1
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Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
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Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
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2
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Vierdag WMAM, Saka SK. A perspective on FAIR quality control in multiplexed imaging data processing. FRONTIERS IN BIOINFORMATICS 2024; 4:1336257. [PMID: 38405548 PMCID: PMC10885342 DOI: 10.3389/fbinf.2024.1336257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
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Affiliation(s)
| | - Sinem K. Saka
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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3
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Gaglia G, Burger ML, Ritch CC, Rammos D, Dai Y, Crossland GE, Tavana SZ, Warchol S, Jaeger AM, Naranjo S, Coy S, Nirmal AJ, Krueger R, Lin JR, Pfister H, Sorger PK, Jacks T, Santagata S. Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma. Cancer Cell 2023; 41:871-886.e10. [PMID: 37059105 PMCID: PMC10193529 DOI: 10.1016/j.ccell.2023.03.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/31/2023] [Accepted: 03/22/2023] [Indexed: 04/16/2023]
Abstract
Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.
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Affiliation(s)
- Giorgio Gaglia
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Megan L Burger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR 97212, USA; School of Medicine, Division of Hematology and Oncology, Oregon Health & Science University, Portland, OR 97212, USA
| | - Cecily C Ritch
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Danae Rammos
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yang Dai
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Grace E Crossland
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sara Z Tavana
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Simon Warchol
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Alex M Jaeger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Robert Krueger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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4
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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5
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Lin JR, Wang S, Coy S, Chen YA, Yapp C, Tyler M, Nariya MK, Heiser CN, Lau KS, Santagata S, Sorger PK. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 2023; 186:363-381.e19. [PMID: 36669472 PMCID: PMC10019067 DOI: 10.1016/j.cell.2022.12.028] [Citation(s) in RCA: 70] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/26/2022] [Accepted: 12/16/2022] [Indexed: 01/20/2023]
Abstract
Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.
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Affiliation(s)
- Jia-Ren Lin
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Shannon Coy
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yu-An Chen
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Madison Tyler
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Maulik K Nariya
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Cody N Heiser
- Program in Chemical & Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Sandro Santagata
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K Sorger
- Ludwig Center at Harvard and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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6
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Warchol S, Krueger R, Nirmal AJ, Gaglia G, Jessup J, Ritch CC, Hoffer J, Muhlich J, Burger ML, Jacks T, Santagata S, Sorger PK, Pfister H. Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:106-116. [PMID: 36170403 PMCID: PMC10043053 DOI: 10.1109/tvcg.2022.3209378] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.
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7
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Park J, Kim J, Lewy T, Rice CM, Elemento O, Rendeiro AF, Mason CE. Spatial omics technologies at multimodal and single cell/subcellular level. Genome Biol 2022; 23:256. [PMID: 36514162 PMCID: PMC9746133 DOI: 10.1186/s13059-022-02824-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.
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Affiliation(s)
- Jiwoon Park
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Junbum Kim
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
| | - Tyler Lewy
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Olivier Elemento
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - André F Rendeiro
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Christopher E Mason
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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8
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Prabhakaran S, Gatenbee C, Robertson-Tessi M, West J, Beg AA, Gray J, Antonia S, Gatenby RA, Anderson AR. Mistic: An open-source multiplexed image t-SNE viewer. PATTERNS (NEW YORK, N.Y.) 2022; 3:100523. [PMID: 35845830 PMCID: PMC9278502 DOI: 10.1016/j.patter.2022.100523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/10/2022] [Accepted: 05/09/2022] [Indexed: 01/02/2023]
Abstract
Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.
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Affiliation(s)
- Sandhya Prabhakaran
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Chandler Gatenbee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Amer A. Beg
- Departments of Immunology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jhanelle Gray
- Departments of Immunology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Scott Antonia
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Robert A. Gatenby
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Alexander R.A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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9
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Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nat Biomed Eng 2022; 6:515-526. [PMID: 34750536 PMCID: PMC9079188 DOI: 10.1038/s41551-021-00789-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/02/2021] [Indexed: 01/20/2023]
Abstract
Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.
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10
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Gaglia G, Kabraji S, Rammos D, Dai Y, Verma A, Wang S, Mills CE, Chung M, Bergholz JS, Coy S, Lin JR, Jeselsohn R, Metzger O, Winer EP, Dillon DA, Zhao JJ, Sorger PK, Santagata S. Temporal and spatial topography of cell proliferation in cancer. Nat Cell Biol 2022; 24:316-326. [PMID: 35292783 PMCID: PMC8959396 DOI: 10.1038/s41556-022-00860-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 01/31/2022] [Indexed: 02/06/2023]
Abstract
Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
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Affiliation(s)
- Giorgio Gaglia
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sheheryar Kabraji
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Danae Rammos
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Dai
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana Verma
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Caitlin E Mills
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Mirra Chung
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Johann S Bergholz
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Rinath Jeselsohn
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Otto Metzger
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Eric P Winer
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean J Zhao
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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11
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Jessup J, Krueger R, Warchol S, Hoffer J, Muhlich J, Ritch CC, Gaglia G, Coy S, Chen YA, Lin JR, Santagata S, Sorger PK, Pfister H. Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:259-269. [PMID: 34606456 PMCID: PMC8805697 DOI: 10.1109/tvcg.2021.3114786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 109 or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.
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