Single-Cell RNA Sequencing in Parkinson's Disease

Abstract

Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson's disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.

Keywords: Parkinson’s disease; bioinformatics; single-cell RNA sequencing.

Figure 1

scRNA-seq of mouse neural progenitors from embryos and early postnatal DaNs. (a,b) Use of ventral midbrain at six embryonic (E) days from E11.5 to E18.5 in mouse embryos. Reprinted from [38], Copyright 2016 Elsevier. (a) Dot plot depicting time distribution of cell types (left), heatmap showing pairwise correlation (middle), and bars showing average number of mRNA molecules per cell (right). (b) t-distributed stochastic neighbor embedding (t-SNE) of cells colored by cell type. (c–e) Use of mesencephalic DaNs at four embryonic (E) days from E10.5 to E13.5 in Lmx1a^EGFP mice. Reprinted from [37], Copyright 2017, with permission from Elsevier. (c) Cells plotted along the first principal component (PC1), colored by embryonic days (top) and the frequency distribution (bottom); yellow: E10.5, orange: E11.5, red: 12.5, and black: E13.5. Relative expression of (d) pan-neuronal markers and (e) DaN markers along PC1 (left) and co-immunostainings of the stated markers (right). (f,g) Use of ventral midbrain at three embryonic (E) days from E13.5 to E18.5 and three postnatal (P) days from P1 to P90 in Pitx3^eGPF/wt mice. Reprinted from [34]. Copyright 2019, Katarína Tiklová et al. (f) Principal component (PC) plot showing 1106 cells colored by developmental stage. (g) Network plot depicting distribution of Pitx3-expressing midbrain neurons colored by developmental stage, pseudotime, and molecularly defined cell type. Dat: Slc6a3, T: Th, N: Nxph4, G: Gad2, A: Aldh1a1, and V: Vip.

Figure 2

scRNA-seq of fetal and human induced pluripotent stem cell (hiPSC) or embryonic stem cell (ESC)-derived neurons in studies of PD. (a–c) Use of WT hiPSC-derived DaNs. Reprinted from [31], Copyright 2020 Elsevier. (a) Immunofluoresence staining, (b) Uniform manifold approxiamation and projection (UMAP) and (c) expression heatmap of WT hiPSC-derived DaNs. TH: tyrosine hydroxylase, TUJ1: beta-3-tubulin. (d–g) Use of human embryonic stem cell (hESC) and fetal ventral midbrain (VM)-derived progenitors. Reprinted from [35], Copyright 2020, Katarína Tiklová et al. (d) Immunohistochemistry of TH in the graft core (six months post-transplantation) (e,f) t-SNE and (g) expression plot using the stated genes (before grafting). “Y”, “O”, “B”, and “G” indicate cell clusters shown in (e).

Figure 3

The snRNA-seq of human postmortem brain tissues in studies of PD. (a,b) Use of SN and cortex tissues derived from five healthy individuals. Reprinted from [30], Copyright 2020, Devika Agarwal et al. (a) UMAP (colored by cell type) and (b) correlation heatmap depicting hierarchical clustering with Pearson correlation as distance metric. (c–e) Use of SN tissues derived from seven healthy individuals. Reprinted from [32], Copyright 2019, with permission from Elsevier. UMAP plots colored by (c) donor and (d) cell type. (e) Violin plots depicting expression of PD-related genes across the identified cell types.

Figure 4

Emerging computational and analytical tools that can be used in sc/snRNA-seq studies of PD. UMAP and RNA velocity trajectories of cells from (a) E10–P45 and (b) E11–E13 developing diencephalon in mice. Reprinted from [92], Copyright 2020, Dong Won Kim et al. (c) Use of LD Score Regression (LDSC) and Multi-Marker Analysis of Genomic Annotation (MAGMA) to assess the associations between previously reported genetic risk variants of different complex “traits”, or brain-related disorders, and SN cell types. Reprinted from [30], Copyright 2020, Devika Agarwal et al. Color of heatmap indicates degrees of statistical significance. *: p value (<0.05), **: Bonferroni corrected q value, Cog.: cognitive phenotypes, Immune: autoimmune diseases, Metabolic/Cardio/Anthropometric: metabolic, cardiovascular and anthropometric traits.