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Global Neurodegeneration Proteomics Consortium,

Publications and source records attributed to Global Neurodegeneration Proteomics Consortium,.

2 recordsLinked to original sources

Patient cerebral organoids capture Alzheimers disease proteomic biomarkers and drug targets

Patient iPSC-derived cerebral organoids are a leading human model of Alzheimers disease, yet their proteome has never been benchmarked against human disease. Clinical cohorts now nominate thousands of biomarkers and drug targets across three proteomic platforms, and whether patient organoids capture these candidates is unknown. Here, we profile AD and control cerebral organoids containing neurons, astrocytes, and microglia on the three platforms driving clinical discovery, mass spectrometry, SomaScan, and Olink, in both conditioned media and lysate. Benchmarked against 121 studies and clinical cohorts of over 17,000 plasma, CSF, and cortex samples, patient organoids detect almost every nominated candidate and reproduce the disease-associated change in roughly one in four of the most reproducible. This convergence spans plasma, CSF, and cortex, and extends to synaptic, mitochondrial, and proteostatic biology. We provide the first multi-platform reference proteome of a patient-derived AD model, establishing it as a translationally relevant system for studying AD.

neuroscience↗

Mapping the circulating proteome across neurodegeneration: A harmonized, consortium-scale framework for uncovering molecular pathophysiology

Large-scale plasma proteomics offers unprecedented opportunities to investigate the systemic biology of neurodegeneration, yet technical heterogeneity, site-specific artifacts, and clinical confounding remain major barriers to reproducible discovery. Leveraging data from 13,733 individuals with Alzheimers disease (AD), Parkinsons disease (PD), frontotemporal dementia (FTD), Parkinsons disease dementia (PDD), amyotrophic lateral sclerosis (ALS), and non-impaired controls in the Global Neurodegeneration Proteomics Consortium (GNPC), we present a scalable and generalizable analytical framework for harmonizing and interpreting consortium-scale proteomic datasets. Using a high-dimensional perturbation framework, we systematically benchmark five commonly used batch correction methods across a range of realistic confounding structures, including site-disease imbalance, nonlinear effects, and heteroskedasticity. Empirical Bayes modelling via limma consistently emerged as the most robust method, optimally balancing removal of site-related technical variance with retention of disease-relevant biological signal. On this harmonized foundation, we resolve neurodegenerative disease plasma signatures, including a shared immune-metabolic axis in AD and PD, neuromuscular disruption in ALS, and proteostatic imbalance in PD. Tissue and cell-type enrichment highlight widespread immune-endocrine involvement in AD and hematopoietic activation in PD. Demographically matched analyses nominate distinct, candidate biomarkers across diseases, including lipid, redox, and complement factors in AD, lysosomal and cytoskeletal proteins in PD, and muscle-derived markers in ALS. This study establishes a scalable analytical framework for integrating real-world proteomic data and provides a disease-resolved catalogue of circulating signatures to inform biomarker development and targeted intervention across neurodegenerative diseases.

neuroscience↗