Idiopathic Parkinson's disease is characterized by a progressive loss of dopaminergic neurons, but the exact disease aetiology remains largely unknown. To date, Parkinson's disease research has ...mainly focused on nigral dopaminergic neurons, although recent studies suggest disease-related changes also in non-neuronal cells and in midbrain regions beyond the substantia nigra. While there is some evidence for glial involvement in Parkinson's disease, the molecular mechanisms remain poorly understood. The aim of this study was to characterize the contribution of all cell types of the midbrain to Parkinson's disease pathology by single-nuclei RNA sequencing and to assess the cell type-specific risk for Parkinson's disease using the latest genome-wide association study. We profiled >41 000 single-nuclei transcriptomes of post-mortem midbrain from six idiopathic Parkinson's disease patients and five age-/sex-matched controls. To validate our findings in a spatial context, we utilized immunolabelling of the same tissues. Moreover, we analysed Parkinson's disease-associated risk enrichment in genes with cell type-specific expression patterns. We discovered a neuronal cell cluster characterized by CADPS2 overexpression and low TH levels, which was exclusively present in idiopathic Parkinson's disease midbrains. Validation analyses in laser-microdissected neurons suggest that this cluster represents dysfunctional dopaminergic neurons. With regard to glial cells, we observed an increase in nigral microglia in Parkinson's disease patients. Moreover, nigral idiopathic Parkinson's disease microglia were more amoeboid, indicating an activated state. We also discovered a reduction in idiopathic Parkinson's disease oligodendrocyte numbers with the remaining cells being characterized by a stress-induced upregulation of S100B. Parkinson's disease risk variants were associated with glia- and neuron-specific gene expression patterns in idiopathic Parkinson's disease cases. Furthermore, astrocytes and microglia presented idiopathic Parkinson's disease-specific cell proliferation and dysregulation of genes related to unfolded protein response and cytokine signalling. While reactive patient astrocytes showed CD44 overexpression, idiopathic Parkinson's disease microglia revealed a pro-inflammatory trajectory characterized by elevated levels of IL1B, GPNMB and HSP90AA1. Taken together, we generated the first single-nuclei RNA sequencing dataset from the idiopathic Parkinson's disease midbrain, which highlights a disease-specific neuronal cell cluster as well as 'pan-glial' activation as a central mechanism in the pathology of the movement disorder. This finding warrants further research into inflammatory signalling and immunomodulatory treatments in Parkinson's disease.
Epstein-Barr virus (EBV) is an oncogenic virus found in about 95% of endemic Burkitt lymphoma (BL) cases. In latently infected cells, EBV DNA is mostly maintained in episomal form, but it can also be ...integrated into the host genome, or both forms can coexist in the infected cells.
In this study, we mapped the chromosomal integration sites of EBV (EBV-IS) into the genome of 21 EBV+ BL cell lines (BL-CL) using metaphase fluorescence in situ hybridization (FISH). The data were used to investigate the EBV-IS distribution pattern in BL-CL, its relation to the genome instability, and to assess its association to common fragile sites and episomes.
We detected a total of 459 EBV-IS integrated into multiple genome localizations with a preference for gene-poor chromosomes. We did not observe any preferential affinity of EBV to integrate into common and rare fragile sites or enrichment of EBV-IS at the chromosomal breakpoints of the BL-CL analyzed here, as other DNA viruses do.
We identified a non-random integration pattern into 13 cytobands, of which eight overlap with the EBV-IS in EBV-transformed lymphoblastoid cell lines and with a preference for gene- and CpGs-poor G-positive cytobands. Moreover, it has been demonstrated that the episomal form of EBV interacts in a non-random manner with gene-poor and AT-rich regions in EBV+ cell lines, which may explain the observed affinity for G-positive cytobands in the EBV integration process. Our results provide new insights into the patterns of EBV integration in BL-CL at the chromosomal level, revealing an unexpected connection between the episomal and integrated forms of EBV.
Thyroid hormone and its receptor TRα1 play an important role in brain development. Several animal models have been used to investigate this function, including mice heterozygous for the TRα1R384C ...mutation, which confers receptor-mediated hypothyroidism. These mice display abnormalities in several autonomic functions, which was partially attributed to a developmental defect in hypothalamic parvalbumin neurons. However, whether other cell types in the hypothalamus are similarly affected remains unknown. Here, we used single-nucleus RNA sequencing to obtain an unbiased view on the importance of TRα1 for hypothalamic development and cellular diversity. Our data show that defective TRα1 signaling has surprisingly little effect on the development of hypothalamic neuronal populations, but it heavily affects hypothalamic oligodendrocytes. Using selective reactivation of the mutant TRα1 during specific developmental periods, we find that early postnatal thyroid hormone action seems to be crucial for proper hypothalamic oligodendrocyte maturation. Taken together, our findings underline the well-known importance of postnatal thyroid health for brain development and provide an unbiased roadmap for the identification of cellular targets of TRα1 action in mouse hypothalamic development.
The structure and function of the circulatory system, including the heart, have undergone substantial changes with the vertebrate evolution. Although the basic function of the heart is to pump blood ...through the body, its size, shape, speed, regeneration capacity, etc. vary considerably across species. Here, we address the differences among vertebrate hearts using a single-cell transcriptomics approach. Published datasets of macaque (
Macaca fascicularis
), mouse, and zebrafish hearts were integrated and compared to the human heart as a reference. While the three mammalian hearts integrated well, the zebrafish heart showed very little overlap with the other species. Our analysis revealed a mouse-specific cell subpopulation of ventricular cardiomyocytes (CM), represented by strikingly different expression patterns of specific genes related to high-energy metabolism. Interestingly, the observed differences between mouse and human CM coincided with actual biological differences between the two species. Smooth muscle and endothelial cells (EC) exhibited species-specific differences in clustering and gene expression, respectively, which we attribute to the tissues selected for sequencing, given different focuses of the original studies. Finally, we compared human and zebrafish heart-specific fibroblasts (FB) and identified a distinctively high expression of genes associated with heart regeneration following injury in zebrafish. Together, our results show that integration of numerous datasets of different species and different sequencing technologies is feasible and that this approach can identify species-specific differences and similarities in the heart.
Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal ...disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.
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Single-cell tissue-specific gene prioritization using machine learning (STIGMA) is an approach to prioritize candidate genes for congenital diseases. STIGMA uses single-cell RNA-seq data to capture the dynamics of gene expression within cell populations across developmental time, making it a powerful tool for the discovery of disease-associated genes.
Mouse models are a critical tool for studying human diseases, particularly developmental disorders
. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the ...developing mouse
. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing
to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions
. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, suggesting that developmental pleiotropy might be 'decomposable' through further scaling of this approach. Overall, our findings show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.
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