Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.
Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.
Exosomes, which are nanovesicles secreted by cells, are promising biomarkers for cancer diagnosis and prognosis, based on their specific surface protein compositions. Here, we demonstrate the correlation of nonsmall cell lung cancer (NSCLC) cell-derived exosomes and potential protein markers by unique Raman scattering profiles and principal component analysis (PCA) for cancer diagnosis. On the basis of surface enhanced Raman scattering (SERS) signals of exosomes from normal and NSCLC cells, we extracted Raman patterns of cancerous exosomes by PCA and clarified specific patterns as unique peaks through quantitative analysis with ratiometric mixtures of cancerous and normal exosomes. The unique peaks correlated well with cancerous exosome ratio (R 2 > 90%) as the unique Raman band of NSCLC exosome. To examine the origin of the unique peaks, we compared these unique peaks with characteristic Raman bands of several exosomal protein markers (CD9, CD81, EpCAM, and EGFR). EGFR had 1.97-fold similarity in Raman profiles than other markers, and it showed dominant expression against the cancerous exosomes in an immunoblotting result. We expect that these results will contribute to studies on exosomal surface protein markers for diagnosis of cancers.
Neuropathic pain following spinal cord injury (SCI) is a devastating disease characterized by spontaneous pain such as hyperalgesia and allodynia. In this study, we investigated the therapeutic potential of ESC-derived spinal GABAergic neurons to treat neuropathic pain in a SCI rat model. Mouse embryonic stem cell-derived neural precursor cells (mESC-NPCs) were cultured in media supplemented with sonic hedgehog (SHH) and retinoic acid (RA) and efficiently differentiated into GABAergic neurons. Interestingly, low doses of SHH and RA induced MGE-like progenitors, which expressed low levels of DARPP32 and Nkx2.1 and high levels of Irx3 and Pax6. These cells subsequently generated the majority of the DARPP32 − GABAergic neurons after in vitro differentiation. The spinal mESC-NPCs were intrathecally transplanted into the lesion area of the spinal cord around T10-T11 at 21 days after SCI. The engrafted spinal GABAergic neurons remarkably increased both the paw withdrawal threshold (PWT) below the level of the lesion and the vocalization threshold (VT) to the level of the lesion (T12, T11, and T10 vertebrae), which indicates attenuation of chronic neuropathic pain by the spinal GABAergic neurons. The transplanted cells were positive for GABA antibody staining in the injured region, and cells migrated to the injured spinal site and survived for more than 7 weeks in L4-L5. The mESC-NPC-derived spinal GABAergic neurons dramatically attenuated the chronic neuropathic pain following SCI, suggesting that the spinal GABAergic mESC-NPCs cultured with low doses of SHH and RA could be alternative cell sources for treatment of SCI neuropathic pain by stem cell-based therapies.
This study was designed to investigate the mechanism by which electric fields affect cell function, and to determine the optimal conditions for electric field inhibition of cancer cell proliferation. Low-intensity (<2 V/cm) and intermediate-frequency (100–300 kHz) alternating electric fields were applied to glioblastoma cell lines. These electric fields inhibited cell proliferation by inducing cell cycle arrest and abnormal mitosis due to the malformation of microtubules. These effects were significantly dependent on the intensity and frequency of applied electric fields.
Isolation of pure extracellular vesicles (EVs), especially from blood, has been a major challenge in the field of EV research. The presence of lipoproteins and soluble proteins often hinders the isolation of high purity EVs upon utilization of conventional separation methods. To circumvent such problems, we designed a single-step dual size-exclusion chromatography (dSEC) column for effective isolation of highly pure EVs from bone marrow derived human plasma. With an aim to select appropriate column design parameters, we analyzed the physiochemical properties of the major substances in bone marrow derived plasma, which include EVs, lipoproteins, and soluble proteins. Based on these findings, we devised a novel dSEC column with two different types of porous beads sequentially stacked each other for efficient separation of EVs from other contaminants. The newly developed dSEC columns exhibited better performance in isolating highly pure EVs from AML plasma in comparison to conventional isolation methods.
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