Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aß plaques in mouse and human brains.

Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aß) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aß plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aß plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aß-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aß plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.

Identification of a possible proteomic biomarker in Parkinson's disease: discovery and replication in blood, brain and cerebrospinal fluid.

Biomarkers to aid diagnosis and delineate the progression of Parkinson's disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson's and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson's Disease Centre and Tracking Parkinson's) and the Parkinson's Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10-4). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson's. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation.