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).
Google Scholar
De Vlaminck, I. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 6, 241ra77 (2014).
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).
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).
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).
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).
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).
Google Scholar
Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes. Science 367, eaau6977 (2020).
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).
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).
Google Scholar
Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat. Biotechnol. 40, 585–597 (2022).
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).
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).
Google Scholar
Loyfer, N. et al. A DNA methylation atlas of normal human cell types. Nature 613, 355–364 (2023).
Google Scholar
Stanley, K. E. et al. Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology. Nat. Commun. 15, 2220 (2024).
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).
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).
Google Scholar
Rostami, A. et al. Senescence, necrosis, and apoptosis govern circulating cell-free DNA release kinetics. Cell Rep. 31, 107830 (2020).
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).
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).
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).
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).
Google Scholar
Medina Diaz, I. et al. Performance of Streck cfDNA blood collection tubes for liquid biopsy testing. PLoS ONE 11, e0166354 (2016).
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).
Google Scholar
exRNAQC Consortium. Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling. Nat. Commun. 16, 4513 (2025).
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).
Google Scholar
Zhou, B. et al. Application of exosomes as liquid biopsy in clinical diagnosis. Signal Transduct. Target. Ther. 5, 144 (2020).
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).
Google Scholar
Kumar, M. A. et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct. Target. Ther. 9, 27 (2024).
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).
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).
Google Scholar
Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).
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).
Google Scholar
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
Google Scholar
Nesselbush, M. C. et al. An ultrasensitive method for detection of cell-free RNA. Nature 641, 759–768 (2025).
Google Scholar
Dor, Y. & Cedar, H. Principles of DNA methylation and their implications for biology and medicine. Lancet 392, 777–786 (2018).
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).
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).
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).
Google Scholar
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
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).
Google Scholar
Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat. Genet. 48, 1273–1278 (2016).
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).
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).
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).
Google Scholar
Moufarrej, M. N. et al. Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature 602, 689–694 (2022).
Google Scholar
Rasmussen, M. et al. RNA profiles reveal signatures of future health and disease in pregnancy. Nature 601, 422–427 (2022).
Google Scholar
Srinivasan, S. et al. Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation. Cell 177, 446–462 (2019).
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).
Google Scholar
Toden, S. & Goel, A. Non-coding RNAs as liquid biopsy biomarkers in cancer. Br. J. Cancer 126, 351–360 (2022).
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).
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).
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).
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).
Google Scholar
Horns, F. et al. Engineering RNA export for measurement and manipulation of living cells. Cell 186, 3642–3658 (2023).
Google Scholar
Meddeb, R. et al. Quantifying circulating cell-free DNA in humans. Sci. Rep. 9, 5220 (2019).
Google Scholar
Jeffery, P. K. & Li, D. Airway mucosa: secretory cells, mucus and mucin genes. Eur. Respir. J. 10, 1655–1662 (1997).
Google Scholar
Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Development 141, 1427–1441 (2014).
Google Scholar
Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).
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).
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).
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).
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).
Google Scholar
Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
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).
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).
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).
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).
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).
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).
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).
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).
Google Scholar
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).
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).
Google Scholar
Im, Y. & Kim, Y. A comprehensive overview of RNA deconvolution methods and their application. Mol. Cells 46, 99–105 (2023).
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).
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).
Google Scholar
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Google Scholar
Caggiano, C. et al. Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE. Nat. Commun. 12, 2717 (2021).
Google Scholar
Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6, eaba2619 (2020).
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).
Google Scholar
Devarajan, K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput. Biol. 4, e1000029 (2008).
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).
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).
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).
Google Scholar
Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 9 (2012).
Google Scholar
Vorperian, S. K. et al. Deconvolution of human urine across the transcriptome and metabolome. Clin. Chem. 70, 1344–1354 (2024).
Google Scholar
Elovitz, M. A. et al. Molecular subtyping of hypertensive disorders of pregnancy. Nat. Commun. 16, 2948 (2025).
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).
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).
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).
Google Scholar
Dimitriadis, E. et al. Pre-eclampsia. Nat. Rev. Dis. Primers 9, 8 (2023).
Google Scholar
De Borre, M. et al. Cell-free DNA methylome analysis for early preeclampsia prediction. Nat. Med. 29, 2206–2215 (2023).
Google Scholar
Adil, M. et al. Preeclampsia risk prediction from prenatal cell-free DNA screening. Nat. Med. 31, 1312–1318 (2025).
Google Scholar
Hulstaert, E. et al. Charting extracellular transcriptomes in the human biofluid RNA atlas. Cell Rep. 33, 108552 (2020).
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).
Google Scholar
Hulstaert, E. et al. RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer. Neoplasia 24, 155–164 (2022).
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).
Google Scholar
Bryzgunova, O. E. & Laktionov, P. P. Extracellular nucleic acids in urine: sources, structure, diagnostic potential. Acta Naturae 7, 48–54 (2015).
Google Scholar
Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).
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).
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).
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).
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).
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).
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).
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).
Google Scholar
Hahn, O. et al. Atlas of the aging mouse brain reveals white matter as vulnerable foci. Cell 186, 4117–4133 (2023).
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).
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).
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).
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).
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).
Google Scholar
Abbott, N. J. Inflammatory mediators and modulation of blood–brain barrier permeability. Cell. Mol. Neurobiol. 20, 131–147 (2000).
Google Scholar
Gaitsch, H., Franklin, R. J. M. & Reich, D. S. Cell-free DNA-based liquid biopsies in neurology. Brain 146, 1758–1774 (2023).
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).
Google Scholar
Frank, R. & Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2, 566–580 (2003).
Google Scholar
Hartl, D. et al. Translational precision medicine: an industry perspective. J. Transl. Med. 19, 245 (2021).
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).
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).
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).
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).
Google Scholar
Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).
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).
Google Scholar
Wishart, D. S. et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).
Google Scholar
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
Google Scholar
Marić, I. et al. Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns 3, 100655 (2022).
Google Scholar
Hédou, J. et al. Discovery of sparse, reliable omic biomarkers with Stabl. Nat. Biotechnol. 42, 1581–1593 (2024).
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).
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).
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).
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).
Google Scholar
