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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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2
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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3
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Cisar C, Keener N, Ruffalo M, Paten B. A unified pipeline for FISH spatial transcriptomics. CELL GENOMICS 2023; 3:100384. [PMID: 37719153 PMCID: PMC10504669 DOI: 10.1016/j.xgen.2023.100384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/23/2023] [Accepted: 07/24/2023] [Indexed: 09/19/2023]
Abstract
High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence in situ hybridization (FISH)-based spatial transcriptomics. We used this pipeline to annotate transcript locations from three real datasets from three different common types of FISH image-based experiments, MERFISH, seqFISH, and targeted in situ sequencing (ISS), and verified that the results were high quality using the internal quality metrics of the pipeline and also a comparison with an orthogonal method of measuring RNA expression. PIPEFISH is a publicly available and open-source tool.
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Affiliation(s)
- Cecilia Cisar
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Nicholas Keener
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mathew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Benedict Paten
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
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4
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Du J, An ZJ, Huang ZF, Yang YC, Zhang MH, Fu XH, Shi WY, Hou J. Novel insights from spatial transcriptome analysis in solid tumors. Int J Biol Sci 2023; 19:4778-4792. [PMID: 37781515 PMCID: PMC10539699 DOI: 10.7150/ijbs.83098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/03/2023] [Indexed: 10/03/2023] Open
Abstract
Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, 341000, China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Xue-Hang Fu
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Wei-Yang Shi
- Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
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5
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Tang X, Chen J, Zhang X, Liu X, Xie Z, Wei K, Qiu J, Ma W, Lin C, Ke R. Improved in situ sequencing for high-resolution targeted spatial transcriptomic analysis in tissue sections. J Genet Genomics 2023; 50:652-660. [PMID: 36796537 DOI: 10.1016/j.jgg.2023.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/17/2023]
Abstract
Spatial transcriptomics enables the study of localization-indexed gene expression activity in tissues, providing the transcriptional landscape that in turn indicates the potential regulatory networks of gene expression. In situ sequencing (ISS) is a targeted spatial transcriptomic technique, based on padlock probe and rolling circle amplification combined with next-generation sequencing chemistry, for highly multiplexed in situ gene expression profiling. Here, we present improved in situ sequencing (IISS) that exploits a new probing and barcoding approach, combined with advanced image analysis pipelines for high-resolution targeted spatial gene expression profiling. We develop an improved combinatorial probe anchor ligation chemistry using a 2-base encoding strategy for barcode interrogation. The new encoding strategy results in higher signal intensity as well as improved specificity for in situ sequencing, while maintaining a streamlined analysis pipeline for targeted spatial transcriptomics. We show that IISS can be applied to both fresh frozen tissue and formalin-fixed paraffin-embedded tissue sections for single-cell level spatial gene expression analysis, based on which the developmental trajectory and cell-cell communication networks can also be constructed.
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Affiliation(s)
- Xinbin Tang
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Jiayu Chen
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Xinya Zhang
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Xuzhu Liu
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Zhaoxiang Xie
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Kaipeng Wei
- Department of Pathology, The 910 Hospital, Quanzhou, Fujian 362000, China
| | - Jianlong Qiu
- Department of Pathology, The 910 Hospital, Quanzhou, Fujian 362000, China
| | - Weiyan Ma
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China
| | - Chen Lin
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China.
| | - Rongqin Ke
- School of Medicine and School of Biomedical Sciences, Huaqiao University, Quanzhou, Fujian 362021, China.
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6
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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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7
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Sallinger K, Gruber M, Müller CT, Bonstingl L, Pritz E, Pankratz K, Gerger A, Smolle MA, Aigelsreiter A, Surova O, Svedlund J, Nilsson M, Kroneis T, El-Heliebi A. Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse. J Transl Med 2023; 21:528. [PMID: 37543577 PMCID: PMC10403907 DOI: 10.1186/s12967-023-04384-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 07/22/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. METHODS The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed. RESULTS We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. CONCLUSIONS In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.
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Affiliation(s)
- Katja Sallinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Michael Gruber
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Christin-Therese Müller
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Lilli Bonstingl
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Elisabeth Pritz
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Karin Pankratz
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Armin Gerger
- Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Maria Anna Smolle
- Department of Orthopaedics and Trauma, Medical University of Graz, Graz, Austria
| | - Ariane Aigelsreiter
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Olga Surova
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
| | - Jessica Svedlund
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
- 10x Genomics, Life City, Solnavägen 3H, 113 63, Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
| | - Thomas Kroneis
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Amin El-Heliebi
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria.
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria.
- Biotechmed, Graz, Austria.
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8
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Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 2023; 41:404-420. [PMID: 36800999 DOI: 10.1016/j.ccell.2023.01.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/01/2022] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics.
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Affiliation(s)
- Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Uri
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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9
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Duan H, Cheng T, Cheng H. Spatially resolved transcriptomics: advances and applications. BLOOD SCIENCE 2023; 5:1-14. [PMID: 36742187 PMCID: PMC9891446 DOI: 10.1097/bs9.0000000000000141] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.
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Affiliation(s)
- Honglin Duan
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Tao Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
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10
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Lomakin A, Svedlund J, Strell C, Gataric M, Shmatko A, Rukhovich G, Park JS, Ju YS, Dentro S, Kleshchevnikov V, Vaskivskyi V, Li T, Bayraktar OA, Pinder S, Richardson AL, Santagata S, Campbell PJ, Russnes H, Gerstung M, Nilsson M, Yates LR. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature 2022; 611:594-602. [PMID: 36352222 PMCID: PMC9668746 DOI: 10.1038/s41586-022-05425-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/07/2022] [Indexed: 11/10/2022]
Abstract
Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1-3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.
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Affiliation(s)
- Artem Lomakin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Wellcome Sanger Institute, Hinxton, UK
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany
| | - Jessica Svedlund
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Carina Strell
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Milana Gataric
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Artem Shmatko
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany
| | - Gleb Rukhovich
- Wellcome Sanger Institute, Hinxton, UK
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany
| | - Jun Sung Park
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Wellcome Sanger Institute, Hinxton, UK
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany
| | - Young Seok Ju
- Laboratory of Cancer Genomics, GSMSE, KAIST, Daejeon, Korea
| | - Stefan Dentro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Wellcome Sanger Institute, Hinxton, UK
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany
| | | | | | - Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Sarah Pinder
- Guys and St Thomas' NHS Trust, London, UK
- School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
| | | | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | | | - Hege Russnes
- Department of Pathology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
- Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany.
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
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11
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Yu Q, Jiang M, Wu L. Spatial transcriptomics technology in cancer research. Front Oncol 2022; 12:1019111. [PMID: 36313703 PMCID: PMC9606570 DOI: 10.3389/fonc.2022.1019111] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/21/2022] [Indexed: 08/25/2023] Open
Abstract
In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.
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Affiliation(s)
- Qichao Yu
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Miaomiao Jiang
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Liang Wu
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
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12
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Moffitt JR, Lundberg E, Heyn H. The emerging landscape of spatial profiling technologies. Nat Rev Genet 2022; 23:741-759. [PMID: 35859028 DOI: 10.1038/s41576-022-00515-3] [Citation(s) in RCA: 120] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 01/04/2023]
Abstract
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.
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Affiliation(s)
- Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Pathology, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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13
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Li Q, Zhang X, Ke R. Spatial Transcriptomics for Tumor Heterogeneity Analysis. Front Genet 2022; 13:906158. [PMID: 35899203 PMCID: PMC9309247 DOI: 10.3389/fgene.2022.906158] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.
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14
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Magoulopoulou A, Qian X, Pediatama Setiabudiawan T, Marco Salas S, Yokota C, Rottenberg ME, Nilsson M, Carow B. Spatial Resolution of Mycobacterium tuberculosis Bacteria and Their Surrounding Immune Environments Based on Selected Key Transcripts in Mouse Lungs. Front Immunol 2022; 13:876321. [PMID: 35663950 PMCID: PMC9157500 DOI: 10.3389/fimmu.2022.876321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Mycobacterium tuberculosis (Mtb) bacilli are the causative agent of tuberculosis (TB), a major killer of mankind. Although it is widely accepted that local interactions between Mtb and the immune system in the tuberculous granuloma determine whether the outcome of infection is controlled or disseminated, these have been poorly studied due to methodological constraints. We have recently used a spatial transcriptomic technique, in situ sequencing (ISS), to define the spatial distribution of immune transcripts in TB mouse lungs. To further contribute to the understanding of the immune microenvironments of Mtb and their local diversity, we here present two complementary automated bacteria-guided analysis pipelines. These position 33 ISS-identified immune transcripts in relation to single bacteria and bacteria clusters. The analysis was applied on new ISS data from lung sections of Mtb-infected C57BL/6 and C3HeB/FeJ mice. In lungs from C57BL/6 mice early and late post infection, transcripts that define inflammatory macrophages were enriched at subcellular distances to bacteria, indicating the activation of infected macrophages. In contrast, expression patterns associated to antigen presentation were enriched in non-infected cells at 12 weeks post infection. T-cell transcripts were evenly distributed in the tissue. In Mtb-infected C3HeB/FeJ mice, transcripts characterizing activated macrophages localized in apposition to small bacteria clusters, but not in organized granulomas. Despite differences in the susceptibility to Mtb, the transcript patterns found around small bacteria clusters of C3HeB/FeJ and C57BL/6 mice were similar. Altogether, the presented tools allow us to characterize in depth the immune cell populations and their activation that interact with Mtb in the infected lung.
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Affiliation(s)
- Anastasia Magoulopoulou
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Xiaoyan Qian
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Todia Pediatama Setiabudiawan
- Department of Microbiology, Tumor and Cell Biology and Centre for Tuberculosis Research, Karolinska Institutet, Solna, Sweden
| | - Sergio Marco Salas
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Martin E Rottenberg
- Department of Microbiology, Tumor and Cell Biology and Centre for Tuberculosis Research, Karolinska Institutet, Solna, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Berit Carow
- Department of Microbiology, Tumor and Cell Biology and Centre for Tuberculosis Research, Karolinska Institutet, Solna, Sweden
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15
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Bassiouni R, Gibbs LD, Craig DW, Carpten JD, McEachron TA. Applicability of spatial transcriptional profiling to cancer research. Mol Cell 2021; 81:1631-1639. [PMID: 33826920 PMCID: PMC8052283 DOI: 10.1016/j.molcel.2021.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/22/2021] [Accepted: 03/10/2021] [Indexed: 12/21/2022]
Abstract
Spatial transcriptional profiling provides gene expression information within the important anatomical context of tissue architecture. This approach is well suited to characterizing solid tumors, which develop within a complex landscape of malignant cells, immune cells, and stroma. In a single assay, spatial transcriptional profiling can interrogate the role of spatial relationships among these cell populations as well as reveal spatial patterns of relevant oncogenic genetic events. The broad utility of this approach is reflected in the array of strategies that have been developed for its implementation as well as in the recent commercial development of several profiling platforms. The flexibility to apply these technologies to both hypothesis-driven and discovery-driven studies allows widespread applicability in research settings. This review discusses available technologies for spatial transcriptional profiling and several applications for their use in cancer research.
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Affiliation(s)
- Rania Bassiouni
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - Lee D Gibbs
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - David W Craig
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - John D Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - Troy A McEachron
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA; Pediatric Oncology Branch, National Cancer Institute, 10 Center Drive, Bethesda, MD 20892, USA.
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16
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Maniatis S, Petrescu J, Phatnani H. Spatially resolved transcriptomics and its applications in cancer. Curr Opin Genet Dev 2021; 66:70-77. [PMID: 33434721 PMCID: PMC7969406 DOI: 10.1016/j.gde.2020.12.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 02/06/2023]
Abstract
Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.
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Affiliation(s)
- Silas Maniatis
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Joana Petrescu
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA; Department of Neurology, Division of Neuromuscular Medicine, Columbia University, New York, NY, USA
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA; Department of Neurology, Division of Neuromuscular Medicine, Columbia University, New York, NY, USA.
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17
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Alon S, Goodwin DR, Sinha A, Wassie AT, Chen F, Daugharthy ER, Bando Y, Kajita A, Xue AG, Marrett K, Prior R, Cui Y, Payne AC, Yao CC, Suk HJ, Wang R, Yu CCJ, Tillberg P, Reginato P, Pak N, Liu S, Punthambaker S, Iyer EPR, Kohman RE, Miller JA, Lein ES, Lako A, Cullen N, Rodig S, Helvie K, Abravanel DL, Wagle N, Johnson BE, Klughammer J, Slyper M, Waldman J, Jané-Valbuena J, Rozenblatt-Rosen O, Regev A, Church GM, Marblestone AH, Boyden ES. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 2021; 371:eaax2656. [PMID: 33509999 PMCID: PMC7900882 DOI: 10.1126/science.aax2656] [Citation(s) in RCA: 173] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/13/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022]
Abstract
Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.
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Affiliation(s)
- Shahar Alon
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Faculty of Engineering, Gonda Brain Research Center and Institute of Nanotechnology, Bar-Ilan University, Ramat Gan, Israel
| | - Daniel R Goodwin
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Anubhav Sinha
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Asmamaw T Wassie
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Fei Chen
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan R Daugharthy
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Yosuke Bando
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Kioxia Corporation, Minato-ku, Tokyo, Japan
| | | | - Andrew G Xue
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | | | | | - Yi Cui
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Andrew C Payne
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chun-Chen Yao
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ho-Jun Suk
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Ru Wang
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Paul Tillberg
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Paul Reginato
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Nikita Pak
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA, USA
| | - Songlei Liu
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Sukanya Punthambaker
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Eswar P R Iyer
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Richie E Kohman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott Rodig
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel L Abravanel
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Nikhil Wagle
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Michal Slyper
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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18
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Abstract
Therapeutic resistance continues to be an indominable foe in our ambition for curative cancer treatment. Recent insights into the molecular determinants of acquired treatment resistance in the clinical and experimental setting have challenged the widely held view of sequential genetic evolution as the primary cause of resistance and brought into sharp focus a range of non-genetic adaptive mechanisms. Notably, the genetic landscape of the tumour and the non-genetic mechanisms used to escape therapy are frequently linked. Remarkably, whereas some oncogenic mutations allow the cancer cells to rapidly adapt their transcriptional and/or metabolic programme to meet and survive the therapeutic pressure, other oncogenic drivers convey an inherent cellular plasticity to the cancer cell enabling lineage switching and/or the evasion of anticancer immunosurveillance. The prevalence and diverse array of non-genetic resistance mechanisms pose a new challenge to the field that requires innovative strategies to monitor and counteract these adaptive processes. In this Perspective we discuss the key principles of non-genetic therapy resistance in cancer. We provide a perspective on the emerging data from clinical studies and sophisticated cancer models that have studied various non-genetic resistance pathways and highlight promising therapeutic avenues that may be used to negate and/or counteract the non-genetic adaptive pathways.
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Affiliation(s)
- Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Sarah-Jane Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
- Center for Cancer Research, The University of Melbourne, Melbourne, VIC, Australia.
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
- Center for Cancer Research, The University of Melbourne, Melbourne, VIC, Australia.
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19
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SCRINSHOT enables spatial mapping of cell states in tissue sections with single-cell resolution. PLoS Biol 2020; 18:e3000675. [PMID: 33216742 PMCID: PMC7717588 DOI: 10.1371/journal.pbio.3000675] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 12/04/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
Changes in cell identities and positions underlie tissue development and disease progression. Although single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT (Single-Cell Resolution IN Situ Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRINSHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity, and quantitative qualities of SCRINSHOT facilitate single-cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions. This study presents SCRINSHOT, an amenable, multiplex RNA-mapping method, applicable to a wide variety of tissue types and conditions. It can function quantitatively across a broad range of expression levels and detect even rare cell types, facilitating the creation of digital tissue maps with single-cell resolution.
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20
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Gyllborg D, Langseth CM, Qian X, Choi E, Salas SM, Hilscher MM, Lein ES, Nilsson M. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res 2020; 48:e112. [PMID: 32990747 PMCID: PMC7641728 DOI: 10.1093/nar/gkaa792] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 08/19/2020] [Accepted: 09/11/2020] [Indexed: 12/16/2022] Open
Abstract
Visualization of the transcriptome in situ has proven to be a valuable tool in exploring single-cell RNA-sequencing data, providing an additional spatial dimension to investigate multiplexed gene expression, cell types, disease architecture or even data driven discoveries. In situ sequencing (ISS) method based on padlock probes and rolling circle amplification has been used to spatially resolve gene transcripts in tissue sections of various origins. Here, we describe the next iteration of ISS, HybISS, hybridization-based in situ sequencing. Modifications in probe design allows for a new barcoding system via sequence-by-hybridization chemistry for improved spatial detection of RNA transcripts. Due to the amplification of probes, amplicons can be visualized with standard epifluorescence microscopes for high-throughput efficiency and the new sequencing chemistry removes limitations bound by sequence-by-ligation chemistry of ISS. HybISS design allows for increased flexibility and multiplexing, increased signal-to-noise, all without compromising throughput efficiency of imaging large fields of view. Moreover, the current protocol is demonstrated to work on human brain tissue samples, a source that has proven to be difficult to work with image-based spatial analysis techniques. Overall, HybISS technology works as a targeted amplification detection method for improved spatial transcriptomic visualization, and importantly, with an ease of implementation.
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Affiliation(s)
- Daniel Gyllborg
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
| | | | - Xiaoyan Qian
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
| | - Eunkyoung Choi
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
| | - Sergio Marco Salas
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65 Solna, Sweden
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