On Jun. 12, 2025, the Wellcome Sanger Institute, Parse Biosciences and the Computational Health Center at Helmholtz Munich announce a collaboration to build the foundation of a single cell atlas, focused on understanding and elucidating cancer plasticity in response to therapies. The collaboration will catalyse an ambitious future phase to develop a cancer plasticity atlas encompassing hundreds of millions of cells.

Utilising novel organoid perturbation and Artificial Intelligence (AI) platforms, the aim is to create a comprehensive dataset to fuel foundational drug discovery models and cancer research.

Dr Mathew Garnett, Group Leader at the Sanger Institute, and Professor Fabian Theis, Director of the Computational Health Center at Helmholtz Munich and Associate Faculty at the Sanger Institute, will be the principal investigators in the collaboration.

Mathew’s research team has generated novel 3D organoid cultures that serve as highly scalable and functional cancer models with the ability to capture hallmarks of patient tumours. The team will use vast numbers of these tumour organoids – mini tumours in a dish – as a model to better understand cancer mechanisms of plasticity and adaptability in response to treatments.

Fabian’s research team has been widely recognised for pioneering computational algorithms to solve complex biological challenges at the intersection of Artificial Intelligence and single-cell genomics, in particular in this context for in silico modelling of drug effects on cellular systems. The initiative will be run through Parse Biosciences’ GigaLab, a state-of-the-art facility purpose-built for the generation of massive scale single-cell RNA sequencing datasets at unprecedented speed.

The Sanger, Helmholtz Munich and Parse teams have developed automated methods to streamline laboratory procedures in addition to the computational methods required to analyse and discover insights within datasets of this size.

The ultimate aim of the collaboration is to build a single-cell reference map that will enable virtual cell modelling and potentially help predict the effect of drugs in cancer patients – where resistance might develop, from which compounds and where to target future treatment efforts.