Catalog
ASAP is committed to accelerating the pace of discovery and informing a path to a cure for Parkinson’s disease through collaboration, research-enabling resources, and data sharing. We’ve created this catalog to showcase the research outputs and tools developed by ASAP-funded programs.
Single-cell transcriptomic and proteomic analysis of Parkinson’s disease Brains
Preprint: The authors provide an extensive single cell analysis profiling nearly 80,000 brain nuclei from prefrontal cortex of late-stage Parkinson’s disease brains, demonstrate that α-synuclein pathology is inversely correlated with chaperone expression in excitatory neurons, found a selective abatement of neuron-astrocyte interactions with enhanced neuroinflammation, and augmented the study with proteomic analysis and cross-comparisons with Alzheimer’s disease datasets.
Teams
Dopamine transporter and synaptic vesicle sorting defects initiate auxilin-linked Parkinson’s disease
Published: Auxilin helps in recycling of synaptic vesicles to facilitate neurotransmission and loss of auxilin is associated with PD. The authors show auxilin knockout mice exhibit typical PD pathology, dopamine transport is disrupted due to slower dopamine reuptake kinetics, and that macroautophagy and defective synaptic vesicle sorting contributes to dopamine dyshomeostasis. View original preprint.
Gut instincts in neuroimmunity from the 18th to 21st century
Review: Authors review the history of studying gut-brain interactions and offer a forward-looking perspective on the future of microbiota-gut-brain research.
Teams
The Immunology of Parkinson’s Disease
Review: Authors provide an overview of the immunobiology of Parkinson’s disease, focusing on the role α-synuclein in the gut-brain axis hypothesis, the innate and adaptive immune responses involved in the disease, and current treatments.
Teams
A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data
The MARBLES, a Markov Random Field model-based approach for differentially expressed gene detection from scRNA-seq data can capture cell-type relationships and account for sample variation by modeling cell-type-specific pseudobulk data. The authors used simulation results to compare this approach to existing methods from two scRNA-seq datasets.
Teams
scNAT: A deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
The authors developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis.
Teams
Gut instincts in neuroimmunity from the eighteenth to twenty-first centuries
In this review, the authors revisit the history of gut-brain interactions in science and medicine, which dates back to at least the eighteenth century, and outline how concepts in this field have shifted and evolved across eras.
Teams
