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Introduction: Hepatocellular Carcinoma (HCC) is considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cellular composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can influence tumor initiation, progress, and response to therapy. Mucosal-associated invariant T (MAIT) cells can represent the most abundant T cell subtype in the human liver and have been found to be impaired in both number and function in liver cancer. These innate-like T cells are assigned crucial roles in regulating immunity and inflammation in the context of infection, albeit their role in HCC remains elusive. Methods: High-dimensional flow cytometry was used to analyze MAIT cell phenotypic changes in murine and human liver cancer. Highly multiplexed immunofluorescence microscopy was used to quantify immune cell infiltration in primary human HCC samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human fresh frozen HCC samples. We applied co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 15 HCC patient samples using whole slide scanning. Initial image analysis was performed using HALO quantitative image analysis software. Finally, we established an image analysis pipeline to quantify the MAIT cell interaction network at the HCC invasive front. Results: Profiling of human and murine HCC using flow cytometry and highly multiplexed CODEX imaging revealed substantial dysregulation/aberrant activation of MAITs in liver cancer. In situ phenotyping of 4,500,000 single cells (including 1,500,000 CD45+ immune cells) allowed for the quantification of 20 distinct immune cell phenotype clusters, differential analysis of activation markers and spatial features of each individual cell. CODEX imaging revealed detailed composition of the MAIT cell niche in human liver cancer tissue allowing for further distinct spatial analysis including infiltration and nearest-neighbor analysis. Importantly, flow cytometry data of paired samples correlated well with image-based immune phenotyping. Beyond that, whole slide imaging revealed spatial relationships and interactions within the MAIT cell hub localized in distinct tissue regions. Conclusion: Here, we demonstrate that spatially resolved, single-cell analysis of human liver cancer tissue allows for in-depth characterization of interacting immune cellular programs underlying MAIT cell dysfunction in HCC. Citation Format: Benjamin Ruf, Noemi Kedei, Matthias Bruhns, Sepideh Babaei, Bernd Heinrich, Varun Subramanyam, Chi Ma, Simon Wabitsch, Benjamin Green, Kylynda C. Bauer, Yuta Myojin, Jonathan Qi, Amran Nur, Justin McCallen, Layla Greten, William G. Telford, Merrill K. Stovroff, Kesha Oza, Jiman Kang, Alexander Kroemer, Manfred Claassen, Firouzeh Korangy, Tim F. Greten. Spatially resolved immune cell atlas of human liver cancer identifies the cellular interaction network underlying mucosal-associated invariant T (MAIT) cell dysfunction in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2106.

Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being climate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is that current methods are not well adapted to climate data challenges. We here present an overview of a NeurIPS 2019 competition on causal discovery for climate time series. The Causality 4 Climate (C4C) competition was hosted on the benchmark platform {www.causeme.net}. C4C offers an extensive number of climate model-based time series datasets with known causal ground truth that incorporate the main challenges of causal discovery in climate research. We give an overview over the benchmark platform, the challenges modeled, how datasets were generated, and implementation details. The goal of C4C is to spur more focused methodological research on causal discovery for understanding our climate system.