Human skin fibroblasts from apparently healthy young, mid-age and old as well as diseased (Progeria) donors were purchased from Coriell Institute or prepared in our lab as described before (Naumann et al., 2018; Pal et al., 2018, 2021). Cells were cultured in Dulbecco-modified Eagle’s medium (DMEM-Glutamax, ThermoFisher Scientific, #10569010) supplemented with 15% fetal bovine serum (FBS superior, ThermoFisher Scientific, #10270106) and penicillin/streptomycin (100 μg/mL, Fisher Scientific, #15140-122) on ventilated culture flasks (75 cm², Fisher Scientific, #658175) at 37°C and 5% CO₂. Fibroblasts were subcultured after trypsinisation (0.25% trypsine-EDTA, ThermoFisher Scientific, #25200-056). Culture medium was changed every 3–4 days.

For determination of PDL, human fibroblasts were seeded into 6-well-plates (Sarstedt, #833.920) at a density of 2x10⁵ per well. When fibroblasts reached confluency, cells were counted and again seeded at a density of 2x10⁵. This was continued over 75–150 days. PDL was calculated as PDL = 3.2*(log_{harvested cells}—log_{seeded cells}) + actual passage number.

Fibroblasts were seeded at a density of 2.5x10⁴ per well in an 8-well µ-Slide (ibidi, #80806) and incubated for 48 h. Cells were fixed with 4% PFA at 37°C for 20 min followed by permeabilization with 0.2% of TritonX and blocked with Blocking Buffer (Pierce Protein-Free T20 (TBS) Blocking Buffer, ThermoFisher Scientific, #37571) at RT for 1 h. Primary antibodies (Anti-phospho-Histone H2A.X (Ser139), #05-636-25ug, 1:500, Millipore; Anti-Histone H3 (di methyl K9), #ab1220, 1:500, abcam; Anti-Lamin B1, #ab16048, 1:500, abcam; Anti-Vimentin, #PA1-16759, 1:300, Thermo Fisher Scientific) were incubated overnight at 4°C. After washing, secondary antibodies (Goat anti-Mouse IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Invitrogen; Goat anti-Rabbit IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Invitrogen) were incubated for 90 min. Finally, after repeated washing, cells were mounted with 4.6-diamidino-2-phenylindole (DAPI) Fluoromount-G (Southern Biotech, #0100-20) and images were taken with a LSM900 confocal microscope (Zeiss). Analysis was done with Fiji software. γH2A.X foci were counted per nucleus (threshold: 20-200; size: 5). Corrected total cell fluorescence (CTCF) was calculated as following: (CTCF) = Integrated Density—(Area of selected Cell/Region * Mean Grey Value of background readings). Nucleus size was determined with area (pixel units) of nucleus with Fiji software.

A standard extraction kit (DNeasy Blood and Tissue Kit, Qiagen, Cat. # 69504) was used for DNA extraction. Mean telomere length was determined using the modified MM-qPCR as described previously (Cawthon, 2009). DNA samples (20 ng/μL) and a reference DNA standard (0.137–100 ng/μL) were assayed in triplicates on different plates and the average of three measurements was used to report the mean telomere length for each sample. A non-template control (water) and a positive control (human leukemia cell line 1301 DNA) were prepared in duplicates and run on every plate. The standard includes DNA samples of 352 healthy donors, with an average age of 40.14 years (18–70 years old; 38.35% males and 61.65% females). The assay was performed using a BioRad CFX384 real-time C1000 thermal cycler with the following profile: 1 cycle of 15 min at 95°C; 2 cycles of 15 s at 94°C, 1 cycle of 15 s at 49°C; 40 cycles of 15 s at 94°C, 1 cycle 10 s at 62°C, 1 cycle 15 s at 72°C with T signal acquisition, 10 s at 85°C, and 15 s at 89°C with signal acquisition. PCR reagents were used at the following final concentrations: 1 U titanium Taq DNA polymerase per reaction with provided titanium Taq PCR buffer (Cat. # 639208), SYBR Green I (Invitrogen, #S7563), 0.2 mM of each dNTP, 1 mM DTT, 1 M betaine, 900 nM of each telomere primer (Telg, Telc) and 300 nM of each single copy gene primer (ALBu, ALBd). Following primer sequences were used: Telg 5′ACA​CTA​AGG​TTT​GGG​TTT​GGG​TTT​GGG​TTT​GGG​TTA​GTG​T′3; Telc 5′TGTTAGGTATCCCT ATC​CCT​ATC​CCT​ATC​CCT​ATC​CCT​AAC​A′3; Albu 5′CGG​CGG​CGG​GCG​GCG​CGG​GCT​GGG​CGG​AAA​TGC​TGC​ACA​GA ATCCTTG′3; Albd 5′GCC​CGG​CCC​GCC​GCG​CCC​GTC​CCG​CCG​GAA​AAG​CAT​GGT​CGC​CTG​TT′3. Ratio of telomere to single-copy gene content (TLR) is taken as relative measurement of telomere length and expressed in arbitrary units. The intra-assay coefficients of variation were <0.3 for all samples.

Total RNA was isolated using the “Quick RNA Miniprep” Kit (Zymo Research, #R1054) according to the manufacturer’s protocol and cDNA was generated from 200 ng isolated RNA with High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, #4368814). mRNA expression levels were determined using Rotor Gene (QIAGEN) with Rotor-Gene SYBR® Green PCR Kit (QIAGEN, #204074) using the following primers: CDKN1A(p21)-Fwd 5′-GAC​ACC​ACT​GGA​GGG​TGA​CT-3′, CDKN1A(p21)-Rev 5′-CAGGTCCACATGGTCT TCCT-3′, CDKN2A(p16)-Fwd 5′-CTC​GTG​CTG​ATG​CTA​CTG​AGG​A-3′, CDKN2A(p16)-Rev 5′-GGTCGGCGCAGTT GGGCTCC-3′. qPCRs were performed in duplicates for each sample (n = 1 correspond to 2 replicates). Data sets were normalized relative to GAPDH (GAPDH-Fwd 5′- GTC​TCC​TCT​GAC​TTC​AAC​AGC​G-3′, GAPDH-Rev 5′- ACC​ACC​CTG​TTG​CTG​TAG​CCA​A-3′) using delta-delta-CT method.

Activity of SA-β-Gal was determined using the “Senescence β-Galaktosidase staining” Kit (Cell signaling, #9860) according to the manufacturer’s protocol and referred to as X-Gal staining in the figures. Cells with cytoplasmic staining were scored as positive. Relative amount to total cell numbers was quantified.

ELISA from cell culture supernatants was performed using “Human IL-6/Interleukin-6 ELISA Kit PicoKine™” (BosterBio, #EK0410) or “Human IL-8/Interleukin-8 ELISA Kit PicoKine™” (BosterBio, #EK0413) according to the manufacturer’s recommended procedures. ELISA were performed in duplicates for each sample (n = 1 correspond to 2 replicates).

Statistical analyses were performed using GraphPad Prism 8 (LaJolla). Experimental groups were compared using one-way-ANOVA (followed by Dunnett’s multiple comparison test). Statistical significance was set up at p-values <0.05 (∗), <0.01 (∗∗), <0.001 (∗∗∗). Data were plotted using GraphPad Prism 8 (LaJolla) showing mean and standard error of the mean.

We first wanted to analyze how selected age markers relate to chronological donor age. Therefore, we performed a systematic comparison of the selected age markers, considering these one by one as single markers, in human fibroblasts from 14 donors from apparently healthy young (2–9 years), mid-age (34–48 years), and old individuals (78–96 years) (Figure 2A; Table 1). Both male and female donors were included. Furthermore, we examined the youngest available cell culture passages of each fibroblast line. To observe growth rates of cells, we investigated the population doubling level (PDL) under stable conditions (37°C, 5% CO₂) (Figure 2B). Primary cells from young (Young 1–5) and mid-age (Midage 1–3) donors showed healthy exponential growth. In contrast, fibroblasts from old (Old 3) and very old donors (Old 4, 5 and 6) displayed slower growth except for the fibroblasts Old 1 and 2, which resembled young donor cells in terms of growth (Figure 2B).

Next, the primary hallmarks of aging (DNA damage, telomere attrition and histone modification) were examined. DNA damage is a driving force of aging because it has a number of molecular consequences that are also hallmarks of aging, such as genomic instability, telomere attrition, epigenetic changes, and/or impaired mitochondrial function (Schumacher et al., 2021). To analyze the number of DNA double-strand breaks (DSBs), we examined the number of γH2A.X foci by immunofluorescence staining (Figure 2C). Human fibroblasts from very old individuals (Old 4 and 5) showed significant higher amounts of DSBs compared with the average number of DSBs in young individuals. We next examined histone modification by immunofluorescence staining and quantifying the corrected total cell fluorescence (CTCF) of H3K9Me3 (Figure 2D). The heterochromatin loss model of aging assumes that heterochromatin domains which are formed early in embryogenesis decrease during aging and contribute to a global loss of heterochromatin-induced gene silencing, resulting in aberrant gene expression patterns (Villeponteau, 1997). In our study, we observed no changes in mid-age (Midage 1–3) but a decrease in H3K9Me3 expression in old donor fibroblasts (Old 2, Old 4, Old 5, and Old 6). In addition, we measured telomere length by monochromatic multiplex qPCR (MM-qPCR) (Figure 2E). Telomeres are specialized chromatin structures at the end of eukaryotic chromosomes that serve to protect chromosome ends. Telomere shortening with aging is observed in most human tissues in which it has been tested (Lange, 2005) and is one of the best understood mechanisms limiting the growth of normal cells in culture, a phenomenon referred to as “replicative senescence” (Collado, Blasco, and Serrano, 2007). Compared to the mean of young donor fibroblasts (Young 1–5), Old 6 was the only cell line exhibiting significant telomere shortening.

Following, single antagonistic age markers were systematically investigated, including induction of cell cycle arrest, expression of SA-βGal, activation of senescence-associated secretory phenotype (SASP), Lamin B1 expression and morphological changes (i.e., size of nucleus). Cell cycle arrest in senescence is largely mediated by activation of one or both of the tumor suppressor pathways p53/p21 and p16/pRB (Kumari and Jat, 2021). These two pathways are very complex, involving many upstream regulators and downstream effectors, as well as several side branches (Chen et al., 2002; Rovillain et al., 2011). Our readout for cell cycle arrest were RNA expression levels of p21 and p16 (Figure 2G). Old 4 and Old 5 showed a raise in RNA expression level of p21. Otherwise, none of the human fibroblasts showed altered RNA expression levels of p21 or p16. Subsequently, we determined the number of cells positive for SA-βGAL, one of the most used markers of cellular senescence. The lysosomal hydrolase β-galactosidase cleaves terminal β-galactose from compounds such as lactose, keratin sulfates, sphingolipids, etc. It is present in almost all tissues. The activity of this enzyme increases with development of cellular senescence (Dimri et al., 1995). We determined the amount of SA-βGAL positive cells (Figure 2F). In comparison to the mean of young donor cells (Young 1–5), old human fibroblasts (Old 1–6) displayed a significant increase of SA- βGAL positive cells, while in mid-age donors, only Midage 3 showed a significant increase in SA- βGAL positive cells. Following, we investigated expression levels of nuclear protein Lamin B1 by determining the CTCF (Figure 2H). Lamin B1 expression decreases with age and is considered as an age marker (Wang et al., 2017; Kristiani, Miri, and Youngjo, 2020). Recent studies have shown that the nuclear lamina regulates both the organization of three-dimensional chromatin structure at the nuclear periphery (Yue, Zheng, and Zheng, 2019) and gene expression, e.g., of inflammatory response genes (Shah et al., 2013). A significant decrease in Lamin B1 expression was observed in mid-age and old donor fibroblasts (Midage 1, Midage 3, Old 1–5) compared with the mean of young donor cells. For studying SASP activation, reflecting senescent cells (Coppé et al., 2008), we examined secreted concentrations of two principle SASP factors, the inflammatory cytokines interleukin 6 (IL-6) and interleukin 8 (IL-8) in conditioned medium of donor cells by ELISA (Figures 2I, J). An increase in concentration of IL-6 was detected in old donor fibroblasts (Old 1-Old 4 and Old 6) in comparison to grouped controls. Additionally, Midage 1 and Old 4 displayed a significant increase in levels of IL-8. Furthermore, it is known that nucleus size changes with age and in pathological conditions (Capell et al., 2005; Glynn and Glover, 2005; Haithcock et al., 2005; Webster, Wikin, and Cohen-Fix, 2009). To study morphological changes occurring with aging or during pathogenesis of age-associated diseases, we examined the size of the nucleus using pixel area of DAPI staining (Figure 2K). Nucleus size was significantly enlarged only in very old human donor fibroblasts (Old 4 and Old 5) compared to grouped young controls (Heckenbach et al., 2022).

In summary, when systematically investigating above mentioned well known single age markers, there was a remarkable heterogeneity between single human donors of different ages and it might thus be hard to judge upon the biological age if only investigating one single aging marker alone. While SA-βGAL was the best single age marker to depict old donor age, it did not perfectly distinguish midage from young donor age. Therefore, we went on to develop a strategy to use above mentioned markers together in one panel to finally yield an integrated AgeScore result.

To take the individual cell line variabilities better into account we used above mentioned single markers to generate one panel with a single absolute value from the tested age markers and asked ourselves whether this value then depicts the chronological age of fibroblasts of different donor ages as good as or even better than single markers (Figure 3A). To create this score (hereinafter referred to as AgeScore) each primary cell culture system examined received an equal weight of “1” for a significant (positive) age marker result and a weight of “0” for each non-significant (negative) result. Subsequently, results of tested age markers were summed up to calculate the (A) primary AgeScore and (B) the antagonistic AgeScore. Finally, both scores together resulted in the (C) full AgeScore that was able to distinguish between young and old donor derived fibroblasts showing an increase with age (Figure 3B). For every used age marker we calculated Pearson’s correlation and most age markers correlate with the AgeScore (Supplementary Figure S1). We observed a very high correlation for the markers H3K9Me3, p21, nucleus size and SA-β-Gal and a high correlation for Lamin B1 and H2AX. However, there is a moderate correlation for telomere length and a low correlation for p16, IL6, and IL8. Whereby all young donor fibroblasts showed a full AgeScore of 0, only the antagonistic AgeScore increased in mid-age fibroblasts (antagonistic AgeScore = 2). In old donor fibroblasts, primary as well as antagonistic AgeScores increased and resulted in the highest full AgeScore calculated. The Pearson`s correlation of R = 0.86 (Figure 3C) demonstrated that our AgeScore tended to increase with donor age of examined human fibroblasts.

We next aimed to examine the external validity of our proposed age-associated marker panel in two specific scenarios of aging, namely, during replicative aging/senescence (Figure 4) and artificial aging (Figure 5) by overexpression of Progerin (Miller et al., 2013).

For replicative aging (Figure 4A) we systematically compared low (p = 5–15) and high passages (p= > 30 except Old 2–4) of young, mid-age and old age donor-derived human fibroblasts. From every age group we choose two different fibroblast lines that display sufficient growth to enable passaging until high passage.

First, we investigated primary age markers. All fibroblasts in high passages (^high) displayed significant increase in DSBs measured by γH2A.X foci in comparison to the mean of all low passages (^low) of young donor cells (Figure 4B). We always compared to the mean of the controls and did not use a paired t-test to the respective low passage cell line to depict not-isogenic conditions. Expression of H3K9Me3 was reduced in Young 4^high, Midage 1^high, Midage 2^high and Old 3^high in comparison to grouped Young^low (Figure 4C). Shortening of telomeres was seen in Young 1^high, Young 2^high, Midage 3^high and Old 3^high compared to the mean of all Young^low (Figure 4D). Nucleus area (Figure 4E) and amount of positive SA-βGal stained cells (Figure 4H) was significantly increased in all cells in high passages. Next, we checked antagonistic age markers. We detected an upregulation of cell cycle arrest markers p21 and p16 in Young 1^high, Midage 1^high, Old 1^high and Old 3^high (Figure 4F). Furthermore, SASP activation tested by release of IL-6 and IL-8 in the cell culture medium was found in all fibroblasts with high passages in comparison to the mean of all Young^low (Figure 4G). Moreover, high passages of all fibroblasts displayed a decreased expression of Lamin B1 (Figure 4I). In summary, all high passaged cells homogenously display the various age markers and showed a high full AgeScore (Figure 4J).

For artificial aging, we used a doxycycline-inducible GFP-Progerin expression system (Kubben et al., 2016) in young donor cells as a model system for artificial aging. This allowed us to prematurely age the cells in a timely well-defined manner. Progerin expression indeed induced a homogenous and very rapid expression of a multitude of age markers. Four days after GFP-Progerin induction with doxycycline (1 μg/mL), prematurely aged human fibroblasts showed formation of age-related nuclear envelope phenotypes paralleled by the expression of various primary age markers. We always compared to the mean of the controls and not paired t-test to the respective low passage cell line to not cause a bias due to isogenic genetic background. All fibroblasts from young donors with GFP-Progerin expression displayed a significant increase of γH2A.X foci in comparison to non-induced fibroblasts (Figure 5B) whereas there were no changes in histone modification (Figure 5C). In comparison to non-induced cells, Young 4 and Young 5 with GFP-Progerin expression showed a shortening of telomere length (Figure 5D). Subsequently, we investigated the antagonistic age markers in prematurely aged fibroblasts from young donors. We observed significant increase in RNA expression of p16 in all fibroblasts with GFP-Progerin expression (Figure 5E). All prematurely aged fibroblasts showed significant increases in SA-βGal positive cell numbers (Figure 5F), nucleus area (Figure 5G) and cytokine release (Figure 5H) in comparison to non-induced cells. Furthermore, Lamin B1 expression was significantly decreased in all prematurely aged fibroblasts (Figure 5I). Generally, the AgeScore showed an increase under GFP-Progerin expression in young donor fibroblasts (Figure 5J). Progerin overexpression rapidly induced a premature aging that was in fact very similar to replicative senescence (Figure 4).

Finally, we wanted to address the question how the different aging markers behave in cell culture models of premature aging diseases. For this we studied low passage fibroblasts from patients suffering from the Progeria syndromes HGPS and WS in comparison to age matched controls, respectively (Figure 6A). Controls for HGPS were thus significantly younger than controls for WS.

The growth rates of progeria fibroblasts were investigated by PDL under stable conditions (37°C, 5% CO₂) (Supplementary Figure S2). Fibroblasts from donors of HGPS patients showed no changes in PDL, whereas WS patient’s fibroblasts displayed slower growth compared with the mean of age-matched controls. Next, we investigated primary age markers. In progeria syndrome’s fibroblasts, we determined an increase in γH2A.X foci in all tested cells (HGPS1, HGPS 2, WS 1, and WS 2) in comparison to the mean of all age-matched controls (Figure 6B). Furthermore, we observed a decrease in H3K9Me3 expression in donor fibroblasts of HGPS patients (HGPS 1 and HGPS 2). There were no changes in fibroblasts from WS patients (Figure 6C). In comparison to grouped age-matched controls, fibroblasts of progeria patients displayed no changes in telomere length (Figure 6D).

Secondly, we checked antagonistic age markers. Activation of cell cycle arrest was detected in WS by upregulated RNA expression levels of p16 (Figure 6E). Furthermore, in all fibroblasts from progeria patients we observed a rise of SA-βGAL positive cells (Figure 6F) and a significant decrease in Lamin B1 expression in HGPS 1, HGPS 2 and WS 1 (Figure 6G). In fibroblasts of HGPS patients we observed a significant increase in concentration of IL-8 in conditioned media. There were no changes in the levels of SASP marker IL-6 or IL-8 in WS fibroblasts (Figures 6H, I). All progeria fibroblasts displayed an increase in nucleus area (Figure 6J). The AgeScores of human fibroblasts of progeria patients (HGPS 1 and 2, WS 1 and 2) were thus higher in comparison to age-matched controls and similar to those of old donor fibroblasts (Figure 6K).

Interestingly, the pattern of individual markers within the AgeScore was different between HGPS cells and the cells of artificial aging, which were aged by overexpression of Progerin (Figure 7). Furthermore, the pattern of individual markers within the AgeScore were very similar between the different donors of the respective progeria syndrome, however did also differ between HGPS and WS (Figure 7). A summary of age marker expression by individual cell lines and aging conditions is presented in Table 2.