Cell type inference in cell-free nucleic acid liquid biopsy

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • De Vlaminck, I. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 6, 241ra77 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Toden, S. et al. Noninvasive characterization of Alzheimer’s disease by circulating, cell-free messenger RNA next-generation sequencing. Sci. Adv. 6, eabb1654 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes. Science 367, eaau6977 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat. Biotechnol. 40, 585–597 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Loyfer, N. et al. A DNA methylation atlas of normal human cell types. Nature 613, 355–364 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stanley, K. E. et al. Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology. Nat. Commun. 15, 2220 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Rostami, A. et al. Senescence, necrosis, and apoptosis govern circulating cell-free DNA release kinetics. Cell Rep. 31, 107830 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kustanovich, A., Schwartz, R., Peretz, T. & Grinshpun, A. Life and death of circulating cell-free DNA. Cancer Biol. Ther. 20, 1057–1067 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Medina Diaz, I. et al. Performance of Streck cfDNA blood collection tubes for liquid biopsy testing. PLoS ONE 11, e0166354 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • exRNAQC Consortium. Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling. Nat. Commun. 16, 4513 (2025).

    Article 

    Google Scholar
     

  • Meddeb, R., Pisareva, E. & Thierry, A. R. Guidelines for the preanalytical conditions for analyzing circulating cell-free DNA. Clin. Chem. 65, 623–633 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhou, B. et al. Application of exosomes as liquid biopsy in clinical diagnosis. Signal Transduct. Target. Ther. 5, 144 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumar, M. A. et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct. Target. Ther. 9, 27 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nesselbush, M. C. et al. An ultrasensitive method for detection of cell-free RNA. Nature 641, 759–768 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dor, Y. & Cedar, H. Principles of DNA methylation and their implications for biology and medicine. Lancet 392, 777–786 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat. Genet. 48, 1273–1278 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Munchel, S. et al. Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia. Sci. Transl. Med. 12, eaaz0131 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Moufarrej, M. N. et al. Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature 602, 689–694 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rasmussen, M. et al. RNA profiles reveal signatures of future health and disease in pregnancy. Nature 601, 422–427 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Srinivasan, S. et al. Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation. Cell 177, 446–462 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Toden, S. & Goel, A. Non-coding RNAs as liquid biopsy biomarkers in cancer. Br. J. Cancer 126, 351–360 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chang, A. et al. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat. Commun. 15, 4949 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tabrizi, S. et al. Modulating cell-free DNA biology as the next frontier in liquid biopsies. Trends Cell Biol. 35, 459–469 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Horns, F. et al. Engineering RNA export for measurement and manipulation of living cells. Cell 186, 3642–3658 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meddeb, R. et al. Quantifying circulating cell-free DNA in humans. Sci. Rep. 9, 5220 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jeffery, P. K. & Li, D. Airway mucosa: secretory cells, mucus and mucin genes. Eur. Respir. J. 10, 1655–1662 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Development 141, 1427–1441 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shen-Orr, S. S. & Gaujoux, R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr. Opin. Immunol. 25, 571–578 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Houseman, E. A. et al. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics 17, 259 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Venet, D., Pecasse, F., Maenhaut, C. & Bersini, H. Separation of samples into their constituents using gene expression data. Bioinformatics 17, S279–S287 (2001).

    Article 
    PubMed 

    Google Scholar
     

  • Shen-Orr, S. S., Tibshirani, R. & Butte, A. J. Gene expression deconvolution in linear space. Nat. Methods 9, 8–9 (2011).


    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, T. et al. Systematic evaluation of methylation-based cell type deconvolution methods for plasma cell-free DNA. Genome Biol. 25, 318 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Im, Y. & Kim, Y. A comprehensive overview of RNA deconvolution methods and their application. Mol. Cells 46, 99–105 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caggiano, C. et al. Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE. Nat. Commun. 12, 2717 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6, eaba2619 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Devarajan, K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput. Biol. 4, e1000029 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barefoot, M. E. et al. Detection of cell types contributing to cancer from circulating, cell-free methylated DNA. Front. Genet. 12, 671057 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 9 (2012).

    Article 

    Google Scholar
     

  • Vorperian, S. K. et al. Deconvolution of human urine across the transcriptome and metabolome. Clin. Chem. 70, 1344–1354 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Elovitz, M. A. et al. Molecular subtyping of hypertensive disorders of pregnancy. Nat. Commun. 16, 2948 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Doss, J. F. et al. A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes. BMC Genomics 16, 952 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dimitriadis, E. et al. Pre-eclampsia. Nat. Rev. Dis. Primers 9, 8 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • De Borre, M. et al. Cell-free DNA methylome analysis for early preeclampsia prediction. Nat. Med. 29, 2206–2215 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Adil, M. et al. Preeclampsia risk prediction from prenatal cell-free DNA screening. Nat. Med. 31, 1312–1318 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hulstaert, E. et al. Charting extracellular transcriptomes in the human biofluid RNA atlas. Cell Rep. 33, 108552 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hulstaert, E. et al. RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer. Neoplasia 24, 155–164 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bryzgunova, O. E. & Laktionov, P. P. Extracellular nucleic acids in urine: sources, structure, diagnostic potential. Acta Naturae 7, 48–54 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Nuzzo, P. V. et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat. Med. 26, 1041–1043 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burnham, P. et al. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 9, 2412 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sin, M. L. Y. et al. Deep sequencing of urinary RNAs for bladder cancer molecular diagnostics. Clin. Cancer Res. 23, 3700–3710 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hahn, O. et al. Atlas of the aging mouse brain reveals white matter as vulnerable foci. Cell 186, 4117–4133 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Ganong, W. F. Circumventricular organs: definition and role in the regulation of endocrine and autonomic function. Clin. Exp. Pharmacol. Physiol. 27, 422–427 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Abbott, N. J. Inflammatory mediators and modulation of blood–brain barrier permeability. Cell. Mol. Neurobiol. 20, 131–147 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gaitsch, H., Franklin, R. J. M. & Reich, D. S. Cell-free DNA-based liquid biopsies in neurology. Brain 146, 1758–1774 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Morgan, P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discov. 17, 167–181 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Frank, R. & Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2, 566–580 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hartl, D. et al. Translational precision medicine: an industry perspective. J. Transl. Med. 19, 245 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Martin-Alonso, C. et al. Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies. Science 383, eadf2341 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Geyer, P. E. et al. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol. Med. 11, e10427 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mann, M., Kumar, C., Zeng, W.-F. & Strauss, M. T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 12, 759–770 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wishart, D. S. et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marić, I. et al. Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns 3, 100655 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hédou, J. et al. Discovery of sparse, reliable omic biomarkers with Stabl. Nat. Biotechnol. 42, 1581–1593 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chung, D. C. et al. A cell-free DNA blood-based test for colorectal cancer screening. N. Engl. J. Med. 390, 973–983 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leave a Comment