A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

Abstract

Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a 'high impact' SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.

Fig. 1 |. Workflow for identification of SASP signatures from the aging plasma proteome.

a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the BLSA and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.

Fig. 2 |. Establishing an IR-induced model of senescence in THP-1 monocytes.

a, Representative fluorescence microscopy images from Edu incorporation assay and, b, corresponding quantification bar plots indicating reduced cellular incorporation of Edu by THP-1 cells 7 days after IR exposure. c, Bar plots showing increased expression of known senescence markers and, d, elevated SPiDER β-gal confirming induction of senescence in IR treated THP-1 cells. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001.

Fig. 3 |. Monocyte SASP is Associated with Age in the BLSA.

a, Principal Component Analysis (PCA) of Neat samples (protein level). b, PCA of 6 NP samples (protein level). c, PCA of 14 NP samples (protein levels). For all PCA, only proteins present in all samples were used. d, Monocyte SASP were identified using nanoparticle processing on the Proteograph. e, The top 200 differentially expressed proteins were used for ontology analysis (p-value < 0.05, q-value <0.1,Biological Process). f, Overlap between monocyte and fibroblast SASP. g, Ontology analysis of overlapping SASP proteins between monocytes and fibroblasts.

Fig. 4 |. Elastic Net Modeling Using SASP of Clinical Traits in the BLSA.

a, 1550 Monocyte SASP are detected in the BLSA 7k SomaScan. b, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values in the test set for each clinical trait. c, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone.

Fig. 5 |. Modeling of the fat content in BLSA using SASP candidates.

a, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values of the test set for each clinical trait. b, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone. d, The correlation between observed waist size and that predicted by Elastic Net Modeling (80% train, 20% test).

Fig. 6 |. SASP-based associations show robust replication in the InCHIANTI aging study.

a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and InCHIANTI (1.3k SomaScan). Elastic Net modeling was used for feature selection in both Inchianti and BLSA, and linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI are shown on the x-axis, and Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA are shown on the y-axis b, Binomial models were trained either using controls (age, sex) or controls + ENSPs in BLSA, then used to predict obesity in Inchianti.

Fig. 7 |. A high-impact SASP panel robustly predicts multiple clinical traits

a, For a 14-trait panel, proteins were ranked by the number of features for which they were selected via Elastic Net in the BLSA, and the most frequently selected proteins are shown with their cross-trait importance on the x-axis. Only proteins that were positively associated with negative traits such as BMI and CRP, and those that were inversely associated with positive traits such as mobility were selected. Stars indicate those that were also detected in InCHIANTI. b, Linear models were trained on 80% of the BLSA cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. c, Linear models were trained on 80% of the InCHIANTI cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. d, Principal Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high impact panel. With the BLSA cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend.