Bone Marrow Stem Cell Niche. Hematological Malignancies. Cancer Immunology, Autoimmune Disorders. Gene Regulation and Systems Epigenetics. Computational Biology and Single Cell Genomics.

Molecular and Computational Hematology-Immunology

Systems epigenetics to study disease mechanisms in hematology and immunology

A crucial part of precision medicine is to quantitatively understand the interplay of genetics, epigenetics, and environmental factors, including the cellular microenvironment. Our vision is to understand how cells integrate genetic and epigenetic information with extrinsic signals from their microenvironment, and how these molecular layers jointly contribute to cellular phenotypes that ultimately define complex disease phenotypes. Our main systems of interest are:

(i) The bone marrow niche, where we aim to understand how hematopoietic stem and progenitor cells interact with their immune and stromal microenvironment, how these interactions are disrupted in hematological malignancies, and how they contribute to disease progression or therapy resistance

(ii) Immune cells and their tissue-interactions in immune dysfunction, infection, and cancer. Here we are interested in understanding the cell-type specific regulatory networks and tissue-interactions that contribute to disease mechanisms.

We are developing system biology approaches that combine deep molecular profiling and advanced computational modelling, and that can be applied to clinical samples. We combine cutting-edge single cell multiomics profiling (RNA, chromatin, spatial), including our own low-cost technologies, and predictive computational models that generate testable mechanistic hypotheses. These hypotheses are tested in appropriate models using genetic perturbations, followed by cellular and high-resolution molecular imaging, including spatial transcriptomics or high-end microscopy.

Future directions

We will integrate single cell and spatial multiomic data (e.g. from our method SUM-seq) with genetics, gene regulatory networks, and spatially resolved methods to study basic biology and translational questions focusing on the bone marrow stem cell niche and misregulation of the immune system.

We will harness cell-type specific gene regulatory networks to study mechanisms of primary immunodeficiencies that are caused by mutations in transcription factor genes (e.g. NFKB, STATs), e.g. within the IMMERGE consortium.

We will expand our computational data integration methods to include additional molecular layers, such as protein levels, spatial transcriptomics, multiplexed imaging, and self-supervised algorithms for electron microscopy/tomography analysis.

Computational frameworks for integrating multi-omics data to gain biological insights

1) Algorithm to estimate differential transcription factor activity (diffTF) based on genome-scale chromatin data (Berest, Cell Rep. 2019). This has proven very sensitive enough to identify gene regulatory changes at early stages of disease (Rasmussen, Gen. Res. 2019).

2) Genome-scale enhancer-mediated regulatory network models, linking transcription factors to enhancers to genes (Kamal, Mol. Syst. Biol. 2023). These networks can interrogate epigenetic alterations at enhancers, which are increasingly recognized to contribute to disease mechanisms (Claringbould, Trends in Mol. Med. 2021).

3) Artificial Intelligence (AI)-driven datamining of cryo-electron tomography data. The AI-based framework can recognise and label organelles and molecular complexes, to produce detailed cellular images (de Teresa, Nat. Met. 2023). This enables automated analysis of tomography data and is crucial for our plans to study the molecular mechanisms of cell-cell interactions. Systems epigenetics to study disease mechanisms in hematology and immunology

Translational research in hematology

1) Novel leukemic stem cell regulator in AML: We identified the transcription factor HLF as a key regulator of leukemic stem cells in AML triple mutated for DNMT3A, NPM1, and FLT3-ITD. Strikingly, the stemness-phenotype of HLF-high AML was only detectable with cells freshly isolated from xenograft mice, but not with in vitro expanded cells, suggesting that interactions with the bone marrow niche are essential. (Garg, Blood 2019)

2) Contribution of specific bone marrow T cells to therapy response in AML. Allogeneic stem cell transplantation (alloSCT) requires donor T cells to recognize and eliminate residual leukemia stem cells. Lack of this capability may lead to relapse. Here we studied how T cell composition post alloSCT is associated with therapy outcome. Single cell RNA-seq showed increased cytotoxic CD8+ effector T cells in remission (n=3) vs relapse (n=3) samples. We further identified a novel surface marker (patent pending) in remission CD8 T cells, and demonstrate by functional experiments that it acts as surrogate for antigen encounter post alloSCT (Mathioudaki et al Blood). Future studies will assess its potential as early marker of therapy outcome.