The Secretome of Senescent Monocytes Predicts Age-Related Clinical Outcomes in Humans

In this study, researchers used Proteograph^® technology to deeply profile the secretome of senescent monocytes in serum-supplemented conditions—conditions that better reflect physiological biology but traditionally present challenges for proteomic analysis. This approach enabled the detection of over 5,000 human proteins, revealing more than 3,900 regulated in the senescent secretome, and determination of a senescence-associated secretory phenotype (SASP) signature.

The resulting monocyte SASP signature was then mapped to plasma proteomic data from over 1,000 participants in the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models identified SASP-derived proteins that robustly predicted clinical traits such as BMI, lipid levels, inflammatory markers, and mobility metrics. These associations were further validated in an independent cohort of 997 participants, InCHIANTI, confirming the reproducibility and clinical relevance of the findings across distinct populations. This work demonstrates how Proteograph-enabled proteomics can bridge cell-based discovery and human biology at scale.