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Irmgard Rossie

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Aug 3, 2024, 10:22:29 AM8/3/24

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This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All raw files are uploaded to the Center for Computational Mass Spectrometry, MassIVE, and the ProteomeXchange Consortium and can be downloaded using the following links: and =PXD013721 (MassIVE ID number: MSV000083750; ProteomeXchange ID number: PXD013721). Data uploads include the protein identification and quantification details, spectral library, and FASTA file used for mass spectrometric analysis. SASP proteomic profiles are available on Panorama ( _Atlas_Buck/begin.view?), a repository for targeted mass spectrometry assays generated in Skyline software. All data are available for viewing and downloading on SASP Atlas (www.saspatlas.com).

Funding: This work was supported by grants from the National Institute on Aging (BS is supported by U01 AG060906-02, Principal Investigator: Schilling; JC is supported by P01AG017242 and R01AG051729, Principal Investigator: Campisi) and a National Institutes of Health Shared Instrumentation Grant (BS is supported by 1S10 OD016281, Buck Institute). NB and OHJ were supported by postdoctoral fellowships from the Glenn Foundation for Medical Research. AK was supported by the SENS Foundation. VS was supported by the University of Washington, Seattle Proteomics Resource (UWPR95794). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

To develop robust and diverse senescence and aging biomarker candidates, a comprehensive profile of the context-dependent and heterogeneous SASP is needed. Several types of stress elicit a senescence and SASP response, which in turn can drive multiple phenotypes and pathologies associated with mammalian aging. These stressors have both shared and distinct secretory components and biological pathways. For example, telomere attrition resulting from repeated cell division (replicative senescence), ionizing radiation, chromatin disruption, and activation of certain oncogenes all can cause senescence-inducing genotoxic stresses, as can genotoxic therapeutic drugs, such as certain anticancer chemotherapies [13] and therapies for HIV treatment or prevention [19]. However, while both ionizing radiation and oncogenes lead to DNA double-strand breaks, ionizing radiation uniquely produces clustered oxidative DNA lesions [20], whereas oncogene activation drives DNA hyper-replication and double-strand breaks [21]. Whether different senescence inducers produce similar or distinct SASPs is at present poorly characterized. Thus, a comprehensive characterization of SASP components is critical to understanding how senescent responses can drive diverse pathological phenotypes in vivo.

The SASP was originally characterized by antibody arrays, which are necessarily biased, to measure the secretion of a small set of pro-inflammatory cytokines, proteases and protease inhibitors, and growth factors [1,2,4,22]. Subsequently, numerous unbiased gene expression studies performed on different tissues and donors of varying ages suggest that the SASP is more complex and heterogeneous [23]; however, a recent meta-analysis of senescent cell transcriptomes confirmed the expression of a few dozen originally characterized SASP factors in multiple senescent cell types [24].

While unbiased transcriptome analyses are valuable, they do not directly assess the presence of secreted proteins. Thus, proteomic studies are needed to accurately and quantitatively identify SASP factors as they are present in the secretomes of senescent cells. Recently, a mass spectrometric study reported several SASP factors induced by genotoxic stress [25], but an in-depth, quantitative, and comparative assessment of SASPs originating from multiple stimuli and different cell types is lacking. Senescent cells also secrete bioactive exosomes [26,27] with both protein and microRNA [28] cargos. Exosomes secreted by senescent cells have been shown to have pro-tumorigenic effects [28], are associated with osteoarthritis [29], and have the ability to induce paracrine senescence [26]. A previous proteomic analysis of protein cargo from senescent extracellular vesicles (EVs) identified few known SASP factors [26], meriting further direct proteomic comparisons between EVs and soluble SASP factors.

We established an efficient and streamlined proteomic workflow to discover novel SASP factors. We collected proteins secreted by senescent and quiescent/control primary human lung fibroblasts (IMR-90) and renal cortical epithelial cells (Fig 1). Briefly, we induced senescence in the cultured cells by X-irradiation (IR), inducible RAS overexpression (RAS), or atazanavir treatment (ATV; a protease inhibitor used in HIV treatment) and allowed 1 to 2 weeks for the senescent phenotype to develop, as described [2]. In parallel, control cells were made quiescent by incubation in 0.2% serum for 3 days and were either mock-irradiated or vehicle treated. Treated and control cells were subsequently cultured in serum-free medium for 24 hours and the conditioned media, containing soluble proteins and exosomes/EVs, was collected. Soluble proteins and exosomes/EVs were separated by ultracentrifugation.

Senescence was induced in cultured primary human lung fibroblasts by either IR, RAS, or ATV. Quiescent control cells were either mock irradiated or vehicle treated. Soluble proteins and exosomes/EVs were then isolated from conditioned media. Samples were digested and subjected to mass spectrometric analysis (DIA), followed by protein identification and quantification using Spectronaut Pulsar [32] and by bioinformatic, pathway, and network analyses in R and Cytoscape [33,34]. ATV, atazanavir treatment; CTL, Control; DIA, data-independent acquisition; EV, extracellular vesicle; IR, X-irradiation; RAS, inducible RAS overexpression; SEN, senescent.

This label-free data-independent acquisition (DIA) approach enabled sensitive and accurate quantification of SASP proteins by integrating the tandem mass spectrometry (MS2) fragment ion chromatograms [30,31]. We quantitatively compared proteins secreted by senescent cells with controls, and significantly changed proteins (q-value 90% of cells and ATV induced senescence in about 65% of cells (S1A and S1B Fig).

We also measured the secretion of known SASP factors (S1D Fig) in the fibroblast sSASP and eSASP, as well as the renal epithelial cell sSASP. These factors included CXCLs, high mobility group box 1 protein (HMGB1), insulin-like growth factor binding proteins (IGFBPs), matrix metalloproteinases (MMPs), laminin subunit beta-1 (LAMB1), and tissue inhibitors of metallopeptidase (TIMPs). In fibroblasts, nearly all previously identified SASP factors were elevated, regardless of the senescence inducer. However, while expression of p16INK4a, IL-6, and SA-β-Gal were also elevated in renal epithelial cells (S1A, S1B and S1C Fig), several SASP proteins identified in fibroblasts were either decreased or unchanged, except for IGFBPs 2/3 and CXCL8. This finding suggests that fibroblast SASP markers do not necessarily pertain to other cell types. Similarly, within exosomes/EVs secreted by senescent fibroblasts, several previously identified key SASP factors were either absent, unchanged, or decreased, including IGFBPs 2/3/5 and LAMB1, and none were consistently elevated in response to more than one inducer (S1D Fig).

To determine how different senescence-inducing stimuli affect the SASPs, we compared the sSASP from human primary fibroblasts induced to senesce by IR, RAS, and ATV. Strikingly, the sSASP was largely distinct among inducers, with an overlap of 150 proteins among 1,091 total increased proteins and no overlap among decreased proteins (S2 Table). Thus, most sSASP protein components and corresponding changes were highly heterogeneous and not shared among inducers (Fig 2C).

To determine whether there are core pathways associated with the sSASPs, we performed pathway and network analyses on overlapping proteins in the sSASPs of each inducer (Fig 2B). The largest pathway associated with all inducers related to tissue and cell structure, including extracellular matrix organization, actin cytoskeleton, integrin interactions, and peptidase regulation.

Tables of top 10 inducer-specific sSASP proteins in (A) IR-induced senescence and (B) RAS-induced senescence. (B) Workflow for generating inducer-specific RNA and protein signatures of senescent cells. Transcriptome analysis of IR- and RAS-induced senescent fibroblasts were obtained from published studies [24,44] and combined. Transcriptome data were filtered for changes that were inducer specific (genes changing exclusively in one inducer but not the other) and were consistent in both studies. Inducer-specific transcriptomes were then compared with inducer-specific secretome changes in the sSASP (from the current study) to produce a combined inducer-specific RNA and protein signature. (D) Log2-fold changes of the top five RAS-specific genes in the sSASP secretome and in two published transcriptome datasets [24,44]. CTL, quiescent control; IR, X-irradiation; RAS, inducible RAS overexpression; SEN, senescent; sSASP, soluble senescence-associated secretory phenotype.

We compared the secretomes of radiation-induced senescent lung fibroblasts and similarly treated senescent renal epithelial cells to determine the cell-type specificity of the sSASP. For renal epithelial cells, the sSASP comprised a mixture of proteins with significantly lower or higher relative secretion (60% increased, 40% decreased), whereas 94% of protein changes in fibroblasts increased in secretion. The magnitude of the fold changes in the sSASP were significantly higher in fibroblasts than in renal epithelial cells, regardless of inducer (S4 Fig, p < 0.0001). For example, 531 of significant protein changes in the fibroblast sSASP were >2-fold, compared to 138 in the renal epithelial cell sSASP. However, for renal epithelial cells, an additional 212 proteins showed significant changes between 1.5- and 2-fold increase or decrease.