Unveiling dose- and time-dependent osteosarcoma cell responses to the γ-secretase inhibitor, DAPT, by confocal Raman microscopy

Analysis of Structural Heterogeneity in Low-Rank Coal and Its Pyrolyzed Char Using Multi-Point Scanning Micro-Raman Spectroscopy

Understanding the changes in carbon structure during the mid-low-temperature pyrolysis of low-rank coal is important for efficient utilization. Raman spectroscopy is commonly used to analyze the structural order of carbonaceous materials, but traditional methods may overlook the heterogeneity of coal/char. This research explores the heterogeneity of char structure derived from low-rank coal at 700 °C through multi-point micro-Raman analysis. The analysis of parameters such as area (A), intensity (I), full width at half maximum (FWHM/W), and peak position (P) reveals that the carbon structure becomes less ordered as coal transforms into char due to the deposition of small molecules on the surface. The study emphasizes the benefits of multi-point detection for gaining in-depth insights into the structural evolution of carbonaceous materials. The increased standard deviation of Raman parameters indicates diverse structural characteristics resulting from pyrolysis at this temperature, which traditional methods may not capture effectively. The mapping method used in this research visually illustrates the distribution of carbon structures in the region.

Investigating the cellular responses of osteosarcoma to cisplatin by confocal Raman microspectroscopy

Confocal Raman Microspectroscopy (CRM) was employed to clarify the cellular response of cisplatin in osteosarcoma (OS) cells with different dosages and incubation times. The K7M2 mouse osteosarcoma cells were treated by cisplatin in 0 μM (UT group), 20 μM (20 T group), and 40 μM (40 T group) doses for 24-h (24H group) and 48-h (48H group), respectively. Raman spectroscopy was utilized to analyze the drug induced variations of intracellular biochemical components in osteosarcoma cells. The spectral results shows that the main changes in its biochemical composition come from nucleic acids. By adopting three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)), principal component analysis combined with support vector machine models (PCA-SVM) was built to address the spectral variations among all investigated groups. Meanwhile, multivariate curve resolution alternating least squares (MCR-ALS) was further utilized to discuss on the chemical interpretation on the acquired spectral results. Moreover, Raman spectral images, which is reconstructed by K-means cluster analysis (KCA) with point-scanned hyperspectral dataset, was applied to illustrate the drug induced compositional and morphological variations in each subcellular region. The achieved results not only prove the application potential of Raman based analytical technique in non-labeled intracellular studies, but also illustrate the detailed compositional and structural information of cisplatin induced OS cell responses from the perspective of multivariate analysis and imaging of Raman spectroscopy.

Unveiling osteosarcoma responses to DAPT combined with cisplatin by using confocal Raman microscopy

The aim of this study was to clarify the dose- and time-dependent effect of the γ-secretase inhibitor (DAPT) combined with cisplatin on osteosarcoma (OS) cells, evaluated by confocal Raman microspectral imaging (CRMI) technology. The intracellular composition significantly changed after combined drug action compared with the sole cisplatin treatment, proving the synergistic effect of DAPT combined with cisplatin on OS cells. The principal component analysis-linear discriminant analysis revealed the main compositional variations by distinguishing spectral characteristics. K-means cluster and univariate imaging were used to visualize the changes in subcellular morphology and biochemical distribution. The results showed that the increase of the DAPT dose and cisplatin treatment time in the combination treatment induced the division of the nucleus in OS cells, and other organelles also showed significant physiological changes compared with the effect of sole cisplatin treatment. After understanding the cellular response to the combined drug treatment at a molecular level, the achieved results provide an experimental fact for developing suitable individualized tumor treatment protocols.

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition

It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.

Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics

Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.