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.

Rapid diagnosis and quarantine of influenza virus mutant-infected people is critical to contain the fatal viral infection spread because effective antiviral drugs are normally not available. Conventional methods, however, cannot be used for the diagnosis because these methods need predefined labels, likely also unavailable for just emerging viruses. Here, we propose label-free identification of cells infected with different influenza viruses based on surface-enhanced Raman spectroscopy (SERS) and principal component analysis (PCA). Viral envelope proteins that are displayed on the surface of cells after infection of influenza viruses were targeted for this identification. Cells that expressed the envelope proteins of A/WSN/33 H1N1 or A/California/04/2009 H1N1 influenza viruses produced distinct SERS signals. Cells that displayed combinations of the envelope proteins from these two viral variants, an indication of emergence of a new virus, also generated characteristic SERS patterns. However, the cell’s own surface proteins often hindered the identification of virally infected cells by producing SERS peaks similar to viral ones. PCA of the obtained SERS patterns could effectively capture the virus-specific signal components from the jumbled SERS peaks. Our study demonstrates a potential of combination of SERS and PCA to identify newly emerging influenza viruses through sensing the cells infected with the viruses.

A series of seven isostructural homodinuclear lanthanide complexes are reported. The magnetic properties (ac and dc SQUID measurements) are discussed on the basis of the X-ray structural properties which show that the two lanthanide sites are structurally different. MCD spectroscopy of the dysprosium(III) and neodymium(III) complexes ([Dy(III)2(L)(OAc)4](+) and [Nd(III)2(L)(OAc)4](+)) allowed us to thoroughly analyze the ligand field, and high-frequency EPR spectroscopy of the gadolinium(III) species ([Gd(III)2(L)(OAc)4](+)) showed the importance of dipolar coupling in these systems. An extensive quantum-chemical analysis of the dysprosium(III) complex ([Dy(III)2(L)(OAc)4](+)), involving an ab initio (CASSCF) wave function, explicit spin-orbit coupling (RASSI-SO), and a ligand field analysis (Lines model and Stevens operators), is in full agreement with all experimental data (SQUID, HF-EPR, MCD) and specifically allowed us to accurately simulate the experimental χT versus T data, which therefore allowed us to establish a qualitative model for all relaxation pathways.

We present the synthesis, structure, magnetic properties, as well as the Mössbauer and electron paramagnetic resonance studies of a ring-shaped [FeLn(Htea)(μ-N)(N)(piv)] (Ln = Y 1, Gd 2, Tb 3, Dy 4, Ho 5, Er, 6) coordination cluster. The Dy, Tb, and Ho analogues show blocking of the magnetization at low temperatures without applied fields. The anisotropy of the 3d ion and the exchange interaction between 3d and 4f ions in FeLn complexes are unambiguously determined by high-field/high-frequency electron paramagnetic resonance measurements at low temperature. Ferromagnetic exchange interaction J is found which decreases upon variation of the Ln ions to larger atomic numbers. This dependence is similar to the behavior shown in the effective barrier values of complexes 3-5. Further information about the anisotropy of the Ln ions was gathered with Fe Mössbauer spectroscopy, and the combination of these methods provides detailed information regarding the electronic structure of these complexes.