1
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Liu Y, Yang C. Computational methods for alignment and integration of spatially resolved transcriptomics data. Comput Struct Biotechnol J 2024; 23:1094-1105. [PMID: 38495555 PMCID: PMC10940867 DOI: 10.1016/j.csbj.2024.03.002] [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: 01/06/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/19/2024] Open
Abstract
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.
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Affiliation(s)
- Yuyao Liu
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
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2
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Nussinov R, Yavuz BR, Jang H. Single cell spatial biology over developmental time can decipher pediatric brain pathologies. Neurobiol Dis 2024; 199:106597. [PMID: 38992777 DOI: 10.1016/j.nbd.2024.106597] [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: 03/27/2024] [Revised: 06/18/2024] [Accepted: 07/07/2024] [Indexed: 07/13/2024] Open
Abstract
Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
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3
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Yu Q, Tian R, Jin X, Wu L. DAIS: a method for identifying spatial domains based on density clustering of spatial omics data. J Genet Genomics 2024; 51:884-887. [PMID: 38599516 DOI: 10.1016/j.jgg.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/17/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Affiliation(s)
- Qichao Yu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China
| | - Ru Tian
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China
| | - Xin Jin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China.
| | - Liang Wu
- BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China.
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4
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Chu X, Tian Y, Lv C. Decoding the spatiotemporal heterogeneity of tumor-associated macrophages. Mol Cancer 2024; 23:150. [PMID: 39068459 PMCID: PMC11282869 DOI: 10.1186/s12943-024-02064-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 07/09/2024] [Indexed: 07/30/2024] Open
Abstract
Tumor-associated macrophages (TAMs) are pivotal in cancer progression, influencing tumor growth, angiogenesis, and immune evasion. This review explores the spatial and temporal heterogeneity of TAMs within the tumor microenvironment (TME), highlighting their diverse subtypes, origins, and functions. Advanced technologies such as single-cell sequencing and spatial multi-omics have elucidated the intricate interactions between TAMs and other TME components, revealing the mechanisms behind their recruitment, polarization, and distribution. Key findings demonstrate that TAMs support tumor vascularization, promote epithelial-mesenchymal transition (EMT), and modulate extracellular matrix (ECM) remodeling, etc., thereby enhancing tumor invasiveness and metastasis. Understanding these complex dynamics offers new therapeutic targets for disrupting TAM-mediated pathways and overcoming drug resistance. This review underscores the potential of targeting TAMs to develop innovative cancer therapies, emphasizing the need for further research into their spatial characteristics and functional roles within the TME.
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Affiliation(s)
- Xiangyuan Chu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110004, P. R. China
| | - Yu Tian
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110004, P. R. China.
| | - Chao Lv
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110004, P. R. China.
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5
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Schott M, León-Periñán D, Splendiani E, Strenger L, Licha JR, Pentimalli TM, Schallenberg S, Alles J, Samut Tagliaferro S, Boltengagen A, Ehrig S, Abbiati S, Dommerich S, Pagani M, Ferretti E, Macino G, Karaiskos N, Rajewsky N. Open-ST: High-resolution spatial transcriptomics in 3D. Cell 2024; 187:3953-3972.e26. [PMID: 38917789 DOI: 10.1016/j.cell.2024.05.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/05/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
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Affiliation(s)
- Marie Schott
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Daniel León-Periñán
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Elena Splendiani
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Leon Strenger
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Jan Robin Licha
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Tancredi Massimo Pentimalli
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Jonathan Alles
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sarah Samut Tagliaferro
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Anastasiya Boltengagen
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sebastian Ehrig
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Stefano Abbiati
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Steffen Dommerich
- Department of Otorhinolaryngology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin 13353, Germany
| | - Massimiliano Pagani
- IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | | | - Giuseppe Macino
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Cellular Biotechnologies and Hematology, La Sapienza University of Rome, 00161 Rome, Italy.
| | - Nikos Karaiskos
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany.
| | - Nikolaus Rajewsky
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Charité - Universitätsmedizin, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Site Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Berlin, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Site Berlin, Berlin, Germany.
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6
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Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 2024; 25:bbae364. [PMID: 39082652 PMCID: PMC11289682 DOI: 10.1093/bib/bbae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 07/17/2024] [Indexed: 08/03/2024] Open
Abstract
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
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Affiliation(s)
| | | | | | | | | | | | - Li Zhuo
- Corresponding author. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Clinic Medical College, Beijing University of Chinese Medicine, 100029 Beijing, China. E-mail:
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7
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Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol 2024:10.1038/s41571-024-00926-7. [PMID: 39043872 DOI: 10.1038/s41571-024-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/25/2024]
Abstract
Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.
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Affiliation(s)
- Julia Chen
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, St George Hospital, Sydney, New South Wales, Australia
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
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8
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Nagasawa S, Zenkoh J, Suzuki Y, Suzuki A. Spatial omics technologies for understanding molecular status associated with cancer progression. Cancer Sci 2024. [PMID: 39042942 DOI: 10.1111/cas.16283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 07/02/2024] [Indexed: 07/25/2024] Open
Abstract
Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.
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Affiliation(s)
- Satoi Nagasawa
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Junko Zenkoh
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Ayako Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
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9
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Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nat Commun 2024; 15:6048. [PMID: 39025895 PMCID: PMC11258319 DOI: 10.1038/s41467-024-50308-x] [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: 02/06/2024] [Accepted: 07/03/2024] [Indexed: 07/20/2024] Open
Abstract
With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.
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Affiliation(s)
- Haoyang Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yingxin Lin
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Wenjia He
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Chencheng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Elva Gao
- The KAUST school, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA.
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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10
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Ishikawa A, Fukui T, Kido A, Katsuya N, Kuraoka K, Uraoka N, Suzuki T, Oka S, Kotachi T, Ashktorab H, Smoot D, Yasui W. Discovering cancer stem-like molecule, nuclear factor I X, using spatial transcriptome in gastric cancer. Cancer Sci 2024. [PMID: 39021298 DOI: 10.1111/cas.16288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/20/2024] Open
Abstract
Gastric cancer (GC) is characterized by significant intratumoral heterogeneity, and stem cells are promising therapeutic targets. Despite advancements in spatial transcriptome analyses, unexplored targets for addressing cancer stemness remain unknown. This study aimed to identify Nuclear Factor IX (NFIX) as a critical regulator of cancer stemness in GC and evaluate its clinicopathological significance and function. Spatial transcriptome analysis of GC was conducted. The correlation between NFIX expression, clinicopathological factors, and prognosis was assessed using immunostaining in 127 GC cases. Functional analyses of cancer cell lines validated these findings. Spatial transcriptome analysis stratified GC tissues based on genetic profiles, identified CSC-like cells, and further refined the classification to identify and highlight the significance of NFIX, as validated by Monocle 3 and CytoTRACE analyses. Knockdown experiments in cancer cell lines have demonstrated the involvement of NFIX in cancer cell proliferation and kinase activity. This study underscores the role of spatial transcriptome analysis in refining GC tissue classification and identifying therapeutic targets, highlighting NFIX as a pivotal factor. NFIX expression is correlated with poor prognosis and drives GC progression, suggesting its potential as a novel therapeutic target for personalized GC therapies.
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Affiliation(s)
- Akira Ishikawa
- Department of Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takafumi Fukui
- Department of Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Aya Kido
- Department of Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Narutaka Katsuya
- Department of Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuya Kuraoka
- Department of Diagnostic Pathology, National Hospital Organization (NHO), Kure Medical Center and Chugoku Cancer Center, Kure, Hiroshima, Japan
| | - Naohiro Uraoka
- Department of Pathology, Kure Kyosai Hospital, Federation of National Public Services and Affiliated Personnel Mutual Aid Associations, Kure, Japan
| | - Takahisa Suzuki
- Department of Surgery, National Hospital Organization (NHO), Kure Medical Center and Chugoku Cancer Center, Kure, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takahiro Kotachi
- Department of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Hassan Ashktorab
- Department of Medicine and Cancer Center, Howard University College of Medicine, Washington, DC, USA
| | - Duane Smoot
- Department of Medicine, Meharry Medical College, Nashville, Tennessee, USA
| | - Wataru Yasui
- Department of Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
- Division of Pathology, Hiroshima City Medical Association Clinical Laboratory, Hiroshima, Japan
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11
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Zhang H, Lu KH, Ebbini M, Huang P, Lu H, Li L. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. NPJ IMAGING 2024; 2:20. [PMID: 39036554 PMCID: PMC11254763 DOI: 10.1038/s44303-024-00025-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Kelly H. Lu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Malik Ebbini
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
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12
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Border S, Ferreira RM, Lucarelli N, Kumar S, Paul A, Manthey D, Barisoni L, Strekalova Y, Ray J, Cheng YH, Rosenberg AZ, Tomaszewski JE, Mimar S, Hodgin JB, El-Achkar TM, Jain S, Eadon MT, Sarder P. FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602778. [PMID: 39026885 PMCID: PMC11257503 DOI: 10.1101/2024.07.09.602778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.
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Affiliation(s)
- Samuel Border
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
| | | | - Nicholas Lucarelli
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
| | - Suhas Kumar
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
| | - Anindya Paul
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
| | | | - Laura Barisoni
- Department of Pathology, Division of AI and Computational Pathology, Duke University, Durham, NC
- Department of Medicine, Division of Nephrology, Duke University, Durham, NC
| | - Yulia Strekalova
- College of Public Health and Health Professions, University of Florida, Gainesville, FL
| | - Jessica Ray
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL
| | - Ying-Hua Cheng
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Avi Z Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine Baltimore, MD
| | - John E Tomaszewski
- Department of Pathology & Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Sayat Mimar
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
| | | | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
- Indianapolis VA Medical Center, Indianapolis, IN
| | - Sanjay Jain
- Department of Medicine, Division of Nephrology, Washington University School of Medicine, St. Louis, MO
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Pinaki Sarder
- Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL
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13
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Yin Z, Huang W, Li K, Fernie AR, Yan S. Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38990529 DOI: 10.1111/tpj.16924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/24/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.
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Affiliation(s)
- Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
- Institute of Advanced Science Facilities, Shenzhen, 518107, Guangdong, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Kun Li
- Guangdong Key Laboratory of Crop Genetic Improvement, Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
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14
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Rocca G, Galli M, Celant A, Stucchi G, Marongiu L, Cozzi S, Innocenti M, Granucci F. Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function. FEBS Lett 2024. [PMID: 38969618 DOI: 10.1002/1873-3468.14962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 07/07/2024]
Abstract
Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single-cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross-talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue-specific functions and aiding newcomers in navigating this field.
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Affiliation(s)
- Giuseppe Rocca
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Marco Galli
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Anna Celant
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Giulia Stucchi
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Laura Marongiu
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Stefano Cozzi
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Metello Innocenti
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
| | - Francesca Granucci
- Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
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15
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Andersson A, Behanova A, Avenel C, Windhager J, Malmberg F, Wählby C. Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data. Cytometry A 2024. [PMID: 38958502 DOI: 10.1002/cyto.a.24884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 07/04/2024]
Abstract
Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces "Points2Regions," a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical andk $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.
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Affiliation(s)
- Axel Andersson
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Jonas Windhager
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Carolina Wählby
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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16
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Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
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Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
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17
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Aihara G, Clifton K, Chen M, Li Z, Atta L, Miller BF, Satija R, Hickey JW, Fan J. SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis. Bioinformatics 2024; 40:btae412. [PMID: 38902953 PMCID: PMC11226864 DOI: 10.1093/bioinformatics/btae412] [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: 02/02/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 06/22/2024] Open
Abstract
MOTIVATION Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. RESULTS To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures. AVAILABILITY AND IMPLEMENTATION SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.
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Affiliation(s)
- Gohta Aihara
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Kalen Clifton
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Mayling Chen
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Zhuoyan Li
- New York Genome Center, New York, NY 10013, United States
| | - Lyla Atta
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Brendan F Miller
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Rahul Satija
- New York Genome Center, New York, NY 10013, United States
- Center for Genomics and Systems Biology, New York University, New York, NY 10003, United States
| | - John W Hickey
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States
| | - Jean Fan
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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18
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Martin-Martin C, Suarez-Alvarez B, González M, Torres IB, Bestard O, Martín JE, Barceló-Coblijn G, Moreso F, Aransay AM, Lopez-Larrea C, Rodriguez RM. Exploring kidney allograft rejection: A proof-of-concept study using spatial transcriptomics. Am J Transplant 2024; 24:1161-1171. [PMID: 38692412 DOI: 10.1016/j.ajt.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/03/2024]
Abstract
In this proof-of-concept study, spatial transcriptomics combined with public single-cell ribonucleic acid-sequencing data were used to explore the potential of this technology to study kidney allograft rejection. We aimed to map gene expression patterns within diverse pathologic states by examining biopsies classified across nonrejection, T cell-mediated acute rejection, interstitial fibrosis, and tubular atrophy. Our results revealed distinct immune cell signatures, including those of T and B lymphocytes, monocytes, mast cells, and plasma cells, and their spatial organization within the renal interstitium. We also mapped chemokine receptors and ligands to study immune cell migration and recruitment. Finally, our analysis demonstrated differential spatial enrichment of transcription signatures associated with kidney allograft rejection across various biopsy regions. Interstitium regions displayed higher enrichment scores for rejection-associated gene expression patterns than tubular areas, which had negative scores. This implies that these signatures are primarily driven by processes unfolding in the renal interstitium. Overall, this study highlights the value of spatial transcriptomics for revealing cellular heterogeneity and immune signatures in renal transplant biopsies and demonstrates its potential for studying the molecular and cellular mechanisms associated with rejection. However, certain limitations must be borne in mind regarding the development and future applications of this technology.
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Affiliation(s)
- Cristina Martin-Martin
- Translational Immunology, Health Research Institute of the Principality of Asturias (ISPA), Avenida de Roma S/N, 33011, Oviedo, Asturias, Spain; RICORS2040, Kidney Disease Research Network, ISCIII, Madrid, Spain
| | - Beatriz Suarez-Alvarez
- Translational Immunology, Health Research Institute of the Principality of Asturias (ISPA), Avenida de Roma S/N, 33011, Oviedo, Asturias, Spain; RICORS2040, Kidney Disease Research Network, ISCIII, Madrid, Spain
| | - Monika González
- CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, 801 bld., 48160, Derio, Bizkaia, Spain
| | - Irina B Torres
- Department of Nephrology, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Nephrology and Renal Transplant Laboratory, Vall Hebron Research Institute (VHIR), Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Oriol Bestard
- Department of Nephrology, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Nephrology and Renal Transplant Laboratory, Vall Hebron Research Institute (VHIR), Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - José E Martín
- CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, 801 bld., 48160, Derio, Bizkaia, Spain
| | - Gwendolyn Barceló-Coblijn
- Lipids in Human Pathology, Institut d'Investigació Sanitària Illes Balears (IdISBa), Ctra. Valldemossa 79, E-07120 Palma, Balearic Islands, Spain; Research Unit, University Hospital Son Espases, Ctra. Valldemossa 79, E-07120 Palma, Balearic Islands, Spain
| | - Francesc Moreso
- Department of Nephrology, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Nephrology and Renal Transplant Laboratory, Vall Hebron Research Institute (VHIR), Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Ana M Aransay
- CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, 801 bld., 48160, Derio, Bizkaia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - Carlos Lopez-Larrea
- Translational Immunology, Health Research Institute of the Principality of Asturias (ISPA), Avenida de Roma S/N, 33011, Oviedo, Asturias, Spain; RICORS2040, Kidney Disease Research Network, ISCIII, Madrid, Spain; Department of Immunology, Hospital Universitario Central de Asturias, 33011, Oviedo, Spain.
| | - Ramon M Rodriguez
- Lipids in Human Pathology, Institut d'Investigació Sanitària Illes Balears (IdISBa), Ctra. Valldemossa 79, E-07120 Palma, Balearic Islands, Spain; Research Unit, University Hospital Son Espases, Ctra. Valldemossa 79, E-07120 Palma, Balearic Islands, Spain
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19
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Huynh KLA, Tyc KM, Matuck BF, Easter QT, Pratapa A, Kumar NV, Pérez P, Kulchar RJ, Pranzatelli TJ, de Souza D, Weaver TM, Qu X, Soares Junior LAV, Dolhnokoff M, Kleiner DE, Hewitt SM, Ferraz da Silva LF, Rocha VG, Warner BM, Byrd KM, Liu J. Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT. RESEARCH SQUARE 2024:rs.3.rs-4536158. [PMID: 38978567 PMCID: PMC11230516 DOI: 10.21203/rs.3.rs-4536158/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.
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Affiliation(s)
- Khoa L. A. Huynh
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
| | - Katarzyna M. Tyc
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
- Massey Cancer Center, Richmond VA, USA
| | - Bruno F. Matuck
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Quinn T. Easter
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Aditya Pratapa
- Department of Cell Biology, Duke University, Durham, NC, USA
| | - Nikhil V. Kumar
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Paola Pérez
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Rachel J. Kulchar
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Thomas J.F. Pranzatelli
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Deiziane de Souza
- Department of Pathology, Medicine School of University of Sao Paulo, SP, BR
| | - Theresa M. Weaver
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Xufeng Qu
- Massey Cancer Center, Richmond VA, USA
| | | | - Marisa Dolhnokoff
- Department of Pathology, Medicine School of University of Sao Paulo, SP, BR
| | - David E. Kleiner
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen M. Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Vanderson Geraldo Rocha
- Department of Hematology, Transfusion and Cell Therapy Service, University of Sao Paulo, Sao Paulo, Brazil
| | - Blake M. Warner
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Kevin M. Byrd
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinze Liu
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
- Massey Cancer Center, Richmond VA, USA
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20
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Frungieri MB, Mayerhofer A. Biogenic amines in the testis: sources, receptors and actions. Front Endocrinol (Lausanne) 2024; 15:1392917. [PMID: 38966220 PMCID: PMC11222591 DOI: 10.3389/fendo.2024.1392917] [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: 02/28/2024] [Accepted: 05/27/2024] [Indexed: 07/06/2024] Open
Abstract
Biogenic amines are signaling molecules with multiple roles in the central nervous system and in peripheral organs, including the gonads. A series of studies indicated that these molecules, their biosynthetic enzymes and their receptors are present in the testis and that they are involved in the regulation of male reproductive physiology and/or pathology. This mini-review aims to summarize the current knowledge in this field and to pinpoint existing research gaps. We suggest that the widespread clinical use of pharmacological agonists/antagonists of these signaling molecules, calls for new investigations in this area. They are necessary to evaluate the relevance of biogenic amines for human male fertility and infertility, as well as the potential value of at least one of them as an anti-aging compound in the testis.
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Affiliation(s)
- Monica Beatriz Frungieri
- Laboratorio de neuro-inmuno-endocrinología testicular, Instituto de Biología y Medicina Experimental (IBYME), Fundación IBYME, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad de Buenos Aires, Argentina
| | - Artur Mayerhofer
- Biomedical Center Munich (BMC), Cell Biology, Anatomy III, Faculty of Medicine, Ludwig Maximilian University of Munich, Planegg-Martinsried, Germany
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21
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Rogal J, Zamproni LN, Nikolakopoulou P, Ygberg S, Wedell A, Wredenberg A, Herland A. Human In Vitro Models of Neuroenergetics and Neurometabolic Disturbances: Current Advances and Clinical Perspectives. Stem Cells Transl Med 2024; 13:505-514. [PMID: 38588471 PMCID: PMC11165162 DOI: 10.1093/stcltm/szae021] [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: 11/01/2023] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
Neurological conditions conquer the world; they are the leading cause of disability and the second leading cause of death worldwide, and they appear all around the world in every age group, gender, nationality, and socioeconomic class. Despite the growing evidence of an immense impact of perturbations in neuroenergetics on overall brain function, only little is known about the underlying mechanisms. Especially human insights are sparse, owing to a shortage of physiologically relevant model systems. With this perspective, we aim to explore the key steps and considerations involved in developing an advanced human in vitro model for studying neuroenergetics. We discuss biological and technological strategies to meet the requirements of a predictive model, aiming at providing a guide and inspiration for future in vitro models of neuroenergetics.
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Affiliation(s)
- Julia Rogal
- Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
- Division of Nanobiotechnology, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology at Science for Life Laboratory, 17165 Solna, Sweden
- Center for the Advancement of Integrated Medical and Engineering Sciences (AIMES), Karolinska Institute and KTH Royal Institute of Technology, 17177 Stockholm, Sweden
| | - Laura Nicoleti Zamproni
- Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
- Department of Biochemistry, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo 04039-032, Brazil
| | - Polyxeni Nikolakopoulou
- Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
- Center for the Advancement of Integrated Medical and Engineering Sciences (AIMES), Karolinska Institute and KTH Royal Institute of Technology, 17177 Stockholm, Sweden
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Sofia Ygberg
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, 17177 Stockholm, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
- Neuropediatric Unit, Karolinska University Hospital, 17177 Stockholm, Sweden
| | - Anna Wedell
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, 17177 Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, 17177 Stockholm, Sweden
| | - Anna Wredenberg
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, 17177 Stockholm, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Anna Herland
- Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
- Division of Nanobiotechnology, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology at Science for Life Laboratory, 17165 Solna, Sweden
- Center for the Advancement of Integrated Medical and Engineering Sciences (AIMES), Karolinska Institute and KTH Royal Institute of Technology, 17177 Stockholm, Sweden
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22
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Du P, Fan R, Zhang N, Wu C, Zhang Y. Advances in Integrated Multi-omics Analysis for Drug-Target Identification. Biomolecules 2024; 14:692. [PMID: 38927095 PMCID: PMC11201992 DOI: 10.3390/biom14060692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 06/08/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.
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Affiliation(s)
- Peiling Du
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Rui Fan
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Nana Zhang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Chenyuan Wu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
| | - Yingqian Zhang
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China; (P.D.); (R.F.); (N.Z.); (C.W.)
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
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23
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Lin S, Cui Y, Zhao F, Yang Z, Song J, Yao J, Zhao Y, Qian BZ, Zhao Y, Yuan Z. Complete spatially resolved gene expression is not necessary for identifying spatial domains. CELL GENOMICS 2024; 4:100565. [PMID: 38781966 PMCID: PMC11228956 DOI: 10.1016/j.xgen.2024.100565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yan Cui
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Zhidong Yang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
| | | | - Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bin-Zhi Qian
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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24
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Blampey Q, Mulder K, Gardet M, Christodoulidis S, Dutertre CA, André F, Ginhoux F, Cournède PH. Sopa: a technology-invariant pipeline for analyses of image-based spatial omics. Nat Commun 2024; 15:4981. [PMID: 38862483 PMCID: PMC11167053 DOI: 10.1038/s41467-024-48981-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024] Open
Abstract
Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.
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Affiliation(s)
- Quentin Blampey
- Paris-Saclay University, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France.
- Paris-Saclay University, Gustave Roussy, Villejuif, France.
| | - Kevin Mulder
- Paris-Saclay University, Gustave Roussy, Villejuif, France
| | - Margaux Gardet
- Paris-Saclay University, Gustave Roussy, Villejuif, France
| | - Stergios Christodoulidis
- Paris-Saclay University, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France
| | | | - Fabrice André
- Paris-Saclay University, Gustave Roussy, Villejuif, France
- Gustave Roussy, Department of Medical Oncology, Villejuif, France
| | | | - Paul-Henry Cournède
- Paris-Saclay University, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France.
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25
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He B, Yao H, Yi C. Advances in the joint profiling technologies of 5mC and 5hmC. RSC Chem Biol 2024; 5:500-507. [PMID: 38846078 PMCID: PMC11151843 DOI: 10.1039/d4cb00034j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/21/2024] [Indexed: 06/09/2024] Open
Abstract
DNA cytosine methylation, a crucial epigenetic modification, involves the dynamic interplay of 5-methylcytosine (5mC) and its oxidized form, 5-hydroxymethylcytosine (5hmC), generated by ten-eleven translocation (TET) DNA dioxygenases. This process is central to regulating gene expression, influencing critical biological processes such as development, disease progression, and aging. Recognizing the distinct functions of 5mC and 5hmC, researchers often employ restriction enzyme-based or chemical treatment methods for their simultaneous measurement from the same genomic sample. This enables a detailed understanding of the relationship between these modifications and their collective impact on cellular function. This review focuses on summarizing the technologies for detecting 5mC and 5hmC together but also discusses the limitations and potential future directions in this evolving field.
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Affiliation(s)
- Bo He
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing China
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies Chengdu China
| | - Haojun Yao
- College of Chemistry and Chemical Engineering, Hunan University Changsha China
| | - Chengqi Yi
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing China
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University Beijing China
- Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University Beijing China
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26
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Huynh KLA, Tyc KM, Matuck BF, Easter QT, Pratapa A, Kumar NV, Pérez P, Kulchar R, Pranzatelli T, de Souza D, Weaver TM, Qu X, Valente Soares LA, Dolhnokoff M, Kleiner DE, Hewitt SM, da Silva LFF, Rocha VG, Warner BM, Byrd KM, Liu J. Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596861. [PMID: 38895230 PMCID: PMC11185514 DOI: 10.1101/2024.05.31.596861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.
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Affiliation(s)
- Khoa L. A. Huynh
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
| | - Katarzyna M. Tyc
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
- Massey Cancer Center, Richmond VA, USA
| | - Bruno F. Matuck
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Quinn T. Easter
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Aditya Pratapa
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Nikhil V. Kumar
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Paola Pérez
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Rachel Kulchar
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Thomas Pranzatelli
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Deiziane de Souza
- Department of Pathology, Medicine School of University of Sao Paulo, SP, BR
| | - Theresa M. Weaver
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Xufeng Qu
- Massey Cancer Center, Richmond VA, USA
| | | | - Marisa Dolhnokoff
- Department of Pathology, Medicine School of University of Sao Paulo, SP, BR
| | - David E. Kleiner
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen M. Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Vanderson Geraldo Rocha
- Department of Hematology, Transfusion and Cell Therapy Service, University of Sao Paulo, Sao Paulo, Brazil
| | - Blake M. Warner
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Kevin M. Byrd
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinze Liu
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
- Massey Cancer Center, Richmond VA, USA
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27
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Garrido-Trigo A, Veny M, Salas A. Uncovering intestinal macrophages through the integration of single-cell and spatial transcriptomics. Genes Immun 2024; 25:254-255. [PMID: 38142267 DOI: 10.1038/s41435-023-00242-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 11/29/2023] [Accepted: 12/07/2023] [Indexed: 12/25/2023]
Affiliation(s)
- Alba Garrido-Trigo
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Madrid, Spain
| | - Marisol Veny
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Madrid, Spain
| | - Azucena Salas
- Inflammatory Bowel Disease Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Madrid, Spain.
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28
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Filipovic D, Kana O, Marri D, Bhattacharya S. Unique challenges and best practices for single cell transcriptomic analysis in toxicology. CURRENT OPINION IN TOXICOLOGY 2024; 38:100475. [PMID: 38645720 PMCID: PMC11027889 DOI: 10.1016/j.cotox.2024.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.
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Affiliation(s)
- David Filipovic
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
| | - Omar Kana
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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29
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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30
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Thuilliez C, Moquin-Beaudry G, Khneisser P, Marques Da Costa ME, Karkar S, Boudhouche H, Drubay D, Audinot B, Geoerger B, Scoazec JY, Gaspar N, Marchais A. CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data. BIOINFORMATICS ADVANCES 2024; 4:vbae081. [PMID: 38915885 PMCID: PMC11194756 DOI: 10.1093/bioadv/vbae081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/02/2024] [Accepted: 05/29/2024] [Indexed: 06/26/2024]
Abstract
Motivation Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates. Results In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis. Availability and implementation CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.
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Affiliation(s)
- Corentin Thuilliez
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Gaël Moquin-Beaudry
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Pierre Khneisser
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Maria Eugenia Marques Da Costa
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Slim Karkar
- University Bordeaux, CNRS, IBGC, UMR, Bordeaux 33077, France
- Bordeaux Bioinformatic Center CBiB, University of Bordeaux, Bordeaux 33000, France
| | - Hanane Boudhouche
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Damien Drubay
- Office of Biostatistics and Epidemiology, Gustave Roussy, Université Paris-Saclay, Villejuif 94805, France
- Inserm, Université Paris-Saclay, CESP U1018, Oncostat, Labeled Ligue Contre le Cancer, Villejuif 94805, France
| | - Baptiste Audinot
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Birgit Geoerger
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Jean-Yves Scoazec
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Nathalie Gaspar
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Antonin Marchais
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
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31
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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Bitto V, Hönscheid P, Besso MJ, Sperling C, Kurth I, Baumann M, Brors B. Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors. NPJ Syst Biol Appl 2024; 10:57. [PMID: 38802379 PMCID: PMC11130291 DOI: 10.1038/s41540-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.
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Affiliation(s)
- Verena Bitto
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Heidelberg, Germany.
- Faculty for Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Pia Hönscheid
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - María José Besso
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Sperling
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ina Kurth
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Michael Baumann
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
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Moreno J, Gluud LL, Galsgaard ED, Hvid H, Mazzoni G, Das V. Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics. PLoS One 2024; 19:e0302853. [PMID: 38768139 PMCID: PMC11104622 DOI: 10.1371/journal.pone.0302853] [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: 12/22/2023] [Accepted: 04/10/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Chronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (L-R) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts. RESULTS The analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models. CONCLUSION Our study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.
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Affiliation(s)
- Jaime Moreno
- Digital Science and Innovation, Computational Biology – AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark
| | - Lise Lotte Gluud
- Gastro Unit, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Dept of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Henning Hvid
- Global Drug Discovery, Novo Nordisk A/S, Maløv, Denmark
| | - Gianluca Mazzoni
- Digital Science and Innovation, Computational Biology – AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark
| | - Vivek Das
- Digital Science and Innovation, Computational Biology – AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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Chen Y, Sun H, Luo Z, Mei Y, Xu Z, Tan J, Xie Y, Li M, Xia J, Yang B, Su B. Crosstalk between CD8 + T cells and mesenchymal stromal cells in intestine homeostasis and immunity. Adv Immunol 2024; 162:23-58. [PMID: 38866438 DOI: 10.1016/bs.ai.2024.02.001] [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: 06/14/2024]
Abstract
The intestine represents the most complex cellular network in the whole body. It is constantly faced with multiple types of immunostimulatory agents encompassing from food antigen, gut microbiome, metabolic waste products, and dead cell debris. Within the intestine, most T cells are found in three primary compartments: the organized gut-associated lymphoid tissue, the lamina propria, and the epithelium. The well-orchestrated epithelial-immune-microbial interaction is critically important for the precise immune response. The main role of intestinal mesenchymal stromal cells is to support a structural framework within the gut wall. However, recent evidence from stromal cell studies indicates that they also possess significant immunomodulatory functions, such as maintaining intestinal tolerance via the expression of PDL1/2 and MHC-II molecules, and promoting the development of CD103+ dendritic cells, and IgA+ plasma cells, thereby enhancing intestinal homeostasis. In this review, we will summarize the current understanding of CD8+ T cells and stromal cells alongside the intestinal tract and discuss the reciprocal interactions between T subsets and mesenchymal stromal cell populations. We will focus on how the tissue residency, migration, and function of CD8+ T cells could be potentially regulated by mesenchymal stromal cell populations and explore the molecular mediators, such as TGF-β, IL-33, and MHC-II molecules that might influence these processes. Finally, we discuss the potential pathophysiological impact of such interaction in intestine hemostasis as well as diseases of inflammation, infection, and malignancies.
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Affiliation(s)
- Yao Chen
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongxiang Sun
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengnan Luo
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yisong Mei
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ziyang Xu
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianmei Tan
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiting Xie
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengda Li
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiaqi Xia
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beichun Yang
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Su
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, The Ministry of Education Key Laboratory of Cell Death and Differentiation, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Jiao Tong University School of Medicine-Yale Institute for Immune Metabolism, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Key Laboratory of Molecular Radiation Oncology of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.
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Li F, Xiang R, Liu Y, Hu G, Jiang Q, Jia T. Approaches and challenges in identifying, quantifying, and manipulating dynamic mitochondrial genome variations. Cell Signal 2024; 117:111123. [PMID: 38417637 DOI: 10.1016/j.cellsig.2024.111123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/14/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Mitochondria, the cellular powerhouses, possess their own unique genetic system, including replication, transcription, and translation. Studying these processes is crucial for comprehending mitochondrial disorders, energy production, and their related diseases. Over the past decades, various approaches have been applied in detecting and quantifying mitochondrial genome variations with also the purpose of manipulation of mitochondria or mitochondrial genome for therapeutics. Understanding the scope and limitations of above strategies is not only fundamental to the understanding of basic biology but also critical for exploring disease-related novel target(s), as well to develop innovative therapies. Here, this review provides an overview of different tools and techniques for accurate mitochondrial genome variations identification, quantification, and discuss novel strategies for the manipulation of mitochondria to develop innovative therapeutic interventions, through combining the insights gained from the study of mitochondrial genetics with ongoing single cell omics combined with advanced single molecular tools.
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Affiliation(s)
- Fei Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Run Xiang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China; Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yue Liu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Guoliang Hu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China; Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Quanbo Jiang
- Light, Nanomaterials, Nanotechnologies (L2n) Laboratory, CNRS EMR 7004, University of Technology of Troyes, 12 rue Marie Curie, 10004 Troyes, France
| | - Tao Jia
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China; CNRS-UMR9187, INSERM U1196, PSL-Research University, 91405 Orsay, France; CNRS-UMR9187, INSERM U1196, Université Paris Saclay, 91405 Orsay, France.
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Jin X, Zhang R, Fu Y, Zhu Q, Hong L, Wu A, Wang H. Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies. Brief Funct Genomics 2024:elae019. [PMID: 38688725 DOI: 10.1093/bfgp/elae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024] Open
Abstract
As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.
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Affiliation(s)
- Xinrong Jin
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Ruohan Zhang
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Yunqi Fu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Qiunan Zhu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Liquan Hong
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Aiwei Wu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Hu Wang
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
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38
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Matchett KP, Paris J, Teichmann SA, Henderson NC. Spatial genomics: mapping human steatotic liver disease. Nat Rev Gastroenterol Hepatol 2024:10.1038/s41575-024-00915-2. [PMID: 38654090 DOI: 10.1038/s41575-024-00915-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.
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Affiliation(s)
- Kylie P Matchett
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Jasmin Paris
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Neil C Henderson
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK.
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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Chen Y, Wu Y, Yan G, Zhang G. Tertiary lymphoid structures in cancer: maturation and induction. Front Immunol 2024; 15:1369626. [PMID: 38690273 PMCID: PMC11058640 DOI: 10.3389/fimmu.2024.1369626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
Tertiary lymphoid structure (TLS) is an ectopic lymphocyte aggregate formed in peripheral non-lymphoid tissues, including inflamed or cancerous tissue. Tumor-associated TLS serves as a prominent center of antigen presentation and adaptive immune activation within the periphery, which has exhibited positive prognostic value in various cancers. In recent years, the concept of maturity regarding TLS has been proposed and mature TLS, characterized by well-developed germinal centers, exhibits a more potent tumor-suppressive capacity with stronger significance. Meanwhile, more and more evidence showed that TLS can be induced by therapeutic interventions during cancer treatments. Thus, the evaluation of TLS maturity and the therapeutic interventions that induce its formation are critical issues in current TLS research. In this review, we aim to provide a comprehensive summary of the existing classifications for TLS maturity and therapeutic strategies capable of inducing its formation in tumors.
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Affiliation(s)
- Yulu Chen
- Department of Phototherapy, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Skin Cancer Center, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Photomedicine, School of Medicine, Tongji University, Shanghai, China
| | - Yuhao Wu
- Department of Phototherapy, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Skin Cancer Center, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Photomedicine, School of Medicine, Tongji University, Shanghai, China
| | - Guorong Yan
- Department of Phototherapy, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Skin Cancer Center, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Photomedicine, School of Medicine, Tongji University, Shanghai, China
| | - Guolong Zhang
- Department of Phototherapy, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Skin Cancer Center, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Photomedicine, School of Medicine, Tongji University, Shanghai, China
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40
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Wang L, Li M, Hwang TH. The 3D Revolution in Cancer Discovery. Cancer Discov 2024; 14:625-629. [PMID: 38571426 DOI: 10.1158/2159-8290.cd-23-1499] [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: 04/05/2024]
Abstract
SUMMARY The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.
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Affiliation(s)
- Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida
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41
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Shugar AL, Konger RL, Rohan CA, Travers JB, Kim YL. Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues. Exp Dermatol 2024; 33:e15076. [PMID: 38610095 PMCID: PMC11034840 DOI: 10.1111/exd.15076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/24/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024]
Abstract
Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.
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Affiliation(s)
- Andrea L. Shugar
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
| | - Raymond L. Konger
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Dermatology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pathology, Richard L. Roudebush Veterans Administration Hospital, Indianapolis, Indiana, USA
| | - Craig A. Rohan
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Dermatology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Medicine, Dayton Veterans Affairs Medical Center, Dayton, Ohio, USA
| | - Jeffrey B. Travers
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Dermatology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Medicine, Dayton Veterans Affairs Medical Center, Dayton, Ohio, USA
| | - Young L. Kim
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
- Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana, USA
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42
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Arulraj T, Wang H, Ippolito A, Zhang S, Fertig EJ, Popel AS. Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. Brief Bioinform 2024; 25:bbae131. [PMID: 38557676 PMCID: PMC10982948 DOI: 10.1093/bib/bbae131] [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: 10/31/2023] [Revised: 02/20/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Hamacher C, Degen R, Franke M, Switacz VK, Fleck D, Katreddi RR, Hernandez-Clavijo A, Strauch M, Horio N, Hachgenei E, Spehr J, Liberles SD, Merhof D, Forni PE, Zimmer-Bensch G, Ben-Shaul Y, Spehr M. A revised conceptual framework for mouse vomeronasal pumping and stimulus sampling. Curr Biol 2024; 34:1206-1221.e6. [PMID: 38320553 PMCID: PMC10965388 DOI: 10.1016/j.cub.2024.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/15/2023] [Accepted: 01/11/2024] [Indexed: 02/08/2024]
Abstract
The physiological performance of any sensory organ is determined by its anatomy and physical properties. Consequently, complex sensory structures with elaborate features have evolved to optimize stimulus detection. Understanding these structures and their physical nature forms the basis for mechanistic insights into sensory function. Despite its crucial role as a sensor for pheromones and other behaviorally instructive chemical cues, the vomeronasal organ (VNO) remains a poorly characterized mammalian sensory structure. Fundamental principles of its physico-mechanical function, including basic aspects of stimulus sampling, remain poorly explored. Here, we revisit the classical vasomotor pump hypothesis of vomeronasal stimulus uptake. Using advanced anatomical, histological, and physiological methods, we demonstrate that large parts of the lateral mouse VNO are composed of smooth muscle. Vomeronasal smooth muscle tissue comprises two subsets of fibers with distinct topography, structure, excitation-contraction coupling, and, ultimately, contractile properties. Specifically, contractions of a large population of noradrenaline-sensitive cells mediate both transverse and longitudinal lumen expansion, whereas cholinergic stimulation targets an adluminal group of smooth muscle fibers. The latter run parallel to the VNO's rostro-caudal axis and are ideally situated to mediate antagonistic longitudinal constriction of the lumen. This newly discovered arrangement implies a novel mode of function. Single-cell transcriptomics and pharmacological profiling reveal the receptor subtypes involved. Finally, 2D/3D tomography provides non-invasive insight into the intact VNO's anatomy and mechanics, enables measurement of luminal fluid volume, and allows an assessment of relative volume change upon noradrenergic stimulation. Together, we propose a revised conceptual framework for mouse vomeronasal pumping and, thus, stimulus sampling.
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Affiliation(s)
- Christoph Hamacher
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Rudolf Degen
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany; Research Training Group 2416 MultiSenses - MultiScales, RWTH Aachen University, 52074 Aachen, Germany
| | - Melissa Franke
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Victoria K Switacz
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany; Research Training Group 2416 MultiSenses - MultiScales, RWTH Aachen University, 52074 Aachen, Germany
| | - David Fleck
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Raghu Ram Katreddi
- Department of Biological Sciences, The RNA Institute, University at Albany, Albany, NY 12222, USA
| | - Andres Hernandez-Clavijo
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Martin Strauch
- Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany
| | - Nao Horio
- Howard Hughes Medical Institute, Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Enno Hachgenei
- Department of Production Metrology, Fraunhofer Institute for Production Technology, 52074 Aachen, Germany
| | - Jennifer Spehr
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Stephen D Liberles
- Howard Hughes Medical Institute, Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Dorit Merhof
- Research Training Group 2416 MultiSenses - MultiScales, RWTH Aachen University, 52074 Aachen, Germany; Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany
| | - Paolo E Forni
- Department of Biological Sciences, The RNA Institute, University at Albany, Albany, NY 12222, USA
| | - Geraldine Zimmer-Bensch
- Research Training Group 2416 MultiSenses - MultiScales, RWTH Aachen University, 52074 Aachen, Germany; Department of Neuroepigenetics, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany
| | - Yoram Ben-Shaul
- Department of Medical Neurobiology, Institute for Medical Research Israel Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Marc Spehr
- Department of Chemosensation, Institute for Biology II, RWTH Aachen University, 52074 Aachen, Germany; Research Training Group 2416 MultiSenses - MultiScales, RWTH Aachen University, 52074 Aachen, Germany.
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44
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Meroueh C, Warasnhe K, Tizhoosh HR, Shah VH, Ibrahim SH. Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials. Hepatology 2024:01515467-990000000-00815. [PMID: 38517078 DOI: 10.1097/hep.0000000000000866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.
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Affiliation(s)
- Chady Meroueh
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Khaled Warasnhe
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hamid R Tizhoosh
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Samar H Ibrahim
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
- Division of Pediatric Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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45
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Chen J, Ke R. Spatial analysis toolkits for RNA in situ sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1842. [PMID: 38605484 DOI: 10.1002/wrna.1842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Jiayu Chen
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
| | - Rongqin Ke
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
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46
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Zhang J, Zhang L, Gongol B, Hayes J, Borowsky A, Bailey-Serres J, Girke T. spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images. NAR Genom Bioinform 2024; 6:lqae006. [PMID: 38312938 PMCID: PMC10836942 DOI: 10.1093/nargab/lqae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/06/2024] Open
Abstract
Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.
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Affiliation(s)
- Jianhai Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Le Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Brendan Gongol
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Jordan Hayes
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Alexander T Borowsky
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Julia Bailey-Serres
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Thomas Girke
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
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47
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Navikas V, Kowal J, Rodriguez D, Rivest F, Brajkovic S, Cassano M, Dupouy D. Semi-automated approaches for interrogating spatial heterogeneity of tissue samples. Sci Rep 2024; 14:5025. [PMID: 38424144 PMCID: PMC10904364 DOI: 10.1038/s41598-024-55387-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.
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Affiliation(s)
| | - Joanna Kowal
- Lunaphore Technologies SA, Tolochenaz, Switzerland
| | | | | | | | | | - Diego Dupouy
- Lunaphore Technologies SA, Tolochenaz, Switzerland.
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48
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Hsu JE, Ruiz L, Hwang Y, Guzman S, Cho CS, Cheng W, Si Y, Macpherson P, Schrank M, Jun G, Kang HM, Kim M, Brooks S, Lee JH. High-Resolution Spatial Transcriptomic Atlas of Mouse Soleus Muscle: Unveiling Single Cell and Subcellular Heterogeneity in Health and Denervation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582103. [PMID: 38464282 PMCID: PMC10925160 DOI: 10.1101/2024.02.26.582103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Skeletal muscle is essential for both movement and metabolic processes, characterized by a complex and ordered structure. Despite its importance, a detailed spatial map of gene expression within muscle tissue has been challenging to achieve due to the limitations of existing technologies, which struggle to provide high-resolution views. In this study, we leverage the Seq-Scope technique, an innovative method that allows for the observation of the entire transcriptome at an unprecedented submicron spatial resolution. By applying this technique to the mouse soleus muscle, we analyze and compare the gene expression profiles in both healthy conditions and following denervation, a process that mimics aspects of muscle aging. Our approach reveals detailed characteristics of muscle fibers, other cell types present within the muscle, and specific subcellular structures such as the postsynaptic nuclei at neuromuscular junctions, hybrid muscle fibers, and areas of localized expression of genes responsive to muscle injury, along with their histological context. The findings of this research significantly enhance our understanding of the diversity within the muscle cell transcriptome and its variation in response to denervation, a key factor in the decline of muscle function with age. This breakthrough in spatial transcriptomics not only deepens our knowledge of muscle biology but also sets the stage for the development of new therapeutic strategies aimed at mitigating the effects of aging on muscle health, thereby offering a more comprehensive insight into the mechanisms of muscle maintenance and degeneration in the context of aging and disease.
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Affiliation(s)
- Jer-En Hsu
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Lloyd Ruiz
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Yongha Hwang
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Space Planning and Analysis, University of Michigan, Ann Arbor, MI, USA
| | - Steve Guzman
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Chun-Seok Cho
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Weiqiu Cheng
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Yichen Si
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Peter Macpherson
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Mitchell Schrank
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Goo Jun
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hyun-Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Myungjin Kim
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Susan Brooks
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Jun Hee Lee
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
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49
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Qiu Y, Wei K, Lin H, Liu Y, Lin C, Ke R. Combined amplification-based single-molecule fluorescence in situ hybridization with immunofluorescence for simultaneous in situ detection of RNAs and proteins. Biochem Biophys Res Commun 2024; 696:149508. [PMID: 38244312 DOI: 10.1016/j.bbrc.2024.149508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
We present a combined amplification-based single-molecule fluorescence in situ hybridization and immunofluorescence (asmFISH-IF) method for the detection of multiple RNAs and proteins simultaneously in cells and formaldehyde-fixed and paraffin-embedded tissue sections. We showed that performing asmFISH before immunofluorescence gives a better IF signal than the opposite. Our asmFISH-IF method could help study the interplay of RNA and protein, helping to understand their functions.
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Affiliation(s)
- Yinghui Qiu
- School of Medicine, Huaqiao University, Xiamen, Fujian, 361021, China; College of Materials Science and Engineering, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Kaipeng Wei
- Department of Medical Laboratory Technology, Quanzhou Medical College, Quanzhou, Fujian, 362011, China
| | - Hui Lin
- Department of Pathology, The 910 Hospital, Quanzhou, 362000, Fujian, China
| | - Yanxiu Liu
- School of Medicine, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Chen Lin
- School of Medicine, Huaqiao University, Xiamen, Fujian, 361021, China.
| | - Rongqin Ke
- School of Medicine, Huaqiao University, Xiamen, Fujian, 361021, China.
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50
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Helms HR, Oyama KA, Ware JP, Ibsen SD, Bertassoni LE. Multiplex Single-Cell Bioprinting for Engineering of Heterogeneous Tissue Constructs with Subcellular Spatial Resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578499. [PMID: 38352428 PMCID: PMC10862823 DOI: 10.1101/2024.02.01.578499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Tissue development, function, and disease are largely driven by the spatial organization of individual cells and their cell-cell interactions. Precision engineered tissues with single-cell spatial resolution, therefore, have tremendous potential for next generation disease models, drug discovery, and regenerative therapeutics. Despite significant advancements in biofabrication approaches to improve feature resolution, strategies to fabricate tissues with the exact same organization of individual cells in their native cellular microenvironment have remained virtually non-existent to date. Here we report a method to spatially pattern single cells with up to eight cell phenotypes and subcellular spatial precision. As proof-of-concept we first demonstrate the ability to systematically assess the influence of cellular microenvironments on cell behavior by controllably altering the spatial arrangement of cell types in bioprinted precision cell-cell interaction arrays. We then demonstrate, for the first time, the ability to produce high-fidelity replicas of a patient's annotated cancer biopsy with subcellular resolution. The ability to replicate native cellular microenvironments marks a significant advancement for precision biofabricated in-vitro models, where heterogenous tissues can be engineered with single-cell spatial precision to advance our understanding of complex biological systems in a controlled and systematic manner.
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Affiliation(s)
- Haylie R Helms
- Knight Cancer Precision Biofabrication Hub, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
- Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR 97201, USA
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
| | - Kody A Oyama
- Knight Cancer Precision Biofabrication Hub, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
| | - Jason P Ware
- Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR 97201, USA
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
| | - Stuart D Ibsen
- Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR 97201, USA
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
| | - Luiz E Bertassoni
- Knight Cancer Precision Biofabrication Hub, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
- Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR 97201, USA
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
- Division of Biomaterials and Biomechanics, Department of Oral Rehabilitation and Biosciences, School of Dentistry, Oregon Health and Science University, Portland, OR 97201, USA
- Center for Regenerative Medicine, School of Medicine, Oregon Health and Science University, Portland, OR 97201, USA
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