Big Data approaches such as genome wide association studies, sequencing, transcriptomics, proteomics, metabolomics, metagenomics and preclinical models are needed to model the complexity of the immune system and understand human disease.
In the era of genomic, transcriptomic and proteomic approaches, new therapeutic interventions have greatly broadened the options for patients with cancer and immune-related diseases. Since its first supplication, only a few decades ago, immunotherapy has become a clinical reality offering nearly too many options to too few patients.
The challenge to clinicians and scientists alike is to understand the mechanism(s) in order to find the optimal treatment regimens. By elucidating the immune mechanisms of the most promising treatments we want to optimize personalized medicine.
Our lab is interested in the discovery of immune signatures and underlying mechanisms during inflammatory processes by integrating high dimensional single-cell technologies with machine-learning computational tools. These new signatures will be able:
a) to define novel correlates of clinical outcomes for a variety of diseases including cancer and autoimmunity,
b) to identify new interventional pathways, and
c) to provide tools for scientific and clinical analysis.
We combine unique expertise from multiple fields, such as single-cell mass spectrometry (a.k.a., mass cytometry or Cytometry by Time-Of-Flight, CyTOF), high parameter sorting and single cell sequencing techniques in conjunction with artificial intelligence-supported analysis tools to analyze clinical samples and perform studies in preclinical mouse models.