
==== Front
Stem Cell Reports
Stem Cell Reports
Stem Cell Reports
2213-6711
Elsevier

S2213-6711(24)00218-2
10.1016/j.stemcr.2024.07.009
Report
Dissecting the impact of differentiation stage, replicative history, and cell type composition on epigenetic clocks
Gorelov Rebecca 1234
Weiner Aaron 1234
Huebner Aaron 1234
Yagi Masaki 1234
Haghani Amin 56
Brooke Robert 7
Horvath Steve 5678
Hochedlinger Konrad khochedlinger@helix.mgh.harvard.edu
12349∗
1 Massachusetts General Hospital Department of Molecular Biology, Boston, MA 02114, USA
2 Massachusetts General Hospital Cancer Center and Center for Regenerative Medicine, Boston, MA 02114, USA
3 Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02139, USA
4 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
5 Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
6 Altos Labs, San Diego, CA 92121, USA
7 Epigenetic Clock Development Foundation, Torrance, CA 90502, USA
8 Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
∗ Corresponding author khochedlinger@helix.mgh.harvard.edu
9 Lead contact

22 8 2024
10 9 2024
22 8 2024
19 9 12421254
3 10 2023
22 7 2024
23 7 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Epigenetic clocks, built on DNA methylation patterns of bulk tissues, are powerful age predictors, but their biological basis remains incompletely understood. Here, we conducted a comparative analysis of epigenetic age in murine muscle, epithelial, and blood cell types across lifespan. Strikingly, our results show that cellular subpopulations within these tissues, including adult stem and progenitor cells as well as their differentiated progeny, exhibit different epigenetic ages. Accordingly, we experimentally demonstrate that clocks can be skewed by age-associated changes in tissue composition. Mechanistically, we provide evidence that the observed variation in epigenetic age among adult stem cells correlates with their proliferative state, and, fittingly, forced proliferation of stem cells leads to increases in epigenetic age. Collectively, our analyses elucidate the impact of cell type composition, differentiation state, and replicative potential on epigenetic age, which has implications for the interpretation of existing clocks and should inform the development of more sensitive clocks.

Graphical abstract

Highlights

• Epigenetic age differs between certain adult stem cells and differentiated progeny

• Stem cell proliferative history impacts epigenetic age

• Forced stem cell proliferation in vivo and in vitro increases epigenetic age

• Age-associated intra-tissue heterogeneity modulates DNA methylation clocks

In this report, the corresponding author and colleagues explore the cellular determinants of widely used DNA methylation age predictors. They uncover a previously unappreciated heterogeneity in epigenetic age across diverse adult stem cell populations, which they link to stem cell proliferative history. Building on this, they experimentally demonstrate that age-associated changes in cell type composition also impact epigenetic clocks.

Keywords

epigenetic clocks
DNA methylation
aging
adult Stem
cell Proliferation
differentiation
Published: August 22, 2024
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pmcIntroduction

Age-related changes to DNA methylation at cytosine-phosphate-guanine (CpG) motifs are a conserved hallmark of mammalian aging (Fraga and Esteller, 2007). Remarkably, changes to the methylation status of only a small subset of CpGs are accurate predictors of chronological age, known as “epigenetic age” or “DNA methylation age.” These predictors, referred to as “epigenetic clocks” or “DNA methylation clocks,” are penalized regression models that select CpGs from a training dataset undergoing hyper- or hypo-methylation with age and are subsequently used to predict a sample’s biological age (Horvath, 2013; Horvath and Raj, 2018b).

Importantly, epigenetic clocks register interventions known to modulate longevity, including but not limited to progerias, calorie restriction, and heterochronic parabiosis (Horvath et al., 2018a; Petkovich et al., 2017; Poganik et al., 2023). However, despite their growing use as robust biomarkers of aging, little is known about the clock’s mechanistic underpinnings. For example, given the majority of studies using epigenetic clocks were conducted on bulk tissue, it remains unclear whether all cells within a tissue exhibit the same epigenetic age and whether age-related shifts in cellular composition within a tissue affect the epigenetic clock. This is particularly relevant in tissues composed of heterogeneous cell types which are known to (1) have different DNA methylation profiles and (2) change their frequencies with age (Bock et al., 2012; Tabula Muris Consortium, 2020; Young et al., 2021; Zhang et al., 2022). Previous work using human samples suggest that certain tissues (e.g., breast), as well as specific anatomical regions within a tissue (e.g., brain), exhibit distinct epigenetic ages compared to the chronological age of that individual, yet the cellular basis for these observations is unclear (Horvath et al., 2015a; Sehl et al., 2017). Recently, single-cell approaches have been employed in an effort to address the contribution of cellular heterogeneity to epigenetic clocks. However, these efforts are limited by reduced sensitivity and have yet to be applied to diverse tissues (Hernando-Herraez et al., 2019; Trapp et al., 2021). Resolving these fundamental questions is therefore critical to gain insights into the mechanisms that drive the epigenetic clock and to build improved and more sensitive clocks in the future.

Results

Epigenetic age comparison between adult stem cells and differentiated cells across tissues

To determine the epigenetic age of functionally different cell types within a tissue, we sought to compare the DNA methylation age of stem cells versus differentiated cells. We were particularly interested in this comparison given their differing capacities for regeneration across tissues as well as previously published in vitro data suggesting cultured stem cells and differentiated cells may have distinct epigenetic ages (Kabacik et al., 2022; Sheng et al., 2018).

To address this fundamental question in vivo, we analyzed immature and mature cell populations from muscle, blood, and various epithelia using three previously established types of epigenetic clocks widely used in the field, including (1) tissue-specific clocks that measure age for a particular tissue, (2) pan-tissue clocks that measure epigenetic age across tissue types, and (3) universal clocks that measure epigenetic age across tissues and mammalian species (Lu et al., 2023; Meer et al., 2018; Mozhui et al., 2022; Petkovich et al., 2017). For the majority of our analyses, samples were analyzed with the HorvathMammalMethylChip320, and clocks built off this methylation array were used for analysis, unless otherwise noted (Arneson et al., 2022). Importantly, these clocks were trained largely on bulk tissues including but not limited to the bone marrow, muscle, intestine, and epidermis. This is of particular relevance for the current study where we probe the effect of differentiation status on existing clocks, which have been trained on tissues comprising a combination of stem, progenitor, and differentiated cells. More detailed descriptions of the clocks used in this work can be found in the experimental procedures section.

We first examined the epigenetic age of muscle stem cells, termed satellite cells, and of whole muscle, which is largely composed of postmitotic myofibers (Ma et al., 2020). To isolate satellite cells, we first dissociated whole-muscle tissue and then purified mononucleated cells based on the expression of the satellite cell-specific Pax7-nGFP reporter using fluorescence-activated cell sorting (FACS) (Figure 1A) (Sambasivan et al., 2009). According to the mouse muscle-specific and pan-tissue clocks, the epigenetic age of bulk muscle was highly correlated with the chronological age of the samples, as expected. Satellite cells, on the other hand, exhibited a striking reduction in epigenetic age (∼50%–60%) compared to their chronological age and the bulk muscle samples they were derived from (Figures 1B and S1A). Notably, this effect was more subtle with both universal clocks. However, across all examined clocks, satellite cells did age overtime, albeit at a slower rate compared to whole-muscle tissue. These findings support and extend previous studies that analyzed the epigenetic age of single-cell and pseudo-bulk populations of satellite cells from young and old mice (Hernando-Herraez et al., 2019; Trapp et al., 2021).Figure 1 Comparing DNA methylation age between adult stem and differentiated cells

(A) Experimental scheme including a representative FACS plot for satellite cells isolated from Pax7-nGFP mice.

(B) Measurement of DNA methylation age of muscle and satellite cells using a mouse pan-tissue clock and a universal clock. Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗) p < 0.05.

(C) Experimental scheme including a representative FACS plot of HSPCs isolated from bone marrow.

(D) Measurement of DNA methylation age of various subpopulations in the bone marrow using a mouse pan-tissue clock and a universal clock. Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗) p < 0.05.

(E) Experimental scheme including a representative FACS plot of intestinal stem cells and differentiated cells isolated from the small intestinal epithelium of Lgr5-eGFP mice.

(F) Measurement of DNA methylation age of intestinal stem and differentiated cells using a mouse pan-tissue clock and a universal clock. Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice).

(G) Experimental scheme including a representative FACS plot of basal stem cells and differentiated cells from airway epithelium isolated from a ΔNp63-GFP mouse.

(H) Measurement of DNA methylation age of basal epithelial stem and differentiated cells using a mouse pan-tissue clock and a universal clock. Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice), except trachea and lungs (n = 2 mice). See also Figure S1.

To determine whether the reduced epigenetic age of satellite cells is a general feature of tissue stem cells, we next examined hematopoietic stem and progenitor cells (HSPCs) as well as various epithelial stem cell populations. To isolate stem, progenitor, and differentiated cell types from the blood system, we FACS-purified bone marrow subpopulations using endogenous surface markers that allowed us to distinguish differentiated T cells (CD3+), B cells (B220+), granulocytes (Gr1+), macrophages (Mac1+), and red blood cells (Ter119+), collectively termed “Lineage+” cells, from myeloid-committed progenitors (Lineage−, cKit+, Sca1-), lymphoid-committed progenitors (Lineage−, cKitmid, Sca1mid, Il7r+, Flt3+), and multipotent HSPCs (Lineage−, cKit+, Sca1+ or LSK) (Figures 1C and S1B).

HSPCs, myeloid-committed progenitors, and differentiated hematopoietic cells aged at similar rates to the chronological age of the sample they were derived from according to blood, pan-tissue, and universal clocks (Figures 1D and S1C). However, lymphoid-committed progenitors registered a younger epigenetic age compared to differentiated cells in the bone marrow as well as their own chronological age. Importantly, we were able to recapitulate these differences in epigenetic age using a previously published bisulfite sequencing dataset of stem and progenitor cell populations within the bone marrow, which we analyzed with two independent clocks, a bisulfite sequencing-based pan-tissue mouse clock (whole lifespan mouse multi-tissue clock, or BS-WLMT Clock) and a blood-specific mouse clock (BS-Mouse Blood Clock) (Figure S1D) (Bock et al., 2012; Meer et al., 2018; Petkovich et al., 2017). More information on these clocks is available in the experimental procedures section.

It is important to acknowledge that the HSPC population is heterogeneous as it contains both hematopoietic stem cells (HSCs) and multipotent progenitors. We therefore sought to examine epigenetic age in more defined subpopulations of HSPCs. Given that the isolation of sufficient numbers of bona fide HSCs for bulk methylation profiling with the methylation arrays is technically challenging, we used a previously published bisulfite sequencing dataset of HSPCs from young and old mice that was further enriched for HSCs using the stem cell marker CD150 (Lineage−, cKit+, Sca1+, CD150+) (Sun et al., 2014). Consistent with our results in unfractionated HSPCs, we observed that CD150+ HSPCs have an epigenetic age that largely correlates with the chronological age of the sample using either the BS-WLMT Clock or the BS-Mouse Blood Clock (Figure S1E).

Next, we used Lgr5-eGFP reporter mice to purify intestinal epithelial stem cells (Lgr5-eGFP+, EpCAM+ ISCs) and their differentiated progeny (Lgr5-eGFP−, EpCAM+), which are largely composed of absorptive, secretory, and enteroendocrine cells (Figure 1E) (Tian et al., 2011). Unlike the muscle and bone marrow, intestinal stem and differentiated cells aged at the same rate across all examined clocks (Figures 1F and S1F). Our result differs from previously published work suggesting that human intestinal crypts, where ISCs reside, have a decreased epigenetic age compared to intestinal villi, which are largely composed of differentiated cells (Lewis et al., 2020). However, as crypts contain a heterogeneous population of cells, it is possible that non-ISCs or non-epithelial cells accounted for the observed reduction in epigenetic age. We also cannot rule out species-specific effects that underlie these differences.

To expand upon our finding in other highly regenerative tissues, we next examined epithelia maintained by basal stem cells. We utilized a ΔNp63-GFP mouse, where one copy of the Trp63 gene is replaced by a GFP reporter, to distinguish stem cells residing in the epithelial basal layer (p63-GFP+) and differentiated cells residing in the suprabasal layers (p63-GFP−). Using this model, we isolated p63-GFP+ and p63-GFP− cells from a variety of epithelia from the same mouse including the esophagus, tongue, skin, and airway (trachea and lungs) (Figure 1G) (Romano et al., 2012). The stem versus differentiated cells from the esophagus, tongue, and skin exhibited a similar epigenetic age. Strikingly, however, the differentiated epithelial cells from the airways showed an epigenetic age that was 2–30 times higher than the matched stem cell population, paralleling our observations in muscle (Figures 1H and S1G).

Taken together, it appears that certain stem or progenitor populations do not have a meaningfully different epigenetic age compared to their differentiated progeny, namely HSPCs and myeloid-committed progenitors, as well as intestinal, skin, esophagus, and tongue epithelium. By contrast, other stem cell populations do in fact show notable differences in epigenetic age, including satellite cells, lymphoid-committed progenitors, and airway epithelial stem cells. Interestingly, intestinal, esophageal, and tongue epithelia are known to turn over within a few days, skin within a few weeks, and certain myeloid progenitors differentiate into mature myeloid cells within less than 24 h, representing some of the most proliferative tissues in the body (Barker, 2014; Hsieh et al., 2004; Passegue et al., 2005; Piedrafita et al., 2020; Potten et al., 2002). On the other hand, satellite cells are largely quiescent under homeostatic conditions, airway epithelium turns over on the order of months, and lymphoid-committed progenitors generate differentiated cells at a much slower rate compared to their myeloid counterparts (Cheung et al., 2012; Passegue et al., 2005; Ruysseveldt et al., 2021). These stark differences in epigenetic age within tissues therefore suggest that proliferative history and turnover rate may play a role in the accumulation of molecular changes registered by epigenetic clocks.

Proliferation impacts the epigenetic age of adult stem cells

To probe a possible link between stem cell proliferation rates and epigenetic aging in a more defined system, we returned to epithelial tissues maintained by basal stem cells (p63-GFP+). This system allowed us to compare the proliferation rates of matched epithelial stem cell populations from the esophagus, tongue, skin, and airway, where we had observed variability in epigenetic age (Figure 1H, blue bars). Indeed, we detected striking differences in the proliferative ability of these different stem cell populations using in vivo labeling with 5-ethynyl-2'-deoxyuridine (EdU), a nucleoside analog that incorporates into newly formed DNA, serving as a proxy of cell division. Specifically, we observed a gradient of proliferative indices ranging from the esophagus (∼10% EdU+) and tongue (∼6% EdU+) to the skin (∼3% EdU+) and trachea (∼2% EdU+) (Figures 2A, S2A, and S2B). When we correlated these values with the previously measured epigenetic ages of these populations, we observed a clear positive relationship, with the slowest cycling stem cell populations exhibiting a younger epigenetic age compared to faster cycling cells (Figure 2B).Figure 2 Cell division impacts the epigenetic age of adult stem cells

(A) In vivo EdU incorporation in p63-GFP+ cells from the trachea, skin, tongue, and esophagus. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗∗) p < 0.01.

(B) Correlative plot depicting the relationship between epigenetic age and proliferation (measured by EdU incorporation) among basal epithelial stem cells from different tissues. Gray dashed line is a simple linear regression depicting the positive correlation.

(C) Experimental scheme depicting the stomach epithelial culture system derived from 2-month-old mice (left) and measurement of DNA methylation age using a mouse-pan tissue clock and two universal clocks (right). Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗∗∗) p < 0.001, (∗) p < 0.05.

(D) Experimental scheme depicting satellite cell culture system derived from 3-month-old mice (left) and measurement of DNA methylation age using a mouse-pan tissue clock and two universal clocks (right). Red dashed line indicates chronological age. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗∗∗) p < 0.001, (∗∗) p < 0.01, (∗) p < 0.05.

(E) Experimental scheme depicting HSC transplantation experiment adapted from Beerman and colleagues (Beerman et al., 2013) (top) and measurement of DNA methylation age using the BS-WLMT Clock and BS-Mouse Blood Clock (bottom). Young HSCs were harvested from 3.5-month-old mice, old HSCs were harvested from 25-month-old mice, and donor mice were between 3 and 3.5 months old at the time of transplant. Red dashed lines indicate chronological age or age range of the samples at time of harvest for bisulfite sequencing. Error bars indicate mean ± SD (n = 3 mice), except young and old HSC samples (n = 2 mice).

(F) Experimental scheme depicting 5-FU experimental design adapted from Beerman and colleagues (Beerman et al., 2013) (top) and measurement of DNA methylation age using the BS-WLMT Clock and BS-Mouse Blood Clock (bottom). Mice were between 3 and 3.5 months old when the experiment was initiated. Red dashed lines indicate the chronological age range of the samples at time of harvest. Error bars indicate mean ± SD (n = 3 mice), except 4X treated samples (n = 2 mice). See also Figure S2.

To experimentally test the effect of forced proliferation on various stem cell populations, we utilized a recently developed in vitro culture system that maintains gastric stem cells derived from the antral stomach epithelium (Figure 2C left) (Huebner et al., 2023). We found that continuous passaging of these cells, over the course of several months, led to significant increases in epigenetic age across all relevant clocks (Figure 2C right). Our observation confirms and extends a recent study that showed keratinocyte cultures have an increased epigenetic age with passage (Kabacik et al., 2022). However, as both keratinocyte and gastric stem cell cultures undergo a certain degree of spontaneous differentiation, we cannot exclude the possibility that differentiated cells contributed to the observed change in epigenetic age.

To determine the effects of forced proliferation on epigenetic age using a purer stem cell culturing system, we harvested quiescent satellite cells from adult mice and explanted them in vitro for 5 days, which triggers activation and proliferation (Figure 2D left). Importantly, satellite cells remained Pax7+ after short-term culture, indicating they retain muscle stem cell identity as they proliferate (Figure S2C). After 5 days in culture, explanted satellite cells exhibited a significant increase in epigenetic age compared to matched, quiescent samples that were harvested before culture, supporting the notion that proliferation indeed contributes to epigenetic age (Figure 2D right).

To explore whether these observations apply to other regenerative tissues, we analyzed two complementary in vivo systems, where HSCs were forced to proliferate. First, we utilized a previously published dataset, where HSCs were isolated from adult mice and then transplanted at low numbers (10 HSCs) into recipient animals. As the recipient mice had been irradiated, the transplanted donor HSCs were forced to proliferate extensively in order to reconstitute the recipient’s blood system. After 20 weeks, when reconstitution was complete, donor-derived HSCs were harvested from recipients and processed for bisulfite sequencing (Figure 2E top) (Beerman et al., 2013). We analyzed these samples as well as young and old non-transplanted HSCs using the previously described BS-WLMT Clock and BS-Mouse Blood Clock (Meer et al., 2018; Petkovich et al., 2017). Interestingly, transplanted HSCs exhibited an epigenetic age that was almost double the chronological age of the HSCs (age of donor at transplant plus 20 weeks of engraftment) (Figure 2E bottom), consistent with the interpretation that forced cycling of adult stem cells contributes to epigenetic aging. Our finding also aligns with recent work in humans demonstrating that transplantation of small numbers of cord blood HSPCs results in accelerated epigenetic aging (Onizuka et al., 2023).

To exclude the possibility that the transplantation procedure itself contributed to accelerated aging, we also analyzed an independent dataset where young adult mice were injected with 5-fluorouracil (5-FU). 5-FU is a drug that forces quiescent HSCs into the cell cycle to restore hematopoiesis. Specifically, mice received multiple doses of 5-FU, after which HSCs were harvested and processed for bisulfite sequencing (Figure 2F top) (Beerman et al., 2013). Compared to the age-matched controls, HSCs from mice injected with 5-FU exhibited an increased epigenetic age (Figure 2F bottom). Of relevance, HSCs isolated post-transplant and after 5-FU injections reportedly show other features of aging as well, including immunophenotypic changes and functional defects (Beerman et al., 2013). Overall, our analyses in basal, gastric, muscle, and hematopoietic stem cells strongly suggest that changes in stem cell division rates underlie the observed variability in epigenetic ages in these cell populations.

Age-associated changes in cell type composition modulate DNA methylation clocks

We next explored whether the correlation between epigenetic age and proliferation potential we detected across different stem cells extends to cell types within a heterogeneous tissue. We therefore returned to muscle, which is largely composed of multinucleated myofibers but also contains multiple other cell types including differentiated hematopoietic cells, fibro-adipogenic progenitors (FAPs), endothelial cells, and the previously examined satellite cells (Figure 3A left). Critically, FAPs, endothelial cells, and satellite cells were previously shown to be quiescent, while differentiated blood cells arise from highly proliferative hematopoietic precursor cells (Cheung et al., 2012; Negroni et al., 2022; Passegue et al., 2005; Ricard et al., 2021). We utilized cell type-specific surface markers to isolate each of these cell types from young, middle-aged, and old mice and measured their epigenetic age (Figure S3A). In line with our findings in stem cells, quiescent cells had a younger epigenetic age compared to bulk muscle and hematopoietic cells in middle-aged and old mice (Figure 3A right).Figure 3 Dissecting how age-associated changes in cellular composition affect DNA methylation age predictors

(A) Experimental scheme depicting isolated cell types (left) and DNA methylation age of various subpopulations within the muscle using a mouse pan-tissue clock and mouse-muscle clock (right). Red dashed lines indicate chronological age. Young mice were 4 months old, middle-aged mice were 9–10 months old, and old mice were 24 months old. Error bars indicate mean ± SD (n = 3–4 mice). Two-tailed unpaired Student’s t test: (∗∗∗) p < 0.001, (∗∗) p < 0.01, (∗) p < 0.05.

(B) Quantification of the change in cellular composition in the peripheral blood with age. Error bars indicate mean ± SD (n = 4 mice). Two-tailed unpaired Student’s t test: (∗∗∗) p < 0.001, (∗∗) p < 0.01.

(C) DNA methylation age of various subpopulations within the blood using universal, pan-tissue, and blood clocks (n = 3–4 mice). Red dashed lines indicate chronological age. Error bars indicate mean ± SD (n = 3–4 mice). Two-tailed unpaired Student’s t test: (∗∗) p < 0.01, (∗) p < 0.05.

(D) Experimental scheme outlining how peripheral blood from old mice was mixed in the cellular proportions seen in young mice and vice versa.

(E) Measurement of DNA methylation age of whole and mixed blood samples using a mouse pan-tissue clock. Red dashed lines indicate chronological age. Error bars indicate mean ± SD (n = 3 mice). Two-tailed unpaired Student’s t test: (∗) p < 0.05. See also Figure S3.

Of note, cell types with a decreased epigenetic age in the muscle including satellite cells, endothelial cells, and FAPs were recently shown to be depleted with age, which we independently confirmed here (Figure S3B) (Lukjanenko et al., 2019; Zhang et al., 2022). These observations raise the important question of whether epigenetic clocks trained on bulk tissue could be impacted by age-related shifts in subpopulations of cells with distinct methylation profiles. Specifically, our data generated in muscle support the hypothesis that cell types that become less abundant with age have a lower epigenetic age. However, it is difficult to measure this effect in muscle as many of the relevant cell types are quite rare, making it unlikely that their age-dependent loss has a meaningful impact on the clock.

We therefore turned to the peripheral hematopoietic system as it undergoes more significant age-related compositional changes. Specifically, we investigated the well-characterized increase in myeloid cells, particularly granulocytes, and the reciprocal decrease in lymphoid cells, or T and B cells, in the peripheral blood of aging mice (Figures 3B and S3C). Indeed, granulocytes exhibited an elevated epigenetic age compared to T and B cells with the majority of clocks examined (Figure 3C). We confirmed these effects using a different dataset of sorted peripheral blood cell types using the BS-WLMT Clock and BS-Mouse Blood Clock (Figure S3D). Our findings support the previous observations that (1) the global DNA methylomes of myeloid and lymphoid cells are quite distinct from one another, (2) the elevated epigenetic age of peripheral blood samples of individuals with Parkinson’s disease can be partly attributed to an increased level of circulating granulocytes, and (3) considering age-associated changes in blood cell composition can enhance the predictive power of epigenetic clocks (Bock et al., 2012; Chen et al., 2016; Horvath and Ritz, 2015b; Zhang et al., 2024). Critically, as it relates to the latter, previous studies incorporating the contribution of blood cell composition to epigenetic clocks relied on extrapolations of cell type composition based on methylation sequencing data from bulk blood samples rather than from purified subpopulations. Hence, the actual contribution of cellular heterogeneity to epigenetic clocks remains experimentally unverified to our knowledge.

To experimentally assess the contribution of age-related changes in cell type composition to the epigenetic clock, we performed mixing experiments between different blood cell types known to change in frequency with age. Specifically, we purified granulocytes, T cells, and B cells from the peripheral blood of young and old mice and then immediately re-mixed these cell types in proportions seen in the opposite age group. Namely, sorted populations of granulocytes, T cells, and B cells from old mice were re-mixed in the proportions seen in young mice, and similarly sorted granulocytes, T cells, and B cells from young mice were re-mixed in the proportions seen in old mice. Un-mixed bulk blood samples from young and old mice, which had a normal, age-appropriate blood cell composition, served as controls (Figure 3D). Intriguingly, old blood mixed in the proportions of young blood registered a ∼20% reduction in epigenetic age (Figures 3E and S3E). This result indicates that, while age-associated changes in cellular composition clearly modulate the clock, they are not the sole driver. Moreover, mixing young blood in the composition of old blood did not significantly affect the sample’s epigenetic age, suggesting that the impact of compositional changes is accentuated with age.

Discussion

Epigenetic clocks have emerged as robust and powerful biomarkers of aging using bulk tissue methylation analysis, which facilitates studies that (1) investigate how diseases affect the aging process, (2) evaluate the effectiveness of therapeutic aging interventions, and (3) correlate age with overall health among the general public (Cao et al., 2022; Fahy et al., 2019). It is therefore imperative to understand the cellular drivers of epigenetic clocks. Our work contributes to this important question by highlighting the intra-tissue heterogeneity in epigenetic aging rates. Specifically, our results suggest that, based on currently available methylation clocks, certain populations of stem and progenitor cells age at different rates. We observed a marked difference in epigenetic age between more proliferative stem cell populations such as esophageal basal cells and small intestinal crypt cells and more quiescent stem/progenitor cell populations such as satellite cells and lymphoid-committed progenitors.

Our observation on the impact of proliferation on the epigenetic clock is notable in light of previous work. As mentioned earlier, breast tissue from pre-menopausal women had an increased epigenetic age compared to matched peripheral blood samples. Interestingly, this difference diminished with age in post-menopausal women. Breast tissue epithelium of pre-menopausal women undergoes bouts of cell cycling. However, these bouts of proliferation are known to decrease with age and the onset of menopause, leading the authors to speculate that the increased epigenetic age of pre-menopausal breast tissue may be the result of increased cell cycling (Sehl et al., 2017). More recent work has demonstrated that naive lymphoid cells, which are typically quiescent, register a younger epigenetic age compared to matched activated lymphoid cells that have undergone proliferation in response to antigen (Jonkman et al., 2022; Mi et al., 2024). And lastly, interventions that are known to slow down the epigenetic clock such as calorie restriction are linked to lower levels of cell turnover (Hsieh et al., 2004).

Our findings also call to mind the premise of a different DNA methylation-based predictor termed epigenetic time of cancer (epiTOC), which uses DNA methylation data to predict stem cell division rates across tissues (Teschendorff, 2020; Yang et al., 2016). This suggests that both epigenetic age predictors and mitotic clocks, like epiTOC, depend on similar changes in DNA methylation. Indeed, mitotic clocks and epigenetic age predictors rely on age- and/or division-associated hypermethylation at genomic regions that are targeted by the epigenetic modifier polycomb repressive complex 2 (PRC2) (Lu et al., 2023; Teschendorff, 2020; Yang et al., 2016). Similarly, in a recent study that built epigenetic clocks based on the HorvathMammalMethylChip320, Lu and colleagues noted that aging proliferative tissues exhibited greater amounts of hypermethylation at PRC2 target sites compared to less proliferative aging tissues, underscoring a potential mechanism by which proliferative history may impact the DNA methylome in a way that drives the clock (Lu et al., 2023). Moreover, recent work has shown that changes in DNA methylation at PRC2 targets are also shared between physiological aging and in vitro culturing of differentiated cell types, such as mouse embryonic fibroblasts (i.e., serial passaging) (Minteer et al., 2022). However, it is important to note that (1) even quiescent cell types had increased epigenetic ages with time and (2) long-term culture of embryonic stem cells and induced pluripotent stem cells does not lead to increases in epigenetic age, highlighting that proliferative history likely contributes to epigenetic clocks but is not their sole driver (Kabacik et al., 2022). Regardless, the notion that proliferative history is associated with both aging across different stem cell populations and cancer provides intriguing mechanistic links between stem cell exhaustion, aging, and cancer (Beerman et al., 2013; Tao et al., 2019; Tomasetti and Vogelstein, 2015).

In this work, we have also showed that, in the muscle and blood, cells that undergo age-associated changes in abundance register a distinct epigenetic age from bulk tissues and other tissue-resident cell types. While this finding has previously been assumed through deconvolution of bulk sequencing datasets in peripheral blood, we have experimentally demonstrated that intra-tissue heterogeneity can indeed impact epigenetic clocks to a certain extent (Chen et al., 2016; Zhang et al., 2024).

Overall, we have highlighted how stem cell proliferation history and age-associated changes in cell type composition impact an aging tissue’s bulk DNA methylome in a way that influences the epigenetic clock. We anticipate that these findings will provide an important framework for interpreting data generated by epigenetic clocks and potentially inform strategies to generate improved epigenetic clocks. From a mechanistic point of view, it will be important to assess in future studies what molecular changes correlate with and possibly drive the observed differences in epigenetic age among the different cell types in a tissue. For example, in our dataset aged HSPCs had a higher epigenetic age compared to aged lymphoid progenitors, and aged HSCs and multipotent progenitors are thought to accrue more DNA damage with age than lymphoid progenitors, raising the intriguing possibility that DNA damage directly or indirectly impacts epigenetic clocks (Beerman et al., 2014).

Experimental procedures

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to the lead contact Dr. Konrad Hochedlinger.

Materials availability

This study did not generate unique reagents.

Data and code availability

The accession number for the data reported in this paper is GEO: GSE238172.

Isolation of cellular subpopulations

Muscle

Mononuclear cells were isolated from Pax7-nGFP, Rosa-M2rtTA, Col1a1-tetO-Myod1 mice as previously described (Maesner et al., 2016).

Epithelium

The small intestines of Lgr5-DTR-eGFP mice as well as airway, skin, tongue, and esophagus from ΔNp63-GFP) mice were isolated as previously described with minor modifications (Mou et al., 2016; O’Rourke et al., 2016; Weiner et al., 2022).

Hematopoietic organs

The blood and bone marrow from C57BL/6 mice were isolated as previously described with minor modifications (Brumbaugh et al., 2019; Rodriguez-Fraticelli et al., 2020).

For more details on all isolation protocols, see the supplemental experimental procedures. Of note, all procedures which involved mice adhered to MGH's Institutional Animal Care and Use Committee (IACUC) protocol no. 2006N000104.

In vitro culturing experiments

Stomach epithelia monolayer culture

Gastric glands were isolated, cultured, and passaged as previously described from C57BL/6 mice (Huebner et al., 2023).

Satellite cell culture

Satellite cells from Pax7-nGFP, Rosa-M2rtTA, Col1a1-tetO-Myod1 mice were collected and cultured as previously described (Maesner et al., 2016; Yagi et al., 2021).

DNA methylation profiling

Mouse DNA samples were subjected to the HorvathMammalMethyl320 Beadchip run on the Infinium microarray platform at AKESOgen Inc (Arneson et al., 2022).

DNA methylation clock analysis

Epigenetic ages from Universal Clocks 2 & 3, Mouse Pan-Tissue Clock, Mouse Muscle Clock, and Mouse Blood Clocks were calculated as previously described (Lu et al., 2023; Mozhui et al., 2022). BS-WLMT and BS-Mouse Blood Clocks were also applied on published bisulfite sequencing datasets as previously described (Meer et al., 2018; Petkovich et al., 2017). More detailed information on the clocks can be found in the supplemental experimental procedures. DNA methylation sequencing datasets from previously published work were accessed through GEO: GSE44117 and GSE47819 as well as http://invivomethylation.computational-epigenetics.org/.

Statistical analysis

Statistical analyses were performed using Prism software (GraphPad). Details for statistical analyses, including replicate numbers, are included in the figure legends.

Supplemental information

Document S1. Figures S1–S3

Document S2. Article plus supplemental information

Acknowledgments

We thank the Hochedlinger lab as well as Zhixun Dou and Vadim Gladyshev for their critical reading of this work. We also wish to acknowledge the HSCI-CRM Flow Cytometry Core Facility for their help with FACS. K.H. was supported by 10.13039/100005294 MGH , the Milky Way Research Foundation, the 10.13039/100000002 NIH (5R01AR077695 ), and the Gerald R. and Darlene Jordan Chair in Regenerative Medicine. Figures were made with BioRender.com.

Author contributions

R.G. and K.H. conceived the study and wrote the manuscript. R.G., A.W., A. Huebner, and M.Y. performed experiments. A. Haghani, R.B., and S.H. performed methylation sequencing using the HorvathMammalMethylChip320 and conducted epigenetic clock analyses.

Declaration of interests

S.H. is a founder and R.B. is the executive director of the nonprofit Epigenetic Clock Development Foundation that distributes the mammalian methylation array platform (HorvathMammalMethylChip320) used here.

Supplemental information can be found online at https://doi.org/10.1016/j.stemcr.2024.07.009.
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