Classification of fixed urological cells using Raman tweezers

Classification of formalin-fixed bladder cancer cells with laser tweezer Raman spectroscopy

Bladder cancer is a common cancer that is relatively hard to detect at an early stage because of its non-obvious symptoms. It is known that bladder cells can be found in urine samples which potentially could be used for early detection of bladder cancer. Raman spectroscopy is a powerful non-invasive tool for accessing biochemical information of cells. Combined with laser tweezers, to allow isolation of single cells, Raman spectroscopy has been used to characterise a number of bladder cells that might be found in a urine sample. Using principal component-canonical variates analysis (PC-CVA) and k-fold validation, the results shows that the invasive bladder cancer cells can be identified with accuracy greater than 87%. This demonstrates the potential of developing an early detection method that identifies the invasive bladder cancer cells in urine samples.

Vibrational spectroscopies for biochemical investigation of X-ray exposure effects on SH-SY5Y human neuroblastoma cells

Neuroblastoma is the most recurring cancer in childhood and adolescence. The SH-SY5Y neuroblastoma cell line is generally adopted for elaborating new therapeutical approaches and/or elaborating strategies for the prevention of central nervous system disturbances. In fact, it represents a valid model system for investigating in vitro the effects on the brain of X-ray exposure using vibrational spectroscopies that can detect early radiation-induced molecular alterations of potential clinical usefulness. In recent years, we dedicated significant efforts in the use of Fourier-transform and Raman microspectroscopy techniques for characterizing such radiation-induced effects on SH-SY5Y cells by examining the contributions from different cell components (DNA, proteins, lipids, and carbohydrates) to the vibrational spectra. In this review, we aim at revising and comparing the main results of our studies to provide a wide outlook of the latest outcomes and a framework for future radiobiology research using vibrational spectroscopies. A short description of our experimental approaches and data analysis procedures is also reported.

Reliable cell preparation protocol for Raman imaging to effectively differentiate normal leukocytes and leukemic blasts

Leukemias are a remarkably diverse group of malignancies originating from abnormal progenitor cells in the bone marrow. Leukemia subtypes are classified according to the cell type that has undergone neoplastic transformation using demanding and time-consuming methods. Alternative is Raman imaging that can be used both for living and fixed cells. However, considering the diversity of leukemic cell types and normal leukocytes, and the availability of different sample preparation protocols, the main objective of this work was to verify them for leukemia and normal blood cell samples for Raman imaging. The effect of glutaraldehyde (GA) fixation in a concentration gradient (0.1 %, 0.5 %, and 2.5 % GA) on the molecular structure of T-cell acute lymphoblastic leukemia (T-ALL) and peripheral blood mononuclear cells (PBMCs) was verified. Changes in the secondary structure of proteins within cells were indicated as the main effect of fixation, as shown by an increase in band intensity at 1041 cm^-1, characteristic for in-plane δ(CH) deformation in phenylalanine (Phe). Different sensitivity of mononuclear and leukemic cells to fixation was observed. While the 0.1 % concentration of GA was too low to preserve the cell structure for an extended period of time, a GA concentration of 0.5 % seemed optimal for both normal and malignant cells. Chemical changes in PBMCs samples stored for 11 days were also investigated, which manifested in numerous modifications in the secondary structure of proteins and the content of nucleic acids. The impact of cell preculturing for 72 h after unbanking was verified, and there was no significant effect on the molecular structure of cells fixed with 0.5 % GA. In summary, the developed protocol for the preparation of samples for Raman imaging allows for the effective differentiation of fixed normal leukocytes from malignant T lymphoblasts.

Application and Progress of Raman Spectroscopy in Male Reproductive System

Raman spectroscopy is a fast-developing, unmarked, non-invasive, non-destructive technique which allows for real-time scanning and sampling of biological samples in situ, reflecting the subtle biochemical composition alterations of tissues and cells through the variations of spectra. It has great potential to identify pathological tissue and provide intraoperative assistance in clinic. Raman spectroscopy has made many exciting achievements in the study of male reproductive system. In this review, we summarized literatures about the application and progress of Raman spectroscopy in male reproductive system from PubMed and Ovid databases, using MeSH terms associated to Raman spectroscopy, prostate, testis, seminal plasma and sperm. The existing challenges and development opportunities were also discussed and prospected.

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky-Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky-Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

In Vitro Spectroscopy-Based Profiling of Urothelial Carcinoma: A Fourier Transform Infrared and Raman Imaging Study

Simple Summary

The mortality and recurrence associated with urothelial carcinoma are high. High heterogeneity makes it hard to detect with currently available methods such as cytology and histology. We propose here vibrational spectroscopic imaging as an additional diagnostic tool for the classification of bladder cancer. Our study revealed that chemism-induced spectroscopic features of the cancer cells of various stages and invasiveness were specifically detected.

Abstract

Markers of bladder cancer cells remain elusive, which is a major cause of the low recognition of this malignant neoplasm and its recurrence. This implies an urgent need for additional diagnostic tools which are based on the identification of the chemism of bladder cancer. In this study, we employed label-free techniques of molecular imaging—Fourier Transform Infrared and Raman spectroscopic imaging—to investigate bladder cancer cell lines of various invasiveness (T24a, T24p, HT-1376, and J82). The urothelial HCV-29 cell line was the healthy control. Specific biomolecules discriminated spatial distribution of the nucleus and cytoplasm and indicated the presence of lipid bodies and graininess in some cell lines. The most prominent discriminators are the total content of lipids and sugar moieties as well as the presence of glycogen and other carbohydrates, un/saturated lipids, cytochromes, and a level of S-S bridges in proteins. The combination of the obtained hyperspectral database and chemometric methods showed a clear differentiation of each cell line at the level of the nuclei and cytoplasm and pointed out spectral signals which differentiated bladder cancer cells. Registered spectral markers correlated with biochemical composition changes can be associated with pathogenesis and potentially used for the diagnosis of bladder cancer and response to experimental therapies.

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

To explore the effect in nasopharyngeal carcinoma (NPC) cells after treatment with chemodrugs, Raman profiles were characterized by laser tweezer Raman spectroscopy. Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment include changes in protein structure (e.g. 1655 cm^-1), changes in DNA/RNA content and structure (e.g. 830 cm^-1), destruction of DNA/RNA base pairs (e.g. 785 cm^-1), and reduction in lipids (e.g. 970 cm^-1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

Raman Trapping Microscopy for Non-invasive Analysis of Biological Samples

Raman microscopy is an emerging tool in biomedicine. It provides label-free and non-invasive analysis of biological cells. Due to its high biochemical specificity, Raman spectroscopy can be used to acquire spectral fingerprints that allow characterizing cells types and states. Here, we present a methodological approach for implementing Raman microscopy in skin cell measurements. Raman spectra can clearly identify keratinocytes, fibroblasts, and melanocytes cells that are involved in the production of autologous skin grafts. Consequently, Raman microscopy is a promising tool that can be used to analyze single cells and to test the quality of therapeutic cell products.

Multivariate Analysis of Difference Raman Spectra of the Irradiated Nucleus and Cytoplasm Region of SH-SY5Y Human Neuroblastoma Cells

Previous works showed that spatially resolved Raman spectra of cytoplasm and nucleus region of single cells exposed to X-rays evidence different features. The present work aims to introduce a new approach to profit from these differences to deeper investigate X-ray irradiation effects on single SH-SY5Y human neuroblastoma cells. For this aim, Raman micro-spectroscopy was performed in vitro on single cells after irradiation by graded X-ray doses (2, 4, 6, 8 Gy). Spectra from nucleus and cytoplasm regions were selectively acquired. The examination by interval Principal Component Analysis (i-PCA) of the difference spectra obtained by subtracting each cytoplasm-related spectrum from the corresponding one detected at the nucleus enabled us to reveal the subtle modifications of Raman features specific of different spatial cell regions. They were discussed in terms of effects induced by X-ray irradiation on DNA/RNA, lipids, and proteins. The proposed approach enabled us to evidence some features not outlined in previous investigations.

An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Combination of High-Resolution Optical Coherence Tomography and Raman Spectroscopy for Improved Staging and Grading in Bladder Cancer

We present a combination of optical coherence tomography (OCT) and Raman spectroscopy (RS) for improved diagnosis and discrimination of different stages and grades of bladder cancer ex vivo by linking the complementary information provided by these two techniques. Bladder samples were obtained from biopsies dissected via transurethral resection of the bladder tumor (TURBT). As OCT provides structural information rapidly, it was used as a red-flag technology to scan the bladder wall for suspicious lesions with the ability to discriminate malignant tissue from healthy urothelium. Upon identification of degenerated tissue via OCT, RS was implemented to determine the molecular characteristics via point measurements at suspicious sites. Combining the complementary information of both modalities allows not only for staging, but also for differentiation of low-grade and high-grade cancer based on a multivariate statistical analysis. OCT was able to clearly differentiate between healthy and malignant tissue by tomogram inspection and achieved an accuracy of 71% in the staging of the tumor, from pTa to pT2, through texture analysis followed by k-nearest neighbor classification. RS yielded an accuracy of 93% in discriminating low-grade from high-grade lesions via principal component analysis followed by k-nearest neighbor classification. In this study, we show the potential of a multi-modal approach with OCT for fast pre-screening and staging of cancerous lesions followed by RS for enhanced discrimination of low-grade and high-grade bladder cancer in a non-destructive, label-free and non-invasive way.

Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound

Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.

Raman micro-spectroscopy applied to treatment resistant and sensitive human ovarian cancer cells

Despite the many advances intended to enhance the response to treatment, the survival rate of patients with ovarian cancer has only marginally improved in the past few decades. One major cause for this, is the lack of diagnostics for platinum-resistant disease. The goal of this study was to determine whether Raman micro-spectroscopy in conjunction with multivariate statistical analysis could discriminate between chemically fixed cisplatin-resistant (A2780cp) and cisplatin-sensitive (A2780s) human ovarian carcinoma cells. Raman spectra collected from individual cells were pre-processed and subsequently analyzed with Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA). Statistically significant differences (P <  0.0001) were observed between the Raman spectra of A2780s and A2780cp cells. A diagnostic accuracy of 82% was obtained using the PCA-LDA classifier model for the discrimination between the A2780s and A2780cp cells. The loading plot analysis suggests that relative increases in proteins and glutathione in the cisplatin-resistant cells compared to the cisplatin-sensitive cells are most likely the major source of discrimination between the two types of cells. These results support the potential application of Raman spectroscopy in the identification of chemo-resistant tumors prior to treatment.

Noninvasive Diagnosis of High-Grade Urothelial Carcinoma in Urine by Raman Spectral Imaging

The current gold standard for the diagnosis of bladder cancer is cystoscopy, which is invasive and painful for patients. Therefore, noninvasive urine cytology is usually used in the clinic as an adjunct to cystoscopy; however, it suffers from low sensitivity. Here, a novel noninvasive, label-free approach with high sensitivity for use with urine is presented. Coherent anti-Stokes Raman scattering imaging of urine sediments was used in the first step for fast preselection of urothelial cells, where high-grade urothelial cancer cells are characterized by a large nucleus-to-cytoplasm ratio. In the second step, Raman spectral imaging of urothelial cells was performed. A supervised classifier was implemented to automatically differentiate normal and cancerous urothelial cells with 100% accuracy. In addition, the Raman spectra not only indicated the morphological changes that are identified by cytology with hematoxylin and eosin staining but also provided molecular resolution through the use of specific marker bands. The respective Raman marker bands directly show a decrease in the level of glycogen and an increase in the levels of fatty acids in cancer cells as compared to controls. These results pave the way for "spectral" cytology of urine using Raman microspectroscopy.

Automated analysis of single cells using Laser Tweezers Raman Spectroscopy

In recent years, significant progress has been made into the label-free detection and discrimination of individual cancer cells using Laser Tweezers Raman Spectroscopy (LTRS). However, the majority of examples reported have involved manual trapping of cells, which is time consuming and may lead to different cell lines being analysed in discrete batches. A simple, low-cost microfluidic flow chamber is introduced which allows single cells to be optically trapped and analysed in an automated fashion, greatly reducing the level of operator input required. Two implementations of the flow chamber are discussed here; a basic single-channel device in which the fluid velocity is controlled manually, and a dual-channel device which permits the automated capture and analysis of multiple cell lines with no operator input. Results are presented for the discrimination of live epithelial prostate cells and lymphocytes, together with a consideration of the consequences of traditional 'batch analysis' typically used for LTRS of live cells.

Raman activated cell sorting

Single cell Raman spectra (SCRS) are intrinsic biochemical profiles and 'chemical images' of single cells which can be used to characterise phenotypic changes, physiological states and functions of cells. On the base of SCRS, Raman activated cell sorting (RACS) provides a label-free cell sorting approach, which can link single cells to their chemical or phenotypic profiles. Overcoming naturally weak Raman signals, establishing Raman biomarker as sorting criteria to RACS and improving specific sorting technology are three challenges of developing RACS. Advances on Raman spectroscopy such as stimulated Raman scattering (SRS) and pre-screening helped to increase RACS sorting speed. Entire SCRS can be characterised using pattern recognition methods, and specific Raman bands can be extracted as biomarkers for RACS. Recent advances on cell sorting technologies based on microfluidic device and surface-ejection enable accurate and reliable single cell sorting from complex samples. A high throughput RACS will be achievable in near future by integrating fast Raman detection system such as SRS with microfluidic RACS and Raman activated cell ejection (RACE).

Vibrational spectroscopic methods for cytology and cellular research

The use of vibrational spectroscopy, FTIR and Raman, for cytology and cellular research has the potential to revolutionise the approach to cellular analysis. Vibrational spectroscopy is non-destructive, simple to operate and provides direct information. Importantly it does not require expensive exogenous labels that may affect the chemistry of the cell under analysis. In addition, the advent of spectroscopic microscopes provides the ability to image cells and acquire spectra with a subcellular resolution. This introductory review focuses on recent developments within this fast paced field and highlights potential for the future use of FTIR and Raman spectroscopy. We particularly focus on the development of live cell research and the new technologies and methodologies that have enabled this.

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.

Raman spectroscopy for medical diagnostics--From in-vitro biofluid assays to in-vivo cancer detection

Raman spectroscopy is an optical technique based on inelastic scattering of light by vibrating molecules and can provide chemical fingerprints of cells, tissues or biofluids. The high chemical specificity, minimal or lack of sample preparation and the ability to use advanced optical technologies in the visible or near-infrared spectral range (lasers, microscopes, fibre-optics) have recently led to an increase in medical diagnostic applications of Raman spectroscopy. The key hypothesis underpinning this field is that molecular changes in cells, tissues or biofluids, that are either the cause or the effect of diseases, can be detected and quantified by Raman spectroscopy. Furthermore, multivariate calibration and classification models based on Raman spectra can be developed on large "training" datasets and used subsequently on samples from new patients to obtain quantitative and objective diagnosis. Historically, spontaneous Raman spectroscopy has been known as a low signal technique requiring relatively long acquisition times. Nevertheless, new strategies have been developed recently to overcome these issues: non-linear optical effects and metallic nanoparticles can be used to enhance the Raman signals, optimised fibre-optic Raman probes can be used for real-time in-vivo single-point measurements, while multimodal integration with other optical techniques can guide the Raman measurements to increase the acquisition speed and spatial accuracy of diagnosis. These recent efforts have advanced Raman spectroscopy to the point where the diagnostic accuracy and speed are compatible with clinical use. This paper reviews the main Raman spectroscopy techniques used in medical diagnostics and provides an overview of various applications.

Raman spectroscopic studies on screening of myopathies

Myopathies are among the major causes of mortality in the world. There is no complete cure for this heterogeneous group of diseases, but a sensitive, specific, and fast diagnostic tool may improve therapy effectiveness. In this study, Raman spectroscopy is applied to discriminate between muscle mutants in Drosophila on the basis of associated changes at the molecular level. Raman spectra were collected from indirect flight muscles of mutants, upheld(1) (up(1)), heldup(2) (hdp(2)), myosin heavy chain(7) (Mhc(7)), actin88F(KM88) (Act88F(KM88)), upheld(101) (up(101)), and Canton-S (CS) control group, for both 2 and 12 days old flies. Difference spectra (mutant minus control) of all the mutants showed an increase in nucleic acid and β-sheet and/or random coil protein content along with a decrease in α-helix protein. Interestingly, the 12th day samples of up(1) and Act88F(KM88) showed significantly higher levels of glycogen and carotenoids than CS. A principal components based linear discriminant analysis classification model was developed based on multidimensional Raman spectra, which classified the mutants according to their pathophysiology and yielded an overall accuracy of 97% and 93% for 2 and 12 days old flies, respectively. The up(1) and Act88F(KM88) (nemaline-myopathy) mutants form a group that is clearly separated in a linear discriminant plane from up(101) and hdp(2) (cardiomyopathy) mutants. Notably, Raman spectra from a human sample with nemaline-myopathy formed a cluster with the corresponding Drosophila mutant (up(1)). In conclusion, this is the first demonstration in which myopathies, despite their heterogeneity, were screened on the basis of biochemical differences using Raman spectroscopy.

Discrimination of bladder cancer cells from normal urothelial cells with high specificity and sensitivity: combined application of atomic force microscopy and modulated Raman spectroscopy

Atomic force microscopy (AFM) and modulated Raman spectroscopy (MRS) were used to discriminate between living normal human urothelial cells (SV-HUC-1) and bladder tumour cells (MGH-U1) with high specificity and sensitivity. MGH-U1 cells were 1.5-fold smaller, 1.7-fold thicker and 1.4-fold rougher than normal SV-HUC-1 cells. The adhesion energy was 2.6-fold higher in the MGH-U1 cells compared to normal SV-HUC-1 cells, which possibly indicates that bladder tumour cells are more deformable than normal cells. The elastic modulus of MGH-U1 cells was 12-fold lower than SV-HUC-1 cells, suggesting a higher elasticity of the bladder cancer cell membranes. The biochemical fingerprints of cancer cells displayed a higher DNA and lipid content, probably due to an increase in the nuclear to cytoplasm ratio. Normal cells were characterized by higher protein contents. AFM studies revealed a decrease in the lateral dimensions and an increase in thickness of cancer cells compared to normal cells; these studies authenticate the observations from MRS. Nanostructural, nanomechanical and biochemical profiles of bladder cells provide qualitative and quantitative markers to differentiate between normal and cancerous cells at the single cellular level. AFM and MRS allow discrimination between adhesion energy, elasticity and Raman spectra of SV-HUC-1 and MGH-U1 cells with high specificity (83, 98 and 95%) and sensitivity (97, 93 and 98%). Such single-cell-level studies could have a pivotal impact on the development of AFM-Raman combined methodologies for cancer profiling and screening with translational significance.

Applications of Raman spectroscopy to the urinary bladder for cancer diagnostics

Biomolecular changes associated with cancer progression can be identified using Raman spectroscopy, allowing for this technique to be utilized as a non-invasive tool for the diagnosis of bladder cancer. Applications of Raman spectroscopy for diagnostics in real-time have consistently produced higher sensitivities and specificities than current clinical methods. This technique can be applied

Highlighting a need to distinguish cell cycle signatures from cellular responses to chemotherapeutics in SR-FTIR spectroscopy

Previous research has seen difficulties in establishing clear discrimination by principal component analysis (PCA) between drug-treated cells analysed by single point SR-FTIR spectroscopy, relative to multisampling cell monolayers by conventional FTIR. It is suggested that the issue arises due to signal mixing between cellular-response signatures and cell cycle phase contributions in individual cells. Consequently, chemometric distinction of cell spectra treated with multiple drugs is difficult even with supervised methods. In an effort to separate cell cycle chemistry from cellular response chemistry in the spectra, renal carcinoma cells were stained with propidium iodide and fluorescent-activated cell sorted (FACS) after exposure to a number of chemotherapeutic compounds; 5-fluorouracil (5FU) and a set of novel gold-based experimental compounds. The cell spectra were analysed separately by PCA in G(1), S or G(2)/M phase. The mode of action of established drug 5FU, known to disrupt S phase, was confirmed by FACS analysis. The chemical signature of 5FU-treated cells discriminated against both the control and gold-compound (KF0101)-treated cell spectra, suggesting a different mode of action due to a difference in cellular response.

Raman microspectroscopy: shining a new light on reproductive medicine

BACKGROUND The last 20 years have seen an enormous upsurge in the number of publications reporting findings obtained by Raman spectroscopy, a non-invasive, non-destructive method which uses the inelastic scattering of light to provide a 'fingerprint' of the sample's chemical composition and constituents. Long neglected because of practical difficulties, the technique has been transformed by recent technological advances into a powerful analytical tool capable of opening avenues of investigation that were previously out of the reach of biomedical scientists. Beyond introducing the approach and describing its relative merits and weaknesses, the aim of this review is to provide a spur for discussion of what may become an invaluable tool for biomedical investigations. METHODS A comprehensive review of the literature was conducted searching PubMed and Ovid databases using numerous MeSH terms associated with reproductive medicine. Furthermore, the reference lists of all reported literature were explored. The searches were restricted to English language articles published in the last 50 years. RESULTS Beginning with simple characterizations of biologically and medically important substances, aided by increasing technological sophistication, the use of Raman spectroscopy in biomedicine has quickly expanded to the investigation of complex biochemical interactions, the assessment of organelles and now the evaluation of living cells and tissue. The first Raman investigations of reproductive organs were primarily oncological in nature; however, the past few years have seen an increase in the application of the technique for the assessment and evaluation of both male and female gametes. In particular, progress has been made in the characterization, identification and localization of sperm nuclear DNA damage. CONCLUSIONS The use of Raman spectroscopy has already provided many tantalizing glimpses into the potential that the technique has to answer many of the unresolved issues in investigative and therapeutic reproductive medicine. However, without stringent assessment and the clear representation of the methods' findings, their true meaning cannot be revealed nor should any conclusions be hastily derived. For the potential of Raman microspectroscopy to be truly realized, the dependability and reliability of the technique and its results can only be ascertained by multidisciplinary collaborations that undertake carefully conducted, controlled and analysed studies.

FTIR microspectroscopy of selected rare diverse sub-variants of carcinoma of the urinary bladder

Urothelial carcinomas of the bladder are a heterogeneous group of tumours, although some histological sub-variants are rare and sparsely reported in the literature. Diagnosis of sub-variants from conventional urothelial carcinoma can be challenging, as they may mimic the morphology of other malignancies or benign tumours and therefore their distinction is important. For the first time, the spectral pathology of some of these sub-variants has been documented by infrared microspectroscopy and an attempt made to profile their biochemistry. It is important not only to identify and separate the cancer-associated epithelial tissue spectra from common tissue features such as stroma or blood, but also to detect the signatures of tumour sub-variants. As shown, their spectroscopic signals can change dramatically as a consequence of differentiation. Example cases are discussed and compared with histological evaluations.

Recent advances in laser tweezers Raman spectroscopy (LTRS) for label-free analysis of single cells

Laser tweezers Raman spectroscopy (LTRS), a technique that integrates optical tweezers with confocal Raman spectroscopy, is a variation of micro-Raman spectroscopy that enables the manipulation and biochemical analysis of single biological particles in suspension. This article provides an overview of the LTRS method, with an emphasis on highlighting recent advances over the past several years in the development of the technology and several new biological and biomedical applications that have been demonstrated. A perspective on the future developments of this powerful cytometric technology will also be presented.

Raman spectroscopy in biomedicine - non-invasive in vitro analysis of cells and extracellular matrix components in tissues

Raman spectroscopy is an established laser-based technology for the quality assurance of pharmaceutical products. Over the past few years, Raman spectroscopy has become a powerful diagnostic tool in the life sciences. Raman spectra allow assessment of the overall molecular constitution of biological samples, based on specific signals from proteins, nucleic acids, lipids, carbohydrates, and inorganic crystals. Measurements are non-invasive and do not require sample processing, making Raman spectroscopy a reliable and robust method with numerous applications in biomedicine. Moreover, Raman spectroscopy allows the highly sensitive discrimination of bacteria. Rama spectra retain information on continuous metabolic processes and kinetics such as lipid storage and recombinant protein production. Raman spectra are specific for each cell type and provide additional information on cell viability, differentiation status, and tumorigenicity. In tissues, Raman spectroscopy can detect major extracellular matrix components and their secondary structures. Furthermore, the non-invasive characterization of healthy and pathological tissues as well as quality control and process monitoring of in vitro-engineered matrix is possible. This review provides comprehensive insight to the current progress in expanding the applicability of Raman spectroscopy for the characterization of living cells and tissues, and serves as a good reference point for those starting in the field.

Introduction: why analyze single cells?

Powerful methods in molecular biology are abundant; however, in many fields including hematology, stem cell biology, tissue engineering, and cancer biology, data from tools and assays that analyze the average signals from many cells may not yield the desired result because the cells of interest may be in the minority-their behavior masked by the majority-or because the dynamics of the populations of interest are offset in time. Accurate characterization of samples with high cellular heterogeneity may only be achieved by analyzing single cells. In this chapter, we discuss the rationale for performing analyses on individual cells in more depth, cover the fields of study in which single-cell behavior is yielding new insights into biological and clinical questions, and speculate on how single-cell analysis will be critical in the future.

Analysis of single eukaryotic cells using Raman Tweezers

Raman Tweezers is a technique that combines optical trapping with Raman spectroscopy and has enabled the spectroscopic analysis of single cells. Applications of this technique include the identification and discrimination of different types of cells, including healthy and non-healthy cells (e.g. cancer cells). In addition, the interaction of cells with stimuli, e.g. drugs, can also be studied on a single-cell basis. Herein, a generic protocol for the analysis of fixed and living single eukaryotic cells is described, including the considerations required to build a Raman Tweezers systems.

Modulated Raman spectroscopy for enhanced identification of bladder tumor cells in urine samples

Standard Raman spectroscopy (SRS) is a noninvasive technique that is used in the biomedical field to discriminate between normal and cancer cells. However, the presence of a strong fluorescence background detracts from the use of SRS in real-time clinical applications. Recently, we have reported a novel modulated Raman spectroscopy (MRS) technique to extract the Raman spectra from the background. In this paper, we present the first application of MRS to the identification of human urothelial cells (SV-HUC-1) and bladder cancer cells (MGH) in urine samples. These results are compared to those obtained by SRS. Classification using the principal component analysis clearly shows that MRS allows discrimination between Raman spectra of SV-HUC-1 and MGH cells with high sensitivity (98%) and specificity (95%). MRS is also used to distinguish between SV-HUC-1 and MGH cells after exposure to urine for up to 6 h. We observe a marked change in the MRS of SV-HUC-1 and MGH cells with time in urine, indicating that the conditions of sample collection will be important for the application of this methodology to clinical urine samples.

Light forces the pace: optical manipulation for biophotonics

The biomedical sciences have benefited immensely from photonics technologies in the last 50 years. This includes the application of minute forces that enable the trapping and manipulation of cells and single molecules. In terms of the area of biophotonics, optical manipulation has made a seminal contribution to our understanding of the dynamics of single molecules and the microrheology of cells. Here we present a review of optical manipulation, emphasizing its impact on the areas of single-molecule studies and single-cell biology, and indicating some of the key experiments in the fields.

Factors influencing the discrimination and classification of prostate cancer cell lines by FTIR microspectroscopy

In this study we obtained Fourier transform infrared (FTIR) spectra of fixed prostate cell lines of differing types as well as the primary epithelial cells from benign prostatic hyperplasia (BPH). Results showed that by using multivariate chemometric analysis it was possible to discriminate and classify these cell lines, which gave rise to sensitivity and specificity values of >94% and >98%, respectively. Following on from these results the possible influences of different factors on the discrimination and classification of the prostate cell lines were examined. Firstly, the effect of using different growth media during cell culturing was investigated, with results indicating that this did not influence chemometric discrimination. Secondly, differences in the nucleus-to-cytoplasm (N/C) ratio were examined, and it was concluded that this factor was not the main reason for the discrimination and classification of the prostate cancer (CaP) cell lines. In conclusion, given the fact that neither growth media nor N/C ratio could totally explain the classification it is likely that actual biochemical differences between the cell lines is the major contributing factor.