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      Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes

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          Abstract

          The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high-dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics.

          eLife digest

          To diagnose a disease such as cancer, doctors sometimes take small tissue samples called biopsies from the affected area. These biopsies are then thinly sliced and treated with dyes to identify healthy and cancerous cells. However, clinicians and scientists often need to look into what happens inside individual cells in the tissues so they can understand how cancers arise and progress. This helps them to identify different types of tumor cells and to tailor the best treatment for the patient.

          To do so, a number of proteins (the molecules involved in nearly all life’s processes) need to be tracked in healthy and diseased cells and tissues. This can be done thanks to a range of methods known as immunofluorescence microscopy, but following different proteins on the same slice of a sample is difficult. However, a new type of immunofluorescence known as t-CyCIF may be a solution.

          With this technique, a fluorescent compound is applied that will bind to a specific protein of interest. A microscope can pick up the light from the compound when the sample is imaged, which reveals the protein’s location in the cell or tissue. Then, a substance is used that deactivates the fluorescence signal. After this, another compound that binds to a new type of protein is used, and imaged. This cycle is repeated several times to locate different proteins. Lastly, the individual images are processed and stitched together to reveal the cells and their internal structures.

          Here, Lin, Izar et al. showed that t-CyCIF could be used to study biopsies and to obtain images that covered a large area of healthy human tissues and tumors. The technique helped to track over 60 different proteins in normal and tumor tissue samples from human patients. Several sets of experiments showed that t-CyCIF could uncover the molecular mechanisms that are disrupted during cancer, but also reveal the complexity of a single tumor. In fact, as shown with biopsies of brain cancer, cancerous cells in a tumor can be strikingly different, even when they are close to each other. Finally, the method helped to pinpoint which types of immune cells are involved in fighting a kidney tumor. Overall, such information cannot be obtained with conventional methods, yet is crucial for diagnosis and treatment.

          Most laboratories can readily use t-CyCIF since the technique is open source and requires equipment that is easily accessible. In fact, the technique should soon be used to assess how well certain drugs help the immune system combat cancer. Ultimately, better use of biopsies is key to customizing cancer care.

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          Most cited references27

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          Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.

          Previously undescribed prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit markers either of proliferation or of angiogenesis and mesenchyme. Upon recurrence, tumors frequently shift toward the mesenchymal subclass. Chromosomal locations of genes distinguishing tumor subclass parallel DNA copy number differences between subclasses. Functional relevance of tumor subtype molecular signatures is suggested by the ability of cell line signatures to predict neurosphere growth. A robust two-gene prognostic model utilizing PTEN and DLL3 expression suggests that Akt and Notch signaling are hallmarks of poor prognosis versus better prognosis gliomas, respectively.
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            Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues.

            RNA-sequencing (RNA-seq) measures the quantitative change in gene expression over the whole transcriptome, but it lacks spatial context. In contrast, in situ hybridization provides the location of gene expression, but only for a small number of genes. Here we detail a protocol for genome-wide profiling of gene expression in situ in fixed cells and tissues, in which RNA is converted into cross-linked cDNA amplicons and sequenced manually on a confocal microscope. Unlike traditional RNA-seq, our method enriches for context-specific transcripts over housekeeping and/or structural RNA, and it preserves the tissue architecture for RNA localization studies. Our protocol is written for researchers experienced in cell microscopy with minimal computing skills. Library construction and sequencing can be completed within 14 d, with image analysis requiring an additional 2 d.
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              Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes.

              Flow cytometry allows highly quantitative analysis of complex dissociated populations at the cost of neglecting their tissue localization. In contrast, conventional microscopy methods provide spatial information, but visualization and quantification of cellular subsets defined by complex phenotypic marker combinations is challenging. Here, we describe an analytical microscopy method, "histo-cytometry," for visualizing and quantifying phenotypically complex cell populations directly in tissue sections. This technology is based on multiplexed antibody staining, tiled high-resolution confocal microscopy, voxel gating, volumetric cell rendering, and quantitative analysis. We have tested this technology on various innate and adaptive immune populations in murine lymph nodes (LNs) and were able to identify complex cellular subsets and phenotypes, achieving quantitatively similar results to flow cytometry, while also gathering cellular positional information. Here, we employ histo-cytometry to describe the spatial segregation of resident and migratory dendritic cell subsets into specialized microanatomical domains, suggesting an unexpected LN demarcation into discrete functional compartments. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Senior Editor
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                11 July 2018
                2018
                : 7
                : e31657
                Affiliations
                [1 ]deptLaboratory of Systems Pharmacology Harvard Medical School BostonUnited States
                [2 ]deptLudwig Center for Cancer Research at Harvard Harvard Medical School BostonUnited States
                [3 ]deptDepartment of Medical Oncology Dana-Farber Cancer Institute BostonUnited States
                [4 ]Broad Institute of MIT and Harvard CambridgeUnited States
                [5 ]deptHarvard Graduate Program in Biophysics Harvard University CambridgeUnited States
                [6 ]deptDepartment of Pathology Brigham and Women’s Hospital, Harvard Medical School BostonUnited States
                [7 ]deptDepartment of Oncologic Pathology Dana-Farber Cancer Institute BostonUnited States
                Massachusetts Institute of Technology United States
                University of Pennsylvania United States
                University of Pennsylvania United States
                Helmholtz Zentrum München Germany
                Biological Research Centre of the Hungarian Academy of Sciences Hungary
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-4702-7705
                http://orcid.org/0000-0003-2379-6702
                http://orcid.org/0000-0002-3364-1838
                Article
                31657
                10.7554/eLife.31657
                6075866
                29993362
                e62dffa9-c2a5-4905-94d6-a1cc2a37a0c9
                © 2018, Lin et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 September 2017
                : 29 June 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P50GM107618
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008126, Dana-Farber/Harvard Cancer Center;
                Award ID: GI SPORE Developmental Research Project Award
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U54HL127365
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R41-CA224503
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008126, Dana-Farber/Harvard Cancer Center;
                Award ID: Claudia Adams Barr Program
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: K08CA222663
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Cancer Biology
                Computational and Systems Biology
                Custom metadata
                t-CyCIF can be used to collect spatially-encoded, multiparametric data from fixed and embedded research or clinical specimens making it possible to probe the organization of tumors and tissues at a single-cell level.

                Life sciences
                immunopathology,multiplexed imaging,single-cell method,human
                Life sciences
                immunopathology, multiplexed imaging, single-cell method, human

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