PhD - Leuven | More than two weeks ago
Spatial omics using ultra-high-resolution electrically synthesized DNA arrays
Many of the current cancer therapies often fail as the genetic architecture of cancers remains largely unique within each patient, emphasizing the need for personalized diagnostics and treatments. Pathological assessment of cancerous tissue sections is similarly complex, making the interpretation of such data and its related diagnostic decision to some extent subjective. While conventional procedures analyze cells in bulk independent of their micro-environment, recent advances in spatial transcriptomics has made it possible to combine morphological data with full transcription profiles resulting in an objective method to understand the behavior of cancer cells in their distinctive micro-environment. Further simplifying this technology and pushing resolution to subcellular level will undoubtedly generate accurate, specific and quantitative pathological data, enabling detailed patient stratification, prognosis and therapy.
In this PhD, we want to leverage both CMOS scaling and the recent boost in next-generation sequencing and nucleic acid synthesis technologies. Ultra-high-density electrode arrays with feature sizes well below 2 µm can be readily fabricated using standard CMOS processes. This allows to develop novel methods to synthesize DNA sequences on each miniaturized electrode on demand. We use electrochemically triggered DNA synthesis, with the emphasis of maintaining lateral resolution and accurate synthesis quality control to ensure suitable sequence lengths. The aim of the PhD is to generate information on the tissue-specific expression of genes at a resolution that was never attainable before. This will support many high-end biological applications and ultimately allows digitizing pathology to revolutionize the use of tissue sections for in-vitro diagnostics.
Required background: Biomedical engineering, chemical engineering, nanoscience/nanotechnology, (electro)chemistry, physics
Type of work: literature study: 10%, modelling and data analysis: 40%, setup construction and device measurements: 50%
Supervisor: Liesbet Lagae
Daily advisor: Kathrin Hoelz
The reference code for this position is 2022-093. Mention this reference code on your application form.