Macrophages are essential cells of the innate immune system, playing crucial roles in coordinating inflammatory responses and maintaining homeostasis. In cases of chronic inflammation, damaged tissues release abnormal signals that attract macrophages precursors to the local environment and drive macrophages toward a proinflammatory phenotype. This creates a feedback loop that sustains inflammation. The consequences to society are huge: chronic inflammatory diseases are among the leading causes of death and disability worldwide. Recent single-cell multi-omics studies have identified dysregulated macrophages as central players in chronic inflammation, but the molecular mechanisms sustaining these responses remain poorly understood. MACROCODE aims to unravel the molecular mechanisms governing the altered function of macrophages during chronic inflammation. The specific aims of this project are: 1. Use CRISPR-Cas9 technology with single-cell read-outs to perturb genes in macrophages that show dysregulated expression between healthy and diseased states in chronic inflammatory conditions . Genes will be selected by creating an integrated single-cell transcriptomics atlas of macrophages in both health and disease. 2. Develop a computational framework using bioinformatics and AI tools to link these perturbed genes to transcriptomic programs, cellular functions and in vivo disease phenotypes. 3. Validate key genes by modulating their expression using dCas9-VPR or KRAB systems, and evaluate the effects on macrophage differentiation, identity and function. Altogether, MACROCODE will decode the genetic regulation of macrophage dysfunction in chronic inflammation. This multidisciplinary project will equip me with advanced skills in genomics and bioinformatics, enhance my leadership and transferable skills, and prepare me for a future as an independent leader in the field of gene therapy for immune diseases.

The advent of single cell technologies has enabled the characterization of cell types and developmental processes. Observations from different cells allow one to identify underlying patterns at higher resolution than convoluted bulk data, and integration of different omics data can yield a more differentiated picture of mechanistic connections. In this proposal, Gene REgulatory Cell States (GReCS) from multi-modal data, I plan to develop a computational method that combines these aspects to generate insights into gene regulation at the level of single cells. Measurements of chromatin accessibility in single cells are becoming increasingly common. The method I propose to develop combines sc/sn-ATAC- and scRNA-sequencing data to characterize gene regulation. My approach will integrate and use transcriptomics and open chromatin data to filter comprehensive prior information about candidate interactions and predict cell-specific gene regulatory network versions using machine learning, while sparse single cell measurements are imputed using local cell similarities. In this way, rare measurements across cell types and a larger condition space for network inference can be exploited, using the natural potential of chromatin accessibility data as a filter to map interactions into a cell-specific context. A distinguishing feature of the proposed method is the characterization of local gene regulatory states, which allows the observation of continuous changes throughout a cell-cell similarity embedding. This will be useful to examine changes during cell differentiation and along gradients in spatial reconstructions, for example of embryonic development. The developed methods will be made available to the community as a computational toolkit to improve the characterization of gene regulation by combining different types of data.