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Kamel M, Sarangi A, Senin P, Villordo S, Sunaal M, Barot H, Wang S, Solbas A, Cano L, Classe M, Bar-Joseph Z, Pla Planas A. SpatialOne: end-to-end analysis of visium data at scale. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae509. [PMID: 39152991 PMCID: PMC11374018 DOI: 10.1093/bioinformatics/btae509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/08/2024] [Accepted: 08/15/2024] [Indexed: 08/19/2024]
Abstract
MOTIVATION Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose. RESULTS Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale. AVAILABILITY AND IMPLEMENTATION SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.
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Affiliation(s)
- Mena Kamel
- Digital R&D, Sanofi, Paris 75017, France
| | | | | | | | | | - Het Barot
- Digital R&D, Sanofi, Paris 75017, France
| | | | - Ana Solbas
- Digital R&D, Sanofi, Paris 75017, France
| | - Luis Cano
- Precision Medicine & Computational Biology, Sanofi, Vitry-sur-Seine 94400, France
| | - Marion Classe
- Precision Medicine & Computational Biology, Sanofi, Vitry-sur-Seine 94400, France
| | - Ziv Bar-Joseph
- Digital R&D, Sanofi, Water Street 450, Cambridge, MA 02141, USA
| | - Albert Pla Planas
- Digital R&D, Sanofi, Carrer de Rosselló i Porcel 21, Barcelona 08016, Spain
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Mateiou C, Lokhande L, Diep LH, Knulst M, Carlsson E, Ek S, Sundfeldt K, Gerdtsson A. Spatial tumor immune microenvironment phenotypes in ovarian cancer. NPJ Precis Oncol 2024; 8:148. [PMID: 39026018 PMCID: PMC11258306 DOI: 10.1038/s41698-024-00640-8] [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: 12/04/2023] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
Immunotherapy has largely failed in ovarian carcinoma (OC), likely due to that the vast tumor heterogeneity and variation in immune response have hampered clinical trial outcomes. Tumor-immune microenvironment (TIME) profiling may aid in stratification of OC tumors for guiding treatment selection. Here, we used Digital Spatial Profiling combined with image analysis to characterize regions of spatially distinct TIME phenotypes in OC to assess whether immune infiltration pattern can predict presence of immuno-oncology targets. Tumors with diffuse immune infiltration and increased tumor-immune spatial interactions had higher presence of IDO1, PD-L1, PD-1 and Tim-3, while focal immune niches had more CD163 macrophages and a preliminary worse outcome. Immune exclusion was associated with presence of Tregs and Fibronectin. High-grade serous OC showed an overall stronger immune response and presence of multiple targetable checkpoints. Low-grade serous OC was associated with diffuse infiltration and a high expression of STING, while endometrioid OC had higher presence of CTLA-4. Mucinous and clear cell OC were dominated by focal immune clusters and immune-excluded regions, with mucinous tumors displaying T-cell rich immune niches.
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Affiliation(s)
- Claudia Mateiou
- Department of Pathology and Cytology, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | | | - Lan Hoa Diep
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Mattis Knulst
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Elias Carlsson
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Sara Ek
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Karin Sundfeldt
- Department of Obstetrics and Gynecology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Anna Gerdtsson
- Department of Immunotechnology, Lund University, Lund, Sweden.
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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [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] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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Pang JMB, Byrne DJ, Bergin ART, Caramia F, Loi S, Gorringe KL, Fox SB. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology. Histopathology 2024; 84:577-586. [PMID: 37991396 DOI: 10.1111/his.15093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023]
Abstract
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
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Affiliation(s)
- Jia-Min B Pang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David J Byrne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alice R T Bergin
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Kylie L Gorringe
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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Eliason J, Rao A. Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567108. [PMID: 38014073 PMCID: PMC10680696 DOI: 10.1101/2023.11.14.567108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model , to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.
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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. BIOLOGICAL 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] [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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