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Project

Bioinformatics for single-cell sequence and in situ RNA analytics to study cell niches and cell-cell interaction

single-cell RNA-seq procedures alone are insufficient to deliver comprehensive insight in organ cell atlases in health and disease. Indeed, as most of our organ functions are executed by the concerted action of individual cells in a spatially organized context, it is paramount to research single cells in their native spatial context. This is not only important to understand normal organ development and function, but also to investigate how (subpopulations of) cells are perturbed in diseased conditions as cancer or neurological disorders. This has spurred recent key advances in spatial transcriptomics methodologies. RNA molecules no longer need to be extracted from cells, but can be directly visualized in their original tissue context by using in situ hybridization (ISH) technologies or in situ sequencing (ISS) technologies. Alternatively, using in situ capturing (ISC) technologies. In this doctoral dissertation, we propose the development of novel data analysis methodologies and experimental designs required for the interpretation of these cutting-edge spatial omic technologies by leveraging state-of-the-art deep learning methods.

 

Date:10 May 2021 →  Today
Keywords:spatial omics
Disciplines:Single-cell data analysis, Computational biomodelling and machine learning
Project type:PhD project