The epigenetic clock is a key tool to quantify the process of biological aging and evaluate the risk of diseases. The standardization and high efficiency of its detection process are the core premises to promote the technology from laboratory to clinical application. From sample collection to final result interpretation, the standardization of each step directly affects the accuracy and reliability of detection data, and then determines its application value in health assessment, disease early warning, and other scenarios.

This study focuses on the optimization of the whole process of epigenetic clock detection, and takes the four core steps of sample processing, nucleic acid extraction, epigenetic analysis, and result calibration as the framework to systematically sort out the key technical parameters and quality control points of each link.

Sample Collection: Blood, Saliva, and Tissue Biopsies

Sample collection is the cornerstone of epigenetic clock detection. Different types of samples have their own advantages and disadvantages, and preserved tissue samples (FFPE) have special requirements and precautions in clinical research.

Blood

Blood samples are widely used in epigenetic clock detection, with significant advantages. Venous blood collection is mature and common, and it is convenient to obtain. As a systemic body fluid, blood can reflect the physiological state of various tissues and organs of the body, and blood cells, plasma, and other components carry rich biological information, which can provide a comprehensive and representative data basis for detection.

But blood samples also have disadvantages. Venous blood collection is an invasive operation, which may bring pain and discomfort to the subjects, make some people feel psychological pressure, and affect their enthusiasm for participating in the test. The time of blood collection has a great influence on the test results. The changes of hormone level and metabolic state in different periods of time will cause the fluctuation of DNA methylation level in blood, and individual physiological state, such as stress, acute disease, recent diet and exercise, will also interfere with the test results and affect the accuracy and stability.

Saliva

Saliva sample collection has the advantages of being non-invasive, the subject can spit saliva, without suffering trauma and pain, convenient collection, high flexibility and accessibility, low collection cost, no need for professional blood collection equipment and technicians, and reduced manpower and material resources investment.

However, saliva samples have obvious limitations. Saliva cells are complex in composition, including oral epithelial cells, a large number of bacteria and viruses, and their DNA may be mixed with human DNA to interfere with the detection results. Moreover, the content of DNA in saliva is low, which requires high detection technology and equipment, and the detection method is not sensitive and may not be able to accurately detect DNA methylation information. Oral microorganisms are influenced by many factors, such as diet, oral hygiene habits, oral diseases, etc., and environmental pollutants and food residues may also be mixed in, which increases the uncertainty of test results.

Tissue Biopsy Sample

Tissue biopsy samples can provide accurate local tissue information, which is of great significance for studying the aging process of specific tissues or organs and related diseases. For example, in tumor research, tumor tissue biopsy and epigenetic clock detection can deeply understand the biological age and aging characteristics of tumor cells and provide key information for tumor diagnosis, treatment and prognosis evaluation; In the field of neuroscience, the analysis of brain biopsy samples is helpful to reveal the aging mechanism and the pathogenesis of neurodegenerative diseases in specific areas of the brain.

However, tissue biopsy samples also have shortcomings. Tissue biopsy is an invasive operation that requires special instruments to obtain tissue samples, which may cause damage to the subject's body and cause complications such as bleeding and infection. Patients with weak bodies or basic diseases are at higher risk. Biopsy requires high technical skills for operators, and needs rich clinical experience and professional knowledge to ensure that the obtained tissue samples are representative and do not cause too much damage to the samples during the operation. The sample size of tissue biopsy is usually limited, which will limit the follow-up detection items and analysis methods, and it is impossible to carry out large-scale, quantitative detection, which will affect the comprehensiveness and depth of research.

Clustering heatmap for external validation white blood cell data (Houseman et al., 2012)

Special Points for Attention in Clinical Research of FFPE

FFPE samples play a key role in clinical research because they can preserve the morphological structure and biological information of isolated tissues to the greatest extent, and can be preserved at room temperature for decades, which is conducive to long-term research and the establishment of a sample bank, and is a common biological material in tumor research. The preparation process is as follows:

• Fixation: Fixation is the key step, and 10% neutral buffered formalin (pH 7.2-7.4) is commonly used. Surgery or biopsy tissue should be fixed as soon as possible, within 30 minutes, to prevent autolysis and degradation of nucleic acid and protein.
• Material selection: Standard material selection is the premise of obtaining excellent slices. The size of the tissue block should be 1.5cm×2.0cm×0.2-0.3cm, and there should be room around and up and down the embedding box to facilitate tissue fixation and dehydration.
• Dehydration: It is necessary to completely dehydrate the tissues by using ethanol with different concentrations to avoid the degradation of nucleic acids. It is usually done by a dehydrator. Replace the dehydrator liquid in time and use fresh, high-quality, and undiluted reagents.
• Transparency: Transparent agent (such as xylene) replaces the dehydrating agent, because the refractive index of the transparent agent is close to that of protein, which makes the tissue transparent.
• Wax dipping: Soak the tissue in melted paraffin after it is transparent. It is suggested to use paraffin with low solubility to avoid the degradation of nucleic acid and protein caused by the increase in wax soaking temperature.
• Storage: When FFPE samples are stored for a long time, the integrity of RNA will be higher after being stored at 4℃ for one year. Appropriate storage temperature should be selected to ensure the effective preservation of the biological information of samples.

DNA Extraction and Quality Assessment

DNA extraction and quality evaluation are the core and basic steps of molecular biology research. Obtaining high-quality DNA directly determines the accuracy and repeatability of subsequent experiments such as PCR, sequencing, and gene cloning. This process needs to be realized through cell lysis, protein removal, impurity purification, and nucleic acid purification, while the quality evaluation relies on agarose gel electrophoresis, ultraviolet spectrophotometry, and other technologies to quantitatively detect the purity, integrity, and concentration of DNA.

Standardized extraction process and accurate quality evaluation are the premises to ensure the reliability of downstream molecular experimental data, and also an important technical support to promote research in genetics, medicine, and other fields.

Influence of Degradation on the Accuracy of Results

Sample degradation is the key factor that affects the accuracy of epigenetic clock detection, and the essence is that sample DNA molecules are broken and damaged, resulting in integrity destruction. In principle, DNA is broken into small fragments by sample degradation, which cannot completely contain methylation information. DNA methylation is a key indicator of epigenetic clock detection, and accurate detection of its pattern and level is very important for predicting individual biological age and disease risk.

DNA fragmentation will cut off the continuous methylation sites, resulting in incomplete or biased methylation information, which will affect data analysis and the interpretation of results. Sample degradation may also change DNA methylation modification, such as the increase or decrease of methylation sites, interfere with the algorithm's recognition of real methylation patterns, and affect the detection accuracy.

There are various reasons for sample degradation, and the common ones are:

• Improper storage: Samples that are not processed in time or stored in poor conditions (such as high temperature, high humidity, light, etc.) will accelerate degradation. When blood samples are stored at room temperature for a long time, DNA will be degraded due to increased nuclease activity. In addition, the material of the container and the composition of the preservation solution also affect the stability of the sample. The adsorption of DNA by the container will cause loss, and the impurities or microorganisms in the preservation solution will cause degradation.
• Error in extraction operation: When cells are lysed, the DNA structure will be excessively damaged if the concentration of lysis reagent is too high or the action time is too long. In the steps of DNA separation and precipitation, violent vibration and high-speed centrifugation will cause mechanical damage; When DNA is purified, improper elution conditions (such as pH value and temperature of eluent) will affect the stability of DNA.

Sample degradation affects the accuracy of epigenetic clock detection results in many ways:

• Age prediction bias: The loss or change of DNA methylation information will make the algorithm miscalculate the biological age and affect the evaluation of the individual aging process.
• Misjudgment of disease risk: Wrong age prediction will lead to wrong disease risk assessment, which may misdiagnose low-risk individuals as high-risk and make unnecessary examination and treatment; Or misdiagnose high-risk individuals as low-risk individuals and miss the opportunity of early intervention.

Drop-BS for differentiating cell types based on cellular-resolution CH methylation in human brain tissues (Zhang et al., 2023)

Laboratory Processing: Bisulfite Conversion and Array/Sequencing

The laboratory treatment of bisulfite transformation and chip/sequencing is the core technology of epigenetic DNA methylation research. The specific conversion of unmethylated cytosine to uracil was realized by bisulfite modification, which laid the foundation for subsequent detection.

Chip technology can realize Qualcomm screening of methylation sites, while sequencing technology can provide methylation information with single-base resolution. The combination or separate application of the two methods can accurately analyze the genome methylation pattern and provide key experimental data support for disease mechanism research and biomarker screening.

Detailed Explanation of Bisulfite Conversion

Bisulfite conversion is the core of methylation analysis, and the principle is based on the difference in chemical reaction between unmethylated and methylated cytosine. Under the action of bisulfite, unmethylated cytosine is deaminated and converted into uracil, while methylated cytosine remains unchanged due to methyl protection. This chemical modification difference can be used to accurately identify the two in subsequent analysis.

• A. Transformation process
  • a) DNA denaturation: The hydrogen bond of double-stranded DNA is destroyed under high temperature or alkaline conditions, and the single-stranded DNA can fully react with bisulfite.
  • b) Addition reaction: denatured single-stranded DNA is mixed with bisulfite solution under acidic conditions, and unmethylated cytosine is added with bisulfite ion to form C-bisulfite adduct.
  • c) Hydrolytic deamination: under the conditions of heating and weak alkalinity, the C-bisulfite adduct undergoes hydrolytic deamination to produce U-bisulfite.
  • d) Desulfurization conversion: removing bisulfite ions and finally converting U-bisulfite into uracil.
• B. Library Preparation and Platform Selection.
  • a) DNA fragmentation: cuts long DNA molecules into short segments suitable for analysis. The common methods are:
  • b) Ultrasonic fragmentation method: DNA is mechanically broken in solution by using ultrasonic energy, which is simple to operate and uniform in fragment length distribution, but it requires special ultrasonic equipment and high control of experimental conditions.
  • c) Enzymatic digestion method: DNA is specifically cut by restriction endonucleases, and DNA fragments with a specific length can be obtained by selecting different enzymes.
  • d) Linker: Linker is a DNA fragment with a known sequence, which can be connected with both ends of a DNA fragment to provide a primer binding site for subsequent PCR amplification and sequencing. Usually, T4 DNA ligase is used for ligation, and the reaction temperature, time, and enzyme dosage should be strictly controlled to ensure efficient ligation. After adding the linker, it is necessary to purify and quality test the library, remove the unconnected linker and impurities, and ensure the quality and purity of the library reach the standard.
• C. Detection platform
  • a) Chip technology: For example, the Infinium methylation chip of Illumina, based on magnetic bead technology combined with 50nt target-specific oligonucleotide, hybridizes with bisulfite-transformed genomic DNA, and methylation sites are detected by two Infinium chemical methods.
  • b) Sequencing technology: Taking the whole genome bisulfite sequencing (WGBS) as an example, it can detect the methylation sites of the whole genome without bias. The principle is to amplify and sequence the DNA fragments in the library, and analyze the sequencing data to determine the base methylation state.

Blocks of DNA methylation overlap exons, histone H3K36me3, and histone H3K4me2 marks (Hodges et al., 2009)

Data Analysis: Applying the Epigenetic Clock Algorithm

The epigenetic clock algorithm is a mathematical model to predict the biological age of individuals based on DNA methylation data. The core principle is to construct a prediction model by using the change pattern of DNA methylation sites related to age.

In the process of human aging, the DNA methylation pattern undergoes a certain change and regularity. This algorithm captures this rule and establishes the mathematical relationship between methylation level and age by analyzing a large number of sample methylation data and modeling, thus realizing the prediction of individual biological age.

The steps of applying the epigenetic clock algorithm are as follows:

• Data preprocessing: Clean and standardize the original methylation data, remove noise and abnormal values, and ensure data quality and reliability. Wrong data points may be introduced during data collection due to experimental errors and sample pollution. If they are not processed, the subsequent analysis results will be affected. Data preprocessing can be used for quality control to improve data accuracy and consistency.
• Model training: A large number of sample data of known age are used to train the model to learn the relationship between methylation data and age. Usually, the sample data is divided into a training set and a test set; the former is used to train the model, and the latter is used to evaluate the model's performance. When training, choose appropriate machine learning algorithms, such as linear regression, support vector machine, neural network, etc., to model the training set data, and constantly adjust the model parameters, so that the model can accurately predict the sample age.
• Prediction calculation: After the model is trained and verified by the test set, it is applied to the sample data of unknown age to predict the biological age. The pretreated methylation data are input into the trained model, and the model calculates the predicted age (DNAmAge) of the sample according to the learned relationship between methylation and age.

Output and Interpretation of Results

Epigenetic clock detection results are usually output in the form of DNAmAge and age acceleration:

• DNAmAge: Refers to the biological age of an individual predicted according to the epigenetic clock algorithm, reflecting the aging degree of the individual at the molecular level, which is closely related to the individual's cognition and physical function, and can assist in evaluating the health status.
• Age acceleration: That is, the difference between DNAmAge and actual age is a key indicator to measure the aging speed of individuals, and it can also reflect health risks, which are closely related to the risk of various age-related diseases.

Aspects needing attention in the interpretation of results:

• Individual differences: Genetic background, lifestyle, and environmental factors will affect DNA methylation patterns and the aging process, leading to different test results.
• Limitations of sample quality and detection methods: Sample degradation and pollution will make methylation data inaccurate, and different detection methods (such as chip technology and sequencing technology) have differences in coverage and accuracy of detection sites. When using chip technology to detect, important methylation sites may be missed due to limited detection sites, which makes the results incomplete.

Distribution of age-associated differentially methylated CpG positions (aDMPs) with their effect size in beta values and significance p value in the African American (AA) and white participants of the HANDLS study (Tajuddin et al., 2019)

Conclusion

Epigenetic clock detection technology is a precise process, including sample collection, DNA extraction, bisulfite transformation, library preparation, detection, data analysis, and result output. Looking forward to the future, this technology has a broad prospect in biomedical research and clinical application, which will provide support for exploring the aging mechanism and the occurrence and development of diseases, and play a greater role in early diagnosis, risk prediction, and personalized treatment of diseases.

With the development of technology, sample collection, detection methods, and data analysis will be continuously optimized. Epigenetic clock detection technology provides a new perspective and method for understanding aging and diseases. Although there are challenges, with the deepening of research and technical development, it will surely make greater breakthroughs in the biomedical field and make important contributions to human health.

FAQ

1. For personal epigenetic clock detection, which sample type (blood, saliva, tissue biopsy) is the most suitable choice?

It depends on your detection purpose and acceptance of invasiveness. If you want a balance of convenience, representativeness, and moderate invasiveness, blood samples are the best choice. If you are sensitive to invasive operations, saliva samples are more suitable. Tissue biopsy samples are only recommended when studying specific organs, as they are highly invasive and have strict requirements on operators and sample size.

2. If the detected DNAmAge is older than the actual age, does it mean the detection result is wrong?

No. DNAmAge being older than the actual age reflects "accelerated epigenetic aging" rather than a detection error. This phenomenon may be affected by multiple factors: on the one hand, it may be related to your genetic background, lifestyle, or environmental exposure. on the other hand, it may also be an early signal of potential health risks.

References

1. Houseman EA, Accomando WP, Koestler DC, et al. "DNA methylation arrays as surrogate measures of cell mixture distribution." BMC Bioinformatics. 2012 13: 86.
2. Zhang Q, Ma S, Liu Z, et al. "Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes." Nat Commun. 2023 14(1): 4672.
3. Hodges E, Smith AD, Kendall J, et al. "High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing." Genome Res. 2009 19(9): 1593-1605.
4. Tajuddin SM, Hernandez DG, Chen BH, et al. "Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites." Clin Epigenetics. 2019 11(1): 119.