AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.

Background: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).

Methods: We applied artificial intelligence (AI)-based imaging analyses to Cell Paint-stained cells exposed to drug libraries, identifying CON as a marker for ICD.

Mononuclear cell composition and activation in blood and mucosal tissue of eosinophilic esophagitis.

Introduction: Eosinophilic esophagitis (EoE) is a chronic, inflammatory, antigen-driven disease of the esophagus. Tissue EoE pathology has previously been extensively characterized by novel transcriptomics and proteomic platforms, however the majority of surface marker determination and screening has been performed in blood due to mucosal tissue size limitations. While eosinophils, CD4 T cells, mast cells and natural killer (NK) T cells were previously investigated in the context of EoE, an accurate picture of the composition of peripheral blood mononuclear cells (PBMC) and their activation is missing.

Tumor microenvironment-derived monoacylglycerol lipase provokes tumor-specific immune responses and lipid profiles.

We recently described that monoacylglycerol lipase (MGL) is present in the tumor microenvironment (TME), increasing tumor growth. In this study we compare the implications of MGL deficiency in the TME in different tumor types. We show that subcutaneous injection of KP (Kras/p53, mouse lung adenocarcinoma) or B16-F10 cells (mouse melanoma) induced tumor growth in MGL wild type (WT) and knockout (KO) mice.