SP 3 - Fibromap
Computational detection of single cellular identity and cross-talk during fibrosis progression
Single cell isolation can be coupled with RNA sequencing (RNA-seq) or open chromatin sequencing (ATAC-seq) to measure the transcriptional and regulatory states of several thousand cells in either homeostasis or disease conditions. The project has as a first aim the development of statistical machine learning methods for quantification of temporal changes in cells during disease progression: normal, pre-fibrotic and fibrotic states. The problem of comparing changes in cell populations between two or more biological conditions from single cell sequencing data has been poorly explored so far. The proposed methods will answer the following questions: 1) Which cell types are present during disease progression? 2) Are there changes in the populations of particular cell types? 3) Do particular cell types change transcriptional and epigenetic programs during progression? 4) Which biological and regulatory pathways are behind these changes? 5) These are relevant in the study of microenvironment-driven/supported diseases as in fibrosis, as some cells are only present in fibrotic states, i.e. myelofibroblasts; and some cells types as megakaryocytes or monocytes are expected to change their transcriptional/epigenetic state in fibrosis due to malignant transformations. As a second aim, this subproject will use receptor-ligand expression and cellular colocalization to dissect changes in cellular crosstalk during fibrosis progression. We will use multiscale models for analysis of cellular crosstalk by integrating receptor-ligand information from scRNA-seq (molecular level) and colocalization of cells from imaging (cellular level ). This will allow us to detect pairs of cell types with changes in interactions during fibrosis. Moreover, receptor-ligand pairs will provide molecular cues of underlying signal transduction pathways. These represent potential targets of therapeutic interventions.