Parkinson's disease (PD) is a prevalent neurodegenerative disorder where recent evidence suggests pathogenesis may be mediated by inflammatory processes. The molecular architecture of the disease remains to be fully elucidated. We performed single-nucleus transcriptomics and unbiased proteomics using postmortem tissue obtained from the prefrontal cortex of 12 individuals with late-stage PD and age-matched controls. We analyzed ~80,000 nuclei and identified eight major cell types, including brain-resident T cells, each with distinct transcriptional changes in line with the known genetics of PD. By analyzing Lewy body pathology in the same postmortem tissue, we found that a-synuclein pathology is inversely correlated with chaperone expression in excitatory neurons. Examining cell-cell interactions, we found a selective abatement of neuron-astrocyte interactions and enhanced neuroinflammation. Proteomic analyses of the same brains identified synaptic proteins in prefrontal cortex that were preferentially downregulated in PD. Strikingly, comparing this dataset to a regionally similar published analysis for Alzheimer's disease (AD), we found no common differentially expressed genes in neurons, but identified many shared differentially expressed genes in glial cells, suggesting that disease etiology in PD and AD are likely distinct. These data are presented as a resource for interrogating the molecular and cellular basis of PD and other neurodegenerative diseases.
Longitudinal analyses of single cell lineages over prolonged periods have been challenging particularly in processes characterized by high cell turn-over such as inflammation, proliferation, or cancer. RGB marking has emerged as an elegant approach for enabling such investigations. However, methods for automated image analysis continue to be lacking. Here, to address this, we created a number of different multicolored poly- and monoclonal cancer cell lines for in vitro and in vivo use. To classify these cells in large scale data sets, we subsequently developed and tested an automated algorithm based on hue selection. Our results showed that this method allows accurate analyses at a fraction of the computational time required by more complex color classification methods. Moreover, the methodology should be broadly applicable to both in vitro and in vivo analyses.
A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.
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