PMC:4845325 / 1561-2697 JSONTXT

{"project":"2_test","denotations":[{"id":"27112350-16900146-72613472","span":{"begin":318,"end":319},"obj":"16900146"},{"id":"27112350-20622844-72613472","span":{"begin":318,"end":319},"obj":"20622844"},{"id":"27112350-24121966-72613472","span":{"begin":318,"end":319},"obj":"24121966"}],"text":"Plasma proteins provide a sampling of biological processes throughout the organism and have been applied to diagnose or monitor human disease. However, in neurodegenerative disorders it has so far been more difficult to use unbiased large-scale proteomic approaches to discover blood-based biomarkers for diagnostics [1–3]. While individual patient samples might be insufficient for reliable classification tasks based on plasma proteins alone, patient populations could instead be used to smoothen variability and identify underlying common changes linked to disease mechanisms. To achieve this, we propose a medium-scale proteomic strategy that concentrates on secreted signaling proteins involved in cellular communication. Changes in these signaling proteins may result from pathogenic processes or indicate cellular responses to disease. A screen focused on these proteins may not only reduce the proteome test space dramatically but also provide mechanistic insight [4]. Here, we examined whether this approach can robustly identify proteins and biological pathways linked to sporadic late-onset Alzheimer’s disease dementia (AD)."}