Neurodegenerative diseases such as Alzheimer’s and frontotemporal dementia develop silently for years before symptoms appear, limiting the impact of current treatments and slowing progress toward prevention. Earlier detection, clearer differentiation among disease subtypes, and better tools to track biological change are urgently needed. Achieving these goals has been hindered by fragmented datasets, limited access to large, deeply characterized cohorts, and molecular approaches that do not scale from discovery to broad clinical application.

High‑coverage proteomic platforms now offer a path forward by enabling unbiased discovery of disease‑related biology. However, translating these insights requires validation across complementary cohorts and refinement into targeted assays suitable for scalable biomarker development. Deeply phenotyped longitudinal cohorts such as Bluefield provide an essential foundation for this work, supporting discovery in both frontotemporal dementia (FTD) and Alzheimer’s disease (AD) and enabling findings that generalize across populations and disease stages.

This project brings together the Global Neurodegeneration Proteomics Consortium (GNPC) and the NULISA Neuro Data Commons (NNDC) to generate and integrate proteomic data from two well‑characterized cohorts, beginning with the Bluefield cohort. The effort includes 2,060 longitudinal plasma samples from hundreds of participants spanning presymptomatic and symptomatic genetic FTD, with tight alignment between biospecimens and clinical measures—ideal for identifying dynamic proteomic changes associated with disease onset and progression.

Bluefield samples will be analyzed within the GNPC vMultiplatform Cycle, covering four assays across three major proteomic platforms and measuring approximately 17,000 proteins per sample. In parallel, the NNDC will generate NULISA CNS assay data while engaging a broader network of international partners. Embedding Bluefield within GNPC and NNDC’s harmonized data‑sharing framework will produce a landmark, reusable dataset that enhances existing resources, enables cross‑cohort analyses, and expands the scientific questions that can be addressed.