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      Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging

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          Summary

          A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/ lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/ lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

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          Highlights

          • Autoimmunity analyzed by multiplexed DNA-tagged antibody staining (CODEX)

          • CODEX data reveal pairwise interactions and niches changing with disease

          • First tier of neighbors significantly impacts marker expression in the index cells

          • Changes in splenic morphology correlate with shifts in cell frequencies

          Abstract

          A DNA barcoding-based imaging technique uses multiplexed tissue antigen staining to enable the characterization of cell types and dynamics in a model of autoimmune disease.

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          Most cited references11

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          Transcriptional Programs Define Molecular Characteristics of Innate Lymphoid Cell Classes and Subsets

          The diversity of innate lymphoid cells (ILCs) is rapidly expanding. Three ILC classes have emerged, ILC1, ILC2, and ILC3, with ILC1 and ILC3 including several subsets. The classification of some subsets is unclear and it remains controversial whether NK cells and ILC1 are distinct cell types. To address these issues, we analyzed ILCs and NK cells gene expression within mouse small intestine, spleen, and liver, as part of the Immunological Genome Project. Results identify unique gene-expression patterns for some ILCs and overlapping patterns between ILC1 and NK cells, whereas few ILC subsets remain indistinguishable. A transcriptional program shared by small intestine ILCs and a core ILC signature is identified. Transcripts that suggest novel ILC functions and developmental paths are revealed and discussed.
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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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              Automated Mapping of Phenotype Space with Single-Cell Data

              Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                09 August 2018
                09 August 2018
                : 174
                : 4
                : 968-981.e15
                Affiliations
                [1 ]Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA
                Author notes
                []Corresponding author gnolan@ 123456stanford.edu
                [2]

                These authors contributed equally

                [3]

                Present address: Smart Tube, Inc., 922 Industrial Avenue, Palo Alto, CA 94303, USA

                [4]

                Lead Contact

                Article
                S0092-8674(18)30904-8
                10.1016/j.cell.2018.07.010
                6086938
                30078711
                794e6030-3277-4065-a42a-725184685c5c
                © 2018 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 September 2017
                : 5 February 2018
                : 3 July 2018
                Categories
                Article

                Cell biology
                multiplexed imaging,multidimensional imaging,niche,microenvironment,tissue architecture,autoimmunity,immune tissue,codex

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