Research

Merging Stem Cells with Immunology by Cell Fate Reprogramming. Learn more about our research lines:

Modeling Hematopoietic Stem Cell Generation with Cell Reprogramming

Whether from bone marrow, mobilized peripheral blood, or cord blood, Hematopoietic Stem Cell (HSC) transplantation is the best example of regenerative cell therapy. HSCs have remarkable therapeutic applications in a broad range of diseases that affect blood-forming and the immune system. Nevertheless, a shortage of universal, genetically matched material remains. We employ an innovative approach to investigate the establishment of HSC identity: we screen for factors that recreate this unique cell state from fibroblasts. This approach leads to the identification of master transcription factors, cell surface phenotypes, and gene expression signatures of induced cell fate. We then ask whether a similar logic applies to the specification of HSCs during mouse and human embryogenesis and use this information to uncover general principles of blood cell specification. We have demonstrated the direct reprogramming of mouse and human fibroblasts into clonogenic hematopoietic progenitors using the combination of transcription factors GATA2, GFI1B, and FOS. We further established that cellular reprogramming approaches can inform the developmental specification of hematopoietic stem cells and showed that cooperative transcription factor binding mediates human hemogenic induction.

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Understanding Immune Cell Diversity with Direct Cell Reprogramming

Cellular reprogramming rewires the transcriptional and epigenetic network of one cell state to that of a different cell type. Somatic cells can be reprogrammed into induced pluripotent stem cells by the expression of transcription factor (TF) combinations. Somatic cell fates can also be instructed by direct reprogramming using TFs specifying target-cell identity, as demonstrated for myocytes, neurons, cardiomyocytes, among others. In hematopoiesis, progenitors and mature cells have also been generated employing this strategy. Nevertheless, reprogramming approaches have been mainly restricted to regenerative medicine and there is momentum to bring these concepts to immunology. Hence, we have shown for the first time that dendritic cell identity and antigen presentation can be programmed by a small combination of transcription factors – PU.1, IRF8, and BATF3 – providing evidence that immune cells and triggered immune responses can be modulated through cell reprogramming. We now extend our reprogramming studies to the specification of immune cell-types with therapeutic interest and broader types of reprogramming. The minimal networks identified by direct induction of immune cell-types will be instrumental to elucidate the establishment of different cell identities and the exclusion of alternative cell fates throughout immune cell development.

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Harnessing Dendritic Cell Reprogramming for Cancer Immunotherapy

An important hallmark of cancer is the ability to evade the immune system. Genetic mutations in tumor cells result in the accumulation of tumor antigens. However, increased cell heterogeneity, downregulation of antigen presentation, or inhibition of immune cell infiltration allow cancer cells to escape immune surveillance. For the first time, direct cell reprogramming offers exciting opportunities to overcome these challenges. The Pereira Lab is harnessing dendritic cell minimal TF networks to reprogram cancer cells into antigen-presenting dendritic cells, providing a new strategy to restore anti-tumor immunity. With this approach, we aim to combine DCs’ antigen processing and presenting abilities with the endogenous generation of tumor antigens. We aim to establish a new modality for cancer immunotherapy based on dendritic cell reprogramming in vivo, paving the way for first-in-human trials of a transdifferentiation approach in the form of a cancer gene therapy. Overall, in this research line, we aim to merge cell reprogramming and cancer immunotherapy, paving the way for entirely new approaches for cancer therapy.

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