While almost all animals are able to at least partially replace some lost parts, regeneration abilities vary considerably across species. Here we study gene expression patterns in distantly related species to investigate conserved regeneration strategies. To this end, we collect from the literature transcriptomic data obtained during the regeneration of three species (Hydra magnipapillata, Schmidtea mediterranea, and Apostichopus japonicus), and compare them with gene expression during regeneration in vertebrates and mammals. This allows us to identify a common set of differentially expressed genes and relevant shared pathways that are conserved across species during the early stage of the regeneration process. We also find a set of differentially expressed genes that in mammals are associated to the presence of macrophages and to the epithelial–mesenchymal transition. This suggests that features of the sophisticated wound healing strategy of mammals are already observable in earlier emerging metazoans.

Artificial intelligence (AI)–powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non–small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. Significance: Leveraging artificial intelligence–powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

There is still no successful strategy to treat Huntington’s disease, an inherited autosomal disorder associated with the aggregation of mutated forms of the huntingtin protein containing polyglutamine tracts with more than 36 repeats. Recent experimental evidence is challenging the conventional view of the disease by revealing transcellular transfer of mutated huntingtin proteins which are able to seed oligomers involving wild type forms of the protein. Here we decipher the molecular mechanism of this unconventional heterogeneous oligomerization by performing discrete molecular dynamics simulations. We identify the most probable oligomer conformations and the molecular regions that can be targeted to destabilize them. Our computational findings are complemented experimentally by fluorescence-lifetime imaging microscopy/fluorescence resonance energy transfer (FLIM-FRET) of cells co-transfected with huntingtin proteins containing short and large polyglutamine tracts. Our work clarifies the structural features responsible for heterogeneous huntingtin aggregation with possible implications to contrast the prion-like spreading of Huntington’s disease.

The ability of bats to coexist with viruses without being harmed is an interesting issue that is still under investigation. Here we use a mathematical model to show that the pattern of body temperature variations observed in bats between day and night is responsible for their ability to keep viruses in check. From the dynamical systems point of view, our model displays an intriguing quasi-periodic behaviour that might be relevant in making the system robust by avoiding viral escape due to perturbations in the body temperature cycle.