Biology:Fragmentomics

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Fragmentomics refers to the study of cell-free DNA (cfDNA) fragmentation patterns. Programmed cell death leads to the non-random fragmentation of DNA, and the resulting fragments can be released by cells into the blood as cell-free DNA[1]. Using physical features such as size distribution, end motifs, and nucleosome positioning, cfDNA fragments can be profiled to infer epigenetic and transcriptional states[2]. Differences in these features can identify fragments from particular sources, such as circulating tumour DNA (ctDNA) and cell-free fetal DNA (cffDNA). Furthermore, the cell of origin and mechanism of cfDNA release lead to specific fragmentation signatures, providing information about cell type, gene expression, cell physiology or pathology, or action of treatment[3][4]

Although fragmentomic approaches have primarily been developed and validated using whole-genome sequencing data — which provides the depth and breadth of coverage needed to resolve subtle genome-wide patterns — their integration with targeted sequencing panels has gained increasing attention as a more clinically scalable alternative.[5]

History

cfDNA circulates in multiple body fluids but is most abundantly detected in blood plasma and serum. It is released from tissues through apoptosis, necrosis, and active secretion, and can exist either freely or within extracellular vesicles. In the bloodstream, cfDNA is highly fragmented and exhibits characteristic size profiles.[6] cfDNA was first detected in 1948 by scientists Mandal and Metais in the blood serum of cancer patients.[7] Decades later in the 1990s three big discoveries, made using PCR-based assays, revolutionized the understanding of cfDNA fragments.[8] The first was the discovery of tumour cfDNA in cancer patients by detecting tumor specific variants (microsatellite repeats) in circulating plasma.[9] The second was the detection of fetal cfDNA in pregnant women carrying male fetuses, achieved by identifying Y-chromosome markers in maternal plasma.[10] The third was the detection of donor-specific cell-free DNA in the plasma of kidney and liver transplant recipients by identifying sex-mismatched genetic sequences (Y-chromosome markers).[11]The term fragmentomics emerged in the 2010s to describe the study of the fragmentation patterns of cfDNA, with the complete structural and functional landscape of cfDNA fragments collectively referred to as the "fragmentome".[12]

Fragmentomic features

Fragment size distribution

Fragment size is highly dependent on the placement of histones on the DNA of cells undergoing apoptosis. During programmed cell death, DNA is degraded by caspase-activated DNAases.[13] Nucleosomes protect DNA from digestion by DNAases, leading to cuts exclusively occurring at exposed sites between nucleosomes.[2] This non-random, caspase-dependent cleavage is reflected in the fragment size distribution of cfDNA; the median size of cfDNA fragments is ~167 bp, representing the length of DNA wrapped around a single nucleosome.[1][3][4] A smaller peak in the distribution is observed at ~320 bp, representing a dinucleosomal unit.[14]

Given the consistency in distribution, fragment size can be used to classify fragment type. Tumour-derived fragments tend to be shorter overall compared to normal cfDNA, with enrichment for fragment sizes between 90 and 150 bp.[3] The ratio of short-to-long fragments can serve as a feature for early cancer detection and disease monitoring.[15][16] Similarly, fetal cfDNA in maternal plasma is shorter than maternal cfDNA.[17][18] The majority of cffDNA fragments are shorter than 300 bp, while roughly 20% of maternal cfDNA fragments are longer than 300 bp.[17][18][19] Separating fragments by size and extracting those under 300 bp can help enrich for cffDNA.[18]

End motifs

cfDNA fragments have been found to terminate at preferred nucleotide contexts, with particular tetranucleotide sequences being highly enriched at the ends of fragments.[20] These end motifs are shaped by the nucleases responsible for cleavage, such as DFFB, DNASE1L3, and DNASE1.[21][22][23] Plasma DNA fragment end motif frequencies have been shown to be affected by disease. For example, in a study of patients with hepatocellular carcinoma (HCC), patients with HCC had lower abundance of the CCCA fragment motif compared to healthy patients.[24] Nuclease dysregulation can also cause changes in end motif frequency. Deletion of Dnase1l3 in mice led to the preferential cleavage of fragments, with the top six motifs all beginning with CC.[25]

While some plasma DNA fragments are cleaved with blunt ends, other nucleases can produce jagged ends.[20] Jagged ends refer to the asymmetry between the 5′ and 3′ termini on opposing strands where the cuts on DNA strands are often slightly offset, leading to overhangs.[20] The degree and directionality of this jaggedness encodes information about the specific enzymatic mechanism of cleavage, with DNASE1 and DNASE1L3 leading to an increase and decrease in the frequency of jagged edges, respectively.[26] Jagged ends have also been found to be more frequent in fetal DNA in maternal cfDNA and ctDNA in cancer patients.[26]

Nucleosome positioning

cfDNA fragmentation patterns can reveal underlying nucleosome occupancy in the cell-of-origin. Nucleosome-protected DNA is resistant to cleavage by nucleases, so cuts primarily occur in nucleosome-depleted regions (NDR).[4][20] Consequently, the ends of cfDNA fragments are strongly biased to fall within regions of linker DNA. Accessible genomic regions are areas of open chromatin that frequently contain actively bound transcription factor binding sites.[27] By clustering fragments to specific genomic positions, the location of NDRs can be determined and quantified as a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp genomic window minus those with endpoints inside it.[4] Nucleosome organization can also be inferred from sequencing metrics, with a loss of sequencing coverage reflecting DNA degradation at unprotected binding sites and peaks of coverage occurring around nucleosome-protected regions.[27]Nucleosome maps constructed from fragmentation patterns reflect chromatin state in the cell-of-origin and can reveal sites of active transcription.[28] In oncology, nucleosome positioning can be leveraged for early screening and non-invasive cancer subtyping with ctDNA.[29][30]

Methods

Standard workflow

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A standard fragmentomics workflow. Blood samples are taken for analysis from the source of interest (e.g., cancer patients, placenta), and DNA fragments are extracted from plasma or serum. The isolated fragments are constructed into libraries and subject to next-generation sequencing (NGS). The resulting sequences then undergo bioinformatic analysis with fragmentomic pipelines.

Fragmentomics analyses begin with peripheral blood draw, followed by the isolation of blood plasma and/or serum to obtain circulating cfDNA. Isolated cfDNA fragments then undergo next-generation sequencing (NGS). Sequencing data are then processed through bioinformatic pipelines to extract fragmentomic features and/or DNA methylation signals. This can then be used for tissue-of-origin deconvolution, and additional downstream analyses involving statistical and machine learning approaches.[citation needed]

EPIC-seq

EPIC-seq (Epigenetic Expression Inference from Cell-free DNA Sequencing) is a fragmentomics-based method that infers gene expression from cfDNA by analyzing fragmentation patterns at transcription start sites (TSS).[31]

cfMeDIP-seq

cfMeDIP-seq (cell-free methylated DNA immunoprecipitation and high-throughput sequencing) is a fragment-based method used to profile DNA methylation patterns from cfDNA.[32] DNA methylation, the addition of a methyl group to the 5′ position of cytosine residue (5mC), is a key epigenetic mechanism regulating gene expression, and its patterns are tissue- and cell-type specific. Changes in DNA methylation also reflect development, aging, environmental exposures, and disease states. In cfMeDIP-seq, cfDNA is first extracted from plasma and quantified despite its low input and fragment size (~167 bp due to nucleosome protection). In this assay, a 5mC antibody binds to the methylated fragments, enriching for methylated regions across the genome.[32] Fragments are quantified using spike-in and filler DNA.[32]

EM-seq

EM-seq (Enzymatic methyl sequencing) also profiles cfDNA methylation though enzymatic conversion.[33] The method preserves cfDNA fragment length from degradation and provides high resolution information from low input amounts. In EM-seq, the 5mC and 5hmC is protected by TET family enzymes, then unmethylated cytosines are deaminated, thereby being read-out as thymines during sequencing.[33]

Limitations

Despite its utility, fragmentomics faces some notable limitations. Biologically, plasma cfDNA is a composite signal from multiple tissues, and at low tumour fractions (i.e., <1%, as often seen in early stage cancer), tumour-derived fragmentomic signals are overwhelmed by background noise from normal cells, making reliable detection difficult without high sensitivity.[31][34] Fragmentomics is also constrained by sequencing depth. Many fragmentomic features (particularly nucleosome positioning at specific loci) require high-coverage whole-genome sequencing, which remains costly and limits clinical scalability.[5] While targeted panels enable deeper sequencing,[5] they limit information to a select few genomic regions and miss global fragmentation patterns.

Applications

Tissue-of-origin detection

cfDNA fragments carry signatures reflecting their tissue-of-origin. The tissue and cell-type contributions of circulating cfDNA fragments can be computationally inferred in a process called cfDNA deconvolution.[35][36] Fragmentomic features can be quantified by calculating statistical scores at defined genomic loci for predictable characteristics such as fragment size distributions, fragment end motifs, strand orientation, fragment convergence patterns, and nucleosomal positioning around TSSs.[36] These fragmentation signatures are then used to reconstruct chromatin accessibility landscapes and computationally correlated with reference epigenomic maps and cell atlases to rank candidate tissues and cells-of-origin.[36]

Cancer detection and tumour profiling

Liquid biopsies are increasingly being used for cancer profiling, where tumor-derived DNA is analyzed to detect somatic variants, monitor disease progression, assess minimal residual disease, and guide targeted therapy decisions. Plasma DNA fragments from tumour cells tend to be shorter and have different end motif profiles compared to cell-free DNA from normal cells, improving the identification of tumour DNA.[8] Fragmentomics has been applied to several areas of cancer care, including early detection,[16][12] monitoring,[4] and subtyping.[27][29]

Non-invasive prenatal testing

In prenatal medicine, cfDNA analysis is widely used for fetal sex determination and non-invasive prenatal testing (NIPT), including parent-of-origin and haplotype-based analyses using paternal SNPs. However, the availability of fetal cfDNA in maternal cfDNA is low, comprising only 2–19% of the total volume[18]. Since all cffDNA is less than 300 bp long,[18] selecting for fragments by size can help enrich for fetal DNA.

Transplant rejection detection

While organ transplants are often life saving procedures, their efficacy and success are threatened by the possibility of rejection. Identifying rejection responses early is critical to save the graft, prevent irreversible damage, and provide treatment in a timely manner. Donor-derived cell-free DNA (ddcfDNA) has been developed as a biomarker of graft integrity, and fragmentomics can be applied to detect transplant rejection. A previous study found that patients who experience acute rejection had higher ddcfDNA concentrations and showed enrichment for fragments between 100 and 250 bp.[37]

Biomarker discovery

cfDNA is increasingly investigated as a biomarker in neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, as well as in autoimmune, cardiovascular, and inflammatory conditions, where tissue-specific methylation patterns can reveal the cell of origin.[6] In obstetrics, placental cfDNA dynamics are being studied in pregnancy complications such as preeclampsia.[38][39]

References

  1. ↑ 1.0 1.1 Ivanov, Maxim; Baranova, Ancha; Butler, Timothy; Spellman, Paul; Mileyko, Vladislav (2015). "Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation". BMC Genomics 16 (Suppl 13). doi:10.1186/1471-2164-16-S13-S1. PMID 26693644. 
  2. ↑ 2.0 2.1 Helzer, Kyle T.; Sharifi, Marina N.; Sperger, Jamie M.; Chrostek, Matthew R.; Bootsma, Matthew L.; Reese, Shannon R.; Taylor, Amy; Kaufmann, Katie R. et al. (2025). "Analysis of cfDNA fragmentomics metrics and commercial targeted sequencing panels". Nature Communications 16 (1). doi:10.1038/s41467-025-64153-z. PMID 41087333. 
  3. ↑ 3.0 3.1 3.2 Mouliere, Florent et al. (2018). "Enhanced detection of circulating tumor DNA by fragment size analysis". Science Translational Medicine 10 (466). doi:10.1126/scitranslmed.aat4921. PMID 30404863. PMC 6483061. https://www.repository.cam.ac.uk/handle/1810/286423. 
  4. ↑ 4.0 4.1 4.2 4.3 4.4 Snyder, Matthew W.; Kircher, Martin; Hill, Andrew J.; Daza, Riza M.; Shendure, Jay (2016). "Cell-free DNA Comprises an in Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin". Cell 164 (1–2): 57–68. doi:10.1016/j.cell.2015.11.050. PMID 26771485. .
  5. ↑ 5.0 5.1 5.2 Helzer, K.T. et al. (2023). "Fragmentomic analysis of circulating tumor DNA-targeted cancer panels". Annals of Oncology 34 (9): 813–825. doi:10.1016/j.annonc.2023.06.001. PMID 37330052. 
  6. ↑ 6.0 6.1 Aydın, Şeyma; Özdemir, Selçuk; Adıgüzel, Ahmet (2025). "The Potential of cfDNA as Biomarker: Opportunities and Challenges for Neurodegenerative Diseases". Journal of Molecular Neuroscience 75 (1). doi:10.1007/s12031-025-02317-8. PMID 40080233. 
  7. ↑ Nagy, Balint (2019). "Cell-Free Nucleic Acids". International Journal of Molecular Sciences 20 (22): 5645. doi:10.3390/ijms20225645. PMID 31718077. 
  8. ↑ 8.0 8.1 Tsui, W.H. Adrian; Jiang, Peiyong; Lo, Y.M. Dennis (2025). "Cell-free DNA fragmentomics in cancer". Cancer Cell 43 (10): 1792–1814. doi:10.1016/j.ccell.2025.09.006. PMID 41043439. 
  9. ↑ Qi Chen, Xu; Stroun, Maurice; Magnenat, Jean-Luc; Nicod, Laurent P.; Kurt, Anne-Marie; Lyautey, Jacqueline; Lederrey, Christine; Anker, Philippe (1996). "Microsatellite alterations in plasma DNA of small cell lung cancer patients". Nature Medicine 2 (9): 1033–1035. doi:10.1038/nm0996-1033. PMID 8782463. 
  10. ↑ Lo, Y M Dennis; Corbetta, Noemi; Chamberlain, Paul F.; Rai, Vik; Sargent, Ian L.; Redman, Christopher WG; Wainscoat, James S. (1997). "Presence of fetal DNA in maternal plasma and serum". The Lancet 350 (9076): 485–487. doi:10.1016/S0140-6736(97)02174-0. PMID 9274585. 
  11. ↑ Lo, YM Dennis; Tein, Mark SC; Pang, Calvin CP; Yeung, Chung K.; Tong, Kwok-Lung; Hjelm, N Magnus (1998). "Presence of donor-specific DNA in plasma of kidney and liver-transplant recipients". The Lancet 351 (9112): 1329–1330. doi:10.1016/S0140-6736(05)79055-3. PMID 9643800. 
  12. ↑ 12.0 12.1 Thierry, A.R. (2023). "Circulating DNA fragmentomics and cancer screening". Cell Genomics 3 (1). doi:10.1016/j.xgen.2022.100242. PMID 36777187. 
  13. ↑ Nagata, S.; Nagase, H.; Kawane, K.; Mukae, N.; Fukuyama, H. (2003). "Degradation of chromosomal DNA during apoptosis". Cell Death & Differentiation 10 (1): 108–116. doi:10.1038/sj.cdd.4401161. PMID 12655299. 
  14. ↑ Sanchez, Cynthia; Roch, Benoit; Mazard, Thibault; Blache, Philippe; Dache, Zahra Al Amir; Pastor, Brice; Pisareva, Ekaterina; Tanos, Rita et al. (2021). "Circulating nuclear DNA structural features, origins, and complete size profile revealed by fragmentomics". JCI Insight 6 (7). doi:10.1172/jci.insight.144561. PMID 33571170. 
  15. ↑ Fang, Jingwen; Luo, Songwen; Li, Shouzhen; Xu, Yehong; Wang, Jing; Shan, Benjie; Hu, Mingjun; Yu, Qiaoni et al. (2026). "Detection of primary cancer types via fragment size selection in circulating cell-free extrachromosomal circular DNA". Genome Medicine 18 (1). doi:10.1186/s13073-025-01595-6. PMID 41495807. 
  16. ↑ 16.0 16.1 Dong, Wanyu; Hu, W.; Lu, Y.; Zheng, Q. (2025). "Cell-free DNA fragmentomics: A universal framework for early cancer detection and monitoring". American Journal of Clinical and Experimental Immunology 14 (4): 237–240. doi:10.62347/EBRY4326. PMID 40977920. 
  17. ↑ 17.0 17.1 Lo, Y M Dennis; Huang, Dolly W S.; Lo, Kwok-Wai; Leung, Tse N.; Lau, Tze K.; Wong, Nathalie; Hui, Angela B Y.; Zhang, Jun et al. (2004). "Size Distributions of Maternal and Fetal DNA in Maternal Plasma". Clinical Chemistry 50 (1): 88–92. doi:10.1373/clinchem.2003.024893. PMID 14709639. 
  18. ↑ 18.0 18.1 18.2 18.3 18.4 Quake, Stephen R.; Hudgins, Louanne; Chitkara, Usha; Blumenfeld, Yair J.; Fan, H Christina (2010). "Analysis of the Size Distributions of Fetal and Maternal Cell-Free DNA by Paired-End Sequencing". Clinical Chemistry 56 (8): 1279–1286. doi:10.1373/clinchem.2010.144188. PMID 20558635. 
  19. ↑ Yang, Qiwei; Du, Zhenwu; Song, Yang; Gao, Sujie; Yu, Shan; Zhu, He; Ren, Ming; Zhang, Guizhen (2017). "Size-selective separation and overall-amplification of cell-free fetal DNA fragments using PCR-based enrichment". Scientific Reports 7. doi:10.1038/srep40936. PMID 28102322. 
  20. ↑ 20.0 20.1 20.2 20.3 Lo, Y. M. Dennis; Han, Diana S. C.; Jiang, Peiyong; Chiu, Rossa W. K. (2021). "Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies". Science 372 (6538). doi:10.1126/science.aaw3616. PMID 33833097. 
  21. ↑ Han, Diana S.C.; Lo, Y.M. Dennis (2021). "The Nexus of cfDNA and Nuclease Biology". Trends in Genetics 37 (8): 758–770. doi:10.1016/j.tig.2021.04.005. PMID 34006390. 
  22. ↑ Han, Diana S.C.; Ni, Meng; Chan, Rebecca W.Y.; Chan, Vicken W.H.; Lui, Kathy O.; Chiu, Rossa W.K.; Lo, Y.M. Dennis (2020). "The Biology of Cell-free DNA Fragmentation and the Roles of DNASE1, DNASE1L3, and DFFB". The American Journal of Human Genetics 106 (2): 202–214. doi:10.1016/j.ajhg.2020.01.008. PMID 32004449. 
  23. ↑ Zhou, Ze; Ma, Mary-Jane L.; Chan, Rebecca W. Y.; Lam, W. K. Jacky; Peng, Wenlei; Gai, Wanxia; Hu, Xi; Ding, Spencer C. et al. (2023). "Fragmentation landscape of cell-free DNA revealed by deconvolutional analysis of end motifs". Proceedings of the National Academy of Sciences 120 (17). doi:10.1073/pnas.2220982120. PMID 37075072. 
  24. ↑ Jiang, Peiyong; Sun, Kun; Peng, Wenlei; Cheng, Suk Hang; Ni, Meng; Yeung, Philip C.; Heung, Macy M.S.; Xie, Tingting et al. (2020). "Plasma DNA End-Motif Profiling as a Fragmentomic Marker in Cancer, Pregnancy, and Transplantation". Cancer Discovery 10 (5): 664–673. doi:10.1158/2159-8290.CD-19-0622. PMID 32111602. 
  25. ↑ Serpas, Lee; Chan, Rebecca W. Y.; Jiang, Peiyong; Ni, Meng; Sun, Kun; Rashidfarrokhi, Ali; Soni, Chetna; Sisirak, Vanja et al. (2019). "Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA". Proceedings of the National Academy of Sciences 116 (2): 641–649. doi:10.1073/pnas.1815031116. PMID 30593563. 
  26. ↑ 26.0 26.1 Jiang, Peiyong; Xie, Tingting; Ding, Spencer C.; Zhou, Ze; Cheng, Suk Hang; Chan, Rebecca W.Y.; Lee, Wing-Shan; Peng, Wenlei et al. (2020). "Detection and characterization of jagged ends of double-stranded DNA in plasma". Genome Research 30 (8): 1144–1153. doi:10.1101/gr.261396.120. PMID 32801148. 
  27. ↑ 27.0 27.1 27.2 Doebley, Anna-Lisa; Ko, Minjeong; Liao, Hanna; Cruikshank, A. Eden; Santos, Katheryn; Kikawa, Caroline; Hiatt, Joseph B.; Patton, Robert D. et al. (2022). "A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA". Nature Communications 13 (1). doi:10.1038/s41467-022-35076-w. PMID 36463275. 
  28. ↑ Vanderstichele, Adriaan; Busschaert, Pieter; Landolfo, Chiara; Olbrecht, Siel; Coosemans, An; Froyman, Wouter; Loverix, Liselore; Concin, Nicole et al. (2022). "Nucleosome footprinting in plasma cell-free DNA for the pre-surgical diagnosis of ovarian cancer". npj Genomic Medicine 7 (1). doi:10.1038/s41525-022-00300-5. PMID 35484288. 
  29. ↑ 29.0 29.1 Ulz, Peter; Perakis, Samantha; Zhou, Qing; Moser, Tina; Belic, Jelena; Lazzeri, Isaac; Wölfler, Albert; Zebisch, Armin et al. (2019). "Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection". Nature Communications 10 (1). doi:10.1038/s41467-019-12714-4. PMID 31604930. 
  30. ↑ Cristiano, Stephen et al. (2019). "Genome-wide cell-free DNA fragmentation in patients with cancer". Nature 570 (7761): 385–389. doi:10.1038/s41586-019-1272-6. PMID 31142840. 
  31. ↑ 31.0 31.1 Esfahani, Mohammad Shahrokh; Hamilton, Emily G.; Mehrmohamadi, Mahya; Nabet, Barzin Y.; Alig, Stefan K.; King, Daniel A.; Steen, Chloé B.; MacAulay, Charles W. et al. (2022). "Inferring gene expression from cell-free DNA fragmentation profiles". Nature Biotechnology 40 (4): 585–597. doi:10.1038/s41587-022-01222-4. PMID 35361996. 
  32. ↑ 32.0 32.1 32.2 Shen, Shu Yi; Burgener, Justin M.; Bratman, Scott V.; De Carvalho, Daniel D. (2019). "Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA". Nature Protocols 14 (10): 2749–2780. doi:10.1038/s41596-019-0202-2. PMID 31471598. 
  33. ↑ 33.0 33.1 Černoša, Iva; Trincado-Alonso, Fernando; Canal-Noguer, Pol; Kruusmaa, Kristi; Perera-Lluna, Alexandre (2024). "Use of Enzymatically Converted Cell-Free DNA (CfDNA) Data for Copy Number Variation-Linked Fragmentation Analysis Allows for Early Colorectal Cancer Detection". International Journal of Molecular Sciences 25 (6): 3502. doi:10.3390/ijms25063502. PMID 38542469. 
  34. ↑ Jiang, Peiyong; Lo, Y. M. Dennis (2022). "Enhanced cancer detection from cell-free DNA". Nature Biotechnology 40 (4): 473–474. doi:10.1038/s41587-021-01207-9. PMID 35361997. 
  35. ↑ Liu, Yaping (2022). "At the dawn: Cell-free DNA fragmentomics and gene regulation". British Journal of Cancer 126 (3): 379–390. doi:10.1038/s41416-021-01635-z. PMID 34815523. 
  36. ↑ 36.0 36.1 36.2 Stanley, Kate E.; Jatsenko, Tatjana; Tuveri, Stefania; Sudhakaran, Dhanya; Lannoo, Lore; Van Calsteren, Kristel; De Borre, Marie; Van Parijs, Ilse et al. (2024). "Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology". Nature Communications 15 (1). doi:10.1038/s41467-024-46435-0. PMID 38472221. 
  37. ↑ Fernández-Galán, Esther; Badenas, Celia; Fondevila, Constantino; Jiménez, Wladimiro; Navasa, Miquel; Puig-Butillé, Joan Anton; Brunet, Mercè (2022). "Monitoring of Donor-Derived Cell-Free DNA by Short Tandem Repeats: Concentration of Total Cell-Free DNA and Fragment Size for Acute Rejection Risk Assessment in Liver Transplantation". Liver Transplantation 28 (2): 257–268. doi:10.1002/lt.26272. PMID 34407295. 
  38. ↑ Adil, Mohamed; Kolarova, Teodora R.; Doebley, Anna-Lisa; Chen, Leah A.; Tobey, Cara L.; Galipeau, Patricia; Rosen, Sam; Yang, Michael et al. (2025). "Preeclampsia risk prediction from prenatal cell-free DNA screening". Nature Medicine 31 (4): 1312–1318. doi:10.1038/s41591-025-03509-w. PMID 39939524. 
  39. ↑ Yuen, Natalie; Lemaire, Melanie; Wilson, Samantha L. (2024). "Cell-free placental DNA: What do we really know?". PLOS Genetics 20 (12). doi:10.1371/journal.pgen.1011484. PMID 39652523. 



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