Article pubs.acs.org/ac
Transmission FT-IR Chemical Imaging on Glass Substrates: Applications in Infrared Spectral Histopathology Paul Bassan,† Joe Mellor,‡ Jonathan Shapiro,‡ Kaye J Williams,§ Michael P. Lisanti,∥ and Peter Gardner*,† †
Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K. School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL, U.K. § Manchester Pharmacy School, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K. ∥ Manchester Breast Centre and Breakthrough Breast Cancer Research Unit, Paterson Institute for Cancer Research, School of Cancer, Enabling Sciences and Technology, University of Manchester, Manchester M13 9PL, U.K. ‡
ABSTRACT: Fourier transform-infrared (FT-IR) chemical imaging in transmission mode has traditionally been performed on expensive mid-IR transparent windows such as barium/ calcium fluoride, which are more fragile than glass, making preparation in the histopathology laboratories more cumbersome. A solution is presented here by using cheap glass substrates for the FT-IR chemical imaging, which has a high-wavenumber transmission window allowing measurement of the C−H, N−H, and O−H stretches occurring at ca. 2500−3800 cm−1. The “fingerprint” region of the IR spectrum occurring below 1800 cm−1 is not obtainable; however, we demonstrate that a wealth of information is contained in the high wavenumber range using 71 patients on a breast tissue microarray (TMA) as a model for investigation. Importantly, we demonstrate that the tissue can be classified into four basic tissue cell types and that using just the epithelial cells, reasonable discrimination of normal and malignant tissue can be found.
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modality can be used to analyze biopsy samples correctly, reducing subjectivity.5−7 This high degree of diagnostic accuracy coupled with the prospect of high throughput biopsy screening makes the further development of this technique particularly attractive.8 Infrared spectroscopy, however, is not without its drawbacks. It is well-known that glass is quite unsuitable for any type of optics (e.g., lenses, widows, or sample slides) because of its opacity over most of the useful mid-infrared region of the spectrum.9−11 In order to analyze a biopsy sample, therefore, the slice of tissue either has to be placed on a mid-infrared transparent substrate such a calcium fluoride (CaF2) or barium fluoride (BaF2) for a transmission measurement or on a highly reflecting substrate for a, so-called, transflection measurement. In the case of a transmission measurement the substrates are expensive (typically greater than $40 per slide) and are far from robust. Their extreme brittleness makes them unsuitable for incorporation into conventional automated tissue sample preparation equipment and thus sample preparation has to be carried out manually in a separate procedure.12 In the transflection experiment, the most popular substrate is a Ag/SnO2-coated glass slide often referred to as a low-e (e for emissivity) slide.13−19 The measurement is made in reflection mode with the infrared beam passing through the tissue, reflecting off the surface of the substrate and through the tissue a
very year in the UK over a quarter of a million people are diagnosed with cancer. The four most common types of cancer, breast, lung, bowel, and prostate, make up over half of all these cases. The problem is particularly present in certain cancers such as prostate and breast, since they are strongly correlated with age and indeed are endemic in the older population. The issue is exacerbated by the fact that the world population aged 60 or over is currently increasing at the fastest pace ever, 3.7% annually in the period 2010−2015, and the numbers aged over 80 growing faster still.1 This will put a considerable strain on all parts of the health care systems and will have dramatic effects on health care costs. In most cases of suspected cancer, the taking of a biopsy is required to accurately assess if the disease is present and, if so, to ascertain its degree of progression. For each case, numerous thin slices of tissue sample needs to be carefully examined by a highly trained pathologist peering down an optical microscope. This is both a laborious and time-consuming process. More importantly, the process is subjective and prone to both intraand interobserver error.2 Pressure of increasing workload, rising costs, and a subjective analysis are all key drivers for the development of faster (high throughput), cheaper, and more reliable methods of tissue analysis. During the past decade, the use of infrared microspectroscopy to study biomedical samples has increased significantly.3,4 In the field of cancer diagnosis, it has been demonstrated, in the case of prostate cancer for example, that this investigational © 2014 American Chemical Society
Received: October 21, 2013 Accepted: January 10, 2014 Published: January 10, 2014 1648
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value with most work focused on the protein and fingerprint regions below 1800 cm−1; however, we believe that much of the problem has been caused by a lack of data mining in this region of the spectrum. Recently, we have demonstrated that FT-IR imaging can be used to rapidly analyze large areas27 (whole organ cross sections) of tissue at high spatial resolution, and in this paper use similar methodology to analyze breast tissue microarrays (TMAs) on glass substrates. We test the hypothesis that using FT-IR spectra of just the high wavenumber region of the spectrum, it is possible to (a) classify breast tissue according to cell-type and (b) extract spectral biomarkers discriminating malignant and nonmalignant epithelial cells.
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MATERIALS AND METHODS Breast Tissue Microarray. A breast tissue microarray (BR20832, US Biomax Inc., Rockville, MD) containing 1 mm diameter tissue cores was sectioned onto two standard histology glass substrates. The first slide was subsequently dewaxed and stained with hematoxylin and eosin (H&E), a standard step in the histopathology laboratories, as it provides contrasts between different cell types and extracellular tissue components. The second slide (hereafter referred to as the unstained glass section) did not undergo any chemical treatment such as dewaxing and hence remained embedded in paraffin. The reason for this being to prevent any further chemical alteration of the sample due to reagents and also to create some degree of refractive index matching, thus reducing the spectral distortion due to Mie scattering.25,28 FT-IR Chemical Imaging. FT-IR chemical images were measured using a Varian 670-IR coupled with a Varian 620-IR microscope (Agilent Technologies, Santa Clara, CA). The system was equipped with a liquid nitrogen cooled mercury−cadmium− telluride (MCT) detector comprising 128 × 128 elements, producing a focal plane array infrared camera. Chemical images were measured at a spectral resolution of 8 cm−1 using 32 and 8 coadded scans for the background and sample, respectively. During spectral processing, the interferograms were processed using 2 levels of zero filling to give a data spacing of ca. 2 cm−1 in the wavenumber domain. A triangular apodization function was used throughout during spectral processing. Each core took approximately 1 min 45 s to measure. Computational Methods. Histology Spectral Database Construction. All data handling and analysis steps were implemented in MATLAB 2012a (The MathWorks Inc., Natick, MA). A database was constructed containing spectra from regions of epithelium, blood, necrosis, and stroma (essentially comprising the extracellular matrix and stromal cells), using the methods reported by Levin and Bhargava29 and Fernandez et al.8 Histological review and annotation was performed using the GPU Image Manipulation Program (GIMP), software available at http://www.gimp.org/. Signal Processing. Since the tissue samples were left embedded paraffin, the scattering, predominantly resonant Mie scattering,25,28 was reduced, leaving the broad oscillations from nonresonant Mie scattering.30,31 This negates the need to use computationally intensive scatter correction algorithms32,33 and allows a simple linear baseline subtraction between 3100 and 3600 cm−1, as used elsewhere.8 Spectra were then normalized to the absorbance value at 3298 cm−1. Cell-Type Spectral Pattern Recognition. With the use of a similar approach to Bhargava et al.,34 spectral biomarkers to discriminate between the 4 cell-types were found using a sequential feed-forward feature selection method based on
Figure 1. (a) Single beam spectrum through a clean glass slide. (b) Absorbance spectrum of breast epithelial cells from the red cross marked in Figure 2(panels c and d).
second time. The advantage of these slides are that they are cheap (∼$1), robust, and give enhanced signal due to the double pass through the tissue. Recently, however, a number of papers have cast doubt on the suitability of transflection method for infrared measurement, particularly biomedical applications.20−24 The problem largely (in addition to scattering25) is that the interaction between the incident and reflected electric fields of the infrared radiation results in an electric field standing wave (EFSW) above the reflecting surface.26 This has the effect of modulating absorption at different heights above the surface. Unfortunately, since this is a wavelength dependent effect, the spectrum of a thin (few micrometers) tissue sample on the surface is modulated such that the intensity of the absorption peaks vary differently across different parts of the spectrum. These perturbations in the spectrum can be quite pronounced and render data obtained by this method potentially unreliable. At this present point in time, therefore, researchers in the field have to revert to the more expensive and physically fragile CaF2 and BaF2 substrates. This is a significant barrier to getting this technology adopted in the clinical environment. Clearly, there is a need for a cheaper, stronger alternative substrate that can be used in automated sample preparation systems currently in use for conventional histopathology. In this paper, we explore the possibility of using conventional glass as a transmission substrate. Despite the fact that glass is widely regarded as unsuitable for mid-infrared spectroscopy, it does have a narrow transmission window covering the N−H, O−H, and C−H stretching regions occurring at ca. 2500−3800 cm−1, hereafter referred to as the “high wavenumber range”. Traditionally, this region has not been viewed as having particular diagnostic 1649
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Figure 2. (a) FT-IR chemical image of the breast TMA comprising 15 noncancerous and 56 cancerous patients (totalling 71 cases). Image rendered by computing absorbance ratio of 3298: 3350 cm−1 and contains ca. 6.2 million pixels, each of which is an IR spectrum. H&E micrograph from the section adjacent to (a). (c and d) show an expanded view of the two cores used for constructing the histology spectral database. The red cross marks the position from where the spectrum in Figure 1b was acquired. Each core has a diameter of 1 mm and is represented by ca. 26000 IR spectra.
linear kernel support vector machine (SVM) classifier from the LIBSVM library,35 software available at http://www.csie.ntu. edu.tw/∼cjlin/libsvm. With the use of the selected features (spectral biomarkers), a Random Forest algorithm,36 software available at http://code.google.com/p/randomforest-matlab, was used to construct a classifier. The Random Forest approach was chosen as the classification prediction gives a probability estimate for each class in the form of vote fractions.
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RESULTS
Infrared Spectra Using Glass Slides. Figure 1a shows the single beam transmission spectrum of a clean 1 mm thickness glass slide. The spectrum clearly shows that the glass has a significant transmission window between ca. 3700 and 2000 cm−1. Therefore, although the strong amide I and II bands and fingerprint region of the spectrum are inaccessable, it is evident that the high wavenumber region of the spectrum can be recorded from a tissue sample using such a glass substrate. Figure 1b shows a spectrum of epithelial cells from the unstained section of a breast tissue microarry on a glass slide (red cross in Figure 2). The spectrum has been truncated at ca. 2600 cm−1 as the signal-tonoise ratio degrades rapidly below this range as the transmission through the glass decreases. The spectrum exhibits a missing region between ca. 2700−3000 cm−1, the C−H stretching region, which have been truncated due to absorption saturation from the strong absorption of the paraffin as the slides have not been dewaxed.
Figure 3. Mean spectra of epithelium, stroma, blood, and necrosis for the high wavenumber region.
FT-IR Chemical Imaging on Glass Slides. Figure 2a shows an FT-IR chemical image of the breast TMA from the unstained glass section. The image is a false color intensity image of the ratio of absorbances: 3298: 3350 cm−1, which is able to show the tissue architecture. Figure 2b shows the H&E image from the serial section, providing a guide for the cell-types, with epithelium, stroma, necrosis, and blood labeled in the expanded set of cores in (d). For clarity, 2 cores have been expanded, and the chemical image in (c) shows the morphology of the tissue architecture clearly with excellent correspondence to the H&E image in (d). 1650
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Figure 4. False color classification image of breast TMA FT-IR chemical image, where green = epithelium, purple = stroma, red = blood, and orange = necrosis. The highlighted cores were used for building the histology model.
Histology Using High Wavenumber Region. From the highlighted breast tissue cores in Figure 2 (panels b and c), IR spectra were extracted and inserted in a database containing 3792 epithelial, 8088 stromal, 838 blood, and 1801 necrosis spectra. Figure 3 shows the mean spectra for each of the classes in the database. Although at first sight this looks like a single feature, it is clear that there is a variation in peak position, peak width, and the intensity of shoulders on either side of the main peak. This database was used to find the spectral biomarkers that discriminate between the different classes and then to train a Random Forest classifier as stated in Cell-Type Spectral Pattern Recognition. The four features/metrics found to be informative were (1) ratio of absorbances, 3380: 3400 cm−1; (2) ratio of absorbances, 3520: 3530 cm−1; (3) ratio of absorbances, 3350: 3390 cm−1; and (4) area under spectral region from 3109 to 3580 cm−1. The breast TMA FT-IR chemical image shown in Figure 2a was prepared such that each spectrum was individually subjected to the classifier, which gave a probability estimate of it belonging to each of the four cell types. A false color system (green = epithelium, purple = stroma, red = blood, and orange = necrosis) was employed to visually display the result of this classification procedure and is shown in Figure 4 and qualitatively has excellent agreement with the H&E image in Figure 2b.
Figure 5. Receiver operator curve (ROC) for the Random Forest classifier output for the independent test data set. The area under curve (AUC) values are epithelium = 0.9845, stroma = 0.9956, blood = 0.9922, necrosis = 0.9932.
For a quantitative evaluation, a second database was constructed containing the spectra that were not used for training the classifier (i.e., the 69 cores that are outside the highlighted region in Figure 4). These remaining 69 cores provide a test data set containing 149343 IR spectra that were completely independent of the training data set (the two 1651
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found to be (1) ratio of absorbances, 3160:3170 cm−1; (2) ratio of absorbances, 3190:3200 cm−1, (3) ratio of absorbances, 3200:3220 cm−1, (4) ratio of absorbances, 3110: 3220 cm−1, and (5) ratio of absorbances, 3120:3460 cm−1. The joint probability distribution of the top two features is shown in Figure 6b, where two distinct 2 distributions can be seen. A popular way to visualize patterns in spectroscopic data is to perform principal components analysis (PCA) and plotting the scores of a pair of components in a scatter plot. This approach has not been adopted here due to complexity of interpreting the chemical information from the scores and loadings combination. Instead we have produced a scatter plot in Figure 7 of the top two metrics stated above which produces
highlighted cores). The output from the Random Forest classifier assigns to each individual spectrum, a probability estimate of it belonging to each of the 4 cell-types (these numbers sum to 1), allowing the option to reject a prediction if the probability estimate is below a certain threshold. Receiver operator curves (ROCs) are a powerful tool to visualize the classifier output, and the area under the curve (AUC) forms helpful summary statistic for understanding performance. Figure 5 shows the ROC curves for the 4 classes, optimal performance is shown when the curve is as close to the top left corner as possible, meaning that there is a high true positive rate while having a low false positive rate. The resulting AUC from this ideal scenario approaches the maximum value of 1, meaning that it is a perfect test (i.e., perfect sensitivity and specificity). Setting a probability estimate acceptance threshold of 0.95 (95%), the correctly classified percentage for the independent test data set was epithelium = 98.25%, stroma = 99.94%, blood = 100.00%, and necrosis = 97.22%. These classification percentages are comparable to the results achieved for prostate tissue histology using the full IR spectrum as published in the seminal paper by Fernandez et al.8 Spectral Biomarkers for Malignancy. To investigate the potential of developing a breast cancer detection system using just the high wavenumber region, a third database was constructed containing just epithelium from TMA cores that were nonmalignant (15 patients providing 15077 spectra) and malignant (56 patients providing 17859 spectra), the mean spectra of which are shown in Figure 6a.
Figure 7. Scatter plot of absorbance ratio 3190:3200 cm−1 against absorbance ratio 3160:3170 cm−1, where each point is an individual spectrum. The visualization shows that using these two metrics, a pattern can be seen in the data separating the malignant from the nonmalignant epithelial breast tissue.
a scores plot type of visualization. The benefit of this is that the grouping of the data can be related to the spectrum in a simpler way, no interpretation of loadings is required. This result shows that there are chemical differences in the malignant to nonmalignant epithelial breast tissue in the high wavenumber region.
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DISCUSSION This paper has shown that FT-IR chemical imaging can be performed using glass slides as the sample support, allowing high quality spectra to be measured in the high-frequency region of the IR spectrum. The false color classification image in Figure 4 shows that a robust classifier can be constructed using just the high wavenumber region of the spectrum. The ROC curves, AUC values, and correct classification percentages also show that predicting the cell type using just the high wavenumber region of the spectrum can be done to a high level of accuracy using just four metrics that are information derived from the spectrum. The method stated here used only four spectral biomarkers, but there could well be other biomarkers with better performance; it was beyond the scope of this paper to perform an exhaustive search of the possible features. The validation shown here was for just one set of data, and further validation on a greater number of patients needs to be done for clinical testing. The biochemical significance of these biomarkers have not been discussed here due to complexity
Figure 6. (a) Mean spectra from the nonmalignant (15 patients providing, 15077 spectra) and malignant (56 patients providing 17859 spectra). (b) Joint probability distribution, P(x,y) of x axis: ratio (3160:3170) and y axis: ratio (3190:3200).
Using the method described in Cell-Type Spectral Pattern Recognition, feature selection (spectral biomarker identificaton) was performed using a randomly selected 1000 spectra from each of two classes and using the remaining as the validation set. The top discriminatory metrics/features were 1652
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(11) Günzler, H.; Gremlich, H.-U.; Blümich, M.-J. IR Spectroscopy: An introduction. Wiley-VCH: Weinheim, Germany, 2002; Vol. 69469. (12) Hughes, C.; Iqbal-Wahid, J.; Brown, M.; Shanks, J. H.; Eustace, A.; Denley, H.; Hoskin, P. J.; West, C.; Clarke, N. W.; Gardner, P. J. Biophotonics 2013, 6, 73−87. (13) Dukor, R. K.; Marcott, C. A. Method and system for performing infrared study on a biological sample. U.S. Patent 6274871, 2001. (14) Bird, B.; Miljkovic, M.; Romeo, M. J.; Smith, J.; Stone, N.; George, M. W.; Diem, M. BMC Clin. Pathol. 2008, 8, 8. (15) Gazi, E.; Dwyer, J.; Lockyer, N.; Gardner, P.; Vickerman, J. C.; Miyan, J.; Hart, C. A.; Brown, M.; Shanks, J. H.; Clarke, N. Faraday Discuss. 2004, 126, 41−59. (16) Quaroni, L.; Casson, A. G. Analyst 2009, 134, 1240−1246. (17) Steiner, G.; Koch, E. Anal. Bioanal. Chem. 2009, 394, 671−678. (18) Wood, B. R.; Bambery, K. R.; Evans, C. J.; Quinn, M. A.; McNaughton, D. BMC Med. Imaging 2006, 6, 12. (19) Yang, T. A. T.; Weng, S. F.; Zheng, N.; Pan, Q. H.; Cao, H. L.; Liu, L. A.; Zhang, H. D.; Mu, D. W. Forensic Sci. Int. 2011, 207, E34− E39. (20) Brooke, H.; Perkins, D. L.; Setlow, B.; Setlow, P.; Bronk, B. V.; Myrick, M. L. Appl. Spectrosc. 2008, 62, 881−888. (21) Filik, J.; Frogley, M. D.; Pijanka, J. K.; Wehbe, K.; Cinque, G. Analyst 2012, 137, 853−861. (22) Davis, B. J.; Carney, P. S.; Bhargava, R. Anal. Chem. 2010, 82, 3474−3486. (23) Davis, B. J.; Carney, P. S.; Bhargava, R. Anal. Chem. 2010, 82, 3487−3499. (24) Bassan, P.; Lee, J.; Sachdeva, A.; Pissardini, J.; Dorling, K. M.; Fletcher, J. S.; Henderson, A.; Gardner, P. Analyst 2013, 138, 144− 157. (25) Bassan, P.; Byrne, H. J.; Bonnier, F.; Lee, J.; Dumas, P.; Gardner, P. Analyst 2009, 134, 1586−1593. (26) Greenler, R. G. J. Chem. Phys. 1966, 44, 310−316. (27) Bassan, P.; Sachdeva, A.; Shanks, J. H.; Brown, M. D.; Clarke, N. W.; Gardner, P. Analyst 2013, 138, 7066−7069. (28) Bassan, P.; Kohler, A.; Martens, H.; Lee, J.; Byrne, H. J.; Dumas, P.; Gazi, E.; Brown, M.; Clarke, N.; Gardner, P. Analyst 2010, 135, 268−277. (29) Levin, I. W.; Bhargava, R. Annu. Rev. Phys. Chem. 2005, 56, 429−474. (30) Mohlenhoff, B.; Romeo, M.; Diem, M.; Woody, B. R. Biophys. J. 2005, 88, 3635−3640. (31) Kohler, A.; Sule-Suso, J.; Sockalingum, G. D.; Tobin, M.; Bahrami, F.; Yang, Y.; Pijanka, J.; Dumas, P.; Cotte, M.; van Pittius, D. G.; Parkes, G.; Martens, H. Appl. Spectrosc. 2008, 62, 259−266. (32) Bassan, P.; Sachdeva, A.; Kohler, A.; Hughes, C.; Henderson, A.; Boyle, J.; Shanks, J. H.; Brown, M.; Clarke, N. W.; Gardner, P. Analyst 2012, 137, 1370−1377. (33) Bassan, P.; Kohler, A.; Martens, H.; Lee, J.; Jackson, E.; Lockyer, N.; Dumas, P.; Brown, M.; Clarke, N.; Gardner, P. J. Biophotonics 2010, 3, 609−620. (34) Bhargava, R.; Fernandez, D. C.; Hewitt, S. M.; Levin, I. W. Biochim. Biophys. Acta, Biomembr. 2006, 1758, 830−845. (35) Chang, C. C.; Lin, C. J., ACM Transactions on Interactive Intelligent Systems and Technology; 2011, 2, 127. (36) Breiman, L. Machine Learning 2001, 45, 5−32. (37) Bhargava, R.; Wang, S. Q.; Koenig, J. L. Appl. Spectrosc. 1998, 52, 323−328. (38) Jimenez-Hernandez, M.; Hughes, C.; Bassan, P.; Ball, F.; Brown, M. D.; Clarke, N. W.; Gardner, P. Analyst 2013, 138, 3957−3966. (39) Papamarkakis, K.; Bird, B.; Schubert, J. M.; Miljkovic, M.; Wein, R.; Bedrossian, K.; Laver, N.; Diem, M. Lab. Invest. 2010, 90, 589− 598. (40) Rohleder, D.; Kocherscheidt, G.; Gerber, K.; Kiefer, W.; Ko, W.; Mo, J.; Petrich, W. J. Biomed. Opt. 2005, 10, 031108−03110810.
and the possibly unintelligible nature of the vibrations of overlapping absorption bands from different chemicals with the same vibrations. In addition to classifying the cell-type from just the high wavenumber region of the spectrum, spectral biomarkers discriminating malignant from nonmalignant breast epithelium was demonstrated. Although just a preliminary result illustrating the concept, it is envisaged that it would be perfectly possible to develop a simple screening tool for the detection of malignancy. With further work, it may also be possible to extract cancer-grading information similar to that obtained from the full IR spectrum.5,6 The use of glass substrates for IR chemical imaging is highly compatible with indium antimonide (InSb) detectors, which are sensitive in the wavelength range of ca. 1−5 μm, corresponding to the high wavenumber range of the IR spectrum. These detectors have been used for IR chemical imaging37 and are available in large formats and can operate much faster than MCT technology. This could potentially reduce the time for imaging TMAs to seconds rather than minutes.
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CONCLUSION This paper shows that if a clinical problem such as cancer detection using FT-IR chemical imaging of biopsy tissue can be achieved using spectral biomarkers in the high wavenumber region, then glass slides are the perfect low cost and physically robust substrate. In addition, glass substrates allow transmission measurements instead of transflection mode using the low-e substrate, which negates the problems of the electric-field standing waves.21,24 This method of using glass slides need not be limited to tissue but could be used for single biological cell drug investigations,38 oral cell cytology,39 and blood serum40 applications also.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We would like to thank the University of Manchester Research Institute (UMRI) for a pump-priming award to support this research.
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REFERENCES
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