Macrophages are key regulators of inflammation and repair, but their heterogeneity and multiple roles in the liver are not fully understood. We aimed herein to map the intrahepatic macrophage populations and their function(s) during acute liver injury. We used flow cytometry, gene expression analysis, multiplex-immunofluorescence, 3D-reconstruction, and spatial image analysis to characterize the intrahepatic immune landscape in mice post-CCl4-induced acute liver injury during three distinct phases: necroinflammation, and early and late repair. We observed hepatocellular necrosis and a reduction in liver resident lymphocytes during necroinflammation accompanied by the infiltration of circulating myeloid cells and upregulation of inflammatory cytokines. These parameters returned to baseline levels during the repair phase while pro-repair chemokines were upregulated. We identified resident CLEC4F+ Kupffer cells (KCs) and infiltrating IBA1+CLEC4F- monocyte-derived macrophages (MoMFs) as the main hepatic macrophage populations during this response to injury. While occupying most of the necrotic area, KCs and MoMFs exhibited distinctive kinetics, distribution and morphology at the site of injury. The necroinflammation phase was characterized by low levels of KCs and a remarkable invasion of MoMFs suggesting their potential role in phagoctosing necrotic hepatocytes, while opposite kinetics/distribution were observed during repair. During the early repair phase, yolksac - derived KCs were restored, whereas MoMFs diminished gradually then dissipated during late repair. MoMFs interacted with hepatic stellate cells during the necroinflammatory and early repair phases, potentially modulating their activation state and influencing their fibrogenic and pro-repair functions that are critical for wound healing. Altogether, our study reveals novel and distinct spatial and temporal distribution of KCs and MoMFs and provides insights into their complementary roles during acute liver injury.

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The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cellbased high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using userdefined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section. Video Link The video component of this article can be found at https://www.jove.com/video/60740/ 1. The spatial organization of the different cellular and structural components of the tumor tissue and the dynamic exchange between the cancer and neighboring non-cancer cells ultimately modulate tumor progression and response to therapy 2,3,4. It has been shown that the immune response in cancer is spatiotemporally regulated 5,6. Different immune cell populations infiltrating the neoplastic lesion and the adjacent tissue exhibit distinctive spatial distribution patterns and varied activation and differentiation states associated with different functions (e.g., pro-versus antitumor). These different immune populations and their parameters coevolve overtime with the tumor and the stromal compartments. The emergence of technologies allowing single cell multiomics profiling has exponentially increased our understanding of the numerous cellular and molecular networks regulating carcinogenesis and tumor progression. However, most single cell-based high-throughput analytical tools require tissue disruption and single cell isolation, resulting in loss of information about the spatial organization of cells and cell-cell interactions 7. Because the location and arrangement of specific immune cells in the TME have diagnostic and prognostic value, technologies allowing spatial resolution are an essential complement of single cell-based immune profiling techniques. Traditionally, imaging techniques like immunohistochemistry (IHC) and multiplex immunofluorescence (mIF) have been restricted to a small number of biomarkers...

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cellbased high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using userdefined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

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