Highlights d CytoMAP is a user-friendly, comprehensive platform for spatial analysis of tissues d Allows quantification of cellular positioning and global tissue structure d Enables exploration of cellular and tissue microenvironment heterogeneity d CytoMAP reveals fundamental features of myeloid cell organization in lymph nodes

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Recently developed approaches for highly-multiplexed 2-dimensional (2D) and 3D imaging have revealed complex patterns of cellular positioning and cell-cell interactions with important roles in both cellular and tissue level physiology. However, robust and accessible tools to quantitatively study cellular patterning and tissue architecture are currently lacking. Here, we developed a spatial analysis toolbox, Histo-Cytometric Multidimensional Analysis Pipeline (CytoMAP), which incorporates neural network based data clustering, positional correlation, dimensionality reduction, and 2D/3D region reconstruction to identify localized cellular networks and reveal fundamental 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 SIRPadermal DCs, as well as preferential association of resident DCs with select LN vasculature. These studies describe DC organization in LNs, and provide a comprehensive analytics toolbox for in-depth exploration of quantitative imaging datasets.

In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.