Single-cell sequencing analysis of striatal gene regulatory networks in Parkinson's disease

Objetivo

Parkinson’s disease (PD) is the fastest growing Neurodegenerative disorder, currently affecting an estimate of 7 to 10 million people worldwide. The disease is characterized by the loss of dopaminergic neurons in the substantia nigra and the accumulation of protein aggregates in the remaining ones. The loss of these cells leads to a dopamine deficit in the striatum which causes the motor symptoms characteristic of PD and the general malfunction of the neuronal circuitry in the basal ganglia. The existing treatment for PD is dopamine replacement therapy with levodopa. This drug alleviates the motor symptoms but has a number of side effects, loses efficacy with time and does not address the cognitive decline caused by PD. In order to gain a better understanding of the disease and open new paths towards an effective treatment, the PARKSEQ project will use the latest advanced in single-cell RNA sequencing and in situ hybridization to determine which cell-type specific gene regulatory networks (GRNs) are affected by PD in the striatum. To this end, I will use single-cell RNA sequencing (scRNA-seq) to study post-mortem human striatum samples from PD and control subjects, under the supervision and guidance of Ana Muñoz, a leading expert in PD and scRNA-seq studies. I will draw from my experience in Bioinformatics and in single cell data analysis to determine the cell types and subtypes existing in the human striatum. On each subtype, I will establish which genes are differentially expressed and, considering their co-expression, I will formulate putative altered GRNs. Then, during a secondment period, I will join Mats Nilsson laboratory to validate the scRNA-seq findings using in situ hybridization. Dr. Nilsson has advanced this technique making it possible to use verify the presence of over 100 genes simultaneously on intact tissue slices. This will enable me to verify the PD-induced alterations in the GRNs suggested by the scRNA-seq data.