PloS one · 2026
Optimization of extracellular vesicle extraction from hepatic tissue interstitial fluid and analysis of their ncRNA expression profiles.
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Liu Shubo, Fu Yuxuan, Guo Hua, Li Hailin, Zhang Lei, Du Shuangshuang, Guo Chunyue, Lou Cheng, Zhang Jinjuan, Lu Chengjun, Gao Yingtang
Аннотация
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.
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.