Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Collision Enhanced Raman Scattering (CERS): An Ultra-High Efficient Raman Enhancement Technique for Hollow Core Photonic Crystal Fiber Based Raman Spectroscopy Gas Analyzer.

Raman enhancement techniques are essential for gas analysis to increase the detection sensitivity of a Raman spectroscopy system. We have developed an efficient Raman enhancement technique called the collision-enhanced Raman scattering (CERS), where the active Raman gas as the analyte is mixed with a buffer gas inside the hollow-core photonic-crystal fiber (HCPCF) of a fiber-enhanced Raman spectroscopy (FERS) system. This results in an enhanced Raman signal from the analyte gas. In this study, we first showed that the intensity of the 587 cm-1 stimulated Raman scattering (SRS) peak of H2 confined in an HCPCF is enhanced by as much as five orders of magnitude by mixing with a buffer gas such as helium or N2. Secondly, we showed that the magnitudes of Raman enhancement depend on the type of buffer gas, with helium being more efficient compared to N2. This makes helium a favorable buffer gas for CERS. Thirdly, we applied CERS for Raman measurements of propene, a metabolically interesting volatile organic compound (VOC) with an association to lung cancer. CERS resulted in a substantial enhancement of propene Raman peaks. In conclusion, the CERS we developed is a simple and efficient Raman-enhancing mechanism for improving gas analysis. It has great potential for application in breath analysis for lung cancer detection.

A New Gas Analysis Method Based on Single-Beam Excitation Stimulated Raman Scattering in Hollow Core Photonic Crystal Fiber Enhanced Raman Spectroscopy.

We previously developed a hollow-core photonic crystal fiber (HCPCF) based Raman scattering enhancement technique for gas/human breath analysis. It enhances photon-gas molecule interactions significantly but is still based on CW laser excitation spontaneous Raman scattering, which is a low-probability phenomenon. In this work, we explored nanosecond/sub-nanosecond pulsed laser excitation in HCPCF based fiber enhanced Raman spectroscopy (FERS) and successfully induced stimulated Raman scattering (SRS) enhancement. Raman measurements of simple and complex gases were performed using the new system to assess its feasibility for gas analysis. We studied the gas Raman scattering characteristics, the relationship between Raman intensities and pump energies, and the energy threshold for the transition from spontaneous Raman scattering to SRS. H2, CO2, and propene (C3H6) were used as test gases. Our results demonstrated that a single-beam pulsed pump combined with FERS provides an effective Raman enhancement technique for gas analysis. Furthermore, an energy threshold for SRS initiation was experimentally observed. The SRS-capable FERS system, utilizing a single-beam pulsed pump, shows great potential for analyzing complex gases such as propene, which is a volatile organic compound (VOC) gas, serving as a biomarker in human breath for lung cancer and other human diseases. This work contributes to the advancement of gas analysis and opens alternative avenues for exploring novel Raman enhancement techniques.

Micro-relief characterization of benign and malignant skin lesions by polarization speckle analysis in vivo.

BACKGROUND/PURPOSE: A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. METHODS: The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). RESULTS: The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 µm and 21 ± 3 µm, respectively. Normal skin has rms roughness of 31 ± 3 µm, other lesions have roughness of 35 ± 10 µm (AK), 35 ± 7 µm (SCC), 31 ± 4 µm (SK), and 30 ± 5 µm (BCC). CONCLUSION: An independent-samples Kruskal-Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection.

STAT3 inhibitor BBI608 reduces patient-specific primary cell viability of cervical and endometrial cancer at a clinical-relevant concentration.

PURPOSE: Aberrant activation of STAT3 signal pathway promotes tumor progression in many solid tumor types, including cervical cancer and endometrial cancer. BBI608, the STAT3 inhibitor had been reported in previous studies for restraining cancer stem cells. However, whether BBI608 is available for inhibiting the proliferation of cervical cancer or endometrial cancer remains poorly understood. This study investigated the anti-tumor effect and molecular mechanism of BBI608 on the patient-specific primary cells (PSPC) generated from cervical and endometrial cancer in vitro. METHODS: PSPCs were obtained from four patients via biopsy. The cell viability was analyzed by the CCK8 assay. The PSPCs were treated with various concentrations of BBI608 or/and paclitaxel; and then, western blot was applied to investigate the expression of phosphorylated STAT3 (pSTAT3). RESULTS: The PSPCs cell viability was reduced after treated with BBI608 at a lower concentration. Western blot results showed a reduction trend of pSTAT3 after PSPCs treated with BBI608. Our results demonstrated that BBI608 at the certain concentrations worked well in reducing the cell viability of PSPC from the patients who suffered from cervical cancer and endometrial cancer. CONCLUSIONS: In this study, the patient-specific primary cell (PSPC) was used as the pre-clinical model for investigating the efficiency of BBI608 in reducing cancer cells viability. BBI608, at a clinical-relevant concentration, had valid efficiency in PSPCs from the patients. The dose of drugs treatment and the measured results were more valuable for further guiding clinical trials.

Raman microspectroscopy based TNM staging and grading of breast cancer.

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, ß-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Microscopic Raman illustrating antitumor enhancement effects by the combination drugs of γ-secretase inhibitor and cisplatin on osteosarcoma cells.

By using Raman microspectroscopy, it aims to elucidate the cellular variations caused by the combination drug of γ-secretase inhibitor (DAPT) and cisplatin in osteosarcoma (OS) cells. Illustrated by the obtained results of spectral analysis, the intracellular composition significantly changed after combined drug actions compared to the solo DAPT treatment, indicating the synergistic effect of DAPT combined with cisplatin on OS cells. Meanwhile, multivariate curve resolution-alternating least squares (MCR-ALS) algorithm was utilized to address the biochemical constitution changes in all investigated groups including the untreated (UT), DAPT (40D) and combined drug (40D + 20C) treated cells. K-means cluster and univariate imaging were both utilized to visualize the changes in subcellular morphology and biochemical distribution. The presented study provides a unique understanding on the cellular responses to DAPT combined with cisplatin from the natural biochemical perspectives, and laids an experimental foundation for exploring the therapeutic strategies of other combined anticancer drugs in cancer cell model.

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.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples.

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm-1 and 2800 to 3000 cm-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection.

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm-1 were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin.

Confocal Raman microspectral analysis and imaging were used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT group) and treated with 20 µM (20T group) and 40 µM (40T group) cisplatin for 24 hours. Statistical analysis of the changes in specific Raman signals was performed using a one-way ANOVA and multiple Tukey's honest significant difference (HSD) post hoc tests. Principal component analysis-linear discriminant analysis (PCA-LDA) was used to highlight the featured cellular drug responses based on the obtained spectral information. For spectral imaging analysis, k-means cluster analysis (KCA) was adopted to clarify the effect of cisplatin dose changes on the subcellular structure and its biochemical composition. The results suggest that the major biochemical changes induced by cisplatin in OS cells undergoing apoptosis are reduced protein and nucleic acid content. Through univariate analysis, the changes in the distribution of nucleic acids in OS cells induced by different doses of cisplatin were obtained. The combination of Raman spectroscopy and multivariate analysis shows that cisplatin mainly acts on the nucleus and causes changes in the secondary structure of proteins. These results indicate that Raman imaging technology has the potential to offer the basis of dose optimization for personalized cancer treatment by helping to understand in vitro cellular drug interactions.

Correlation of surface-enhanced Raman spectroscopic fingerprints of kidney transplant recipient urine with kidney function parameters.

Routine monitoring of kidney transplant function is required for the standard care in post-transplantation management, including frequent measurements of serum creatinine with or without kidney biopsy. However, the invasiveness of these methods with potential for clinically significant complications makes them less than ideal. The objective of this study was to develop a non-invasive tool to monitor the kidney transplant function by using Surface-Enhanced Raman Spectroscopy (SERS). Urine and blood samples were collected from kidney transplant recipients after surgery. Silver nanoparticle-based SERS spectra of the urine were measured and evaluated using partial least squires (PLS) analysis. The SERS spectra were compared with conventional chemical markers of kidney transplant function to assess its predictive ability. A total of 110 kidney transplant recipients were included in this study. PLS results showed significant correlation with urine protein (R2 = 0.4660, p < 0.01), creatinine (R2 = 0.8106, p < 0.01), and urea (R2 = 0.7808, p < 0.01). Furthermore, the prediction of the blood markers of kidney transplant function using the urine SERS spectra was indicated by R2 = 0.7628 (p < 0.01) for serum creatinine and R2 = 0.6539 (p < 0.01) for blood urea nitrogen. This preliminary study suggested that the urine SERS spectral analysis could be used as a convenient method for rapid assessment of kidney transplant function.

Intratumoral levels and prognostic significance of Fusobacterium nucleatum in cervical carcinoma.

Growing evidence suggests that microbes can influence the onset of cancer and its consequent development. By researching samples from patients afflicted by cervical cancer, we aimed to explore the associated dynamics and prognostic value of intratumoral levels of F. nucleatum. We used qPCR to analyze tumor tissues obtained from 112 cervical cancer patients in order to characterize the levels and influences of intratumoral levels of the F. nucleatum. Especially for recurrent tissues, there was a distinct observation of higher levels of F. nucleatum in cervical cancer. Patients with high burdens of F. nucleatum intratumoral infiltration exhibited correspondingly poor rates of both overall survival and progression-free survival. Measures of the levels of F. nucleatum were found to have been reliable independent prognostic factors that could predict rates of PFS for afflicted patients (HR = 4.8, 95%CI = 1.2-18.6, P = 0.024). Notably, the levels ofF. nucleatum were positively correlated with tumor differentiation. Cancer cells from patients with relatively high levels of F. nucleatum were observed to possess the characteristics of cancer stem cells (CSCs). We propose that F. nucleatum might be one potential cervical cancer diagnostic and prognostic biomarker, and these findings will help to provide a sound rationale and merit for further study of this bacterium.

Mechanistic insights into ceramidase inhibitor LCL521-enhanced tumor cell killing by photodynamic and thermal ablation therapies.

Our recent investigation uncovered that the acid ceramidase inhibitor LCL521 enhances the direct tumor cell killing effect of photodynamic therapy (PDT) treatment. The present study aimed at elucidating the mechanisms underlying this effect. Exposing mouse squamous cell carcinoma SCCVII cells treated with temoporfin-based PDT to LCL521 (rising ceramide concentration) produced a much greater decrease in cell survival than comparable exposure to the sphingosine kinase-1 inhibitor PF543 (that reduces sphingosine-1-phosphate concentration). This is consistent with recognizing the rising levels of pro-apoptotic sphingolipid ceramide as being more critical in promoting the death of PDT-treated cells than the reduction in the availability of pro-survival acting sphingosine-1 phosphate. This pro-apoptotic impact of LCL521, which was suppressed by the apoptosis inhibitor bongkrekic acid, involves the interaction with the cellular stress signaling network. Hence, inhibiting the key elements of these pathways markedly influenced the adjuvant effect of LCL521 on the PDT response. Particularly effective was the inositol-requiring element-1 (IRE1) kinase inhibitor STF-083010 that dramatically enhanced the killing of cells treated with PDT plus LCL521. An important role in the survival of these cells was exhibited by master transcription factors STAT3 and HIF-1α. The STAT3 inhibitor NSC 74859 was especially effective in further reducing the cell survival rates, suggesting its possible exploitation for therapeutic gain. An additional finding in this study is that LCL521-promoted PDT-mediated cell killing through ceramide-mediated lethal effects is extended to the interaction with other cancer treatment modalities with a rapid cellular stress impact such as photothermal therapy (PTT) and cryoablation therapy (CAT).

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

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose- and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 µM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network.

BACKGROUND: Multiphoton microscopy (MPM) offers a feasible approach for the biopsy in clinical medicine, but it has not been used in clinical applications due to the lack of efficient image processing methods, especially the automatic segmentation technology. Segmentation technology is still one of the most challenging assignments of the MPM imaging technique. METHODS: The MPM imaging segmentation model based on deep learning is one of the most effective methods to address this problem. In this paper, the practicability of using a convolutional neural network (CNN) model to segment the MPM image of skin cells in vivo was explored. A set of MPM in vivo skin cells images with a resolution of 128×128 was successfully segmented under the Python environment with TensorFlow. A novel deep-learning segmentation model named Dense-UNet was proposed. The Dense-UNet, which is based on U-net structure, employed the dense concatenation to deepen the depth of the network architecture and achieve feature reuse. This model included four expansion modules (each module consisted of four down-sampling layers) to extract features. RESULTS: Sixty training images were taken from the dorsal forearm using a femtosecond Ti:Sa laser running at 735 nm. The resolution of the images is 128×128 pixels. Experimental results confirmed that the accuracy of Dense-UNet (92.54%) was higher than that of U-Net (88.59%), with a significantly lower loss value of 0.1681. The 90.60% Dice coefficient value of Dense-UNet outperformed U-Net by 11.07%. The F1-Score of Dense-UNet, U-Net, and Seg-Net was 93.35%, 90.02%, and 85.04%, respectively. CONCLUSIONS: The deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM in vivo images had been adopted by introducing a deep CNN to bridge this gap in Dense-UNet technology. Dense-UNet has reached ultramodern performance for MPM images, especially for in vivo images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images in vivo.

Confocal Raman microspectroscopic analysis on the time-dependent impact of DAPT, a γ-secretase inhibitor, to osteosarcoma cells.

Confocal Raman microspectroscopy (CRM) analysis provides subcellular compositional and morphology related information. In this study, we used CRM in conjunction with multivariate statistical analysis to elucidate the time-dependent impact of the γ-secretase inhibitor, DAPT (N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester) of osteosarcoma (OS) cells. The interactions of DAPT (20 µM) with a murine OS cell line K7M2 at 24 and 48 h were monitored. The spectral characteristics of drug action were identified to illustrate the cellular compositional alterations, showing that DAPT induced apoptosis by reducing the protein, lipid and nucleic acid content and structural changes. Multivariate algorithms, principal component analysis (PCA) and linear discriminant analysis (LDA) revealed a clear separation among cells in the untreated control (UT), 24H (DAPT-treated for 24 h), and 48H (DAPT-treated for 48 h) groups, achieving sensitivities of 100%, 96%, 100% and specificities of 98%, 100%, 100%, respectively. After point-scanned spectral imaging, K-means clustering analysis (KCA) was further used to visualize sub-cellular morphological changes and the underlying spectral characteristics in a temporal sequence. Compared with the UT group, Raman imaging results exhibited gradually increased nuclear division of OS cells with DAPT treatment duration extension, along with changes in the physiology of other organelles within the cell. By providing a unique perspective for understanding the temporary cellular responses to DAPT at molecular level, the achieved results form the foundation of strategies for the application of CRM and other Raman-based techniques for studying the therapeutic responses of other anticancer agents in cancer model systems.

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells.

Confocal Raman microspectral imaging was adopted to elucidate the cellular drug responses of osteosarcoma cells (OC) to N-[N-(3, 5-difluorophenyl acetyl)-L-alanyl]-sphenylglycine butyl ester (DAPT), a γ-secretase inhibitor, by identifying the drug induced subcellular compositional and structural changes. Methods: Spectral information were acquired from cultured osteosarcoma cells treated with 0 (Untreated Group, UT), 10 (10 µM DAPT treated, 10T), 20 µM (20 µM DAPT treated, 20T) DAPT for 24 hours. A one-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple test were sequentially applied to address spectral features among three groups. Multivariate algorithms such as K-means clustering analysis (KCA) and Principal component analysis (PCA) were used to highlight the structural and compositional differences, while, univariate imaging was applied to illustrate the distribution pattern of certain cellular components after drug treatment. Results: Major biochemical changes in DAPT-induced apoptosis came from changes in the content and structure of proteins, lipids, and nucleic acids. By adopted multivariate algorithms, the drug induced cellular changes was identified by the morphology and spectral characteristics between untreated cells and treated cells, testified that DAPT mainly acted in the nuclear region. With the increase of the drug concentration, the content of main subcellular compositions, such nucleic acid, protein, and lipid decreased. In an addition, DAPT-induced nuclear fragmentation and apoptosis was depicted by the univariate Raman image of major cellular components (nucleic acids, proteins and lipids). Conclusions: The achieved Raman spectral and imaging results illustrated detailed DAPT-induced subcellular compositional and structural variations as a function of drug dose. Such observations can not only explain drug therapeutic mechanisms of OC DAPT treatment, and also provide new insights for accessing the medicine curative efficacy and predicting prognosis.

N-dihydrogalactochitosan-supported tumor control by photothermal therapy and photothermal therapy-generated vaccine.

Photothermal therapy (PTT) is recently clinically established cancer therapy that uses near-infrared light for thermal ablation of solid tumors. The biopolymer N-dihydrogalactochitosan (GC) was shown in multiple reports to act as a very effective adjunct to tumor PTT. In the present study, mouse tumor model SCCVII (squamous cell carcinoma) was used with two protocols, in situ tumor PTT and therapeutic PTT vaccine for tumors, for investigating the effects of GC. The results reveal that GC can potentiate tumoricidal action of PTT through both direct and indirect mechanisms. In addition to previously known capacity of GC for activating immune effector cells, the indirect means is shown to include reducing the populations of immunoregulatory T cells (Tregs) in PTT-treated tumors. Testing the effects of GC on PTT-treated SCCVII tumor cells in vitro uncovered the existence of a direct mechanism evident by reduced colony survival of these cells. Fluorescence microscopy demonstrated increased binding of fluorescein-labeled GC to PTT-treated compared to untreated SCCVII cells that can be blocked by pre-exposure to annexin V. The results of additional in vitro testing with specific inhibitors demonstrate that these direct mechanisms do not involve the engagement of death surface receptors that trigger extrinsic apoptosis pathway signaling but may be linked to pro-survival activity of caspase-1. Based on the latter, it can be suggested that GC-promoted killing of PTT-treated cells stems from interference of GC bound to damaged membrane components with the repair of these structures that consequently hinders cell survival.