Cancer is a real-time evolutionary phenomenon. As the disease progresses, malignant cells and the tumor microenvironment (TME) co-evolve into an activated state characterized by high cellular heterogeneity, profound plasticity and dynamic paracrine communication that regulate cancer growth. Through an innovative set of integrated basic, pre-clinical and translational studies, we aim to investigate the heterogeneity of microenvironmental cell types by ascribing functional roles to subgroups and elucidating cell functional niches. Using single cell RNAseq we have characterized the biological heterogeneity of cancer associated fibroblasts (CAFs) and are on our way to propose a universal CAF nomenclature system that can be used regardless of the species, tissue or cancer progression stage. We have also started to characterize functionally diverse pericyte and macrophage subgroups in a high-grade glioma mouse model. Single cell RNA seq data of tumours from pericyte-poor genetically engineered mouse model is being compared with those from mice with a normal pericyte coverage to investigate how pericyte absence affects the intra- and inter- tumoral expression heterogeneity and cellular composition of other cell types. Nonetheless, deeper bioinformatic analysis of this dataset is needed to fully exploit its potential and shed light on the effects of pericyte depletion on tumour composition. In the near future, we aim to fully exploit our dataset and continue investigating the phenotypic heterogeneity of different cell types and their interactions. We shall also validate our findings in diverse publicly available human datasets. As a final step, we want to better understand the spatial distribution and cellular communication of niches, adapted tumour cells and glioma influencing stromal cells in the glioma topography. To that end, we have performed a spatial transcriptomics experiment with pericyte poor and control tumour tissue applying the Visium platform (10X Genomics). Data has been recently sequenced and ready to be analysed. Raw data consists of 130 GiB which exceeds our current assigned storage for our small compute project SNIC 2022/22-329. This is why we are asking for storage resources while we work on these data. Combining the resolution power spatial transcriptomics with immune fluorescence imaging, we anticipate gaining valuable knowledge on the complex interaction of pericytes, immune cells and tumour cells in glioma.