The progressive accumulation, aggregation, and spread of α-synuclein (αSN) are common hallmarks of Parkinson's disease (PD) pathology. Moreover, numerous proteins interact with αSN species, influencing its toxicity in the brain. In the present study, we extended analyses of αSN-interacting proteins to cerebrospinal fluid (CSF). Using coimmunoprecipitation, followed by mass spectrometry, we found that αSN colocalize with apolipoproteins on lipoprotein vesicles. We confirmed these interactions using several methods, including the enrichment of lipoproteins with a recombinant αSN, and the subsequent uptake of prepared vesicles by human dopaminergic neuronal-like cells. Further, we report an increased level of ApoE in CSF from early PD patients compared with matched controls in 3 independent cohorts. Moreover, in contrast to controls, we observed the presence of ApoE-positive neuromelanin-containing dopaminergic neurons in substantia nigra of PD patients. In conclusion, the cooccurrence of αSN on lipoprotein vesicles, and their uptake by dopaminergic neurons along with an increase of ApoE in early PD, proposes a mechanism(s) for αSN spreading in the extracellular milieu of PD.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.