eccDNA in Somatic Cells and Aging Research: What We Know and How to Profile It

A decade ago, most conversations about circular DNA revolved around cancer amplicons. Today, researchers are asking a broader question: what do extrachromosomal circular DNAs (eccDNAs) look like in healthy tissues, and how might they relate to aging? This ultimate guide synthesizes what's known about eccDNA in somatic cells (our primary SEO focus: eccdna somatic cells), where the evidence remains thin, and how to profile these molecules with rigor in research-use-only (RUO) projects.

If you'd like a concise primer on concepts and vocabulary before diving in, see the overview resource, "eccDNA Sequencing Explained," which provides a non-technical foundation for the methods discussed here.

We take a conservative stance throughout: current human data mostly support correlations between eccDNA features and aging phenotypes rather than causation. We'll separate yeast models from mammalian observations, highlight tissue-specific differences, and detail practical workflows (with ATAC-seq outward-facing reads as the method hero) to help you generate auditable, reproducible evidence.

The Aging Connection: What "eccDNA Aging" Does and Doesn't Tell Us

Aging research owes much of its eccDNA vocabulary to yeast. In budding yeast, recombination within the rDNA locus generates extrachromosomal rDNA circles (ERCs). For years, ERC accumulation in mother cells was viewed as a central driver of replicative senescence. However, recent work urges caution.

In 2023, Zylstra and colleagues reported that the senescence entry point and global expression changes are tightly associated with amplified linear fragments from the right arm of chromosome XII (ChrXIIr), while ERC accumulation showed little impact on these hallmarks. The implication is that ERCs may be correlative rather than causal in yeast senescence. See the open-access article for details: "Senescence in yeast is associated with amplified linear fragments from the right arm of chromosome XII" (PLOS Biology 2023; DOI: 10.1371/journal.pbio.3002250).
- Source: Zylstra AR et al., 2023. PLOS Biology article
A companion study the same year showed that dietary switching maintained a youthful phenotype during replicative aging without caloric restriction, again pointing away from ERCs as the sole explanatory factor and toward ChrXIIr-related changes. See "Dietary change without caloric restriction maintains a youthful phenotype during replicative ageing in yeast" (PLOS Biology 2023; DOI: 10.1371/journal.pbio.3002245).
- Source: Horkai D et al., 2023. PLOS Biology article

What does this mean for eccdna aging? At minimum, yeast evidence has diversified: ERCs exist and correlate with aging, but amplified linear DNA from ChrXIIr appears more tightly linked to the senescence entry point. That's a cautionary tale for over-interpreting any single circular DNA class as causal.

Figure 1. Yeast model schematic summarizing ERC formation and asymmetric retention in mother cells alongside amplified linear ChrXIIr fragments. Attribution: Original diagram created for this article.

Mammalian data add different nuance. A 2021 Nature paper showed that many eccDNAs arise during apoptosis and are potent innate immunostimulants via cGAS–STING, with activity dependent on circularity rather than sequence. In other words, eccDNAs can be readouts of cell death processes and, in turn, modulators of inflammation—both relevant to aging biology. See "eccDNAs are apoptotic products with high innate immunostimulatory activity" (Nature 2021; DOI: 10.1038/s41586-021-04009-w).

PubMed entry: Nature 2021 on immunostimulatory eccDNAs

In human somatic cells, several studies report age-associated differences, especially in stem cell models under replicative senescence, but causal links remain unproven. For example, senescent human bone marrow mesenchymal stem cells (BM-MSCs) show distinct eccDNA landscapes relative to young MSCs, including shorter circles and altered genomic distributions that correlate with pathways tied to senescence. See "Genome-wide sequencing identified extrachromosomal circular DNA dynamics in young vs. senescent human bone marrow mesenchymal stem cells" (Frontiers in Cellular Neuroscience 2024; DOI: 10.3389/fncel.2024.1421342).

Article: Frontiers in Cellular Neuroscience 2024

What about telomeres? It's reasonable to hypothesize that telomere attrition and replication stress could foster eccDNA formation. Yet, cross-tissue human evidence directly linking telomere shortening to quantitative eccDNA accumulation remains limited. With today's data, it's safer to treat telomeres as part of the broader genomic instability context rather than a proven driver of eccdna aging.

Tissue-Specific Views of eccDNAs: Brain, Muscle, and Blood

The phrase eccdna somatic cells covers diverse tissues, each with its own turnover rate, chromatin state, and damage/repair milieu. It isn't surprising that eccDNA profiles differ by tissue, independent of chronological age.

Figure 2. Anatomy schematic highlighting tissues frequently profiled for eccDNAs, with evidence notes. Attribution: Original diagram created for this article.

Skeletal muscle vs blood. In a study of aged males, skeletal muscle displayed far more eccDNAs than matched blood, and physical activity status (sedentary vs active) did not show large differences in circle number/length distributions in that cohort. Correlation, not causation: the main effect was tissue type. See "Skeletal Muscles of Sedentary and Physically Active Aged Males Exhibit Similar Extrachromosomal Circular DNA Profiles" (Frontiers in Genetics 2023; DOI: 10.3389/fgene.2023.1081365).

Article: Frontiers in Genetics 2023

Peripheral blood mononuclear cells (PBMCs). PBMC eccDNA studies characterize circle size and distribution across healthy donors, but robust, replicated age-stratified differences are still sparse. A representative study is "The characteristics of extrachromosomal circular DNA in peripheral blood mononuclear cells" (Frontiers in Genetics 2023; DOI: 10.3389/fgene.2023.1125046).

Article: Frontiers in Genetics PBMC 2023

Brain. Reports in mammals suggest neuron-biased eccDNA features and potential age-related patterns, but comprehensive, replicated atlases in humans remain an open area. Mouse datasets and early catalogs in specific regions are promising and should be treated as preliminary when extrapolating to humans.

A critical confounder across tissues is cell death. Apoptosis can inflate eccDNA counts and simultaneously trigger innate immune signaling, complicating interpretation of "aging-related" increases. If you plan to sample tissues with variable turnover or stress, build explicit controls for apoptosis and inflammation into your design. For conceptual background on how apoptosis generates circles and activates cGAS–STING, see our series article on interpretation: Are eccDNAs Apoptotic Products?

How to Profile eccDNA in Somatic Cells (RUO)

Across healthy tissues, eccDNAs can be rare and heterogeneous. Robust profiling therefore hinges on: careful sample handling; fit-for-purpose methods; stringent bioinformatics; and orthogonal validation. Below, we focus on the ATAC-seq outward-facing reads strategy as the "method hero," then compare it with Circle-Seq enrichment and single-cell add-ons.

ATAC-seq outward-facing reads: a practical, high-signal route

Principle. Standard paired-end ATAC-seq libraries sometimes capture paired reads pointing away from one another (outward-facing) at a location that only makes sense if two distant genomic positions are now adjacent—i.e., a circular junction. Sensitive alignment and junction-aware parsing can retrieve such signatures and propose candidate circles.

Foundational resources and tools. The approach was introduced and vetted in "ATAC-seq identifies thousands of extrachromosomal circular DNAs in cancer and cell lines" (Science Advances 2020; DOI: 10.1126/sciadv.aba2489) and expanded with stepwise tutorials (e.g., Su et al., 2021, open-access protocol).

Science Advances 2020: Kumar P. et al. (PMC: Open access)
Protocol/tutorial 2021: ATAC-Seq-based Identification of eccDNA

Wet-lab notes (nuclei and libraries). In healthy tissues, maximize nuclei integrity and reduce mitochondrial contamination. Gentle nuclei isolation, consistent transposition times, and clean size-selection improve signal-to-noise for eccDNA junction reads. If you already run ATAC for chromatin accessibility, you can mine the same libraries for outward-facing reads—cost-efficient and minimally invasive to your process.

Computational pipeline (outline). Align with a sensitive mapper (e.g., BWA-MEM) allowing split/soft-clipped reads. Extract candidate junctions where paired-end reads face outward and where split reads support adjacency of distant genomic positions. Re-align candidate-supporting reads with guided realignment tools (e.g., Circle-Map Realign) and score junctions. Require a minimum set of evidentiary reads per candidate (e.g., ≥2 split reads plus ≥2 discordant pairs) and screen out candidates in highly repetitive regions unless reproducible across replicates.

Quality controls. Predefine thresholds and apply the same gating across samples to avoid cherry-picking. Track the fraction of outward-facing reads, the number of high-confidence junctions per million mapped reads, mitochondrial read fraction, and the proportion of candidates reproduced in technical or biological replicates. Consider spike-in circles as positive controls for end-to-end sensitivity.

When to choose ATAC-seq outward-facing reads. This route shines when your primary assay is chromatin accessibility and tissue nuclei can be prepared consistently (brain nuclei, skeletal muscle nuclei, PBMC-derived nuclei). It's also attractive for exploratory surveys in eccdna somatic cells because it limits extra library prep complexity.

Neutral service pathway (disclosure). If you need an external RUO provider for ATAC libraries and junction-aware analysis, CD Genomics can support the workflow objectively. Disclosure: CD Genomics is our product. See the ATAC-seq service for typical inputs/outputs and the lab's public ATAC-seq protocol for stepwise considerations. Mentions here are informational; no clinical or performance claims are implied.

Circle-Seq enrichment plus RCA: sensitive but bias-prone without strict controls

Principle. Circle-Seq depletes linear DNA using exonucleases (e.g., Plasmid-Safe DNase), selectively retains circles, and amplifies them by phi29 rolling circle amplification (RCA) prior to library prep. First described broadly in yeast and adapted widely since, Circle-Seq remains a go-to for enriched detection of small eccDNAs.

Key references include "Extrachromosomal circular DNA is common in yeast" (PNAS 2015; DOI: 10.1073/pnas.1508825112), an in-depth methods chapter "Circle-Seq: Isolation and Sequencing of Chromosome-derived eccDNAs" (Methods in Molecular Biology 2020; DOI: 10.1007/978-1-0716-0323-9_15), and a video walk-through in JoVE (2016; DOI: 10.3791/54239).

PNAS 2015: Møller HD et al.
Methods Mol Biol 2020: Circle-Seq chapter
JoVE 2016: Genome-wide Purification of eccDNA

Common pitfalls. Incomplete digestion of linear DNA generates false positives. RCA favors smaller templates, skewing size distributions. Mis-mapping in repeats can conjure spurious junctions. Plan enzyme controls (with/without exonuclease), spike-in circle controls to quantify depletion/amplification efficiency, and validate candidate junctions orthogonally (PCR + Sanger; ddPCR/qPCR).

Neutral service pathway (informational). For labs that prefer to outsource portions of enrichment or sequencing under RUO terms, see the broader eccDNA Sequencing Services. These pages describe inputs, library options, and typical deliverables without implying clinical use.

Dissecting Tissue Heterogeneity and Capturing Rare Events via Targeted Approaches

Rationale. Aging tissues present a mosaic of diverse cell types and states. When eccDNA signals are sparse or confined to micro-regions, analyzing specific tissue coordinates or enriched populations allows for the identification of contributing compartments without the need for granular, cell-by-cell analysis.

Practical enrichment strategies. To enhance effective signal, isolate the target population or micro-region prior to library preparation. Effective methods include fluorescence-activated nuclei sorting (FANS) to concentrate neuronal or muscle nuclei, laser-capture microdissection (LCM) for sampling distinct histological zones, and immuno-enrichment of dissociated populations. These techniques significantly reduce background noise from dominant cell types and improve the recovery of junction-supporting reads in bulk data.

Library and sequencing choices for rare circles. For discovery phases, couple nucleus- or population-enriched ATAC libraries with deep paired-end sequencing to maximize the detection of outward-facing junction reads. For targeted follow-up, utilize capture panels designed to tile across high-confidence junction zones, or employ long-read bulk sequencing (such as Oxford Nanopore or PacBio) to resolve complex breakpoints and repetitive sequences that are often misaligned by short-read platforms.

Designs to boost confidence and reduce bias. Incorporate synthetic circular spike-ins and enzymatic controls during enrichment or exonuclease steps to rigorously document recovery rates and depletion efficiency. Enhance reliability through extensive technical and biological replication across different donors and tissue regions. Establish and consistently apply pre-defined filtering thresholds—such as minimum counts for split-reads and discordant pairs—to prevent subjectivity in data gating.

Orthogonal validation and quantification. Treat identified junctions as testable hypotheses. Validate a subset using junction PCR followed by Sanger sequencing to verify breakpoint sequences. Subsequently, quantify validated targets across larger cohorts using junction-spanning qPCR or ddPCR. For candidates located in repetitive or structurally complex regions, support short-read findings with long-read data or targeted capture to prove contiguous support for the circular architecture.

When to use these strategies. Opt for population-based or spatial enrichment combined with deep, targeted, or long-read sequencing when heterogeneity or rare circles are suspected, but logistical factors—such as throughput, cost, or validation requirements—render the analysis of individual cells isolated from the tissue impractical. This workflow maintains the link between eccDNA findings and specific tissue compartments while ensuring scalability and auditability.

Bioinformatics rigor and artifact filtering (don't skip this)

Detection sensitivity in healthy tissues invites artifacts. Use pipelines that explicitly model eccDNA junction evidence and repeats, such as Circle-Map (BMC Bioinformatics 2019; DOI: 10.1186/s12859-019-3160-3), ECCsplorer (Nucleic Acids Research 2022; DOI: 10.1093/nar/gkab1255), and ecc_finder (Frontiers in Plant Science 2021; DOI: 10.3389/fpls.2021.743742). Pair tool choice with transparent thresholds and reproducibility criteria.

For a deeper dive into filtering strategies, reproducibility gates, and reporting expectations, see our companion resource: Bioinformatics for eccDNA: Detection Algorithms, Filtering Artifacts, and Reporting Standards.

Recommended evidentiary gates for eccdna somatic cells projects (tunable by dataset): require at least two supporting split reads plus two outward-facing pairs per junction; exclude candidates in low-complexity or high-copy repeats unless reproduced across independent libraries; and report a circle score (when available) alongside uncertainty intervals for abundance estimates.

Validation, QC, and Reporting: From Candidate Circles to Auditable Results

Healthy-tissue eccDNAs are typically low-frequency; validation and QC are not optional. Below is a compact, SOP-style checklist you can adapt to your lab's quality system.

Assay controls and depletion efficiency. Quantify linear DNA depletion in Circle-Seq with enzyme controls and spike-in circles; for ATAC, track mitochondrial fraction and nuclei integrity metrics. Document lot numbers, enzyme activities, and run conditions.
Junction-level evidence. For each reported circle, include the number of split reads, number of discordant outward-facing pairs, and any circle score provided by the software. Provide genomic coordinates, repeat annotations, and flanking sequence context.
Orthogonal verification. Validate a subset of candidates with junction PCR and Sanger sequencing to confirm breakpoint identity. Where quantification is needed across cohorts, apply ddPCR/qPCR assays designed to span the junction.
Reproducibility. Demonstrate technical replicate consistency and, if possible, biological replicate consistency. Report the fraction of candidates replicated across libraries and donors.
Artifact flags. Mark candidates overlapping known problematic repeats or tandem duplications and those with excessive local soft-clipping not consistent with a circular junction model.
Audit trail. Archive pipeline versions, parameters, reference genomes, and random seeds; include a manifest of intermediate files sufficient to reproduce circle calls and abundance metrics.

Short Case Vignettes and Figures

Two concise vignettes illustrate how the above principles come together in practice for eccdna somatic cells. Figures are illustrative; one contains simulated data, as labeled.

Vignette A: Hippocampus nuclei — ATAC-seq outward-facing reads in an aging study

Aging question. Do hippocampal neurons in older donors show distinct eccDNA junction profiles relative to younger donors, after controlling for nuclei quality and cell death markers?

Design snapshot. Prepare high-quality neuronal nuclei from post-mortem hippocampus samples using consistent isolation steps and QC for integrity. Generate paired-end ATAC libraries with harmonized transposition conditions across donors. Align reads (BWA-MEM), extract outward-facing pairs and split reads, and score candidate junctions using Circle-Map Realign. Set gating thresholds a priori (e.g., split≥2, pairs≥2, remove repeat-only candidates unless replicated). Include negative controls and, if possible, add spike-in circles.

Expected outcomes. In well-matched cohorts, you may observe a modest increase in total candidate junctions per million reads with age, but variance is likely high and confounded by neuronal loss markers. Independent replication across donors and brain subregions is essential before making any statements about eccdna aging.

Validation. Select 10–20 candidates for junction PCR + Sanger. If pursuing quantification, design ddPCR assays for 3–5 best-supported junctions and estimate relative abundance across donors. Report uncertainty and effect sizes rather than binary "present/absent" calls.

Vignette B: Skeletal muscle — Circle-Seq with ddPCR quantification

Aging question. Does aged skeletal muscle differ from younger muscle in the abundance of specific small eccDNAs after normalization for input and digestion efficiency?

Design snapshot. Extract total DNA under conditions that minimize shearing. Apply exonuclease digestion (with enzyme-minus controls), spike in a known synthetic circle to monitor depletion and amplification, then perform RCA and library prep. Sequence to a depth appropriate for the expected circle size distribution. Call circles with Circle-Map and ECCsplorer; filter by split/pair evidence and replicate consistency. Normalize abundance to spike-in recovery.

Expected outcomes. Skeletal muscle commonly shows higher eccDNA load than blood independent of age. Age-related differences, if present, may affect specific junction classes rather than global abundance. Treat any differences as correlations pending replicated cohorts and orthogonal validation.

Validation. Confirm 10–15 candidates by junction PCR + Sanger and quantify a subset with ddPCR across the cohort. Provide a reproducibility summary and disclose any candidates sensitive to mapping in repetitive regions.

Figure 3. Simulated data illustrating eccDNA abundance per million reads across Young vs Old in brain, skeletal muscle, and blood. Note: Simulated for illustration only; not empirical results. Attribution: Original plot created for this article.

Where This Field Is Headed (Conservative Outlook)

The case for eccdna aging as a biomarker class is intriguing but not settled. Yeast studies now paint a more complex picture in which ERCs coexist with amplified linear DNA changes tied to senescence. In mammals, eccDNAs intersect with apoptosis and innate immunity, implying that changes with age could reflect shifts in death/clearance dynamics as much as in ongoing circle biogenesis. That's not a dismissal—just a reminder to treat eccDNa signals as state markers that demand context.

What should the next wave of studies do?

Prioritize standardized workflows and reporting, especially in low-frequency contexts (healthy tissues). Share thresholds, spike-in strategies, and replicate criteria.
Blend modalities—e.g., ATAC-seq outward-facing reads as the primary discovery route, with Circle-Seq enrichment on matched inputs to chase specific classes, and single-cell strategies where heterogeneity is central.
Incorporate orthogonal validation by default (junction PCR + Sanger; ddPCR/qPCR) and quantify reproducibility across independent cohorts.
Publish negative results and boundary conditions—knowing where a method lacks power is as valuable as a positive call when aiming for a reliable biomarker.

For comparative insights beyond human tissues, cross-check findings in model systems where life-stage and stress variables are controllable. Our companion resource offers examples in Drosophila and rice that can inspire study designs without implying direct translational equivalence: Plant & Model Systems Spotlight: Drosophila Life Cycle and Rice Nutrient Stress eccDNA Dynamics.

Finally, remember search intent: if your goal is to profile eccdna somatic cells to explore aging correlations, clarity and auditability will matter more than maximal circle counts. Set your thresholds before you look at outcomes, and think of each candidate junction as a hypothesis that deserves independent tests.

Author

Yang H. — Senior Scientist, CD Genomics; University of Florida.

Yang is a genomics researcher with over 10 years of research experience in genetics, molecular and cellular biology, sequencing workflows, and bioinformatic analysis. Skilled in both laboratory techniques and data interpretation, Yang supports RUO study design and NGS-based projects.

References:

Zylstra AR, et al. Senescence in yeast is associated with amplified linear fragments from the right arm of chromosome XII. PLOS Biology. 2023. DOI: 10.1371/journal.pbio.3002250. https://pmc.ncbi.nlm.nih.gov/articles/PMC10464983/
Horkai D, et al. Dietary change without caloric restriction maintains a youthful phenotype during replicative ageing in yeast. PLOS Biology. 2023. DOI: 10.1371/journal.pbio.3002245. https://pmc.ncbi.nlm.nih.gov/articles/PMC10464975/
Wang Y, et al. eccDNAs are apoptotic products with high innate immunostimulatory activity. Nature. 2021. DOI: 10.1038/s41586-021-04009-w. https://pubmed.ncbi.nlm.nih.gov/34671165/
Kumar P, et al. ATAC-seq identifies thousands of extrachromosomal circular DNAs in cancer and cell lines. Science Advances. 2020. DOI: 10.1126/sciadv.aba2489. https://www.science.org/doi/10.1126/sciadv.aba2489 (PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC7228742/)
Su Z, et al. ATAC-Seq-based Identification of Extrachromosomal Circular DNA in Human Cancer Cells. 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8161110/
Møller HD, et al. Extrachromosomal circular DNA is common in yeast. Proceedings of the National Academy of Sciences (PNAS). 2015. DOI: 10.1073/pnas.1508825112. https://www.pnas.org/doi/10.1073/pnas.1508825112
Møller HD. Circle-Seq: Isolation and Sequencing of Chromosome-derived eccDNAs. Methods in Molecular Biology. 2020. DOI: 10.1007/978-1-0716-0323-9_15. https://pubmed.ncbi.nlm.nih.gov/31989524/
Haase K, et al. Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotes. Journal of Visualized Experiments (JoVE). 2016. DOI: 10.3791/54239. https://www.jove.com/t/54239/genome-wide-purification-extrachromosomal-circular-dna-from
Prada-Luengo I, et al. Sensitive detection of circular DNAs at single-nucleotide resolution using guided realignment of partially aligned reads (Circle-Map). BMC Bioinformatics. 2019. DOI: 10.1186/s12859-019-3160-3. https://pubmed.ncbi.nlm.nih.gov/31830908/
Mann L, et al. ECCsplorer: a pipeline to detect extrachromosomal circular DNA in eukaryotes. Nucleic Acids Research. 2022. DOI: 10.1093/nar/gkab1255. https://pmc.ncbi.nlm.nih.gov/articles/PMC8760651/
Zhang P, et al. ecc_finder: A Robust and Accurate Tool for Detecting Extrachromosomal Circular DNA. Frontiers in Plant Science. 2021. DOI: 10.3389/fpls.2021.743742. https://www.frontiersin.org/articles/10.3389/fpls.2021.743742/full
Gerovska D, et al. Skeletal Muscles of Sedentary and Physically Active Aged Males Exhibit Similar Extrachromosomal Circular DNA Profiles. Frontiers in Genetics. 2023. DOI: 10.3389/fgene.2023.1081365. https://pmc.ncbi.nlm.nih.gov/articles/PMC9917053/
Peng Y, et al. The characteristics of extrachromosomal circular DNA in peripheral blood mononuclear cells. Frontiers in Genetics. 2023. DOI: 10.3389/fgene.2023.1125046. https://pmc.ncbi.nlm.nih.gov/articles/PMC10041755/
Yang W, et al. Genome-wide sequencing identified extrachromosomal circular DNA dynamics in young vs. senescent human bone marrow mesenchymal stem cells. Frontiers in Cellular Neuroscience. 2024. DOI: 10.3389/fncel.2024.1421342. https://www.frontiersin.org/articles/10.3389/fncel.2024.1421342/full
Fan X, et al. SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform. Genome Biology. 2021. DOI: 10.1186/s13059-021-02406-y. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02406-y
Chamorro González R, et al. Parallel sequencing of extrachromosomal circular DNAs and transcriptomes from single cells reveals somatic evolution of ecDNA in cancer (scEC&T-seq). Nature Genetics. 2023. DOI: 10.1038/s41588-023-01386-y. https://refubium.fu-berlin.de/handle/fub188/40987

Notes on figures and attributions: All figures in this article are original, created for explanatory purposes; Figure 2 displays simulated data and is labeled accordingly. All content is provided for research use only (RUO); no clinical use is intended or implied.

For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.

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