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Benmelech S, Le T, McKay M, Nam J, Subramaniam K, Tellez D, Vlasak G, Mak M. Biophysical and biochemical aspects of immune cell-tumor microenvironment interactions. APL Bioeng 2024; 8:021502. [PMID: 38572312 PMCID: PMC10990568 DOI: 10.1063/5.0195244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 03/19/2024] [Indexed: 04/05/2024] Open
Abstract
The tumor microenvironment (TME), composed of and influenced by a heterogeneous set of cancer cells and an extracellular matrix, plays a crucial role in cancer progression. The biophysical aspects of the TME (namely, its architecture and mechanics) regulate interactions and spatial distributions of cancer cells and immune cells. In this review, we discuss the factors of the TME-notably, the extracellular matrix, as well as tumor and stromal cells-that contribute to a pro-tumor, immunosuppressive response. We then discuss the ways in which cells of the innate and adaptive immune systems respond to tumors from both biochemical and biophysical perspectives, with increased focus on CD8+ and CD4+ T cells. Building upon this information, we turn to immune-based antitumor interventions-specifically, recent biophysical breakthroughs aimed at improving CAR-T cell therapy.
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Affiliation(s)
- Shoham Benmelech
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Thien Le
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Maggie McKay
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Jungmin Nam
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Krupakar Subramaniam
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Daniela Tellez
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Grace Vlasak
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Michael Mak
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
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2
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Rossi M, Radisky DC. Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum. Cancers (Basel) 2024; 16:1615. [PMID: 38730568 PMCID: PMC11083340 DOI: 10.3390/cancers16091615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.
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Affiliation(s)
| | - Derek C. Radisky
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL 32224, USA;
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3
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Røgenes H, Finne K, Winge I, Akslen LA, Östman A, Milosevic V. Development of 42 marker panel for in-depth study of cancer associated fibroblast niches in breast cancer using imaging mass cytometry. Front Immunol 2024; 15:1325191. [PMID: 38711512 PMCID: PMC11070582 DOI: 10.3389/fimmu.2024.1325191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/05/2024] [Indexed: 05/08/2024] Open
Abstract
Imaging Mass Cytometry (IMC) is a novel, and formidable high multiplexing imaging method emerging as a promising tool for in-depth studying of tissue architecture and intercellular communications. Several studies have reported various IMC antibody panels mainly focused on studying the immunological landscape of the tumor microenvironment (TME). With this paper, we wanted to address cancer associated fibroblasts (CAFs), a component of the TME very often underrepresented and not emphasized enough in present IMC studies. Therefore, we focused on the development of a comprehensive IMC panel that can be used for a thorough description of the CAF composition of breast cancer TME and for an in-depth study of different CAF niches in relation to both immune and breast cancer cell communication. We established and validated a 42 marker panel using a variety of control tissues and rigorous quantification methods. The final panel contained 6 CAF-associated markers (aSMA, FAP, PDGFRa, PDGFRb, YAP1, pSMAD2). Breast cancer tissues (4 cases of luminal, 5 cases of triple negative breast cancer) and a modified CELESTA pipeline were used to demonstrate the utility of our IMC panel for detailed profiling of different CAF, immune and cancer cell phenotypes.
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Affiliation(s)
- Hanna Røgenes
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kenneth Finne
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ingeborg Winge
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Lars A. Akslen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Arne Östman
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Vladan Milosevic
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Goodwin RJA, Platz SJ, Reis-Filho JS, Barry ST. Accelerating Drug Development Using Spatial Multi-omics. Cancer Discov 2024; 14:620-624. [PMID: 38571424 DOI: 10.1158/2159-8290.cd-24-0101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
SUMMARY Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility of spatial biology data in accelerating drug programs, however, requires balancing exploratory discovery investigations against scalable and clinically applicable spatial biomarker analysis.
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Affiliation(s)
- Richard J A Goodwin
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Stefan J Platz
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Jorge S Reis-Filho
- Cancer Biomarker Development, Early Oncology, AstraZeneca, Gaithersburg, Maryland
| | - Simon T Barry
- Bioscience, Early Oncology, AstraZeneca, Cambridge, United Kingdom
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5
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Li M. Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology. Microsc Res Tech 2024; 87:631-659. [PMID: 38053519 DOI: 10.1002/jemt.24467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/07/2023]
Abstract
Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.
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Affiliation(s)
- Mi Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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Ermann J, Lefton M, Wei K, Gutierrez-Arcelus M. Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling. Curr Rheumatol Rep 2024; 26:144-154. [PMID: 38227172 DOI: 10.1007/s11926-023-01132-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis. RECENT FINDINGS The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.
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Affiliation(s)
- Joerg Ermann
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Micah Lefton
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Harvard Medical School, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Rius Rigau A, Li YN, Matei AE, Györfi AH, Bruch PM, Koziel S, Devakumar V, Gabrielli A, Kreuter A, Wang J, Dietrich S, Schett G, Distler JHW, Liang M. Characterization of Vascular Niche in Systemic Sclerosis by Spatial Proteomics. Circ Res 2024; 134:875-891. [PMID: 38440901 DOI: 10.1161/circresaha.123.323299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND Systemic sclerosis (SSc) is a connective tissue disease that can serve as a model to study vascular changes in response to inflammation, autoimmunity, and fibrotic remodeling. Although microvascular changes are the earliest histopathologic manifestation of SSc, the vascular pathophysiology remains poorly understood. METHODS We applied spatial proteomic approaches to deconvolute the heterogeneity of vascular cells at the single-cell level in situ and characterize cellular alterations of the vascular niches of patients with SSc. Skin biopsies of patients with SSc and control individuals were analyzed by imaging mass cytometry, yielding a total of 90 755 cells including 2987 endothelial cells and 4096 immune cells. RESULTS We identified 7 different subpopulations of blood vascular endothelial cells (VECs), 2 subpopulations of lymphatic endothelial cells, and 3 subpopulations of pericytes. A novel population of CD34+;αSMA+ (α-smooth muscle actin);CD31+ VECs was more common in SSc, whereas endothelial precursor cells were decreased. Co-detection by indexing and tyramide signal amplification confirmed these findings. The microenvironment of CD34+;αSMA+;CD31+ VECs was enriched for immune cells and myofibroblasts, and CD34+;αSMA+;CD31+ VECs expressed markers of endothelial-to-mesenchymal transition. The density of CD34+;αSMA+;CD31+ VECs was associated with clinical progression of fibrosis in SSc. CONCLUSIONS Using spatial proteomics, we unraveled the heterogeneity of vascular cells in control individuals and patients with SSc. We identified CD34+;αSMA+;CD31+ VECs as a novel endothelial cell population that is increased in patients with SSc, expresses markers for endothelial-to-mesenchymal transition, and is located in close proximity to immune cells and myofibroblasts. CD34+;αSMA+;CD31+ VEC counts were associated with clinical outcomes of progressive fibrotic remodeling, thus providing a novel cellular correlate for the crosstalk of vasculopathy and fibrosis.
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Affiliation(s)
- Aleix Rius Rigau
- Department of Internal Medicine 3, Rheumatology and Clinical Immunology (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Deutsches Zentrum Immuntherapie (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
| | - Yi-Nan Li
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
| | - Alexandru-Emil Matei
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
| | - Andrea-Hermina Györfi
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
| | - Peter-Martin Bruch
- Department of Haematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Germany (P.-M.B., S.K., S.D.)
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf, Aachen Bonn Cologne, Germany (P.-M.B., S.K., S.D.)
- Molecular Medicine Partnership Unit, Heidelberg, Germany (P.-M.B., S.K., S.D.)
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Germany (P.-M.B., S.D.)
| | - Sarah Koziel
- Department of Haematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Germany (P.-M.B., S.K., S.D.)
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf, Aachen Bonn Cologne, Germany (P.-M.B., S.K., S.D.)
- Molecular Medicine Partnership Unit, Heidelberg, Germany (P.-M.B., S.K., S.D.)
- Düsseldorf School of Oncology, Germany (S.K.)
| | - Veda Devakumar
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
| | - Armando Gabrielli
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Fondazione di Medicina Molecolare e Terapia Cellulare, Università Politecnica delle Marche, Ancona, Italy (A.G.)
| | - Alexander Kreuter
- Department of Dermatology, Venerology and Allergology, Helios St. Johannes Klinik Duisburg, Germany (A.K.)
- Department of Dermatology, Venerology and Allergology, Helios St. Elisabeth Klinik Oberhausen, University Witten-Herdecke, Germany (A.K.)
| | - Jiucun Wang
- Department of Rheumatology, Huashan Hospital (J.W., M.L.), Fudan University, Shanghai, P. R. China
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, and Human Phenome Institute, Fudan University, Shanghai, P. R. China (J.W.)
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, P. R. China (J.W.)
| | - Sascha Dietrich
- Department of Haematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Germany (P.-M.B., S.K., S.D.)
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf, Aachen Bonn Cologne, Germany (P.-M.B., S.K., S.D.)
- Molecular Medicine Partnership Unit, Heidelberg, Germany (P.-M.B., S.K., S.D.)
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Germany (P.-M.B., S.D.)
| | - Georg Schett
- Department of Internal Medicine 3, Rheumatology and Clinical Immunology (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Deutsches Zentrum Immuntherapie (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
| | - Jörg H W Distler
- Department of Internal Medicine 3, Rheumatology and Clinical Immunology (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Deutsches Zentrum Immuntherapie (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
| | - Minrui Liang
- Department of Internal Medicine 3, Rheumatology and Clinical Immunology (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Deutsches Zentrum Immuntherapie (A.R.R., G.S., J.H.W.D., M.L.), Friedrich-Alexander-University Erlangen-Nürnberg and University Hospital Erlangen, Germany
- Clinic for Rheumatology (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Hiller Research Center (Y.-N.L., A.-E.M., A.-H.G., V.D., A.G., J.H.W.D., M.L.), University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University, Germany
- Department of Rheumatology, Huashan Hospital (J.W., M.L.), Fudan University, Shanghai, P. R. China
- Huashan Rare Disease Center (M.L.), Fudan University, Shanghai, P. R. China
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Chen R, Xu J, Wang B, Ding Y, Abdulla A, Li Y, Jiang L, Ding X. SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging. Nat Commun 2024; 15:2708. [PMID: 38548720 PMCID: PMC10978886 DOI: 10.1038/s41467-024-46989-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/15/2024] [Indexed: 04/01/2024] Open
Abstract
Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics. However, the precision of subsequent clinical analysis is constrained by imaging noise and resolution. Here, we propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement. SpiDe-Sr effectively resists noise and improves resolution by 4 times. We demonstrate SpiDe-Sr respectively with cells, mouse and human tissues, resulting 18.95%/27.27%/21.16% increase in peak signal-to-noise ratio and 15.95%/31.63%/15.52% increase in cell extraction accuracy. We further apply SpiDe-Sr to study the tumor microenvironment of a 20-patient clinical breast cancer cohort with 269,556 single cells, and discover the invasion of Gram-negative bacteria is positively correlated with carcinogenesis markers and negatively correlated with immunological markers. Additionally, SpiDe-Sr is also compatible with fluorescence microscopy imaging, suggesting SpiDe-Sr an alternative tool for microscopy image super-resolution.
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Grants
- This work was supported by National Key R&D Program of China (2022YFC2601700, 2022YFF0710202) and NSFC Projects (T2122002, 22077079, 81871448), Shanghai Municipal Science and Technology Project(22Z510202478), Shanghai Municipal Education Commission Project(21SG10), Shanghai Jiao Tong University Projects (YG2021ZD19, Agri-X20200101, 2020 SJTU-HUJI), Shanghai Municipal Health Commission Project (2019CXJQ03). Thanks for AEMD SJTU, Shanghai Jiao Tong University Laboratory Animal Center for the supporting.
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Affiliation(s)
- Rui Chen
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jiasu Xu
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Boqian Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Aynur Abdulla
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiyang Li
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xianting Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.
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10
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Forjaz A, Vaz E, Romero VM, Joshi S, Braxton AM, Jiang AC, Fujikura K, Cornish T, Hong SM, Hruban RH, Wu PH, Wood LD, Kiemen AL, Wirtz D. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues. bioRxiv 2024:2023.12.04.569986. [PMID: 38106231 PMCID: PMC10723352 DOI: 10.1101/2023.12.04.569986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.
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Affiliation(s)
- André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Eduarda Vaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Valentina Matos Romero
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Ann C. Jiang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Kohei Fujikura
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Toby Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Laura D. Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
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11
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Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
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Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
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12
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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13
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Ferri-Borgogno S, Burks JK, Seeley EH, McKee TD, Stolley DL, Basi AV, Gomez JA, Gamal BT, Ayyadhury S, Lawson BC, Yates MS, Birrer MJ, Lu KH, Mok SC. Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses. Cancers (Basel) 2024; 16:846. [PMID: 38473208 PMCID: PMC10930466 DOI: 10.3390/cancers16050846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Most platforms used for the molecular reconstruction of the tumor-immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell-cell or cell-extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.
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Affiliation(s)
- Sammy Ferri-Borgogno
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | - Jared K. Burks
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Erin H. Seeley
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Trevor D. McKee
- Pathomics, Inc., Toronto, ON M4C 3K2, Canada; (T.D.M.); (S.A.)
| | - Danielle L. Stolley
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Akshay V. Basi
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Javier A. Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Basant T. Gamal
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | | | - Barrett C. Lawson
- Department of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Melinda S. Yates
- Department of Pathology and Laboratory Medicine, The University of North Carolina, Chapel Hill, NC 27599, USA
| | - Michael J. Birrer
- Winthrop P. Rockefelle Cancer Institute, The University of Arkanasas for Medical Sciences, Little Rock, AR 72205, USA
| | - Karen H. Lu
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | - Samuel C. Mok
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
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14
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Scharf MM, Humphrys LJ, Berndt S, Di Pizio A, Lehmann J, Liebscher I, Nicoli A, Niv MY, Peri L, Schihada H, Schulte G. The dark sides of the GPCR tree - research progress on understudied GPCRs. Br J Pharmacol 2024. [PMID: 38339984 DOI: 10.1111/bph.16325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/24/2023] [Accepted: 01/08/2024] [Indexed: 02/12/2024] Open
Abstract
A large portion of the human GPCRome is still in the dark and understudied, consisting even of entire subfamilies of GPCRs such as odorant receptors, class A and C orphans, adhesion GPCRs, Frizzleds and taste receptors. However, it is undeniable that these GPCRs bring an untapped therapeutic potential that should be explored further. Open questions on these GPCRs span diverse topics such as deorphanisation, the development of tool compounds and tools for studying these GPCRs, as well as understanding basic signalling mechanisms. This review gives an overview of the current state of knowledge for each of the diverse subfamilies of understudied receptors regarding their physiological relevance, molecular mechanisms, endogenous ligands and pharmacological tools. Furthermore, it identifies some of the largest knowledge gaps that should be addressed in the foreseeable future and lists some general strategies that might be helpful in this process.
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Affiliation(s)
- Magdalena M Scharf
- Karolinska Institutet, Dept. Physiology & Pharmacology, Sec. Receptor Biology & Signaling, Stockholm, Sweden
| | - Laura J Humphrys
- Institute of Pharmacy, University of Regensburg, Regensburg, Germany
| | - Sandra Berndt
- Rudolf Schönheimer Institute for Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
- Chemoinformatics and Protein Modelling, Department of Molecular Life Science, School of Life Science, Technical University of Munich, Freising, Germany
| | - Juliane Lehmann
- Rudolf Schönheimer Institute for Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Ines Liebscher
- Rudolf Schönheimer Institute for Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
- Chemoinformatics and Protein Modelling, Department of Molecular Life Science, School of Life Science, Technical University of Munich, Freising, Germany
| | - Masha Y Niv
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Lior Peri
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Hannes Schihada
- Institute of Pharmaceutical Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Gunnar Schulte
- Karolinska Institutet, Dept. Physiology & Pharmacology, Sec. Receptor Biology & Signaling, Stockholm, Sweden
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15
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Quail DF, Park M, Welm AL, Ekiz HA. Breast Cancer Immunity: It is TIME for the Next Chapter. Cold Spring Harb Perspect Med 2024; 14:a041324. [PMID: 37188526 PMCID: PMC10835621 DOI: 10.1101/cshperspect.a041324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.
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Affiliation(s)
- Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec H3A 1A3, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Morag Park
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec H3A 1A3, Canada
- Departments of Biochemistry, Oncology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Alana L Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - H Atakan Ekiz
- Department of Molecular Biology and Genetics, Izmir Institute of Technology, Gulbahce, 35430 Urla, Izmir, Turkey
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16
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Magrill J, Moldoveanu D, Gu J, Lajoie M, Watson IR. Mapping the single cell spatial immune landscapes of the melanoma microenvironment. Clin Exp Metastasis 2024:10.1007/s10585-023-10252-4. [PMID: 38217840 DOI: 10.1007/s10585-023-10252-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.
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Affiliation(s)
- Jamie Magrill
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Dan Moldoveanu
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Jiayao Gu
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mathieu Lajoie
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Ian R Watson
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Department of Biochemistry, McGill University, Montréal, QC, Canada.
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada.
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17
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Hunter B, Nicorescu I, Foster E, McDonald D, Hulme G, Fuller A, Thomson A, Goldsborough T, Hilkens CMU, Majo J, Milross L, Fisher A, Bankhead P, Wills J, Rees P, Filby A, Merces G. OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration. Cytometry A 2024; 105:36-53. [PMID: 37750225 PMCID: PMC10952805 DOI: 10.1002/cyto.a.24803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 09/27/2023]
Abstract
Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.
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Affiliation(s)
- Bethany Hunter
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Ioana Nicorescu
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Emma Foster
- Image Analysis Unit, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - David McDonald
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Gillian Hulme
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew Fuller
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Amanda Thomson
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | | | - Catharien M. U. Hilkens
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Joaquim Majo
- Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Luke Milross
- Transplantation and Regenerative Medicine, Newcastle University Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew Fisher
- Transplantation and Regenerative Medicine, Newcastle University Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Peter Bankhead
- Centre for Genomic and Experimental Medicine, CRUK Scotland Centre, and Edinburgh PathologyUniversity of EdinburghEdinburghUK
| | - John Wills
- Department of Veterinary MedicineCambridge UniversityCambridgeUK
- Department of Biomedical EngineeringSwansea UniversitySwansea, WalesUK
| | - Paul Rees
- Department of Biomedical EngineeringSwansea UniversitySwansea, WalesUK
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusettsUSA
| | - Andrew Filby
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - George Merces
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Image Analysis Unit, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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18
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Lin HC, Makhlouf A, Vazquez Echegaray C, Zawada D, Simões F. Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs. Development 2023; 150:dev202300. [PMID: 38078653 PMCID: PMC10753584 DOI: 10.1242/dev.202300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.
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Affiliation(s)
- Hsiu-Chuan Lin
- Department of Biosystems Science and Engineering, ETH Zürich, 4057 Basel, Switzerland
| | - Aly Makhlouf
- MRC Laboratory of Molecular Biology, University of Cambridge, Cambridge CB2 0QH, UK
| | - Camila Vazquez Echegaray
- Molecular Medicine and Gene Therapy, Lund Stem Cell Centre, Wallenberg Centre for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Dorota Zawada
- First Department of Medicine, Cardiology, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
- German Center for Cardiovascular Research (DZHK), Munich Heart Alliance, 80636 Munich, Germany
- Regenerative Medicine in Cardiovascular Diseases, First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
| | - Filipa Simões
- Department of Physiology, Anatomy and Genetics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford OX3 7TY, UK
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19
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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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20
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Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
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Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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21
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Bhattacharyya S, Ehsan SF, Karacosta LG. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. Front Netw Physiol 2023; 3:1256104. [PMID: 37964768 PMCID: PMC10642209 DOI: 10.3389/fnetp.2023.1256104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/16/2023]
Abstract
In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.
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Affiliation(s)
- Sayantan Bhattacharyya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shafqat F. Ehsan
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Loukia G. Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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22
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Kim EN, Chen PZ, Bressan D, Tripathi M, Miremadi A, di Pietro M, Coussens LM, Hannon GJ, Fitzgerald RC, Zhuang L, Chang YH. Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole-slide imaging and accurate segmentation. Cell Rep Methods 2023; 3:100595. [PMID: 37741277 PMCID: PMC10626190 DOI: 10.1016/j.crmeth.2023.100595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/05/2023] [Accepted: 08/30/2023] [Indexed: 09/25/2023]
Abstract
Imaging mass cytometry (IMC) is a powerful technique capable of detecting over 30 markers on a single slide. It has been increasingly used for single-cell-based spatial phenotyping in a wide range of samples. However, it only acquires a rectangle field of view (FOV) with a relatively small size and low image resolution, which hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dimensional IMC on the same tissue slide. Our computational pipeline uses the whole-slide image (WSI) of IF as a spatial reference and integrates small-FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images, and demonstrated the advantage of the dual-modality imaging strategy.
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Affiliation(s)
- Eun Na Kim
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, USA; Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | - Dario Bressan
- Cancer Research UK Cambridge Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Monika Tripathi
- Early Cancer Institute, University of Cambridge, Cambridge, UK
| | - Ahmad Miremadi
- Early Cancer Institute, University of Cambridge, Cambridge, UK
| | | | - Lisa M Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Gregory J Hannon
- Cancer Research UK Cambridge Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | | | - Lizhe Zhuang
- Early Cancer Institute, University of Cambridge, Cambridge, UK.
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, USA.
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23
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Burlingame E, Ternes L, Lin JR, Chen YA, Kim EN, Gray JW, Chang YH. 3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding. Front Bioinform 2023; 3:1275402. [PMID: 37928169 PMCID: PMC10620917 DOI: 10.3389/fbinf.2023.1275402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction: Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that use MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region of interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. Methods: To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. Results and discussion: We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
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Affiliation(s)
- Erik Burlingame
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Luke Ternes
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Jia-Ren Lin
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Yu-An Chen
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Eun Na Kim
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Joe W. Gray
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
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24
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Akbari B, Huber BR, Sherman JH. Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Crit Rev Anal Chem 2023:1-30. [PMID: 37847593 DOI: 10.1080/10408347.2023.2266838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.
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Affiliation(s)
- Behnaz Akbari
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | - Bertrand Russell Huber
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- US Department of Veteran Affairs, VA Boston Healthcare System, Boston, Massachusetts USA
- US Department of Veterans Affairs, National Center for PTSD, Boston, Massachusetts USA
| | - Janet Hope Sherman
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
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25
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Watson ECR, Baker W, Ahern D, Loi D, Cribbs AP, Oppermann U. Mass cytometry as a tool in target validation and drug discovery. Methods Enzymol 2023; 690:541-574. [PMID: 37858540 DOI: 10.1016/bs.mie.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Mass cytometry provides highly multiparametric data at a single cell level, coupling the specificity and sensitivity of time-of-flight mass spectrometry with the single-cell throughput of flow cytometry. It offers great value in interrogating the potentially heterogenous impact that a drug may have on a biological system, allowing an investigator to capture not just changes in cell behavior, but how these changes may differ between cell subtypes. In this chapter, we review the technical details of the platform as well as its limitations, before describing our approach to planning and running a mass cytometry experiment. A series of method modules, spanning the staining process through to data cleaning, are described that are then combined to create three separate experiments. The first experiment illustrates a core process in mass cytometry: the validation and titration of a metal-conjugated antibody reporter. The second experiment explores the impact of a kinase inhibitor on cell cycle and apoptosis pathways of a human myeloma cell line. And the third experiment exploits the multiparametric capability of mass cytometry, by exploring the differential expression changes in a transcription factor upon drug treatment across the cellular compartments of a peripheral blood mononuclear cell sample.
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Affiliation(s)
- Edmund C R Watson
- Botnar Research Centre, NIHR Biomedical Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom; Oxford Translational Myeloma Centre, Botnar Research Centre, University of Oxford, United Kingdom
| | - Warren Baker
- Botnar Research Centre, NIHR Biomedical Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom; Oxford Translational Myeloma Centre, Botnar Research Centre, University of Oxford, United Kingdom
| | - David Ahern
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom
| | - Danson Loi
- Botnar Research Centre, NIHR Biomedical Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom; Oxford Translational Myeloma Centre, Botnar Research Centre, University of Oxford, United Kingdom
| | - Adam P Cribbs
- Botnar Research Centre, NIHR Biomedical Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom; Oxford Translational Myeloma Centre, Botnar Research Centre, University of Oxford, United Kingdom
| | - Udo Oppermann
- Botnar Research Centre, NIHR Biomedical Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, United Kingdom; Oxford Translational Myeloma Centre, Botnar Research Centre, University of Oxford, United Kingdom.
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26
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Lei JT, Jaehnig EJ, Smith H, Holt MV, Li X, Anurag M, Ellis MJ, Mills GB, Zhang B, Labrie M. The Breast Cancer Proteome and Precision Oncology. Cold Spring Harb Perspect Med 2023; 13:a041323. [PMID: 37137501 PMCID: PMC10547392 DOI: 10.1101/cshperspect.a041323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.
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Affiliation(s)
- Jonathan T Lei
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Hannah Smith
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Matthew V Holt
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xi Li
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Meenakshi Anurag
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Matthew J Ellis
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Marilyne Labrie
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
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27
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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28
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Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
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29
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Strotton M, Hosogane T, di Michiel M, Moch H, Varga Z, Bodenmiller B. Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging. Nat Methods 2023; 20:1310-1322. [PMID: 37653120 PMCID: PMC10482696 DOI: 10.1038/s41592-023-01977-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 07/05/2023] [Indexed: 09/02/2023]
Abstract
Rapid, highly multiplexed, nondestructive imaging that spans the molecular to the supra-cellular scale would be a powerful tool for tissue analysis. However, the physical constraints of established imaging methods limit the simultaneous improvement of these parameters. Whole-organism to atomic-level imaging is possible with tissue-penetrant, picometer-wavelength X-rays. To enable highly multiplexed X-ray imaging, we developed multielement Z-tag X-ray fluorescence (MEZ-XRF) that can operate at kHz speeds when combined with signal amplification by exchange reaction (SABER)-amplified Z-tag reagents. We demonstrated parallel imaging of 20 Z-tag or SABER Z-tag reagents at subcellular resolution in cell lines and multiple human tissues. We benchmarked MEZ-XRF against imaging mass cytometry and demonstrated the nondestructive multiscale repeat imaging capabilities of MEZ-XRF with rapid tissue overview scans, followed by slower, more sensitive imaging of low-abundance markers such as immune checkpoint proteins. The unique multiscale, nondestructive nature of MEZ-XRF, combined with SABER Z-tags for high sensitivity or enhanced speed, enables highly multiplexed bioimaging across biological scales.
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Affiliation(s)
- Merrick Strotton
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Tsuyoshi Hosogane
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | | | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Zsuzsanna Varga
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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30
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Abstract
Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.
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Affiliation(s)
- Dario Bressan
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Giorgia Battistoni
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Gregory J. Hannon
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
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31
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Fu X, Sahai E, Wilkins A. Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response. J Pathol 2023; 260:578-591. [PMID: 37551703 PMCID: PMC10952145 DOI: 10.1002/path.6153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 08/09/2023]
Abstract
In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Xiao Fu
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | - Erik Sahai
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
| | - Anna Wilkins
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Division of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
- Royal Marsden Hospitals NHS TrustLondonUK
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Wang J, Zhang T, Liao Y, Chen W, Liang S, Xu S, Fang H, Wang M, Zheng L, Gu Z, Zhang Z, Wang B, Bi Y, Feng W. 3D-imaging and quantitative assessment for size-related penetration of HfO 2 nanoparticles in breast cancer tumor by synchrotron radiation microcomputed tomography. Anal Chim Acta 2023; 1266:341352. [PMID: 37244662 DOI: 10.1016/j.aca.2023.341352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/22/2023] [Accepted: 05/09/2023] [Indexed: 05/29/2023]
Abstract
The development of quantitative analytical methods to assess the heterogeneous distribution and penetration of nanodrugs in solid tumors is of great importance for anticancer nanomedicine. Herein, Expectation-Maximization (EM) iterate algorithm and threshold segmentation methods were used to visualize and quantify the spatial distribution patterns, penetration depth and diffusion features of two-sized hafnium oxide nanoparticles (s-HfO2 NPs in 2 nm and l-HfO2 NPs in 50 nm sizes) in mouse models of breast cancer using synchrotron radiation micro-computed tomography (SR-μCT) imaging technique. The three-dimensional (3D) SR-μCT images were reconstructed based on the EM iterate algorithm thus clearly displayed the size-related penetration and distribution within the tumors after intra-tumoral injection of HfO2 NPs and X-ray irradiation treatment. The obtained 3D animations clearly show that a considerable amount of s-HfO2 and l-HfO2 NPs diffused into tumor tissues at 2 h post-injection and displayed the obvious increase in the tumor penetration and distribution area within the tumors at day 7 after combination with low-dose X-ray irradiation treatment. A thresholding segmentation for 3D SR-μCT image was developed to assess the penetration depth and quantity of HfO2 NPs along the injection sites in tumors. The developed 3D-imaging techniques revealed that the s-HfO2 NPs presented more homogeneous distribution pattern, diffused more quickly and penetrated more deeply within tumor tissues than the l-HfO2 NPs did. Whereas, the low-dose X-ray irradiation treatment greatly enhanced the wide distribution and deep penetration of both s-HfO2 and l-HfO2 NPs. This developed method may provide quantitative distribution and penetration information for the X-ray sensitive high-Z metal nanodrugs in the cancer imaging and therapy.
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Affiliation(s)
- Jiali Wang
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005, China; CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Tingfeng Zhang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - You Liao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wei Chen
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shanshan Liang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Si Xu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005, China; CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Fang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Meng Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingna Zheng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhanjun Gu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhiyong Zhang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Bing Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China.
| | - Yi Bi
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005, China.
| | - Weiyue Feng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
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He W, Ye Z, Li M, Yan Y, Lu W, Xing G. Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN. Front Plant Sci 2023; 14:1181322. [PMID: 37560031 PMCID: PMC10407792 DOI: 10.3389/fpls.2023.1181322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.
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Affiliation(s)
- Wei He
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Zhihao Ye
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Mingshuang Li
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Yulu Yan
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Wei Lu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Guangnan Xing
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
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Ghose S, Ju Y, McDonough E, Ho J, Karunamurthy A, Chadwick C, Cho S, Rose R, Corwin A, Surrette C, Martinez J, Williams E, Sood A, Al-Kofahi Y, Falo LD, Börner K, Ginty F. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Commun Biol 2023; 6:718. [PMID: 37468758 PMCID: PMC10356782 DOI: 10.1038/s42003-023-04991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/11/2023] [Indexed: 07/21/2023] Open
Abstract
Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.
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Affiliation(s)
- Soumya Ghose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yingnan Ju
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA
| | | | - Jonhan Ho
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | | | | | - Sanghee Cho
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Rachel Rose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Alex Corwin
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | | | - Jessica Martinez
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Eric Williams
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Anup Sood
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yousef Al-Kofahi
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Louis D Falo
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | - Katy Börner
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA.
| | - Fiona Ginty
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA.
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35
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Bai Y, Zhu B, Oliveria JP, Cannon BJ, Feyaerts D, Bosse M, Vijayaragavan K, Greenwald NF, Phillips D, Schürch CM, Naik SM, Ganio EA, Gaudilliere B, Rodig SJ, Miller MB, Angelo M, Bendall SC, Rovira-Clavé X, Nolan GP, Jiang S. Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nat Commun 2023; 14:4013. [PMID: 37419873 PMCID: PMC10329015 DOI: 10.1038/s41467-023-39616-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 07/09/2023] Open
Abstract
Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.
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Affiliation(s)
- Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - John-Paul Oliveria
- Department of Translational Medicine, Genentech, Inc., South San Francisco, CA, USA
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Bryan J Cannon
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | | | - Darci Phillips
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Samuel M Naik
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael B Miller
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Xavier Rovira-Clavé
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA.
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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36
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Nabhan M, Egan D, Kreileder M, Zhernovkov V, Timosenko E, Slidel T, Dovedi S, Glennon K, Brennan D, Kolch W. Deciphering the tumour immune microenvironment cell by cell. Immunooncol Technol 2023; 18:100383. [PMID: 37234284 PMCID: PMC10206805 DOI: 10.1016/j.iotech.2023.100383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.
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Affiliation(s)
- M. Nabhan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - D. Egan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - M. Kreileder
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - V. Zhernovkov
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - E. Timosenko
- ICC, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, , UK
| | - T. Slidel
- Oncology Data Science, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - S. Dovedi
- ICC, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, , UK
| | - K. Glennon
- UCD Gynaecological Oncology Group, UCD School of Medicine Mater Misericordiae University Hospital, Dublin, Ireland
| | - D. Brennan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
- UCD Gynaecological Oncology Group, UCD School of Medicine Mater Misericordiae University Hospital, Dublin, Ireland
| | - W. Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Ireland
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37
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Wang V, Liu Z, Martinek J, Zhou J, Boruchov H, Ray K, Palucka K, Chuang J. Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments. Res Sq 2023:rs.3.rs-2968528. [PMID: 37398220 PMCID: PMC10312981 DOI: 10.21203/rs.3.rs-2968528/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then generate melanoma histocytometry whole slide images and verify that CISA can detect similar interactions across data modalities. Interestingly, CISA histoctyometry analysis also reveals that T-cell:macrophage synapse formation is associated with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types.
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Affiliation(s)
| | - Zichao Liu
- 1The Jackson Laboratory for Genomic Medicine
| | | | - Jie Zhou
- The Jackson Laboratory for Genomic Medicine
| | | | - Kelly Ray
- The Jackson Laboratory for Genomic Medicine
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38
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Almagro J, Messal HA. Volume imaging to interrogate cancer cell-tumor microenvironment interactions in space and time. Front Immunol 2023; 14:1176594. [PMID: 37261345 PMCID: PMC10228654 DOI: 10.3389/fimmu.2023.1176594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/26/2023] [Indexed: 06/02/2023] Open
Abstract
Volume imaging visualizes the three-dimensional (3D) complexity of tumors to unravel the dynamic crosstalk between cancer cells and the heterogeneous landscape of the tumor microenvironment (TME). Tissue clearing and intravital microscopy (IVM) constitute rapidly progressing technologies to study the architectural context of such interactions. Tissue clearing enables high-resolution imaging of large samples, allowing for the characterization of entire tumors and even organs and organisms with tumors. With IVM, the dynamic engagement between cancer cells and the TME can be visualized in 3D over time, allowing for acquisition of 4D data. Together, tissue clearing and IVM have been critical in the examination of cancer-TME interactions and have drastically advanced our knowledge in fundamental cancer research and clinical oncology. This review provides an overview of the current technical repertoire of fluorescence volume imaging technologies to study cancer and the TME, and discusses how their recent applications have been utilized to advance our fundamental understanding of tumor architecture, stromal and immune infiltration, vascularization and innervation, and to explore avenues for immunotherapy and optimized chemotherapy delivery.
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Affiliation(s)
- Jorge Almagro
- Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, United States
| | - Hendrik A. Messal
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, Netherlands
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39
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Murdaugh RL, Anastas JN. Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma. Front Pharmacol 2023; 14:1002296. [PMID: 37205910 PMCID: PMC10191214 DOI: 10.3389/fphar.2023.1002296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.
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Affiliation(s)
- Rebecca L. Murdaugh
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
- Program in Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
| | - Jamie N. Anastas
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
- Program in Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
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40
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Visalakshan RM, Lowrey MK, Sousa MGC, Helms HR, Samiea A, Schutt CE, Moreau JM, Bertassoni LE. Opportunities and challenges to engineer 3D models of tumor-adaptive immune interactions. Front Immunol 2023; 14:1162905. [PMID: 37081897 PMCID: PMC10110941 DOI: 10.3389/fimmu.2023.1162905] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/14/2023] [Indexed: 04/09/2023] Open
Abstract
Augmenting adaptive immunity is a critical goal for developing next-generation cancer therapies. T and B cells infiltrating the tumor dramatically influence cancer progression through complex interactions with the local microenvironment. Cancer cells evade and limit these immune responses by hijacking normal immunologic pathways. Current experimental models using conventional primary cells, cell lines, or animals have limitations for studying cancer-immune interactions directly relevant to human biology and clinical translation. Therefore, engineering methods to emulate such interplay at local and systemic levels are crucial to expedite the development of better therapies and diagnostic tools. In this review, we discuss the challenges, recent advances, and future directions toward engineering the tumor-immune microenvironment (TME), including key elements of adaptive immunity. We first offer an overview of the recent research that has advanced our understanding of the role of the adaptive immune system in the tumor microenvironment. Next, we discuss recent developments in 3D in-vitro models and engineering approaches that have been used to study the interaction of cancer and stromal cells with B and T lymphocytes. We summarize recent advancement in 3D bioengineering and discuss the need for 3D tumor models that better incorporate elements of the complex interplay of adaptive immunity and the tumor microenvironment. Finally, we provide a perspective on current challenges and future directions for modeling cancer-immune interactions aimed at identifying new biological targets for diagnostics and therapeutics.
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41
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Abstract
Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.
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Affiliation(s)
- Jingwen Yang
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Yamei Chen
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Ying Jing
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA
| | - Michael R Green
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Leng Han
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA.
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42
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Marcassa G, Dascenco D, de Wit J. Proteomics-based synapse characterization: From proteins to circuits. Curr Opin Neurobiol 2023; 79:102690. [PMID: 36805717 DOI: 10.1016/j.conb.2023.102690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/15/2022] [Accepted: 01/10/2023] [Indexed: 02/19/2023]
Abstract
The highly heterogeneous nature of neuronal cell types and their connections presents a major challenge to the characterization of neural circuits at the protein level. New approaches now enable an increasingly sophisticated dissection of cell type- and cellular compartment-specific proteomes, as well as the profiling of the protein composition of specific synaptic connections. Here, we provide an overview of these approaches and discuss how they hold considerable promise toward unravelling the molecular mechanisms of neural circuit formation and function. Finally, we provide an outlook of technological developments that may bring the characterization of synaptic proteomes at the single-synapse level within reach.
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Affiliation(s)
- Gabriele Marcassa
- VIB Center for Brain & Disease Research, Herestraat 49, 3000 Leuven, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Herestraat 49, 3000 Leuven, Belgium
| | - Dan Dascenco
- VIB Center for Brain & Disease Research, Herestraat 49, 3000 Leuven, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Herestraat 49, 3000 Leuven, Belgium. https://twitter.com/ddascenco
| | - Joris de Wit
- VIB Center for Brain & Disease Research, Herestraat 49, 3000 Leuven, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Herestraat 49, 3000 Leuven, Belgium.
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43
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Gebreyesus ST, Muneer G, Huang CC, Siyal AA, Anand M, Chen YJ, Tu HL. Recent advances in microfluidics for single-cell functional proteomics. Lab Chip 2023; 23:1726-1751. [PMID: 36811978 DOI: 10.1039/d2lc01096h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.
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Affiliation(s)
- Sofani Tafesse Gebreyesus
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Gul Muneer
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | | | - Asad Ali Siyal
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
| | - Mihir Anand
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
| | - Hsiung-Lin Tu
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
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44
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Lu P, Oetjen KA, Bender DE, Ruzinova MB, Fisher DAC, Shim KG, Pachynski RK, Brennen WN, Oh ST, Link DC, Thorek DLJ. IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry. Nat Commun 2023; 14:1601. [PMID: 36959190 PMCID: PMC10036333 DOI: 10.1038/s41467-023-37123-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 03/02/2023] [Indexed: 03/25/2023] Open
Abstract
Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments.
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Affiliation(s)
- Peng Lu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, USA
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA
- Program in Quantitative Molecular Therapeutics, Washington University School of Medicine, St. Louis, USA
| | - Karolyn A Oetjen
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
| | - Diane E Bender
- The Bursky Center for Human Immunology and Immunotherapy Programs Immunomonitoring Laboratory, Washington University School of Medicine, St. Louis, USA
| | - Marianna B Ruzinova
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, USA
| | - Daniel A C Fisher
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
| | - Kevin G Shim
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
| | - Russell K Pachynski
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
| | - W Nathaniel Brennen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins University, Baltimore, USA
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Stephen T Oh
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
- The Bursky Center for Human Immunology and Immunotherapy Programs Immunomonitoring Laboratory, Washington University School of Medicine, St. Louis, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, USA
| | - Daniel C Link
- Department of Medicine, Washington University School of Medicine, St. Louis, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, USA
| | - Daniel L J Thorek
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, USA.
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA.
- Program in Quantitative Molecular Therapeutics, Washington University School of Medicine, St. Louis, USA.
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, USA.
- Oncologic Imaging Program, Siteman Cancer Center, Washington University School of Medicine, St. Louis, USA.
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45
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Yang M, Unsihuay D, Hu H, Nguele Meke F, Qu Z, Zhang ZY, Laskin J. Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution. Anal Chem 2023; 95:5214-5222. [PMID: 36917636 DOI: 10.1021/acs.analchem.2c04795] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.
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Affiliation(s)
- Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Daisy Unsihuay
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Frederick Nguele Meke
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zihan Qu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zhong-Yin Zhang
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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46
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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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47
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Zhang H, Delafield DG, Li L. Mass spectrometry imaging: the rise of spatially resolved single-cell omics. Nat Methods 2023; 20:327-330. [PMID: 36899158 DOI: 10.1038/s41592-023-01774-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA.
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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48
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:387-399. [PMID: 36797409 DOI: 10.1038/s41592-023-01773-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023]
Abstract
Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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49
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Song X, Zang Q, Li C, Zhou T, Zare RN. Immuno-Desorption Electrospray Ionization Mass Spectrometry Imaging Identifies Functional Macromolecules by Using Microdroplet-Cleavable Mass Tags. Angew Chem Int Ed Engl 2023; 62:e202216969. [PMID: 36622964 DOI: 10.1002/anie.202216969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/02/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
We present immunoassay-based desorption electrospray ionization mass spectrometry imaging (immuno-DESI-MSI) to visualize functional macromolecules such as drug targets and cascade signaling factors. A set of boronic acid mass tags (BMTs) were synthesized to label antibodies as MSI probes. The boronic ester bond is employed to cross-link the BMT with the galactosamine-modified antibody. The BMT can be released from its tethered antibody by ultrafast cleavage of the boronic ester bond caused by the acidic condition of sprayed DESI microdroplets containing water. The fluorescent moiety enables the BMT to work in both optical and MS imaging modes. The positively charged quaternary ammonium group enhances the ionization efficiency. The introduction of the boron element also makes mass tags readily identified because of its unique isotope pattern. Immuno-DESI-MSI provides an appealing strategy to spatially map macromolecules beyond what can be observed by conventional DESI-MSI, provided antibodies are available to the targeted molecules of interest.
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Affiliation(s)
- Xiaowei Song
- Department of Chemistry, Stanford University, Stanford, CA-94305, USA.,Department of Chemistry, Fudan University, Shanghai, 200016, China
| | - Qingce Zang
- Institution of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Chao Li
- Department of Medicine, Stanford University School of Medicine, Palo Alto, CA-94304, USA
| | - Tianhao Zhou
- National Clinical Research Center of Cancer, Tianjin Medical University, Tianjin, 300060, China
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA-94305, USA
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50
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Komura D, Onoyama T, Shinbo K, Odaka H, Hayakawa M, Ochi M, Herdiantoputri RR, Endo H, Katoh H, Ikeda T, Ushiku T, Ishikawa S. Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists. Patterns (N Y) 2023; 4:100688. [PMID: 36873900 DOI: 10.1016/j.patter.2023.100688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/30/2022] [Accepted: 01/12/2023] [Indexed: 02/12/2023]
Abstract
Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models. This study presents SegPath-the largest annotation dataset (>10 times larger than publicly available annotations)-for the segmentation of hematoxylin and eosin (H&E)-stained sections for eight major cell types in cancer tissue. The SegPath generating pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. We found that SegPath is comparable with, or outperforms, pathologist annotations. Moreover, annotations by pathologists are biased toward typical morphologies. However, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for machine-learning research in histopathology.
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