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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 1, Iss. 3 — Oct. 1, 2010
  • pp: 1014–1025
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Detection of colonic inflammation with Fourier transform infrared spectroscopy using a flexible silver halide fiber

Vinay K. Katukuri, John Hargrove, Sharon J. Miller, Kinan Rahal, John Y. Kao, Rolf Wolters, Ellen M. Zimmermann, and Thomas D. Wang  »View Author Affiliations


Biomedical Optics Express, Vol. 1, Issue 3, pp. 1014-1025 (2010)
http://dx.doi.org/10.1364/BOE.1.001014


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Abstract

Persistent colonic inflammation increases risk for cancer, but mucosal appearance on conventional endoscopy correlates poorly with histology. Here we demonstrate the use of a flexible silver halide fiber to collect mid-infrared absorption spectra and an interval model to distinguish colitis from normal mucosa in dextran sulfate sodium treated mice. The spectral regime between 950 and 1800 cm−1 was collected from excised colonic specimens and compared with histology. Our model identified 3 sub-ranges that optimize the classification results, and the performance for detecting inflammation resulted in a sensitivity, specificity, accuracy, and positive predictive value of 92%, 88%, 90%, and 88%, respectively.

© 2010 OSA

1. Introduction

Ulcerative colitis (UC) and Crohn’s disease are inflammatory processes in the colon that significantly increase the risk for development of colorectal cancer [1

1. J. Xie and S. H. Itzkowitz, “Cancer in inflammatory bowel disease,” World J. Gastroenterol. 14(3), 378–389 (2008). [CrossRef] [PubMed]

]. Unlike sporadic carcinoma, colitis-associated cancer develops from non-polypoid mucosa and follows an inflammation-dysplasia-carcinoma sequence [2

2. T. Ullman, V. Croog, N. Harpaz, D. Sachar, and S. Itzkowitz, “Progression of flat low-grade dysplasia to advanced neoplasia in patients with ulcerative colitis,” Gastroenterology 125(5), 1311–1319 (2003). [CrossRef] [PubMed]

]. In addition to the duration and anatomic extent of the disease, the severity of microscopic inflammation over time is also a risk factor in patients with long-standing inflammatory bowel disease (IBD) [3

3. R. B. Gupta, N. Harpaz, S. Itzkowitz, S. Hossain, S. Matula, A. Kornbluth, C. Bodian, and T. Ullman, “Histologic inflammation is a risk factor for progression to colorectal neoplasia in ulcerative colitis: a cohort study,” Gastroenterology 133(4), 1099–1105, quiz 1340–1341 (2007). [CrossRef] [PubMed]

]. Thus, there is a clinical need to monitor the degree and severity of colonic inflammation [4

4. M. Rutter, B. Saunders, K. Wilkinson, S. Rumbles, G. Schofield, M. Kamm, C. Williams, A. Price, I. Talbot, and A. Forbes, “Severity of inflammation is a risk factor for colorectal neoplasia in ulcerative colitis,” Gastroenterology 126(2), 451–459 (2004). [CrossRef] [PubMed]

]. Clinical indices, such as the Powell-Tuck Activity Score, do not correlate well with disease activity [5

5. P. Gomes, C. du Boulay, C. L. Smith, and G. Holdstock, “Relationship between disease activity indices and colonoscopic findings in patients with colonic inflammatory bowel disease,” Gut 27(1), 92–95 (1986). [CrossRef] [PubMed]

]. Moreover, endoscopy with biopsy is not effective for localizing microscopic inflammation in normal appearing mucosa on endoscopy. Furthermore, an assessment of inflammation throughout the various anatomic segments of the colon is important for evaluating the effectiveness of new drugs. Thus, a novel diagnostic technique that can perform a rapid assessment of inflammation in the colon would have great clinical potential.

The absorption of infrared light by the inter-atomic bonds in tissue biomolecules can be evaluated using Fourier transform infrared (FTIR) spectroscopy [6

6. T. D. Wang, G. Triadafilopoulos, J. M. Crawford, L. R. Dixon, T. Bhandari, P. Sahbaie, S. Friedland, R. Soetikno, and C. H. Contag, “Detection of endogenous biomolecules in Barrett’s esophagus by Fourier transform infrared spectroscopy,” Proc. Natl. Acad. Sci. U.S.A. 104(40), 15864–15869 (2007). [CrossRef] [PubMed]

10

10. B. Rigas, S. Morgello, I. S. Goldman, and P. T. Wong, “Human colorectal cancers display abnormal Fourier-transform infrared spectra,” Proc. Natl. Acad. Sci. U.S.A. 87(20), 8140–8144 (1990). [CrossRef] [PubMed]

]. The characteristic spectra of these absorption peaks provide a molecular “fingerprint” that can be used to perform histological classification of tissue [11

11. D. C. Fernandez, R. Bhargava, S. M. Hewitt, and I. W. Levin, “Infrared spectroscopic imaging for histopathologic recognition,” Nat. Biotechnol. 23(4), 469–474 (2005). [CrossRef] [PubMed]

]. Recently, flexible endoscope-compatible optical fibers have been developed with silver halide materials that can be used to collect infrared spectra remotely [12

12. C. Charlton, A. Katzir, and B. Mizaikoff, “Infrared evanescent field sensing with quantum cascade lasers and planar silver halide waveguides,” Anal. Chem. 77(14), 4398–4403 (2005). [CrossRef] [PubMed]

14

14. M. A. Mackanos, J. Hargrove, R. Wolters, C. B. Du, S. Friedland, R. M. Soetikno, C. H. Contag, M. R. Arroyo, J. M. Crawford, and T. D. Wang, “Use of an endoscope-compatible probe to detect colonic dysplasia with Fourier transform infrared spectroscopy,” J. Biomed. Opt. 14(4), 044006 (2009). [CrossRef] [PubMed]

]. Silver halides have features that provide significant advantages for in vivo use, including broad spectral transmission, good mechanical flexibility, low optical attenuation, long term stability, and no tissue toxicity.

Mouse models are important for the study of colitis, and have made significant contributions to our understanding of inflammation pathways, cancer transformation, and chemoprevention strategies [15

15. S. Wirtz, C. Neufert, B. Weigmann, and M. F. Neurath, “Chemically induced mouse models of intestinal inflammation,” Nat. Protoc. 2(3), 541–546 (2007). [CrossRef] [PubMed]

17

17. H. S. Cooper, L. Everley, W. C. Chang, G. Pfeiffer, B. Lee, S. Murthy, and M. L. Clapper, “The role of mutant Apc in the development of dysplasia and cancer in the mouse model of dextran sulfate sodium-induced colitis,” Gastroenterology 121(6), 1407–1416 (2001). [CrossRef] [PubMed]

]. Dextran sulfate sodium (DSS) mouse models have been shown to replicate important immunological and histopathological aspects of IBD that occur in human disease. The role of inflammation and the effect of disease activity on the development of cancer in this model have been accurately correlated with disease severity.

Here, we aim to demonstrate the use of this fiber material to collect infrared spectra for detecting the presence of mucosal inflammation in the DSS animal model of colitis. We also aim to show that FTIR is sensitive to changes in tissue biochemistry that occur in inflammation prior to the appearance of histological findings. Moreover, we aim to show that the diagnostic performance can be preserved with use of only 3 wavenumbers from the fingerprint region, allowing for a future laser-based system to be developed as a part of a clinical endoscope-compatible instrument.

2. Methods

2.1 DSS colitis model

2.2 Collection of FTIR spectra

The colonic specimens were placed onto a slide, and a grid overlay was used to achieve accurate localization of the sites measured. Spectra were obtained after reducing the effect of water absorption in the collected spectra by gently blowing cool air onto the mucosal surface for several seconds to remove ambient moisture. A spectrometer (Nexus 470, Thermo Electron, Madison, WI) with a mercury cadmium telluride (MCT) detector cooled with liquid nitrogen was used. The detection scheme is based on a Michelson interferometer, shown in Fig. 1
Fig. 1 FTIR instrument. Flexible silver halide fiber inserted into signal arm of Michelson interferometer is used to collect infrared (IR) spectra. Details provided in text.
, where a moving mirror varies the length of one optical path relative to the other, and creates an interferogram that is mathematically converted to an absorbance spectrum by a Fourier transform. The spectrometer was continuously purged with air to remove water vapor and CO2. A 900 μm diameter silver halide fiber is used in the attenuated total reflectance configuration resulting in a total length of 1 meter (Multi-Loop-MIRTM, Harrick Scientific, Pleasantville, NY). In the distal tip of the instrument, the fiber is arranged in a loop (diameter of 6.4 mm) that is placed into gentle contact with mucosal surface of the specimens for collection of mid-infrared absorption spectra. A gain of 4 was used to record the spectra with a resolution of 4 cm−1 using 36 co-added scans for both the background and tissue. Wavenumbers less than 900 cm−1 and greater than 1800 cm−1 were not collected because of the relatively high absorbance of silver halide outside of this range. Spectra were collected from the right (R), transverse (T), and left (L) anatomic segments of the colon from each mouse.

2.3 Processing and classification of FTIR spectra

The FTIR spectra were classified using partial least squares (PLS) discriminant analysis [18

18. S. Wold, A. Ruhe, H. Wold, and W. Dunn III, “The Collinearity Problem in Linear-Regression - the Partial Least-Squares (PLS) Approach to Generalized Inverses,” SIAM J. Sci. Stat. Comput. 5(3), 735–743 (1984). [CrossRef]

,19

19. P. Geladi and B. Kowalski, “Partial Least-Squares Regression - a Tutorial,” Anal. Chim. Acta 185(1), 1–17 (1986). [CrossRef]

]. The classification models were implemented using the SIMPLS algorithm in PLS_Toolbox, (Eigenvector Research, Inc, Wenatchee, WA) [20

20. S. de Jong, “SIMPLS - an Alternative Approach to Partial Least-Squares Regression,” Chemom. Intell. Lab. Syst. 18(3), 251–263 (1993). [CrossRef]

,21

21. B. Wise, N. Gallagher, R. Bro, J. Shaver, W. Windig, and R. Koch, PLS_Toolbox Version 4.0 Manual (2006).

]. An interval model was developed that uses continuous regions of each spectra. This model determines the best performance for classifying the DSS and normal colonic segments using the complete spectral data set. Also, a discrete model was developed to use only 3 wavenumbers from each spectra to design a future clinical instrument with a laser source.

2.4 Interval model

2.5 Discrete model

In this method, interval PLS discriminant analysis was employed as described above but using only combinations of 3 individual wavenumbers from each spectrum. Because of the large number of possibilities, a subset of 25 candidate wavenumbers was first identified from a VIP analysis. First, a PLS discrimination model using the spectral absorbance from all of the wavenumbers was calibrated. Then, the 25 highest local maxima based on VIP score were selected. Pre-processing was performed as described above, and the PLS model with the best performance is reported below.

2.6 Evaluation of histology

Histology was evaluated to determine the severity of mucosal damage. A score for each specimen was provided using a standard colitis index (CI) [24

24. D. J. Law, E. M. Labut, R. D. Adams, and J. L. Merchant, “An isoform of ZBP-89 predisposes the colon to colitis,” Nucleic Acids Res. 34(5), 1342–1350 (2006). [CrossRef] [PubMed]

]. The criteria are defined by 4 categories based on the extent of crypt damage, inflammation, sub-mucosal edema, and hemorrhage, and scored according to severity over a range from 0 to 3. The score for each category was multiplied by an extent factor, defined as 1 for <10-25%, 2 for 25-50%, and 3 for >50%, corresponding to the proportion of surface area of diseased colon. In addition, 4 points were added for transmural involvement.

3. Results

3.1 DSS colitis model

3.2 Processing and classification of FTIR spectra

Representative FTIR spectra collected with the silver halide fiber from the DSS and control specimens of colon over the full spectral regime (900 to 1800 cm−1) from the right (R), transverse (T), and left (L) anatomic segments are shown in Fig. 2
Fig. 2 Representative FTIR spectra for DSS (red) and normal (black) colonic mucosa over fingerprint region (900 to 1800 cm−1)) from the right (R), transverse (T), and left (L) anatomic segments demonstrate very good SNR and reveal numerous absorbance peaks in 6 separate sub-ranges.
, and demonstrate very good SNR. We identified the following 6 sub-ranges that optimized the interval model: 1) 1000-1188 cm−1; 2) 1188-1325 cm−1; 3) 1325-1485 cm−1; 4) 1485-1585 cm−1; 5) 1585-1696 cm−1; and 6) 1696-1774 cm−1. These sub-ranges are denoted by the vertical dotted lines in Fig. 2. The spectra reveal numerous absorbance peaks over this regime. Prominent peaks in sub-range 1 include 1037 cm−1 (CO stretch vibration of deoxyribose) and 1088 cm−1 (symmetric PO2 stretch) from nucleic acids. Notable bands in sub-range 2 include 1240 cm−1 (coupled CN stretch to NH bend of amide III), and significant peaks in sub-range 3 include 1398 and 1454 cm−1 (CH bend in aliphatic side groups of amino acids). In addition, large peaks are seen at 1545 cm−1 (CN stretch and NH bend of amide II) and 1640 cm−1 (C=O stretch of amide I). A complete list of peak assignments are summarized in Table 1

Table 1. Band assignments for inter-atomic bonds found in common tissue biomolecules for primary mid-infrared absorption peaks in molecular fingerprint regime are shown (νs – symmetric, νas – asymmetric stretch). References cited are within 5 cm−1 of those found in this study

table-icon
View This Table
.

3.3 Interval model

The best interval model for distinguishing between the DSS and control spectra used sub-ranges 1 to 3 only, as shown in Fig. 3
Fig. 3 Pre-processing of spectra prior to classification. (A) The mean unprocessed spectra for the DSS (red) and control (black) mice from sub-ranges 1 to 3. (B) Along-spectrum derivation (Savitsky-Golay algorithm) followed by normalization enhances spectral differences between groups. (C) Unit-variance scaling further amplifies spectral differences. (D) VIP score reveals relative contribution of each wavenumber to the performance of the classification model.
. Sub-ranges 4 and 5 were found to be sensitive to the presence of water, and eliminated in this model. First, the mean unprocessed spectra for the DSS (red) and control (black) groups for sub-ranges 1 to 3 are shown in Fig. 3(A). Next, the mean spectra obtained after pre-processing with along-spectrum derivation (Savitsky-Golay algorithm) followed by normalization with the area under the curve are shown in Fig. 3(B). These spectra demonstrate how pre-processing enhances differences between the two groups. The length of smoothing window used was 11 points (~42 cm−1) for sub-range 1, 15 points (~58 cm−1) for sub-range 2, and 7 points (~28 cm−1) for sub-range 3. The spectra are further processed by unit-variance scaling, as shown in Fig. 3(C), resulting in greater enhancement of the class differences in all 3 sub-ranges. The mean regression coefficients over the spectral regime, shown magnified by a factor of 10 (blue), determine the relative contribution of each DSS and control value to the overall prediction. The VIP scores shown in Fig. 3(D) reveal the relative contribution of each wavenumber to the performance of the classification model. The horizontal dashed line with a value of 1 denotes the average contribution. Prominent peaks are seen between 1060 and 1130 cm−1 in sub-range 1, where vibrational modes for nucleic acids and glycoproteins are located. Also, significant peaks can be seen in sub-range 3 between 1360 and 1500 cm−1, where vibrational modes for amino acids and glycolipids occur.

The processed spectra from the interval model were then evaluated using the PLS algorithm. The classification values for the 3 colonic segments, right (R), transverse (T) and left (L), are shown in Fig. 4(A)
Fig. 4 Performance for n = 8 DSS (red) and n = 8 control (black) mice in right (R), transverse (T), and left (L) colonic segments using the classification value of 1.5 (dashed horizontal line) as threshold. (A) Best interval model produced a sensitivity, specificity, accuracy, and positive predictive value of 92%, 88%, 90%, and 88%, respectively. (B) Best discrete model using 3 wavenumbers of 1072, 1088, and 1740 cm−1 produced results of 92%, 83%, 88%, and 85%, respectively. C) Colitis index (CI) scores from histology are shown for comparison.
for each animal. The DSS specimens are shown on the left (red) and the controls are presented on the right (black). A classification value of 1.5 (dashed horizontal line) was used as the diagnostic threshold. The ratio of DSS samples above this value (22/24) corresponds to a sensitivity of 92%, and that of control specimens below (21/24) correspond to a specificity of 88%. The overall accuracy and positive predictive value was found to be 90% and 88%, respectively.

3.4 Discrete model

The best 3 wavenumber discrete model for distinguishing between the DSS and control spectra was found to have absorbances at 1072, 1088, and 1740 cm−1. These 3 wavenumbers correspond to the CNstretch of glycoproteins, symmetricPO2stretch of nucleic acids, and C=O stretch of phospholipids. The processed spectra from this model were then evaluated using the PLS algorithm. The classification values for each of the 3 colonic segments are shown in Fig. 4(B) for each animal. A classification value of 1.5 (dashed horizontal line) was used as the diagnostic threshold. The ratio of DSS samples above this value (22/24) corresponds to a sensitivity of 92%, and that of control specimens below (20/24) correspond to a specificity of 83%. The overall accuracy and positive predictive value was found to be 88% and 85%, respectively.

3.5 Evaluation of histology

Furthermore, the CI score evaluated from histology for each colonic segment is shown in Fig. 4(C) for each animal. The DSS mice had a mean CI score of 5.1, 26.4 and 20.4 for the right, transverse, and left colonic segments, respectively. In comparison, the control mice had a significantly lower mean CI score of 2.1, 4.1 and 2.7, respectively. Interestingly, less inflammation was observed in the right colon of the DSS mice compared to the transverse and the left colonic segments. As expected, no particular segment of the control specimens was more likely to be misclassified. Representative histology from the transverse segment of the DSS and control mice are shown in Figs. 5(A)
Fig. 5 Histology (H&E). Representative sections from (A) DSS colitis shows features of mucosal damage, including distorted crypt morphology, dot hemorrhages, and edema, and for (B) control (normal colonic mucosa) shows regular-spaced crypts, no hemorrhages, and absence of edema, scale bar 50 μm.
and 5(B), respectively, scale bar 50 μm. The DSS sections show features of mucosal damage, including distorted crypts, dot hemorrhages, and mucosal edema. The control sections show regular-spaced crypts, no hemorrhages, and absence of edema.

The ROC curves for the performance of the interval (solid) and discrete (dashed) models, shown in Fig. 6
Fig. 6 ROC curves show comparison of performance between the interval (solid) and discrete (dashed) models for detection of colitis with optical fiber, resulting in an AUC of 0.92 and 0.88, respectively.
, reveal only a small reduction in the AUC from 0.92 to 0.88 with use of only 3 wavenumbers in the classification algorithm.

4.Discussion

Here, we demonstrate the use of a flexible silver halide fiber to collect infrared spectra remotely from freshly excised colonic mucosa from DSS mice to detect the presence of inflammation. To the best of our knowledge, this is the first demonstration of the use of an optical fiber to collect infrared absorption spectra to identify biochemical changes associated with inflammation. This technique is promising because endogenous tissue biomolecules that affect inflammatory pathways can be assessed with very high signal-to-noise in a simple instrument. Moreover, there is a significant clinical need for new technologies that can monitor the presence of inflammation in the digestive tract, as white light endoscopy alone is not sufficiently sensitive. This is particularly true for assessing the proximal (right sided) extent of disease in ulcerative colitis where microscopic inflammation can be found in macroscopically normal appearing mucosa [25

25. M. L. Mutinga, R. D. Odze, H. H. Wang, J. L. Hornick, and F. A. Farraye, “The clinical significance of right-sided colonic inflammation in patients with left-sided chronic ulcerative colitis,” Inflamm. Bowel Dis. 10(3), 215–219 (2004). [CrossRef] [PubMed]

].

Acknowledgments

Research supported in part by grants from the National Institutes of Health, including P50 CA93990 and U54 CA136429 (TDW), and the Department of Defense USAMRCMC W81XWH-07-C-0086 (RW).

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2.

T. Ullman, V. Croog, N. Harpaz, D. Sachar, and S. Itzkowitz, “Progression of flat low-grade dysplasia to advanced neoplasia in patients with ulcerative colitis,” Gastroenterology 125(5), 1311–1319 (2003). [CrossRef] [PubMed]

3.

R. B. Gupta, N. Harpaz, S. Itzkowitz, S. Hossain, S. Matula, A. Kornbluth, C. Bodian, and T. Ullman, “Histologic inflammation is a risk factor for progression to colorectal neoplasia in ulcerative colitis: a cohort study,” Gastroenterology 133(4), 1099–1105, quiz 1340–1341 (2007). [CrossRef] [PubMed]

4.

M. Rutter, B. Saunders, K. Wilkinson, S. Rumbles, G. Schofield, M. Kamm, C. Williams, A. Price, I. Talbot, and A. Forbes, “Severity of inflammation is a risk factor for colorectal neoplasia in ulcerative colitis,” Gastroenterology 126(2), 451–459 (2004). [CrossRef] [PubMed]

5.

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6.

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12.

C. Charlton, A. Katzir, and B. Mizaikoff, “Infrared evanescent field sensing with quantum cascade lasers and planar silver halide waveguides,” Anal. Chem. 77(14), 4398–4403 (2005). [CrossRef] [PubMed]

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OCIS Codes
(070.4790) Fourier optics and signal processing : Spectrum analysis
(070.5010) Fourier optics and signal processing : Pattern recognition
(110.2350) Imaging systems : Fiber optics imaging
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(300.6300) Spectroscopy : Spectroscopy, Fourier transforms

ToC Category:
Optics in Cancer Research

History
Original Manuscript: July 30, 2010
Revised Manuscript: September 16, 2010
Manuscript Accepted: September 19, 2010
Published: September 21, 2010

Citation
Vinay K. Katukuri, John Hargrove, Sharon J. Miller, Kinan Rahal, John Y. Kao, Rolf Wolters, Ellen M. Zimmermann, and Thomas D. Wang, "Detection of colonic inflammation with Fourier transform infrared spectroscopy using a flexible silver halide fiber," Biomed. Opt. Express 1, 1014-1025 (2010)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-1-3-1014


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References

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