Raman microspectroscopy for the early detection of pre-malignant changes in cervical tissue

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms

Cervical cancer is the most common reproductive malignancy in the female reproductive system. The incidence rate and mortality rate of cervical cancer among women in China are high. In this study, Raman spectroscopy was used to collect tissue sample data from patients with cervicitis, cervical precancerous low-grade lesions, cervical precancerous high-grade lesions, well differentiated squamous cell carcinoma, moderately differentiated squamous cell carcinoma, poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and derivatives. Convolutional neural network (CNN) and residual neural network (ResNet) classification models were constructed to classify and identify seven types of tissue samples. The attention mechanism efficient channel attention network (ECANet) module and squeeze-and-excitation network (SENet) module were combined with the established CNN and ResNet network models, respectively, to make the models have better diagnostic performance. The results showed that efficient channel attention convolutional neural network (ECACNN) had the best discrimination, and the average accuracy, recall, F1 and AUC values after five cross-validations could reach 94.04%, 94.87%, 94.43% and 96.86%, respectively.

Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women’s Health Issue—A Review

This review focuses on recent advances and future perspectives in the use of Raman spectroscopy for cervical cancer, a global women’s health issue. Cervical cancer is the fourth most common women’s cancer in the world, and unfortunately mainly affects younger women. However, when detected at the early precancer stage, it is highly treatable. High-quality cervical screening programmes and the introduction of the human papillomavirus (HPV) vaccine are reducing the incidence of cervical cancer in many countries, but screening is still essential for all women. Current gold standard methods include HPV testing and cytology for screening, followed by colposcopy and histopathology for diagnosis. However, these methods are limited in terms of sensitivity/specificity, cost, and time. New methods are required to aid clinicians in the early detection of cervical precancer. Over the past 20 years, the potential of Raman spectroscopy together with multivariate statistical analysis has been shown for the detection of cervical cancer. This review discusses the research to date on Raman spectroscopic approaches for cervical cancer using exfoliated cells, biofluid samples, and tissue ex vivo and in vivo.

Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)

The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set.

Application of Raman spectroscopy for characterization of the functional polarization of macrophages into M1 and M2 cells

Macrophages are key cells in the immune inflammatory response that can be differentiated into M1 and M2 phenotypes. Polarization has a critical therapeutic value, especially in diseases in which an M1/M2 imbalance plays a pathophysiological role. Raman spectroscopy has proven to be a promising bioanalytical technique for discriminating different cell types. However, to our knowledge, its application to identify the functional polarization of macrophages into M1 or M2 cells is yet to be investigated. In this work, Raman spectroscopy was applied to the analysis of macrophage polarization, and the spectral datasets were analyzed using principal component analysis (PCA). In vitro, resting J774.1 macrophages were treated with LPS/IFN-γ to induce the M1 phenotype or with IL-4 to induce the M2 phenotype. The resulting Raman spectra showed sufficient biochemical information to distinguish between M1 and M2 phenotypes when analyzed by PCA, reflecting the changes in cell markers caused by differentiation. The Raman spectra collected from LPS-stimulated M1 and M2 macrophages were more intense. The functional phenotype of M1 macrophages was confirmed by IL-6 secretion and TNF-α mRNA expression, while M2 macrophages produced IL-10 and Arg-1 mRNA, as well as by the morphological changes observed by scanning electron microscopy. Taken together, the results indicate that Raman spectroscopy combined with PCA analysis is a useful tool to identify the functional phenotypes of macrophages, providing an alternative way to distinguish between cells in distinct differentiation stages.

Tracking the Differentiation Status of Human Neural Stem Cells through Label-Free Raman Spectroscopy and Machine Learning-Based Analysis

The ability to noninvasively monitor stem cells' differentiation is important to stem cell studies. Raman spectroscopy is a non-harmful imaging approach that acquires the cellular biochemical signatures. Herein, we report the first use of label-free Raman spectroscopy to characterize the gradual change during the differentiation process of live human neural stem cells (NSCs) in the in vitro cultures. Raman spectra of 600-1800 cm-1 were measured with human NSC cultures from the undifferentiated stage (NSC-predominant) to the highly differentiated one (neuron-predominant) and subsequently analyzed using various mathematical methods. Hierarchical cluster analysis distinguished two cell types (NSCs and neurons) through the spectra. The subsequently derived differentiation rate matched that measured by immunocytochemistry. The key spectral biomarkers were identified by time-dependent trend analysis and principal component analysis. Furthermore, through machine learning-based analysis, a set of eight spectral data points were found to be highly accurate in classifying cell types and predicting the differentiation rate. The predictive accuracy was the highest using the artificial neural network (ANN) and slightly lowered using the logistic regression model and linear discriminant analysis. In conclusion, label-free Raman spectroscopy with the aid of machine learning analysis can provide the noninvasive classification of cell types at the single-cell level and thus accurately track the human NSC differentiation. A set of eight spectral data points combined with the ANN method were found to be the most efficient and accurate. Establishing this non-harmful and efficient strategy will shed light on the in vivo and clinical studies of NSCs.

Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening

Simple Summary

Persistent high-risk human papillomavirus (HPV) infection can lead to cervical precancer and cancer. Recently, HPV testing has been introduced for primary cervical screening, but the HPV DNA test cannot distinguish between transient and persistent HPV infection. Thus, there is an unmet clinical need to develop a new test to identify women with a high-risk persistent HPV infection. Raman spectra were recorded from cervical smear samples (n = 60) and, on the basis of HPV DNA and HPV mRNA test results, a classifier was developed to identify persistent HPV infection. A further blinded independent test set (n = 14) was used to validate the model, and sensitivity of 90% and specificity of 100% were achieved. Improved triage would allow women with a high-risk persistent HPV infection to be referred for immediate treatment, while women with a low-risk transient infection could avoid overtreatment.

Abstract

The role of persistent high-risk human papillomavirus (HPV) infection in the development of cervical precancer and cancer is now well accepted, and HPV testing has recently been introduced for primary cervical screening. However, the low specificity of HPV DNA testing can result in large numbers of women with an HPV-positive result, and additional triage approaches are needed to avoid over-referral to colposcopy and overtreatment. The aim of this study was to assess Raman spectroscopy as a potential triage test to discriminate between transient and persistent HPV infection. HPV DNA status and mRNA status were confirmed in ThinPrep^® cervical samples (n = 60) using the Cobas 4800 and APTIMA HPV test, respectively. Raman spectra were recorded from single-cell nuclei and subjected to partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 14). Sensitivity of 85% and specificity of 92% were achieved for the classification of transient and persistent HPV infection, and this increased to 90% sensitivity and 100% specificity when mean sample spectra were used instead of individual cellular spectra. This study showed that Raman spectroscopy has potential as a triage test for HPV-positive women to identify persistent HPV infection.

The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions

Simple Summary

Raman spectroscopy, a light scattering technique that provides the biochemical fingerprint of a sample, was used on samples taken from patients with cancer and precancerous lesions. This information was then used to build a classifier to identify cancer and the precancerous phases. The ability to distinguish cancerous tissue from normal and precancerous tissue is diagnostically crucial as it can alter the patients’ prognosis and management. Moreover, as cellular changes are often present at the tumour margin, the ability to distinguish these changes from cancer can help in preserving more of the tissue and maintaining aesthetics and functionality for the patient.

Abstract

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter- and intra-observer errors and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%.

Raman spectroscopic study of cervical precancerous lesions and cervical cancer

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d’Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.

Novel endoscopic optical diagnostic technologies in medical trial research: recent advancements and future prospects

Novel endoscopic biophotonic diagnostic technologies have the potential to non-invasively detect the interior of a hollow organ or cavity of the human body with subcellular resolution or to obtain biochemical information about tissue in real time. With the capability to visualize or analyze the diagnostic target in vivo, these techniques gradually developed as potential candidates to challenge histopathology which remains the gold standard for diagnosis. Consequently, many innovative endoscopic diagnostic techniques have succeeded in detection, characterization, and confirmation: the three critical steps for routine endoscopic diagnosis. In this review, we mainly summarize researches on emerging endoscopic optical diagnostic techniques, with emphasis on recent advances. We also introduce the fundamental principles and the development of those techniques and compare their characteristics. Especially, we shed light on the merit of novel endoscopic imaging technologies in medical research. For example, hyperspectral imaging and Raman spectroscopy provide direct molecular information, while optical coherence tomography and multi-photo endomicroscopy offer a more extensive detection range and excellent spatial-temporal resolution. Furthermore, we summarize the unexplored application fields of these endoscopic optical techniques in major hospital departments for biomedical researchers. Finally, we provide a brief overview of the future perspectives, as well as bottlenecks of those endoscopic optical diagnostic technologies. We believe all these efforts will enrich the diagnostic toolbox for endoscopists, enhance diagnostic efficiency, and reduce the rate of missed diagnosis and misdiagnosis.

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.

Rapid and complete paraffin removal from human tissue sections delivers enhanced Raman spectroscopic and histopathological analysis

Incomplete removal of paraffin and organic contaminants from tissues processed for diagnostic histology has been a profound barrier to the introduction of Raman spectroscopic techniques into clinical practice. We report a route to rapid and complete paraffin removal from a range of formalin-fixed paraffin embedded tissues using super mirror stainless steel slides. The method is equally effective on a range of human and animal tissues, performs equally well with archived and new samples and is compatible with standard pathology lab procedures. We describe a general enhancement of the Raman scatter and enhanced staining with antibodies used in immunohistochemistry for clinical diagnosis. We conclude that these novel slide substrates have the power to improve diagnosis through anatomical pathology by facilitating the simultaneous combination of improved, more sensitive immunohistochemical staining and simplified, more reliable Raman spectroscopic imaging, analysis and signal processing.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma based on Raman spectroscopy and support vector machine

In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized mean Raman spectra of the two tissues and pointed out the major differences in the biochemical composition between the two tissues. The PCA-SVM model that was used to distinguish the two types of cervical cancer tissues was established. The accuracy of the model in differentiating cervical adenocarcinoma from cervical squamous cell carcinoma was 93.125%. The results of this study indicate that Raman spectroscopy of cervical adenocarcinoma and cervical squamous cell carcinoma tissue in combination with SVM (support vector analysis) and PCA (principal component analysis) can be useful for the classification of cervical adenocarcinoma and cervical squamous cell carcinoma tissues and for the exploration of the differences in biochemical compositions between the two types of cervical tissue. This study lays a foundation to further study Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm^-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

Raman spectroscopic detection of high-grade cervical cytology: Using morphologically normal appearing cells

This study aims to detect high grade squamous intraepithelial cells (HSIL) by investigating HSIL associated biochemical changes in morphologically normal appearing intermediate and superficial cells using Raman spectroscopy. Raman spectra (n = 755) were measured from intermediate and superficial cells from negative cytology ThinPrep specimens (n = 18) and from morphologically normal appearing intermediate and superficial cells from HSIL cytology ThinPrep specimens (n = 17). The Raman data was subjected to multivariate algorithms including the standard principal component analysis (PCA)-linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) together with random subsets cross-validation for discriminating negative cytology from HSIL. The PCA-LDA method yielded sensitivities of 74.9%, 72.8%, and 75.6% and specificities of 89.9%, 81.9%, and 84.5%, for HSIL diagnosis based on the dataset obtained from intermediate, superficial and mixed intermediate/superficial cells, respectively. The PLS-DA method provided improved sensitivities of 95.5%, 95.2% and 96.1% and specificities of 92.7%, 94.7% and 93.5% compared to the PCA-LDA method. The results demonstrate that the biochemical signatures of morphologically normal appearing cells can be used to discriminate between negative and HSIL cytology. In addition, it was found that mixed intermediate and superficial cells could be used for HSIL diagnosis as the biochemical differences between negative and HSIL cytology were greater than the biochemical differences between intermediate and superficial cell types.

Screening and staging for non-small cell lung cancer by serum laser Raman spectroscopy

OBJECTIVE

Lung cancer is the leading cause of cancer-related death worldwide. Current clinical screening methods to detect lung cancer are expensive and associated with many complications. Raman spectroscopy is a spectroscopic technique that offers a convenient method to gain molecular information about biological samples. In this study, we measured the serum Raman spectral intensity of healthy volunteers and patients with different stages of non-small cell lung cancer. The purpose of this study was to evaluate the application of serum laser Raman spectroscopy as a low cost alternative method in the screening and staging of non-small cell lung cancer (NSCLC).

METHODS

The Raman spectra of the sera of peripheral venous blood were measured with a LabRAM HR 800 confocal Micro Raman spectrometer for individuals from five groups including 14 healthy volunteers (control group), 23 patients with stage I NSCLC (stage I group), 24 patients with stage II NSCLC (stage II group), 19 patients with stage III NSCLC (stage III group), 11 patients with stage IV NSCLC (stage IV group). Each serum sample was measured 3 times at different spots and the average spectra represented the signal of Raman spectra in each case. The Raman spectrum signal data of the five groups were statistically analyzed by analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and cross-validation.

RESULTS

Raman spectral intensity was sequentially reduced in serum samples from control group, stage I group, stage II group and stage III/IV group. The strongest peak intensity was observed in the control group, and the weakest one was found in the stage III/IV group at bands of 848 cm^-1, 999 cm^-1, 1152 cm^-1, 1446 cm^-1 and 1658 cm^-1 (P < 0.05). Linear discriminant analysis showed that the sensitivity to identify healthy people, stage I, stage II, and stage III/IV NSCLC was 86%, 65%, 75%, and 87%, respectively; the specificity was 95%, 94%, 88%, and 93%, respectively; and the overall accuracy rate was 92% (71/77).

CONCLUSION

The laser Raman spectroscopy can effectively identify patients with stage I, stage II or stage III/IV Non-Small Cell Lung cancer using patient serum samples.

Raman chemical imaging, a new tool in kidney stone structure analysis: Case-study and comparison to Fourier Transform Infrared spectroscopy

BACKGROUND AND OBJECTIVES

The kidney stone's structure might provide clinical information in addition to the stone composition. The Raman chemical imaging is a technology used for the production of two-dimension maps of the constituents' distribution in samples. We aimed at determining the use of Raman chemical imaging in urinary stone analysis.

MATERIAL AND METHODS

Fourteen calculi were analyzed by Raman chemical imaging using a confocal Raman microspectrophotometer. They were selected according to their heterogeneous composition and morphology. Raman chemical imaging was performed on the whole section of stones. Once acquired, the data were baseline corrected and analyzed by MCR-ALS. Results were then compared to the spectra obtained by Fourier Transform Infrared spectroscopy.

RESULTS

Raman chemical imaging succeeded in identifying almost all the chemical components of each sample, including monohydrate and dihydrate calcium oxalate, anhydrous and dihydrate uric acid, apatite, struvite, brushite, and rare chemicals like whitlockite, ammonium urate and drugs. However, proteins couldn't be detected because of the huge autofluorescence background and the small concentration of these poor Raman scatterers. Carbapatite and calcium oxalate were correctly detected even when they represented less than 5 percent of the whole stones. Moreover, Raman chemical imaging provided the distribution of components within the stones: nuclei were accurately identified, as well as thin layers of other components. Conversion of dihydrate to monohydrate calcium oxalate was correctly observed in the centre of one sample. The calcium oxalate monohydrate had different Raman spectra according to its localization.

CONCLUSION

Raman chemical imaging showed a good accuracy in comparison with infrared spectroscopy in identifying components of kidney stones. This analysis was also useful in determining the organization of components within stones, which help locating constituents in low quantity, such as nuclei. However, this analysis is time-consuming, making it more suitable for research studies rather than routine analysis.

Confocal Raman spectroscopic imaging for in vitro monitoring of active ingredient penetration and distribution in reconstructed human epidermis model

Topically applied active cosmetic ingredients (ACI) or active pharmaceutical ingredients (API) efficacy is directly related to their efficiency of penetration in the skin. In vitro reconstructed human epidermis surrogate models offer in vivo like skin samples for transdermal studies. Using Delipidol®, an ACI currently used in the cosmetics industry, the capabilities to deliver accurate distribution maps and penetration profiles of this molecule by means of confocal Raman spectroscopic imaging have been demonstrated. Using a non-negative constrained least squares (NCLS) approach, contribution of specific molecules can be estimated at each point of spectral maps in order to deliver semi-quantitative heat maps representing the ACI levels in the different skin layers. The concentration profiles obtained are approximately single exponential for all 3 time points evaluated, with a consistent decay constant, which is independent of the sublayer structure. Notably, however, there is no significant penetration into the lower basal layers until a critical concentration is built up, after 3 hours. Combination of Raman confocal imaging with spectral unmixing methods such as NCLS is demonstrated to be a relevant approach for in vitro biological evaluation of cosmetic and pharmaceutical active ingredients and could easily be implemented as a screening tool for industrial use.

Raman spectroscopy for cytopathology of exfoliated cervical cells

Cervical cancer is the fourth most common cancer affecting women worldwide but mortality can be decreased by early detection of pre-malignant lesions. The Pap smear test is the most commonly used method in cervical cancer screening programmes. Although specificity is high for this test, it is widely acknowledged that sensitivity can be poor mainly due to the subjective nature of the test. There is a need for new objective tests for the early detection of pre-malignant cervical lesions. Over the past two decades, Raman spectroscopy has emerged as a promising new technology for cancer screening and diagnosis. The aim of this study was to evaluate the potential of Raman spectroscopy for cervical cancer screening using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) classification terminology. ThinPrep® Pap samples were recruited from a cervical screening population. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Normal and abnormal ThinPrep® samples were discriminated based on the biochemical fingerprint of the cells using Principal Component Analysis (PCA). Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) was employed to build classification models based on either CIN or SIL terminology. This study has shown that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and that the use of CIN terminology resulted in improved sensitivity for high grade cases.

A monolithic microsphere-fiber probe for spatially resolved Raman spectroscopy: Application to head and neck squamous cell carcinomas

The ability to identify precise cancer margins in vivo during a surgical excision is critical to the well-being of the patient. Decreased operative time has been linked to shorter patient recovery time, and there are risks associated with removing either too much or too little tissue from the surgical site. The more rapidly and accurately a surgeon can identify and excise diseased tissue, the better the prognosis for the patient. To this end, we investigate both malignant and healthy oral cavity tissue using the Raman spectroscopy, with a monolithic microsphere-fiber probe. Our results indicate that this probe has decreased the size of the analyzed area by more than an order of magnitude, as compared to a conventional fiber reflection probe. Scanning the probe across the tissues reveals variations in the Raman spectra that enable us to differentiate between malignant and healthy tissues. Consequently, we anticipate that the high spatial resolution afforded by the probe will permit us to identify tumor margins in detail, thereby optimizing tissue removal and improving patient outcomes.

Raman spectral signatures of cervical exfoliated cells from liquid-based cytology samples

It is widely accepted that cervical screening has significantly reduced the incidence of cervical cancer worldwide. The primary screening test for cervical cancer is the Papanicolaou (Pap) test, which has extremely variable specificity and sensitivity. There is an unmet clinical need for methods to aid clinicians in the early detection of cervical precancer. Raman spectroscopy is a label-free objective method that can provide a biochemical fingerprint of a given sample. Compared with studies on infrared spectroscopy, relatively few Raman spectroscopy studies have been carried out to date on cervical cytology. The aim of this study was to define the Raman spectral signatures of cervical exfoliated cells present in liquid-based cytology Pap test specimens and to compare the signature of high-grade dysplastic cells to each of the normal cell types. Raman spectra were recorded from single exfoliated cells and subjected to multivariate statistical analysis. The study demonstrated that Raman spectroscopy can identify biochemical signatures associated with the most common cell types seen in liquid-based cytology samples; superficial, intermediate, and parabasal cells. In addition, biochemical changes associated with high-grade dysplasia could be identified suggesting that Raman spectroscopy could be used to aid current cervical screening tests.

Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the "Fingerprint" Region

The ability to rapidly and accurately discriminate between healthy and malignant tissue offers surgeons a tool for in vivo analysis that would potentially reduce operating time, facilitate quicker recovery, and improve patient outcomes. To this end, we investigate discrimination between diseased tissue and adjacent healthy controls from patients with head and neck cancer using near-infrared Raman spectroscopy. Our results indicate previously unreported peaks in the Raman spectra that lie outside the conventional "fingerprint" region (400 cm-1-1800 cm -1) played an important role in our analysis and in discriminating between the tissue classes. Preliminary multivariate statistical analyses of the Raman spectra indicate that discrimination between diseased and healthy tissue is possible based on these peaks.

Differentiating responses of lung cancer cell lines to Doxorubicin exposure: in vitro Raman micro spectroscopy, oxidative stress and bcl-2 protein expression

The potential of Raman micro spectroscopy as an in vitro, non-invasive tool for clinical applications has been demonstrated in recent years, specifically for cancer research. To further illustrate its potential as a high content and label free technique, it is important to show its capability to elucidate drug mechanisms of action and cellular resistances. In this study, cytotoxicity assays were employed to establish the toxicity profiles for 24 hr exposure of lung cancer cell lines, A549 and Calu-1, to the commercially available drug, doxorubicin (DOX). Raman spectroscopy, coupled with Confocal Laser Scanning Microscopy and Flow Cytometry, was used to track the DOX mechanism of action, at a subcellular level, and to study the mechanisms of cellular resistance to DOX. Biomarkers related to the drug mechanism of action and cellular resistance to apoptosis, namely reactive oxygen species (ROS) and bcl-2 protein expression, respectively, were also measured and correlated to Raman spectral profiles. Calu-1 cells are shown to exhibit spectroscopic signatures of both direct DNA damage due to intercalation in the nucleus and indirect damage due to oxidative stress in the cytoplasm, whereas the A549 cell line only exhibits signatures of the former mechanism of action. PCA of nucleolar, nuclear and cytoplasmic regions of A549 and Calu-1 with corresponding loadings of PC1 and PC2.

Optical techniques for cervical neoplasia detection

This paper provides an overview of the current research in the field of optical techniques for cervical neoplasia detection and covers a wide range of the existing and emerging technologies. Using colposcopy, a visual inspection of the uterine cervix with a colposcope (a binocular microscope with 3- to 15-fold magnification), has proven to be an efficient approach for the detection of invasive cancer. Nevertheless, the development of a reliable and cost-effective technique for the identification of precancerous lesions, confined to the epithelium (cervical intraepithelial neoplasia) still remains a challenging problem. It is known that even at early stages the neoplastic transformations of cervical tissue induce complex changes and modify both structural and biochemical properties of tissues. The different methods, including spectroscopic (diffuse reflectance spectroscopy, induced fluorescence and autofluorescence spectroscopy, Raman spectroscopy) and imaging techniques (confocal microscopy, optical coherence tomography, Mueller matrix imaging polarimetry, photoacoustic imaging), probe different tissue properties that may serve as optical biomarkers for diagnosis. Both the advantages and drawbacks of these techniques for the diagnosis of cervical precancerous lesions are discussed and compared.

Using Raman spectroscopy to characterize biological materials

Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia

BACKGROUND

Cervical cancer remains a major health problem, especially in developing countries. Colposcopic examination is used to detect high-grade lesions in patients with a history of abnormal pap smears. New technologies are needed to improve the sensitivity and specificity of this technique. We propose to test the potential of fluorescence confocal microscopy to identify high-grade lesions.

METHODS

We examined the quantification of ex vivo confocal fluorescence microscopy to differentiate among normal cervical tissue, low-grade Cervical Intraepithelial Neoplasia (CIN), and high-grade CIN. We sought to (1) quantify nuclear morphology and tissue architecture features by analyzing images of cervical biopsies; and (2) determine the accuracy of high-grade CIN detection via confocal microscopy relative to the accuracy of detection by colposcopic impression. Forty-six biopsies obtained from colposcopically normal and abnormal cervical sites were evaluated. Confocal images were acquired at different depths from the epithelial surface and histological images were analyzed using in-house software.

RESULTS

The features calculated from the confocal images compared well with those features obtained from the histological images and histopathological reviews of the specimens (obtained by a gynecologic pathologist). The correlations between two of these features (the nuclear-cytoplasmic ratio and the average of three nearest Delaunay-neighbors distance) and the grade of dysplasia were higher than that of colposcopic impression. The sensitivity of detecting high-grade dysplasia by analysing images collected at the surface of the epithelium, and at 15 and 30 μm below the epithelial surface were respectively 100, 100, and 92 %.

CONCLUSIONS

Quantitative analysis of confocal fluorescence images showed its capacity for discriminating high-grade CIN lesions vs. low-grade CIN lesions and normal tissues, at different depth of imaging. This approach could be used to help clinicians identify high-grade CIN in clinical settings.

Raman spectroscopy for screening and diagnosis of cervical cancer

Cervical cancer is the fourth most common cancer in women worldwide and mainly affects younger women. The mortality associated with cervical cancer can be reduced if the disease is detected at the pre-cancer stage. Current best-practice methods include cytopathology, HPV testing, and histopathology, but these methods are limited in terms of subjectivity, cost, and time. There is an unmet clinical need for new methods to aid clinicians in the early detection of cervical pre-cancer. These methods should be objective and rapid and require minimal sample preparation. Raman spectroscopy is a vibrational spectroscopic technique by which incident radiation is used to induce vibrations in the molecules of a sample and the scattered radiation may be used to characterise the sample in a rapid and non-destructive manner. Raman spectroscopy is sensitive to subtle biochemical changes occurring at the molecular level, enabling spectral variations corresponding to disease onset to be detected. Over the past 15 years, there have been numerous reports revealing the potential of Raman spectroscopy together with multivariate statistical analysis for the detection of a variety of cancers. This paper discusses the recent advances and challenges for cervical-cancer screening and diagnosis and offers some perspectives for the future.

Raman spectroscopy for physiological investigations of tissues and cells

Raman micro-spectroscopy provides a convenient non-destructive and location-specific means of probing cellular physiology and tissue physiology at sub-micron length scales. By probing the vibrational signature of molecules and molecular groups, the distribution and metabolic products of small molecules that cannot be labeled with fluorescent dyes can be analyzed. This method works well for molecular concentrations in the micro-molar range and has been demonstrated as a valuable tool for monitoring drug-cell interactions. If the small molecule of interest does not contain groups that would allow for a discrimination against cytoplasmic background signals, "labeling" of the molecule by isotope substitution or by incorporating other unique small groups, e.g. alkynes provides a stable signal even for time-lapse imaging such compounds in living cells. In this review we highlight recent progress in assessing the physiology of cells and tissue by Raman spectroscopy and imaging.

Cellular discrimination using in vitro Raman micro spectroscopy: the role of the nucleolus

Raman micro spectroscopy is employed to discriminate between cell lines. Results show the importance of the nuclear sub-cellular organelle, the nucleoli, to differentiate between cancer cell lines with high specificity and sensitivity.