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Lei J, Huang Y, Zhao Y, Zhou Z, Mao L, Liu Y. Nanotechnology as a new strategy for the diagnosis and treatment of gliomas. J Cancer 2024; 15:4643-4655. [PMID: 39006067 PMCID: PMC11242339 DOI: 10.7150/jca.96859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/19/2024] [Indexed: 07/16/2024] Open
Abstract
Glioma is the most common malignant tumor of the central nervous system (CNS), and is characterized by high aggressiveness and a high recurrence rate. Currently, the main treatments for gliomas include surgical resection, temozolomide chemotherapy and radiotherapy. However, the prognosis of glioma patients after active standardized treatment is still poor, especially for glioblastoma (GBM); the median survival is still only 14.6 months, and the 5-year survival rate is only 4-5%. The current challenges in glioma treatment include difficulty in complete surgical resection, poor blood‒brain barrier (BBB) drug permeability, therapeutic resistance, and difficulty in tumor-specific targeting. In recent years, the rapid development of nanotechnology has provided new directions for diagnosing and treating gliomas. Nanoparticles (NPs) are characterized by excellent surface tunability, precise targeting, excellent biocompatibility, and high safety. In addition, NPs can be used for gene therapy, photodynamic therapy, and antiangiogenic therapy and can be combined with biomaterials for thermotherapy. In recent decades, breakthroughs in diagnosing and treating gliomas have been made with various functional NPs, and NPs are expected to become a new strategy for glioma diagnosis and treatment. In this paper, we review the main obstacles in the treatment of glioma and discuss the potential and challenges of the latest nanotechnology in the diagnosis and treatment of glioma.
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Affiliation(s)
- Jun Lei
- Department of Neurosurgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu 610200, China
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yiyang Huang
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yichuan Zhao
- Southwest Medical University, Luzhou 646000, China
| | - Zhi Zhou
- Department of Neurosurgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu 610200, China
| | - Lei Mao
- Department of Neurosurgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu 610200, China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu 610041, China
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2
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Yang Y, Jin X, Xie Y, Ning C, Ai Y, Wei H, Xu X, Ge X, Yi T, Huang Q, Yang X, Jiang T, Wang X, Piao Y, Jin X. The CEBPB + glioblastoma subcluster specifically drives the formation of M2 tumor-associated macrophages to promote malignancy growth. Theranostics 2024; 14:4107-4126. [PMID: 38994023 PMCID: PMC11234274 DOI: 10.7150/thno.93473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/24/2024] [Indexed: 07/13/2024] Open
Abstract
Rationale: The heterogeneity of tumor cells within the glioblastoma (GBM) microenvironment presents a complex challenge in curbing GBM progression. Understanding the specific mechanisms of interaction between different GBM cell subclusters and non-tumor cells is crucial. Methods: In this study, we utilized a comprehensive approach integrating glioma single-cell and spatial transcriptomics. This allowed us to examine the molecular interactions and spatial localization within GBM, focusing on a specific tumor cell subcluster, GBM subcluster 6, and M2-type tumor-associated macrophages (M2 TAMs). Results: Our analysis revealed a significant correlation between a specific tumor cell subcluster, GBM cluster 6, and M2-type TAMs. Further in vitro and in vivo experiments demonstrated the specific regulatory role of the CEBPB transcriptional network in GBM subcluster 6, which governs its tumorigenicity, recruitment of M2 TAMs, and polarization. This regulation involves molecules such as MCP1 for macrophage recruitment and the SPP1-Integrin αvβ1-Akt signaling pathway for M2 polarization. Conclusion: Our findings not only deepen our understanding of the formation of M2 TAMs, particularly highlighting the differential roles played by heterogeneous cells within GBM in this process, but also provided new insights for effectively controlling the malignant progression of GBM.
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Affiliation(s)
- Yongchang Yang
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
- Tianjin Medical University, Tianjin 300060, China
| | - Xingyu Jin
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
- Tianjin Medical University, Tianjin 300060, China
| | - Yang Xie
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
- Tianjin Medical University, Tianjin 300060, China
| | - Chunlan Ning
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
- Tianjin Medical University, Tianjin 300060, China
| | - Yiding Ai
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
- Tianjin Medical University, Tianjin 300060, China
| | - Haotian Wei
- Tianjin Medical University, Tianjin 300060, China
| | - Xing Xu
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Xianglian Ge
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Tailong Yi
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Qiang Huang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xuejun Yang
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, People's Republic of China
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaoguang Wang
- Department of Neuro-Oncology and Neurosurgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin China
| | - Yingzhe Piao
- Department of Neuro-Oncology and Neurosurgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin China
| | - Xun Jin
- Department of Biochemistry and Molecular Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
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3
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Read RD, Tapp ZM, Rajappa P, Hambardzumyan D. Glioblastoma microenvironment-from biology to therapy. Genes Dev 2024; 38:360-379. [PMID: 38811170 PMCID: PMC11216181 DOI: 10.1101/gad.351427.123] [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] [Indexed: 05/31/2024]
Abstract
Glioblastoma (GBM) is the most aggressive primary brain cancer. These tumors exhibit high intertumoral and intratumoral heterogeneity in neoplastic and nonneoplastic compartments, low lymphocyte infiltration, and high abundance of myeloid subsets that together create a highly protumorigenic immunosuppressive microenvironment. Moreover, heterogeneous GBM cells infiltrate adjacent brain tissue, remodeling the neural microenvironment to foster tumor electrochemical coupling with neurons and metabolic coupling with nonneoplastic astrocytes, thereby driving growth. Here, we review heterogeneity in the GBM microenvironment and its role in low-to-high-grade glioma transition, concluding with a discussion of the challenges of therapeutically targeting the tumor microenvironment and outlining future research opportunities.
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Affiliation(s)
- Renee D Read
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, Georgia 30322, USA;
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia 30322, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | - Zoe M Tapp
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio 43205, USA
| | - Prajwal Rajappa
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio 43205, USA;
- Department of Pediatrics, The Ohio State University Wexner Medical Center, Columbus, Ohio 43215, USA
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio 43215, USA
| | - Dolores Hambardzumyan
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
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Chen J, Zhou M, Wu W, Zhang J, Li Y, Li D. STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics. ARXIV 2024:arXiv:2406.06393v2. [PMID: 38947920 PMCID: PMC11213178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.
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Affiliation(s)
| | | | - Wenrong Wu
- University of North Carolina at Chapel Hill
| | | | - Yun Li
- University of North Carolina at Chapel Hill
| | - Didong Li
- University of North Carolina at Chapel Hill
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5
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Pan Z, Zhou R, Wang Y. ShinySRT: shareable and interactive visualization of spatially resolved data. J Genet Genomics 2024:S1673-8527(24)00145-0. [PMID: 38897429 DOI: 10.1016/j.jgg.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/05/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Affiliation(s)
- Zhenzhong Pan
- Department of Neurosurgery and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Ran Zhou
- Department of Neurosurgery and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Yuan Wang
- Department of Neurosurgery and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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Duo H, Li Y, Lan Y, Tao J, Yang Q, Xiao Y, Sun J, Li L, Nie X, Zhang X, Liang G, Liu M, Hao Y, Li B. Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios. Genome Biol 2024; 25:145. [PMID: 38831386 PMCID: PMC11149245 DOI: 10.1186/s13059-024-03290-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: 10/27/2023] [Accepted: 05/28/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. RESULTS We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. CONCLUSIONS No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.
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Affiliation(s)
- Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People's Republic of China
| | - Yang Lan
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical University, Chongqing, 400038, People's Republic of China
| | - Jingxin Tao
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, People's Republic of China
| | - Yingxue Xiao
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Jing Sun
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Lei Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Mingwei Liu
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China.
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, People's Republic of China.
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7
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Nasir-Moin M, Wadiura LI, Sacalean V, Juros D, Movahed-Ezazi M, Lock EK, Smith A, Lee M, Weiss H, Müther M, Alber D, Ratna S, Fang C, Suero-Molina E, Hellwig S, Stummer W, Rössler K, Hainfellner JA, Widhalm G, Kiesel B, Reichert D, Mischkulnig M, Jain R, Straehle J, Neidert N, Schnell O, Beck J, Trautman J, Pastore S, Pacione D, Placantonakis D, Oermann EK, Golfinos JG, Hollon TC, Snuderl M, Freudiger CW, Heiland DH, Orringer DA. Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy. Nat Biomed Eng 2024; 8:672-688. [PMID: 38987630 DOI: 10.1038/s41551-024-01217-3] [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: 04/03/2022] [Accepted: 04/20/2024] [Indexed: 07/12/2024]
Abstract
The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.
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Affiliation(s)
- Mustafa Nasir-Moin
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Vlad Sacalean
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center - University of Freiburg, Freiburg, Germany
| | - Devin Juros
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Emily K Lock
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Andrew Smith
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Hannah Weiss
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Michael Müther
- Department of Neurosurgery, Münster University Hospital, Münster, Germany
| | - Daniel Alber
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Camila Fang
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Eric Suero-Molina
- Department of Neurosurgery, Münster University Hospital, Münster, Germany
| | - Sönke Hellwig
- Department of Neurosurgery, Münster University Hospital, Münster, Germany
| | - Walter Stummer
- Department of Neurosurgery, Münster University Hospital, Münster, Germany
| | - Karl Rössler
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Johannes A Hainfellner
- Division of Neuropathology and Neurochemistry (Obersteiner Institute), Department of Neurology, Medical University Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - David Reichert
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Mario Mischkulnig
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Jakob Straehle
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Berta-Ottenstein Clinician Scientist Program, Faculty of Medicine, University Freiburg, Freiburg, Germany
| | - Nicolas Neidert
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Berta-Ottenstein Clinician Scientist Program, Faculty of Medicine, University Freiburg, Freiburg, Germany
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Translational NeuroOncology Research Group, Medical Center - University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for NeuroModulation (NeuroModul), University of Freiburg, Freiburg, Germany
| | | | | | - Donato Pacione
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Eric Karl Oermann
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
- Center for Data Science, New York University, New York, USA
| | - John G Golfinos
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Todd C Hollon
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matija Snuderl
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Microenvironment and Immunology Research Laboratory, Medical Center - University of Freiburg, Freiburg, Germany.
- Comprehensive Cancer Center Freiburg (CCCF), Medical Center - University of Freiburg, Freiburg, Germany.
- German Cancer Consortium (DKTK), partner site Freiburg, Freiburg, Germany.
| | - Daniel A Orringer
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
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Hu H, Wang C, Tao R, Liu B, Peng D, Chen Y, Zhang W. Evidences of neurological injury caused by COVID-19 from glioma tissues and glioma organoids. CNS Neurosci Ther 2024; 30:e14822. [PMID: 38923860 PMCID: PMC11199819 DOI: 10.1111/cns.14822] [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: 04/18/2024] [Revised: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
INTRODUCTION Despite the extensive neurological symptoms induced by COVID-19 and the identification of SARS-CoV-2 in post-mortem brain samples from COVID-19 patients months after death, the precise mechanisms of SARS-CoV-2 invasion into the central nervous system remain unclear due to the lack of research models. METHODS We collected glioma tissue samples from glioma patients who had a recent history of COVID-19 and examined the presence of the SARS-CoV-2 spike protein. Subsequently, spatial transcriptomic analyses were conducted on normal brain tissues, glioma tissues, and glioma tissues from glioma patients with recent COVID-19 history. Additionally, single-cell sequencing data from both glioma tissues and glioma organoids were collected and analyzed. Glioma organoids were utilized to evaluate the efficacy of potential COVID-19 blocking agents. RESULTS Glioma tissues from glioma patients with recent COVID-19 history exhibited the presence of the SARS-CoV-2 spike protein. Differences between glioma tissues from glioma patients who had a recent history of COVID-19 and healthy brain tissues primarily manifested in neuronal cells. Notably, neuronal cells within glioma tissues of COVID-19 history demonstrated heightened susceptibility to Alzheimer's disease, depression, and synaptic dysfunction, indicative of neuronal aberrations. Expressions of SARS-CoV-2 entry factors were confirmed in both glioma tissues and glioma organoids. Moreover, glioma organoids were susceptible to pseudo-SARS-CoV-2 infection and the infections could be partly blocked by the potential COVID-19 drugs. CONCLUSIONS Gliomas had inherent traits that render them susceptible to SARS-CoV-2 infection, leading to their representability of COVID-19 neurological symptoms. This established a biological foundation for the rationality and feasibility of utilization of glioma organoids as research and blocking drug testing model in SARS-CoV-2 infection within the central nervous system.
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Affiliation(s)
- Huimin Hu
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Chen Wang
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Rui Tao
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Bohan Liu
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Dazhao Peng
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Yankun Chen
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
| | - Wei Zhang
- Department of Molecular Neuropathology, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Center of Brain Tumor, Beijing Institute for Brain DisordersBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Chinese Glioma Genome Atlas Network (CGGA)BeijingChina
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9
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Wu Q, Berglund AE, Macaulay RJ, Etame AB. The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma. Cells 2024; 13:942. [PMID: 38891074 PMCID: PMC11171993 DOI: 10.3390/cells13110942] [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: 04/30/2024] [Revised: 05/26/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.
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Affiliation(s)
- Qiong Wu
- Department of Neuro-Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Anders E. Berglund
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Robert J. Macaulay
- Departments of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Arnold B. Etame
- Department of Neuro-Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
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10
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Ospina OE, Soupir AC, Manjarres-Betancur R, Gonzalez-Calderon G, Yu X, Fridley BL. Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models. Sci Rep 2024; 14:10967. [PMID: 38744956 PMCID: PMC11094014 DOI: 10.1038/s41598-024-61758-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Xiaoqing Yu
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Biostatistics and Epidemiology Core, Division of Health Services & Outcomes Research, Children's Mercy, Kansas City, MO, USA.
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11
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Wang W, Li T, Cheng Y, Li F, Qi S, Mao M, Wu J, Liu Q, Zhang X, Li X, Zhang L, Qi H, Yang L, Yang K, He Z, Ding S, Qin Z, Yang Y, Yang X, Luo C, Guo Y, Wang C, Liu X, Zhou L, Liu Y, Kong W, Miao J, Ye S, Luo M, An L, Wang L, Che L, Niu Q, Ma Q, Zhang X, Zhang Z, Hu R, Feng H, Ping YF, Bian XW, Shi Y. Identification of hypoxic macrophages in glioblastoma with therapeutic potential for vasculature normalization. Cancer Cell 2024; 42:815-832.e12. [PMID: 38640932 DOI: 10.1016/j.ccell.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/21/2024] [Accepted: 03/25/2024] [Indexed: 04/21/2024]
Abstract
Monocyte-derived tumor-associated macrophages (Mo-TAMs) intensively infiltrate diffuse gliomas with remarkable heterogeneity. Using single-cell transcriptomics, we chart a spatially resolved transcriptional landscape of Mo-TAMs across 51 patients with isocitrate dehydrogenase (IDH)-wild-type glioblastomas or IDH-mutant gliomas. We characterize a Mo-TAM subset that is localized to the peri-necrotic niche and skewed by hypoxic niche cues to acquire a hypoxia response signature. Hypoxia-TAM destabilizes endothelial adherens junctions by activating adrenomedullin paracrine signaling, thereby stimulating a hyperpermeable neovasculature that hampers drug delivery in glioblastoma xenografts. Accordingly, genetic ablation or pharmacological blockade of adrenomedullin produced by Hypoxia-TAM restores vascular integrity, improves intratumoral concentration of the anti-tumor agent dabrafenib, and achieves combinatorial therapeutic benefits. Increased proportion of Hypoxia-TAM or adrenomedullin expression is predictive of tumor vessel hyperpermeability and a worse prognosis of glioblastoma. Our findings highlight Mo-TAM diversity and spatial niche-steered Mo-TAM reprogramming in diffuse gliomas and indicate potential therapeutics targeting Hypoxia-TAM to normalize tumor vasculature.
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Affiliation(s)
- Wenying Wang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Tianran Li
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Yue Cheng
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Fei Li
- Department of Neurosurgery and Glioma Medical Research Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Shuhong Qi
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, and MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P.R. China
| | - Min Mao
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Jingjing Wu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Qing Liu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Xiaoning Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Xuegang Li
- Department of Neurosurgery and Glioma Medical Research Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Lu Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Haoyue Qi
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Lan Yang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Kaidi Yang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Zhicheng He
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Shuaishuai Ding
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Zhongyi Qin
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China; Department of Gastroenterology, Chongqing Key Laboratory of Digestive Malignancies, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing 400042, P.R. China
| | - Ying Yang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Xi Yang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Chunhua Luo
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Ying Guo
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Chao Wang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Xindong Liu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Lei Zhou
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Yuqi Liu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Weikai Kong
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Jingya Miao
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Shuanghui Ye
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Min Luo
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Lele An
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Lujing Wang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Linrong Che
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive Malignancies, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing 400042, P.R. China
| | - Qin Niu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Qinghua Ma
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Xia Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China
| | - Zhihong Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, and MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P.R. China
| | - Rong Hu
- Department of Neurosurgery and Glioma Medical Research Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Hua Feng
- Department of Neurosurgery and Glioma Medical Research Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Yi-Fang Ping
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China; Chongqing Advanced Pathology Research Institute, Jinfeng Laboratory, Chongqing 400039, P. R. China; Yu-Yue Scientific Research Center for Pathology, Jinfeng Laboratory, Chongqing 400039, P.R. China.
| | - Xiu-Wu Bian
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China; Chongqing Advanced Pathology Research Institute, Jinfeng Laboratory, Chongqing 400039, P. R. China; Yu-Yue Scientific Research Center for Pathology, Jinfeng Laboratory, Chongqing 400039, P.R. China.
| | - Yu Shi
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Amy Medical University), and The Key Laboratory of Tumor Immunopathology, The Ministry of Education of China, Chongqing 400038, P.R. China; Chongqing Advanced Pathology Research Institute, Jinfeng Laboratory, Chongqing 400039, P. R. China; Yu-Yue Scientific Research Center for Pathology, Jinfeng Laboratory, Chongqing 400039, P.R. China.
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12
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Lin H, Liu C, Hu A, Zhang D, Yang H, Mao Y. Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives. J Hematol Oncol 2024; 17:31. [PMID: 38720342 PMCID: PMC11077829 DOI: 10.1186/s13045-024-01544-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/10/2024] [Indexed: 05/12/2024] Open
Abstract
Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45+ cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.
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Affiliation(s)
- Hao Lin
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Neurosurgical Institute of Fudan University, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Chaxian Liu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Neurosurgical Institute of Fudan University, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Ankang Hu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Neurosurgical Institute of Fudan University, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Duanwu Zhang
- Children's Hospital of Fudan University, and Shanghai Key Laboratory of Medical Epigenetics, International Co-Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Hui Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
- Institute for Translational Brain Research, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Neurosurgical Institute of Fudan University, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Neurosurgical Institute of Fudan University, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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13
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Johnson AL, Lopez-Bertoni H. Cellular diversity through space and time: adding new dimensions to GBM therapeutic development. Front Genet 2024; 15:1356611. [PMID: 38774283 PMCID: PMC11106394 DOI: 10.3389/fgene.2024.1356611] [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: 12/15/2023] [Accepted: 04/15/2024] [Indexed: 05/24/2024] Open
Abstract
The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.
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Affiliation(s)
- Amanda L. Johnson
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
| | - Hernando Lopez-Bertoni
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
- Oncology, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, United States
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14
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Laury AR, Zheng S, Aho N, Fallegger R, Hänninen S, Saez-Rodriguez J, Tanevski J, Youssef O, Tang J, Carpén OM. Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma. Mod Pathol 2024; 37:100508. [PMID: 38704029 DOI: 10.1016/j.modpat.2024.100508] [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: 12/08/2023] [Revised: 04/04/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024]
Abstract
Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.
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Affiliation(s)
- Anna Ray Laury
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland.
| | - Shuyu Zheng
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Niina Aho
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Robin Fallegger
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Satu Hänninen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Jovan Tanevski
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany; Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Omar Youssef
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Clinical and Chemical Pathology Department, National Cancer Institute, Cairo University, Cairo, Egypt
| | - Jing Tang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Olli Mikael Carpén
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
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15
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Yabo YA, Heiland DH. Understanding glioblastoma at the single-cell level: Recent advances and future challenges. PLoS Biol 2024; 22:e3002640. [PMID: 38814900 PMCID: PMC11139343 DOI: 10.1371/journal.pbio.3002640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.
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Affiliation(s)
- Yahaya A Yabo
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Dieter Henrik Heiland
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, Faculty of Medicine, Medical Center University of Freiburg, Freiburg, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- German Cancer Consortium (DKTK) partner site, Freiburg, Germany
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16
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Liu M, Ji Z, Jain V, Smith VL, Hocke E, Patel AP, McLendon RE, Ashley DM, Gregory SG, López GY. Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche. Acta Neuropathol Commun 2024; 12:64. [PMID: 38650010 PMCID: PMC11036705 DOI: 10.1186/s40478-024-01769-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/30/2024] [Indexed: 04/25/2024] Open
Abstract
Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.
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Affiliation(s)
- Mengyi Liu
- Computational Biology and Bioinformatics Program, Duke University School of Medicine, Durham, NC, 27710, USA
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27705, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Vaibhav Jain
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27705, USA
| | - Vanessa L Smith
- Department of Pathology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Emily Hocke
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27705, USA
| | - Anoop P Patel
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Roger E McLendon
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Pathology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - David M Ashley
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Simon G Gregory
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA.
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27705, USA.
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA.
| | - Giselle Y López
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, NC, 27710, USA.
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA.
- Department of Pathology, Duke University School of Medicine, Durham, NC, 27710, USA.
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17
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Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [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: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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18
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Sussman JH, Oldridge DA, Yu W, Chen CH, Zellmer AM, Rong J, Parvaresh-Rizi A, Thadi A, Xu J, Bandyopadhyay S, Sun Y, Wu D, Emerson Hunter C, Brosius S, Ahn KJ, Baxter AE, Koptyra MP, Vanguri RS, McGrory S, Resnick AC, Storm PB, Amankulor NM, Santi M, Viaene AN, Zhang N, Raedt TD, Cole K, Tan K. A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583588. [PMID: 38496580 PMCID: PMC10942465 DOI: 10.1101/2024.03.06.583588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.
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Affiliation(s)
- Jonathan H. Sussman
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Derek A. Oldridge
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Wenbao Yu
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
| | - Chia-Hui Chen
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Abigail M. Zellmer
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Jiazhen Rong
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Statistics and Data Science, University of
Pennsylvania, Philadelphia, PA
| | | | - Anusha Thadi
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Jason Xu
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Shovik Bandyopadhyay
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Cellular and Molecular Biology Graduate Group, Perelman School of
Medicine, University of Pennsylvania, PA
| | - Yusha Sun
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Neuroscience Graduate Group, Perelman School of Medicine,
University of Pennsylvania, PA
| | - David Wu
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - C. Emerson Hunter
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephanie Brosius
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kyung Jin Ahn
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Amy E. Baxter
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Mateusz P. Koptyra
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Rami S. Vanguri
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Stephanie McGrory
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Adam C. Resnick
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Phillip B. Storm
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Nduka M. Amankulor
- Department of Neurosurgery, Perelman School of Medicine,
Philadelphia, PA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Angela N. Viaene
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Nancy Zhang
- Department of Statistics and Data Science, University of
Pennsylvania, Philadelphia, PA
| | - Thomas De Raedt
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Kristina Cole
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
| | - Kai Tan
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
- Center for Single Cell Biology, Children’s Hospital of
Philadelphia, Philadelphia, PA
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19
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Yan K, Liu Q, Huang R, Jiang Y, Bian Z, Li S, Li L, Shen F, Tsuneyama K, Zhang Q, Lian Z, Guan H, Xu B. Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment. Clin Transl Med 2024; 14:e1594. [PMID: 38426403 PMCID: PMC10905537 DOI: 10.1002/ctm2.1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. METHODS In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis. RESULTS The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment. CONCLUSIONS The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.
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Affiliation(s)
- Kai Yan
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Qing‐Zhi Liu
- Chronic Disease LaboratoryInstitutes for Life SciencesSouth China University of TechnologyGuangzhouChina
| | - Rong‐Rong Huang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Yi‐Hua Jiang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhouChina
| | - Zhen‐Hua Bian
- School of Biomedical Sciences and EngineeringSouth China University of TechnologyGuangzhou International CampusGuangzhouChina
| | - Si‐Jin Li
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Liang Li
- Medical Research InstituteGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Fei Shen
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory MedicineInstitute of Biomedical SciencesTokushima University Graduate SchoolTokushimaJapan
| | - Qing‐Ling Zhang
- Department of PathologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Zhe‐Xiong Lian
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Haixia Guan
- Department of EndocrinologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Bo Xu
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
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20
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Singhal V, Chou N, Lee J, Yue Y, Liu J, Chock WK, Lin L, Chang YC, Teo EML, Aow J, Lee HK, Chen KH, Prabhakar S. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet 2024; 56:431-441. [PMID: 38413725 PMCID: PMC10937399 DOI: 10.1038/s41588-024-01664-3] [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: 04/03/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.
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Affiliation(s)
- Vipul Singhal
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nigel Chou
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Joseph Lee
- Faculty of Science, National University of Singapore, Singapore, Republic of Singapore
| | - Yifei Yue
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Republic of Singapore
| | - Jinyue Liu
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wan Kee Chock
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Li Lin
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | | | - Jonathan Aow
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- School of Computing, National University of Singapore, Singapore, Republic of Singapore
- Singapore Eye Research Institute, Singapore, Republic of Singapore
- International Research Laboratory on Artificial Intelligence, Singapore, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Republic of Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Kok Hao Chen
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Shyam Prabhakar
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore.
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21
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Ran X, Zheng J, Chen L, Xia Z, Wang Y, Sun C, Guo C, Lin P, Liu F, Wang C, Zhou J, Sun C, Liu Q, Ma J, Qin Z, Zhu X, Xie Q. Single-Cell Transcriptomics Reveals the Heterogeneity of the Immune Landscape of IDH-Wild-Type High-Grade Gliomas. Cancer Immunol Res 2024; 12:232-246. [PMID: 38091354 PMCID: PMC10835213 DOI: 10.1158/2326-6066.cir-23-0211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/21/2023] [Accepted: 12/11/2023] [Indexed: 02/03/2024]
Abstract
Isocitrate dehydrogenase (IDH)-wild-type (WT) high-grade gliomas, especially glioblastomas, are highly aggressive and have an immunosuppressive tumor microenvironment. Although tumor-infiltrating immune cells are known to play a critical role in glioma genesis, their heterogeneity and intercellular interactions remain poorly understood. In this study, we constructed a single-cell transcriptome landscape of immune cells from tumor tissue and matching peripheral blood mononuclear cells (PBMC) from IDH-WT high-grade glioma patients. Our analysis identified two subsets of tumor-associated macrophages (TAM) in tumors with the highest protumorigenesis signatures, highlighting their potential role in glioma progression. We also investigated the T-cell trajectory and identified the aryl hydrocarbon receptor (AHR) as a regulator of T-cell dysfunction, providing a potential target for glioma immunotherapy. We further demonstrated that knockout of AHR decreased chimeric antigen receptor (CAR) T-cell exhaustion and improved CAR T-cell antitumor efficacy both in vitro and in vivo. Finally, we explored intercellular communication mediated by ligand-receptor interactions within the tumor microenvironment and PBMCs and revealed the unique cellular interactions present in the tumor microenvironment. Taken together, our study provides a comprehensive immune landscape of IDH-WT high-grade gliomas and offers potential drug targets for glioma immunotherapy.
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Affiliation(s)
- Xiaojuan Ran
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Jian Zheng
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Linchao Chen
- Department of Neurosurgery, Huashan Hospital Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhen Xia
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Yin Wang
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Chengfang Sun
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Medicine, Zhejiang University, Hangzhou, China
| | - Chen Guo
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Peng Lin
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Fuyi Liu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chun Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianguo Zhou
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
| | - Chongran Sun
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qichang Liu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianzhu Ma
- Institute of AI Industrial Research, Tsinghua University, Beijing, China
| | - Zhiyong Qin
- Department of Neurosurgery, Huashan Hospital Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiangdong Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qi Xie
- Westlake Disease Modeling Laboratory, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute of Advanced Study, Hangzhou, Zhejiang, China
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22
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Zheng Y, Pizurica M, Carrillo-Perez F, Noor H, Yao W, Wohlfart C, Marchal K, Vladimirova A, Gevaert O. Digital profiling of cancer transcriptomes from histology images with grouped vision attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.28.560068. [PMID: 37808782 PMCID: PMC10557714 DOI: 10.1101/2023.09.28.560068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.
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Affiliation(s)
- Yuanning Zheng
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Marija Pizurica
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
- Internet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium
| | - Francisco Carrillo-Perez
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Humaira Noor
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Wei Yao
- Roche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA
| | | | - Kathleen Marchal
- Internet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium
| | - Antoaneta Vladimirova
- Roche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA
| | - Olivier Gevaert
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, USA
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23
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Deng Y, Lu Y, Li M, Shen J, Qin S, Zhang W, Zhang Q, Shen Z, Li C, Jia T, Chen P, Peng L, Chen Y, Zhang W, Liu H, Zhang L, Rong L, Wang X, Chen D. SCAN: Spatiotemporal Cloud Atlas for Neural cells. Nucleic Acids Res 2024; 52:D998-D1009. [PMID: 37930842 PMCID: PMC10767991 DOI: 10.1093/nar/gkad895] [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: 08/14/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.
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Affiliation(s)
- Yushan Deng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yubao Lu
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Mengrou Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wei Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Qiang Zhang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Zhaoyang Shen
- Life Sciences and Technology College, China Pharmaceutical University, Nanjing 211198, China
| | - Changxiao Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Tengfei Jia
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Peixin Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Lingmin Peng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yangfeng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wensheng Zhang
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Liangming Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Limin Rong
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Dongsheng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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24
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Jin L, Jin A, Wang L, Qi X, Jin Y, Zhang C, Niu M. NRP1 Induces Enhanced Stemness and Chemoresistance in Glioma Cells via YAP. Biol Pharm Bull 2024; 47:166-174. [PMID: 38220212 DOI: 10.1248/bpb.b23-00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Neuropilin-1 (NRP1), a transmembrane glycoprotein, plays an important role in the malignant progression of gliomas; however, its role in chemoresistance is not fully understood. In this study, we observed the effects of NRP1 on the stemness and chemoresistance of glioma cells and the mediating role of Yes-associated protein (YAP). We constructed NRP1 overexpressing LN-229 glioma cells. Cells were treated with recombinant NRP1 protein (rNRP1) and the YAP inhibitor Super-TDU when necessary. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to detect the sensitivity of cells to temozolomide (TMZ). Sphere and clone formation assays were performed to detect the sphere- and clone-forming abilities of cells. Western blotting was performed to detect cellular CD133, CD44, p-LATS1, and p-YAP protein expression. Immunofluorescence and flow cytometry were used to detect the subcellular localization of YAP and apoptosis, respectively. We found that both NRP1 overexpression and rNRP1 treatment enhanced self-renewal, TMZ resistance, and CD133 and CD44 protein expression in LN-229 cells. NRP1 overexpression and rNRP1 treatment also induced LATS1 and YAP dephosphorylation and YAP nuclear translocation. Super-TDU inhibits NRP1 overexpression-induced enhanced self-renewal and TMZ resistance in LN-229 cells. Our study suggests that NRP1 induces increased stemness in glioma cells, resulting in chemoresistance, and that this effect is associated with YAP activation.
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Affiliation(s)
| | - Ai Jin
- Cangzhou People's Hospital
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25
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Nafe R, Hattingen E. Cellular Components of the Tumor Environment in Gliomas-What Do We Know Today? Biomedicines 2023; 12:14. [PMID: 38275375 PMCID: PMC10813739 DOI: 10.3390/biomedicines12010014] [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: 11/13/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
A generation ago, the molecular properties of tumor cells were the focus of scientific interest in oncology research. Since then, it has become increasingly apparent that the tumor environment (TEM), whose major components are non-neoplastic cell types, is also of utmost importance for our understanding of tumor growth, maintenance and resistance. In this review, we present the current knowledge concerning all cellular components within the TEM in gliomas, focusing on their molecular properties, expression patterns and influence on the biological behavior of gliomas. Insight into the TEM of gliomas has expanded considerably in recent years, including many aspects that previously received only marginal attention, such as the phenomenon of phagocytosis of glioma cells by macrophages and the role of the thyroid-stimulating hormone on glioma growth. We also discuss other topics such as the migration of lymphocytes into the tumor, phenotypic similarities between chemoresistant glioma cells and stem cells, and new clinical approaches with immunotherapies involving the cells of TEM.
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Affiliation(s)
- Reinhold Nafe
- Department of Neuroradiology, Clinics of Johann Wolfgang Goethe-University, Schleusenweg 2-16, D-60528 Frankfurt am Main, Germany;
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26
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Zhang W, Cao L, Yang J, Zhang S, Zhao J, Shi Z, Liao K, Wang H, Chen B, Qian Z, Xu H, Wu L, Liu H, Wang H, Ma C, Qiu Y, Ge J, Chen J, Lin Y. AEP-cleaved DDX3X induces alternative RNA splicing events to mediate cancer cell adaptation in harsh microenvironments. J Clin Invest 2023; 134:e173299. [PMID: 37988165 PMCID: PMC10849765 DOI: 10.1172/jci173299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023] Open
Abstract
Oxygen and nutrient deprivation are common features of solid tumors. Although abnormal alternative splicing (AS) has been found to be an important driving force in tumor pathogenesis and progression, the regulatory mechanisms of AS that underly the adaptation of cancer cells to harsh microenvironments remain unclear. Here, we found that hypoxia- and nutrient deprivation-induced asparagine endopeptidase (AEP) specifically cleaved DDX3X in a HIF1A-dependent manner. This cleavage yields truncated carboxyl-terminal DDX3X (tDDX3X-C), which translocates and aggregates in the nucleus. Unlike intact DDX3X, nuclear tDDX3X-C complexes with an array of splicing factors and induces AS events of many pre-mRNAs; for example, enhanced exon skipping (ES) in exon 2 of the classic tumor suppressor PRDM2 leads to a frameshift mutation of PRDM2. Intriguingly, the isoform ARRB1-Δexon 13 binds to glycolytic enzymes and regulates glycolysis. By utilizing in vitro assays, glioblastoma organoids, and animal models, we revealed that AEP/tDDX3X-C promoted tumor malignancy via these isoforms. More importantly, high AEP/tDDX3X-C/ARRB1-Δexon 13 in cancerous tissues was tightly associated with poor patient prognosis. Overall, our discovery of the effect of AEP-cleaved DDX3X switching on alternative RNA splicing events identifies a mechanism in which cancer cells adapt to oxygen and nutrient shortages and provides potential diagnostic and/or therapeutic targets.
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Affiliation(s)
- Wenrui Zhang
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Cao
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Yang
- Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuai Zhang
- Department of Neurosurgery, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jianyi Zhao
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhonggang Shi
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keman Liao
- Brain Injury Center, Shanghai Institute of Head Trauma and
| | - Haiwei Wang
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defects, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Binghong Chen
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zhongrun Qian
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, China
| | - Haoping Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linshi Wu
- Department of Biliary-Pancreatic Surgery and
| | - Hua Liu
- Department of General Surgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongxiang Wang
- Department of Neurosurgery, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chunhui Ma
- Department of Orthopedics, Shanghai General Hospital of Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Qiu
- Brain Injury Center, Shanghai Institute of Head Trauma and
| | - Jianwei Ge
- Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Lin
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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27
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Shireman JM, Cheng L, Goel A, Garcia DM, Partha S, Quiñones-Hinojosa A, Kendziorski C, Dey M. Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough? Front Oncol 2023; 13:1266397. [PMID: 37916170 PMCID: PMC10618006 DOI: 10.3389/fonc.2023.1266397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.
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Affiliation(s)
- Jack M. Shireman
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amiti Goel
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Diogo Moniz Garcia
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States
| | - Sanil Partha
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Mahua Dey
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
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28
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Zhou R, Yang G, Zhang Y, Wang Y. Spatial transcriptomics in development and disease. MOLECULAR BIOMEDICINE 2023; 4:32. [PMID: 37806992 PMCID: PMC10560656 DOI: 10.1186/s43556-023-00144-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
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Affiliation(s)
- Ran Zhou
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gaoxia Yang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Zhang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yuan Wang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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29
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Zhao S, Wang Q, Ni K, Zhang P, Liu Y, Xie J, Ji W, Cheng C, Zhou Q. Combining single-cell sequencing and spatial transcriptome sequencing to identify exosome-related features of glioblastoma and constructing a prognostic model to identify BARD1 as a potential therapeutic target for GBM patients. Front Immunol 2023; 14:1263329. [PMID: 37727789 PMCID: PMC10505933 DOI: 10.3389/fimmu.2023.1263329] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/17/2023] [Indexed: 09/21/2023] Open
Abstract
Background Glioblastoma (GBM) is a malignant primary brain tumor. This study focused on exploring the exosome-related features of glioblastoma to better understand its cellular composition and molecular characteristics. Methods Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome RNA sequencing (stRNA-seq) were used to analyze the heterogeneity of glioblastomas. After data integration, cell clustering, and annotation, five algorithms were used to calculate scores for exosome-related genes(ERGs). Cell trajectory analysis and intercellular communication analysis were performed to explore exosome-related communication patterns. Spatial transcriptome sequencing data were analyzed to validate the findings. To further utilize exosome-related features to aid in clinical decision-making, a prognostic model was constructed using GBM's bulk RNA-seq. Results Different cell subpopulations were observed in GBM, with Monocytes/macrophages and malignant cells in tumor samples showing higher exosome-related scores. After identifying differentially expressed ERGs in malignant cells, pseudotime analysis revealed the cellular status of malignant cells during development. Intercellular communication analysis highlighted signaling pathways and ligand-receptor interactions. Spatial transcriptome sequencing confirmed the high expression of exosome-related gene features in the tumor core region. A prognostic model based on six ERGs was shown to be predictive of overall survival and immunotherapy outcome in GBM patients. Finally, based on the results of scRNA-seq and prognostic modeling as well as a series of cell function experiments, BARD1 was identified as a novel target for the treatment of GBM. Conclusion This study provides a comprehensive understanding of the exosome-related features of GBM in both scRNA-seq and stRNA-seq, with malignant cells with higher exosome-related scores exhibiting stronger communication with Monocytes/macrophages. In terms of spatial data, highly scored malignant cells were also concentrated in the tumor core region. In bulk RNA-seq, patients with a high exosome-related index exhibited an immunosuppressive microenvironment, which was accompanied by a worse prognosis as well as immunotherapy outcomes. Prognostic models constructed using ERGs are expected to be independent prognostic indicators for GBM patients, with potential implications for personalized treatment strategies for GBM. Knockdown of BARD1 in GBM cell lines reduces the invasive and value-added capacity of tumor cells, and thus BARD1-positively expressing malignant cells are a risk factor for GBM patients.
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Affiliation(s)
- Songyun Zhao
- Department of Neurosurgery, Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Kaixiang Ni
- Department of Neurosurgery, Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Pengpeng Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yuan Liu
- Department of General Surgery, Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Wei Ji
- Department of Neurosurgery, Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Chao Cheng
- Department of Neurosurgery, Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Qiang Zhou
- Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, China
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30
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Zheng Y, Carrillo-Perez F, Pizurica M, Heiland DH, Gevaert O. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun 2023; 14:4122. [PMID: 37433817 DOI: 10.1038/s41467-023-39933-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/30/2023] [Indexed: 07/13/2023] Open
Abstract
Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes.
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Affiliation(s)
- Yuanning Zheng
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, 94305, USA
| | - Francisco Carrillo-Perez
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, 94305, USA
- Department of Architecture and Computer Technology (ATC), University of Granada, Granada, 18014, Spain
| | - Marija Pizurica
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, 94305, USA
- Internet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium
| | - Dieter Henrik Heiland
- Microenvironment and Immunology Research Laboratory, Medical Center, University of Freiburg, Freiburg, 79106, Germany
- Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, 79106, Germany
| | - Olivier Gevaert
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, 94305, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
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31
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Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. J Pharm Anal 2023; 13:694-710. [PMID: 37577383 PMCID: PMC10422112 DOI: 10.1016/j.jpha.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 08/15/2023] Open
Abstract
Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.
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Affiliation(s)
- Yijia Fangma
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Mengting Liu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
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32
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Li Y, Zhu Q, Zhou S, Chen J, Du A, Qin C. Combined bulk RNA and single-cell RNA analyses reveal TXNL4A as a new biomarker for hepatocellular carcinoma. Front Oncol 2023; 13:1202732. [PMID: 37305572 PMCID: PMC10248245 DOI: 10.3389/fonc.2023.1202732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Hepatocellular carcinoma (HCC) has a high mortality rate worldwide. The dysregulation of RNA splicing is a major event leading to the occurrence, progression, and drug resistance of cancer. Therefore, it is important to identify new biomarkers of HCC from the RNA splicing pathway. Methods We performed the differential expression and prognostic analyses of RNA splicing-related genes (RRGs) using The Cancer Genome Atlas-liver hepatocellular carcinoma (LIHC). The International Cancer Genome Consortium (ICGC)-LIHC dataset was used to construct and validate prognostic models, and the PubMed database was used to explore genes in the models to identify new markers. The screened genes were subjected to genomic analyses, including differential, prognostic, enrichment, and immunocorrelation analyses. Single-cell RNA (scRNA) data were used to further validate the immunogenetic relationship. Results Of 215 RRGs, we identified 75 differentially expressed prognosis-related genes, and a prognostic model incorporating thioredoxin like 4A (TXNL4A) was identified using least absolute shrinkage and selection operator regression analysis. ICGC-LIHC was used as a validation dataset to confirm the validity of the model. PubMed failed to retrieve HCC-related studies on TXNL4A. TXNL4A was highly expressed in most tumors and was associated with HCC survival. Chi-squared analyses indicated that TXNL4A expression positively correlated positively with the clinical features of HCC. Multivariate analyses revealed that high TXNL4A expression was an independent risk factor for HCC. Immunocorrelation and scRNA data analyses indicated that TXNL4A was correlated with CD8 T cell infiltration in HCC. Conclusion Therefore, we identified a prognostic and immune-related marker for HCC from the RNA splicing pathway.
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Affiliation(s)
- Yifan Li
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Qiaozhen Zhu
- Infection and Immunity Institute and Translational Medical Center, Huaihe Hospital, Kaifeng, Henan, China
| | - Shuchang Zhou
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Jiangtao Chen
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Aoyu Du
- Department of Plastic Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Changjiang Qin
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
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Sussman JH, Xu J, Amankulor N, Tan K. Dissecting the tumor microenvironment of epigenetically driven gliomas: Opportunities for single-cell and spatial multiomics. Neurooncol Adv 2023; 5:vdad101. [PMID: 37706202 PMCID: PMC10496944 DOI: 10.1093/noajnl/vdad101] [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] [Indexed: 09/15/2023] Open
Abstract
Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.
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Affiliation(s)
- Jonathan H Sussman
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Medical Scientist Training Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jason Xu
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Medical Scientist Training Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nduka Amankulor
- Department of Neurosurgery, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kai Tan
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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