Integrating spatial transcriptomics and known regulatory elements to elucidate mechanisms of Parkinson’s disease

Principal Investigator: Xianjun Dong

Authors: Jie Yuan, Nathan Haywood, Zhixiang Liao, Idil Tuncali, Xian Adiconis, Sean Simmons, Yuliya Kuras, Zechuan Lin, Jacob Parker, Su-Chun Zhang, Mel B. Feany, Clemens Scherzer, Joshua Z. Levin, and Xianjun Dong
Lay Abstract

We are attempting to answer the where and when of Parkinson’s Disease (PD) pathology. To do this, we prepare and analyze brain sections of controls and PD patients using spatial transcriptomics to quantify how gene expression differs in cortical layers of the brain. We also collect single cell RNA-seq data to determine whether PD-related genes expression is specific to particular cell types or to particular inferred pseudo-times. Lastly, we combine these results with known gene regulatory features. Ultimately, we wish to integrate all these lines of evidence to identify plausible genetic pathways of PD risk with high confidence.

Scientific Abstract

Parkinson’s Disease (PD) is highly complex, with multiple known clinical presentations and proposed molecular mechanisms. Genome-wide association studies (GWAS) have identified several genomic loci associated with PD but reveal little about their relevance to disease etiology. Recent studies have leveraged evidence from multi-omics, including single-cell expression, chromatin accessibility, and chromatin conformation, to create plausible functional hypotheses for PD-associated alleles. However, little is known about the spatial and temporal dimensions of PD pathology. For example, where does the disease start, and how does it spread?

In the Parkinson 5D project, we will generate spatial transcriptomics data for 100 brains representing controls, prodromal, and clinically manifest PD as well as single-cell gene expression, and unify these with publicly available omics data. For genes implicated in PD, we assess whether expression is localized to particular cortical layers, cell types, and pseudo-times, and whether these genes exhibit distinct spatial expression patterns depending on PD status. We also integrate additional omics features, such as enhancer RNAs identified from the BRAINcode project and open chromatin regions detected by scATACseq, to assess whether risk alleles intersect these regions. Lastly, we intend to develop interactive visualization tools to summarize these integrated spatial and omics data.

Clinical Implications
We hope to provide further insights into the genetic risk factors and gene regulatory pathways associated with Parkinson’s Disease. These results may lead to better predictive models of PD and therapeutic targets.

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