---
title: "Optimized extraction of hepatic TIF-sEVs and ncRNA profiles for HCC biomarker discovery"
id: "plos-one-5-optimization-of-extracellular-vesicle-extraction-from-hepatic-tissue"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-5-optimization-of-extracellular-vesicle-extraction-from-hepatic-tissue"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355303"
published_at: "2026-08-03T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Optimized extraction of hepatic TIF-sEVs and ncRNA profiles for HCC biomarker discovery
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-5-optimization-of-extracellular-vesicle-extraction-from-hepatic-tissue
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355303)
- **Published At:** 2026-08-03T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This study optimized a protocol to isolate small extracellular vesicles (**TIF-sEVs**) from hepatic tissue interstitial fluid to improve tissue specificity of EV-derived biomarkers for hepatocellular carcinoma (HCC). - Optimal enzymatic digestion used **Collagenase D (2 mg/mL)** plus **DNase I (40 U/mL)** for 30 minutes, combined with differential and ultracentrifugation. - Isolated vesicles showed typical EV morphology by TEM, particle sizes below 200 nm by NTA, and expression of canonical EV markers **ALIX** and **CD63**, as well as the hepatocyte membrane protein **ASGPR**, supporting hepatic origin. - Comparative assessment indicated shorter digestion (30 min) and repeated differential centrifugation reduced non-vesicular contaminants versus 60-min digestion or single centrifugation. - qRT-PCR profiling of 33 candidate non-coding RNAs (11 lncRNAs and 22 miRNAs from TCGA-LIHC and GSE302990 sources) found enrichment of six lncRNAs (examples: **AL031985**, **TMCC1-AS1**) and eleven miRNAs (examples: **miR-1224-5p**, **miR-483-5p**) in TIF-sEVs. - Integration of all 33 candidate ncRNAs with clinical parameters using LASSO regression produced a combined diagnostic signature: **ALB**, **PLT**, **DBIL**, lncRNA **GAS5**, and **miR-194-5p**, which achieved an area under the curve (**AUC**) of **0.960** for distinguishing HCC. - The authors conclude the protocol yields an efficient, reproducible system for isolating liver-derived TIF-sEVs and identifies multiple **ncRNAs** as potential diagnostic biomarkers for HCC; further validation beyond this exploratory work is implied.
## Clinical Analysis & Structured Key Points
Optimization of extracellular vesicle extraction from hepatic tissue interstitial fluid and analysis of their ncRNA expression profiles | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Background To address the limited tissue specificity of extracellular vesicles (EVs) derived from blood and other body fluids, this study isolated small EVs (sEVs) from the tissue interstitial fluid (TIF) of hepatocellular carcinoma (HCC) and adjacent tissues. The expression profiles of non-coding RNAs (ncRNAs) were analyzed to identify more specific diagnostic biomarkers. Methods An optimized protocol for TIF-sEV extraction was established, which combined enzymatic digestion (Collagenase D and DNase I) with differential and ultracentrifugation. The isolated sEVs were characterized using nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and western blotting (WB). The expression of 33 candidate ncRNAs (11 lncRNAs from TCGA-LIHC and 22 miRNAs from TCGA-LIHC and GSE302990) in TIF-sEVs was analyzed by qRT-PCR and integrated with clinical parameters via LASSO regression. Results The optimal extraction conditions were determined to be digestion with Collagenase D (2 mg/mL) and DNase I (40 U/mL) for 30 minutes. The obtained EVs exhibited typical morphology, a particle size below 200 nm, and expressed canonical EV marker proteins (ALIX, CD63) as well as the hepatocyte-specific membrane protein ASGPR. qRT-PCR analysis revealed specific enrichment of six lncRNAs (e.g., AL031985, TMCC1-AS1) and eleven miRNAs (e.g., miR-1224-5p, miR-483-5p) in TIF-sEVs. LASSO regression applied to all 33 candidate ncRNAs and clinical parameters identified a combined diagnostic signature comprising ALB, PLT, DBIL, lncRNA GAS5, and miR-194-5p, which achieved an area under the curve (AUC) of 0.960. Conclusions This study establishes an efficient and stable system for the isolation and characterization of TIF-sEVs from liver tissue. Furthermore, it identifies multiple non-coding RNAs (ncRNAs) that are enriched in TIF-sEVs, thereby providing potential novel diagnostic biomarkers for the differential diagnosis and prognosis evaluation of HCC. Citation: Liu S, Fu Y, Guo H, Li H, Zhang L, Du S, et al. (2026) Optimization of extracellular vesicle extraction from hepatic tissue interstitial fluid and analysis of their ncRNA expression profiles. PLoS One 21(8): e0355303. https://doi.org/10.1371/journal.pone.0355303 Editor: Boyan Grigorov, CRCL: Centre de Recherche en Cancerologie de Lyon, FRANCE Received: February 15, 2026; Accepted: July 20, 2026; Published: August 3, 2026 Copyright: © 2026 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the Tianjin Municipal Health Commission (Grant No. TJWJ2021ZD003, received by Y. Gao) and the Natural Science Foundation of Tianjin Science and Technology Bureau (Grant No. 21JCZDJC01050, received by Y. Gao). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors declare no conflicts of interest. 1 Introduction Extracellular vesicles (EVs), a class of phospholipid bilayer vesicles actively secreted by cells, have emerged as highly promising sources of disease biomarkers due to their stable presence in various body fluids and their capacity to carry bioactive molecules, such as proteins and nucleic acids, which reflect the state of parental cells [ 1 – 4 ]. The diagnostic value of biofluid-derived EVs (Bf-EVs) has been evaluated across various diseases [ 5 – 8 ]. For instance, in HCC, the level of LC3B-positive EVs in patient plasma is significantly elevated [ 7 ], and specific miRNAs, such as miR-122-5p, within serum EVs have demonstrated diagnostic potential [ 8 ]. However, the application of Bf-EVs faces several challenges. Due to their diverse sources and lack of tissue specificity, accurately tracing their organ origins remains difficult, which limits the specificity and sensitivity of related biomarkers [ 3 , 9 – 11 ]. To address this issue, research focus has gradually shifted toward the tumor microenvironment closer to the primary lesion, specifically the tissue interstitial fluid (TIF). TIF is the fluid present in the interstitium of tissues and serves as the main component of extracellular fluid, forming the immediate internal environment for cell survival. It contains abundant EVs secreted by local cells, which may retain the specific molecular fingerprints of their originating tissues at an earlier stage, thereby providing direct evidence for the tissue tracing of Bf-EVs [ 12 – 15 ]. This strategy is emerging as an ideal bridge connecting the precision of ‘tissue biopsy’ with the convenience of ‘liquid biopsy,’ demonstrating significant potential across multiple disease areas. Examples include the identification of miR-483-5p as a diagnostic biomarker through high-throughput sequencing of HCC tissue interstitial fluid and subsequent validation [ 16 ], proteins derived from skin interstitial fluid EVs have been utilized for point-of-care detection of melanoma [ 17 ], the use of dermal interstitial fluid EV miRNAs as markers for burn depth assessment [ 18 ], SERS spectral features of EVs in bronchoalveolar lavage fluid have been applied to diagnose non-small cell lung cancer [ 19 ]; miRNAs and proteins in peritoneal fluid-derived EVs have been employed for ovarian cancer diagnosis [ 20 , 21 ]; and proteomics of of cerebrospinal fluid EVs have been used in the differential diagnosis of neurodegenerative diseases [ 22 , 23 ], multiple sclerosis [ 24 ], and medulloblastoma [ 25 ]. To date, the sampling methods for TIF-EVs from various tissues exhibit significant variability, and no unified standards have been established. For instance, brain tissues are obtained through microdialysis [ 12 , 22 – 25 ], while skin tissues are collected using microneedles [ 17 ]. In contrast, visceral tissues, such as the liver, are acquired through surgical resection or minimally invasive procedures, followed by enzymatic digestion and differential/ultracentrifugation [ 16 , 26 , 27 ]. Specifically, TIF-EVs from HCC tissues are isolated via enzymatic digestion combined with differential/ultracentrifugation [ 16 , 26 , 27 ]. Although this method has been characterized through transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting (WB), confirming its adherence to the fundamental requirements of the MISEV2023 guidelines, the specific operational procedures have yet to be standardized. Furthermore, research into its diagnostic biomarkers remains in the preliminary exploratory stage [ 16 ]. Therefore, the present study aims to refine the isolation method for liver tissue-derived TIF-sEVs and to explore the non-coding RNAs they contain, with the goal of providing potential biomarkers for the differential diagnosis and precise intervention of HCC. 2 Results 2.1 Isolation and characterization of TIF-sEVs TEM results demonstrated that EVs isolated from the tissue interstitial fluid of non-tumor adjacent tissue (NEVs) and tumor tissue (TEVs), analyzed under various digestion times (0 min, 30 min, and 60 min) and centrifugation repetitions (one differential centrifugation step versus repeated differential centrifugation), exhibited the classic ‘cup-shaped’ morphology. The majority of particles within the fields of view were relatively uniform in size, consistent with the characteristics of EVs, thereby confirming the successful extraction of tissue interstitial fluid-derived small extracellular vesicles (TIF-sEVs) ( Fig 1A and S1A - S1D Fig ). Specifically, the 0-min and 30-min groups displayed morphologically typical EVs with clean backgrounds and minimal impurities, whereas the 60-min group exhibited a cluttered background with numerous protein aggregates, lipoproteins, and other non-vesicular structures alongside EVs. Furthermore, TIF-sEVs subjected to repeated differential centrifugation contained fewer impurities compared to those processed with a single centrifugation step. Finally, to assess the storage stability of TIF-sEVs, this study compared the morphological integrity of samples stored at 4°C versus −80°C for one week. The results indicated that TIF-sEVs stored at −80°C exhibited significantly fewer background contaminants, such as protein aggregates and non-vesicular structures, than those stored at 4°C, suggesting that storage at −80°C is more conducive to maintaining the morphological purity and stability of TIF-sEVs. Download: PNG larger image TIFF original image Fig 1. Characterization of TIF-EVs derived from adjacent non-tumor and tumor tissues. (A) TEM images of EVs under different enzyme incubation durations; (B) NTA size distribution of EVs under different enzyme incubation durations; (C) NTA particle diameter analysis of NEVs and TEVs under different enzyme incubation durations; (D) Western Blot results after 30-minute enzyme incubation; (E) Western Blot results after 60-minute enzyme incubation. Legend for Western Blot figures: NEVs: Protein from non-tumor adjacent tissue-derived TIF-sEVs. TEVs: Protein from tumor tissue-derived TIF-sEVs. ZNX: Protein from non-tumor adjacent tissue pellet after EV extraction. ZTX: Protein from tumor tissue pellet after EV extraction. NUS: Protein from supernatant after ultracentrifugation pellet of non-tumor adjacent tissue EVs. TUS: Protein from supernatant after ultracentrifugation pellet of tumor tissue EVs. ZN: Protein from non-tumor adjacent tissue. ZT: Protein from tumor tissue. https://doi.org/10.1371/journal.pone.0355303.g001 NTA analysis revealed that the average particle diameters of both NEVs and TEVs extracted at different digestion times (0 min, 30 min, and 60 min) were consistently below 200 nm, which aligns with the characteristics of sEVs and confirms the successful extraction of TIF-sEVs ( Fig 1B - 1C ). The average particle diameter of NEVs exhibited a non-significant increasing trend with longer digestion times. In contrast, the average particle diameter of TEVs decreased significantly as digestion time increased. Notably, the average particle diameter of NEVs was significantly larger than that of TEVs at all digestion time points. Furthermore, particle concentration progressively increased with digestion time, with statistically significant differences observed between most groups, except for the comparison between the 0-min TEVs group and the 30-min group ( Table 1 ). Download: PNG larger image TIFF original image Table 1. Characterization of TIF-EVs: Total protein concentration and particle properties. https://doi.org/10.1371/journal.pone.0355303.t001 BCA assay results indicated that the total protein concentration of TIF-sEVs displayed an increasing trend with prolonged digestion time, with statistically significant differences noted between groups. However, no significant difference was found between NEVs and TEVs ( Table 1 ). Western Blot results demonstrated that under same loading amount ( Fig 1D - 1E and S1E Fig ), the positive markers CD63, CD9, TSG101, and Alix were all detected in TIF-sEVs. Among these, CD9 exhibited a pronounced signal in the 30-minute TEVs, while a weaker signal was observed in the 60-minute TEVs. TSG101 predominantly appeared as a 47 kDa band at 30 minutes, whereas both 52 kDa and 47 kDa bands were observed at 60 minutes. Alix was primarily present as a 100 kDa subunit in TIF-sEVs, displaying a pattern distinct from the three molecular weight forms (100, 90, and 80 kDa) observed in tissue and the two forms (100 and 90 kDa) observed in cells. The negative marker Calnexin was detectable in NEVs and 60-minute TEVs, albeit with weaker signal intensity than that of the positive markers. The 0-minute control group was essentially negative, confirming that simple mechanical disruption does not release detectable EVs. Regarding source markers, both hepatocyte markers ASGPR and ALB were positive, with ASGPR exhibiting markedly higher signal intensity compared to the cell control. The signals for the biliary epithelial marker CK19 and the vascular endothelial marker CD34 were both weaker than those observed in cells. These results demonstrate that the isolated vesicles conform to the characteristics of EVs and suggest that hepatocytes are one of their principal sources. Based on the characterization analysis, TIF-sEVs derived from paracancerous tissues and tumor tissues exhibit differences in particle size, concentration, and cargo content. This indicates that TIF-sEVs possess high tissue specificity and demonstrate potential as biomarkers for liver cancer. Additionally, after a comprehensive consideration of vesicle yield and purity, the final protocol for subsequent TIF-sEVs experiments was established to include a 30-minute enzymatic digestion, one round of differential centrifugation, and storage at −80°C. 2.2 Expression characteristics of lncRNA molecules 2.2.1 Analysis of lncRNA CT values. The detection of lncRNAs in TIF-sEVs, tissues, and US was conducted ( Table 2 ). The CT value results indicated that five lncRNAs, namely AL158166, TMCC1-AS1, CERNA2, LINC00622, and AL031985, exhibited CT values that were 2–6 cycles lower in TIF-sEVs compared to tissues, suggesting their enrichment in vesicles. Conversely, the reference gene GAPDH and six lncRNAs (GAS5, H19, LINC00839, SNHG1, LINC03067, and ST8SIA6-AS1) displayed CT values that were 1–7 cycles higher in TIF-sEVs than in tissues. Furthermore, with the exception of cases where LINC03067 was undetected in some NEVs, and the TUS of LINC03067 and NUS of ST8SIA6-AS1 were undetected (rendering CT values incomparable), the CT values of other lncRNAs in TIF-sEVs were at least 4 cycles lower than those in US. This finding suggests the presence of free lncRNAs or other Non-Vesicular Extracellular Particles (NVEPs), such as exomeres or supermeres, in US. It can be speculated that five lncRNA molecules, including AL031985 and AL158166, may be selectively packaged and enriched in TIF-sEVs, categorizing them as TIF-sEVs-enriched ncRNAs. Download: PNG larger image TIFF original image Table 2. Quartile statistics of lncRNA expression CT values across sample groups. https://doi.org/10.1371/journal.pone.0355303.t002 2.2.2 Relative expression levels of LncRNAs. For relative quantification analysis, the CT values of samples without amplification signals were set to the maximum cycle threshold of 45. The results indicated that the relative expression levels of seven lncRNAs (AL031985, AL158166, CERNA2, TMCC1-AS1, LINC00622, GAS5, and H19) in TIF-sEVs were higher than those in tissues, with statistically significant differences observed for all except H19 ( Fig 2A - 2G ). This finding further suggests that these lncRNAs may be specifically enriched in TIF-sEVs. Conversely, four lncRNAs, including LINC00839, LINC03067, SNHG1, and ST8SIA6-AS1, exhibited high expression levels in tissues ( Fig 2H - 2K ). Except for LINC03067, which showed no significant difference between TEVs and tumor tissues (ZT), the others displayed statistically significant differences. Additionally, the relative expression level of H19 in adjacent non-tumor tissues (ZN) was significantly higher than that in ZT ( Fig 2G ). In paired t-tests, ST8SIA6-AS1 was significantly higher in ZT than in ZN, while both ST8SIA6-AS1 and SNHG1 were significantly higher in TEVs than in NEVs. However, these differences did not reach statistical significance after multiple comparison correction. Based on this speculation, seven lncRNA molecules, including AL031985 and AL158166, may be selectively packaged and enriched in TIF-sEVs, categorizing them as TIF-sEVs-enriched ncRNAs. Furthermore, H19 may exhibit lower expression levels in HCC, while ST8SIA6-AS1 and SNHG1 may demonstrate higher expression levels in HCC, representing HCC-specific ncRNAs. Download: PNG larger image TIFF original image Fig 2. Relative expression levels of lncRNAs in tissue and TIF-EVs. A-G: lncRNAs enriched in TIF-EVs; H-K: lncRNAs not enriched in TIF-EVs. https://doi.org/10.1371/journal.pone.0355303.g002 2.3 Expression characteristics of miRNA molecules 2.3.1 Analysis of miRNA CT values. Detection of miRNAs in TIF-sEVs and tissues ( Table 3 ) revealed that the CT value results indicated four miRNAs (miR-1224-5p, miR-4306, miR-2114-5p, and miR-483-5p) exhibited CT values that were 2–11 cycles lower in TIF-sEVs compared to tissues. Additionally, three miRNAs (miR-628-5p, miR-1269a, and miR-885-3p) showed only a 0.14–4 cycle reduction in CT values in TIF-sEVs from the paracancerous group compared to their corresponding tissues. In contrast, the reference gene U6 and the remaining 15 miRNAs (including miR-16-5p, miR-21-3p, and miR-21-5p) exhibited CT values that were 0.42–11 cycles higher in TIF-sEVs than in tissues. This suggests that four miRNA molecules, including miR-1224-5p and miR-483-5p, may be selectively packaged and enriched in TIF-sEVs, thus belonging to the category of TIF-sEVs-enriched ncRNAs. Download: PNG larger image TIFF original image Table 3. Quartile statistics of miRNA expression CT values across sample groups. https://doi.org/10.1371/journal.pone.0355303.t003 2.3.2 Relative expression levels of miRNAs. The relative quantitative analysis indicated that 17 miRNAs exhibited elevated relative expression levels in TIF-sEVs compared to tissues ( Fig 3A - 3Q ). Among these, 11 miRNAs—miR-1224-5p, miR-1269a, miR-4306, miR-483-5p, miR-2114-5p, miR-628-5p, miR-885-3p, miR-130a-3p, miR-197-3p, miR-885-5p, and miR-214-3p—demonstrated statistically significant differences ( Fig 3A - 3K ). It is hypothesized that these miRNAs may be relatively enriched in TIF-sEVs. miR-122-3p and miR-21-3p showed higher relative expression levels exclusively in NEVs compared to ZN ( Fig 3R - 3S ). In contrast, three miRNAs, namely miR-16-5p, miR-21-5p, and miR-122-5p ( Fig 3T - 3V ), exhibited higher relative expression levels in tissues, with these three miRNAs demonstrating statistically significant differences in the TEVs versus ZT comparison. However, in the NEVs versus ZN comparison, only miR-16-5p displayed a statistically significant difference. Furthermore, the relative expression of miR-122-3p in NEVs was significantly higher than that in TEVs ( Fig 3R ). Based on this hypothesis, 11 miRNA molecules, including miR-1224-5p and miR-483-5p, may be selectively packaged and enriched in TIF-sEVs, categorizing them as TIF-sEVs-enriched ncRNAs. Additionally, miR-122-3p may exhibit lower expression levels in HCC, suggesting its role as an HCC-specific ncRNA. Download: PNG larger image TIFF original image Fig 3. Relative expression levels of miRNAs in tissue and TIF-EVs. A-Q: miRNAs enriched in TIF-EVs; R-S: miRNAs enriched in TIF-EVs only in the non-tumor adjacent group; T-V: miRNAs not enriched in TIF-EVs. https://doi.org/10.1371/journal.pone.0355303.g003 2.4 Receiver operating characteristic (ROC) curve analysis To preliminarily evaluate diagnostic efficacy, 23 patients were categorized into HCC and non-HCC groups. LASSO regression was performed on 33 candidate ncRNAs and clinical parameters. The final model retained five key variables: albumin (ALB), platelets (PLT), direct bilirubin (DBIL), lncRNA GAS5, and miRNA hsa-194-5p ( Fig 4 ). This composite indicator demonstrated a significantly improved predictive performance for HCC, yielding an area under the curve (AUC) of 0.960 (95% confidence interval 0.89–1.0
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