CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

Stoltzfus, Caleb; Filipek, Jakub; Gern, Benjamin H.; Olin, Brandy E.; Leal, Joseph M.; Lyons-Cohen, Miranda R; Huang, Jessica; Paz-Stoltzfus, Clarissa L.; Plumlee, Courtney R.; Pöschinger, Thomas; Urdahl, Kevin B.; Perro, Mario; Gerner, Michael Y.

doi:10.2139/ssrn.3493253

Cited by 19 publications

(41 citation statements)

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“…Focusing on the iTME, other investigators applying multiplexed imaging have explored cellular functional states, spatial and functional interactions between multiple cell types, as well as how these interactions are modulated by tissue context ( Ali et al., 2020 ; Arnol et al., 2019 ; Jackson et al., 2020 ; Keren et al., 2018 ; Schapiro et al., 2017 ; Stoltzfus et al., 2019 ; Zhu et al., 2018 ). We reasoned that viewing the tissue only with respect to interacting cell types (CTs) limits the ability to capture the tissue-level processes that emerge from such spatial relationships.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Schürch

Bhate

Barlow

et al. 2020

Cell

622

638

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Summary Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We re-engineered co-detection by indexing (CODEX) for paraffin-embedded tissue microarrays, enabling simultaneous profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers. We identified nine conserved, distinct cellular neighborhoods (CNs)—a collection of components characteristic of the CRC iTME. Enrichment of PD-1 + CD4 + T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating how complex biological processes, such as antitumoral immunity, occur through concerted actions of cells and spatial domains.

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Section: Introductionmentioning

confidence: 99%

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Schürch

Bhate

Barlow

et al. 2020

Cell

622

638

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“…It differs from existing spatial data analysis and/or visualization pipelines 8,35,36,38,43,[44][45][46] , and is complementary to alternative strategies that computationally infer spatial information from single-cell RNAseq analysis 47 . To our knowledge, Giotto is the first demonstrated general-purpose toolbox for spatial transcriptomic/proteomic data analysis, while the other methods are designed for specific data types 44,[48][49][50] or tasks, such as the identification of cell types 43 , marker genes 35,36 or domain patterns 38 . Although both Seurat and Scanpy have now adopted spatial transcriptomic analysis functionalities, an important distinction is that our design of the giotto object is specifically targeted for spatial transcriptomic data analysis, whereas the data structure in the other packages were originally designed for single-cell RNAseq analysis.…”

Section: Discussionmentioning

confidence: 99%

Giotto, a toolbox for integrative analysis and visualization of spatial expression data

Dries

Zhu

Dong

et al. 2019

Preprint

173

323

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The rapid development of novel spatial transcriptomics technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational algorithms and pipelines. Here we present Giotto, a comprehensive, flexible, robust, and open-source pipeline for spatial transcriptomic data analysis and visualization. The data analysis module implements a wide range of algorithms ranging from basic tasks such as data pre-processing to innovative approaches for cell-cell interaction characterization. The data visualization module provides a user-friendly workspace that allows users to interactively visualize, explore and compare multiple layers of information. These two modules can be used iteratively for refined analysis and hypothesis development. We illustrate the functionalities of Giotto by using the recently published seqFISH+ dataset for mouse brain. Our analysis highlights the utility of Giotto for characterizing tissue spatial organization as well as for the interactive exploration of multi-layer information in spatial transcriptomic and imaging data. We find that single-cell resolution spatial information is essential for the investigation of ligandreceptor mediated cell-cell interactions. Giotto is generally applicable and can be easily integrated with external software packages for multi-omic data integration. Giotto facilitates the comprehensive analysis of single-cell spatial transcriptomic dataGiotto Analyzer is written in the popular language R. The core data structure is a simple and flexible S4 object ( Fig. 2A). Raw and processed count matrices are represented as a base matrix in R, while other annotations and metadata is encoded by an igraph network or a data.table. The former is a powerful library to work with networks, and the latter is a simple but intuitive table format with excellent performance for large-scale operations. In total, the Giotto uncovers different layers of spatial expression variabilityA key component of Giotto Analyzer is the implementation of a wide range of computational methods for spatial gene expression pattern identification. On a basic level, Giotto Analyzer can reduce the single-cell resolution data to a spatial grid through averaging (Supplementary Fig. 2A, B). Principal component analysis (PCA) is applied to the gridaverage data and significant principal components, along with their associated genes, are identified and reported. Using the aforementioned seqFISH+ dataset as an example, we found that the first principal component (PC) separates the outer layer extremities from the other layers. This is likely due to differences in cell-type compositions as most layers correlate with Slc17a7 expression, a marker for glutamatergic neurons, while the extremities show higher abundance of astrocytes and oligodendrocytes (Fig. 3A, top, Fig. 2D). In contrast, the second PC separates the outer and inner layers, which have similar cell-type composit...

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“…In skin-dLNs, CD301b + DCs account for a major subset of the dermal-derived migratory cDC2s (Kumamoto et al, 2009;Kumamoto et al, 2013). (Gerner et al, 2012;Gerner et al, 2015;Kissenpfennig et al, 2005;Kumamoto et al, 2009;Kumamoto et al, 2013;Stoltzfus et al, 2020). Thus, CD301b + DCs in the skin-dLNs likely represent a subset of migratory DCs that interact with T cells on their entry route through the HEVs into the paracortex (Bajenoff et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…This latter signal likely represents the direct antigen presentation to the cognate CD4T cell clones. We and others showed previously that, in a model of hapten-induced contact hypersensitivity, CD301b + DCs are enriched at the T-B cell border area in the dLN and particularly at areas surrounding HEVs, whereas CD207 + DCs (including epidermal Langerhans cells and dermal CD103 + DCs) are segregated from the CD301b + DCs and localized more preferentially to the deeper T cell zone (Kissenpfennig et al, 2005;Kumamoto et al, 2009;Stoltzfus et al, 2020), suggesting that CD301b + DCs are the first DC population encountered by CD4T cells as they home to the LN through the HEVs. In the dLNs of WT mice immunized with OVA and papain, CD301b + DCs were also enriched at the T-B boundary areas and located in closer proximity the HEVs than CD207 + DCs ( Figure Notably, when the mice were immunized with OVA and CFA, both CD301b + DCs and CD103 + DCs took up the antigen at similar levels ( Figure S5C).…”

Section: Cd301b + Dcs Directly Present Soluble Foreign Antigens To CDmentioning

confidence: 93%

Optimal CD4T Cell Priming in Lymph Nodes Requires Repertoire Scanning by CD301b⁺Migratory cDC2 Cells

Tatsumi

Codrington

Kumamoto

2020

Preprint

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SummaryActivation of CD4T cells by conventional dendritic cells (cDC) is pivotal in adaptive immunity. However, while the activation mechanism of antigen-specific CD4T cells has been extensively studied, the cellular mechanism that leads to the selection of cognate CD4T cell clones out of the polyclonal pool is incompletely understood. Here, we show that, in the reactive lymph nodes, newly homed naive polyclonal CD4T cells are temporarily retained before leaving the lymph node. This stop-and-go traffic of CD4T cells provides an adequate time window for efficient scanning and timely priming of antigen-specific clones. Mechanistically, upon immunization, CD301b+ DCs, a major subset of migratory cDC2 cells, quickly migrate to the draining lymph node and settle in the areas near the high endothelial venules, where they retain incoming polyclonal CD4T cells through MHCII-dependent but antigen-independent mechanisms while concurrently providing cognate stimuli to prime antigen-specific CD4T cells. These results indicate that CD301b+ DCs function as an immunological “display window” for CD4T cells to efficiently scan their antigen specificity.Graphical AbstractHighlightsNewly homed polyclonal CD4T cells are temporarily retained in the reactive lymph nodes.Depletion of CD301b+ DCs results in shorter dwell time of CD4T cells in the draining lymph node and delayed priming of antigen-specific clones.The transient retention of polyclonal CD4T cells in the draining lymph node requires MHCII expression on CD301b+ DCs but not cognate antigen.CD301b+ DCs are required for robust expansion of rare antigen-specific CD4T cell clones and their skewing toward Th2 cells.

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CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

Cited by 19 publications

References 0 publications

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Giotto, a toolbox for integrative analysis and visualization of spatial expression data

Optimal CD4T Cell Priming in Lymph Nodes Requires Repertoire Scanning by CD301b⁺Migratory cDC2 Cells

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CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

Cited by 19 publications

References 0 publications

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

Giotto, a toolbox for integrative analysis and visualization of spatial expression data

Optimal CD4T Cell Priming in Lymph Nodes Requires Repertoire Scanning by CD301b+Migratory cDC2 Cells

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Optimal CD4T Cell Priming in Lymph Nodes Requires Repertoire Scanning by CD301b⁺Migratory cDC2 Cells