In recent years, 3D cell cultures (organoids and tumor spheroids) has generated great interest in biological engineering because they seem to allow a better representation of biological complexity than monolayer cell cultures. However, few works have yet focused on the detailed analysis of the levels of molecular and cellular similarity between different 3D culture models and with the corresponding reference tissues. We have developed a bioinformatics framework to facilitate the advanced analysis of transcriptomes from multiple experimental conditions of 3D cell cultures. We have setup a strategy to generate controlled cell mixtures from single cell RNA-seq data of reference tissues and to use them to assess the biological complexity of 3D cell cultures. We applied our approach to analyze the transcriptomes of blood vessel organoids and we were able to identify the experimental conditions allowing to better recapitulate the biology of the reference tissue.