N2-0236

We will build an autonomous system that intelligently observes biological and clinical samples at a single-cell level and infers how cellular mechanisms work. Recently, the partners have been developing state of the art machine learning methods, to infer how complex systems work and to ease the building of decision support systems, at the Slovenian site. Microscopy-based deep learning techniques to most precisely isolate and collect single-cells, and characterize their molecular properties by various methods, including single-cell sequencing and mass spectrometry, has been established at the Hungarian site. To most efficiently connect information between multi-omics modalities, we will develop together a framework that consists of single-cell imaging, annotation and image analysis using deep learning, cell selection and an inference system. The proposed workflow will be capable of analyzing biological problems and will provide a better description of ccRCC (clear cell Renal Cell Carcinoma) at a single-cell level, thus giving a fully automated, unbiased, and human-interpretable description of observed samples.