Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).
De Vlaminck, I. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 6, 241ra77 (2014).
Fan, H. C., Blumenfeld, Y. J., Chitkara, U., Hudgins, L. & Quake, S. R. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc. Natl Acad. Sci. USA 105, 16266–16271 (2008).
Koh, W. et al. Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. Proc. Natl Acad. Sci. USA 111, 7361–7366 (2014).
Toden, S. et al. Noninvasive characterization of Alzheimer’s disease by circulating, cell-free messenger RNA next-generation sequencing. Sci. Adv. 6, eabb1654 (2020).
Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136 (2018).
Heitzer, E., Auinger, L. & Speicher, M. R. Cell-free DNA and apoptosis: how dead cells inform about the living. Trends Mol. Med. 26, 519–528 (2020).
Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes. Science 367, eaau6977 (2020).
Sun, K. et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc. Natl Acad. Sci. USA 112, E5503–E5512 (2015).
Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M. & Shendure, J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164, 57–68 (2016).
Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat. Biotechnol. 40, 585–597 (2022).
Klatt, E. C. Robbins & Cotran Atlas of Pathology (Elsevier, 2021).
Kumar, V., Abbas, A. K. & Aster, J. C. Robbins and Cotran Pathologic Basis of Disease (Elsevier, 2015).
Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium & Quake, S. R. Cell types of origin of the cell-free transcriptome. Nat. Biotechnol. 40, 855–861 (2022).
Sadeh, R. et al. ChIP–seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin. Nat. Biotechnol. 39, 586–598 (2021).
Loyfer, N. et al. A DNA methylation atlas of normal human cell types. Nature 613, 355–364 (2023).
Stanley, K. E. et al. Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology. Nat. Commun. 15, 2220 (2024).
Tsang, J. C. H. et al. Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics. Proc. Natl Acad. Sci. USA 114, E7786–E7795 (2017).
Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).
Tabula Sapiens Consortium et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Rostami, A. et al. Senescence, necrosis, and apoptosis govern circulating cell-free DNA release kinetics. Cell Rep. 31, 107830 (2020).
Kustanovich, A., Schwartz, R., Peretz, T. & Grinshpun, A. Life and death of circulating cell-free DNA. Cancer Biol. Ther. 20, 1057–1067 (2019).
De Sota, R. E., Quake, S. R., Sninsky, J. J. & Toden, S. Decoding bioactive signals of the RNA secretome: the cell-free messenger RNA catalogue. Expert Rev. Mol. Med. 26, e12 (2024).
Wang, C. & Liu, H. Factors influencing degradation kinetics of mRNAs and half-lives of microRNAs, circRNAs, lncRNAs in blood in vitro using quantitative PCR. Sci. Rep. 12, 7259 (2022).
Larson, M. H. et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat. Commun. 12, 2357 (2021).
Medina Diaz, I. et al. Performance of Streck cfDNA blood collection tubes for liquid biopsy testing. PLoS ONE 11, e0166354 (2016).
Kowarsky, M. et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc. Natl Acad. Sci. USA 114, 9623–9628 (2017).
exRNAQC Consortium. Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling. Nat. Commun. 16, 4513 (2025).
Meddeb, R., Pisareva, E. & Thierry, A. R. Guidelines for the preanalytical conditions for analyzing circulating cell-free DNA. Clin. Chem. 65, 623–633 (2019).
Zhou, B. et al. Application of exosomes as liquid biopsy in clinical diagnosis. Signal Transduct. Target. Ther. 5, 144 (2020).
Liang, Y., Lehrich, B. M., Zheng, S. & Lu, M. Emerging methods in biomarker identification for extracellular vesicle-based liquid biopsy. J. Extracell. Vesicles 10, e12090 (2021).
Kumar, M. A. et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct. Target. Ther. 9, 27 (2024).
Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).
Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).
Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).
Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
Nesselbush, M. C. et al. An ultrasensitive method for detection of cell-free RNA. Nature 641, 759–768 (2025).
Dor, Y. & Cedar, H. Principles of DNA methylation and their implications for biology and medicine. Lancet 392, 777–786 (2018).
Lehmann-Werman, R. et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc. Natl Acad. Sci. USA 113, E1826–E1834 (2016).
Guler, G. D. et al. Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA. Nat. Commun. 11, 5270 (2020).
Song, C.-X. et al. 5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages. Cell Res. 27, 1231–1242 (2017).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Cui, X.-L. et al. A human tissue map of 5-hydroxymethylcytosines exhibits tissue specificity through gene and enhancer modulation. Nat. Commun. 11, 6161 (2020).
Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat. Genet. 48, 1273–1278 (2016).
Ibarra, A. et al. Non-invasive characterization of human bone marrow stimulation and reconstitution by cell-free messenger RNA sequencing. Nat. Commun. 11, 400 (2020).
Chalasani, N. et al. Noninvasive stratification of nonalcoholic fatty liver disease by whole transcriptome cell-free mRNA characterization. Am. J. Physiol. Gastrointest. Liver Physiol. 320, G439–G449 (2021).
Munchel, S. et al. Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia. Sci. Transl. Med. 12, eaaz0131 (2020).
Moufarrej, M. N. et al. Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature 602, 689–694 (2022).
Rasmussen, M. et al. RNA profiles reveal signatures of future health and disease in pregnancy. Nature 601, 422–427 (2022).
Srinivasan, S. et al. Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation. Cell 177, 446–462 (2019).
Schwarzenbach, H., Nishida, N., Calin, G. A. & Pantel, K. Clinical relevance of circulating cell-free microRNAs in cancer. Nat. Rev. Clin. Oncol. 11, 145–156 (2014).
Toden, S. & Goel, A. Non-coding RNAs as liquid biopsy biomarkers in cancer. Br. J. Cancer 126, 351–360 (2022).
Loy, C. J. et al. Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C. Cell Rep. Med. 4, 101034 (2023).
Chang, A. et al. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat. Commun. 15, 4949 (2024).
Tabrizi, S. et al. Modulating cell-free DNA biology as the next frontier in liquid biopsies. Trends Cell Biol. 35, 459–469 (2024).
Sorrentino, S. The eight human ‘canonical’ ribonucleases: molecular diversity, catalytic properties, and special biological actions of the enzyme proteins. FEBS Lett. 584, 2194–2200 (2010).
Horns, F. et al. Engineering RNA export for measurement and manipulation of living cells. Cell 186, 3642–3658 (2023).
Meddeb, R. et al. Quantifying circulating cell-free DNA in humans. Sci. Rep. 9, 5220 (2019).
Jeffery, P. K. & Li, D. Airway mucosa: secretory cells, mucus and mucin genes. Eur. Respir. J. 10, 1655–1662 (1997).
Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Development 141, 1427–1441 (2014).
DomÃnguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).
Moss, J. et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 5068 (2018).
Zhou, J. et al. Human body single-cell atlas of 3D genome organization and DNA methylation. Preprint at bioRxiv https://doi.org/10.1101/2025.03.23.644697 (2025).
Bai, D. et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat. Biotechnol. 43, 85–96 (2025).
CZI Cell Science Program et al. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Nucleic Acids Res. 53, D886–D900 (2025).
Tabula Sapiens Consortium & Quake, S. R. Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues. Preprint at bioRxiv https://doi.org/10.1101/2024.12.03.626516 (2025).
Pisco, A. O., Tojo, B. & McGeever, A. Single-cell analysis for whole-organism datasets. Annu. Rev. Biomed. Data Sci. 4, 207–226 (2021).
Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Zhu, T. et al. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat. Methods 19, 296–306 (2022).
Teschendorff, A. E., Zhu, T., Breeze, C. E. & Beck, S. EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-seq data. Genome Biol. 21, 221 (2020).
Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3, 505–517 (2022).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Shen-Orr, S. S. & Gaujoux, R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr. Opin. Immunol. 25, 571–578 (2013).
Mohammadi, S., Zuckerman, N., Goldsmith, A. & Grama, A. A critical survey of deconvolution methods for separating cell types in complex tissues. Proc. IEEE 105, 340–366 (2017).
Houseman, E. A. et al. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics 17, 259 (2016).
Venet, D., Pecasse, F., Maenhaut, C. & Bersini, H. Separation of samples into their constituents using gene expression data. Bioinformatics 17, S279–S287 (2001).
Shen-Orr, S. S., Tibshirani, R. & Butte, A. J. Gene expression deconvolution in linear space. Nat. Methods 9, 8–9 (2011).
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P. & De Preter, K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat. Commun. 11, 5650 (2020).
Sun, T. et al. Systematic evaluation of methylation-based cell type deconvolution methods for plasma cell-free DNA. Genome Biol. 25, 318 (2024).
Im, Y. & Kim, Y. A comprehensive overview of RNA deconvolution methods and their application. Mol. Cells 46, 99–105 (2023).
Qiao, W. et al. PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions. PLoS Comput. Biol. 8, e1002838 (2012).
Gong, T. et al. Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples. PLoS ONE 6, e27156 (2011).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Caggiano, C. et al. Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE. Nat. Commun. 12, 2717 (2021).
Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6, eaba2619 (2020).
Keukeleire, P., Makrodimitris, S. & Reinders, M. Cell type deconvolution of methylated cell-free DNA at the resolution of individual reads. NAR Genom. Bioinform. 5, lqad048 (2023).
Devarajan, K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput. Biol. 4, e1000029 (2008).
Barefoot, M. E. et al. Detection of cell types contributing to cancer from circulating, cell-free methylated DNA. Front. Genet. 12, 671057 (2021).
Li, S. et al. Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Proc. Natl Acad. Sci. USA 120, e2305236120 (2023).
Yan, S. et al. Pathway-enhanced Transformer-based model for robust enumeration of cell types from the cell-free transcriptome. Preprint at bioRxiv https://doi.org/10.1101/2024.02.28.582494 (2024).
Zaitsev, K., Bambouskova, M., Swain, A. & Artyomov, M. N. Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat. Commun. 10, 2209 (2019).
Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 9 (2012).
Vorperian, S. K. et al. Deconvolution of human urine across the transcriptome and metabolome. Clin. Chem. 70, 1344–1354 (2024).
Elovitz, M. A. et al. Molecular subtyping of hypertensive disorders of pregnancy. Nat. Commun. 16, 2948 (2025).
Moss, J. et al. Megakaryocyte- and erythroblast-specific cell-free DNA patterns in plasma and platelets reflect thrombopoiesis and erythropoiesis levels. Nat. Commun. 14, 7542 (2023).
Doss, J. F. et al. A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes. BMC Genomics 16, 952 (2015).
Akirav, E. M. et al. Detection of β cell death in diabetes using differentially methylated circulating DNA. Proc. Natl Acad. Sci. USA 108, 19018–19023 (2011).
Dimitriadis, E. et al. Pre-eclampsia. Nat. Rev. Dis. Primers 9, 8 (2023).
De Borre, M. et al. Cell-free DNA methylome analysis for early preeclampsia prediction. Nat. Med. 29, 2206–2215 (2023).
Adil, M. et al. Preeclampsia risk prediction from prenatal cell-free DNA screening. Nat. Med. 31, 1312–1318 (2025).
Hulstaert, E. et al. Charting extracellular transcriptomes in the human biofluid RNA atlas. Cell Rep. 33, 108552 (2020).
Tivey, A., Church, M., Rothwell, D., Dive, C. & Cook, N. Circulating tumour DNA — looking beyond the blood. Nat. Rev. Clin. Oncol. 19, 600–612 (2022).
Hulstaert, E. et al. RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer. Neoplasia 24, 155–164 (2022).
Haeberle, L. et al. Molecular analysis of cyst fluids improves the diagnostic accuracy of pre-operative assessment of pancreatic cystic lesions. Sci. Rep. 11, 2901 (2021).
Bryzgunova, O. E. & Laktionov, P. P. Extracellular nucleic acids in urine: sources, structure, diagnostic potential. Acta Naturae 7, 48–54 (2015).
Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).
Cheng, T. H. T. et al. Noninvasive detection of bladder cancer by shallow-depth genome-wide bisulfite sequencing of urinary cell-free DNA for methylation and copy number profiling. Clin. Chem. 65, 927–936 (2019).
Green, E. A. et al. Clinical utility of cell-free and circulating tumor DNA in kidney and bladder cancer: a critical review of current literature. Eur. Urol. Oncol. 4, 893–903 (2021).
Nuzzo, P. V. et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat. Med. 26, 1041–1043 (2020).
Burnham, P. et al. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 9, 2412 (2018).
Sin, M. L. Y. et al. Deep sequencing of urinary RNAs for bladder cancer molecular diagnostics. Clin. Cancer Res. 23, 3700–3710 (2017).
Monteiro, M. B. et al. Urinary sediment transcriptomic and longitudinal data to investigate renal function decline in type 1 diabetes. Front. Endocrinol. 11, 238 (2020).
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).
Hahn, O. et al. Atlas of the aging mouse brain reveals white matter as vulnerable foci. Cell 186, 4117–4133 (2023).
Mathys, H. et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell 186, 4365–4385 (2023).
Pan, W., Gu, W., Nagpal, S., Gephart, M. H. & Quake, S. R. Brain tumor mutations detected in cerebral spinal fluid. Clin. Chem. 61, 514–522 (2015).
Seoane, J., De Mattos-Arruda, L., Le Rhun, E., Bardelli, A. & Weller, M. Cerebrospinal fluid cell-free tumour DNA as a liquid biopsy for primary brain tumours and central nervous system metastases. Ann. Oncol. 30, 211–218 (2019).
De Sota, R. E. et al. Transcriptome profiling of cerebrospinal fluid in Alzheimer’s disease reveals molecular dysregulations associated with disease. Preprint at medRxiv https://doi.org/10.1101/2023.11.21.23298852 (2023).
András, I. E. & Toborek, M. Extracellular vesicles of the blood–brain barrier. Tissue Barriers 4, e1131804 (2016).
Ganong, W. F. Circumventricular organs: definition and role in the regulation of endocrine and autonomic function. Clin. Exp. Pharmacol. Physiol. 27, 422–427 (2000).
Abbott, N. J. Inflammatory mediators and modulation of blood–brain barrier permeability. Cell. Mol. Neurobiol. 20, 131–147 (2000).
Gaitsch, H., Franklin, R. J. M. & Reich, D. S. Cell-free DNA-based liquid biopsies in neurology. Brain 146, 1758–1774 (2023).
Morgan, P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discov. 17, 167–181 (2018).
Frank, R. & Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2, 566–580 (2003).
Hartl, D. et al. Translational precision medicine: an industry perspective. J. Transl. Med. 19, 245 (2021).
FDA. Nucleic Acid Based Tests https://www.fda.gov/medical-devices/in-vitro-diagnostics/nucleic-acid-based-tests (2025).
Milbury, C. A. et al. Clinical and analytical validation of FoundationOne®CDx, a comprehensive genomic profiling assay for solid tumors. PLoS ONE 17, e0264138 (2022).
Woodhouse, R. et al. Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS ONE 15, e0237802 (2020).
Martin-Alonso, C. et al. Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies. Science 383, eadf2341 (2024).
Geyer, P. E. et al. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol. Med. 11, e10427 (2019).
Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).
Mann, M., Kumar, C., Zeng, W.-F. & Strauss, M. T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 12, 759–770 (2021).
Wishart, D. S. et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
Marić, I. et al. Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns 3, 100655 (2022).
Hédou, J. et al. Discovery of sparse, reliable omic biomarkers with Stabl. Nat. Biotechnol. 42, 1581–1593 (2024).
Chung, D. C. et al. A cell-free DNA blood-based test for colorectal cancer screening. N. Engl. J. Med. 390, 973–983 (2024).
Alexander, G. E. et al. Analytical validation of a multi-cancer early detection test with cancer signal origin using a cell-free DNA-based targeted methylation assay. PLoS ONE 18, e0283001 (2023).
Mirvie. Mirvie Receives FDA Breakthrough Device Designation for First Test Designed to Indicate Risk of Preeclampsia Months Before Symptoms Occur https://www.mirvie.com/mirvie-media-releases/fda-breakthrough-device-designation (2022).
Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).
Isakova, A., Neff, N. & Quake, S. R. Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states. Proc. Natl Acad. Sci. USA 118, e2113568118 (2021).






