In our group we apply a wide range of multi-omics techniques, machine learning approaches and advanced statistical modelling to analyze multimodal sequencing data. We employ droplet-based single-cell sequencing to gain insights into cellular heterogeneity in tissues and identify novel cellular states in disease. We further use this technique to define new marker genes associated with a cell type and/or disease phenotype and define subpopulations within known cell types based on the gene expression profile. We also utilize bulk RNA and ATAC sequencing as well as ChIP-seq. To gain spatial information on a single-cell level we use the Visium Spatial platform from 10X Genomics and combine immunohistochemistry with transcriptomic data.

Using these techniques, we aim to uncover underlying disease mechanisms with primary patient material ranging from PBMCs to spinal cord and brain tissue and in α-syn and TDP-43 mouse models. 

The utilization of multi-omics sequencing analysis, particularly at a single-cell resolution, offers a more precise understanding of the disease-related changes at the molecular level. The goal is to identify key molecular signatures, pathways, and cell types affected by e.g. the overexpression of α-syn at different ages or pharmacological treatments. Furthermore, we hope to elucidate the mechanisms behind PD and ALS to increase the number of biomarkers and therapeutics for patients in our future projects using multi-omics sequencing approaches.

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