CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues
Author(s) -
Caleb Stoltzfus,
Jakub Filipek,
Benjamin H. Gern,
Brandy Olin,
Joseph M. Leal,
Yajun Wu,
Miranda R. LyonsCohen,
Jessica Huang,
Clarissa L. Paz-Stoltzfus,
Courtney R. Plumlee,
Thomas Pöschinger,
Kevin B. Urdahl,
Mario Perro,
Michael Y. Gerner
Publication year - 2020
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2020.107523
Subject(s) - biology , compartmentalization (fire protection) , computational biology , myeloid , cluster analysis , microbiology and biotechnology , cellular architecture , cell , neuroscience , computer science , immunology , genetics , artificial intelligence , reference architecture , software architecture , software , programming language , biochemistry , enzyme
SUMMARY Recently developed approaches for highly multiplexed imaging have revealed complex patterns of cellular positioning and cell-cell interactions with important roles in both cellular- and tissue-level physiology. However, tools to quantitatively study cellular patterning and tissue architecture are currently lacking. Here, we develop a spatial analysis toolbox, the histo-cytometric multidimensional analysis pipeline (CytoMAP), which incorporates data clustering, positional correlation, dimensionality reduction, and 2D/3D region reconstruction to identify localized cellular networks and reveal features of tissue organization. We apply CytoMAP to study the microanatomy of innate immune subsets in murine lymph nodes (LNs) and reveal mutually exclusive segregation of migratory dendritic cells (DCs), regionalized compartmentalization of SIRPa dermal DCs, and preferential association of resident DCs with select LN vasculature. The findings provide insights into the organization of myeloid cells in LNs and demonstrate that CytoMAP is a comprehensive analytics toolbox for revealing features of tissue organization in imaging datasets.
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