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Abstract
The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.
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Affiliation(s)
- Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Michael T Eadon
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
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2
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Zhang Y, Yu B, Ming W, Zhou X, Wang J, Chen D. SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data. SCIENCE ADVANCES 2024; 10:eadp4942. [PMID: 39331720 PMCID: PMC11430467 DOI: 10.1126/sciadv.adp4942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 08/21/2024] [Indexed: 09/29/2024]
Abstract
Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.
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Affiliation(s)
- Yuelei Zhang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Bianjiong Yu
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Wenxuan Ming
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Xiaolong Zhou
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Jin Wang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Dijun Chen
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
- Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, China
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3
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Gong D, Arbesfeld-Qiu JM, Perrault E, Bae JW, Hwang WL. Spatial oncology: Translating contextual biology to the clinic. Cancer Cell 2024:S1535-6108(24)00349-0. [PMID: 39366372 DOI: 10.1016/j.ccell.2024.09.001] [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] [Received: 05/17/2024] [Revised: 08/01/2024] [Accepted: 09/06/2024] [Indexed: 10/06/2024]
Abstract
Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.
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Affiliation(s)
- Dennis Gong
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeanna M Arbesfeld-Qiu
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ella Perrault
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jung Woo Bae
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William L Hwang
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
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4
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Tirosh I, Suva ML. Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors. Cancer Cell 2024; 42:1497-1506. [PMID: 39214095 DOI: 10.1016/j.ccell.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/22/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.
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Affiliation(s)
- Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel.
| | - Mario L Suva
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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5
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Liu Z, Sun Y, Li Y, Ma A, Willaims NF, Jahanbahkshi S, Hoyd R, Wang X, Zhang S, Zhu J, Xu D, Spakowicz D, Ma Q, Liu B. An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2403393. [PMID: 39225619 DOI: 10.1002/advs.202403393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/26/2024] [Indexed: 09/04/2024]
Abstract
Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.
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Affiliation(s)
- Zhaoqian Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
- College of Sciences, Xi'an University of Science and Technology, Xi'an, Shanxi, 710054, China
| | - Yuhan Sun
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Yingjie Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Nyelia F Willaims
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Shiva Jahanbahkshi
- Department of Food Science and Technology, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH, 43210, USA
| | - Rebecca Hoyd
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Xiaoying Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Shiqi Zhang
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, OH, 43210, USA
| | - Jiangjiang Zhu
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, OH, 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65201, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65201, USA
| | - Daniel Spakowicz
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
- Shandong National Center for Applied Mathematics, Jinan, Shandong, 250199, China
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6
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Matchett KP, Paris J, Teichmann SA, Henderson NC. Spatial genomics: mapping human steatotic liver disease. Nat Rev Gastroenterol Hepatol 2024; 21:646-660. [PMID: 38654090 DOI: 10.1038/s41575-024-00915-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.
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Affiliation(s)
- Kylie P Matchett
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Jasmin Paris
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Neil C Henderson
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK.
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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7
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Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol 2024; 21:660-674. [PMID: 39043872 DOI: 10.1038/s41571-024-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/25/2024]
Abstract
Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.
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Affiliation(s)
- Julia Chen
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, St George Hospital, Sydney, New South Wales, Australia
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
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8
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de Matos Rodrigues J, Lokhande L, Olsson LM, Hassan M, Johansson A, Janská A, Kumar D, Schmidt L, Nikkarinen A, Hollander P, Glimelius I, Porwit A, Gerdtsson AS, Jerkeman M, Ek S. CD163+ macrophages in mantle cell lymphoma induce activation of prosurvival pathways and immune suppression. Blood Adv 2024; 8:4370-4385. [PMID: 38959399 PMCID: PMC11375268 DOI: 10.1182/bloodadvances.2023012039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024] Open
Abstract
ABSTRACT Mantle cell lymphoma (MCL) is dependent on a supportive tumor immune microenvironment (TIME) in which infiltration of CD163+ macrophages has a negative prognostic impact. This study explores how abundance and spatial localization of CD163+ cells are associated with the biology of MCL, using spatial multiomic investigations of tumor and infiltrating CD163+ and CD3+ cells. A total of 63 proteins were measured using GeoMx digital spatial profiling in tissue microarrays from 100 diagnostic MCL tissues. Regions of interest were selected in tumor-rich and tumor-sparse tissue regions. Molecular profiling of CD163+ macrophages, CD20+ MCL cells, and CD3+ T-cells was performed. To validate protein profiles, 1811 messenger RNAs were measured in CD20+ cells and 2 subsets of T cells. Image analysis was used to extract the phenotype and position of each targeted cell, thereby allowing the exploration of cell frequencies and cellular neighborhoods. Proteomic investigations revealed that CD163+ cells modulate their immune profile depending on their localization and that the immune inhibitory molecules, V-domain immunoglobulin suppressor of T-cell activation and B7 homolog 3, have higher expression in tumor-sparse than in tumor-rich tissue regions and that targeting should be explored. We showed that MCL tissues with more abundant infiltration of CD163+ cells have a higher proteomic and transcriptional expression of key components of the MAPK pathway. Thus, the MAPK pathway may be a feasible therapeutic target in patients with MCL with CD163+ cell infiltration. We further showed the independent and combined prognostic values of CD11c and CD163 beyond established risk factors.
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Affiliation(s)
| | | | - Lina M Olsson
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - May Hassan
- Department of Immunotechnology, Lund University, Lund, Sweden
| | | | - Anna Janská
- Department of Immunotechnology, Lund University, Lund, Sweden
| | | | - Lina Schmidt
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Anna Nikkarinen
- Department of Immunology, Genetics and Pathology, Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
| | - Peter Hollander
- Department of Immunology, Genetics and Pathology, Clinical and Experimental Pathology, Uppsala University, Uppsala, Sweden
| | - Ingrid Glimelius
- Department of Immunology, Genetics and Pathology, Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
| | - Anna Porwit
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | | | - Mats Jerkeman
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Sara Ek
- Department of Immunotechnology, Lund University, Lund, Sweden
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Huang J, Yuan C, Jiang J, Chen J, Badve SS, Gokmen-Polar Y, Segura RL, Yan X, Lazar A, Gao J, Epstein M, Wang L, Hu J. MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.24.609528. [PMID: 39253421 PMCID: PMC11383057 DOI: 10.1101/2024.08.24.609528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.
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10
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Adhikari SD, Steele NG, Theisen B, Wang J, Cui Y. SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.23.609477. [PMID: 39229093 PMCID: PMC11370608 DOI: 10.1101/2024.08.23.609477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis.
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Affiliation(s)
- Sikta Das Adhikari
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI
- Department of Statistics and Probability, Michigan State University, East Lansing, MI
| | - Nina G Steele
- Department of Surgery, Henry Ford Pancreatic Cancer Center, Henry Ford Hospital, Detroit, MI
- Department of Pathology, Wayne State University, Detroit, MI
- Department of Oncology, Wayne State University, Detroit, MI
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI
| | - Brian Theisen
- Department of Pathology, Henry Ford Health, Detroit, MI
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA
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11
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Kumaran G, Carroll L, Muirhead N, Bottomley MJ. How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases? J Invest Dermatol 2024:S0022-202X(24)01926-2. [PMID: 39177547 DOI: 10.1016/j.jid.2024.07.006] [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: 03/01/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 08/24/2024]
Abstract
Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.
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Affiliation(s)
- Girishkumar Kumaran
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Liam Carroll
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Matthew J Bottomley
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
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12
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Hatton-Jones KM, West NP, Barcelon J, Cox AJ. The effect of Proteinase K treatment on GeoMx digital spatial profiling data quality from formalin-fixed, paraffin-embedded tissue. Pathology 2024:S0031-3025(24)00191-0. [PMID: 39227250 DOI: 10.1016/j.pathol.2024.06.004] [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: 11/27/2023] [Revised: 05/20/2024] [Accepted: 06/06/2024] [Indexed: 09/05/2024]
Abstract
The emergence of spatial profiling technologies in recent years has accelerated opportunities to profile in detail the molecular attributes of a wide range of tissue pathologies using archival specimens. However, tissue treatment for fixation and storage does not always support generation of high-quality genomic data. The purpose of this study was to investigate the impacts of Proteinase K (ProtK) treatment, as a way to increase target transcript exposure, on downstream sequencing data quality metrics for spatial transcriptomic data using formalin-fixed, paraffin-embedded samples. In a series of four independent assessments using different tissue types (nasal mucosa, tonsil, pancreas), varying concentrations of ProtK (ranging from 0.1 to 1 μg/mL) were used as part of the sample processing workflow to generate transcriptomic data using the Nanostring GeoMx DSP and Illumina NextSeq 2000 platforms. Use of higher concentrations of ProtK was generally found to increase total reads (2-4-fold). However, negative probe counts also tended to be increased (2-12-fold), resulting in reductions in the signal-to-noise ratio (10-70% lower) and the number of genes detected above background (50-80% lower). These effects were not seen in all tissues and impacts of tissue handling and processing, beyond ProtK treatment, on data quality metrics, also require consideration. Regardless, these observations highlight the need for careful consideration of a range of sample processing factors and benefits that may be achieved through the optimisation of sample processing workflows for specific tissues as a way to maximise the generation of quality data using spatial transcriptomic approaches.
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Affiliation(s)
- Kyle M Hatton-Jones
- Menzies Health Institute Queensland, Griffith University, Southport, Qld, Australia
| | - Nicholas P West
- Menzies Health Institute Queensland, Griffith University, Southport, Qld, Australia; School of Pharmacy and Medical Science, Griffith University, Southport, Qld, Australia
| | - Jean Barcelon
- Menzies Health Institute Queensland, Griffith University, Southport, Qld, Australia
| | - Amanda J Cox
- Menzies Health Institute Queensland, Griffith University, Southport, Qld, Australia; School of Pharmacy and Medical Science, Griffith University, Southport, Qld, Australia.
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13
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Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends Genet 2024:S0168-9525(24)00169-0. [PMID: 39127549 DOI: 10.1016/j.tig.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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14
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Liu C, Li K, Sui X, Zhao T, Zhang T, Chen Z, Wu H, Li C, Li H, Yang F, Liu Z, Lu Y, Wang J, Chen X, Liu P. Patient-Derived Tumor Organoids Combined with Function-Associated ScRNA-Seq for Dissecting the Local Immune Response of Lung Cancer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400185. [PMID: 38896792 PMCID: PMC11336893 DOI: 10.1002/advs.202400185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/03/2024] [Indexed: 06/21/2024]
Abstract
In vitro models coupled with multimodal approaches are needed to dissect the dynamic response of local tumor immune microenvironment (TIME) to immunotherapy. Here the patient-derived primary lung cancer organoids (pLCOs) are generated by isolating tumor cell clusters, including the infiltrated immune cells. A function-associated single-cell RNA sequencing (FascRNA-seq) platform allowing both phenotypic evaluation and scRNA-seq at single-organoid level is developed to dissect the TIME of individual pLCOs. The analysis of 171 individual pLCOs derived from seven patients reveals that pLCOs retain the TIME heterogeneity in the parenchyma of parental tumor tissues, providing models with identical genetic background but various TIME. Linking the scRNA-seq data of individual pLCOs with their responses to anti-PD-1 (αPD-1) immune checkpoint blockade (ICB) allows to confirm the central role of CD8+ T cells in anti-tumor immunity, to identify potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity, including CD99 gene. In summary, the study constructs a joint phenotypic and transcriptomic FascRNA-seq platform to dissect the dynamic response of local TIME under ICB treatment, providing a promising approach to evaluate novel immunotherapies and to understand the underlying molecular mechanisms.
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Affiliation(s)
- Chang Liu
- School of Biomedical EngineeringTsinghua UniversityBeijing100084China
| | - Kaiyi Li
- School of Biomedical EngineeringTsinghua UniversityBeijing100084China
| | - Xizhao Sui
- Department of Thoracic SurgeryPeople's HospitalPeking UniversityBeijing100034China
| | - Tian Zhao
- School of Biomedical EngineeringTsinghua UniversityBeijing100084China
| | - Ting Zhang
- Beijing Advanced Innovation Centre for Biomedical EngineeringKey Laboratory for Biomechanics and Mechanobiology of Ministry of EducationSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Zhongyao Chen
- School of Biomedical EngineeringTsinghua UniversityBeijing100084China
| | - Hainan Wu
- Beijing Advanced Innovation Centre for Biomedical EngineeringKey Laboratory for Biomechanics and Mechanobiology of Ministry of EducationSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Chao Li
- Department of Thoracic SurgeryPeople's HospitalPeking UniversityBeijing100034China
| | - Hao Li
- Department of Thoracic SurgeryPeople's HospitalPeking UniversityBeijing100034China
| | - Fan Yang
- Department of Thoracic SurgeryPeople's HospitalPeking UniversityBeijing100034China
| | - Zhidong Liu
- Beijing Chest HospitalCapital Medical University & Beijing Tuberculosis and Thoracic Tumor Research InstituteBeijing101125China
| | - You‐Yong Lu
- Laboratory of Molecular OncologyKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education)School of OncologyBeijing Cancer Hospital and InstitutePeking UniversityBeijing100142China
| | - Jun Wang
- Department of Thoracic SurgeryPeople's HospitalPeking UniversityBeijing100034China
| | - Xiaofang Chen
- Beijing Advanced Innovation Centre for Biomedical EngineeringKey Laboratory for Biomechanics and Mechanobiology of Ministry of EducationSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Peng Liu
- School of Biomedical EngineeringTsinghua UniversityBeijing100084China
- Changping LaboratoryBeijing102299China
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15
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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16
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Love NR, Williams C, Killingbeck EE, Merleev A, Saffari Doost M, Yu L, McPherson JD, Mori H, Borowsky AD, Maverakis E, Kiuru M. Melanoma progression and prognostic models drawn from single-cell, spatial maps of benign and malignant tumors. SCIENCE ADVANCES 2024; 10:eadm8206. [PMID: 38996022 PMCID: PMC11244543 DOI: 10.1126/sciadv.adm8206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 06/06/2024] [Indexed: 07/14/2024]
Abstract
Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we used single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi and primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative two-dimensional analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included up-regulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 patients with melanoma via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.
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Affiliation(s)
- Nick R Love
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - Claire Williams
- NanoString Technologies, a Bruker Company, Seattle, WA 98109, USA
| | | | - Alexander Merleev
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | | | - Lan Yu
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - John D McPherson
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Hidetoshi Mori
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Alexander D Borowsky
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Emanual Maverakis
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - Maija Kiuru
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
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17
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Ospina OE, Manjarres-Betancur R, Gonzalez-Calderon G, Soupir AC, Smalley I, Tsai K, Markowitz J, Vallebuona E, Berglund A, Eschrich S, Yu X, Fridley BL. spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601050. [PMID: 39005315 PMCID: PMC11244876 DOI: 10.1101/2024.06.27.601050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
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Affiliation(s)
- Oscar E. Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | - Alex C. Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Inna Smalley
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kenneth Tsai
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Joseph Markowitz
- Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ethan Vallebuona
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Steven Eschrich
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Xiaoqing Yu
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L. Fridley
- Division of Health Services and Outcomes Research, Children’s Mercy, Kansas City, MO, USA
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18
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Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, Travis RC, Smith-Byrne K. Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics. EBioMedicine 2024; 105:105168. [PMID: 38878676 PMCID: PMC11233900 DOI: 10.1016/j.ebiom.2024.105168] [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: 10/16/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING This work was supported by Cancer Research UK (grant no. C8221/A29017).
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Affiliation(s)
- Trishna A Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
| | - Åsa K Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
| | - Sandy Figiel
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Wencheng Yin
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Eleanor L Watts
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Joshua R Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Marc J Gunter
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ian G Mills
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Alastair D Lamb
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim J Key
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
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19
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Alhaddad H, Ospina OE, Khaled ML, Ren Y, Vallebuona E, Boozo MB, Forsyth PA, Pina Y, Macaulay R, Law V, Tsai KY, Cress WD, Fridley B, Smalley I. Spatial transcriptomics analysis identifies a tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease. Cell Rep Med 2024; 5:101606. [PMID: 38866016 PMCID: PMC11228800 DOI: 10.1016/j.xcrm.2024.101606] [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: 12/20/2023] [Revised: 03/08/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024]
Abstract
Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. Here, we characterize the tumor microenvironment of LMD and patient-matched extra-cranial metastases using spatial transcriptomics in a small number of clinical specimens (nine tissues from two patients) with extensive in vitro and in vivo validation. The spatial landscape of melanoma LMD is characterized by a lack of immune infiltration and instead exhibits a higher level of stromal involvement. The tumor-stroma interactions at the leptomeninges activate tumor-promoting signaling, mediated through upregulation of SERPINA3. The meningeal stroma is required for melanoma cells to survive in the cerebrospinal fluid (CSF) and promotes MAPK inhibitor resistance. Knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in tumor cell death and re-sensitization to MAPK-targeting therapy. Our data provide a spatial atlas of melanoma LMD, identify the tumor-promoting role of meningeal stroma, and demonstrate a mechanism for overcoming microenvironment-mediated drug resistance in LMD.
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Affiliation(s)
- Hasan Alhaddad
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Oscar E Ospina
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, FL, USA
| | - Mariam Lotfy Khaled
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA; Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Yuan Ren
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ethan Vallebuona
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA; Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA
| | | | - Peter A Forsyth
- Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL, USA; Department of NeuroOncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Yolanda Pina
- Department of NeuroOncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Macaulay
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Vincent Law
- Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL, USA; Department of NeuroOncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kenneth Y Tsai
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - W Douglas Cress
- Department of Molecular Oncology at the Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke Fridley
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, FL, USA; Division of Health Services & Outcomes Research, Children's Mercy Hospital, Kansas City, MO 64108, USA.
| | - Inna Smalley
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA; Department of Cutaneous Oncology at the Moffitt Cancer Center, Tampa, FL, USA.
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20
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Steiner D, Sultan L, Sullivan T, Liu H, Zhang S, LeClerc A, Alekseyev YO, Liu G, Mazzilli SA, Zhang J, Rieger-Christ K, Burks EJ, Beane J, Lenburg ME. Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597993. [PMID: 38915565 PMCID: PMC11195124 DOI: 10.1101/2024.06.07.597993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI+ compared with VI- tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42×10-6) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI- LUAD (HR=2.76; p=0.003). To determine our VI predictor's robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2×10-16) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.
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Affiliation(s)
- Dylan Steiner
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Lila Sultan
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Travis Sullivan
- Department of Translational Research, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Hanqiao Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sherry Zhang
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Ashley LeClerc
- Boston University Microarray and Sequencing Resource Core Facility, Boston, MA, USA
| | - Yuriy O Alekseyev
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Gang Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A Mazzilli
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jiarui Zhang
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Kimberly Rieger-Christ
- Department of Translational Research, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Eric J Burks
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jennifer Beane
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA, Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
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21
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Yu Y, He Y, Xie Z. Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity. Biomolecules 2024; 14:674. [PMID: 38927077 PMCID: PMC11201407 DOI: 10.3390/biom14060674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/01/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local or global spatial relationships between spots, resulting in an underutilization of spatial information. To this end, we propose SECE, a deep learning-based method that captures both local and global relationships among spots and aggregates their information using expression similarity and spatial similarity. We benchmarked SECE against eight state-of-the-art methods on six real spatial transcriptomics datasets spanning four different platforms. SECE consistently outperformed other methods in spatial domain identification accuracy. Moreover, SECE produced spatial embeddings that exhibited clearer patterns in low-dimensional visualizations and facilitated a more accurate trajectory inference.
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Affiliation(s)
- Yuanyuan Yu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China;
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China;
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China;
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
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22
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Mi J, Ren L, Andersson O. Leveraging zebrafish to investigate pancreatic development, regeneration, and diabetes. Trends Mol Med 2024:S1471-4914(24)00124-2. [PMID: 38825440 DOI: 10.1016/j.molmed.2024.05.002] [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: 01/31/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 06/04/2024]
Abstract
The zebrafish has become an outstanding model for studying organ development and tissue regeneration, which is prominently leveraged for studies of pancreatic development, insulin-producing β-cells, and diabetes. Although studied for more than two decades, many aspects remain elusive and it has only recently been possible to investigate these due to technical advances in transcriptomics, chemical-genetics, genome editing, drug screening, and in vivo imaging. Here, we review recent findings on zebrafish pancreas development, β-cell regeneration, and how zebrafish can be used to provide novel insights into gene functions, disease mechanisms, and therapeutic targets in diabetes, inspiring further use of zebrafish for the development of novel therapies for diabetes.
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Affiliation(s)
- Jiarui Mi
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; Department of Gastroenterology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, China.
| | - Lipeng Ren
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden
| | - Olov Andersson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; Department of Medical Cell Biology, Uppsala University, Biomedical Centre, Uppsala, Sweden.
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23
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Augustin RC, Cai WL, Luke JJ, Bao R. Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies. Clin Cancer Res 2024; 30:1724-1732. [PMID: 38236069 PMCID: PMC11062841 DOI: 10.1158/1078-0432.ccr-22-2241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/28/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.
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Affiliation(s)
- Ryan C. Augustin
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
- Mayo Clinic, Department of Medical Oncology, Rochester, MN
| | - Wesley L. Cai
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Jason J. Luke
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Riyue Bao
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
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24
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Guo C, Ye W, Cao D, Shi M, Zhang W, Cheng Y, Wang Y, Xia XQ. Unraveling the stereoscopic gene transcriptional landscape of zebrafish using FishSED, a fish spatial expression database with multispecies scalability. SCIENCE CHINA. LIFE SCIENCES 2024; 67:843-846. [PMID: 37758906 DOI: 10.1007/s11427-023-2418-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 07/18/2023] [Indexed: 09/29/2023]
Affiliation(s)
- Cheng Guo
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Weidong Ye
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Danying Cao
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Mijuan Shi
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Wanting Zhang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yingyin Cheng
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yaping Wang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiao-Qin Xia
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China.
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
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25
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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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26
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Liu F, Yang Y, Xu XS, Yuan M. MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping. Comput Biol Chem 2024; 109:108009. [PMID: 38219419 DOI: 10.1016/j.compbiolchem.2023.108009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/16/2024]
Abstract
Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.
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Affiliation(s)
- Fengrong Liu
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | | | - Min Yuan
- School of Public Health Administration, Anhui Medical University, Hefei 230032, China.
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27
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Diazzi S, Ablain J. Nonepithelial cancer dissemination: specificities and challenges. Trends Cancer 2024; 10:356-368. [PMID: 38135572 DOI: 10.1016/j.trecan.2023.11.006] [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: 10/03/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
Epithelial cancers have served as a paradigm to study tumor dissemination but recent data have highlighted significant differences with nonepithelial cancers. Here, we review the current knowledge on nonepithelial tumor dissemination, drawing examples from the latest developments in melanoma, glioma, and sarcoma research. We underscore the importance of the reactivation of developmental processes during cancer progression and describe the nongenetic mechanisms driving nonepithelial tumor spread. We also outline therapeutic opportunities and ongoing clinical approaches to fight disseminating cancers. Finally, we discuss remaining challenges and emerging questions in the field. Defining the core principles underlying nonepithelial cancer dissemination may uncover actionable vulnerabilities of metastatic tumors and help improve the prognosis of patients with cancer.
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Affiliation(s)
- Serena Diazzi
- Centre de Recherche en Cancérologie de Lyon, Centre Léon Bérard, INSERM U1052, CNRS UMR5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Julien Ablain
- Centre de Recherche en Cancérologie de Lyon, Centre Léon Bérard, INSERM U1052, CNRS UMR5286, Université Claude Bernard Lyon 1, Lyon, France.
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28
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Pisano C, Leitenberger JJ, Pugliano-Mauro M, Carroll BT. Updates in Skin Cancer in Transplant Recipients and Immunosuppressed Patients: Review of the 2022-2023 Scientific Symposium of the International Immunosuppression and Transplant Skin Cancer Collaborative. Transpl Int 2024; 37:12387. [PMID: 38562207 PMCID: PMC10982388 DOI: 10.3389/ti.2024.12387] [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: 11/06/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024]
Abstract
The International Immunosuppression and Transplant Skin Cancer Collaborative (ITSCC) and its European counterpart, Skin Care in Organ Transplant Patients-Europe (SCOPE) are comprised of physicians, surgeons, and scientist who perform integrative collaborative research focused on cutaneous malignancies that arise in solid organ transplant recipients (SOTR) and patients with other forms of long-term immunosuppression. In October 2022, ITSCC held its biennial 4-day scientific symposium in Essex, Massachusetts. This meeting was attended by members of both ITSCC and SCOPE and consisted of specialists including Mohs micrographic and dermatologic oncology surgeons, medical dermatologists, transplant dermatologists, transplant surgeons, and transplant physicians. During this symposium scientific workshop groups focusing on consensus standards for case reporting of retrospective series for invasive squamous cell carcinoma (SCC), defining immunosuppressed patient status for cohort reporting, development of multi-institutional registry for reporting rare tumors, and development of a KERACON clinical trial of interventions after a SOTRs' first cutaneous SCC were developed. The majority of the symposium focused on presentation of the most up to date research in cutaneous malignancy in SOTR and immunosuppressed patients with specific focus on chemoprevention, immunosuppression regimens, immunotherapy in SOTRs, spatial transcriptomics, and the development of cutaneous tumor registries. Here, we present a summary of the most impactful scientific updates presented at the 2022 ITSCC symposium.
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Affiliation(s)
- Catherine Pisano
- Department of Dermatology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Justin J. Leitenberger
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
| | - Melissa Pugliano-Mauro
- Department of Dermatology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Bryan T. Carroll
- Department of Dermatology, Case Western Reserve University School of Medicine, Cleveland, OH, United States
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29
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Liu YC, Chen P, Chang R, Liu X, Jhang JW, Enkhbat M, Chen S, Wang H, Deng C, Wang PY. Artificial tumor matrices and bioengineered tools for tumoroid generation. Biofabrication 2024; 16:022004. [PMID: 38306665 DOI: 10.1088/1758-5090/ad2534] [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: 08/10/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
The tumor microenvironment (TME) is critical for tumor growth and metastasis. The TME contains cancer-associated cells, tumor matrix, and tumor secretory factors. The fabrication of artificial tumors, so-called tumoroids, is of great significance for the understanding of tumorigenesis and clinical cancer therapy. The assembly of multiple tumor cells and matrix components through interdisciplinary techniques is necessary for the preparation of various tumoroids. This article discusses current methods for constructing tumoroids (tumor tissue slices and tumor cell co-culture) for pre-clinical use. This article focuses on the artificial matrix materials (natural and synthetic materials) and biofabrication techniques (cell assembly, bioengineered tools, bioprinting, and microfluidic devices) used in tumoroids. This article also points out the shortcomings of current tumoroids and potential solutions. This article aims to promotes the next-generation tumoroids and the potential of them in basic research and clinical application.
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Affiliation(s)
- Yung-Chiang Liu
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
| | - Ping Chen
- Cancer Centre, Faculty of Health Sciences, MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR 999078, People's Republic of China
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, People's Republic of China
| | - Ray Chang
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
| | - Xingjian Liu
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
| | - Jhe-Wei Jhang
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
| | - Myagmartsend Enkhbat
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
| | - Shan Chen
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
| | - Hongxia Wang
- State Key Laboratory of Oncogenes and Related Genes, Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Chuxia Deng
- Cancer Centre, Faculty of Health Sciences, MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR 999078, People's Republic of China
| | - Peng-Yuan Wang
- Oujiang Laboratory; Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang 325024, People's Republic of China
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30
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Jogi HR, Smaraki N, Nayak SS, Rajawat D, Kamothi DJ, Panigrahi M. Single cell RNA-seq: a novel tool to unravel virus-host interplay. Virusdisease 2024; 35:41-54. [PMID: 38817399 PMCID: PMC11133279 DOI: 10.1007/s13337-024-00859-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: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 06/01/2024] Open
Abstract
Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.
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Affiliation(s)
- Harsh Rajeshbhai Jogi
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Nabaneeta Smaraki
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Dhaval J. Kamothi
- Division of Pharmacology and Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
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31
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You Y, Chen Z, Hu WW. The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets? Pharmacol Ther 2024; 255:108606. [PMID: 38346477 DOI: 10.1016/j.pharmthera.2024.108606] [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: 10/31/2023] [Revised: 01/05/2024] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.
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Affiliation(s)
- Yi You
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhong Chen
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Wei-Wei Hu
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China.
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32
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Pong A, Mah CK, Yeo GW, Lewis NE. Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment. Curr Opin Biotechnol 2024; 85:103048. [PMID: 38142648 PMCID: PMC11168798 DOI: 10.1016/j.copbio.2023.103048] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/11/2023] [Accepted: 11/25/2023] [Indexed: 12/26/2023]
Abstract
Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.
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Affiliation(s)
- Avery Pong
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Clarence K Mah
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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33
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Feng L, Wang R, Zhao Q, Wang J, Luo G, Xu C. Racial disparities in metastatic colorectal cancer outcomes revealed by tumor microbiome and transcriptome analysis with bevacizumab treatment. Front Pharmacol 2024; 14:1320028. [PMID: 38357363 PMCID: PMC10864621 DOI: 10.3389/fphar.2023.1320028] [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: 10/11/2023] [Accepted: 12/11/2023] [Indexed: 02/16/2024] Open
Abstract
Background: Metastatic colorectal cancer (mCRC) is a heterogeneous disease, often associated with poor outcomes and resistance to therapies. The racial variations in the molecular and microbiological profiles of mCRC patients, however, remain under-explored. Methods: Using RNA-SEQ data, we extracted and analyzed actively transcribing microbiota within the tumor milieu, ensuring that the identified bacteria were not merely transient inhabitants but engaged in the tumor ecosystem. Also, we independently acquired samples from 12 mCRC patients, specifically, 6 White individuals and 6 of Black or African American descent. These samples underwent 16S rRNA sequencing. Results: Our study revealed notable racial disparities in the molecular signatures and microbiota profiles of mCRC patients. The intersection of these data showcased the potential modulating effects of specific bacteria on gene expression. Particularly, the bacteria Helicobacter cinaedi and Sphingobium herbicidovorans emerged as significant influencers, with strong correlations to the genes SELENBP1 and SNORA38, respectively. Discussion: These findings underscore the intricate interplay between host genomics and actively transcribing tumor microbiota in mCRC's pathogenesis. The identified correlations between specific bacteria and genes highlight potential avenues for targeted therapies and a more personalized therapeutic approach.
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Affiliation(s)
- Lei Feng
- Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Department of Surgical Oncology, Hanzhong People’s Hospital, Hanzhong, Shaanxi, China
| | - Rui Wang
- Department of Thoracic Surgery, Cancer Centre, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Qian Zhao
- Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Jun Wang
- Tongji Hospital Tongji Medical College of HUST, Wuhan, China
| | - Gang Luo
- Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Department of Surgical Oncology, Hanzhong People’s Hospital, Hanzhong, Shaanxi, China
| | - Chongwen Xu
- Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
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34
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [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: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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35
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Chen C, Kim HJ, Yang P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol 2024; 25:18. [PMID: 38225676 PMCID: PMC10789051 DOI: 10.1186/s13059-023-03145-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: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. RESULTS Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. CONCLUSIONS Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.
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Affiliation(s)
- Carissa Chen
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
| | - Pengyi Yang
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
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36
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Valdeolivas A, Amberg B, Giroud N, Richardson M, Gálvez EJC, Badillo S, Julien-Laferrière A, Túrós D, Voith von Voithenberg L, Wells I, Pesti B, Lo AA, Yángüez E, Das Thakur M, Bscheider M, Sultan M, Kumpesa N, Jacobsen B, Bergauer T, Saez-Rodriguez J, Rottenberg S, Schwalie PC, Hahn K. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis Oncol 2024; 8:10. [PMID: 38200223 PMCID: PMC10781769 DOI: 10.1038/s41698-023-00488-4] [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: 02/24/2023] [Accepted: 12/04/2023] [Indexed: 01/12/2024] Open
Abstract
The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.
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Affiliation(s)
- Alberto Valdeolivas
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
| | - Bettina Amberg
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Nicolas Giroud
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Marion Richardson
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Eric J C Gálvez
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Solveig Badillo
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Alice Julien-Laferrière
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Demeter Túrós
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | | | - Isabelle Wells
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Benedek Pesti
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Amy A Lo
- Genentech, Inc, San Francisco, CA, USA
| | - Emilio Yángüez
- Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland
| | | | - Michael Bscheider
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Marc Sultan
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Nadine Kumpesa
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Björn Jacobsen
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Tobias Bergauer
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Julio Saez-Rodriguez
- Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Sven Rottenberg
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Bern Center for Precision Medicine (BCPM), University of Bern, Bern, Switzerland
| | - Petra C Schwalie
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Kerstin Hahn
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
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37
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Varrone M, Tavernari D, Santamaria-Martínez A, Walsh LA, Ciriello G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat Genet 2024; 56:74-84. [PMID: 38066188 DOI: 10.1038/s41588-023-01588-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/23/2023] [Indexed: 12/20/2023]
Abstract
Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.
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Affiliation(s)
- Marco Varrone
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniele Tavernari
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Albert Santamaria-Martínez
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Cancer Center Léman, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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38
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Wang H, Huang R, Nelson J, Gao C, Tran M, Yeaton A, Felt K, Pfaff KL, Bowman T, Rodig SJ, Wei K, Goods BA, Farhi SL. Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570603. [PMID: 38106230 PMCID: PMC10723440 DOI: 10.1101/2023.12.07.570603] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.
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Affiliation(s)
- Huan Wang
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Ruixu Huang
- Thayer School of Engineering, Molecular and Systems Biology, and Program in Quantitative Biomedical Sciences at Dartmouth College, Hanover, NH 03755, USA
| | - Jack Nelson
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Ce Gao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Miles Tran
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Anna Yeaton
- Present affiliation: Immunai, New York, NY 10016, USA
| | - Kristen Felt
- ImmunoProfile, Brigham & Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kathleen L. Pfaff
- Center for Immuno-Oncology, Tissue Biomarker Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Teri Bowman
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Scott J. Rodig
- Center for Immuno-Oncology, Tissue Biomarker Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Brittany A. Goods
- Thayer School of Engineering, Molecular and Systems Biology, and Program in Quantitative Biomedical Sciences at Dartmouth College, Hanover, NH 03755, USA
| | - Samouil L. Farhi
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
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Alhaddad H, Ospina OE, Khaled ML, Ren Y, Forsyth P, Pina Y, Macaulay R, Law V, Tsai KY, Cress WD, Fridley B, Smalley I. Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572266. [PMID: 38187574 PMCID: PMC10769278 DOI: 10.1101/2023.12.18.572266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. The inaccessible nature of the disease site and lack of understanding of the biology of this unique metastatic site are major barriers to developing efficacious therapies for patients with melanoma LMD. Here, we characterize the tumor microenvironment of the leptomeningeal tissues and patient-matched extra-cranial metastatic sites using spatial transcriptomic analyses with in vitro and in vivo validation. We show the spatial landscape of melanoma LMD to be characterized by a lack of immune infiltration and instead exhibit a higher level of stromal involvement. We show that the tumor-stroma interactions at the leptomeninges activate pathways implicated in tumor-promoting signaling, mediated through upregulation of SERPINA3 at the tumor-stroma interface. Our functional experiments establish that the meningeal stroma is required for melanoma cells to survive in the CSF environment and that these interactions lead to a lack of MAPK inhibitor sensitivity in the tumor. We show that knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in re-sensitization of the tumor to MAPK-targeting therapy and tumor cell death in the leptomeningeal environment. Our data provides a spatial atlas of melanoma LMD, identifies the tumor-promoting role of meningeal stroma, and demonstrates a mechanism for overcoming microenvironment-mediated drug resistance unique to this metastatic site.
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Affiliation(s)
- Hasan Alhaddad
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Oscar E. Ospina
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Mariam Lotfy Khaled
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Egypt
| | - Yuan Ren
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Peter Forsyth
- Department of Tumor Biology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Yolanda Pina
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Robert Macaulay
- Department of Pathology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Vincent Law
- Department of Tumor Biology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Kenneth Y. Tsai
- Department of Pathology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - W Douglas Cress
- Department of Molecular Oncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Brooke Fridley
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, Florida, USA
- Division of Health Services & Outcomes Research, Children’s Mercy Hospital, Kansas City, MO 64108
| | - Inna Smalley
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of Cutaneous Oncology at the Moffitt Cancer Center, Tampa, Florida, USA
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40
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Li Z, Patel ZM, Song D, Yan G, Li JJ, Pinello L. Benchmarking computational methods to identify spatially variable genes and peaks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.02.569717. [PMID: 38076922 PMCID: PMC10705556 DOI: 10.1101/2023.12.02.569717] [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: 12/17/2023]
Abstract
Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.
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Affiliation(s)
- Zhijian Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Zain M. Patel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Dongyuan Song
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Luca Pinello
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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41
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Toninelli M, Rossetti G, Pagani M. Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer 2023; 9:1085-1096. [PMID: 37673713 DOI: 10.1016/j.trecan.2023.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/08/2023]
Abstract
In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.
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Affiliation(s)
- Mattia Toninelli
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Grazisa Rossetti
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Massimiliano Pagani
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi, Milan, Italy.
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Xiao X, Juan C, Drennon T, Uytingco CR, Vishlaghi N, Sokolowskei D, Xu L, Levi B, Sammarco MC, Tower RJ. Spatial transcriptomic interrogation of the murine bone marrow signaling landscape. Bone Res 2023; 11:59. [PMID: 37926705 PMCID: PMC10625929 DOI: 10.1038/s41413-023-00298-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 09/19/2023] [Accepted: 09/28/2023] [Indexed: 11/07/2023] Open
Abstract
Self-renewal and differentiation of skeletal stem and progenitor cells (SSPCs) are tightly regulated processes, with SSPC dysregulation leading to progressive bone disease. While the application of single-cell RNA sequencing (scRNAseq) to the bone field has led to major advancements in our understanding of SSPC heterogeneity, stem cells are tightly regulated by their neighboring cells which comprise the bone marrow niche. However, unbiased interrogation of these cells at the transcriptional level within their native niche environment has been challenging. Here, we combined spatial transcriptomics and scRNAseq using a predictive modeling pipeline derived from multiple deconvolution packages in adult mouse femurs to provide an endogenous, in vivo context of SSPCs within the niche. This combined approach localized SSPC subtypes to specific regions of the bone and identified cellular components and signaling networks utilized within the niche. Furthermore, the use of spatial transcriptomics allowed us to identify spatially restricted activation of metabolic and major morphogenetic signaling gradients derived from the vasculature and bone surfaces that establish microdomains within the marrow cavity. Overall, we demonstrate, for the first time, the feasibility of applying spatial transcriptomics to fully mineralized tissue and present a combined spatial and single-cell transcriptomic approach to define the cellular components of the stem cell niche, identify cell‒cell communication, and ultimately gain a comprehensive understanding of local and global SSPC regulatory networks within calcified tissue.
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Affiliation(s)
- Xue Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Conan Juan
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tingsheng Drennon
- Department of Cell Biology & Applications, 10x Genomics, Pleasanton, CA, USA
| | - Cedric R Uytingco
- Department of Cell Biology & Applications, 10x Genomics, Pleasanton, CA, USA
| | - Neda Vishlaghi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Dimitri Sokolowskei
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin Levi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Mimi C Sammarco
- Department of Surgery, Tulane School of Medicine, New Orleans, LA, USA
| | - Robert J Tower
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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43
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Xiang J, Liu W, Liu S, Wang T, Tang H, Yang J. Deciphering the implications of mitophagy-related signatures in clinical outcomes and microenvironment heterogeneity of clear cell renal cell carcinoma. J Cancer Res Clin Oncol 2023; 149:16015-16030. [PMID: 37689589 DOI: 10.1007/s00432-023-05349-y] [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: 07/14/2023] [Accepted: 08/25/2023] [Indexed: 09/11/2023]
Abstract
BACKGROUND The role of mitophagy in various cancer-associated biological processes is well recognized. Nonetheless, the comprehensive implications of mitophagy in clear cell renal cell carcinoma (ccRCC) necessitate further exploration. METHODS Based on the transcriptomic data encompassing 25 mitophagy-related genes (MRGs), we identified the distinct mitophage patterns in 763 ccRCC samples. Subsequently, a mitophage-related predictive signature with machine learning algorithms was constructed, designated as RiskScore, to quantify the individual mitophagy status in ccRCC patients. Employing multispectral immunofluorescence (mIF) and immunohistochemistry (IHC) staining, we detected the effect of PTEN-induced putative kinase 1 (PINK1) in the prognosis and immune microenvironment of ccRCC. RESULTS Our analysis initially encompassed a comprehensive assessment of the expression profiling, genomic variations, and interactions among the 25 MRGs in ccRCC. Subsequently, the consensus clustering algorithm was applied to stratify ccRCC patients into three clusters with distinct prognostic outcomes, tumor microenvironment (TME) characteristics, and underlying biological pathways. We screened eight pivotal genes (CLIC4, PTPRB, SLC16A12, ENPP5, FLRT3, HRH2, PDK4, and SCD5) to construct a mitophagy-related predictive signature, which showed excellent prognostic value for ccRCC patients. Moreover, patient subgroups divided by the RiskScore showed contrasting expression levels of immune checkpoints (ICPs), abundance of immune cells, and immunotherapy response. Additionally, a nomogram was established with robust predictive power integrating the RiskScore and clinical features. Notably, we observed that PINK1 expression markedly correlated with favorable treatment response and advanced maturation stages of tertiary lymphoid structures, which potentially shed light on enhancing anti-tumor immunity of ccRCC. CONCLUSION Collectively, this study initially developed a signature associated with mitophagy, which demonstrated an excellent ability to predict the clinical prognosis, TME characterization, and responsiveness to targeted therapy and immunotherapy for ccRCC patients. Of particular note is the pivotal role of PINK1 in mediating the treatment response and immune microenvironment for ccRCC patients.
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Affiliation(s)
- Jianfeng Xiang
- Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wangrui Liu
- Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shifan Liu
- Department of Medical Imaging, Medical School of Nantong University, Nantong, China
| | - Tao Wang
- Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Haidan Tang
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
| | - Jianfeng Yang
- Department of Surgery, Shangnan Branch of Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. PLoS Genet 2023; 19:e1010983. [PMID: 37862362 PMCID: PMC10619839 DOI: 10.1371/journal.pgen.1010983] [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: 04/06/2023] [Revised: 11/01/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023] Open
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
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Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, Travis RC, Smith-Byrne K. Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.21.23295864. [PMID: 37790472 PMCID: PMC10543057 DOI: 10.1101/2023.09.21.23295864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Background Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.
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Affiliation(s)
- Trishna A Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Åsa K Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
| | - Sandy Figiel
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Wencheng Yin
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Eleanor L Watts
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
| | - Joshua R Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124 Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124 Uppsala, Sweden
| | - Marc J Gunter
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
- Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
- Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ian G Mills
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Alastair D Lamb
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Tim J Key
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
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Wu S, Qiu Y, Cheng X. ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities. Brief Bioinform 2023; 24:bbad395. [PMID: 37965808 DOI: 10.1093/bib/bbad395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/15/2023] [Accepted: 10/14/2023] [Indexed: 11/16/2023] Open
Abstract
Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities.
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Affiliation(s)
- Siyao Wu
- School of Mathematics and Statistics, Xi'an Jiaotong University, 710049 Shanxi, China
| | - Yushan Qiu
- School of Mathematical Sciences, Shenzhen University, 518000 Guangdong, China
| | - Xiaoqing Cheng
- School of Mathematics and Statistics, Xi'an Jiaotong University, 710049 Shanxi, China
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48
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Shi X, Zhu J, Long Y, Liang C. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks. Brief Bioinform 2023; 24:bbad278. [PMID: 37544658 DOI: 10.1093/bib/bbad278] [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: 04/18/2023] [Revised: 06/27/2023] [Accepted: 07/14/2023] [Indexed: 08/08/2023] Open
Abstract
MOTIVATION Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging. RESULTS To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.
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Affiliation(s)
- Xuejing Shi
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Juntong Zhu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Yahui Long
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, 138648, Singapore
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
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Wang G, Zeng D, Sweren E, Miao Y, Chen R, Chen J, Wang J, Liao W, Hu Z, Kang S, Garza LA. N6-methyladenosine RNA Methylation Correlates with Immune Microenvironment and Immunotherapy Response of Melanoma. J Invest Dermatol 2023; 143:1579-1590.e5. [PMID: 36842525 PMCID: PMC10363194 DOI: 10.1016/j.jid.2023.01.027] [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: 09/30/2022] [Revised: 01/05/2023] [Accepted: 01/22/2023] [Indexed: 02/26/2023]
Abstract
RNA methylation normally inhibits the self-recognition and immunogenicity of RNA. As such, it is likely an important inhibitor of cancer immune recognition in the tumor microenvironment, but how N6-methyladenosine (m6A) affects prognosis and treatment response remains unknown. In eight independent melanoma cohorts (1,564 patients), the modification patterns of 21 m6A gene signatures were systematically correlated with the immune cell infiltration of melanoma tumor microenvironment. m6A modification patterns for each patient were quantified using the principal component analysis method, yielding an m6Ascore that reflects the abundance of m6A RNA modifications. Two different m6A modification patterns were observed in patients with melanoma, separated into high and low m6Ascores that correlated with survival and treatment response. Low m6Ascores were characterized by an immune-inflamed phenotype, with 61.1% 5-year survival. High m6Ascores were characterized by an immune-excluded phenotype, with 52.2% 5-year survival. Importantly, lower m6Ascores correlated with more sensitive anti-PD-1 and anti-CTLA4 treatment responses, with 90% of patients with low m6Ascore responding, whereas 10% of those with high m6Ascore nonresponding (in cohort GSE63557). At single-cell and spatial transcriptome resolution, m6Ascore reflects melanoma malignant progression, immune exhaustion, and resistance to immune checkpoint blockade therapy. Hence, the m6Ascore correlates to an important facet of tumor immune escape as a tool for personalized medicine to guide immunotherapy in patients with melanoma.
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Affiliation(s)
- Gaofeng Wang
- Department of Plastic and Aesthetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dongqiang Zeng
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Evan Sweren
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yong Miao
- Department of Plastic and Aesthetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ruosi Chen
- Department of Plastic and Aesthetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Junjun Chen
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jin Wang
- Department of Plastic and Aesthetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wangjun Liao
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhiqi Hu
- Department of Plastic and Aesthetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sewon Kang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Luis A Garza
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Cell Biology, Johns Hopkins University, Baltimore, Maryland, USA; Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA.
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Guan YT, Zhang C, Zhang HY, Wei WL, Yue W, Zhao W, Zhang DH. Primary cilia: Structure, dynamics, and roles in cancer cells and tumor microenvironment. J Cell Physiol 2023; 238:1788-1807. [PMID: 37565630 DOI: 10.1002/jcp.31092] [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: 05/08/2023] [Revised: 06/24/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
Despite the initiation of tumor arises from tumorigenic transformation signaling in cancer cells, cancer cell survival, invasion, and metastasis also require a dynamic and reciprocal association with extracellular signaling from tumor microenvironment (TME). Primary cilia are the antenna-like structure that mediate signaling sensation and transduction in different tissues and cells. Recent studies have started to uncover that the heterogeneous ciliation in cancer cells and cells from the TME in tumor growth impels asymmetric paracellular signaling in the TME, indicating the essential functions of primary cilia in homeostasis maintenance of both cancer cells and the TME. In this review, we discussed recent advances in the structure and assembly of primary cilia, and the role of primary cilia in tumor and TME formation, as well as the therapeutic potentials that target ciliary dynamics and signaling from the cells in different tumors and the TME.
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Affiliation(s)
- Yi-Ting Guan
- Zhanjiang Institute of Clinical Medicine, Central People's Hospital of Zhanjiang, Guangdong Medical University Zhanjiang Central Hospital, Zhanjiang, P. R. China
| | - Chong Zhang
- Zhanjiang Institute of Clinical Medicine, Central People's Hospital of Zhanjiang, Guangdong Medical University Zhanjiang Central Hospital, Zhanjiang, P. R. China
| | - Hong-Yong Zhang
- Zhanjiang Institute of Clinical Medicine, Central People's Hospital of Zhanjiang, Guangdong Medical University Zhanjiang Central Hospital, Zhanjiang, P. R. China
| | - Wen-Lu Wei
- Zhanjiang Institute of Clinical Medicine, Central People's Hospital of Zhanjiang, Guangdong Medical University Zhanjiang Central Hospital, Zhanjiang, P. R. China
| | - Wei Yue
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Wei Zhao
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, P. R. China
- Department of Posthodontics, College of Stomatology, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Dong-Hui Zhang
- Zhanjiang Institute of Clinical Medicine, Central People's Hospital of Zhanjiang, Guangdong Medical University Zhanjiang Central Hospital, Zhanjiang, P. R. China
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