{"doi":"10.1002/ctm2.1341","title":"Method optimisation to enrich small extracellular vesicles from saliva samples","abstract":"Salivary small extracellular vesicles (sEV) contain cancer-derived biomolecules, and sEVs can mediate cancer progression and metastasis.1 Given their biological roles during cancer pathogenesis,2 sEVs can be used as non-invasive markers for disease diagnosis, prognosis, therapy selection and monitoring. However, our understanding of the influence of sEV isolation method on downstream analysis (e.g., proteomics) is limited. Here, we have evaluated four isolation methods for salivary sEVs, and compared them with plasma sEVs protocols. Figure S1 depicts the flowchart of sEV isolation using saliva and plasma samples. We have compared size exclusion chromatography (SEC), ultracentrifugation (UC), ultracentrifugation plus filtration (UCF) and density gradient (DG) to isolate sEVs. In order to evaluate the yields of sEVs, the particle size and abundance were assessed using nanoparticle tracking analysis (NTA). Mean and mode sizes, the average and most frequent population of particle sizes, respectively, were determined. No significant variations were observed between means and modes of salivary sEVs (Figure 1A). For plasma sEVs, DG resulted in a significantly larger particle size than the UC and UCF (Figure 1B). In addition, sEVs with larger mean and mode sizes were detected in saliva in comparison to plasma (Figure S2A,B). DG and SEC resulted in the highest sEV yield for saliva and plasma, respectively (Figure 1C,D). Next, according to the number of particles per microgram of protein, calculated using a BCA method (Figure 1E,F), the purity of sEVs was assessed (Figure 1G,H). Overall, DG and SEC provided two- to six-fold higher yields and purity of sEVs derived from saliva and plasma, respectively. To further assess the purities of the isolated sEVs, sEV and non-sEV markers as well as alpha-amylase (α-Amy) and albumin (ALB) as the most abundant saliva and plasma proteins, respectively, were analysed using Western blot. Among all methods, DG method could isolate relatively purer salivary sEVs (positive for a battery of sEV markers CD9, CD63, CD81, TSG101 and Syntenin-1) (Figure 1I and Figure S2C). In contrast, plasma samples isolated by SEC (Figure 1J and Figure S2FC) were enriched for all positive sEV markers (CD9, CD63, CD81, HSP70, TSG101 and Syntenin-1). Some markers were either weakly or not detected in sEVs isolated using the other methods, which can be due to below reasons. First, this may be due to the distinct sEV subtypes isolated by each method.3 Second, it may be due to enrichment of non-sEV lipid particles and/or empty vesicles.4 Third, some markers are cell-type specific and not released in large quantities into biofluids such as saliva and blood.5 The presence of α-Amy and ALB in samples isolated by UC and UCF indicates that the absence of cell organelle markers is useful, but not sufficient, to rule out other protein contaminations. Next, the size, morphology and integrity of sEVs were evaluated using transmission electron microscopy (TEM) (Figure 2 and Figure S3). Salivary sEVs isolated by UC, UCF and SEC demonstrated particle clustering in some regions (Figure S3A–C). The tendency of sEVs (yellow arrows) to aggregate was more evident in samples isolated using UC than those isolated by other methods (Figure S3A7–10). In contrast, for plasma samples, UC and UCF displayed average sizes of sEVs (Figure S3E,F). Also, protein clusters and/or cell debris were observed in UCF (blue arrows). SEC and DG showed a range of small to large EVs, with protein aggregates and cell debris only found in DG (Figure S3G,H). Furthermore, saliva-derived sEVs were dispersed, larger and fewer than plasma, which can be partly explained by their properties. While salivary glands, either ductal or acinar cells, have been mainly implicated in secretion of sEVs, the origin of sEVs in human plasma is largely a mixture of components derived from circulating immune cells.6, 7 Overall, TEM demonstrated that isolated particles display the expected char","journal":"Clinical and Translational Medicine","year":2023,"id":348279,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9604,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":873641,"name":"Juliana Müller Bark","orcid":"0000-0003-0291-0094","position":1,"is_corresponding":false},{"id":1040302,"name":"Lucas Trevisan França de Lima","orcid":"0000-0002-4993-5945","position":2,"is_corresponding":false},{"id":334920,"name":"Luize G. Lima","orcid":"0000-0002-4167-6723","position":3,"is_corresponding":false},{"id":334921,"name":"Andreas Möller","orcid":"0000-0002-8618-6998","position":4,"is_corresponding":false},{"id":901788,"name":"Liz Kenny","orcid":"0000-0002-7556-0542","position":5,"is_corresponding":false},{"id":901789,"name":"Sarju Vasani","orcid":"0000-0003-1471-9692","position":6,"is_corresponding":false},{"id":901790,"name":"Sudha Rao","orcid":"0000-0001-9547-947X","position":7,"is_corresponding":false},{"id":594818,"name":"Riccardo Dolcetti","orcid":"0000-0003-1625-9853","position":8,"is_corresponding":false},{"id":901787,"name":"Abolfazl Jangholi","orcid":"0000-0001-7385-6123","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:12:06.040073Z","pmid":"37587263","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}