Classification of fixed urological cells using Raman tweezers

Harvey, Tim J; Hughes, Caryn; Ward, Andrew D.; Faria, Elsa Correia; Henderson, Alex; Clarke, Noel W.; Brown, Michael D.; Snook, Richard D.; Gardner, Peter

doi:10.1002/jbio.200810061

Cited by 57 publications

(62 citation statements)

References 58 publications

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“…Also, one can apply thresholding methods in the compressed domain based on the SD of data in the compressed domain to eliminate such outliers [41]. If a given spectrum is very different from all the other spectra in the data set with respect to a particular variable(s) in the compressed domain i.e.…”

Section: Outlier Removalmentioning

confidence: 99%

“…So, these score plots can be used for outlier detection, identification of trends, groups, exploration of patterns etc. [38,41,45,47,[57][58][59]. This is illustrated by performing PCA analysis (Software used in this work: PLS and MIA Tool Box from Eigenvector Inc. USA with MATLAB (Ver.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…In the new dimensional space, each observation would have fewer variables (dimensionality reduction) and at the same time observations belonging to the same class will form lumps (clusters) and each cluster would be clearly differentiated from the other [41,70].…”

Section: A) Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

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Review of multidimensional data processing approaches for Raman and infrared spectroscopy

et al. 2015

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Raman and Infrared (IR) spectroscopies provide information about the structure, functional groups and environment of the molecules in the sample. In combination with a microscope, these techniques can also be used to study molecular distributions in heterogeneous samples. Over the past few decades Raman and IR microspectroscopy based techniques have been extensively used to understand fundamental biology and responses of living systems under diverse physiological and pathological conditions. The spectra from biological systems are complex and diverse, owing to their heterogeneous nature consisting of bio-molecules such as proteins, lipids, nucleic acids, carbohydrates etc. Sometimes minor differences may contain critical information. Therefore, interpretation of the results obtained from Raman and IR spectroscopy is difficult and to overcome these intricacies and for deeper insight we need to employ various data mining methods. These methods must be suitable for handling large multidimensional data sets and for exploring the complete spectral information simultaneously. The effective implementation of these multivariate data analysis methods requires the pretreatment of data. The preprocessing of raw data helps in the elimination of noise (unwanted signals) and the enhancement of discriminating features. This review provides an outline of the state-of-the-art data processing tools for multivariate analysis and the various preprocessing methods that are widely used in Raman and IR spectroscopy including imaging for better qualitative and quantitative analysis of biological samples.

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Section: Outlier Removalmentioning

confidence: 99%

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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Review of multidimensional data processing approaches for Raman and infrared spectroscopy

et al. 2015

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“…Cells can also be readily distinguished on the basis of their phenotypic differences and this has been used to discriminate between bone [21] and lung cells, [22] and more importantly in terms of disease diagnosis between cancerous and non-cancerous cells. [23,24] This was not completed with a clinical sample but the authors highlighted the desire to extend analysis into urine.…”

Section: Raman Spectroscopy and In Vitro Cell Analysismentioning

confidence: 99%

Surface enhanced Raman spectroscopy (SERS): Potential applications for disease detection and treatment

McAughtrie

Faulds

Graham

2014

Journal of Photochemistry and Photobiology C: Photochemistry Re

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This version is available at https://strathprints.strath.ac.uk/49189/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the +44 (0) Highlights: A range of diseases can be detected by Raman and SERS in vitro, ex vivo and in vivo  In vivo multiplexed detection of cancer was achieved  SERS is also a key step in disease treatment  Studies must be further extended to a physiologically relevant medium and in vivo  The potential exists for the application of the techniques in a clinical setting

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“…3 RS has considerable potential as a diagnostic tool for the classification of normal and cancer cells due to its noninvasive nature and capability to predict pathological diagnosis in real time. 4 However, the use of this spectroscopic technique is dramatically limited by the presence of strong fluorescence signals.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2011

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Abstract. 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. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Classification of fixed urological cells using Raman tweezers

Cited by 57 publications

References 58 publications

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

Surface enhanced Raman spectroscopy (SERS): Potential applications for disease detection and treatment

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

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