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Cheng X, Cao Y, Liu X, Li Y, Li Q, Gao D, Yu Q. Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis. Clin Transl Med 2024; 14:e70036. [PMID: 39350478 PMCID: PMC11442492 DOI: 10.1002/ctm2.70036] [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: 05/05/2024] [Revised: 08/14/2024] [Accepted: 09/16/2024] [Indexed: 10/04/2024] Open
Abstract
Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.
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Affiliation(s)
- Xifu Cheng
- Department of Gastroenterology and Hepatologythe Second Affiliated HospitalJiangxi Medical CollegeNanchang UniversityNanchangChina
- Department of Pathogen Biology and ImmunologySchool of Basic Medical SciencesJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Yuke Cao
- Department of Gastroenterology and Hepatologythe Second Affiliated HospitalJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Xiangyi Liu
- Queen Mary SchoolJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Yuanheng Li
- Queen Mary SchoolJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Qing Li
- Department of Oncologythe Second Affiliated HospitalJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Dian Gao
- Department of Gastroenterology and Hepatologythe Second Affiliated HospitalJiangxi Medical CollegeNanchang UniversityNanchangChina
- Department of Pathogen Biology and ImmunologySchool of Basic Medical SciencesJiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Qiongfang Yu
- Department of Gastroenterology and Hepatologythe Second Affiliated HospitalJiangxi Medical CollegeNanchang UniversityNanchangChina
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2
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Wang J, Alhaskawi A, Dong Y, Tian T, Abdalbary SA, Lu H. Advances in spatial multi-omics in tumors. TUMORI JOURNAL 2024; 110:327-339. [PMID: 39185632 DOI: 10.1177/03008916241271458] [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: 08/27/2024]
Abstract
Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.
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Affiliation(s)
- Junyan Wang
- The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Ahmad Alhaskawi
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yanzhao Dong
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Tu Tian
- Department of Plastic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Sahar Ahmed Abdalbary
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Department of Orthopedic Physical Therapy, Faculty of Physical Therapy, Nahda University in Beni Suef, Beni Suef, Egypt
| | - Hui Lu
- The First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
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3
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Pedrotti S, Castiglioni I, Perez-Estrada C, Zhao L, Chen JP, Crosetto N, Bienko M. Emerging methods and applications in 3D genomics. Curr Opin Cell Biol 2024; 90:102409. [PMID: 39178735 DOI: 10.1016/j.ceb.2024.102409] [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: 05/13/2024] [Revised: 07/21/2024] [Accepted: 07/22/2024] [Indexed: 08/26/2024]
Abstract
Since the advent of Hi-C in 2009, a plethora of high-throughput sequencing methods have emerged to profile the three-dimensional (3D) organization of eukaryotic genomes, igniting the era of 3D genomics. In recent years, the genomic resolution achievable by these approaches has dramatically increased and several single-cell versions of Hi-C have been developed. Moreover, a new repertoire of tools not based on proximity ligation of digested chromatin has emerged, enabling the investigation of the higher-order organization of chromatin in the nucleus. In this review, we summarize the expanding portfolio of 3D genomic technologies, highlighting recent developments and applications from the past three years. Lastly, we present an outlook of where this technology-driven field might be headed.
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Affiliation(s)
- Simona Pedrotti
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
| | | | - Cynthia Perez-Estrada
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden; Science for Life Laboratory, Solna, 17165, Sweden
| | - Linxuan Zhao
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden; Science for Life Laboratory, Solna, 17165, Sweden
| | - Jinxin Phaedo Chen
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden; Science for Life Laboratory, Solna, 17165, Sweden
| | - Nicola Crosetto
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy; Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden; Science for Life Laboratory, Solna, 17165, Sweden.
| | - Magda Bienko
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy; Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden; Science for Life Laboratory, Solna, 17165, Sweden.
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4
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Crouigneau R, Li YF, Auxillos J, Goncalves-Alves E, Marie R, Sandelin A, Pedersen SF. Mimicking and analyzing the tumor microenvironment. CELL REPORTS METHODS 2024:100866. [PMID: 39353424 DOI: 10.1016/j.crmeth.2024.100866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 07/22/2024] [Accepted: 09/09/2024] [Indexed: 10/04/2024]
Abstract
The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.
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Affiliation(s)
- Roxane Crouigneau
- Section for Cell Biology and Physiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yan-Fang Li
- Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Jamie Auxillos
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - Eliana Goncalves-Alves
- Section for Cell Biology and Physiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rodolphe Marie
- Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Albin Sandelin
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark.
| | - Stine Falsig Pedersen
- Section for Cell Biology and Physiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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5
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Gong D, Arbesfeld-Qiu JM, Perrault E, Bae JW, Hwang WL. Spatial oncology: Translating contextual biology to the clinic. Cancer Cell 2024:S1535-6108(24)00349-0. [PMID: 39366372 DOI: 10.1016/j.ccell.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 08/01/2024] [Accepted: 09/06/2024] [Indexed: 10/06/2024]
Abstract
Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.
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Affiliation(s)
- Dennis Gong
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeanna M Arbesfeld-Qiu
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ella Perrault
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jung Woo Bae
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William L Hwang
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
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6
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Du Y, Ding X, Ye Y. The spatial multi-omics revolution in cancer therapy: Precision redefined. Cell Rep Med 2024; 5:101740. [PMID: 39293393 DOI: 10.1016/j.xcrm.2024.101740] [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: 04/06/2024] [Revised: 07/11/2024] [Accepted: 08/21/2024] [Indexed: 09/20/2024]
Abstract
Spatially resolved multi-omics revolutionizes cancer therapy by decoding the cellular and molecular heterogeneity of the tumor microenvironment through spatial coordinates. This commentary discusses the roles of spatial multi-omics in identifying precise therapeutic targets and predicting treatment responses while also highlighting the challenges that impede its integration into precision medicine.
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Affiliation(s)
- Yanhua Du
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyu Ding
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Youqiong Ye
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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7
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Chen JH, Elmelech L, Tang AL, Hacohen N. Powerful microscopy technologies decode spatially organized cellular networks that drive response to immunotherapy in humans. Curr Opin Immunol 2024; 91:102463. [PMID: 39277910 DOI: 10.1016/j.coi.2024.102463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 08/22/2024] [Accepted: 08/24/2024] [Indexed: 09/17/2024]
Abstract
In tumors, immune cells organize into networks of different sizes and composition, including complex tertiary lymphoid structures and recently identified networks centered around the chemokines CXCL9/10/11 and CCL19. New commercially available highly multiplexed microscopy using cyclical RNA in situ hybridization and antibody-based approaches have the potential to establish the organization of the immune response in human tissue and serve as a foundation for future immunology research.
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Affiliation(s)
- Jonathan H Chen
- Northwestern University, Feinberg School of Medicine, Department of Pathology, Chicago, IL, USA; Northwestern University, Feinberg School of Medicine, Center for Human Immunobiology, Chicago, IL, USA; Krantz Family Center for Cancer Research, Massachusetts General Hospital (MGH) Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Pathology, MGH, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Liad Elmelech
- Krantz Family Center for Cancer Research, Massachusetts General Hospital (MGH) Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Pathology, MGH, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Alexander L Tang
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Nir Hacohen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital (MGH) Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
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8
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Jena SG, Verma A, Engelhardt BE. Answering open questions in biology using spatial genomics and structured methods. BMC Bioinformatics 2024; 25:291. [PMID: 39232666 PMCID: PMC11375982 DOI: 10.1186/s12859-024-05912-5] [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: 10/10/2023] [Accepted: 08/22/2024] [Indexed: 09/06/2024] Open
Abstract
Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.
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Affiliation(s)
- Siddhartha G Jena
- Department of Stem Cell and Regenerative Biology, Harvard, 7 Divinity Ave, Cambridge, MA, USA
| | - Archit Verma
- Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
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9
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Guo Y, Ren C, He Y, Wu Y, Yang X. Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review). Mol Med Rep 2024; 30:157. [PMID: 38994768 PMCID: PMC11258600 DOI: 10.3892/mmr.2024.13281] [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: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 07/13/2024] Open
Abstract
The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.
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Affiliation(s)
- Yajing Guo
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China
| | - Chao Ren
- Graduate School, Hunan University of Traditional Chinese Medicine, Changsha, Hunan 410208, P.R. China
| | - Yuxi He
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Yue Wu
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Xiaojun Yang
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
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10
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You Y, Fu Y, Li L, Zhang Z, Jia S, Lu S, Ren W, Liu Y, Xu Y, Liu X, Jiang F, Peng G, Sampath Kumar A, Ritchie ME, Liu X, Tian L. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat Methods 2024; 21:1743-1754. [PMID: 38965443 PMCID: PMC11399101 DOI: 10.1038/s41592-024-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/29/2024] [Indexed: 07/06/2024]
Abstract
Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.
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Affiliation(s)
- Yue You
- Guangzhou National Laboratory, Guangzhou, China
| | - Yuting Fu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Lanxiang Li
- Guangzhou National Laboratory, Guangzhou, China
| | | | - Shikai Jia
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Shihong Lu
- Guangzhou National Laboratory, Guangzhou, China
| | - Wenle Ren
- Guangzhou National Laboratory, Guangzhou, China
| | - Yifang Liu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Yang Xu
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Xiaojing Liu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Fuqing Jiang
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, University of Chinese Academy of Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, University of Chinese Academy of Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
| | - Abhishek Sampath Kumar
- Department of Stem Cell and Regenerative Biology, Harvard University. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew E Ritchie
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Xiaodong Liu
- School of Life Sciences, Westlake University, Hangzhou, China.
- Research Center for Industries of the Future, Westlake University, Hangzhou, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Westlake Institute for Advanced Study, Hangzhou, China.
| | - Luyi Tian
- Guangzhou National Laboratory, Guangzhou, China.
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.
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11
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Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, Liu B, Xu Y, Ding W, Li L, Cao C, Wu P. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol 2024; 17:72. [PMID: 39182134 PMCID: PMC11344930 DOI: 10.1186/s13045-024-01596-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024] Open
Abstract
The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.
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Affiliation(s)
- Xiaojie Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Peng
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Miaochun Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shitong Lin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bai Hu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tian Chu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Binghan Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yashi Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wencheng Ding
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Li Li
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Canhui Cao
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Peng Wu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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12
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [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: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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13
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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14
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Li B, Bao F, Hou Y, Li F, Li H, Deng Y, Dai Q. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model. Nat Commun 2024; 15:6541. [PMID: 39095360 PMCID: PMC11297205 DOI: 10.1038/s41467-024-50837-5] [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: 12/09/2023] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.
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Affiliation(s)
- Bohan Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Feng Bao
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China
- Department of Automation, Tsinghua University, Beijing, China
| | - Yimin Hou
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Fengji Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Hongjue Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Yue Deng
- School of Artificial Intelligence, Beihang University, Beijing, China.
| | - Qionghai Dai
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China.
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China.
- Department of Automation, Tsinghua University, Beijing, China.
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15
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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16
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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17
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Di Mauro F, Arbore G. Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine. Cancer Immunol Res 2024; 12:800-813. [PMID: 38657223 PMCID: PMC11217735 DOI: 10.1158/2326-6066.cir-23-0699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.
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Affiliation(s)
- Francesco Di Mauro
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Giuseppina Arbore
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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18
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Wang Y, Woyshner K, Sriworarat C, Stein-O’Brie G, Goff LA, Hansen KD. Multi-sample non-negative spatial factorization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.01.599554. [PMID: 39005356 PMCID: PMC11244884 DOI: 10.1101/2024.07.01.599554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
It is important to model biological variation when analyzing spatial transcriptomics data from multiple samples. One approach to multi-sample analysis is to spatially align samples, but this is a challenging problem. Here, we provide an alignment-free framework for generalizing a one-sample spatial factorization model to multi-sample data. Using this framework, we develop a method, called multi-sample non-negative spatial factorization (mNSF) that extends the one-sample non-negative spatial factorization (NSF) framework to a multi-sample dataset. Our model allows for a sample-specific model for the spatial correlation structure and extracts a low-dimensional representation of the data. We illustrate the performance of mNSF by simulation studies and real data. mNSF identifies true factors in simulated data, identifies shared anatomical regions across samples in real data and reveals region-specific biological functions. mNSFs performance is similar to alignment based methods when alignment is possible, but extends analysis to situations where spatial alignment is impossible. We expect multi-sample factorization methods to be a powerful class of methods for analyzing spatially resolved transcriptomics data.
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Affiliation(s)
- Yi Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
| | - Kyla Woyshner
- Department of Genetic Medicine, Johns Hopkins School of Medicine
| | | | - Genevieve Stein-O’Brie
- Department of Genetic Medicine, Johns Hopkins School of Medicine
- Department of Neuroscience, Johns Hopkins School of Medicine
- Kavli Neurodiscovery Institute, Johns Hopkins School of Medicine
- Quantitative Sciences Division, Department of Oncology, Johns Hopkins School of Medicine
| | - Loyal A Goff
- Department of Genetic Medicine, Johns Hopkins School of Medicine
- Department of Neuroscience, Johns Hopkins School of Medicine
- Kavli Neurodiscovery Institute, Johns Hopkins School of Medicine
| | - Kasper D. Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
- Department of Genetic Medicine, Johns Hopkins School of Medicine
- Department of Biomedical Engineering, Johns Hopkins School of Medicine
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19
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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20
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Kim H, Kim KE, Madan E, Martin P, Gogna R, Rhee HW, Won KJ. Unveiling contact-mediated cellular crosstalk. Trends Genet 2024:S0168-9525(24)00132-X. [PMID: 38906738 DOI: 10.1016/j.tig.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.
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Affiliation(s)
- Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Kwang-Eun Kim
- Department of Convergence Medicine, Yonsei University Wonju College of Medicine, Wonju, South Korea; Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Esha Madan
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Rajan Gogna
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Hyun-Woo Rhee
- Department of Chemistry, Seoul National University, Seoul, South Korea.
| | - Kyoung-Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA.
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21
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Shi W, Zhang J, Huang S, Fan Q, Cao J, Zeng J, Wu L, Yang C. Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry. JACS AU 2024; 4:1723-1743. [PMID: 38818076 PMCID: PMC11134576 DOI: 10.1021/jacsau.4c00118] [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] [Received: 02/07/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.
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Affiliation(s)
- Weixiong Shi
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jing Zhang
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
| | - Shanqing Huang
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qian Fan
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiao Cao
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zeng
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lingling Wu
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Chaoyong Yang
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
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22
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Fujiwara N, Kimura G, Nakagawa H. Emerging Roles of Spatial Transcriptomics in Liver Research. Semin Liver Dis 2024; 44:115-132. [PMID: 38574750 DOI: 10.1055/a-2299-7880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.
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Affiliation(s)
- Naoto Fujiwara
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Genki Kimura
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
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23
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Berleant JD, Banal JL, Rao DK, Bathe M. Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.12.24305660. [PMID: 38699348 PMCID: PMC11064994 DOI: 10.1101/2024.04.12.24305660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.
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Affiliation(s)
- Joseph D. Berleant
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James L. Banal
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Present address: Cache DNA, Inc. 733 Industrial Rd., San Carlos, CA 94070 USA
| | | | - Mark Bathe
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139 USA
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24
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White B, Swietach P. What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes? Pflugers Arch 2024; 476:673-688. [PMID: 37999800 PMCID: PMC11006749 DOI: 10.1007/s00424-023-02876-y] [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: 09/25/2023] [Revised: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
Acidosis is a chemical signature of the tumour microenvironment that challenges intracellular pH homeostasis. The orchestrated activity of acid-base transporters of the solute-linked carrier (SLC) family is critical for removing the end-products of fermentative metabolism (lactate/H+) and maintaining a favourably alkaline cytoplasm. Given the critical role of pH homeostasis in enabling cellular activities, mutations in relevant SLC genes may impact the oncogenic process, emerging as negatively or positively selected, or as driver or passenger mutations. To address this, we performed a pan-cancer analysis of The Cancer Genome Atlas simple nucleotide variation data for acid/base-transporting SLCs (ABT-SLCs). Somatic mutation patterns of monocarboxylate transporters (MCTs) were consistent with their proposed essentiality in facilitating lactate/H+ efflux. Among all cancers, tumours of uterine corpus endometrial cancer carried more ABT-SLC somatic mutations than expected from median tumour mutation burden. Among these, somatic mutations in SLC4A3 had features consistent with meaningful consequences on cellular fitness. Definitive evidence for ABT-SLCs as 'cancer essential' or 'driver genes' will have to consider microenvironmental context in genomic sequencing because bulk approaches are insensitive to pH heterogeneity within tumours. Moreover, genomic analyses must be validated with phenotypic outcomes (i.e. SLC-carried flux) to appreciate the opportunities for targeting acid-base transport in cancers.
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Affiliation(s)
- Bobby White
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford, OX1 3PT, UK.
| | - Pawel Swietach
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford, OX1 3PT, UK
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25
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [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: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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26
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Kim Y, Cheng W, Cho CS, Hwang Y, Si Y, Park A, Schrank M, Hsu JE, Xi J, Kim M, Pedersen E, Koues OI, Wilson T, Jun G, Kang HM, Lee JH. Seq-Scope Protocol: Repurposing Illumina Sequencing Flow Cells for High-Resolution Spatial Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587285. [PMID: 38617262 PMCID: PMC11014489 DOI: 10.1101/2024.03.29.587285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Spatial transcriptomics (ST) technologies represent a significant advance in gene expression studies, aiming to profile the entire transcriptome from a single histological slide. These techniques are designed to overcome the constraints faced by traditional methods such as immunostaining and RNA in situ hybridization, which are capable of analyzing only a few target genes simultaneously. However, the application of ST in histopathological analysis is also limited by several factors, including low resolution, a limited range of genes, scalability issues, high cost, and the need for sophisticated equipment and complex methodologies. Seq-Scope-a recently developed novel technology-repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, thereby overcoming these limitations. Here we provide a detailed step-by-step protocol to implement Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell that allows for the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. In addition to detailing how to prepare a frozen tissue section for both histological imaging and sequencing library preparation, we provide comprehensive instructions and a streamlined computational pipeline to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.
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Affiliation(s)
- Yongsung Kim
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Weiqiu Cheng
- Department of Biostatistics, University of Michigan School of Public Health
| | - Chun-Seok Cho
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Yongha Hwang
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
- Space Planning and Analysis, University of Michigan Medical School
| | - Yichen Si
- Department of Biostatistics, University of Michigan School of Public Health
| | - Anna Park
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Mitchell Schrank
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Jer-En Hsu
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Jingyue Xi
- Department of Biostatistics, University of Michigan School of Public Health
| | - Myungjin Kim
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Ellen Pedersen
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan
| | - Olivia I. Koues
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan
| | - Thomas Wilson
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan
- Department of Human Genetics, University of Michigan Medical School
- Department of Pathology, University of Michigan Medical School
| | - Goo Jun
- Human Genetics Center, School of Public Health, University of Texas Health Science Center
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health
| | - Jun Hee Lee
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
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27
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Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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28
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Liu Z, Deng C, Zhou Z, Xiao Y, Jiang S, Zhu B, Naler LB, Jia X, Yao DD, Lu C. Epigenomic tomography for probing spatially defined chromatin state in the brain. CELL REPORTS METHODS 2024; 4:100738. [PMID: 38508188 PMCID: PMC10985265 DOI: 10.1016/j.crmeth.2024.100738] [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: 04/24/2023] [Revised: 12/24/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024]
Abstract
Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Single-cell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5 mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.
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Affiliation(s)
- Zhengzhi Liu
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Chengyu Deng
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Zirui Zhou
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Ya Xiao
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - Shan Jiang
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Bohan Zhu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Lynette B Naler
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Xiaoting Jia
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | | | - Chang Lu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA.
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29
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Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol 2024; 25:73. [PMID: 38504325 PMCID: PMC10949687 DOI: 10.1186/s13059-024-03213-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: 08/14/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.
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Affiliation(s)
- Bin Duan
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| | - Shaoqi Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Xiaojie Cheng
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
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30
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Mirzakhel Z, Reddy GA, Boman J, Manns B, Veer ST, Katira P. "Patchiness" in mechanical stiffness across a tumor as an early-stage marker for malignancy. BMC Ecol Evol 2024; 24:33. [PMID: 38486161 PMCID: PMC10938681 DOI: 10.1186/s12862-024-02221-6] [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: 09/17/2023] [Accepted: 03/03/2024] [Indexed: 03/17/2024] Open
Abstract
Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a "patchiness" index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the "patchiness" of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.
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Affiliation(s)
- Zibah Mirzakhel
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
| | - Gudur Ashrith Reddy
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - Jennifer Boman
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
| | - Brianna Manns
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
| | - Savannah Ter Veer
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
| | - Parag Katira
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA.
- Computational Science Research Center, San Diego State University, San Diego, CA, USA.
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31
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Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [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: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
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Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
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32
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Mortlock RD, Choate KA. How Efforts to Understand Somatic Mosaicism Will Impact Dermatology. J Invest Dermatol 2024; 144:453-455. [PMID: 38395493 PMCID: PMC11009867 DOI: 10.1016/j.jid.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 10/04/2023] [Indexed: 02/25/2024]
Affiliation(s)
- Ryland D Mortlock
- Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA; Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Keith A Choate
- Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.
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33
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [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: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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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
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, 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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34
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Singhal V, Chou N, Lee J, Yue Y, Liu J, Chock WK, Lin L, Chang YC, Teo EML, Aow J, Lee HK, Chen KH, Prabhakar S. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet 2024; 56:431-441. [PMID: 38413725 PMCID: PMC10937399 DOI: 10.1038/s41588-024-01664-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.
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Affiliation(s)
- Vipul Singhal
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nigel Chou
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Joseph Lee
- Faculty of Science, National University of Singapore, Singapore, Republic of Singapore
| | - Yifei Yue
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Republic of Singapore
| | - Jinyue Liu
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wan Kee Chock
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Li Lin
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | | | - Jonathan Aow
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- School of Computing, National University of Singapore, Singapore, Republic of Singapore
- Singapore Eye Research Institute, Singapore, Republic of Singapore
- International Research Laboratory on Artificial Intelligence, Singapore, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Republic of Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Kok Hao Chen
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Shyam Prabhakar
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore.
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35
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Rao S, Verrill C, Cerundolo L, Alham NK, Kaya Z, O'Hanlon M, Hayes A, Lambert A, James M, Tullis IDC, Niederer J, Lovell S, Omer A, Lopez F, Leslie T, Buffa F, Bryant RJ, Lamb AD, Vojnovic B, Wedge DC, Mills IG, Woodcock DJ, Tomlinson I, Hamdy FC. Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features. Genome Med 2024; 16:35. [PMID: 38374116 PMCID: PMC10877771 DOI: 10.1186/s13073-024-01302-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: 07/19/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification. METHODS We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants. RESULTS In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees. CONCLUSIONS Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.
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Affiliation(s)
- Srinivasa Rao
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
- Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK.
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lucia Cerundolo
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Zeynep Kaya
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Miriam O'Hanlon
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alicia Hayes
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Adam Lambert
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Martha James
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Jane Niederer
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Shelagh Lovell
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Altan Omer
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Francisco Lopez
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Tom Leslie
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Richard J Bryant
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alastair D Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Boris Vojnovic
- Department of Oncology, University of Oxford, Oxford, UK
| | - David C Wedge
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Dan J Woodcock
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ian Tomlinson
- Department of Oncology, University of Oxford, Oxford, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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36
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Lai J, Liu Y, Scharpf RB, Karchin R. Evaluation of simulation methods for tumor subclonal reconstruction. ARXIV 2024:arXiv:2402.09599v1. [PMID: 38410652 PMCID: PMC10896360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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37
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Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
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Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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38
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Yan S, Guo Y, Lin L, Zhang W. Breaks for Precision Medicine in Cancer: Development and Prospects of Spatiotemporal Transcriptomics. Cancer Biother Radiopharm 2024; 39:35-45. [PMID: 38181185 DOI: 10.1089/cbr.2023.0116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
With the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.
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Affiliation(s)
- Shiqi Yan
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China
| | - Yilin Guo
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China
| | - Lizhong Lin
- Department of Clinical Laboratory, The First People's Hospital of Changde City, Changde, Hunan, People's Republic of China
| | - Wenling Zhang
- Department of Laboratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
- Department of Laboratory Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China
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39
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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40
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Lin S, Li D, Yang Y, Yu M, Zhao R, Li J, Peng L. Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma. J Cancer 2024; 15:1093-1109. [PMID: 38230205 PMCID: PMC10788724 DOI: 10.7150/jca.92185] [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: 11/13/2023] [Accepted: 12/16/2023] [Indexed: 01/18/2024] Open
Abstract
Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.
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Affiliation(s)
- Sen Lin
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Danfei Li
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Yang
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mengjiao Yu
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ruiqi Zhao
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinghao Li
- Department of Traditional Chinese Medicine, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China
| | - Lisheng Peng
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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41
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Varrone M, Tavernari D, Santamaria-Martínez A, Walsh LA, Ciriello G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat Genet 2024; 56:74-84. [PMID: 38066188 DOI: 10.1038/s41588-023-01588-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/23/2023] [Indexed: 12/20/2023]
Abstract
Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.
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Affiliation(s)
- Marco Varrone
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniele Tavernari
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Albert Santamaria-Martínez
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Cancer Center Léman, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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42
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Iorgulescu JB, Yoon CH, Wu CJ, Macosko EZ, Chen F. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 2024; 625:101-109. [PMID: 38093010 PMCID: PMC10764288 DOI: 10.1038/s41586-023-06837-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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Affiliation(s)
- Andrew J C Russell
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Jackson A Weir
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | - Naeem M Nadaf
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Vipin Kumar
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sandeep Kambhampati
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard University, Boston, MA, USA
| | - Ruth Raichur
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Sophia Liu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biophysics Program, Harvard University, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Vignesh Shanmugam
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Guangzhou Laboratory, Guangdong, China
| | - J Bryan Iorgulescu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
- Molecular Diagnostics Laboratory, Department of Hematopathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles H Yoon
- Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine J Wu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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43
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [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: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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44
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Ivanov RA, Lashin SA. Intratumor heterogeneity: models of malignancy emergence and evolution. Vavilovskii Zhurnal Genet Selektsii 2023; 27:815-819. [PMID: 38213707 PMCID: PMC10777286 DOI: 10.18699/vjgb-23-94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 01/13/2024] Open
Abstract
Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.
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Affiliation(s)
- R A Ivanov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - S A Lashin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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45
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Kim H, Kumar A, Lövkvist C, Palma AM, Martin P, Kim J, Bhoopathi P, Trevino J, Fisher P, Madan E, Gogna R, Won KJ. CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data. Mol Syst Biol 2023; 19:e11670. [PMID: 37815040 PMCID: PMC10632736 DOI: 10.15252/msb.202311670] [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: 03/23/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023] Open
Abstract
Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.
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Affiliation(s)
- Hyobin Kim
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Amit Kumar
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Cecilia Lövkvist
- Novo Nordisk Foundation Center for Stem Cell Medicine, reNEWUniversity of CopenhagenCopenhagenDenmark
| | - António M Palma
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Instituto Superior TecnicoUniversidade de LisboaLisboaPortugal
| | - Patrick Martin
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Junil Kim
- School of Systems Biomedical ScienceSoongsil UniversitySeoulKorea
| | - Praveen Bhoopathi
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Jose Trevino
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Paul Fisher
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Esha Madan
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Rajan Gogna
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Kyoung Jae Won
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
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46
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Poos AM, Prokoph N, Przybilla MJ, Mallm JP, Steiger S, Seufert I, John L, Tirier SM, Bauer K, Baumann A, Rohleder J, Munawar U, Rasche L, Kortüm KM, Giesen N, Reichert P, Huhn S, Müller-Tidow C, Goldschmidt H, Stegle O, Raab MS, Rippe K, Weinhold N. Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis. Blood 2023; 142:1633-1646. [PMID: 37390336 PMCID: PMC10733835 DOI: 10.1182/blood.2023019758] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.
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Affiliation(s)
- Alexandra M. Poos
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Nina Prokoph
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Moritz J. Przybilla
- Division Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Jan-Philipp Mallm
- Single Cell Open Lab, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Simon Steiger
- Division of Chromatin Networks, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Isabelle Seufert
- Division of Chromatin Networks, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Lukas John
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Stephan M. Tirier
- Division of Chromatin Networks, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Katharina Bauer
- Single Cell Open Lab, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Anja Baumann
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Jennifer Rohleder
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Umair Munawar
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Leo Rasche
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
- Mildred Scheel Early Career Center, University Hospital of Würzburg, Würzburg, Germany
| | - K. Martin Kortüm
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Nicola Giesen
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Philipp Reichert
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
| | - Stefanie Huhn
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
| | - Carsten Müller-Tidow
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, GMMG-Study Group at University Hospital Heidelberg, Heidelberg, Germany
| | - Oliver Stegle
- Division Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Marc S. Raab
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Karsten Rippe
- Division of Chromatin Networks, German Cancer Research Center and BioQuant, Heidelberg, Germany
| | - Niels Weinhold
- Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany
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47
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Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
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Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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48
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Fu R, Walters K, Kaufman ML, Koc K, Baldwin A, Clay MR, Basham KJ, Kiseljak-Vassiliades K, Fishbein L, Mukherjee N. In Situ Spatial Reconstruction of Distinct Normal and Pathological Cell Populations Within the Human Adrenal Gland. J Endocr Soc 2023; 7:bvad131. [PMID: 37953901 PMCID: PMC10638100 DOI: 10.1210/jendso/bvad131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Indexed: 11/14/2023] Open
Abstract
The human adrenal gland consists of concentrically organized, functionally distinct regions responsible for hormone production. Dysregulation of adrenocortical cell differentiation alters the proportion and organization of the functional zones of the adrenal cortex leading to disease. Current models of adrenocortical cell differentiation are based on mouse studies, but there are known organizational and functional differences between human and mouse adrenal glands. This study aimed to investigate the centripetal differentiation model in the human adrenal cortex and characterize aldosterone-producing micronodules (APMs) to better understand adrenal diseases such as primary aldosteronism. We applied spatially resolved in situ transcriptomics to human adrenal tissue sections from 2 individuals and identified distinct cell populations and their positional relationships. The results supported the centripetal differentiation model in humans, with cells progressing from the outer capsule to the zona glomerulosa, zona fasciculata, and zona reticularis. Additionally, we characterized 2 APMs in a 72-year-old woman. Comparison with earlier APM transcriptomes indicated a subset of core genes, but also heterogeneity between APMs. The findings contribute to our understanding of normal and pathological cellular differentiation in the human adrenal cortex.
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Affiliation(s)
- Rui Fu
- RNA Biosciences Initiative and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
- Computational Biology, New York Genome Center, New York, NY 10013, USA
| | - Kathryn Walters
- RNA Biosciences Initiative and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Michael L Kaufman
- RNA Biosciences Initiative and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Katrina Koc
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Amber Baldwin
- RNA Biosciences Initiative and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Michael R Clay
- Department of Pathology, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Kaitlin J Basham
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Katja Kiseljak-Vassiliades
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
- Research Service Veterans Affairs Medical Center, Aurora, CO 80045, USA
| | - Lauren Fishbein
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
| | - Neelanjan Mukherjee
- RNA Biosciences Initiative and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine at Colorado Anschutz Medical Campus Aurora, Aurora, CO 80045, USA
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49
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Bhattacharyya S, Ehsan SF, Karacosta LG. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1256104. [PMID: 37964768 PMCID: PMC10642209 DOI: 10.3389/fnetp.2023.1256104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/16/2023]
Abstract
In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.
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Affiliation(s)
- Sayantan Bhattacharyya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shafqat F. Ehsan
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Loukia G. Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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50
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Greenstreet L, Afanassiev A, Kijima Y, Heitz M, Ishiguro S, King S, Yachie N, Schiebinger G. DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods. Cell Syst 2023; 14:844-859.e4. [PMID: 37751737 DOI: 10.1016/j.cels.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 04/19/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).
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Affiliation(s)
- Laura Greenstreet
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Anton Afanassiev
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Yusuke Kijima
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Department of Aquatic Bioscience, The University of Tokyo, Tokyo, Japan
| | - Matthieu Heitz
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Soh Ishiguro
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Samuel King
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Nozomu Yachie
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka, Japan; Graduate School of Media and Governance, Keio University, Fujisawa, Japan.
| | - Geoffrey Schiebinger
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada; School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada.
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