Dual size-exclusion chromatography for efficient isolation of extracellular vesicles from bone marrow derived human plasma

Jung, Jik Han; Back, Woojin; Yoon, Junyong; Han, Hyeonjeong; Kang, Ka-Won; Choi, Byeonghyeon; Jeong, Hyesun; Park, Jaena; Shin, Hyunku; Hur, Woojune; Choi, Yeonho; Hong, Sunghoi; Kim, Hyun Koo; Park, Yong; Park, Ji Ho

doi:10.1038/s41598-020-80514-8

Cited by 13 publications

(21 citation statements)

References 38 publications

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“…In accordance with the results of western blots, the vesicles with an apparent membrane structure, whose diameter is in the range of 30–120 nm, were visible in F2, and cup-shaped exosomes (Figure d) were also observed. By comparison, the lipoprotein nanoparticles in F1 without an apparent membrane structure aggregated and fused to a larger size, which might be due to the processes of freezing and thawing, resulting in earlier elution than exosomes. Small-sized white spheres belonging to HDLs and protein aggregates were visible in F3.…”

Section: Resultsmentioning

confidence: 96%

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

et al. 2022

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Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently based on deep learning fingerprints of plasma exosomes. Sequential sizeexclusion chromatography (SSEC) was developed efficiently for separating exosomes from human plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a single sample with high purity such that the discard rates of high-density lipoproteins and low/ very low-density lipoproteins were 93 and 85%, respectively. Benefitting from the rapid and high-purity isolation, the contents encapsulated in exosomes, covered by plasma proteins, were well profiled by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS). We further analyzed 220 clinical samples, including 79 breast cancer patients, 57 pancreatic cancer patients, and 84 healthy controls. After MS data preprocessing and feature selection, the extracted MS feature peaks were utilized as inputs for constructing a multi-classifier artificial neural network (denoted as Exo-ANN) model. The optimized model avoided overfitting and performed well in both training cohorts and test cohorts. For the samples in the independent test cohort, it realized a diagnosed accuracy of 80.0% with an area under the curve of 0.91 for the whole group. These results suggest that our integrated pipeline may become a generic tool for liquid biopsy based on the analysis of plasma exosomes in clinics.

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Section: Resultsmentioning

confidence: 96%

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

et al. 2022

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“…Exosome purity and surface chemical status are significant factors in our label-free detection because other biomolecules and chemicals on the exosome surface affect the resulting Raman signals. Accordingly, we employed size exclusion chromatography (SEC) that isolates exosomes based on the hydrodynamic size of vesicles 35 , 36 . Since SEC does not use additional chemical reagents that produce undesired Raman signals in the isolation process, the disturbance of signals can be minimized in label-free SERS detection.…”

Section: Resultsmentioning

confidence: 99%

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

Shin¹,

Choi

Shim³

et al. 2023

Nat Commun

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119

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Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

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“…Tetraspanins specific to the endosomes, such as CD9, CD63 and CD81 are enriched in human EVs (Ref. 70) and are considered as typical EV markers (Ref. 71).…”

Section: Tetraspanins For Extracellular Vesicle Detectionmentioning

confidence: 99%

Extracellular vesicle isolation, purification and evaluation in cancer diagnosis

Mortezaee

Majidpoor

Fathi

2022

Expert Rev. Mol. Med.

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Strategies for non-invasive biomarker discovery in early detection of cancer are an urgent need. Extracellular vesicles (EVs) have generated increasing attention from the scientific community and are under intensive investigations due to their unique biological profiles and their non-invasive nature. EVs are membrane-enclosed vesicles with variable sizes and function. Such vesicles are actively secreted from multiple cell types and are considered as key vehicles for inter-cellular communications and signalling. The stability and potential to easily cross biological barriers enable EVs for exerting durable effects on target cells. These along with easy access to such vesicles, the consistent secretion from tumour during all stages of tumorigenesis and their content providing a reservoir of molecules as well as mirroring the identity of the cell of origin are virtues that have made EVs appealing to be assessed in liquid biopsy approaches and for using as a promising resource of biomarkers in cancer diagnosis and therapy and monitoring targeted cancer therapy. Early detection of EVs will guide time-scheduled personalised therapy. Surveying reliable and sensitive methods for rapid isolation of EVs from biofluids, the purity of isolated vesicles and their molecular profiling and marker specification for clinical translation in patients with cancer are issues in the area and the hot topics of many recent studies. Here, the focus is over methods for EV isolation and stratification for digging more information about liquid biopsy-based diagnosis. Extending knowledge regarding EV-based strategies is a key to validate independent patient follow-up for cancer diagnosis at early stages and inspecting the efficacy of therapeutics.

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Dual size-exclusion chromatography for efficient isolation of extracellular vesicles from bone marrow derived human plasma

Cited by 13 publications

References 38 publications

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

Extracellular vesicle isolation, purification and evaluation in cancer diagnosis

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