1
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Kukanja P, Langseth CM, Rubio Rodríguez-Kirby LA, Agirre E, Zheng C, Raman A, Yokota C, Avenel C, Tiklová K, Guerreiro-Cacais AO, Olsson T, Hilscher MM, Nilsson M, Castelo-Branco G. Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology. Cell 2024; 187:1990-2009.e19. [PMID: 38513664 DOI: 10.1016/j.cell.2024.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 12/13/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.
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Affiliation(s)
- Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
| | - Christoffer M Langseth
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Leslie A Rubio Rodríguez-Kirby
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Chao Zheng
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Amitha Raman
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Christophe Avenel
- Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden; BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, 751 05 Uppsala, Sweden
| | - Katarina Tiklová
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - André O Guerreiro-Cacais
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Tomas Olsson
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
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2
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Pielawski N, Andersson A, Avenel C, Behanova A, Chelebian E, Klemm A, Nysjö F, Solorzano L, Wählby C. TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data. Heliyon 2023; 9:e15306. [PMID: 37131430 PMCID: PMC10149187 DOI: 10.1016/j.heliyon.2023.e15306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 05/04/2023] Open
Abstract
Background and objectives Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples. Methods Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data. Results We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods. Conclusion TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.
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Affiliation(s)
- Nicolas Pielawski
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Axel Andersson
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Eduard Chelebian
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Anna Klemm
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Fredrik Nysjö
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Leslie Solorzano
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Wählby
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
- Corresponding author.
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3
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Li X, Andrusivova Z, Czarnewski P, Langseth CM, Andersson A, Liu Y, Gyllborg D, Braun E, Larsson L, Hu L, Alekseenko Z, Lee H, Avenel C, Kallner HK, Åkesson E, Adameyko I, Nilsson M, Linnarsson S, Lundeberg J, Sundström E. Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin. Nat Neurosci 2023; 26:891-901. [PMID: 37095395 PMCID: PMC10166856 DOI: 10.1038/s41593-023-01312-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/20/2023] [Indexed: 04/26/2023]
Abstract
The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.
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Affiliation(s)
- Xiaofei Li
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Zaneta Andrusivova
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Paulo Czarnewski
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Stockholm, Sweden
| | | | - Alma Andersson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Artificial Intelligence and Machine Learning, Research and Early Development, Genentech. Inc., South San Francisco, CA, USA
| | - Yang Liu
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Daniel Gyllborg
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Emelie Braun
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Zhanna Alekseenko
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Hower Lee
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Christophe Avenel
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, Sweden
| | - Helena Kopp Kallner
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
- Department of Obstetrics and Gynecology, Danderyd Hospital, Danderyd, Sweden
| | - Elisabet Åkesson
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- R&D Unit, Stockholms Sjukhem, Stockholm, Sweden
| | - Igor Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Erik Sundström
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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4
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Behanova A, Avenel C, Andersson A, Chelebian E, Klemm A, Wik L, Östman A, Wählby C. Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3. Biol Imaging 2023; 3:e6. [PMID: 38487686 PMCID: PMC10936381 DOI: 10.1017/s2633903x23000053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/18/2023] [Accepted: 02/13/2023] [Indexed: 03/17/2024]
Abstract
Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.
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Affiliation(s)
- Andrea Behanova
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Axel Andersson
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Eduard Chelebian
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Anna Klemm
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Lina Wik
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Arne Östman
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Carolina Wählby
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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5
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Sountoulidis A, Marco Salas S, Braun E, Avenel C, Bergenstråhle J, Theelke J, Vicari M, Czarnewski P, Liontos A, Abalo X, Andrusivová Ž, Mirzazadeh R, Asp M, Li X, Hu L, Sariyar S, Martinez Casals A, Ayoglu B, Firsova A, Michaëlsson J, Lundberg E, Wählby C, Sundström E, Linnarsson S, Lundeberg J, Nilsson M, Samakovlis C. A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung. Nat Cell Biol 2023; 25:351-365. [PMID: 36646791 PMCID: PMC9928586 DOI: 10.1038/s41556-022-01064-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 11/23/2022] [Indexed: 01/18/2023]
Abstract
The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.
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Affiliation(s)
- Alexandros Sountoulidis
- grid.452834.c0000 0004 5911 2402Science for Life Laboratory, Solna, Sweden ,grid.10548.380000 0004 1936 9377Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Sergio Marco Salas
- grid.452834.c0000 0004 5911 2402Science for Life Laboratory, Solna, Sweden ,grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Emelie Braun
- grid.4714.60000 0004 1937 0626Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Christophe Avenel
- grid.8993.b0000 0004 1936 9457Department of Information Technology, Uppsala University, Uppsala, Sweden ,grid.452834.c0000 0004 5911 2402BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, Sweden
| | - Joseph Bergenstråhle
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jonas Theelke
- grid.452834.c0000 0004 5911 2402Science for Life Laboratory, Solna, Sweden ,grid.10548.380000 0004 1936 9377Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Marco Vicari
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Paulo Czarnewski
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Andreas Liontos
- grid.452834.c0000 0004 5911 2402Science for Life Laboratory, Solna, Sweden ,grid.10548.380000 0004 1936 9377Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Xesus Abalo
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Žaneta Andrusivová
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Reza Mirzazadeh
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Michaela Asp
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaofei Li
- grid.4714.60000 0004 1937 0626Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lijuan Hu
- grid.4714.60000 0004 1937 0626Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Sanem Sariyar
- grid.5037.10000000121581746Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Anna Martinez Casals
- grid.5037.10000000121581746Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Burcu Ayoglu
- grid.5037.10000000121581746Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Alexandra Firsova
- grid.452834.c0000 0004 5911 2402Science for Life Laboratory, Solna, Sweden ,grid.10548.380000 0004 1936 9377Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Jakob Michaëlsson
- grid.4714.60000 0004 1937 0626Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Emma Lundberg
- grid.5037.10000000121581746Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Carolina Wählby
- grid.8993.b0000 0004 1936 9457Department of Information Technology, Uppsala University, Uppsala, Sweden ,grid.452834.c0000 0004 5911 2402BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, Sweden
| | - Erik Sundström
- grid.4714.60000 0004 1937 0626Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Sten Linnarsson
- grid.4714.60000 0004 1937 0626Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Joakim Lundeberg
- grid.5037.10000000121581746Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Solna, Sweden. .,Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
| | - Christos Samakovlis
- Science for Life Laboratory, Solna, Sweden. .,Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden. .,Molecular Pneumology, Cardiopulmonary Institute, Justus Liebig University, Giessen, Germany.
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6
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Bekkhus T, Avenel C, Hanna S, Boger MF, Klemm A, Bacovia DV, Wärnberg F, Wählby C, Ulvmar MH. Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep learning tool HEV-finder. J Pathol 2022; 258:4-11. [PMID: 35696253 PMCID: PMC9543492 DOI: 10.1002/path.5981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 12/02/2022]
Abstract
Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor‐draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high‐throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep‐learning model and created a graphical user interface for visualization of the results. The tool, named the HEV‐finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV‐finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large‐scale, fully automated, and user‐independent, image‐based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Tove Bekkhus
- The Beijer Laboratory, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of Information Technology, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility, SciLifeLab, Uppsala, Sweden
| | - Sabella Hanna
- The Beijer Laboratory, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Mathias Franzén Boger
- The Beijer Laboratory, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Anna Klemm
- Department of Information Technology, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility, SciLifeLab, Uppsala, Sweden
| | - Daniel Vasiliu Bacovia
- The Beijer Laboratory, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Fredrik Wärnberg
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Carolina Wählby
- Department of Information Technology, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility, SciLifeLab, Uppsala, Sweden
| | - Maria H Ulvmar
- The Beijer Laboratory, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
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7
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Chelebian E, Avenel C, Kartasalo K, Marklund M, Tanoglidi A, Mirtti T, Colling R, Erickson A, Lamb AD, Lundeberg J, Wählby C. Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer. Cancers (Basel) 2021; 13:4837. [PMID: 34638322 PMCID: PMC8507756 DOI: 10.3390/cancers13194837] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 11/16/2022] Open
Abstract
Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
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Affiliation(s)
- Eduard Chelebian
- Science for Life Laboratory, Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden;
| | - Christophe Avenel
- Science for Life Laboratory, Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden;
| | - Kimmo Kartasalo
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77 Stockholm, Sweden;
| | - Maja Marklund
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, 171 65 Solna, Sweden; (M.M.); (J.L.)
| | - Anna Tanoglidi
- Department of Clinical Pathology, Uppsala University Hospital, 752 37 Uppsala, Sweden;
| | - Tuomas Mirtti
- Department of Pathology, Research Program in Systems Oncology, University of Helsinki, Helsinki University Hospital, 00100 Helsinki, Finland;
| | - Richard Colling
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 7DQ, UK; (R.C.); (A.E.); (A.D.L.)
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Andrew Erickson
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 7DQ, UK; (R.C.); (A.E.); (A.D.L.)
| | - Alastair D. Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 7DQ, UK; (R.C.); (A.E.); (A.D.L.)
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7LE, UK
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, 171 65 Solna, Sweden; (M.M.); (J.L.)
| | - Carolina Wählby
- Science for Life Laboratory, Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden;
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Avenel C, Tolf A, Dragomir A, Carlbom IB. Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology. Front Bioeng Biotechnol 2019; 7:125. [PMID: 31334225 PMCID: PMC6624635 DOI: 10.3389/fbioe.2019.00125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 05/07/2019] [Indexed: 01/04/2023] Open
Abstract
Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.
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Affiliation(s)
| | - Anna Tolf
- Department of Pathology, Uppsala University Hospital, Uppsala, Sweden
| | - Anca Dragomir
- Department of Pathology, Uppsala University Hospital, Uppsala, Sweden.,Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ingrid B Carlbom
- CADESS Medical AB, Uppsala, Sweden.,Department for Information Technology, Uppsala University, Uppsala, Sweden
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