1
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Junaid M, Lee EJ, Lim SB. Single-cell and spatial omics: exploring hypothalamic heterogeneity. Neural Regen Res 2025; 20:1525-1540. [PMID: 38993130 DOI: 10.4103/nrr.nrr-d-24-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024] Open
Abstract
Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Eun Jeong Lee
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
- Department of Brain Science, Ajou University School of Medicine, Suwon, South Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
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2
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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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3
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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 2024:3008916241271458. [PMID: 39185632 DOI: 10.1177/03008916241271458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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4
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Mante J, Groover KE, Pullen RM. Environmental community transcriptomics: strategies and struggles. Brief Funct Genomics 2024:elae033. [PMID: 39183066 DOI: 10.1093/bfgp/elae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024] Open
Abstract
Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.
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Affiliation(s)
- Jeanet Mante
- Oak Ridge Associated Universities, Oak Ridge, 37831, TN, USA
| | - Kyra E Groover
- Department of Molecular Biosciences, University of Texas at Austin, Austin, 78705, TX, USA
| | - Randi M Pullen
- DEVCOM Army Research Laboratory, Adelphi, 20783, MD, USA
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5
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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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6
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Kumaran G, Carroll L, Muirhead N, Bottomley MJ. How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases? J Invest Dermatol 2024:S0022-202X(24)01926-2. [PMID: 39177547 DOI: 10.1016/j.jid.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 08/24/2024]
Abstract
Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.
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Affiliation(s)
- Girishkumar Kumaran
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Liam Carroll
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Matthew J Bottomley
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
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7
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Zaman F, Smith ML, Balagopal A, Durand CM, Redd AD, Tobian AAR. Spatial technologies to evaluate the HIV-1 reservoir and its microenvironment in the lymph node. mBio 2024; 15:e0190924. [PMID: 39058091 PMCID: PMC11324018 DOI: 10.1128/mbio.01909-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024] Open
Abstract
The presence of the HIV-1 reservoir, a group of immune cells that contain intact, integrated, and replication-competent proviruses, is a major challenge to cure HIV-1. HIV-1 reservoir cells are largely unaffected by the cytopathic effects of viruses, antiviral immune responses, or antiretroviral therapy (ART). The HIV-1 reservoir is seeded early during HIV-1 infection and augmented during active viral replication. CD4+ T cells are the primary target for HIV-1 infection, and recent studies suggest that memory T follicular helper cells within the lymph node, more precisely in the B cell follicle, harbor integrated provirus, which contribute to viral rebound upon ART discontinuation. The B cell follicle, more specifically the germinal center, possesses a unique environment because of its distinct property of being partly immune privileged, potentially allowing HIV-1-infected cells within the lymph nodes to be protected from CD8+ T cells. This modified immune response in the germinal center of the follicle is potentially explained by the exclusion of CD8+ T cells and the presence of T regulatory cells at the junction of the follicle and extrafollicular region. The proviral makeup of HIV-1-infected cells is similar in lymph nodes and blood, suggesting trafficking between these compartments. Little is known about the cell-to-cell interactions, microenvironment of HIV-1-infected cells in the follicle, and trafficking between the lymph node follicle and other body compartments. Applying a spatiotemporal approach that integrates genomics, transcriptomics, and proteomics to investigate the HIV-1 reservoir and its neighboring cells in the lymph node has promising potential for informing HIV-1 cure efforts.
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Affiliation(s)
- Fatima Zaman
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
| | - Melissa L. Smith
- Department of
Biochemistry and Molecular Genetics, University of Louisville School of
Medicine, Louisville,
Kentucky, USA
| | - Ashwin Balagopal
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Christine M. Durand
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Andrew D. Redd
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
- Laboratory of
Immunoregulation, National Institute of Allergy and Infectious Diseases,
National Institutes of Health,
Bethesda, Maryland, USA
- Institute of
Infectious Disease and Molecular Medicine, University of Cape
Town, Cape Town,
South Africa
| | - Aaron A. R. Tobian
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
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8
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Dezem FS, Arjumand W, DuBose H, Morosini NS, Plummer J. Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:131-153. [PMID: 38768396 DOI: 10.1146/annurev-biodatasci-102523-103640] [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: 05/22/2024]
Abstract
Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.
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Affiliation(s)
- Felipe Segato Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Wani Arjumand
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Hannah DuBose
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Natalia Silva Morosini
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Jasmine Plummer
- Department of Cellular and Molecular Biology and Comprehensive Cancer Center, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
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9
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Zhu E, Xie Q, Huang X, Zhang Z. Application of spatial omics in gastric cancer. Pathol Res Pract 2024; 262:155503. [PMID: 39128411 DOI: 10.1016/j.prp.2024.155503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/13/2024]
Abstract
Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.
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Affiliation(s)
- Erran Zhu
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Qi Xie
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Xinqi Huang
- Excellent Class, Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Zhiwei Zhang
- Cancer Research Institute of Hengyang Medical College, University of South China; Key Laboratory of Cancer Cellular and Molecular Pathology of Hunan; Department of Pathology, Department of Pathology of Hengyang Medical College, University of South China; The First Affiliated Hospital of University of South China, China.
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10
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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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11
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Nagasawa S, Zenkoh J, Suzuki Y, Suzuki A. Spatial omics technologies for understanding molecular status associated with cancer progression. Cancer Sci 2024. [PMID: 39042942 DOI: 10.1111/cas.16283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 07/02/2024] [Indexed: 07/25/2024] Open
Abstract
Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.
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Affiliation(s)
- Satoi Nagasawa
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Junko Zenkoh
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Ayako Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
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12
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Fortner A, Bucur O. Multiplexed spatial transcriptomics methods and the application of expansion microscopy. Front Cell Dev Biol 2024; 12:1378875. [PMID: 39105173 PMCID: PMC11298486 DOI: 10.3389/fcell.2024.1378875] [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: 01/30/2024] [Accepted: 06/10/2024] [Indexed: 08/07/2024] Open
Abstract
While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.
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Affiliation(s)
- Andra Fortner
- Medical School, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- Victor Babes National Institute of Pathology, Bucharest, Romania
| | - Octavian Bucur
- Victor Babes National Institute of Pathology, Bucharest, Romania
- Genomics Research and Development Institute, Bucharest, Romania
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13
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Herranz E, Treaba CA, Barletta VT, Mehndiratta A, Ouellette R, Sloane JA, Ionete C, Babu S, Mastantuono M, Magon S, Loggia ML, Makary MM, Hooker JM, Catana C, Kinkel RP, Nicholas R, Klawiter EC, Magliozzi R, Mainero C. Characterization of cortico-meningeal translocator protein expression in multiple sclerosis. Brain 2024; 147:2566-2578. [PMID: 38289855 PMCID: PMC11224595 DOI: 10.1093/brain/awae030] [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: 09/21/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/01/2024] Open
Abstract
Compartmentalized meningeal inflammation is thought to represent one of the key players in the pathogenesis of cortical demyelination in multiple sclerosis. PET targeting the 18 kDa mitochondrial translocator protein (TSPO) is a molecular-specific approach to quantifying immune cell-mediated density in the cortico-meningeal tissue compartment in vivo. This study aimed to characterize cortical and meningeal TSPO expression in a heterogeneous cohort of multiple sclerosis cases using in vivo simultaneous MR-PET with 11C-PBR28, a second-generation TSPO radioligand, and ex vivo immunohistochemistry. Forty-nine multiple sclerosis patients (21 with secondary progressive and 28 with relapsing-remitting multiple sclerosis) with mixed or high affinity binding for 11C-PBR28 underwent 90-min 11C-PBR28 simultaneous MR-PET. Tracer binding was measured using 60-90 min normalized standardized uptake value ratios sampled at mid-cortical depth and ∼3 mm above the pial surface. Data in multiple sclerosis patients were compared to 21 age-matched healthy controls. To characterize the nature of 11C-PBR28 PET uptake, the meningeal and cortical lesion cellular expression of TSPO was further described in post-mortem brain tissue from 20 cases with secondary progressive multiple sclerosis and five age-matched healthy donors. Relative to healthy controls, patients with multiple sclerosis exhibited abnormally increased TSPO signal in the cortex and meningeal tissue, diffusively in progressive disease and more localized in relapsing-remitting multiple sclerosis. In multiple sclerosis, increased meningeal TSPO levels were associated with increased Expanded Disability Status Scale scores (P = 0.007, by linear regression). Immunohistochemistry, validated using in situ sequencing analysis, revealed increased TSPO expression in the meninges and adjacent subpial cortical lesions of post-mortem secondary progressive multiple sclerosis cases relative to control tissue. In these cases, increased TSPO expression was related to meningeal inflammation. Translocator protein immunostaining was detected on meningeal MHC-class II+ macrophages and cortical-activated MHC-class II+ TMEM119+ microglia. In vivo arterial blood data and neuropathology showed that endothelial binding did not significantly account for increased TSPO cortico-meningeal expression in multiple sclerosis. Our findings support the use of TSPO-PET in multiple sclerosis for imaging in vivo inflammation in the cortico-meningeal brain tissue compartment and provide in vivo evidence implicating meningeal inflammation in the pathogenesis of the disease.
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Affiliation(s)
- Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Valeria T Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Russell Ouellette
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Clinical Neuroscience, Karolinska Institutet, 141 86 Stockholm, Sweden
- Department of Radiology, Karolinska University Hospital, 141 86 Stockholm, Sweden
| | - Jacob A Sloane
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Carolina Ionete
- Department of Neurology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Suma Babu
- Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Marina Mastantuono
- Neurology Section, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona 53593, Italy
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel 4058, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel 4058, Switzerland
| | - Marco L Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Meena M Makary
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
| | - Jacob M Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Revere P Kinkel
- University of California San Diego, Department of Neuroscience, San Diego, CA 92093, USA
| | - Richard Nicholas
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London SW7 2BX, UK
| | - Eric C Klawiter
- Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Roberta Magliozzi
- Neurology Section, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona 53593, Italy
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London SW7 2BX, UK
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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14
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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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15
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Wu X, Xu W, Deng L, Li Y, Wang Z, Sun L, Gao A, Wang H, Yang X, Wu C, Zou Y, Yan K, Liu Z, Zhang L, Du G, Yang L, Lin D, Yue J, Wang P, Han Y, Fu Z, Dai J, Cao G. Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nat Biomed Eng 2024; 8:872-889. [PMID: 38745110 DOI: 10.1038/s41551-024-01205-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.
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Affiliation(s)
- Xiaofeng Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Weize Xu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lulu Deng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yue Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhongchao Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Leqiang Sun
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Anran Gao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Haoqi Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaodan Yang
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengchao Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yanyan Zou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Keji Yan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhixiang Liu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingkai Zhang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Guohua Du
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Liyao Yang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Da Lin
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Junqiu Yue
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Wang
- Britton Chance Centre for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Centre for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yunyun Han
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenfang Fu
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Jinxia Dai
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
| | - Gang Cao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
- Faculty of Life and Health Sciences, and Shenzhen-Hong Kong Institute of Brain Science and The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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16
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Jin Y, Zuo Y, Li G, Liu W, Pan Y, Fan T, Fu X, Yao X, Peng Y. Advances in spatial transcriptomics and its applications in cancer research. Mol Cancer 2024; 23:129. [PMID: 38902727 PMCID: PMC11188176 DOI: 10.1186/s12943-024-02040-9] [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: 04/28/2024] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.
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Affiliation(s)
- Yang Jin
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuanli Zuo
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gang Li
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China
| | - Wenrong Liu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yitong Pan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ting Fan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xin Fu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojun Yao
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China.
| | - Yong Peng
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Frontier Medical Center, Tianfu Jincheng Laboratory, Chengdu, 610212, China.
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17
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Skinner OP, Asad S, Haque A. Advances and challenges in investigating B-cells via single-cell transcriptomics. Curr Opin Immunol 2024; 88:102443. [PMID: 38968762 DOI: 10.1016/j.coi.2024.102443] [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: 04/30/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
Single-cell RNA sequencing (scRNAseq) and Variable, Diversity, Joining (VDJ) profiling have improved our understanding of B-cells. Recent scRNAseq-based approaches have led to the discovery of intermediate B-cell states, including preplasma cells and pregerminal centre B-cells, as well as unveiling protective roles for B-cells within tertiary lymphoid structures in respiratory infections and cancers. These studies have improved our understanding of transcriptional and epigenetic control of B-cell development and of atypical and memory B-cell differentiation. Advancements in temporal profiling in parallel with transcriptomic and VDJ sequencing have consolidated our understanding of the trajectory of B-cell clones over the course of infection and vaccination. Challenges remain in studying B-cell states across tissues in humans, in relating spatial location with B-cell phenotype and function, in examining antibody isotype switching events, and in unequivocal determination of clonal relationships. Nevertheless, ongoing multiomic assessments and studies of cellular interactions within tissues promise new avenues for improving humoral immunity and combatting autoimmune conditions.
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Affiliation(s)
- Oliver P Skinner
- Department of Microbiology & Immunology, University of Melbourne, Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Parkville, Melbourne, VIC 3000, Australia.
| | - Saba Asad
- Department of Microbiology & Immunology, University of Melbourne, Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Parkville, Melbourne, VIC 3000, Australia
| | - Ashraful Haque
- Department of Microbiology & Immunology, University of Melbourne, Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Parkville, Melbourne, VIC 3000, Australia.
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18
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Jain S, Eadon MT. Spatial transcriptomics in health and disease. Nat Rev Nephrol 2024:10.1038/s41581-024-00841-1. [PMID: 38719971 DOI: 10.1038/s41581-024-00841-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 07/05/2024]
Abstract
The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.
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Affiliation(s)
- Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Michael T Eadon
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
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19
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Johnson AL, Lopez-Bertoni H. Cellular diversity through space and time: adding new dimensions to GBM therapeutic development. Front Genet 2024; 15:1356611. [PMID: 38774283 PMCID: PMC11106394 DOI: 10.3389/fgene.2024.1356611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/15/2024] [Indexed: 05/24/2024] Open
Abstract
The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.
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Affiliation(s)
- Amanda L. Johnson
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
| | - Hernando Lopez-Bertoni
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
- Oncology, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, United States
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20
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Yeo S, Schrader AW, Lee J, Asadian M, Han HS. Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics. Anal Chem 2024; 96:6517-6522. [PMID: 38621224 PMCID: PMC11076048 DOI: 10.1021/acs.analchem.3c05686] [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] [Indexed: 04/17/2024]
Abstract
Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.
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Affiliation(s)
- Seokjin Yeo
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alex W Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Juyeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hee-Sun Han
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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21
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Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int J Mol Sci 2024; 25:4485. [PMID: 38674070 PMCID: PMC11050520 DOI: 10.3390/ijms25084485] [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: 03/08/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
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Affiliation(s)
| | | | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (J.T.); (X.B.)
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22
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Mah CK, Ahmed N, Lopez NA, Lam DC, Pong A, Monell A, Kern C, Han Y, Prasad G, Cesnik AJ, Lundberg E, Zhu Q, Carter H, Yeo GW. Bento: a toolkit for subcellular analysis of spatial transcriptomics data. Genome Biol 2024; 25:82. [PMID: 38566187 PMCID: PMC11289963 DOI: 10.1186/s13059-024-03217-7] [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: 08/17/2023] [Accepted: 03/14/2024] [Indexed: 04/04/2024] Open
Abstract
The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.
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Affiliation(s)
- Clarence K Mah
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Sanford Stem Cell Institute Innovation Center, La Jolla, CA, USA
| | - Noorsher Ahmed
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Sanford Stem Cell Institute Innovation Center, La Jolla, CA, USA
| | - Nicole A Lopez
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Dylan C Lam
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Sanford Stem Cell Institute Innovation Center, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Avery Pong
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Alexander Monell
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Colin Kern
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Yuanyuan Han
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Gino Prasad
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Anthony J Cesnik
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Emma Lundberg
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Quan Zhu
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Sanford Stem Cell Institute Innovation Center, La Jolla, CA, USA.
- Stem Cell Program, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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23
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Haviv D, Remšík J, Gatie M, Snopkowski C, Takizawa M, Pereira N, Bashkin J, Jovanovich S, Nawy T, Chaligne R, Boire A, Hadjantonakis AK, Pe'er D. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol 2024:10.1038/s41587-024-02193-4. [PMID: 38565973 DOI: 10.1038/s41587-024-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
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Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ján Remšík
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohamed Gatie
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Catherine Snopkowski
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meril Takizawa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adrienne Boire
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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24
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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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25
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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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26
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Grones C, Eekhout T, Shi D, Neumann M, Berg LS, Ke Y, Shahan R, Cox KL, Gomez-Cano F, Nelissen H, Lohmann JU, Giacomello S, Martin OC, Cole B, Wang JW, Kaufmann K, Raissig MT, Palfalvi G, Greb T, Libault M, De Rybel B. Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics. THE PLANT CELL 2024; 36:812-828. [PMID: 38231860 PMCID: PMC10980355 DOI: 10.1093/plcell/koae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/17/2023] [Accepted: 10/24/2023] [Indexed: 01/19/2024]
Abstract
Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.
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Affiliation(s)
- Carolin Grones
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
- VIB Single Cell Core Facility, Ghent 9052, Belgium
| | - Dongbo Shi
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
- Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Manuel Neumann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Lea S Berg
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Yuji Ke
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Rachel Shahan
- Department of Biology, Duke University, Durham, NC 27708, USA
- Howard Hughes Medical Institute, Duke University, Durham, NC 27708, USA
| | - Kevin L Cox
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Fabio Gomez-Cano
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Jan U Lohmann
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefania Giacomello
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, 17165 Solna, Sweden
| | - Olivier C Martin
- Universities of Paris-Saclay, Paris-Cité and Evry, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay, Gif-sur-Yvette 91192, France
| | - Benjamin Cole
- DOE-Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jia-Wei Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences (CAS), Shanghai 200032, China
| | - Kerstin Kaufmann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Michael T Raissig
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Gergo Palfalvi
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Thomas Greb
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Marc Libault
- Division of Plant Science and Technology, Interdisciplinary Plant Group, College of Agriculture, Food, and Natural Resources, University of Missouri-Columbia, Columbia, MO 65201, USA
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
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27
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Chen J, Ke R. Spatial analysis toolkits for RNA in situ sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1842. [PMID: 38605484 DOI: 10.1002/wrna.1842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Jiayu Chen
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
| | - Rongqin Ke
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
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28
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Zhang J, Zhang L, Gongol B, Hayes J, Borowsky A, Bailey-Serres J, Girke T. spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images. NAR Genom Bioinform 2024; 6:lqae006. [PMID: 38312938 PMCID: PMC10836942 DOI: 10.1093/nargab/lqae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/06/2024] Open
Abstract
Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.
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Affiliation(s)
- Jianhai Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Le Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Brendan Gongol
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Jordan Hayes
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Alexander T Borowsky
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Julia Bailey-Serres
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Thomas Girke
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
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29
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [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: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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30
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Smith KD, Prince DK, MacDonald JW, Bammler TK, Akilesh S. Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies. GLOMERULAR DISEASES 2024; 4:49-63. [PMID: 38600956 PMCID: PMC11006413 DOI: 10.1159/000538344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/10/2024] [Indexed: 04/12/2024]
Abstract
Background The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future. Summary In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy. Key Message The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.
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Affiliation(s)
- Kelly D. Smith
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - James W. MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Theo K. Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Shreeram Akilesh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, Seattle, WA, USA
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31
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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32
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Zhang R, Huang Y, Li M, Wang L, Li B, Xia A, Li Y, Yang S, Jin F. High-throughput, microscopy-based screening and quantification of genetic elements. MLIFE 2023; 2:450-461. [PMID: 38818273 PMCID: PMC10989126 DOI: 10.1002/mlf2.12096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/08/2023] [Accepted: 10/10/2023] [Indexed: 06/01/2024]
Abstract
Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is a powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8 × 12-well agarose pad plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs.
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Affiliation(s)
- Rongrong Zhang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yajia Huang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Mei Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Lei Wang
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Bing Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Aiguo Xia
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Ye Li
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Shuai Yang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Chengdu Documentation and Information CenterChinese Academy of SciencesChengduChina
| | - Fan Jin
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
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33
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Cui Z, Wei H, Goding C, Cui R. Stem cell heterogeneity, plasticity, and regulation. Life Sci 2023; 334:122240. [PMID: 37925141 DOI: 10.1016/j.lfs.2023.122240] [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: 09/08/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/06/2023]
Abstract
As a population of homogeneous cells with both self-renewal and differentiation potential, stem cell pools are highly compartmentalized and contain distinct subsets that exhibit stable but limited heterogeneity during homeostasis. However, their striking plasticity is showcased under natural or artificial stress, such as injury, transplantation, cancer, and aging, leading to changes in their phenotype, constitution, metabolism, and function. The complex and diverse network of cell-extrinsic niches and signaling pathways, together with cell-intrinsic genetic and epigenetic regulators, tightly regulate both the heterogeneity during homeostasis and the plasticity under perturbation. Manipulating these factors offers better control of stem cell behavior and a potential revolution in the current state of regenerative medicine. However, disruptions of normal regulation by genetic mutation or excessive plasticity acquisition may contribute to the formation of tumors. By harnessing innovative techniques that enhance our understanding of stem cell heterogeneity and employing novel approaches to maximize the utilization of stem cell plasticity, stem cell therapy holds immense promise for revolutionizing the future of medicine.
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Affiliation(s)
- Ziyang Cui
- Department of Dermatology and Venerology, Peking University First Hospital, Beijing 100034, China.
| | - Hope Wei
- Department of Biology, Boston University, 5 Cummington Mall, Boston, MA 02215, United States of America
| | - Colin Goding
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Headington, Oxford OX37DQ, UK
| | - Rutao Cui
- Skin Disease Research Institute, The 2nd Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
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34
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Avtanski D, Hadzi-Petrushev N, Josifovska S, Mladenov M, Reddy V. Emerging technologies in adipose tissue research. Adipocyte 2023; 12:2248673. [PMID: 37599422 PMCID: PMC10443968 DOI: 10.1080/21623945.2023.2248673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023] Open
Abstract
Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.
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Affiliation(s)
- Dimiter Avtanski
- Friedman Diabetes Institute, Lenox Hill Hospital, New York, NY, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Nikola Hadzi-Petrushev
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Slavica Josifovska
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Mitko Mladenov
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Varun Reddy
- New York Institute of Technology College of Osteopathic Medicine, Old Westbury, NY, USA
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35
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Handler K, Bach K, Borrelli C, Piscuoglio S, Ficht X, Acar IE, Moor AE. Fragment-sequencing unveils local tissue microenvironments at single-cell resolution. Nat Commun 2023; 14:7775. [PMID: 38012149 PMCID: PMC10681997 DOI: 10.1038/s41467-023-43005-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Cells collectively determine biological functions by communicating with each other-both through direct physical contact and secreted factors. Consequently, the local microenvironment of a cell influences its behavior, gene expression, and cellular crosstalk. Disruption of this microenvironment causes reciprocal changes in those features, which can lead to the development and progression of diseases. Hence, assessing the cellular transcriptome while simultaneously capturing the spatial relationships of cells within a tissue provides highly valuable insights into how cells communicate in health and disease. Yet, methods to probe the transcriptome often fail to preserve native spatial relationships, lack single-cell resolution, or are highly limited in throughput, i.e. lack the capacity to assess multiple environments simultaneously. Here, we introduce fragment-sequencing (fragment-seq), a method that enables the characterization of single-cell transcriptomes within multiple spatially distinct tissue microenvironments. We apply fragment-seq to a murine model of the metastatic liver to study liver zonation and the metastatic niche. This analysis reveals zonated genes and ligand-receptor interactions enriched in specific hepatic microenvironments. Finally, we apply fragment-seq to other tissues and species, demonstrating the adaptability of our method.
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Affiliation(s)
- Kristina Handler
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Karsten Bach
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Costanza Borrelli
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Salvatore Piscuoglio
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research Laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Xenia Ficht
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Andreas E Moor
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland.
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36
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Robles-Remacho A, Sanchez-Martin RM, Diaz-Mochon JJ. Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling. Anal Chem 2023; 95:15450-15460. [PMID: 37814884 PMCID: PMC10603609 DOI: 10.1021/acs.analchem.3c02029] [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: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
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Affiliation(s)
- Agustín Robles-Remacho
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Rosario M. Sanchez-Martin
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Juan J. Diaz-Mochon
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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37
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Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
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Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
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38
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Cohn DE, Forder A, Marshall EA, Vucic EA, Stewart GL, Noureddine K, Lockwood WW, MacAulay CE, Guillaud M, Lam WL. Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging. Front Immunol 2023; 14:1275890. [PMID: 37936700 PMCID: PMC10627006 DOI: 10.3389/fimmu.2023.1275890] [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: 08/10/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023] Open
Abstract
The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment..
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Affiliation(s)
- David E. Cohn
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Aisling Forder
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Erin A. Marshall
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Emily A. Vucic
- Department of Biochemistry and Molecular Pharmacology, New York University (NYU) Langone Medical Center, New York, NY, United States
| | - Greg L. Stewart
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Kouther Noureddine
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - William W. Lockwood
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Calum E. MacAulay
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Martial Guillaud
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Wan L. Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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Sounart H, Lázár E, Masarapu Y, Wu J, Várkonyi T, Glasz T, Kiss A, Borgström E, Hill A, Rezene S, Gupta S, Jurek A, Niesnerová A, Druid H, Bergmann O, Giacomello S. Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections. Genome Biol 2023; 24:237. [PMID: 37858234 PMCID: PMC10588020 DOI: 10.1186/s13059-023-03080-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.
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Affiliation(s)
- Hailey Sounart
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Enikő Lázár
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Yuvarani Masarapu
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Jian Wu
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Tibor Várkonyi
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
| | - Tibor Glasz
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
| | - András Kiss
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
| | | | | | - Sefanit Rezene
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Soham Gupta
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Henrik Druid
- Department of Oncology-Pathology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Olaf Bergmann
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- Center for Regenerative Therapies Dresden (CRTD), TU Dresden, Dresden, Germany
- Universitätsmedizin Göttingen, Institute of Pharmacology and Toxicology, Göttingen, Germany
| | - Stefania Giacomello
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden.
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40
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Eichenberger BT, Griesbach E, Mitchell J, Chao JA. Following the Birth, Life, and Death of mRNAs in Single Cells. Annu Rev Cell Dev Biol 2023; 39:253-275. [PMID: 37843928 DOI: 10.1146/annurev-cellbio-022723-024045] [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: 10/18/2023]
Abstract
Recent advances in single-molecule imaging of mRNAs in fixed and living cells have enabled the lives of mRNAs to be studied with unprecedented spatial and temporal detail. These approaches have moved beyond simply being able to observe specific events and have begun to allow an understanding of how regulation is coupled between steps in the mRNA life cycle. Additionally, these methodologies are now being applied in multicellular systems and animals to provide more nuanced insights into the physiological regulation of RNA metabolism.
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Affiliation(s)
- Bastian T Eichenberger
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
- University of Basel, Basel, Switzerland
| | - Esther Griesbach
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
| | - Jessica Mitchell
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
| | - Jeffrey A Chao
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
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41
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Pan J, Chang Z, Zhang X, Dong Q, Zhao H, Shi J, Wang G. Research progress of single-cell sequencing in tuberculosis. Front Immunol 2023; 14:1276194. [PMID: 37901241 PMCID: PMC10611525 DOI: 10.3389/fimmu.2023.1276194] [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: 08/14/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.
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Affiliation(s)
| | | | | | | | | | - Jingwei Shi
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
| | - Guoqing Wang
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
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42
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Zhou R, Yang G, Zhang Y, Wang Y. Spatial transcriptomics in development and disease. MOLECULAR BIOMEDICINE 2023; 4:32. [PMID: 37806992 PMCID: PMC10560656 DOI: 10.1186/s43556-023-00144-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
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Affiliation(s)
- Ran Zhou
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gaoxia Yang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Zhang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yuan Wang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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43
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Jung N, Kim TK. Spatial transcriptomics in neuroscience. Exp Mol Med 2023; 55:2105-2115. [PMID: 37779145 PMCID: PMC10618223 DOI: 10.1038/s12276-023-01093-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 10/03/2023] Open
Abstract
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
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Affiliation(s)
- Namyoung Jung
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Tae-Kyung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.
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44
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Miao H, Kuruoğlu EE, Xu T. Non-homogeneous Poisson and renewal processes as spatial models for cancer mutation. Comput Biol Chem 2023; 106:107922. [PMID: 37499435 DOI: 10.1016/j.compbiolchem.2023.107922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 07/08/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023]
Abstract
Advances in sequencing technology assisted biologists in revealing signatures of DNA cancer mutation process and in demonstrating the mutagenesis behind. However, most of these signatures proposed by majority of work focus only on the type and frequency of mutations, without considering spatial information which is non-negligible in exploring mechanisms of mutation occurrence, e.g., Kataegis. Statistical characterization of the distance between consecutive mutations can give us relative spatial information; however, it ignores location information which is as important as distance information. In this work, we assume that DNA cancer mutations are location-dependent and that integrating the two variables, location and inter-distance, is beneficial to study DNA cancer mutation processes more accurately. Particularly, instead of following a specific distribution over the whole DNA sequence, we found out that the distribution of distance between successive mutations alternates between exponential and power-law distributions. Apart from this, the parameters of either of the two distributions vary with DNA locations. The cancers with kataegis phenomenon, a specific mutation pattern caused by abnormal activity of APOBEC protein family, are more likely to be accompanied by higher parameter values of distance distribution, implying higher occurrence rate of mutation. Therefore, we propose non-homogeneous Poisson and Renewal processes to spatially model DNA cancer mutations and to describe mutation patterns quantitatively and more accurately through a statistical perspective.
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Affiliation(s)
- Hengyuan Miao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Ercan Engin Kuruoğlu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Institute of Data and Information, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Tao Xu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Bio-intelligent Manufacturing and Living Matter Bioprinting Center, Research Institute of Tsinghua University in Shenzhen, Tsinghua University, Shenzhen, China; Department of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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45
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Chen X, Deng R, Su D, Ma X, Han X, Wang S, Xia Y, Yang Z, Gong N, Jia Y, Gao X, Ren X. Visual genetic typing of glioma using proximity-anchored in situ spectral coding amplification. EXPLORATION (BEIJING, CHINA) 2023; 3:20220175. [PMID: 37933281 PMCID: PMC10582607 DOI: 10.1002/exp.20220175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 04/21/2023] [Indexed: 11/08/2023]
Abstract
Gliomas are histologically and genetically heterogeneous tumors. However, classical histopathological typing often ignores the high heterogeneity of tumors and thus cannot meet the requirements of precise pathological diagnosis. Here, proximity-anchored in situ spectral coding amplification (ProxISCA) is proposed for multiplexed imaging of RNA mutations, enabling visual typing of brain gliomas with different pathological grades at the single-cell and tissue levels. The ligation-based padlock probe can discriminate one-nucleotide variations, and the design of proximity primers enables the anchoring of amplicons on target RNA, thus improving localization accuracy. The DNA module-based spectral coding strategy can dramatically improve the multiplexing capacity for imaging RNA mutations through one-time labelling, with low cost and simple operation. One-target-one-amplicon amplification confers ProxISCA the ability to quantify RNA mutation copy number with single-molecule resolution. Based on this approach, it is found that gliomas with higher malignant grades express more genes with high correlation at the cellular and tissue levels and show greater cellular heterogeneity. ProxISCA provides a tool for glioma research and precise diagnosis, which can reveal the relationship between cellular heterogeneity and glioma occurrence or development and assist in pathological prognosis.
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Affiliation(s)
- Xiaolei Chen
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Ruijie Deng
- College of Biomass Science and EngineeringHealthy Food Evaluation Research CenterSichuan UniversityChengduChina
| | - Dongdong Su
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Xiaochen Ma
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Xu Han
- Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina
| | - Shizheng Wang
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Yuqing Xia
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Zifu Yang
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Ningqiang Gong
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaUSA
| | - Yanwei Jia
- State‐Key Laboratory of Analog and Mixed‐Signal VLSIInstitute of MicroelectronicsUniversity of MacauMacauChina
| | - Xueyun Gao
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
| | - Xiaojun Ren
- Department of Chemistry and BiologyFaculty of Environment and Life ScienceBeijing University of TechnologyBeijingChina
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46
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Ziegler C, Mir A, Anandakrishnan S, Martin P, Contreras E, Slemons I, Witkowski B, DeSilva C, Farmer A, Vranic S, Gatalica Z, Richardson D, Derkach DN. Assay-agnostic spatial profiling detects tumor microenvironment signatures: new diagnostic insights for triple-negative breast cancer. Mol Oncol 2023; 17:1953-1961. [PMID: 37666492 PMCID: PMC10552887 DOI: 10.1002/1878-0261.13515] [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: 05/08/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 09/06/2023] Open
Abstract
The role of the tumor microenvironment (TME) in immuno-oncology has driven demand for technologies that deliver in situ, or spatial, molecular information. Compartmentalized heterogeneity that traditional methods miss is becoming key to predicting both acquired drug resistance to targeted therapies and patient response to immunotherapy. Here, we describe a novel method for assay-agnostic spatial profiling and demonstrate its ability to detect immune microenvironment signatures in breast cancer patients that are unresolved by the immunohistochemical (IHC) assessment of programmed cell death ligand-1 (PD-L1) on immune cells, which represents the only FDA microenvironment-based companion diagnostic test that has been approved for triple-negative breast cancer (TNBC). Two distinct physiological states were found that are uncorrelated to tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, and intrinsic cancer subtypes.
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Affiliation(s)
| | - Alain Mir
- Takara Bio USASan FranciscoCaliforniaUSA
| | | | | | | | | | | | | | | | - Semir Vranic
- College of Medicine, QU HealthQatar UniversityDohaQatar
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47
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Jang MJ, Coughlin GM, Jackson CR, Chen X, Chuapoco MR, Vendemiatti JL, Wang AZ, Gradinaru V. Spatial transcriptomics for profiling the tropism of viral vectors in tissues. Nat Biotechnol 2023; 41:1272-1286. [PMID: 36702899 PMCID: PMC10443732 DOI: 10.1038/s41587-022-01648-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023]
Abstract
A barrier to advancing engineered adeno-associated viral vectors (AAVs) for precision access to cell subtypes is a lack of high-throughput, high-resolution assays to characterize in vivo transduction profiles. In this study, we developed an ultrasensitive, sequential fluorescence in situ hybridization (USeqFISH) method for spatial transcriptomic profiling of endogenous and viral RNA with a short barcode in intact tissue volumes by integrating hydrogel-based tissue clearing, enhanced signal amplification and multiplexing using sequential labeling. Using USeqFISH, we investigated the transduction and cell subtype tropisms across mouse brain regions of six systemic AAVs, including AAV-PHP.AX, a new variant that transduces robustly and efficiently across neurons and astrocytes. Here we reveal distinct cell subtype biases of each AAV variant, including a bias of AAV-PHP.N toward excitatory neurons. USeqFISH also enables profiling of pooled regulatory cargos, as we show for a 13-variant pool of microRNA target sites in AAV genomes. Lastly, we demonstrate potential applications of USeqFISH for in situ AAV profiling and multimodal single-cell analysis in non-human primates.
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Affiliation(s)
- Min J Jang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gerard M Coughlin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Cameron R Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Xinhong Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Miguel R Chuapoco
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Julia L Vendemiatti
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alexander Z Wang
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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48
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Magoulopoulou A, Salas SM, Tiklová K, Samuelsson ER, Hilscher MM, Nilsson M. Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annu Rev Genomics Hum Genet 2023; 24:133-150. [PMID: 37018847 DOI: 10.1146/annurev-genom-102722-092013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.
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Affiliation(s)
- Anastasia Magoulopoulou
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Sergio Marco Salas
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Katarína Tiklová
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Erik Reinhold Samuelsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Markus M Hilscher
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Mats Nilsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
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49
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Wang WJ, Chu LX, He LY, Zhang MJ, Dang KT, Gao C, Ge QY, Wang ZG, Zhao XW. Spatial transcriptomics: recent developments and insights in respiratory research. Mil Med Res 2023; 10:38. [PMID: 37592342 PMCID: PMC10433685 DOI: 10.1186/s40779-023-00471-x] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023] Open
Abstract
The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.
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Affiliation(s)
- Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Liu-Xi Chu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Yong He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Ming-Jing Zhang
- Orthopaedic Bioengineering Research Group, Division of Surgery and Interventional Science, University College London, London, HA7 4LP, UK
| | - Kai-Tong Dang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Qin-Yu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zhou-Guang Wang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Xiang-Wei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
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50
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 228] [Impact Index Per Article: 228.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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