The emergence of single-cell and spatial technologies revolutionizes how we study molecular states in tissue samples at high resolution. These methods provide insights into cellular heterogeneity and spatial organization. However, current commercial methods have limitations like limited gene analysis and the potential to compromise tissue integrity. Overcoming these challenges will unlock the full power of single-cell and spatial technologies for comprehensive molecular profiling of tissues.
In this research project, our primary objective is to integrate single-cell RNA-seq with matched or unmatched spatial data from Xenium/MERFISH technologies. By doing so, we aim to achieve a profound spatial gene expression profiling of cancer tissues with high resolution. Furthermore, we will generate atlas-level single-cell data to identify the most informative genes for the spatial profiling process.
Our lab develops and applies new bioinformatics methods to drive discoveries in cancer and translate these into improved outcome for patients.
Our approach is to use mathematics, statistics and computing to make sense of omics data, especially related to cancer evolution spanning initiation, progression and outcome.
The main focus of our computational biology research is rare cancers, melanoma and prostate cancer, but our methodological research is also relevant to cancer in general and other diseases. We have a strong track record in developing methods for analysing genome sequencing data, especially associated with chromosomal rearrangements. A major focus of our research is the development and application of new computational and machine learning tools to analyse large, complex multiomics datasets.