Forscherdatenbank
Christian la Fougére
Department of Radiology Universityhospital Tübingen
Otfried-Müller Str. 14
72076 Tübingen
Programm
Radiation Oncology and Imaging (ROI)
Übersicht
Multiparametric Molecular Imaging
Molecular imaging (MI) is paving the way for precision and personalized medicine. Molecular imaging techniques using radiolabeled pharmaceuticals and positron-emission-tomography (PET) are increasingly used for the assessment of pathobiochemical and pathophysiological changes in oncology and neurology.
We aim to test and to implement new molecular imaging probes for different oncological and neurological diseases, in order to obtain a better characterization of the underlying molecular changes in vivo.
New generation hybrid PET-MR scanner enable the simultaneous acquisition of molecular (PET) as well as morphological and functional (chemical shifts, flow, diffusion, blood oxygenation and perfusion) changes (MR) in vivo, that might give new insights in the oncological processes.
We aim to use those multi-parametric imaging techniques in order to provide an insight in physiological (adaptive) as well as pathophysiological changes, which might lead to an individualized treatment stratification for our patients in the future.
DKTK Junior Group Leader for Cancer Systems Biology
Single-cell approaches have not only revealed a wide variety of cell states, characterized by cells exhibiting striking differences in their transcriptional profile, but have also illuminated the mechanisms underlying state transitions in health and disease. Cellular plasticity and adaptive state changes have recently emerged as a basis for therapeutic resistance in cancer, and a better understanding of how cell state transitions are regulated is critical to develop therapeutic approaches that can overcome therapy resistance.
Our research focuses on understanding the mechanisms driving non-genetic cellular heterogeneity and therapy resistance in malignancy. Using novel single-cell sequencing approaches, we seek to develop new experimental and computational strategies to define altered cell states in both, cancer and immune cells. Our aim is to leverage a data driven strategy combined with single cell genomics and systems biology to address the challenges posed by heterogeneity in cancer, and to develop new strategies to overcome it, with the aim of translating laboratory-based findings into the clinic.