. Moreover, numerous studies of peripheral blood and cerebrospinal fluid from patients with PD suggest alterations in markers of inflammation and immune cell populations that could initiate or exacerbate neuroinflammation and perpetuate the neurodegenerative process. A number of disease genes and risk factors have been identified as modulators of immune function in PD and evidence is mounting for a role of viral or bacterial exposure, pesticides and alterations in gut microbiota in disease pathogenesis. This has led to the hypothesis that complex gene-by-environment interactions combine with an ageing immune system to create the 'perfect storm' that enables the development and progression of PD. We discuss the evidence for this hypothesis and opportunities to harness the emerging immunological knowledge from patients with PD to create better preclinical models with the long-term goal of enabling earlier identification of at-risk individuals to prevent, delay and more effectively treat the disease.
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Affiliation(s)
- Malú Gámez Tansey
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida Health, Gainesville, FL, USA.
| | - Rebecca L Wallings
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, University of Florida College of Medicine, Gainesville, FL, USA
| | - Madelyn C Houser
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA
| | - Mary K Herrick
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, University of Florida College of Medicine, Gainesville, FL, USA
| | - Cody E Keating
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, University of Florida College of Medicine, Gainesville, FL, USA
| | - Valerie Joers
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, University of Florida College of Medicine, Gainesville, FL, USA
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Scaravaglio M, Carbone M. Prognostic Scoring Systems in Primary Biliary Cholangitis: An Update. Clin Liver Dis 2022; 26:629-642. [PMID: 36270720 DOI: 10.1016/j.cld.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Primary biliary cholangitis (PBC) is a complex, chronic disease with a heterogeneous presentation, disease progression, and response to therapy. Several prognostic models based on disease stage and/or treatment response enhance risk stratification and therapeutic management. Recent work on disease modeling proposed early prediction of outcomes at PBC onset, yet this has not been implemented in clinical practice. Although early stratification of patients based on their individual risk of developing end-stage liver disease may prove cost-effective and actually become matter of medical deontology to timely offer the best therapeutic option, given the forthcoming availability of novel, disease-modifying drugs. This review outlines established and novel prognostic systems in PBC and provides some perspectives on the potential role of omics-derived biomarkers in developing reliable risk prediction models and promoting the implementation of personalized medicine in PBC.
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Affiliation(s)
- Miki Scaravaglio
- Division of Gastroenterology and Hepatology, Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza (MB), Italy.
| | - Marco Carbone
- Division of Gastroenterology and Hepatology, Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza (MB), Italy.
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Abstract
Oral and craniofacial tissues are uniquely adapted for continuous and intricate functioning, including breathing, feeding, and communication. To achieve these vital processes, this complex is supported by incredible tissue diversity, variously composed of epithelia, vessels, cartilage, bone, teeth, ligaments, and muscles, as well as mesenchymal, adipose, and peripheral nervous tissue. Recent single cell and spatial multiomics assays-specifically, genomics, epigenomics, transcriptomics, proteomics, and metabolomics-have annotated known and new cell types and cell states in human tissues and animal models, but these concepts remain limitedly explored in the human postnatal oral and craniofacial complex. Here, we highlight the collaborative and coordinated efforts of the newly established Oral and Craniofacial Bionetwork as part of the Human Cell Atlas, which aims to leverage single cell and spatial multiomics approaches to first understand the cellular and molecular makeup of human oral and craniofacial tissues in health and to then address common and rare diseases. These powerful assays have already revealed the cell types that support oral tissues, and they will unravel cell types and molecular networks utilized across development, maintenance, and aging as well as those affected in diseases of the craniofacial complex. This level of integration and cell annotation with partner laboratories across the globe will be critical for understanding how multiple variables, such as age, sex, race, and ancestry, influence these oral and craniofacial niches. Here, we 1) highlight these recent collaborative efforts to employ new single cell and spatial approaches to resolve our collective biology at a higher resolution in health and disease, 2) discuss the vision behind the Oral and Craniofacial Bionetwork, 3) outline the stakeholders who contribute to and will benefit from this network, and 4) outline directions for creating the first Human Oral and Craniofacial Cell Atlas.
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Affiliation(s)
- A J Caetano
- Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - I Sequeira
- Institute of Dentistry, Barts Centre for Squamous Cancer, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - K M Byrd
- Lab of Oral and Craniofacial Innovation, Department of Innovation and Technology Research, ADA Science and Research Institute, Gaithersburg, MD, USA
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Sreenivasan VKA, Balachandran S, Spielmann M. The role of single-cell genomics in human genetics. J Med Genet 2022; 59:827-839. [PMID: 35790352 PMCID: PMC9411920 DOI: 10.1136/jmedgenet-2022-108588] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
Abstract
Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.
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Affiliation(s)
- Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
| | - Saranya Balachandran
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
- Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, Berlin, Germany
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38
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Borck C. Tactile Vision, Epistemic Things and Data Visualization. BERICHTE ZUR WISSENSCHAFTSGESCHICHTE 2022; 45:415-427. [PMID: 36086848 PMCID: PMC9545028 DOI: 10.1002/bewi.202200032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Hans-Jörg Rheinberger constructed his historical epistemology of epistemic things by analyzing experimental practices in molecular biology during the 1970s and 80s. With genetic sequencing and multi-omics approaches, data has become a new resource in the life sciences, questioning the applicability of his concept of experimental system. By historicizing Rheinberger's epistemology, the paper focuses on its relatedness to Ludwik Fleck's notion of an aviso of resistance and points to a gradual shift in Rheinberger's emphasis, moving from an initial focus on writing and its differentiality to work on materials, preparations, and representations. By anchoring visualization in these material practices, Rheinberger also sheds new light on the changing conditions of experimentation in the life sciences due to big data, where visualization emphasizes patterns and correlations rather than substrates.
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Affiliation(s)
- Cornelius Borck
- Institute for History of Medicine and Science StudiesUniversity of Lübeck
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Precision neuro-oncology: a pilot analysis of personalized treatment in recurrent glioma. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04050-w. [PMID: 35953681 DOI: 10.1007/s00432-022-04050-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/29/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE When brain cancer relapses, treatment options are scarce. The use of molecularly matched targeted therapies may provide a feasible and efficacious way to treat individual patients based on the molecular tumor profile. Since little information is available on this strategy in neuro-oncology, we retrospectively analyzed the clinical course of 41 patients who underwent advanced molecular testing at disease relapse. METHODS We performed Sanger sequencing, targeted next generation sequencing, and immunohistochemistry for analysis of potential targets, including programmed death ligand 1, cyclin D1, phosphorylated mechanistic target of rapamycin, telomerase reverse transcriptase promoter mutation, cyclin-dependent kinase inhibitor 2A/B deletion, or BRAF-V600E mutation. In selected patients, whole exome sequencing was conducted. RESULTS The investigation included 41 patients, of whom 32 had isocitrate dehydrogenase (IDH) wildtype glioblastoma. Molecular analysis revealed actionable targets in 31 of 41 tested patients and 18 patients were treated accordingly (matched therapy group). Twenty-three patients received molecularly unmatched empiric treatment (unmatched therapy group). In both groups, 16 patients were diagnosed with recurrent IDH wildtype glioblastoma. The number of severe adverse events was comparable between the therapy groups. Regarding the IDH wildtype glioblastoma patients, median progression-free survival (mPFS) and median overall survival (mOS) were longer in the matched therapy group (mPFS: 3.8 versus 2.0 months, p = 0.0057; mOS: 13.0 versus 4.3 months, p = 0.0357). CONCLUSION These encouraging data provide a rationale for molecularly matched targeted therapy in glioma patients. For further validation, future study designs need to additionally consider the prevalence and persistence of actionable molecular alterations in patient tissue.
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Gehling K, Parekh S, Schneider F, Kirchner M, Kondylis V, Nikopoulou C, Tessarz P. RNA-sequencing of single cholangiocyte-derived organoids reveals high organoid-to organoid variability. Life Sci Alliance 2022; 5:e202101340. [PMID: 35914813 PMCID: PMC9348635 DOI: 10.26508/lsa.202101340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022] Open
Abstract
Over the last decades, organoids have been established from most of the tissue-resident stem and iPS cells. They hold great promise for our understanding of mammalian organ development, but also for the study of disease or even personalised medicine. In recent years, several reports hinted at intraculture organoid variability, but a systematic analysis of such heterogeneity has not been performed before. Here, we used RNA-seq of individual intrahepatic cholangiocyte organoids to address this question. We find that batch-to-batch variation is very low, whereas passage number has a profound impact on gene expression profiles. On the other hand, there is organoid-to-organoid variability within a culture. Using differential gene expression, we did not identify specific pathways that drive this variability, pointing towards possible effects of the microenvironment within the culture condition. Taken together, our study provides a framework for organoid researchers to properly consider experimental design.
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Affiliation(s)
- Kristin Gehling
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
| | - Swati Parekh
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Farina Schneider
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Marcel Kirchner
- FACS and Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Vangelis Kondylis
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Chrysa Nikopoulou
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Peter Tessarz
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
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Bosisio FM, Van Herck Y, Messiaen J, Bolognesi MM, Marcelis L, Van Haele M, Cattoretti G, Antoranz A, De Smet F. Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level. Front Oncol 2022; 12:918900. [PMID: 35992810 PMCID: PMC9389457 DOI: 10.3389/fonc.2022.918900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/20/2022] [Indexed: 01/23/2023] Open
Abstract
Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states. In this review, we provide a comprehensive overview of the available technologies for multiplexed immunohistochemistry, their advantages and challenges. We also provide the principles on how to interpret high-dimensional data in a spatial context. Similar to the fact that no one can just “read” a genome, pathological assessments are in dire need of extended digital data repositories to bring diagnostics and tissue interpretation to the next level.
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Affiliation(s)
- Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
| | | | - Julie Messiaen
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium
| | - Maddalena Maria Bolognesi
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Lukas Marcelis
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Matthias Van Haele
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Giorgio Cattoretti
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Asier Antoranz
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Frederik De Smet
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
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Moreno-Andrés D, Bhattacharyya A, Scheufen A, Stegmaier J. LiveCellMiner: A new tool to analyze mitotic progression. PLoS One 2022; 17:e0270923. [PMID: 35797385 PMCID: PMC9262191 DOI: 10.1371/journal.pone.0270923] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/17/2022] [Indexed: 11/21/2022] Open
Abstract
Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.
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Affiliation(s)
- Daniel Moreno-Andrés
- Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany
- * E-mail: (DMA), (JS)
| | - Anuk Bhattacharyya
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Anja Scheufen
- Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany
| | - Johannes Stegmaier
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
- * E-mail: (DMA), (JS)
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Uniken Venema WTC, Ramírez-Sánchez AD, Bigaeva E, Withoff S, Jonkers I, McIntyre RE, Ghouraba M, Raine T, Weersma RK, Franke L, Festen EAM, van der Wijst MGP. Gut mucosa dissociation protocols influence cell type proportions and single-cell gene expression levels. Sci Rep 2022; 12:9897. [PMID: 35701452 PMCID: PMC9197976 DOI: 10.1038/s41598-022-13812-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/27/2022] [Indexed: 01/15/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of the cellular landscape of organs. Most single-cell protocols require fresh material, which limits sample size per experiment, and consequently, introduces batch effects. This is especially true for samples acquired through complex medical procedures, such as intestinal mucosal biopsies. Moreover, the tissue dissociation procedure required for obtaining single cells is a major source of noise; different dissociation procedures applied to different compartments of the tissue induce artificial gene expression differences between cell subsets. To overcome these challenges, we have developed a one-step dissociation protocol and demonstrated its use on cryopreserved gut mucosal biopsies. Using flow cytometry and scRNA-seq analysis, we compared this one-step dissociation protocol with the current gold standard, two-step collagenase digestion, and an adaptation of a recently published alternative, three-step cold-active Bacillus licheniformus protease digestion. Both cell viability and cell type composition were comparable between the one-step and two-step collagenase dissociation, with the former being more time-efficient. The cold protease digestion resulted in equal cell viability, but better preserves the epithelial cell types. Consequently, to analyze the rarer cell types, such as glial cells, larger total biopsy cell numbers are required as input material. The multi-step protocols affected cell types spanning multiple compartments differently. In summary, we show that cryopreserved gut mucosal biopsies can be used to overcome the logistical challenges and batch effects in large scRNA-seq studies. Furthermore, we demonstrate that using cryopreserved biopsies digested using a one-step collagenase protocol enables large-scale scRNA-seq, FACS, organoid generation and intraepithelial lymphocyte expansion.
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Affiliation(s)
- Werna T C Uniken Venema
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Aarón D Ramírez-Sánchez
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Emilia Bigaeva
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Iris Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | | | - Tim Raine
- Department of Gastroenterology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eleonora A M Festen
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Monique G P van der Wijst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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44
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Conrad T, Altmüller J. Single cell- and spatial 'Omics revolutionize physiology. Acta Physiol (Oxf) 2022; 235:e13848. [PMID: 35656634 DOI: 10.1111/apha.13848] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/24/2022] [Accepted: 05/27/2022] [Indexed: 11/29/2022]
Abstract
Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.
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Affiliation(s)
- Thomas Conrad
- Genomics Technology Platform Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany
| | - Janine Altmüller
- Genomics Technology Platform Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany
- Core Facility Genomics Berlin Institute of Health at Charité ‐ Universitätsmedizin Berlin Berlin Germany
- Center for Molecular Medicine Cologne (CMMC) Cologne Germany
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45
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Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology. Nat Rev Immunol 2022; 22:401-403. [PMID: 35624333 PMCID: PMC9136788 DOI: 10.1038/s41577-022-00740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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46
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Transcription-coupled H3.3 recycling: A link with chromatin states. Semin Cell Dev Biol 2022; 135:13-23. [PMID: 35595602 DOI: 10.1016/j.semcdb.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/09/2022] [Accepted: 05/04/2022] [Indexed: 12/22/2022]
Abstract
Histone variant H3.3 is incorporated into chromatin throughout the cell cycle and even in non-cycling cells. This histone variant marks actively transcribed chromatin regions with high nucleosome turnover, as well as silent pericentric and telomeric repetitive regions. In the past few years, significant progress has been made in our understanding of mechanisms involved in the transcription-coupled deposition of H3.3. Here we review how, during transcription, new H3.3 deposition intermingles with the fate of the old H3.3 variant and its recycling. First, we describe pathways enabling the incorporation of newly synthesized vs old H3.3 histones in the context of transcription. We then review the current knowledge concerning differences between these two H3.3 populations, focusing on their PTMs composition. Finally, we discuss the implications of H3.3 recycling for the maintenance of the transcriptional state and underline the emerging importance of H3.3 as a potent epigenetic regulator for both maintaining and switching a transcriptional state.
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47
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Albers JJ, Pelka K. Listening in on Multicellular Communication in Human Tissue Immunology. Front Immunol 2022; 13:884185. [PMID: 35634333 PMCID: PMC9136009 DOI: 10.3389/fimmu.2022.884185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/14/2022] [Indexed: 11/23/2022] Open
Abstract
Immune responses in human tissues rely on the concerted action of different cell types. Inter-cellular communication shapes both the function of the multicellular interaction networks and the fate of the individual cells that comprise them. With the advent of new methods to profile and experimentally perturb primary human tissues, we are now in a position to systematically identify and mechanistically dissect these cell-cell interactions and their modulators. Here, we introduce the concept of multicellular hubs, functional modules of immune responses in tissues. We outline a roadmap to discover multicellular hubs in human tissues and discuss how emerging technologies may further accelerate progress in this field.
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Affiliation(s)
- Julian J. Albers
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States
- Department of Medicine III, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karin Pelka
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States
- Gladstone-University of California San Francisco (UCSF) Institute of Genomic Immunology, Gladstone Institutes, San Francisco, CA, United States
- *Correspondence: Karin Pelka,
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48
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Li X, Lee EJ, Lilja S, Loscalzo J, Schäfer S, Smelik M, Strobl MR, Sysoev O, Wang H, Zhang H, Zhao Y, Gawel DR, Bohle B, Benson M. A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets. Genome Med 2022; 14:48. [PMID: 35513850 PMCID: PMC9074288 DOI: 10.1186/s13073-022-01048-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01048-4.
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Affiliation(s)
- Xinxiu Li
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Eun Jung Lee
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden.,Department of Otorhinolaryngology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sandra Lilja
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Samuel Schäfer
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Martin Smelik
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Maria Regina Strobl
- Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Oleg Sysoev
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Linköping, Sweden
| | - Hui Wang
- Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Huan Zhang
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Yelin Zhao
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Danuta R Gawel
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Barbara Bohle
- Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Mikael Benson
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden. .,Crown Princess Victoria Children's Hospital, Linköping University Hospital, Linköping, Sweden. .,Division of ENT Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
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49
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Solary E, Abou-Zeid N, Calvo F. Ageing and cancer: a research gap to fill. Mol Oncol 2022; 16:3220-3237. [PMID: 35503718 PMCID: PMC9490141 DOI: 10.1002/1878-0261.13222] [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: 03/01/2022] [Revised: 04/01/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
The complex mechanisms of ageing biology are increasingly understood. Interventions to reduce or delay ageing‐associated diseases are emerging. Cancer is one of the diseases promoted by tissue ageing. A clockwise mutational signature is identified in many tumours. Ageing might be a modifiable cancer risk factor. To reduce the incidence of ageing‐related cancer and to detect the disease at earlier stages, we need to understand better the links between ageing and tumours. When a cancer is established, geriatric assessment and measures of biological age might help to generate evidence‐based therapeutic recommendations. In this approach, patients and caregivers would include the respective weight to give to the quality of life and survival in the therapeutic choices. The increasing burden of cancer in older patients requires new generations of researchers and geriatric oncologists to be trained, to properly address disease complexity in a multidisciplinary manner, and to reduce health inequities in this population of patients. In this review, we propose a series of research challenges to tackle in the next few years to better prevent, detect and treat cancer in older patients while preserving their quality of life.
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Affiliation(s)
- Eric Solary
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université Paris Saclay, Faculté de Médecine, Le Kremlin-Bicêtre, France.,Gustave Roussy Cancer Center, INSERM U1287, Villejuif, France
| | - Nancy Abou-Zeid
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France
| | - Fabien Calvo
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université de Paris, Paris, France
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50
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Villa CE, Cheroni C, Dotter CP, López-Tóbon A, Oliveira B, Sacco R, Yahya AÇ, Morandell J, Gabriele M, Tavakoli MR, Lyudchik J, Sommer C, Gabitto M, Danzl JG, Testa G, Novarino G. CHD8 haploinsufficiency links autism to transient alterations in excitatory and inhibitory trajectories. Cell Rep 2022; 39:110615. [PMID: 35385734 DOI: 10.1016/j.celrep.2022.110615] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/18/2021] [Accepted: 03/13/2022] [Indexed: 12/13/2022] Open
Abstract
Mutations in the chromodomain helicase DNA-binding 8 (CHD8) gene are a frequent cause of autism spectrum disorder (ASD). While its phenotypic spectrum often encompasses macrocephaly, implicating cortical abnormalities, how CHD8 haploinsufficiency affects neurodevelopmental is unclear. Here, employing human cerebral organoids, we find that CHD8 haploinsufficiency disrupted neurodevelopmental trajectories with an accelerated and delayed generation of, respectively, inhibitory and excitatory neurons that yields, at days 60 and 120, symmetrically opposite expansions in their proportions. This imbalance is consistent with an enlargement of cerebral organoids as an in vitro correlate of patients' macrocephaly. Through an isogenic design of patient-specific mutations and mosaic organoids, we define genotype-phenotype relationships and uncover their cell-autonomous nature. Our results define cell-type-specific CHD8-dependent molecular defects related to an abnormal program of proliferation and alternative splicing. By identifying cell-type-specific effects of CHD8 mutations, our study uncovers reproducible developmental alterations that may be employed for neurodevelopmental disease modeling.
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Affiliation(s)
- Carlo Emanuele Villa
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, 20139 Milan, Italy; Human Technopole, Viale Rita Levi Montalcini 1, 20157 Milan, Italy
| | - Cristina Cheroni
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, 20139 Milan, Italy; Human Technopole, Viale Rita Levi Montalcini 1, 20157 Milan, Italy; Department of Oncology and Hemato-oncology, University of Milan, 20122 Milan, Italy
| | - Christoph P Dotter
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Alejandro López-Tóbon
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, 20139 Milan, Italy; Human Technopole, Viale Rita Levi Montalcini 1, 20157 Milan, Italy; Department of Oncology and Hemato-oncology, University of Milan, 20122 Milan, Italy
| | - Bárbara Oliveira
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Roberto Sacco
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Aysan Çerağ Yahya
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Jasmin Morandell
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Michele Gabriele
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, 20139 Milan, Italy
| | - Mojtaba R Tavakoli
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Julia Lyudchik
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Christoph Sommer
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | | | - Johann G Danzl
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
| | - Giuseppe Testa
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, 20139 Milan, Italy; Human Technopole, Viale Rita Levi Montalcini 1, 20157 Milan, Italy; Department of Oncology and Hemato-oncology, University of Milan, 20122 Milan, Italy.
| | - Gaia Novarino
- Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria.
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