Iterative programming of blood cells (ML2Cell)

Our body is made up of a plethora of cells with different characteristics, shapes, and functions. How these diverse cell types develop (or differentiate) from a single founder cell (a zygote) is the focus of ongoing developmental and molecular biology research. Regenerative medicine aims to actively direct the differentiation of stem cells to replace damaged tissues, for example, to generate skin for burn victims or platelets for patients under chemotherapy. Moreover, it may be desirable to change the identity of already differentiated cells, for example, to reprogram cancerous cells toward less malignant cell states. However, finding the right cocktails and sequences of molecules to achieve a specific differentiation outcome is a challenge. There are millions of possible combinations and often the success of the differentiation protocol can only be evaluated fully at the end of the process. To make it possible to refine protocols in real-time during ongoing differentiation experiments, we devised a combined experimental/computational approach (called ML2Cell) that borrows algorithmic principles from machine learning (ML) and integrates them directly in the design of biological experiments. The one key challenge to solve in this prototype project will be to implement an assessment regimen that can inform decision making on-the-fly between different steps of the protocol (that is, at most 24 hours). To this end, we will pair rapid genomics assays with hyper-parallelized bioinformatics analysis. We will benchmark ML2Cell by generating two blood cell types from undifferentiated blood progenitors (hematopoietic stem cells): red blood cells and B cells. These are two highly relevant proof-of-principle examples and there is urgent demand for methods to replace many other types of tissues (apart from blood, especially for skin, cartilage, and bone, but also for internal organs, e.g., liver). If successful, future applications of our approach may also include personalizing the engineering of immunotherapies. On a more abstract level, ML2Cell serves as a proof-of-concept for implementing methods from computer science in biological experiments. In a way, this turns around a long -running trend in which computer science algorithms take inspiration from biology or physics (easily visible in the names of popular algorithms, e.g., neural networks, simulated annealing, genetic algorithms, ant colony optimization). We envision that other concepts and approaches from computer science may find use in experimental study design, for instance, for search and sorting.

No additional funding sources recorded.