Preanalytical framework for routine clinical use of liquid biopsies: combining EVs and cfDNA

Abstract

Aim: Liquid biopsies hold significant potential for the minimally invasive diagnosis of tumors and other diseases. While the clinical application of cell-free DNA (cfDNA) methodologies is emerging, the implementation of tumor-derived extracellular vesicles (EVs) as validated biomarkers is hindered by substantial preanalytical variations. In this work, we standardized the preanalytical procedures of blood collection for subsequent serial isolation of plasma cfDNA and EVs from a single blood collection tube.

Methods: We compared the impact of blood preservation tubes and storage to enable proteomic profiling of resulting EVs in addition to cfDNA extraction and sequencing. Following a stringent method of large EV (lEV) and small EV (sEV) isolation, consisting of differential ultracentrifugation and size exclusion chromatography, we evaluated the protein concentration, particle number, quality and integrity of the isolated EVs. Subsequent proteomic analyses of EV isolates revealed the complexity of the respective tube-biased proteomes, allowing the interpretation of EV origins as well as contamination sources.

Results: While ACD-A and Citrate tubes yield satisfactory results in the preservation of EV proteomes, only Streck RNA, Norgen, and PAX tubes can maintain high cfDNA purity for up to 7 days. When aiming for multiomics analyses, Streck RNA tubes showed the most stable performance across the tested parameters for both bioanalytes. Furthermore, we detected greater variability in protein composition in sEVs than in lEVs after 7 days of storage; thus, sEVs might be more susceptible to storage effects.

Conclusion: Our clinically applicable workflow provides the basis for informed choice of liquid biopsy tubes along with a ready-to-use protocol to retrieve both genomic and EV proteomic biomarker information for multiomics biomarker-based liquid biopsy studies.

Keywords

EV preservation / liquid biopsy / extracellular vesicles / cfDNA / human plasma / translation / isolation protocol / preanalytics

References

[1] Alix-Panabières C.Liquid biopsy: from discovery to clinical implementation.Mol Oncol2021;15:1617-21 PMCID:PMC8169443

[2] Li N,Tang J,Chen D. Circulating non-coding RNAs and exosomes: liquid biopsies for monitoring preeclampsia. In: Huang T, Yang J, Tian G, Editors. Liquid Biopsies. New York: Springer US; 2023. pp. 263-77.

[3] Karaglani M,Cheimonidi C.Liquid biopsy in type 2 diabetes mellitus management: building specific biosignatures via machine learning.J Clin Med2022;11:1045 PMCID:PMC8876363

[4] Siravegna G,Siena S.Integrating liquid biopsies into the management of cancer.Nat Rev Clin Oncol2017;14:531-48

[5] Ignatiadis M,Jeffrey SS.Liquid biopsy enters the clinic - implementation issues and future challenges.Nat Rev Clin Oncol2021;18:297-312

[6] Bronkhorst AJ.A pocket companion to cell-free DNA (cfDNA) preanalytics.Tumour Biol2024;46:S297-308

[7] Diaz IM,Held SAE.Pre-analytical evaluation of streck cell-free DNA blood collection tubes for liquid profiling in oncology.Diagnostics2023;13:1288 PMCID:PMC10093569

[8] Jordaens S,Tjalma W.Urine biomarkers in cancer detection: a systematic review of preanalytical parameters and applied methods.Int J Cancer2023;152:2186-205

[9] Dorpe S, Tummers P, Denys H, Hendrix A. Towards the clinical implementation of extracellular vesicle-based biomarker assays for cancer.Clin Chem2024;70:165-78

[10] Kalluri R.The biology, function, and biomedical applications of exosomes.Science2020;367 PMCID:PMC7717626

[11] Jeppesen DK,Franklin JL.Extracellular vesicles and nanoparticles: emerging complexities.Trends Cell Biol2023;33:667-81 PMCID:PMC10363204

[12] Petroni D,Babboni S,Basta G.Extracellular vesicles and intercellular communication: challenges for in vivo molecular imaging and tracking.Pharmaceutics2023;15:1639 PMCID:PMC10301899

[13] Burbano C,Muñoz-Vahos C.Extracellular vesicles are associated with the systemic inflammation of patients with seropositive rheumatoid arthritis.Sci Rep2018;8:17917 PMCID:PMC6297132

[14] Johnsen KB,Andresen TL.What is the blood concentration of extracellular vesicles?.Biochim Biophys Acta Rev Cancer2019;1871:109-16

[15] Nardi Fda S,Neumann J.High levels of circulating extracellular vesicles with altered expression and function during pregnancy.Immunobiology2016;221:753-60

[16] Gershfeld NL.Thermal instability of red blood cell membrane bilayers: temperature dependence of hemolysis.J Membr Biol1988;101:67-72

[17] Rous P.The preservation of living red blood cells in vitro: II. The transfusion of kept cells.J Exp Med1916;23:239-48 PMCID:PMC2125395

[18] Moore GL.Additive solutions for better blood preservation.Crit Rev Clin Lab Sci1987;25:211-29

[19] Gegner HM,Dugourd A.Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis.Front Mol Biosci2022;9:961448 PMCID:PMC9808085

[20] Geyer PE,Treit PV.Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies.EMBO Mol Med2019;11:e10427 PMCID:PMC6835559

[21] Alberro A,Fernandes A.Extracellular vesicles in blood: sources, effects, and applications.Int J Mol Sci2021;22:8163 PMCID:PMC8347110

[22] Jamaly S,Olsen R.Impact of preanalytical conditions on plasma concentration and size distribution of extracellular vesicles using Nanoparticle Tracking Analysis.Sci Rep2018;8:17216 PMCID:PMC6249294

[23] Sorber L,Jacobs J.Specialized blood collection tubes for liquid biopsy: improving the pre-analytical conditions.Mol Diagn Ther2020;24:113-24

[24] Berg S,Kroeger T.ilastik: interactive machine learning for (bio)image analysis.Nat Methods2019;16:1226-32

[25] Müller T,Longuespée R,Stenzinger A.Automated sample preparation with SP3 for low-input clinical proteomics.Mol Syst Biol2020;16:e9111 PMCID:PMC6966100

[26] Gillet LC,Tate S.Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Mol Cell Proteomics2012;11:O111.016717 PMCID:PMC3433915

[27] Gessulat S,Zolg DP.Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.Nat Methods2019;16:509-18

[28] Consortium. UniProt: the universal protein knowledgebase in 2025.Nucleic Acids Res2025;53:D609-D617 PMCID:PMC11701636

[29] Cox J.MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat Biotechnol2008;26:1367-72

[30] R Core Team. R: a language and environment for statistical computing. MSOR Connect. 2014;1. Available from: https://www.semanticscholar.org/paper/R%3A-A-language-and-environment-for-statistical-Team/659408b243cec55de8d0a3bc51b81173007aa89b [accessed 25 August 2025].

[31] Schwanhäusser B,Li N.Global quantification of mammalian gene expression control.Nature2011;473:337-42

[32] Chitti SV,Kang T.Vesiclepedia 2024: an extracellular vesicles and extracellular particles repository.Nucleic Acids Res2024;52:D1694-8 PMCID:PMC10767981

[33] Mathivanan S,Reid GE.ExoCarta 2012: database of exosomal proteins, RNA and lipids.Nucleic Acids Res2012;40:D1241-4 PMCID:PMC3245025

[34] Aleksander SA,Carbon S.Gene Ontology ConsortiumThe gene ontology knowledgebase in 2023.Genetics2023;224 PMCID:PMC5210579

[35] Ashburner M,Blake JA.Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat Genet2000;25:25-9 PMCID:PMC3037419

[36] Vallejo MC,Elliott EC.A proteomic meta-analysis refinement of plasma extracellular vesicles.Sci Data2023;10:837 PMCID:PMC10684639

[37] Dhondt B,Geeurickx E.Benchmarking blood collection tubes and processing intervals for extracellular vesicle performance metrics.J Extracell Vesicles2023;12:e12315 PMCID:PMC10196222

[38] Larsson J, Gustafsson P, editors. A case study in fitting area-proportional euler dia-grams with ellipses using eulerr. SetVR@ diagrams. 2018;2018:84-91. Available from: https://ceur-ws.org/Vol-2116/paper7.pdf [accessed 25 August 2025].

[39] Krijthe J, van der Maaten L. Rtsne: T-distributed stochastic neighbor embedding using barnes-hut implementation. CRAN 2023. Available from: https://cran.r-project.org/web/packages/Rtsne/Rtsne.pdf [accessed 25 August 2025].

[40] Van der Maaten L, Hinton G. Visualizing high-dimensional data using t-SNE. J Mach Learn Res. 2008;9:2579-605 Available from: https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf [accessed 25 August 2025].

[41] Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014:15:3221-45. Available from: http://www.jmlr.org/papers/volume15/vandermaaten14a/vandermaaten14a.pdf [accessed 25 August 2025].

[42] Gu Z.Complex heatmap visualization.Imeta2022;1:e43 PMCID:PMC10989952

[43] Gu Z,Schlesner M.Complex heatmaps reveal patterns and correlations in multidimensional genomic data.Bioinformatics2016;32:2847-9

[44] Uhlén M,Hallström BM.Proteomics. Tissue-based map of the human proteome.Science2015;347:1260419

[45] Atlas HP. The Human Protein Atlas 2024. Available from: https://www.proteinatlas.org [accessed 25 August 2025].

[46] Wickham H. ggplot2: elegant graphics for data analysis. Cham: Springer; 2016.

[47] Xu S,Feng T,Zhou L.Use ggbreak to effectively utilize plotting space to deal with large datasets and outliers.Front Genet2021;12:774846 PMCID:PMC8593043

[48] Deun J, Mestdagh P, Agostinis P, et al; EV-TRACK Consortium. EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research.Nat Methods2017;14:228-32

[49] Livshits MA,Evtushenko EG.Isolation of exosomes by differential centrifugation: theoretical analysis of a commonly used protocol.Sci Rep2015;5:17319 PMCID:PMC4663484

[50] Théry C,Aikawa E.Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines.J Extracell Vesicles2018;7:1535750 PMCID:PMC6322352

[51] Lucien F,Lenassi M.MIBlood-EV: minimal information to enhance the quality and reproducibility of blood extracellular vesicle research.J Extracell Vesicles2023;12:e12385 PMCID:PMC10704543

[52] Welsh JA,O'Driscoll L.MISEV ConsortiumMinimal information for studies of extracellular vesicles (MISEV2023): from basic to advanced approaches.J Extracell Vesicles2024;13:e12404 PMCID:PMC10850029

[53] Németh K,Ghosal S.Therapeutic and pharmacological applications of extracellular vesicles and lipoproteins.Br J Pharmacol2024;181:4733-49

[54] Szklarczyk D,Koutrouli M.The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic Acids Res2023;51:D638-46 PMCID:PMC9825434

[55] Zorova LD,Popkov VA.Do extracellular vesicles derived from mesenchymal stem cells contain functional mitochondria?.Int J Mol Sci2022;23:7408 PMCID:PMC9266972

[56] Chetty VK,Anchan S.Efficient small extracellular vesicles (EV) isolation method and evaluation of EV-associated DNA role in cell-cell communication in cancer.Cancers2022;14:2068 PMCID:PMC9099953

[57] Murray MJ,Ward D.“Future-Proofing” blood processing for measurement of circulating mirnas in samples from biobanks and prospective clinical trials.Cancer Epidemiol Biomarkers Prev2018;27:208-18 PMCID:PMC5812437

[58] Sato A,Abe T.Investigation of appropriate pre-analytical procedure for circulating free DNA from liquid biopsy.Oncotarget2018;9:31904-14 PMCID:PMC6112748

[59] Chernyshev VS,Tseng YH.Size and shape characterization of hydrated and desiccated exosomes.Anal Bioanal Chem2015;407:3285-301

[60] Rikkert LG,Terstappen LWMM.Quality of extracellular vesicle images by transmission electron microscopy is operator and protocol dependent.J Extracell Vesicles2019;8:1555419 PMCID:PMC6327933

[61] Wu JY,Hu XB,Xiang DX.Preservation of small extracellular vesicles for functional analysis and therapeutic applications: a comparative evaluation of storage conditions.Drug Deliv2021;28:162-70 PMCID:PMC7808382

[62] Nguyen DB,Wesseling MC.Characterization of microvesicles released from human red blood cells.Cell Physiol Biochem2016;38:1085-99

[63] Tutrone R,Neuman B.ExoDx prostate test as a predictor of outcomes of high-grade prostate cancer - an interim analysis.Prostate Cancer Prostatic Dis2023;26:596-601 PMCID:PMC10449627