Analysis of age-dependent DNA methylation changes in plucked hair samples using massive parallel sequencing

Various tissue samples, including plucked head hair from deceased individuals without signs of putrefaction were collected in a previous study in Freiburg, Germany [22] and stored at −80 °C until analysis. Approval for collection and the extended use of the samples for the conducted DNAm analysis was obtained from the ethics committee of the University of Freiburg (263/10, 397/16). Plucked hairs from 49 selected individuals were analyzed covering a wide range of ages (5–88 years) and both sexes (female 22, male 27). From each individual 10 head hairs with roots were pooled for the analysis, if available. Hairs were cleansed prior to cell lysis in the case of visible stains on the hair by incubation in 500 µL DNA-free water for 5 min at 700 rpm. One sample showing a blood stain was additionally cleaned in 500 µL Buffer AL (Qiagen, Hilden, Germany) using an incubation of 2 min and 700 rpm at 37 °C. The DNA was extracted using the QiaAmp Micro Kit (Qiagen). Hair lysis was performed using 160 µL Buffer ATL (Qiagen), 20 µL 1M DTT and 20 µL proteinase K per sample with a 60 min incubation at 60 °C and 1000 rpm. Subsequent steps were performed according to manufacturer’s recommendations. The DNA was quantified using the Quantifiler Human (Thermo Fisher Scientific, TFS, Waltham, MA, USA) or PowerQuant System (Promega, Madison, WI, USA). Reactions were run in a 10 -μL volume on an AB7500 with 7500 System SDS software v2.0.5 (TFS). To compare the DNAm of other tissues (blood, buccal cells, bone, skeletal muscle, brain tissue) DNAm data that were already available for most of the individuals were taken from a previous study [23]. The DNAm of tissue samples from 10 individuals not included in the preceding study was analyzed as described in [23].

Bisulfite conversion was performed with the DNA Methylation EZ Gold Kit (Zymo Research, Irvine, CA, USA) using 30 µL of the DNA eluate in a reduced total volume of 120 µL for the initial deamination step. The single stranded DNA (ssDNA) was eluted in 15 µL H₂O and quantified using the Qubit ssDNA assay kit for the Qubit 2.0 fluorometer (TFS).

Singleplex amplification of 10 selected age-dependent DNAm markers was performed as described in [21], including the markers DDO, ELOVL2, F5, HOXC4, KLF14, MEIS1-AS3, NKIRAS2, RPA2, TRIM59, ZYG11A. Of the converted DNA 1 µL was added to each PCR reaction. For each individual the successfully amplified loci were pooled and the products cleaned with 1.9x magnetic beads (GE Healthcare, Little Chalfont, UK) prepared according to [26]. Of the pooled sample 3–6 µl was added to 12.5 µL Q5 Hot Start Ultra HiFi PCR master mix (New England Biolabs, Ipswich, MA, USA), 5 pmol of each index primer (Biomers, Ulm, Germany, based on the dual Nextera XT indexing system from Illumina [San Diego, CA, USA]), and water ad 25 µL. The runs consisted of 95 ºC 1 min, 72 ºC 5 min; 6 cycles: 95 ºC 30 s, 62 ºC 2 min, 72 ºC 2 min; final elongation at 72 ºC for 3 min. The PCR products were cleaned twice with prepared 1.6x beads (GE healthcare). The DNA quantity of each amplicon pool was measured using the Qubit dsDNA HS assay kit (TFS). Amplicon pools were combined in equimolar ratios. A final 10.5 pM library containing all samples was paired-end sequenced on a MiSeq using the 300 bp v2 chemistry (Illumina, San Diego, CA, USA) on a MiSeq FGx (Verogen, San Diego, CA, USA).

Quality control and DNAm extraction from the raw data were performed as described before [21]. The minimum threshold required was set to 600 reads as a coverage too low leads to increased stochastic variation of the obtained DNAm [17, 20] but a coverage of at least 800–1000 was anticipated. Bisulfite conversion efficiency was checked by analysis of the non-CpG (i.e. CHH and CHG) methylation levels. Further analysis and statistical analysis were performed under the open-source Anaconda Navigator environment (Anaconda Software distribution, https://anaconde.com) using Jupyterlab 1.2.6 and Python 3.6. Correlation analysis between age and DNAm was accomplished using Spearman’s rank correlation coefficient using the package pingouin v0.3.8 [28], along with the 95% confidence interval (95% CI) and the adjusted p-value for multiple testing (based on the Benjamini-Hochberg false detection rate).

Sequence variation was characterized for individuals from whom a successful analysis of the DNA from hair was achieved (according to [21]). The DNA extracted from blood was used for amplification covering the analyzed DNAm loci. Sanger sequencing was performed in a 5µL volume using the BrilliantDye terminator sequencing kit (Nimagen, Nijmegen, The Netherlands) or BigDye v1.1 Terminator kit (TFS). Cycling was conducted as described in the manufacturer’s protocol of the BigDye v1.1 terminator kit (TFS). Afterwards, samples were run on a 3130xL genetic analyzer with foundation data collection software v3.0 (TFS). Data were aligned to the genomic references (GRCh38) using Sequencher v5.4.6 (Gene Codes, Ann Arbor, MI, USA).

The investigated hairs were plucked from deceased individuals and were from different phases of the hair cycle, i.e. growing phase (anagen), transition phase (catagen) and resting phase (telogen) as given at the time of the individual’s death. The amount of ssDNA after bisulfite conversion available for DNAm analysis was between 0.56 ng/µL and 30.6 ng/µL (mean = 6.04 ng/µL, median: 2.56 ng/µL, SD = 7.75 ng/µL). The DNAm of 18 samples could not be successfully determined as either the amount of PCR product and/or sequencing coverage by MPS was too low (15 cases) and the bisulfite conversion efficiency below 98% (3 cases). The obtained results revealed differences in PCR efficiency between the different analyzed loci. This issue has to be considered generally in hair samples as these often contain a low content of DNA and/or fragmented DNA. The F5 and TRIM59 loci were successfully analyzed in all of the 31 samples evaluable but HOXC4 was successfully analyzed in only 14 samples.

The Spearman correlation coefficient (ρ) and the corresponding 95% CI were used to evaluate the correlation between the DNAm for each CpG site and the age of the individual. The 95% CI calculation for MEIS1-AS3 and HOXC4 resulted in a large range of more than ±0.4 with respect to the obtained coefficient (MEIS1-AS3 0.17, CI −0.34, +0.6, HOXC4_2 −0.33, CI −0.73, 0.24), which is also caused by the lower number of samples successfully analyzed for these markers. Therefore, the obtained correlation values of these two markers were not considered as interpretable and the markers not further considered in the study. The CpG site with the highest (absolute) ρ value for each of the other eight loci is provided together with the 95% CI in Table 1 and visualized in Fig. 1. A high correlation between the DNAm and age (absolute ρ value between 0.6 and 1) was observed for ELOVL2, KLF14, RPA2, TRIM59, and ZYG11A. A moderate correlation (absolute ρ value between 0.4 and 0.6) was determined for NKIRAS2. No age dependency was found for the analyzed CpG sites in F5, and DDO. The Spearman’s ρ values of different CpG sites within the same amplicon were often very similar and can be affected by single outliers due to sample size. The calculated 95% CI of the highly correlated markers supports that these markers indeed show an age-dependency (at least moderate) even considering the sample number.

The correlation between age and age-dependent DNAm is not perfect. Influences on the epigenome during life, the genomic background and technical factors can affect the observed DNAm variability [7, 8, 20]. Looking at the genomic background (using unconverted DNA) of the eight DNAm loci considered for the age correlation analysis, no SNPs were found in the regions analyzed at the loci DDO, ELOVL2, NKIRAS2, and TRIM59. The SNPs without a direct effect on the CpG positions analyzed were detected for F5 (9x rs2269648, 3x rs146501594) and RPA2 (1x rs149574926). Furthermore, frequently occurring SNPs in ZYG11A (27x rs533935, 27x rs534070) were also found in a heterozygous or homozygous state in the individuals of this study. Two additional SNPs leading to removal of a CpG site were detected at CpG positions of loci KLF14 (1x rs542947804) and ZYG11A (1x rs564173977). As the CpG position of one chromosome (heterozygote) was affected the measured DNAm value is based only on the CpG of the other chromosome.

An impact on the measured DNAm due to technical/stochastic variation has to be considered in hair, given the high variation in quality and quantity of the DNA available for analysis. A low ssDNA amount after bisulfite conversion in some samples did not explain all of the unexpected results, as samples with high DNA input were also affected. To additionally exclude that outliers might show a higher divergence due to a slightly lower coverage in MPS, a closer look was taken at loci of samples included in the final analysis that showed a coverage in between the minimum threshold and the anticipated number of reads (600–800 reads); however, a systemic bias due to the lower coverage was not identified as loci with a lower coverage did not show a higher tendency to be an outlier. Generally, most variation in the DNAm was observed in DDO and F5. Therefore, it can be assumed that DDO and F5 do indeed show a higher deviation in hair samples due to biological and not technical reasons. This variation seen in hair is in accordance with investigations of other tissues for DDO but stands in contrast to F5, which showed relatively stable results in other tissues (see below and [23]).

The DNAm results from other tissues of the deceased individuals included in this study were either available [23] or newly generated. In total tissues for 25 of the 31 individuals with successfully analyzed hair samples were available for comparison (cf. “Material and methods”). The DNAm values of loci which were found to have a strong (ELOVL2, KLF14, RPA2, TRIM59, ZYG11A) and moderate (NKIRAS2) correlation with age in hair, were also identified as good candidates for age prediction in blood, buccal swabs, skeletal muscle, brain and bone in the previous study [23]. This result is also supported by the analysis of the additional tissue samples; however, it has to be considered that the correlation is not always the best at the same CpG site. No age dependency was found for F5 in hair as for muscle. The DDO locus showed no age dependency, which is a discrepant result with respect to other tissues where a moderate or strong correlation with age was found. The sample size in the study and the high interindividual DNAm variability observed in hair might mask an underlying significant but small age-dependent change.

For detailed comparison between 3 forensically relevant tissues, 23 individuals with DNAm values available from blood, buccal swabs and hair were considered. The DNAm results of the six age-dependent markers showed a generally lower DNAm for ZYG11A in hair (Fig. 2). In ELOVL2, KLF14, TRIM59, the DNAm in hair was close to the DNAm of blood, and for NKIRAS2 values were between those of blood and buccal cells. When an individual showed a DNAm value lying outside the range of values expected in a given tissue, this individual never showed outlying DNAm values in all of the tissues. Interestingly, in the case of a 55-year-old individual, a far higher DNAm than expected was measured at KLF14_2 in blood and buccal cells but not in hair. At least 5 ng of DNA were analyzed in all three sample types from this individual but technical reasons cannot be completely excluded.

The CpG site within a locus with the strongest age correlation in hair analyzed within this study differed from the previously identified CpG sites in blood with the exception of NKIRAS2 (CpG 2). In contrast, the most useful CpG sites of TRIM59 (CpG 6), ZYG11A (CpG 4), RPA2 (CpG 1), and NKIRAS2 (CpG 2) are shared between buccal swabs and hair, which could be due to the shared cell type of keratinocytes; however, closely located CpG sites often had a nearly identical strength of correlation (cf [21, 23]), which indicates that multiple CpG sites per locus correlate with age in all three tissues and are comparable. Nevertheless, the change of %DNAm per year, as indicated by the slope in the case of a linear correlation, varied for the markers. For example, a higher accumulation of DNAm with age at RPA2_1 was seen in the buccal swab samples compared to blood, and hair (Fig. 2). Additionally, the starting DNAm (in children) differed between tissues, and a 10% difference of the DNAm, e.g. in ELOVL2_1, has to be considered, even if the change per year is the same afterwards in buccal swab and hair samples (Fig. 2). These general observations were made in all markers and tissues analyzed albeit of different intensity.

In this study, plucked head hair from deceased individuals was used for DNAm analysis. To avoid possible effects of a long agonal period or putrefaction, only individuals suddenly deceased (e.g. by traffic accident) and without signs of putrefaction were analyzed in this study. An increased time of agony might affect the DNAm and putrefaction leads to DNA degradation [1]. The collected hairs can originate from different phases of the hair cycle, as human hair growth is not synchronized, which may result in varying DNAm patterns [16, 24]. Additionally, it has to be considered that the number of cells obtained by plucking varies and that multiple cell types, such as keratinocytes, melanocytes, mesenchymal cells, as well as stem cells can be (involuntarily) included in the analysis of plucked hairs [27]. In this study, DNA extraction from the plucked hair led to a wide range in the quantity and—as shown by the PCR results—probably also in the quality of DNA. A direct correlation between the extracted DNA amount and the (roughly graded) number of attached cells was not seen.

Additionally, as samples were obtained during routine autopsies limited information on biological characteristics, such as disease, lifestyle and environmental influences was available, which cannot be excluded as possible sources for DNAm outliers. For example, factors such as hair graying due to the loss of active melanocytes of single hairs could be of interest as not all hairs are affected at the same time point and hair graying is generally associated with age [10]. Also, the study concentrated on head hair, and differences in the DNAm between head and body hair cannot be excluded as these present as different types of hair [18]. Analysis of larger cohorts could enable investigation of a possible effect of changes in hair color and hair location on age-dependent DNAm levels. The SNP analysis of the regions of interest was performed to investigate if specific outliers were caused by a variance of the DNA sequence itself. An SNP was found at a CpG site itself only in rare cases. This leads to the removal of a CpG site and can have a direct impact on the overall DNAm. The observed heterozygous mutations at the CpG sites KLF14_1, and ZYG11A_14 did not result in an outlier detection. It can be discussed if the non-affected homologous chromosome outbalances the loss in detectable DNAm. A possible impact of SNPs on a CpG site in close proximity could not be determined in this study; further evaluation would require more samples containing the SNPs under question.

Hairs are a challenging sample source, which makes it difficult to differentiate between technical and biological variation of the true DNAm. Technical variation can either be directly caused by an inefficient analysis or due to stochastic effects in the case of low DNA amounts or quality of the analyzed hair. Technical difficulties of the assay may result from suboptimal primer binding and unbalanced amplicon pooling. Assay optimization (e.g. change of the DNA polymerase) might improve PCR efficiency resulting in more suitable analysis of all markers in the case of low quality DNA. Especially, the PCR efficiency of HOXC4 and MEIS1-AS3 was lower resulting in less successfully analyzed samples. The calculated 95% CI of the obtained ρ of the study samples showed a wide possible range and the determined correlation coefficients were therefore not considered as reliable to demonstrate the exact correlation with age. Furthermore, ssDNA quantity was fluorometrically determined, not reflecting the DNA quality of the sample which is additionally fragmented during the bisulfite conversion step [19]. Therefore, the determined ssDNA amount in this study has be considered as an estimation of the ssDNA amount used for PCR. Strong degradation might be an explanation for outliers with initially high ssDNA input of DNA. In the case of fragmented DNA, MPS results (even with a high coverage) might result from the amplification of only a few template molecules during the initial PCR. These results would then not reflect the actual DNAm of the initial cell material but rather a selection of a few molecules of high quality. Even if short nuclear DNA fragments can be detected in a balanced analysis and therefore reflect the true DNAm value of the sample, the DNAm value of a few molecules will result in high stochastic effects between samples. Another aspect is that external DNA present on the surface of a hair would have an impact on the measured DNAm. Deep cleaning of the hair, e.g. by using NaClO, was not possible as this procedure would have destroyed a lot of material or the hair follicle itself. Therefore, it has to be considered that small traces of environmental DNA might have been present. Head hairs were collected during autopsy wearing gloves and handled carefully afterwards minimizing any external contamination. Only in the case of one sample, an initial mild lysis could not be avoided due to adhering blood; however, it cannot be completely excluded that attached cells (e.g. outer sheath cells) get damaged in case of a mild lysis.

Multiple studies analyzing age-dependent DNAm from different tissues were performed in the past (e.g. [3, 5, 13‐15, 21, 23]); however, a direct comparison is complicated due to differences in the studied individuals, sample collection, and the methods used for analysis and data processing. A comparison of age-dependent DNAm changes in a high number of various tissues was presented by Horvath in 2013 to develop the multitissue clock [13] but the samples included were obtained from various studies and sample populations. Therefore, a direct comparison between the DNAm and age from different tissues is limited, as individuals and the investigated age range differ between the studies. In our study, however, the results of the hair analysis could be compared to data from other tissues from the same individuals allowing a direct comparison between the tissue-dependent DNAm results. Differences between hair and other tissues were observed and only some of the DNAm markers also changed with age in hair, as seen before for the other tissues [23]. Some DNAm markers show a strong correlation with age in hair as well as the other tissues; however, the mere comparison between the obtained correlation coefficients does not permit the conclusion that the age dependency and/or the kind of change of a given DNAm marker is universal. Strong correlations were obtained for several tissues and markers (cf. Figs. 1 and 2) but tissue-specific DNAm values were still observed. Both the initial DNAm at birth as well as the speed of change with age differed between the tissues. This can be due to biological reasons, stochastic effects in DNAm changes as well as to the heterogeneity of cell types in tissues. The CpG site with the highest Spearman correlation coefficient (ρ) detected often differed between tissues; however, the values between neighboring CpG sites were often close and the obtained correlation coefficients and the statistical significance for each CpG site have to be further supported by larger sample sets.