Integrative single-cell transcriptomics to identify novel mediator of human blood progenitor proliferation

The main aim of this project is to improve the availability of haematopoietic stem cells (HSCs) for therapeutic use. HSCs are used in therapy of a variety of conditions including cancer, bone marrow disorders, immune system disorders, haemoglobinopathies and other. Currently, they are harvested either from the umbilical cord or the bone marrow.

While the umbilical cord cells seem to be more "primitive", and therefore better for treatment, it is often hard to harvest sufficient amount to treat an adult patient. On the other hand, harvesting bone marrow cells is extremely invasive. Both approaches have benefits and drawbacks, but the major issue with both is matching the donor cells to the recipient - i.e., limited availability of matched donors.

An alternative source would have to be efficient, more accessible, and more scalable. One such source are pluripotent stem cell (PSC)-derived HSC-like cells. Induced PSCs (iPSCs) can be differentiated into any cell type in the body (in theory), which means that they would make a great alternative source of HSCs.

For this to be successful, the process would have to be perfected. Processes such as maturation and expansion to fully functional HSCs need to be further explored as these might be the obstacles that are preventing PSC-derived HSCs from properly ingrafting and functioning. This can be done by exploring the HSC niche in the body, more precisely - the foetal liver.

Understanding the microenvironment would allow us to improve in-vitro conditions in which the cells are grown. However, there are major ethical issues when it comes to using human embryos.

Therefore, this project will use already existing single-cell transcriptomic datasets to analyse cell populations and their gene expression profiles. This will allow us to better understand the heterogeneity of the foetal liver niche and identify the key regulators of the HSC development. To achieve this, we will leverage single-cell transcriptomic datasets, which allow for the high-resolution analysis of individual cell populations and their gene expression profiles.

Single-cell transcriptomics provides the opportunity to study the heterogeneity of HSCs and their niche cells, identifying key players involved in regulating HSC development. We will use single-cell interaction prediction models to these datasets to map out cell-cell signalling networks that are crucial for HSC maturation. We aim to mimic the conditions of the foetal liver, which is known to be a key site for HSC development during embryogenesis.

Previous studies have identified several transcription factors, such as RUNX1, GATA2, and TAL1, that are involved in HSC development in the foetal liver. In this project, the role of these and other previously detected transcription factors will be tested to determine whether foetal liver conditions can be replicated in-vitro. This approach will help us understand if these conditions are sufficient for generating fully functional HSCs from PSCs.

The project will employ an interdisciplinary approach that combines techniques from bioinformatics, machine learning, and experimental biology. The use of bioinformatics and machine learning will be crucial for analysing the large datasets generated by single-cell transcriptomics and for predicting potential interactions between cells. For example, machine learning algorithms can be used to identify patterns in gene expression that are indicative of key signalling events in HSC development.

These computational approaches will be paired with laboratory techniques, such as CRISPR-Cas9 gene editing and flow cytometry, to validate our findings and manipulate the expression of specific genes involved in HSC development and maturation.

The potential impact of this project is significant. If successful, it could pave the way for a more scalable and cost-effective source of HSCs, which could be used in a wide range of therapeutic applications. This could greatly expand access to HSC transp