Cell2Sample: charting cellular communities via spatial omics data

Spatial transcriptomics combines gene expression data with spatial information, allowing us to see not only which genes are active in cells but also where those cells are located within a tissue. This powerful approach opens up new opportunities to understand how different cell types interact and function together in their native environment.

The Cell2Sample project aims to advance these insights by developing computational methods that explicitly account for spatial context (spatially aware). The project has two main objectives: first, to infer interactions between nearby cells; and second, to link these interactions to intracellular gene regulatory networks. To achieve this, you will develop and adapt machine learning and statistical approaches to model cell-cell communication based on cell-type-specific spatial gene expression, and reconstruct spatially informed gene regulatory networks.

The project offers a balanced focus on method development, implementation, and real-world applications in oncology and neuroscience. You will work with high-quality spatial omics datasets from our collaborators and help establish a novel computational framework for understanding tissue architecture at single-cell resolution.

Would you like to know more about the different phases within the PhD trajectory? You can read more about this on this page.

Your main role is to develop and implement spatially-aware methods to infer interactions between cells and intra-cellular gene regulatory networks. You will critically apply and test these methods to onco- and neurological data sets in collaboration with biomedical researchers. Results will be published in both methodological and biomedical journals.

Finally, you are expected to play an active role in the ADORE research community, in particular the Biocomputational Focus group. You’ll work in close collaboration with another PhD student in the Cell2Sample project, who will work on multi-omics cell type deconvolution methods.

• You hold an MSc in Bioinformatics, AI, Machine Learning or a closely related discipline;
• You have a strong quantitative background, enabling the development of new computational methods;
• You have a strong interest in biomedical applications and life sciences in general;
• You have strong programming skills, preferably in Python and R;
• You are able to explain complex ideas to non-specialists;
• You have good communication skills in English, both written and spoken;
• You are self-disciplined and have a collaborative mindset.