Histology Verification Demonstrates That Biospectroscopy Analysis of Cervical Cytology Identifies Underlying Disease More Accurately than Conventional Screening:Removing the Confounder of Discordance

Background Subjective visual assessment of cervical cytology is flawed, and this can manifest itself by inter- and intra-observer variability resulting ultimately in the degree of discordance in the grading categorisation of samples in screening vs. representative histology. Biospectroscopy methods have been suggested as sensor-based tools that can deliver objective assessments of cytology. However, studies to date have been apparently flawed by a corresponding lack of diagnostic efficiency when samples have previously been classed using cytology screening. This raises the question as to whether categorisation of cervical cytology based on imperfect conventional screening reduces the diagnostic accuracy of biospectroscopy approaches; are these latter methods more accurate and diagnose underlying disease? The purpose of this study was to compare the objective accuracy of infrared (IR) spectroscopy of cervical cytology samples using conventional cytology vs. histology-based categorisation. Methods Within a typical clinical setting, a total of n = 322 liquid-based cytology samples were collected immediately before biopsy. Of these, it was possible to acquire subsequent histology for n = 154. Cytology samples were categorised according to conventional screening methods and subsequently interrogated employing attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. IR spectra were pre-processed and analysed using linear discriminant analysis. Dunn?s test was applied to identify the differences in spectra. Within the diagnostic categories, histology allowed us to determine the comparative efficiency of conventional screening vs. biospectroscopy to correctly identify either true atypia or underlying disease. Results Conventional cytology-based screening results in poor sensitivity and specificity. IR spectra derived from cervical cytology do not appear to discriminate in a diagnostic fashion when categories were based on conventional screening. Scores plots of IR spectra exhibit marked crossover of spectral points between different cytological categories. Although, significant differences between spectral bands in different categories are noted, crossover samples point to the potential for poor specificity and hampers the development of biospectroscopy as a diagnostic tool. However, when histology-based categories are used to conduct analyses, the scores plot of IR spectra exhibit markedly better segregation. Conclusions Histology demonstrates that ATR-FTIR spectroscopy of liquid-based cytology identifies the presence of underlying atypia or disease missed in conventional cytology screening. This study points to an urgent need for a future biospectroscopy study where categories are based on such histology. It will allow for the validation of this approach as a screening tool

Identification of benzoapyrene-induced cell cycle-associated alterations in MCF-7 cells using infrared spectroscopy with computational analysis

Chemical contaminants, such as benzoapyrene (BaP), may modulate transcriptional responses in cells via the activation of aryl hydrocarbon receptor (AhR) or through responses to DNA damage following adduct formation. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy can be employed in a non-destructive fashion to interrogate the biochemical signature of cells via generation of infrared (IR) spectra. By applying to generated spectral datasets subsequent computational approaches such as principal component analysis plus linear discriminant analysis (PCA-LDA), derived data reduction is achieved to facilitate the visualization of wavenumber-related alterations in target cells. Discriminating spectral variables might be associated with lipid or glycogen content, conformational protein changes and phosphorylation, and structural alterations in DNA/RNA. Using this approach, we investigated the dose-related effects of BaP in MCF-7 cells concentrated in S- or G{$_0$}/G{$_1$}-phase. Our findings identified that in PCA-LDA scores plots a clear segregation of IR spectra was evident, with the major spectral alterations associated with DNA/RNA, secondary protein structure and lipid. Dose-related effects were observed and even with exposures as low as 10{$^-$}{$^9$} M BaP, significant (P {$\leq$} 0.001) separation of BaP-treated vs. vehicle control cells was noted. ATR-FTIR spectroscopy with computational analysis is a novel approach to identify the effects of environmental contaminants in target cells

Determination Using Synchrotron Radiation-Based Fourier Transform Infrared Microspectroscopy of Putative Stem Cells in Human Adenocarcinoma of the Intestine: Corresponding Benign Tissue as a Template

The epithelial-cell layer lining the two morphologically and functionally distinct segments of the mammalian intestinal tract, small intestine, and colon is constantly being renewed. This renewal is necessitated by a harsh lumen environment and is hypothesized to be driven by a small population of stem cells (SCs) that are believed to reside at the base of intestinal crypts. A lack of specific markers has hampered previous attempts to identify their exact location. We obtained tissue sections containing small intestine and colon crypts derived from normal (benign) or adenocarcinoma (AC) human intestine. The samples were floated onto BaF2 windows and analyzed using synchrotron radiation-based Fourier transform infrared microspectroscopy via an aperture size of 10 × 10 \ensuremathμm. Derived infrared (IR) spectral data was then analyzed using principal component analysis and/or linear discriminant analysis. Hypothesized cell types (as a function of aperture location along the length of individual crypts) within benign crypts were classed based on exploratory unsupervised IR spectral point clustering. Scores plots derived from individual small intestine crypts consistently generated one or two distinct spectra that clustered away from the remaining cell categories; these were retrospectively classed as ?distinct base region? spectra. In these plots, a clear progression of locations along crypt lengths designated as from putative stem cells (SCs) to transit-amplifying (TA) cells to terminally differentiated (TD) cells was observed in benign small intestine and colon crypts. This progression of spectral points was crypt specific, pointing away from a unifying cell lineage model in human intestinal crypts. On comparison of AC-derived spectra versus corresponding benign, a subpopulation of AC-derived spectra suggested a putative SC-like spectral fingerprint; remaining IR spectra were classed as exhibiting TA cell-like or TD cell-like spectral characteristics. These observations could point to a cancer SC phenotype; an approach capable of identifying their in situ location has enormous therapeutic applications

Classification of agents using Syrian hamster embryo (SHE) cell transformation assay (CTA) with ATR-FTIR spectroscopy and multivariate analysis

The Syrian hamster embryo (SHE) cell transformation assay (pH 6.7) has a reported sensitivity of 87% and specificity of 83%, and an overall concordance of 85% with in vivo rodent bioassay data. To date, the SHE assay is the only in vitro assay that exhibits multistage carcinogenicity. The assay uses morphological transformation, the first stage towards neoplasm, as an endpoint to predict the carcinogenic potential of a test agent. However, scoring of morphologically transformed SHE cells is subjective. We treated SHE cells grown on low-E reflective slides with 2,6-diaminotoluene, N-nitroso-N-ethylnitroguanidine, N-nitroso-N-methylurea, N-nitroso-N-ethylurea, EDTA, dimethyl sulphoxide (DMSO; vehicle control), methyl methanesulfonate, benzoepyrene, mitomycin C, ethyl methanesulfonate, ampicillin or five different concentrations of benzoapyrene. Macroscopically visible SHE colonies were located on the slides and interrogated using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy acquiring five spectra per colony. The acquired IR data were analysed using Fisher?s linear discriminant analysis (LDA) followed by principal component analysis (PCA)-LDA cluster vectors to extract major and minor discriminating wavenumbers for each treatment class. Each test agent vs. DMSO and treatment-induced transformed cells vs. corresponding non-transformed were classified by a unique combination of major and minor discriminating wavenumbers. Alterations associated with Amide I, Amide II, lipids and nucleic acids appear to be important in segregation of classes. Our findings suggest that a biophysical approach of ATR-FTIR spectroscopy with multivariate analysis could facilitate a more objective interrogation of SHE cells towards scoring for transformation and ultimately employing the assay for risk assessment of test agents

Spectral variations amongst histology of HSIL cytology.

<p>Spectral variations amongst histology of HSIL cytology.</p

Analysis of infrared (IR) spectra from cervical cytology samples with subsequent histology (<i>n</i> = 154). LD1 and LD2 were used to plot the confidence ellipses for cancer (violet), high-grade lesion (red), low-grade lesion (blue) and Negative (green).

<p>(<b>A</b>) Comparison of linear discriminant analysis (LDA) scores plots of IR spectra according to conventional cytology grades: Negative <i>vs.</i> LSIL <i>vs.</i> HSIL; (<b>B</b>) Comparison of LDA scores plots according to subsequent histology grades: Normal <i>vs.</i> CIN1 <i>vs.</i> CIN2+ <i>vs.</i> Cancer. In the next two figures, the scores plot for discordant cytology and histology were depicted by double-coloured symbols, with the outer colour determining the cytology grade and the inner colour determining the histology grade; (<b>C</b>) Comparison of LDA scores plots derived from IR spectra analysed using cytology-based categories; and, (<b>D</b>) Comparison of LDA scores plots derived from IR spectra analysed using subsequent histology-based categories.</p

Flow diagram of sample collection, sample preparation, spectroscopy, pre-processing of spectra and feature extraction.

<p>After sampling of the transformation zone (TZ), the cells are suspended in ThinPrep solution. An aliquot of 6 ml of cytology specimen was centrifuged at 1,500 rpm for 5 min, after which the supernatant was then aspirated. The remaining cell pellet was re-suspended in 3 ml autoclaved distilled H₂O and centrifuged at 1500 rpm for 5 min, and the supernatant was again removed. This wash step was repeated three times and, the resulting cell pellet was then re-suspended in 0.5 ml autoclaved distilled H₂O and transferred to a low-E glass microscope slide. Slides were allowed to air-dry and stored in a desiccator until analysis. Infrared (IR) spectra were obtained using a Bruker TENSOR 27 FTIR spectrometer with Helios ATR attachment containing a diamond crystal. Using a CCTV camera, spectra were acquired from 10 independent sample locations. The datasets were processed using MATLAB R2010a software (Mathworks Inc, Natick, MA, USA) with the IRootLab toolbox (<a href="http://irootlab.googlecode.com" target="_blank">http://irootlab.googlecode.com</a>). IR spectra were pre-processed in three steps, which include cutting, baseline correction and normalization. Feature extraction was carried out using linear discriminant analysis, which allowed for segregation of classes and peak detection plots allowed identification of biomarkers.</p

Comparing various cytology grade spectra using multivariate analysis.

<p>(<b>A</b>) Comparison of spectral points for Negative <i>vs.</i> LSIL <i>vs.</i> HSIL (closed symbols) with HPV <i>vs.</i> ASCUS <i>vs.</i> Cancer (open symbols). Scores plots with confidence ellipse showing the relationship of (<b>B</b>) cancer with HSIL; (<b>C</b>) ASCUS with LSIL; and, (<b>D</b>) HPV-like features with Negative cytology; (<b>E</b>) Peak detection plot following linear discriminant analysis (LDA) showing top six wavenumbers responsible for segregation of Negative cytology from ASCUS, HPV and Cancer cytology.</p

Infrared (IR) spectral points of cytology in relation to normal histology.

<p>(<b>A</b>) IR spectra for normal histology classified according to the screening cytology results and in relation to the confidence ellipses of histology grades; and, (<b>B</b>) Analysis of IR spectra from normal cytology classified according to subsequent histology.</p

Top six wavenumbers responsible for separating negative cytology from HPV, ASCUS and Cancer.

<p>Top six wavenumbers responsible for separating negative cytology from HPV, ASCUS and Cancer.</p