A proteomic atlas of senescence-associated secretomes for aging biomarker development.

The senescence-associated secretory phenotype (SASP) has recently emerged as a driver of and promising therapeutic target for multiple age-related conditions, ranging from neurodegeneration to cancer. The complexity of the SASP, typically assessed by a few dozen secreted proteins, has been greatly underestimated, and a small set of factors cannot explain the diverse phenotypes it produces in vivo. Here, we present the "SASP Atlas," a comprehensive proteomic database of soluble proteins and exosomal cargo SASP factors originating from multiple senescence inducers and cell types. Each profile consists of hundreds of largely distinct proteins but also includes a subset of proteins elevated in all SASPs. Our analyses identify several candidate biomarkers of cellular senescence that overlap with aging markers in human plasma, including Growth/differentiation factor 15 (GDF15), stanniocalcin 1 (STC1), and serine protease inhibitors (SERPINs), which significantly correlated with age in plasma from a human cohort, the Baltimore Longitudinal Study of Aging (BLSA). Our findings will facilitate the identification of proteins characteristic of senescence-associated phenotypes and catalog potential senescence biomarkers to assess the burden, originating stimulus, and tissue of origin of senescent cells in vivo.

Introduction

Cellular senescence is a complex stress response that causes an essentially irreversible arrest of cell proliferation and development of a multicomponent senescence-associated secretory phenotype (SASP) [1-4]. The SASP consists of a myriad of cytokines, chemokines (CXCLs), growth factors, and proteases that initiate inflammation, wound healing, and growth responses in nearby cells [5,6]. In young healthy tissues, the SASP is typically transient and tends to contribute to the preservation or restoration of tissue homeostasis [5]. However, senescent cells increase with age, and a chronic SASP is known or suspected to be a key driver of many pathological hallmarks of aging, including chronic inflammation, tumorigenesis, and impaired stem cell renewal [5,7]. Powerful research tools have emerged to investigate the effect of senescence on aging and disease, including two transgenic p16INK4a mouse models that allow the selective elimination of senescent cells [8,9] and compounds that mimic the effect of these transgenes. Data from several laboratories, including our own, strongly support the idea that senescent cells and the SASP drive multiple age-related phenotypes and pathologies, including atherosclerosis [10], osteoarthritis [11], cancer metastasis, cardiac dysfunction [12,13], myeloid skewing [14,15], kidney dysfunction [16], and overall decrements in health span [17]. Recently, senescent cells were shown to secrete bioactive factors into the blood that alter hemostasis and drive blood clotting [18]. SASP factors therefore hold potential as plasma biomarkers for aging and age-related diseases that are marked by the presence of senescent cells.

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.

In this study, we demonstrate that the SASP is not a single phenotype but rather is highly complex, dynamic, and dependent on the senescence inducer and cell type. Here, we also present the “SASP Atlas” (www.SASPAtlas.com), a comprehensive, curated, and expanding online database of the soluble senescence-associated secretory phenotype (sSASP) induced by various stimuli in several cell types. We also analyzed the extracellular vesicle SASP (eSASP), which is largely distinct from the sSASP. Our approach leverages an innovative data-independent mass spectrometry workflow to discover new SASP biomarker candidates. The SASP Atlas can help identify candidate biomarkers of aging and diseases driven by senescent cells. We also show that the SASP is enriched for protein markers of human aging and propose a panel of top SASP-based aging and senescence biomarker candidates.

Results

Cellular senescence extensively alters the secreted proteome

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 (). 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.

Proteomic workflow for isolation and analysis of secreted proteins and exosomes/EVs.

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 <0.05) that had a fold change of at least 1.5-fold (SEN/CTL) were identified. Proteins secreted at significantly higher levels by senescent relative to quiescent cells were defined as SASP factors. In fact, most proteins were secreted at much higher levels by senescent cells compared with non-senescent cells (). Each treatment and control group contained 4–10 biological replicates (see Materials and methods for replicate details and experimental design). Relative protein quantification and statistical details are presented in . Induction of senescence was verified by senescence-associated β-galactosidase (SA-β-Gal) activity and p16INK4a and interleukin-6 (IL-6) mRNA levels (), as described [2]. There was no detectable difference in cell death between senescent and non-senescent cells, as measured by a Sytox Green viability dye assay (). IR and RAS overexpression induced senescence in >90% of cells and ATV induced senescence in about 65% of cells ().

Core sSASP proteins, networks and pathways.

(A) Summary of proteins with significantly altered (q-value <0.05 and >1.5-fold change) secretion by senescent compared with quiescent cells following genotoxic, oncogenic, or ATV treatment stress in senescent human lung fibroblasts. (B) ClueGO [33] pathway enrichment and network analyses of overlapping sSASPs resulting from each senescence inducer. Pathways of the same color have ≥50% similarity. Connecting lines represent Kappa connectivity scores >40%. (C) Venn diagram of proteins showing significantly increased secretion in senescent versus non-senescent fibroblasts following induction of senescence by IR, RAS, or ATV. (D) Unsupervised K-means clustering of proteins significantly increased in the sSASPs of all inducers based on the magnitude of the protein changes (log2-fold change) in senescent versus control groups and partitioned into three clusters. ATV, atazanavir treatment; CXCL1, chemokine C-X-C motif ligand 1; ECM, extracellular matrix; GDF15, growth/differentiation factor 15; IGF, insulin-like growth factor; IGFBP, IGF binding protein; IR, X-irradiation; MMP1, matrix metalloproteinase-1; RAS, RAS oncogene overexpression; ROS, reactive oxygen species; sSASP, soluble senescence-associated secretory phenotype; STC1, stanniocalcin 1; TP53, tumor protein p53.

This unbiased proteomic profiling identified between 441 and 1,693 secreted proteins per senescence inducer, a large fraction of which were significantly up- or down-regulated in the secretome following induction of senescence by IR, RAS, or ATV (). Between 340 and 714 proteins changed significantly in response to each inducer. As expected, most of the significantly changed proteins were markedly up-regulated in the SASP from senescent compared with quiescent cells, but, interestingly, a minority were down-regulated (). Notably, the protein cargo of exosomes/EVs released by senescent cells was distinct compared with that from non-senescent cells (), supporting the existence of an exosome/EV SASP (eSASP) in addition to the sSASP, as described [26]. Most changes in the fibroblast sSASP, independent of inducer, exhibited increased secretion by senescent cells, with only 1%–6% of proteins secreted at lower levels. In contrast, one half to two thirds of all significant protein changes in exosomes/EVs from senescent fibroblasts declined relative to quiescent cells ().

We also measured the secretion of known SASP factors () 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 (), 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 ().

Senescence-inducing stimuli drive largely distinct secretory phenotypes

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 (). Thus, most sSASP protein components and corresponding changes were highly heterogeneous and not shared among inducers ().

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 (). The largest pathway associated with all inducers related to tissue and cell structure, including extracellular matrix organization, actin cytoskeleton, integrin interactions, and peptidase regulation.

To distill the overlapping “core” sSASP proteins into primary components, we performed an unsupervised machine learning analysis (). K-means clustering analysis uncovered three primary clusters among core sSASP components. Strikingly, one cluster, consisting of just three proteins—chemokine C-X-C motif ligand 1 (CXCL1), MMP1, and stanniocalcin 1 (STC1)—were highly represented in the sSASPs of all inducers, suggesting these proteins might serve as surrogate markers of the sSASP. Of note, STC1, among the top sSASP proteins, is a previously unidentified SASP factor and a secreted hormone with many disease associations [35-40]. Our analyses also validate MMP1 and CXCL1 as SASP markers.

We also generated proteomic sSASP signatures that were exclusive to two of the senescence inducers, IR and RAS (), to explore whether we could predict the originating inducer of senescence in published data. Due to the relative scarcity of published proteomic analyses of the sSASP, we compared our secretome analysis to published transcriptomic data. We prepared unique proteomic signatures of IR and RAS from our own data by filtering for secreted proteins that were exclusively increased in the secretomes of either IR- or RAS-induced senescent fibroblasts (), and compared with published transcriptomic signatures of senescent cells for both stimuli [24,41-43]. The meta-transcriptomic profiles, as described by Hernandez-Segura and colleagues [24], are based on whole transcriptome (at least three replicates each) profiles of several fibroblast strains: MRC-5 and HFF datasets were used for analysis of IR-induced senescence and IMR-90 were used for RAS-induced senescence. We combined these analyses with a more recent RNA-sequencing analysis of both IR-induced senescence in WI-38 and IMR-90 human fibroblasts and RAS-induced senescence in WI-38 fibroblasts [44] to generate lists of genes that are exclusively expressed in senescent fibroblasts following IR or RAS (). The combined transcriptome profiles contained 33 gene expression changes exclusive to IR-induced senescence and 1,749 gene expression changes exclusive to RAS-induced senescence. Likely due to the small overlap in IR-specific genes across transcriptomic studies, there was no overlap between IR-specific sSASP factors identified in our study and IR-specific transcriptome changes in senescent cells. However, of the 1,749 exclusively RAS-specific transcriptomic signature genes, we identified 26 proteins were exclusively secreted by RAS-induced senescent fibroblasts in this study (), so we focused on generating a RAS-specific signature. The RAS signature included five proteins—CXCL5, MMP9, MMP3, Cystatin-S (CST4), and C-C motif chemokine 3 (CCL3)—that were both robustly increased in both the secretomes (log2-fold change between 2.8 and 6.9) and transcriptomes (log2-fold change between 4.9 and 9.1) ().

Inducer-specific RNA and protein signatures of the sSASP.

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.

sSASP is largely distinct in composition and regulation in fibroblasts and epithelial cells

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 (, 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.

The sSASP of irradiated fibroblasts and epithelial cells were largely distinct (). Among the proteins increased in the sSASP of each cell type, 9%–23% overlapped, and the magnitude of the changes by renal epithelial cells were, in most cases, lower than in fibroblasts regardless of the senescence inducer, although it is possible that senescent fibroblasts secrete more protein overall than epithelial cells in response to stress. Interestingly, 20%–30% of proteins significantly decreased in the sSASP of renal epithelial cells overlapped with proteins significantly increased in the fibroblast sSASP (). Among the epithelial factors that changed oppositely to the fibroblast factors were IGFBPs 4/7, TIMPs 1 and 2, CXCL1, and most serine protease inhibitors (SERPINs). In all, 17 sSASP factors were shared between all senescence inducers and cell types we examined ().

Epithelial cells and fibroblasts exhibit distinct sSASPs.

(A) Number of proteins identified and significantly altered in the sSASP of irradiated fibroblasts and epithelial cells. (B) Venn diagram comparing proteins significantly increased in the sSASPs of senescent fibroblasts and epithelial cells, both induced by IR (q < 0.05). (C) Venn diagram comparing protein increases in the fibroblast sSASP versus decreases in the epithelial sSASP. (D) Pathway and network analysis of secreted proteins significantly increased in epithelial cell sSASP. (E) Pathway and network analysis of proteins significantly decreased in the epithelial cell sSASP. ECM, extracellular matrix; IR, X-irradiation; ROS, reactive oxygen species; sSASP, soluble senescence-associated secretory phenotype.

Pathway and network analysis of proteins increased in the sSASPs of epithelial cells () showed that most pathways belonged to one of three general categories: protein turnover and secretion, primary metabolism, and cellular detoxification. While not as apparent on a molecule-by-molecule basis, many pathways were commonly enriched in both the epithelial and fibroblast sSASPs (Figs and ), including vesicle-mediated transport and exosomes, glycolytic metabolism, and cellular detoxification. Of notable exceptions, pathways enriched uniquely by epithelial cells included protein translation and degradation (lysosome and phagosome).

Surprisingly, most renal epithelial sSASP proteins with significantly lower secretion by senescent cells were enriched in pathways related to tissue and cell structure, adhesion, and motility (). This finding contrasts with previous reports and our own analyses of fibroblasts (), in which these pathways were increased regardless of inducer. The irradiated epithelial sSASP also had significantly lower levels of proteins involved in RNA processing, in contrast to increased RNA metabolism in the irradiated fibroblast sSASP. Additionally, the epithelial sSASP was significantly depleted in proteins related to proteasome degradation, antigen processing, and the complement system.

Damage-associated molecular patterns (DAMPs; also known as alarmins or danger signals) are released from cells in response to internal and external stress, and are components of the sSASP [45]. HMGB1 is a founding member of the DAMPs, a prominent sSASP marker, and, along with calreticulin (CALR), an important driver of inflammation [45]. Our analysis identified increased secretion of multiple DAMPs, including HMGB1 and CALR, by senescent fibroblasts under all senescence inducers (). However, with some exceptions, the secretion of central DAMPs was unchanged or significantly reduced by senescent renal epithelial cells, demonstrating that some defining sSASP components vary depending on cell type.

Exosome/EV proteomic signatures are altered by cellular senescence

Because proteins are also secreted as EV cargo, we hypothesized that senescent cells would show significant changes in this fraction, which we term the exosome/EV SASP (eSASP). We used ultracentrifugation to enrich conditioned media for exosomes and small EVs released by quiescent and senescent fibroblasts induced by IR and RAS (). We confirmed the quality of exosome/EV–purified fractions by measuring the presence of multiple EV-specific markers, including CD63, CD9, CD81, and CDC42 [46], in our proteomic data (), by independent antibody-based detection of 37 exosomal surface isotopes () and by particle counting and size distribution analysis ().

To determine whether the characteristics of exosomes/EVs from senescent cells are altered, we analyzed particle number and size distribution of exosomes/EVs secreted into the culture medium of senescent and non-senescent cells over a 24-hour period. On average, senescent cells released a greater number of vesicles—about 68 per cell compared with 50 per control cell (). The mean diameter of senescent exosomes/EVs (147 nm) was 2% lower (p < 0.001) than quiescent control exosomes/EVs (150 nm) (). Further work using senolytics may validate whether the number, size, and other characteristics of secreted exosome/EVs are indicators of senescent cell burdens in humans.

The protein content of exosomes/EVs released by IR- versus RAS-induced senescent fibroblasts was largely distinct, sharing only 9 significantly altered proteins (). Exosomes/EVs were reported to contain protein signatures of their originating cells [28,47], offering a unique opportunity to identify senescence biomarkers with a degree of cell type specificity. Thus, exosome/EV proteins might distinguish senescent cells of different origins or resulting from different stressors. The membranes of exosomes are also representative of the originating cells [28,47]. Indeed, about 30% of all the exosome/EV proteins that increased upon senescence are plasma membrane proteins (), suggesting that exosomes/EVs might also identify cell type origins through their cell-surface proteins, although this will need to be experimentally confirmed.

Cellular senescence alters exosome/EV features and composition.

(A) Table showing overlapping significant protein changes in exosomes/EVs secreted by senescent cells induced by IR versus RAS (q < 0.05). (B) Enrichment analysis of gene-ontology/cellular compartments overrepresented among protein contents of exosomes/EVs released by senescent cells. (C) Network analysis of pathways and functions unique to the eSASP. CTL, quiescent control; eSASP, extracellular vesicle senescence-associated secretory phenotype; EV, extracellular vesicle; IR, X-irradiation; RAS, inducible RAS overexpression; SEN, senescent.

Protein changes in exosomes released by senescent cells were also largely distinct from changes in soluble protein secretion obtained from matching cells. Of all 548 soluble proteins secreted at significantly higher levels in senescent fibroblasts following IR, only 51 (9.3%) significantly increased in the eSASP of senescent fibroblasts following IR, and 70 proteins (12.8%) significantly decreased in the eSASP. Accordingly, the pathways enriched within eSASP were largely distinct from the sSASP of irradiated fibroblasts. In addition to enrichment of proteins involved in membrane organization, such as cell adhesion and cell junction assembly proteins, the eSASP is uniquely enriched with signaling pathways such as RAS signaling, G-protein signaling, and prostaglandin synthesis and regulation (), as opposed to the metabolic, growth, and extracellular matrix (ECM)-remodeling pathways found in the cell and inducer-matched sSASP. Full lists of proteins secreted by senescent exosomes are in .

The sSASP contains potential aging and disease biomarkers

As a driver of many aging and disease phenotypes, the sSASP could include known biomarkers of aging and age-related diseases. A recent biomarker study identified 217 proteins that are significantly associated with age in human plasma (adjusted p < 0.00005) [48]. Of these, 20 proteins (9.2%) were present in the originally defined SASP [2]. Strikingly, multiple newly identified SASP factors from our present study were also identified in the study of human plasma [48] (). Of all the originally defined sSASP factors and unique sSASP proteins that we identify here, 92 proteins were also identified as markers of aging in human plasma (42.4% of all plasma aging markers) (). Considering the originally defined SASP in addition to our newly identified “core SASP” (sSASP components common to all senescence inducers), the number of age-associated plasma proteins that are also sSASP proteins is 39, or 18.0% of plasma aging markers (). Thus, plasma biomarkers of aging are highly enriched with sSASP factors.

Human plasma aging markers are enriched for sSASP proteins.

(A) Venn diagram comparing sSASP factors secreted by at least one of IR-, RAS-, or ATV- induced senescent cells with markers of aging identified in human plasma [48]. (B) Overlap between the core sSASP (proteins secreted following all senescence-inducing stimuli) and plasma aging markers. (C) Pie chart showing the proportion of known sSASP factors, newly identified core sSASP factors, and sSASP factors found among plasma markers of aging in humans. (D) Number of proteins contained in the originally identified sSASP, core sSASP, noncore sSASP, and markers of aging in human plasma [48] (p < 0.00005). Top core sSASP factors GDF15, STC1, SERPINs, and MMP1 are among the plasma aging markers. ATV, atazanavir treatment; GDF15, growth/differentiation factor 15; IR, X-irradiation; MMP, matrix metalloproteinase; RAS, inducible RAS overexpression; SASP, senescence-associated secretory phenotype; SERPIN, serine protease inhibitors; sSASP, soluble senescence-associated secretory phenotype; STC1, stanniocalcin 1.

Complement and coagulation cascade proteins [18], particularly protease inhibitors such as SERPINs, were also noted as prominent plasma biomarkers of aging [48]. These proteins and their pathway networks were robustly altered in the sSASPs of cells induced to senesce by all the tested stressors (Figs ), in addition to other top biomarker candidates: MMP1, STC1, and GDF15 (). The protein having the strongest association with aging [48], GDF15 (r = 0.82), was among the most highly secreted proteins in the sSASP induced by IR, RAS and ATV in fibroblasts, and in epithelial cells induced by IR (). Increased secretion of top core sSASP biomarkers plasminogen activator inhibitor 1 (SERPINE1), MMP1, STC1, and GDF15 was confirmed by western blotting in RAS-induced senescent cells compared to controls (). The enrichment of aging and disease biomarkers in the secretomes of senescent cells supports their link to a wide spectrum of age-related diseases.

The sSASP contains aging and disease biomarkers.

(A) Serpins are secreted at high levels by senescent fibroblasts induced by IR, RAS, or ATV. (B) MMP1 and (C) STC1 are among the most highly secreted proteins by senescent fibroblasts. (D) The plasma aging biomarker GDF15 is increased in the sSASPs of fibroblasts induced to senesce by IR, RAS, and ATV and epithelial cells induced by IR. *q < 0.05, **q < 0.01, ***q < 0.001. ATV, atazanavir treatment; CTL, quiescent control; Epi, renal epithelial cell; GDF15, growth/differentiation factor 15; IR, X-irradiation; MMP1, matrix metalloproteinase-1; RAS, inducible RAS overexpression; SEN, senescent; sSASP, soluble senescence-associated secretory phenotype; STC1, stanniocalcin 1.

Discussion

Here, we present the SASP Atlas (www.SASPAtlas.com), the first proteome-based database of SASPs. This database contains the contents of exosome/EV and soluble secretomes, in addition to SASPs originating from multiple senescence-inducing stresses and two distinct cell types. The SASP Atlas will be continuously updated with SASP profiles from new cell types and senescence, including paracrine (or bystander) senescence [49,50] as well as temporal dynamics of the SASP—all generated by our laboratories.

Our proteomic analysis leverages a modern DIA mass spectrometry workflow, which comprehensively acquires label-free, quantitative peptide (MS1), and fragment-level (MS2) data for all peptides in each sample [30-32,51,52]. DIA workflows are not limited by the stochastic peptide MS2 sampling biases characteristic of traditional data-dependent acquisition (DDA) mass spectrometry. In addition to the SASP Atlas database, we provide panels of SASP factors on Panorama Web, a freely available web repository for targeted mass spectrometry assays [53,54]. These resources can be used as a reference and guide to identify and quantify SASP factors that may be associated with specific diseases, and to develop aging and disease-related biomarkers ().

SASP Atlas: A comprehensive resource for SASPs.

SASP Atlas (www.SASPAtlas.com) is a curated and freely available database of the secretomes of senescent cells, including both the soluble and exosome SASP, that can be used to identify SASP components or biomarker candidates for senescence burden, aging, and related diseases. SASP, senescence-associated secretory phenotype.

SASP profiles are needed to develop senescence biomarkers in human plasma or other biofluids, and for identifying individuals to treat with, and measuring the efficacy of, senescence-targeted therapies such as senolytics. Translating senescence- and SASP-targeted interventions to humans will require a comprehensive profile of SASPs, both to identify their deleterious components and to develop human biomarkers to assess senescent cell burden. The SASP, as originally identified, comprised approximately 50 cytokines, CXCLs, growth factors, and proteases that were detected by biased methods (e.g., antibody arrays) and/or transcriptional analyses [1-4,24]. While these comprehensive analyses are valuable in describing the overall phenotype of senescent cells, proteomic analyses are complementary in both confirming transcriptional changes and identifying and quantifying novel SASP factors that are not apparent at the mRNA level. For example, a recent meta-analysis of senescent cell transcriptomes [24] identified >1,000 genes with increased expression specifically in senescent cells induced by IR or oncogenic RAS, and >700 “core” senescence genes (increased expression following all senescence inducers tested). Our analysis identified 548, 644, and 143 proteins in the IR, RAS, and core sSASP, respectively, that were previously unreported at the RNA level (). We expect that the number and nature of these sSASP core proteins will change as we and others interrogate additional cell types and senescence inducers, and we will continue to curate the interactive SASP Atlas. Additionally, the secretion of sSASP factors, such as HMGB1 and other DAMPs, is not generally transcriptionally driven.

DAMP receptor–bearing cells, including cells of the innate immune system, recognize extracellular DAMPs as signals to promote inflammatory and fibrotic responses. Increased circulating DAMPs are hypothesized to play a role in aging [55,56], particularly the age-related inflammation termed “inflammaging” [57]. DAMPs can also serve as biomarkers of a number of diseases, including trauma and cardiovascular, metabolic, neurodegenerative, malignant, and infectious diseases [55,58,59]. In addition, our top “core sSASP” biomarker candidates have been identified as disease biomarkers in human studies. For example, human cohort studies have reported GDF15 as a biomarker of cardiovascular disease, cardiovascular and cancer mortality and morbidity, renal disease, and all-cause mortality independent of cardiovascular mortality [60-66], as well as a driver of senescence-associated colon cancer metastasis [67]. Additionally, two of the top core sSASP proteins identified by an unbiased k-means clustering algorithm—STC1 and MMP1 ()—were reported as significant aging biomarkers [48]. In addition to aging, MMP1 has been identified as a biomarker for several cancers, pulmonary fibrosis, and potentially Alzheimer’s disease [68-71], whereas STC1 has been identified as a diagnostic and prognostic biomarker for cancers, pulmonary fibrosis, renal ischemia/reperfusion injury, and Alzheimer’s disease [35-40].

It is important to note that while this study examines the SASP following several senescence-inducing stimuli, the originating stresses that drive an increasing senescent cell burden in vivo during aging and age-related diseases are unknown. We therefore focused our biomarker candidates on core soluble SASP components, which are presumably contributed by senescent cells originating from multiple stimuli. However, it is likely that this list of core components will decrease as additional stimuli and cell types are added to the SASP Atlas. Therefore, as senescent cells are better characterized in vivo, it will be important to tailor these biomarker candidates to better represent the originating cell types and stresses of senescent cells in the context of aging and specific diseases.

Inducer-specific biomarkers will be critical in cases in which the originating stimuli is known, such as in treatment-induced senescence (i.e., patients taking atazanivir or chemotherapeutics). Senescent cells also have a unique combination of changes in proteasome activity, autophagic flux, and metabolic state depending on their originating stimuli, which have been proposed as inducer-specific biomarkers [72]. We also built inducer-specific senescence signatures based on our proteomic analysis and publicly available transcriptomic data (), and a combined signature for RAS-induced senescence. We propose these signatures will continue to evolve as other senescence-inducing stimuli are studied, and may serve as valuable biomarkers to identify the originating inducers of senescent cells in vivo. We will expand our efforts to build unique molecular profiles to distinguish between senescent cell types, or a subset of senescent cell types, in addition to pursuing a “core” signature.

Surprisingly, many classical features associated with the sSASP, some of which were originally described in cultured fibroblasts, were not present or even changed oppositely in renal epithelial cells. Of note was an absence or reduced secretion of DAMPS, including HMGB1 and CALR, danger signals that initiate inflammatory responses and immune clearance at sites of tissue stress and damage. Epithelial cells also showed significantly reduced secretion of proteins in pathways related to tissue structure and organization that was significantly increased in fibroblasts, including ECM organization, focal adhesion, and other pathways enriched with ECM proteins, proteases, and protease inhibitors. These differences between lung fibroblasts and renal epithelial cells highlight the importance of cell type origin on the heterogeneous senescent phenotype and suggest that the SASP is individualized to cell type. Further characterization of the SASP from both fibroblasts and epithelial cells originating in other tissues will be needed to determine whether these cell-type differences in the SASP are specific to epithelial cells from the kidney or are generalized to other types of epithelial cells.

Our quantitative unbiased proteomic analysis of senescent fibroblasts and epithelial cells reveals a much larger and diverse SASP than initially reported. These SASP profiles contribute a number of new potential senescence, aging, and disease biomarkers. By virtue of this proteomic analysis of secreted proteins, SASP profiles are also ideal candidates for plasma-based biomarkers, which are enriched among secreted proteins [73]. Senescent cells were recently shown to modulate hemostasis and clotting phenotypes in plasma in vivo [18], demonstrating that senescent cells secrete bioactive factors into circulation in vivo. The use of SASP factors as biomarker candidates is further supported by our analysis, which has indicated that core SASP factors are enriched among plasma biomarkers of aging in humans. In addition to general senescence biomarkers, many proteins will likely be specific to the cell type and originating stimulus. Thus, biomarkers present in human patients in vivo will likely vary depending on the affected tissue, originating cell types, and senescence stimuli. Therefore, comprehensive quantitative profiles of the SASP under a variety of physiological conditions will provide biomarker candidates with a higher degree of selectivity to specific pathologies in humans.

Materials and methods

Reagents and resources

A full list of reagents and resources, including vendors and catalog numbers, is available in a Reagent and Resource Table (). Further information and requests for resources and reagents should be directed to the Lead Contact, Birgit Schilling (bschilling@buckinstitute.org).

Human cell culture and primary cell lines

IMR-90 primary human lung fibroblasts (ATCC, Manassas, VA; #CCL-186) were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Thermo Fisher Scientific, Waltham, MA; #12430–054) supplemented with penicillin and streptomycin (5,000 U/mL and 5,000 μg/mL; Thermo Fisher Scientific, Waltham, MA; #15070063) and 10% fetal bovine serum (FBS; Thermo Fisher Scientific, Waltham, MA; #2614079). Primary human renal epithelial cells (ATCC, Manassas, VA; #PCS400011) were cultured in Renal Epithelial Cell Basal Medium (Female, ATCC, Manassas, VA; #PCS-400-030). Both cell types were maintained at 37°C, 10% CO2, and 3% O2. Additional information about cell culture conditions, including seeding density, culture vessels, volume of medium, and final cell counts for each experiment, are available in .

Induction of senescence

IR

Senescence was induced by ionizing radiation (IR;10 Gy X-ray). Quiescent control cells were mock irradiated. Senescent cells were cultured for 10 days to allow development of the senescent phenotype, and quiescent cells were cultured in 0.2% serum for 3 days. Cells were then washed with PBS (Thermo Fisher Scientific, Waltham, MA; #10010–023) and placed in serum- and phenol red–free DMEM (Thermo Fisher Scientific, Waltham, MA; #21063–029), and conditioned media was collected after 24 hours.

RAS overexpression

RASv12 was cloned in pLVX vector (Lenti-X Tet-On; Takara Bio, Mountain View, CA; #632162) to make inducible lentiviruses, which were used to infect early passage IMR-90 cells (PD-30). Transduced cells were selected in puromycin (1 μg/mL) for 24 hours. For induction of RASv12, cells were treated with 1 μg/mL doxycycline in DMSO (Sigma-Aldrich, St. Louis, MO; # D9891) for 4 (early time point) or 7 days. Doxycycline was replaced after every 48 hours. Subsequently, cells were washed with PBS and placed in serum- and phenol red–free DMEM, and conditioned media was collected after 24 hours.

Atazanivir treatment

Cells were cultured in appropriate media containing 20 μM atazanavir, which is a clinically relevant dose, or vehicle (DMSO) for 9 (early time point) or 14 days. Subsequently, cells were washed with PBS and placed in serum- and phenol red–free DMEM, and conditioned media was collected after 24 hours.

Isolation of secreted soluble proteins and exosomes/EVs

Proteins secreted into serum-free medium over a 24-hour period were collected. An ultracentrifugation protocol was used to separate the exosome and small EV fraction from the soluble protein fraction [74]. Briefly, conditioned medium was centrifuged at 10,000g at 4°C for 30 minutes to remove debris. The supernatant was then centrifuged at 20,000g at 4°C for 70 minutes to remove microvesicles, followed by ultracentrifugation at 100,000g at 4°C for 70 minutes to pellet exosomes. The exosome-depleted supernatant was saved as the sSASP. The exosome pellet was then washed twice with PBS and ultracentrifuged again at 100,000g at 4°C for 70 minutes before resuspending in PBS and saving as the eSASP.

Proteomic sample preparation

Chemicals

Acetonitrile (#AH015) and water (#AH365) were from Burdick & Jackson (Muskegon, MI). Iodoacetamide (IAA, #I1149), dithiothreitol (DTT, #D9779), formic acid (FA, #94318-50ML-F), and triethylammonium bicarbonate buffer 1.0 M, pH 8.5 (#T7408), were from Sigma Aldrich (St. Louis, MO), urea (#29700) was from Thermo Scientific (Waltham, MA), sequencing grade trypsin (#V5113) was from Promega (San Luis Obispo, CA), and HLB Oasis SPE cartridges (#186003908) were from Waters (Milford, MA).

Protein concentration and quantification

Samples were concentrated using Amicon Ultra-15 Centrifugal Filter Units with a 3-kDa cutoff (MilliporeSigma, Burlington, MA; #UFC900324) as per the manufacturer instructions and transferred into 8 M urea/50 mM triethylammonium bicarbonate buffer at pH 8. Protein quantitation was performed using a BCA Protein Assay Kit (Pierce, Waltham, MA; #23225).

Digestion

Aliquots of each sample containing 25–100 μg protein were brought to equal volumes with 50 mM triethylammonium bicarbonate buffer at pH 8. The mixtures were reduced with 20 mM DTT (37°C for 1 hour), then alkylated with 40 mM iodoacetamide (30 minutes at RT in the dark). Samples were diluted 10-fold with 50 mM triethylammonium bicarbonate buffer at pH 8 and incubated overnight at 37°C with sequencing grade trypsin (Promega, San Luis Obispo, CA) at a 1:50 enzyme:substrate ratio (wt/wt).

Desalting

Peptide supernatants were collected and desalted with Oasis HLB 30-mg Sorbent Cartridges (Waters, Milford, MA; #186003908), concentrated, and resuspended in a solution containing mass spectrometric “Hyper Reaction Monitoring” retention time peptide standards (HRM, Biognosys, Schlieren, Switzerland; #Kit-3003) and 0.2% formic acid in water.

Mass spectrometry analysis

Samples were analyzed by reverse-phase HPLC-ESI-MS/MS using the Eksigent Ultra Plus nano-LC 2D HPLC system (Dublin, CA) combined with a cHiPLC system directly connected to an orthogonal quadrupole time-of-flight SCIEX TripleTOF 6600 or a TripleTOF 5600 mass spectrometer (SCIEX, Redwood City, CA). Typically, mass resolution in precursor scans was approximately 45,000 (TripleTOF 6600), while fragment ion resolution was approximately 15,000 in “high sensitivity” product ion scan mode. After injection, peptide mixtures were transferred onto a C18 pre-column chip (200 μm × 6 mm ChromXP C18-CL chip, 3 μm, 300 Å; SCIEX, Redwood City, CA) and washed at 2 μL/minute for 10 minutes with the loading solvent (H2O/0.1% formic acid) for desalting. Peptides were transferred to the 75 μm × 15 cm ChromXP C18-CL chip, 3 μm, 300 Å (SCIEX, Redwood City, CA) and eluted at 300 nL/minute with a 3-hour gradient using aqueous and acetonitrile solvent buffers.

All samples were analyzed by DIA, specifically using variable window DIA acquisitions [75]. In these DIA acquisitions, windows of variable width (5 to 90 m/z) are passed in incremental steps over the full mass range (m/z 400–1,250). The cycle time of 3.2 seconds includes a 250-ms precursor ion scan followed by a 45-ms accumulation time for each of the 64 DIA segments. The variable windows were determined according to the complexity of the typical MS1 ion current observed within a certain m/z range using a SCIEX “variable window calculator” algorithm (more narrow windows were chosen in “busy” m/z ranges, wide windows in m/z ranges with few eluting precursor ions) [31]. DIA tandem mass spectra produce complex MS/MS spectra, which are a composite of all the analytes within each selected Q1 m/z window. All collected data were processed in Spectronaut using a panhuman library that provides quantitative DIA assays for approximately 10,000 human proteins [76].

Cell viability assays

Cell viability was assessed with SYTOX Green Nucleic Acid Stain (Invitrogen, Carlsbad, CA; #S7020) or propidium iodide (Thermo, Waltham, MA; #P1304MP) inclusion assay. Senescent and control cells were incubated for 24 hours in serum-free medium containing SYTOX Green with continuous imaging. Cell death was quantified by counting total SYTOX Green positive nuclei (523 nm) after 24 hours. For propidium iodide inclusion assays, cells were incubated with propidium iodide for 1 hour, and cell death was quantified by counting the number of propidium iodide–positive cells (617 nm).

SA-β-Gal staining

SA-β-gal activity was determined using the BioVision Senescence Detection Kit (Milpitas, CA; #K320-250). For each condition and replicate, cells were counted, and 7,000 cells were seeded into 8-well culture slides coated with poly-lysine (Corning, Corning, NY; #354632). After 24 hours, the SA-β-gal staining assay was performed as per the manufacturer’s protocol. For each experiment, approximately 100–150 cells were counted.

RNA extraction and quantitative real-time PCR

Total RNA was prepared using the PureLink Micro-to-Midi total RNA Purification System (Invitrogen, Carlsbad, CA; #12183018A), according to the manufacturer’s protocol. Samples were first treated with DNase I Amp Grade (Invitrogen, Carlsbad, CA; #18068015) to eliminate genomic DNA contamination. RNA was reverse transcribed into cDNA using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA; #4368813) according to the manufacturer’s protocol. Quantitative real-time PCR (qRT-PCR) reactions were performed as described using the Universal Probe Library system (Roche, Basel, Switzerland). Actin and tubulin predeveloped TaqMan assays (Applied Biosystems, Foster City, CA) were used to control for cDNA quantity. qRT-PCR assays were performed on the LightCycler 480 System (Roche, Basel, Switzerland). The primers and probes were as follows: Human actin, F 5′-CCAACCGCGAGAAGATGA, R 5′-TCCATCACGATGCCAGTG, UPL probe #64; Human tubulin, F 5′-CTTCGTCTCCGCCATCAG, R 5′-TTGCCAATCTGGACACCA, UPL Probe #58; Human IL-6, F 5′-GCCCAGCTATGAACTCCTTCT, R 5′-GAAGGCAGCAGGCAACAC, UPL Probe #45; and Human p16INK4a, F 5′-GAGCAGCATGGAGCCTTC, R 5′-CGTAACTATTCGGTGCGTTG, UPL Probe #34.

Exosome characterization and size distribution analysis

Protein determination is performed on exosomes/EVs isolated by ultracentrifugation by direct absorbance, and 20 μg of protein is used for input for the MacsPlex Exosome Kit (Miltenyi, Bergisch Gladbach, Germany) assay. These exosomes are enriched for CD63, CD9, and CD81 surface proteins using antibody beads. This pool of exosomes is then probed for 34 other surface markers used for analysis and comparison across samples. Particle diameter and concentration were assessed by tunable resistive pulse sensing (TRPS) on an IZON (Christchurch, New Zealand) qNano Nanoparticle Characterization instrument using an NP150 nanopore membrane at a 47 calibration with 110-nm carboxylated polystyrene beads at a concentration of 1.2 × 1013 particles/mL (Zen‐bio, Research Triangle, NC).

Processing, quantification, and statistical analysis of MS data

DIA acquisitions were quantitatively processed using the proprietary Spectronaut v12 (12.020491.3.1543) software [32] from Biognosys (Schlieren, Switzerland). A panhuman spectral library was used for Spectronaut processing of the DIA data [76]. Quantitative DIA MS2 data analysis was based on extracted ion chromatograms (XICs) of 6–10 of the most abundant fragment ions in the identified spectra. Relative quantification was performed comparing different conditions (senescent versus control) to assess fold changes. To account for variation in cell number between experimental groups and biological replicates, quantitative analysis of each sample was normalized to cell number by applying a correction factor in the Spectronaut settings. Cell number and correction factors applied to each experiment are available in . The numbers of replicates for each experiment are as follows: X-irradiated fibroblasts, 4 senescent and 4 control replicates; X-irradiated epithelial cells, 5 senescent and 5 control replicates; 4-day RAS-induction fibroblasts, 10 senescent and 10 control replicates; 7-day RAS-induced fibroblasts, 6 senescent and 6 control replicates; atazanavir-treated fibroblasts, 3 senescent (9 days of treatment), 3 senescent (14 days of treatment), and 4 control replicates; X-irradiated fibroblast exosomes, 5 senescent and 5 control replicates; and 7-day RAS-induced fibroblast exosomes, 6 senescent and 6 control replicates. Significance was assessed using FDR-corrected q-values <0.05.

Pathway and network analysis

Gene ontology, pathway, and network analysis was performed using the GlueGO package, version 2.5.3, in Cytoscape (https://cytoscape.org/), version 3.7.1 [33,34]. Curated pathways for enrichment analysis were referenced from the following databases: GO Biological Function, GO Cellular Compartment, Kegg pathways, WikiPathways, and Reactome Pathways. For gene ontology data, testing was restricted to pathways with experimental evidence (EXP, IDA, IPI, IMP, IGI, IEP). The statistical cutoff for enriched pathways was Bonferroni-adjusted p-values <0.01 by right-sided hypergeometric testing. Pathway-connecting edges were drawn for kappa scores >40%. Kappa scores are a measure of inter-pathway agreement among observed proteins that indicate whether pathway agreement is greater than expected by chance based on shared proteins. Pathways with the same color indicate ≥50% similarity in terms.

K-means clustering

Unsupervised clustering was performed in Python with Scikit-learn, a module integrating a wide range of machine learning algorithms [77]. Datasets were preprocessed with the StandardScaler function and clustered with the KMeans algorithm.

Data visualization

Violin plots were visualized in R (https://www.r-project.org/) using the “ggplot2” package [78]. Venn diagrams were constructed using the “VennDiagram” package [79]. Color palettes in R were generated with the “RColorBrewer” package [80]. Pathway and network visualizations were generated and modified using the GlueGO package in Cytoscape [33,34].

Senescence markers induced by IR, RAS, and ATV.

(A) Representative images of SA-β-Gal staining of senescent and control (quiescent) primary human lung fibroblasts and renal epithelial cells following induction of senescence by either IR, RAS, or ATV. (B) Quantification of SA-β-Gal–positive cells. (C) Levels of p16INK4a and IL-6 mRNAs determined by qPCR and expressed as fold change of senescent over control (red line) cells. (D) Commonly reported SASP factors for each inducer, cell type, and fraction. Red up-arrow = significantly increased (q-value < 0.05), blue down-arrow = significantly decreased (q-value < 0.05), hyphen (-) = not significant. ATV, atazanavir treatment; Epi, renal epithelial cell; Fib, fibroblast; IL-6, interleukin 6; IR, X-irradiation; qRT-PCR, quantitative real-time PCR; RAS, inducible RAS overexpression; SA-β-Gal, senescence-associated β-galactosidase; SASP, senescence-associated secretory phenotype.

(TIF)

Click here for additional data file.

Cell viability assays.

Amount of cell death over a 24-hour period as determined by Sytox Green viability dye assay or propidium iodide inclusion assay.

(TIF)

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Western blot confirmation of top core SASP factors.

(A) Western blot exposures of top core SASP factors, GDF15, STC1, SERPINE1, and MMP1, in non-senescent control fibroblasts, early senescent fibroblasts (4 days of RAS induction), and fully senescent fibroblasts (7 days of RAS induction). (B) Densitometry analysis of western blot. *p-value < 0.05 versus CTL. CTL, quiescent control; GDF15, growth/differentiation factor 15; MMP1, matrix metalloproteinase-1; RAS, inducible RAS overexpression; SASP, senescence-associated secretory phenotype; SERPINE1, plasminogen activator inhibitor 1; STC1, stanniocalcin 1.

(TIF)

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Distribution of log2-fold changes in IR-induced fibroblast and epithelial cell secretomes.

(A) Violin plot of all significant (q-value < 0.05) log2-fold changes in the sSASP of IR-induced fibroblasts and epithelial cells. (B) Histogram of all significant log2-fold changes in the sSASP of IR-induced fibroblasts and epithelial cells. IR, X-irradiation; sSASP, soluble senescence-associated secretory phenotype.

(TIF)

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Exosome/EV proteomic markers and size distribution analysis.

(A) Table of exosome- and EV-specific markers identified in exosome and soluble fractions of fibroblasts by mass spectrometry. Multiple peptides from defining exosome/EV markers were identified in the exosome fractions of RAS- and IR-induced senescence experiments, but none were detected in the soluble fractions. (B) Table showing EVs secreted per cell, average EV diameter, and standard deviation of EV diameter in senescent and control cells in complete (10% FBS) medium and low-serum (0.2% FBS) medium. The mean diameter of each condition is significantly different from each other condition (p-value < 0.00001, two-tailed t test). (C) Size distribution analysis of EVs secreted by senescent and control cells in complete and low-serum medium. (D) Exosome/EV-specific markers detected in isolated EV fractions in each treatment group, as measured by MACSPlex exosome detection kit. (E) Median levels of every surface marker measured in exosome/EV fractions by MACSPlex exosome detection kit. EV, extracellular vesicle; FBS, fetal bovine serum; IR, X-irradiation; RAS, RAS oncogene overexpression.

(TIF)

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Comparison of proteomic and transcriptomic changes in the fibroblast SASP.

Transcriptomic changes in the SASP of fibroblasts reported in a recent meta-analysis [24] (Hernandez-Segura and colleagues, 2017) were compared with proteomic changes in the SASP of the current study. (A) Comparison of transcriptomic meta-analysis and proteomic analysis of secretomes in IR-induced senescent cells compared with non-senescent cells. (B) Venn diagram comparing RAS-induced senescence changes at the transcriptome and secreted proteome level. (C) Venn diagram of the core senescent transcriptome signature (genes changed at senescence regardless of inducer) versus changes common to IR- and RAS-induced senescence at the secreted proteome level. (D) Venn diagram comparing the senescent transcriptome and secreted proteome core signatures. IR, X-irradiation; RAS, RAS oncogene overexpression; SASP, senescence-associated secretory phenotype.

(TIF)

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Mass spectrometry quantification for each dataset as separate worksheets in a single excel workbook.

(XLSX)

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Proteins with significantly increased secretion in response to all senescence inducers.

(XLSX)

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Proteins with significantly increased secretion in all cell types in response to all senescence inducers.

(XLSX)

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Age-associated plasma proteins also present in the SASP as determined in our proteomics experiments.

SASP, senescence-associated secretory phenotype.

(XLSX)

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Reagents and resources.

(DOCX)

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Cell culture details for each experiment, including seeding density, culture vessel, cell counts, and correction factors.

(XLSX)

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Inducer-specific secretome, transcriptome, and combined protein/RNA signatures for IR and RAS-induced senescent fibroblasts.

IR, X-irradiation; RAS, inducible RAS overexpression.

(XLSX)

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Underlying numerical data for each figure.

(XLSX)

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Raw western blot images.

(PDF)

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20 Aug 2019

Dear Dr Schilling,

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20 Sep 2019

Dear Dr Schilling,

Thank you very much for submitting your manuscript "A Proteomic Atlas of Senescence-Associated Secretomes for Aging Biomarker Development" for consideration as a Methods and Resources article at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by several independent reviewers.

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REVIEWS:

Reviewer #1:

The paper is well written and is could be another step to identify a molecular signature for aging biomarkers. The effort to identify aging biomarkers that could be used as general markers for all aging-related conditions could be a chimera. Indeed, authors clearly evidenced many differences in the SASP contents of the senescent phenotypes they analyzed. Nevertheless, some markers may be present in many aging-related conditions and this paper gives indication about that. In my opinion, the efforts to identify common aging biomarkers should also consider that every senescent phenotype as a unique molecular profile (see for example: Oncotarget. 2015 Nov 24;6(37):39457-68. doi: 10.18632/oncotarget.6277. PMID: 26540573). I mean starting from differences could better lead to find common features. Authors should address this issue in the discussion.

--

Reviewer #2:

PLOS Biology

Title: A proteomic Atlas of Senescence-Associated Secretomes for Aging Biomarker development.

In this study, Basisty et al., show a comprehensive proteomic analysis on the components of the SASP using two different cell types and three different inducers of cellular senescence. After an exhaustive analysis, the authors determine that the composition of the SASP seems to be depending on the cell type as well as the type of inducer used to induce senescence. Also, the authors have found that senescent cells release more exosomes than control cells and that their protein composition differs from the secretory SASP (sSASP). Finally, by comparing their aSASP's data obtained in cells to the study using biomarkers in human plasma for diseases and aging, the authors conclude that a significant amount of SASP’s components can be considered as a good biomarker for aging and diseases.

Although this is a well written and very interesting work, I felt the authors could go a little more in deep about the meaning of their results., i.e., differences respect sSASP vs. exosomes, how they interpret the differences found in specific pathways in fibroblasts vs. epithelial cells or sSASP vs. exosomes, how the sSASP, that is intracellular phenomenon can be correlated to factors found in the plasma?

Overall, I found this work will significantly move the field forward with respect to cellular senescence and it links to biomarkers for aging and diseases.

Minor comments

1. It called my attention that the authors used 10% CO2, there is a specific reason for that.

2. What would be the importance of having two different compartments (cytosolic) and extracellular vesicles containing SASP components? Which one will be more relevant as a biomarker for aging and diseases?

3. Interestingly miRNA has been involved as an important cargo of exosomes, however, the authors didn't describe if miRNAs were part of the exosomes' analysis. Did they analyze this?

4. Why the exosomes released by senescent cells induce either by IR or RAS have such a different proteomic composition? On the same experiment, is no clear whether the pathway analysis (Figures B and C) are the analysis on one type of stressor or whether it is an average between data from IR and RAS.

5. Using the K means clustering, the authors suggested 3 proteins can be used as a surrogate biomarker of sSASP (lines 218 and 219). However, one of them, CXCL1, in epithelial cells has an opposite change with what was found in fibroblasts. Therefore, this is indicating that these 3 proteins can be used as biomarkers of the SASP for human fibroblasts, and maybe only the IMR-90. Wandering whether each cell type will have their own set of proteins that will serve as a biomarker of SASP to that type of cell.

6. What would be the hypothetical mechanism that allows sSASP proteins (secreted intracellularly) be also found in plasma?

Reviewer #3:

In this article, Basisty et al describe the development of a “proteomic atlas” of senescence-associated secretory pheotypes, which is currently online at www.saspatlas.com. The authors state that current knowledge of SASP factors is limited, due mainly to the array-based and transcriptional-based approaches used to detect soluble SASP factors. Correspondingly, they hypothesize that the SASP is more specific to source and condition than previously appreciated. Thus, the authors aim to stock their proteomic database with SASP factors derived from multiple senescence inducers and cell types, as measured using an unbiased mass spectrometry approach. They begin by inducing cellular senescence via RAS ovexpression, ATV treatment, or IR in primary human lung fibroblasts or primary human renal epithelial cells in vitro. The authors then utilize mass spectrometry to quantify corresponding proteomic changes in secreted soluble factors and exosomal content in comparison to control cells. They find that induction of senescence leads to an overall increase in levels of SASP factors independent of induction method. However, they determine that classical SASP proteins respond differently in epithelial cells compared to fibroblasts, with many classical SASP proteins unchanged or even decreased in epithelial cells. Furthermore, when comparing the SASP induced by IR, RAS, or ATV treatment in fibroblasts, the authors find only a limited number of conserved changes (termed core senescence factors) with the majority exhibiting heterogeneous changes. Finally, the authors analyze exosomal SASP, and find that IR and RAS induce distinct proteomic signatures in eSASP.

Critique:

1. At present, the data provided by the authors does not sufficiently address the gaps in knowledge that they lay out in the introduction. Specifically, the authors state three main aims: 1) “to develop robust and specific senescence and aging biomarkers” with “a comprehensive profile of the context-dependent and heterogenous SASP” (lines 60-61), 2) to build an “in-depth, quantitative and comparative assessment of SASPs originative from multiple stimuli and different cell types” (lines 87-88), and 3) to assess the “proteomic content and function of exosomes…secreted by senescent cells” (lines 91-92). As noted below, a number of issues arise.

• Towards aim 1, the authors state that they are generating an “Atlas of senescence-associated secretomes for aging biomarker development.” To that end, they identify a list of >1000 genes that are “associated with senescence” due to their largely heterogeneous changes induced by IR, RAS, or ATX treatment in fibroblasts or epithelial cells. They then go on to note that 101/217 aging factors identified in Tanaka et al, 2018 overlap with this list, positing a connection between senescence secretomes and aging blood biomarkers. However, given the heterogeneous and large nature of the senescence secretomes they report, we take issue with the grouping of all proteomic changes into one list, termed new SASP. Additionally, given that this paper is framed in the context of aging, it is difficult to say how meaningful the new SASP is to physiological aging, given that IR, RAS-OE, and ATX treatment are not particularly physiological drivers of aging. To appropriately make these claims or comparisons, it would be necessary, and of great interest, to add the secretome of senescent cells and non-senescent counterparts from an aged mouse or aged human to the database.

• Towards aim 2, the authors fail to fully explore context specificity of the SASP, even within the limited scope of renal epithelial cells and lung fibroblasts. Given that the epithelial cell IR secretome shows major differences compared to the fibroblast IR secretome, it would be of great interest to test how the other senescence inducers affect the epithelial secretome.

• Towards Aim 3, the authors characterize the content of exosomes released by IR- vs RAS-induced senescent fibroblasts. Beyond listing the proteins present and comparing them, the authors do not conduct any informative analysis. They propose intriguing hypotheses that “exosome/EV proteins might distinguish senescent cells of different origins or resulting from different stressors” (lines 303-304); however, they do not assess whether any of the SASP factors might be predictive of cell source or senescent induction type. The authors should consider further analyzing their own dataset, or comparing it to published datasets (however limited by technical approach) to determine if such a stress/source signatures exist.

2. The reasoning and details of the methodological approaches chosen by the authors are not clearly described.

• It is unclear why the authors chose human lung fibroblasts and renal cortical epithelial cells for their studies. In the introduction, the authors mention that senescent cells are relevant to aging and disease. Are these cell types specifically relevant to those conditions?

• For the detection of senescence-related secreted proteins, the authors profile conditioned media by mass spectrometry and use a cutoff value of q-value <0.05 and a 1.5-fold change (SEN/CTL). It is unclear if these values are somehow first normalized to cell number prior to comparing protein abundance. If not, it is impossible to determine whether the changes observed are due to altered secretions or artifacts related to differential cell abundance. This is of concern given that the clear majority of changes are observed in the same direction (upregulation). Additionally, if the images in figure S1A are representative, it appears cell density is not the same between CTL and SEN induced cells. These issues also apply to analysis of exosomal content.

• The methods are lacking in experimental detail. For example, they do not report the density at which cells were plated. Please ensure that all information needed to reproduce the results of this study are included in the methods.

• Figure 1 suggests that senescence was induced in fibroblasts and epithelial cells by IR, RAS, ATV, prior to downstream proteomic analysis of secreted factors and exosomes. However, this is misleading as RAS overexpression or ATV treatment were only conducted on cultured fibroblasts, and not on epithelial cells. Similarly, exosomes were only collected from IR- or RAS-fibroblasts.

3. The text and figures are frequently vague, making it difficult for the reader to draw meaningful conclusions about the data being presented.

• The authors state that (line 174) “the magnitude of the fold-changes in the sSASP were generally higher in fibroblasts than in renal epithelial cells, regardless of inducer” (2A). It is unclear where magnitude is represented in figure 2A. If it is the bold numbers with arrows, then the numbers in figure 2A are different than those in the text. To state that the magnitudes of the fold-changes in the sSASPs significantly higher in fibroblasts than epithelial cells, it needs to be supported by statistical approaches and concrete numbers. It is further unclear how the authors reach the conclusion that this change is independent of inducer, when epithelial cells only recieved IR. The authors should make sure to clearly explain what conditions/cell types, etc. were used in their analysis, and to accurately represent their results.

• Throughout the entirety of the text, phrases such as “most proteins” (line 125), “large fraction” (line 146), and “a more even mix” (line 173) need to be replaced with specific numbers and statistics.

• The authors frequently round in the text (ex: “~1700”- line 145). In some cases, it represents improper rounding such as: “about 68 per cell and 49 per control cell” (line 294) rounded from 49.9. This raises concerns as to the rigor of the analysis.

• The authors confirm the presence of beta-galactosidase, p16, and IL-6 expression in each of their conditions (figure S1A-C). They then state that “there was no detectable cell death” (line 131, figure S2). First, this is not reflected in the data: Figure S2 shows viability at ~90% and cell death at ~5%. It is also unclear what cell type and condition is being assessed in figure S2. Cell viability should be reported for all models of senescence, as cell death could be a major contributor to the secretome assessed.

4. The combinations of data employed for pathway analyses throughout the paper are confusing. The fact that the data were combined at all is principally in conflict authors’ idea that the SASP is cell type and senescence-induction specific. Moreover, the conclusions drawn from the pathway analyses presented throughout the paper are primarily speculation, given the lack of experimental data provided to support them.

• The authors perform “pathway and network analysis of proteins increased in the sSASPs of [IR] fibroblasts and [IR] epithelial cells” and conclude that “many pathways were commonly enriched in both the epithelial and fibroblast sSASPs (Fig 3C and Fig 2C)” and that “pathways enriched uniquely by epithelial cells included protein translation and degradation.” These conclusions suggest that the authors did pathway and network analysis on the increased sSASPs from fibroblasts and epithelial cells separately, however this is not what is represented in figure 3C or 2C.

• The authors conclude, for example, that the “enrichment of neurodegeneration-associated proteins and pathways suggests that senescent cells contribute to neurodegenerative diseases” (lines 209-211). This conclusion is very misleading to the reader, as the authors 1) claim cell-type specificity but are working with fibroblasts and epithelial cells to draw conclusions about pathological, aged brain cells and 2) the authors conducted no experimental analysis to validate these findings, or even cross-reference their capacity to be “biomarkers” with data from published plasma biomarker studies.

5. The database itself is an interesting idea, and the current interface is well-made. The graphics generated by the SASP query tool is intuitive and helpful. However, exporting the data is a bit difficult. Currently, downloading the data table generates an excel populated with what appears to be the logRatio – however this is not clearly labeled. It would be nice to have the downloaded table include the logRatio and -log10(qvalue).

Minor.

• Figure 4B: The percent label is vague. What is this a percentage of?

• Figure S4: Where is CD63, CD9, etc. values for CTL fractions?

• Figure S4: The authors report CD81 and CDC42 values in figure S4, but do not discuss in the text.

• Figure S4: The authors state that the mean diameter and size distribution of senescent and control exosomes/EVs were similar. Please provide statistics to show that they are not different.

• Figure S4: Graphs depicting the concentration and diameter of exosomes should use the same x-axis to better facilitate the comparison.

• There are numerous grammatical errors. For example, “Several types of stress elicit a senescence and SASP response, which in turn can drive multiple phenotypes and pathologies associated with mammalian.” (line 63).

--

Reviewer #4:

This is an interesting and valuable report. It is essentially descriptive but the quality and extent of the analyses is relevant. Particularly the finding of common factors present in the SASP and in the plasma of old donors. I have a few comments that the authors may want to address:

1. Perhaps is worth mentioning in the abstract that soluble and exosome SASPs have been analyzed.

2. Regarding this sentence in the Introduction: “We also present the first comprehensive proteomic analysis of the exosomal SASP (eSASP), which is largely distinct from the sSASP”. Please avoid claims of priority and acknowledge (cite, compare and discuss) data in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613042/ (incidentally this study identifies 1,600 proteins in the eSASP)

3. Typos: Fig S1D is mislabeled as S1C, and it says LAMB1 instead of LMNB1.

4. The nomenclature is confusing: “secretome” or “SASP” sometimes refers exclusively to soluble SASP (sSASP) and not exosomes (for example Fig. 2A classifies “secretome” vs “exosomes”), however in other places both are considered SASP (sSASP vs eSASP). There are multiple instances in the text where the meaning has to be inferred from the context. Please, be consistent and simple.

5. Fig. 2A: please clarify the meaning of the numbers:

a. The first and second columns, do they refer to proteins that change both in fibros and epi combined?

b. The third column (labelled “fibro” and “epi”), does it refer to proteins that change with any of the three stressors combined? The fact that the numbers in “fibro” coincide exactly with the numbers in the secretome of IR-senescent cells, is it just a coincidence? Or a typo?

6. In line 332: “core SASP” (SASP components resulting from all senescence inducers). I would rather say: “core SASP” (SASP components common to all senescence inducers).

--------------------

11 Nov 2019

Submitted filename: review_responses.docx

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2 Dec 2019

Dear Dr Schilling,

Thank you for submitting your revised Methods and Resources entitled "A Proteomic Atlas of Senescence-Associated Secretomes for Aging Biomarker Development" for publication in PLOS Biology. I have now obtained advice from the original reviewers and have discussed their comments with the Academic Editor. Based on the reviews, we will probably accept this manuscript for publication, assuming that you will correct the manuscript to address the remaining points raised by Reviewer 4 and carefully revise the entire manuscript to catch all such inconsistencies and errors. Please also make sure to address the data and other policy-related requests noted at the end of this email.

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REVIEWS:

Reviewer #1:

None

Reviewer #2 (Viviana I Perez):

In this revised manuscript the authors addressed all previous reviewers’ questions and concerns, which resulted in an strengthen manuscript.

Reviewer #3:

For the most part, the authors have satisfactorily addressed the majority of our concerns.

Reviewer #4:

Authors have satisfactorily addressed my comments.

However, authors have to revised carefully their data because there are still errors. I have not revised everything, but the few things I checked have errors. A few examples regarding Fig. S1D and the corresponding Table S1.1:

- LMNB1 in AVT-SEN-fibros: the figure says it goes up, however the table says that it goes down

- LAMB1 on the contrary goes up in all three conditions. I wonder if the authors wrongly select as UP in all conditions the protein LMNB1, when it should have been LAMB1

- HMGB1 in exosome of IR-SEN-fibros: the figure says it goes up, the table says it goes down

- HMGB1, MMP2, TIMP1/2, IGFBP3/4/5/7: the figure says all go down, the table says all go up

Please, revise everything. I can understand a few errors, but these seem to me as too many.

11 Dec 2019

Submitted filename: review_responses_v2.docx

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13 Dec 2019

Dear Dr Schilling,

On behalf of my colleagues and the Academic Editor, Manuel Serrano, I am pleased to inform you that we will be delighted to publish your Methods and Resources in PLOS Biology.

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Table 1

DAMPs are a core component of the fibroblast sSASP.

Log2(SEN/CTL)
	IR (Fibroblasts)	RAS	ATV	IR (Epithelial)
HMGB1	2.47	0.59	2.46	NS
CALR	1.22	0.51	1.32	−0.75
CD44	2.25	1.20	1.92	−0.51
S100A11	0.56	1.35	1.88	0.64
LGALS3BP	1.46	1.76	1.79	−1.14
VCAN	1.80	1.32	0.98	−1.39
TNC	1.64	1.40	2.46	0.39
HSPA5	2.03	3.93	1.78	−0.24
HSP90AB1	5.01	2.69	1.65	0.26
HSPA8	2.49	2.98	1.46	0.49
HSPA1A	2.96	2.40	1.45	0.67
HSP90AA1	4.94	3.42	1.34	NS
HSP90B1	2.67	1.61	0.66	NS

All changes are significant (q < 0.05) unless denoted NS.

Abbreviations: ATV, atazanavir treatment; CALR, calreticulin; CTL, quiescent control; DAMP, damage-associated molecular pattern; HMGB1, high mobility group box 1 protein; IR, X-irradiation; NS, not significant; RAS, oncogenic RAS overexpression; SEN, senescent; sSASP, soluble senescence-associated secretory phenotype.