The loss of dopamine (DA) neurons within the substantia nigra pars compacta (SNpc) is a defining pathological hallmark of Parkinson's disease (PD). Nevertheless, the molecular features associated ...with DA neuron vulnerability have not yet been fully identified. Here, we developed a protocol to enrich and transcriptionally profile DA neurons from patients with PD and matched controls, sampling a total of 387,483 nuclei, including 22,048 DA neuron profiles. We identified ten populations and spatially localized each within the SNpc using Slide-seq. A single subtype, marked by the expression of the gene AGTR1 and spatially confined to the ventral tier of SNpc, was highly susceptible to loss in PD and showed the strongest upregulation of targets of TP53 and NR2F2, nominating molecular processes associated with degeneration. This same vulnerable population was specifically enriched for the heritable risk associated with PD, highlighting the importance of cell-intrinsic processes in determining the differential vulnerability of DA neurons to PD-associated degeneration.
The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions ...within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq
-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource ( www.BrainCellData.org ), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases.
Real-world network measurements are critical to building performant and resilient networks at scale. However, access to such data exposes end-users to significant privacy risks; and this is ...particularly true for wireless network measurements. In this paper, we apply six state-of-the-art differentially private (DP) algorithms, that span data-independent/dependent and workload-aware/unaware classes, to privatize queries from real-world WiFi traces on a large-scale campus network. We analyze utility-vs-privacy trade-offs involved in constructing privatized queries for canonical network resource provisioning tasks. We present the following results: (1) for count and histogram queries, the utility of the Laplacian-algorithm shows comparable (or better) performance compared to more complex data-aware DP algorithms, (2) for a given query-type and DP algorithm, the utility-to-noise trade-off varies for each distinct network metric, and finally, (3) we implement a state-of-the-art DP algorithm for trajectory analysis that reveals that there exist significant challenges in accurately reconstructing privatized network mobility trajectories, for relatively small trajectory lengths, even with relaxed privacy budgets.
