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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 2, Iss. 10 — Oct. 31, 2007
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Chemically-selective imaging of brain structures with CARS microscopy

Conor L. Evans, Xiaoyin Xu, Santosh Kesari, X. Sunney Xie, Stephen T.C. Wong, and Geoffrey S. Young  »View Author Affiliations


Optics Express, Vol. 15, Issue 19, pp. 12076-12087 (2007)
http://dx.doi.org/10.1364/OE.15.012076


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Abstract

We demonstrate the use of coherent anti-Stokes Raman scattering (CARS) microscopy to image brain structure and pathology ex vivo. Although non-invasive clinical brain imaging with CT, MRI and PET has transformed the diagnosis of neurologic disease, definitive pre-operative distinction of neoplastic and benign pathologies remains elusive. Definitive diagnosis still requires brain biopsy in a significant number of cases. CARS microscopy, a nonlinear, vibrationally-sensitive technique, is capable of high-sensitivity chemically-selective three-dimensional imaging without exogenous labeling agents. Like MRI, CARS can be tuned to provide a wide variety of possible tissue contrasts, but with sub-cellular spatial resolution and near real time temporal resolution. These attributes make CARS an ideal technique for fast, minimally invasive, non-destructive, molecularly specific intraoperative optical diagnosis of brain lesions. This promises significant clinical benefit to neurosurgical patients by providing definitive diagnosis of neoplasia prior to tissue biopsy or resection. CARS imaging can augment the diagnostic accuracy of traditional frozen section histopathology in needle biopsy and dynamically define the margins of tumor resection during brain surgery. This report illustrates the feasibility of in vivo CARS vibrational histology as a clinical tool for neuropathological diagnosis by demonstrating the use of CARS microscopy in identifying normal brain structures and primary glioma in fresh unfixed and unstained ex vivo brain tissue.

© 2007 Optical Society of America

1. Introduction

Diverse neuropathologies including demyelination (multiple sclerosis, acute disseminated encephalomyelitis), ichemia, viral infection (progressive multifocal leukoencephalopathy), bacterial or fungal abscess, granulomatous disease (sarcoidosis), and primary CNS lymphoma may be difficult to distinguish from primary brain tumor (glioma) by MRI and CT and often require surgical brain biopsy for diagnosis. Similarly, in patients with known brain tumors treated by radiotherapy, it is often difficult to differentiate between radiation injury and recurrent malignancy on MRI [1

1. D. D. Langleben and G. M. Segall, “PET in differentiation of recurrent brain tumor from radiation injury,” J. Nucl. Med. 41, 1861–1867 (2000). [PubMed]

, 2

2. M. C. Preul, R. Leblanc, Z. Caramanos, R. Kasrai, S. Narayanan, and D. L. Arnold, “Magnetic resonance spectroscopy guided brain tumor resection: differentiation between recurrent glioma and radiation change in two diagnostically difficult cases,” Can. J. Neurol. Sci. 25, 13–22 (1998). [PubMed]

]. The risks of brain biopsy are acceptable for patients with new or recurrent glioma because definitive therapy often includes surgical resection prior to radiation and chemotherapy. However, patients with radiation necrosis, demyelination or any of the other diagnoses mentioned do not require surgical resection, and so the morbidity and mortality of brain biopsy is a significant contributor to the injury caused by the disease itself. Although advanced imaging techniques such as MRI diffusion weighted imaging, diffusion tensor imaging, perfusion imaging, permeability imaging and spectroscopy can contribute to the differential diagnosis of masses in the brain, in many cases these techniques are not definitive enough to substitute for pathologic tissue diagnosis [3

3. Y. Ge, M. Law, and R. I. Grossman, “Applications of diffusion tensor MR imaging in multiple sclerosis,” Ann. N Y Acad. Sci. 1064, 202–219 (2005). [CrossRef]

, 4

4. D. Goldberg-Zimring, A. U. Mewes, M. Maddah, and S. K. Warfield, “Diffusion tensor magnetic resonance imaging in multiple sclerosis,” J. Neuroimaging 15, 68S–81S (2005). [CrossRef] [PubMed]

]. In these patients it would be desirable to exclude glioma without resection of brain tissue, in order to minimize the morbidity of neurosurgical biopsy.

Clearly, a technique that could differentiate the aforementioned non-neoplastic conditions and CNS lymphoma from glioma prior to resection of brain tissue would be very desirable. Definitive diagnosis by MRI or PET, however, seems unlikely in the near future. The lower spatial resolution and tissue contrast of these techniques remain inadequate substitutes for the cellular and tissue scale morphologic information that has made microscopic histopathology the diagnostic gold standard for over a century. In the absence of a completely non-invasive pre-operative imaging diagnosis, it would be ideal to diagnose brain lesions in situ through a small burr hole in the skull before deciding whether to perform a larger craniotomy or to resect tissue. To be useful, such a method would need to produce cellular and tissue level images of the intact brain in near real time, with minimal or no tissue damage. A technique that could produce such images without relying on potentially toxic exogenous labeling agents such as fluorescent or heavy metal stains would be even more useful since few labeling agents have been proven safe enough for in vivo human use.

Optical techniques that utilize the intrinsic chemical composition of a specimen to produce microscopic images with different chemical ‘weightings’ offer one promising approach. Endogenous multiphoton fluorescence imaging techniques have found a number of applications in brain imaging [5

5. W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, “Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation,” Proc. Natl. Acad. Sci. U S A 100, 7075–7080 (2003). [CrossRef] [PubMed]

], but the small number of naturally fluorescent compounds limits their general use. Structurally-sensitive imaging modalities such as second harmonic generation microscopy have been used in biomedical studies [6

6. P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Threedimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J. 81, 493–508 (2002). [CrossRef]

, 7

7. W. R. Zipfel, R. M. Williams, and W. W. Webb, “Nonlinear magic: multiphoton microscopy in the biosciences,” Nat. Biotechnol. 21, 1369–1377 (2003). [CrossRef] [PubMed]

], but are limited only to select proteins. Optical coherence tomography, a powerful approach that has found extensive use in biomedical imaging [8

8. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Am. J. of Physiol. 268, H802 (1995).

, 9

9. J. G. Fujimoto, “Optical coherence tomography - a review of the principles and contemporary uses in retinal investigation,” Nat. Biotechnol. 21, 1361–1367 (2003). [CrossRef] [PubMed]

], derives its contrast from changes in the index of refraction of a specimen, which are not always molecularly-specific.

Coherent anti-Stokes Raman scattering (CARS) microscopy [10

10. J. X. Cheng and X. S. Xie, “Coherent anti-Stokes Raman scattering microscopy: instrumentation, theory, and applications,” J. Phys. Chem. B 108, 827–840 (2004). [CrossRef]

] is a highly sensitive [11

11. F. Ganikhanov, C. L. Evans, B. G. Saar, and X. S. Xie, “High sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy,” Opt. Lett. 31, 1872–1874 (2006). [CrossRef] [PubMed]

] vibrationally-selective imaging modality capable of rapid in vivo molecular imaging [12

12. C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U S A 102, 16807–16812 (2005). [CrossRef] [PubMed]

]. Because the contrast in CARS microscopy arises from the intrinsic molecular vibrations in a specimen, CARS is analogous to MRI in its ability to produce images with a variety of chemically selective contrasts. While it lacks the tissue penetration of MRI, CARS microscopy is capable of providing molecular information with sub-micron spatial resolution, allowing the production of images depicting intact tissue and cellular architecture in situ. CARS images thus offer information that is complementary to traditional histopathology techniques and may contain adequate information to substitute for traditional frozen section light microscopy in some intra-operative applications. In CARS, a pump beam, at frequency ωp, and a Stokes beam, at frequency ωs, interact with a sample through a four wave mixing process. When the beat frequency (ωps) is tuned to match the frequency of a Raman active molecular vibration, resonant molecules are coherently driven with the excitation fields, resulting in the generation of a strong “anti-Stokes” signal at ωas=2ωps. Due to the nonlinear nature of the CARS process, the anti-Stokes signal is only generated at a small focal spot that can be scanned in space, allowing the acquisition of high-resolution, three-dimensional image volumes. In thick, turbid tissue such as brain matter, a large percentage of the CARS signal is redirected through multiple scattering events into the backwards (epi) direction, and can be easily collected by a large-area point detector [12

12. C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U S A 102, 16807–16812 (2005). [CrossRef] [PubMed]

].

With a signal strength that is orders of magnitude greater than conventional Raman scattering, and spatial resolutions far better than infrared imaging, CARS microscopy has been used to image DNA and protein concentrations [10

10. J. X. Cheng and X. S. Xie, “Coherent anti-Stokes Raman scattering microscopy: instrumentation, theory, and applications,” J. Phys. Chem. B 108, 827–840 (2004). [CrossRef]

], water distributions [13

13. E. Potma, W. P. de Boeij, P. J. van Haastert, and D. A. Wiersma, “Real-time visualization of intracellular hydrodynamics in single living cells,” Proc. Natl. Acad. Sci. U S A 98, 1577–1582 (2001). [CrossRef] [PubMed]

], and lipid transport in living cells [14

14. X. Nan, E. O. Potma, and X. S. Xie, “Nonperturbative chemical imaging of organelle transport in living cells with coherent anti-stokes Raman scattering microscopy,” Biophys. J. 91, 728–735 (2006). [CrossRef] [PubMed]

]. CARS microscopy has recently been used to image tissue in a living organism at the video-rate [12

12. C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U S A 102, 16807–16812 (2005). [CrossRef] [PubMed]

], allowing for real-time, chemically-selective imaging in vivo. Here we report the use of CARS microscopy to image fresh unfixed normal and diseased brain tissue, revealing microscopic scale structures in the brain without the need for tissue staining.

2. Materials and methods

2.1 Laser source

In order to minimize sample photodamage, the light source was operated in the near infrared. The laser system (Fig. 1) consisted of an optical parametric oscillator (OPO) [15

15. F. Ganikhanov, S. Carrasco, X. Sunney Xie, M. Katz, W. Seitz, and D. Kopf, “Broadly tunable dualwavelength light source for coherent anti-Stokes Raman scattering microscopy,” Opt. Lett. 31, 1292–1294 (2006). [CrossRef] [PubMed]

] (Levante Emerald, APE-Berlin) pumped by a frequency-doubled Nd:YVO4 modelocked laser. The pump laser (High-Q Laser Production GmbH) generates a 7 ps, 76 MHz pulse train of 1064 nm laser light with over 10 W of output power. An external temperature-controlled frequency doubler converts a large fraction of the fundamental, generating 4.5 W of 5 ps-duration 532 nm output. The OPO utilizes a temperature tuned, noncritically phase-matched LBO (LiB3O5) crystal for parametric downconversion. While the temperature of the crystal was set to generate near infrared signal and idler wavelengths, precise wavelength tuning was accomplished through the use of a stacked Lyot filter positioned in the cavity at Brewster’s angle. By using a noncritically phased matched crystal for optical parametric generation, the signal and idler wavelengths exit the laser cavity in a collinear fashion, with pulse trains automatically overlapped in time. The OPO can be continuously tuned over a wide wavelength range from 670 nm to 980 nm for the signal and between 1130 nm and 1450 nm for the idler output. This flexible light source was operated in two configurations to maximize either the total detected CARS signal or penetration depth. To maximize the penetration depth, the signal and idler pulse trains, already collinear and overlapped in time, were used as the pump and Stokes wavelengths, respectively, for the CARS process [Fig. 1(a)]. In a second configuration used to maximize the detected CARS signal, the signal beam was separated from the idler and combined with a portion of the remaining 1064 nm pulse train. Anti-Stokes light generated using this wavelength combination backscattered more efficiently due to its shorter wavelength and was detected at higher quantum efficiencies by a photomultiplier tube. The signal and 1064 nm pulse trains were overlapped in time using a delay stage and sent collinearly into the microscope [Fig. 1(b)].

2.2 Laser scanning microscope

The CARS images were acquired using a modified commercial beam-scanning microscope (Fig. 1(c), Olympus IX71/FV300). Typical commercial microscopes have poor throughput in the near infrared, with less than 1% total throughput at wavelengths near 1250 nm. In order to optimize the microscope throughput, several modifications were made. The stock galvanometric scanning mirrors were replaced with silver galvanometric mirrors and the tube lens was replaced with an Olympus-modified MgF2 coated lens for high throughput throughout the visible and near infrared spectrum. As CARS is a nonlinear process, confocal detectors are not necessary for imaging. The scanning confocal dichroic, therefore, was replaced by a silver mirror to ensure high reflectivity throughout the visible and NIR.

A 20X 0.75NA air objective (UPlanSApo UIS2, Olympus) was used for all imaging. This objective had excellent transmission properties and very low axial and lateral chromatic aberrations, allowing for large CARS signals with a full field of view. The anti-Stokes signal was separated from the pump and Stokes beams by a short-wave pass dichroic (750dcxr for signal and 1064 nm, z850rdc-xr for signal and idler, Chroma Technologies) and isolated using a stack of three 670 nm bandpass filters (30 nm bandwidth 42–3793, Coherent-Ealing) when 1064 nm was used as the Stokes wavelength, or a single 750 nm centered filter (hq750/210m, Chroma Technologies) when the signal and idler combination was used. A red-sensitive photomultiplier tube (R3896, Hammamatsu) was used as a nondescanned epi-detector. A motorized, calibrated scanning stage was used to translate the sample to build up large tissue mosaics.

Fig. 1. Diagrams of the CARS microscopy system. (a) Laser configuration used to maximize the CARS imaging depth. The signal and idler pulse trains of the OPO exit collinearly and overlapped in time. (b) Laser configuration used to maximize the detected epi-CARS signal. The signal of the OPO is combined with the synchronized 1064 nm output of the Nd:VYO4 on a dichroic mirror (DM). A delay stage is used to overlap the two pulse trains in time. (c) Schematic of the CARS microscope. An inverted microscope was modified to use a nondescanned epi-detector to maximize the epi-CARS signal from the tissue.

2.3 Raman spectroscopy

Raman spectra of tissue slices were taken using a Raman microspectrometer (Labram, Horiba Jobin Yvon). Regions of the specimen were identified using reflected light microscopy. Spectra were collected using a long working distance 20X air objective (LWD C A20 PL, 0.4NA, Olympus) with the confocal pinhole fully opened in order to sample a large tissue volume. A 632 nm HeNe laser provided 5 mW of power at the laser focus. The spectra from healthy brain tissues were collected using a 100s integration time and were averaged twice. Spectra collected from diseased brain samples were integrated for 200s and averaged twice.

2.4 Animal handling

Severe combined immune deficiency (SCID) and double transgenic APP and PS1AD mice were acquired from Taconic Farm Inc. All mice were male. The SCID mice were imaged at the age of 6 weeks while the APP and PS1AD mice were imaged at 16 months of age. The APP and PS1AD mice imaged showed no evidence of plaque formation. All animals were maintained and housed according to the guideline of IAUAC and the standards of the Animal Research and Comparative Medicine committee of Brigham and Women’s Hospital. To create a mouse model of a brain tumor, we injected human astrocytoma (a type of glioma) cells intracranially using a stereotactic system. Cells were cultured to subconfluence, trypsinized, washed twice in phosphate buffer solution (PBS), and resuspended (5×107/mL in 2 µl of PBS). 100,000 cells were injected into each mouse. Four weeks after the injection, the mice were sacrificed for brain harvest. After perfusion, the mouse brain was removed and kept in a 1% PBS solution prior to CARS imaging. Imaging sessions lasted no more than 90 minutes to keep the tissue section viable. 4% paraformaldehyde was used for fixation after each imaging session. Coronal sections of the mouse brain were acquired at approximately 2 mm posterior from the bregma. Each section had a thickness of 2 mm.

2.5 Imaging processing

In order to visualize the entire coronal brain slice, 20X, 700×700 µm CARS images were taken spanning the entire tissue sample using the motorized microscope stage. A median filter was used to remove shot noise caused by the photomultiplier tube. A background image taken at a sample location outside the brain region was used to roughly compensate for the uneven brightness of individual images, and a moving average filter was used to remove background noise. The individual images were then divided by the background image, and a MATLAB® (MathWorks, Natick, MA) routine was used to stitch the individual images into a mosaic of the entire specimen.

Fig. 2. Mosaic CARS microscopy image of a healthy APP and PS1AD mouse brain. Each single image is 700×700 µm in size. The brain slice was sectioned 2.8 mm posterior to the bregma. The single image enclosed by the white square is shown in Fig. 3. The pump and Stokes wavelengths were 816.7 and 1064 nm, respectively.

3. Results

3.1 Imaging large-scale brain structures

The 134-image, high-resolution mosaic CARS microscopy in Fig. 2 shows a coronal section from a healthy APP/PS1AD mouse brain. These images were produced by tuning into the 2845 cm-1 lipid CH2 symmetric stretching mode. Brain myelin located in the oligodendrocyte sheaths coating the axonal bundles is rich in CH bonds, and thus the lipid CARS contrast produced intense CARS signal and excellent image contrast [16

16. H. Wang, Y. Fu, P. Zickmund, R. Shi, and J. X. Cheng, “Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues,” Biophys. J. 89, 581–591 (2005). [CrossRef] [PubMed]

]. This section, taken 2.8 mm from the bregma, samples a wide range of brain structures containing both white and grey matter. Prominent white matter tracts that can be identified include the association fiber bundle in the centrum semiovale, the corpus callosum, and corticospinal tracts. Intense lipid band CARS signal arising from deep white matter tracts can be seen traversing the diencephalon and surrounding the deep brain nuclei. Fig. 3(a) shows a single 700×700 µm image of the margin of the corpus callosum, and illustrates the gray-white matter contrast, microstructural anatomy and cellular and tissue level resolution of these images. The ability to produce both intermediate spatial resolution images depicting gross anatomy and corresponding high resolution images from the same acquisition is a major advantage of this technique.

Fig. 3. (a). Single CARS image from Fig. 2. The pump and Stokes wavelengths were 816.7 and 1064 nm, respectively. (b) H&E image of the same region of the same mouse brain. Note the structures observable in both images, from upper left corner to the bottom right are the cortex, corpus callosum, oriens layer, and pyramidal layer.

As the signal strength in CARS images is due to the quadratic density of resonant vibrational modes, the observed contrast often differs significantly from familiar conventional histologic images. In order to validate the identification of structures in our CARS images with the histologic gold standard, tissue layers imaged with CARS were subsequently prepared and imaged using standard staining procedures. Figure 3(b) shows a 20X image of a haematoxylin and eosin (H&E) stained section taken at the same location approximately 50 µm deeper in the tissue from the CARS image in Fig. 3(a). The H&E stain is the most commonly used stain for medical diagnostics. The blue haematoxylin dye stains basophilic structures such as nuclei, while the pink eosin stains acidic cellular features such as proteins and fatty acids. The bright band in the CARS image [Fig. 3(a)] corresponds very well to the pink stained white matter bundle in the histological section. Dark regions in the CARS image at 2845 cm-1 are seen to correspond to the haemotaxylin-stained neuronal cell bodies. By comparing the contrast in the two images, the Oriens, Pyramidal, and CA1 layers of the hippocampus can also be identified in the CARS image, illustrating a level of cytoarchitectonic detail unattainable with conventional medical imaging techniques.

Fig. 4. (3.4 MB) Animation and three-dimensional reconstruction of a 50 µm depth stack in brain tissue taken with CARS microscopy. The first segment of the animation shows an uncorrected depth stack taken at 20x magnification in a coronal section containing the cortex (top) and white matter tracks streaming from the corpus collosum (bottom, not shown). At a depth of ~50 µm in the tissue, scattering from the white matter tracks has significantly decreased the CARS signal, while the CARS signal persists in the less scattering grey matter of the cortex. The second segment of the animation shows the three-dimensional reconstruction of the depth stack. [Media 1]

The maximum imaging depth was highly dependent on the wavelengths used and the local tissue environment. At the moderate laser powers used in this experiment (~100 mW), the 816.7 nm pump wavelength system was able to achieve a depth of approximately 50 µm in grey matter, and 25 µm in the white matter corpus collosum (Fig. 4). The longer wavelength 920 nm pump system, on the other hand, was able to probe much deeper: 80 µm in grey matter such as the cortex and 40 µm deep in the corpus collosum. White matter tracks contain many axon bundles that strongly scatter light. While such scattering does increase the backscattered epi-CARS signal, it can significantly perturb the incoming pump and Stokes light. The highly scattering nature of brain tissue causes an enlargement of the pump and Stokes focal volumes, disrupts the overlap of the two foci, and leads to a reduction in laser focal power at the focus, causing the third-order CARS signal to degrade with depth. This imaging depth will be extended through adaptive approaches in the future [17

17. F. Helmchen and W. Denk, “Deep tissue two-photon microscopy,” Nature 200, 5 (2002).

19

19. P. Marsh, D. Burns, and J. Girkin, “Practical implementation of adaptive optics in multiphoton microscopy,” Opt. Express 11, 1123–1130 (2003). [CrossRef] [PubMed]

].

3.2 Chemically-selective imaging

Fig. 5. Imaging the brain with chemical selectivity. All images were taken from the same brain used in the Fig. 2 mosaic. A nonresonant image was subtracted from (a) and (b). Regions of positive contrast (signal greater than the nonresonant background) are color coded green, while regions of negative contrast are color coded blue. (a) Positive lipid contrast image of CARS microscopy at 2845 cm-1. (b) Negative lipid contrast image of CARS microscopy at 2955 cm-1 showing the cell bodies with positive contrast, (c) Corresponding H&E image. Neuronal cell bodies appear blue due to the haemotaxylin stain, while axons are dyed pink by eosin. The Stokes wavelength was 1064 nm.

Fig. 6. Raman spectra acquired from fresh coronal slices of SCID mouse brain tissue immersed in PBS solution. The red line represents the Raman spectrum from the corpus collosum, while the blue line is the Raman spectrum from the nuclei-rich pyramidal layer of the hippocampus. Raman spectra were acquired in 100s and averaged twice.

The ability of CARS to provide such chemical selectivity allows for the reliable differentiation of cell body and axonal signatures, illustrating the potential of CARS microscopy for in situ imaging of unfixed brain tissues.

Fig. 7. CARS images of astrocytoma in a SCID mouse sacrificed 4 weeks after inoculation of tumor cells. The pump and Stokes wavelengths were 924.0 and 1254.2 nm, respectively. (a) A low resolution, large field of view mosaic CARS microscopy image provides chemically-selective anatomical information. Part (b) illustrates the ability of CARS to produce higher resolution images, in this case corresponding to the area enclosed by the rectangle in (a). This 80X, 175×175 µm image demonstrates the microscopic infiltration at the boundary between the tumor and normal tissue with a precision comparable to the conventional fixed tissue H&E histology images in Fig. 8.

3.3 Visualizing brain tumor margins

The chemically-selective contrast of CARS microscopy can be used to distinguish between healthy and diseased brain tissue. Figure 7(a) shows a full-brain mosaic of a SCID mouse brain containing a human astrocytoma. The CARS image, taken at the CH2 stretch to visualize lipid density, shows the extent of the tumor. The symmetry of the brain has been significantly altered by tumor growth with infiltration of the margin of the corpus callosum in the right hemisphere. Examining the tumor margin reveals the characteristic infiltration of normal white matter by invasive glioma [Fig. 7(b)]. In comparison, Fig. 8 shows an H&E microscopy image of a corresponding brain section from a different SCID mouse in the same group as the mouse imaged in Fig. 7. Comparison of this section with Fig. 7 reveals that the CARS image defines the tumor margin with comparable accuracy to the H&E stain. The tumor contrast in the CARS image arises from the destruction and displacement of normal white matter by the comparatively highly cellular and thus lipid deficient astrocytic glioma (Fig. 9). The low CARS signal level from the tumor made depth measurements difficult, though the penetration depth under these conditions is estimated to be ~50 µm when using a 920 nm pump and ~35 µm when using a 816.7 nm pump, based on literature tissue scattering parameters [25

25. S. C. Gebhart, W. C. Lin, and A. Mahadevan-Jansen, “In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling,” Phys. Med. Biol. 51, 2011–2027 (2006). [CrossRef] [PubMed]

].

Fig. 8. H&E images of a brain tumor harvested from a different SCID mouse in the same group as the mouse imaged in Fig. 7. The brain slice was taken at approximately the same location as that of Fig. 7. (a) The boundary between the tumor and normal tissue at 4X, (b) 10X, and (c) 20X magnification.

4. Discussion

The 20X magnification, coronal section lipid band CARS images clearly depict major mouse brain white and gray matter structures, including the corpus callosum, layers of the hippocampus, the thalamus, and cingulum. In addition, the high sensitivity and chemical selectivity achievable by CARS without the use of labeling agents should allow far more specific tissue contrast specificity in the future. By tuning the Raman shift to image DNA, RNA, and protein bands it should be possible to overlay images acquired at different vibrational bands to produce composite images reflecting the complex molecular combination of brain tissue. While imaging at such bands is currently limited by a nonresonant electronic background in the CARS signal, technological improvements [11

11. F. Ganikhanov, C. L. Evans, B. G. Saar, and X. S. Xie, “High sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy,” Opt. Lett. 31, 1872–1874 (2006). [CrossRef] [PubMed]

, 26

26. E. O. Potma, C. L. Evans, and X. S. Xie, “Heterodyne coherent anti-Stokes Raman scattering (CARS) imaging,” Opt Lett 31, 241–243 (2006). [CrossRef] [PubMed]

] over the past few years have lead to a dramatic increase in the sensitivity and selectivity of CARS microscopy. In particular, Frequency-Modulation CARS (FM-CARS) microscopy [11

11. F. Ganikhanov, C. L. Evans, B. G. Saar, and X. S. Xie, “High sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy,” Opt. Lett. 31, 1872–1874 (2006). [CrossRef] [PubMed]

] is especially promising for in vivo imaging. By rapidly toggling the Raman shift on and off a CARS resonance, FM-CARS suppresses the nonresonant background. In preliminary studies, this has increased sensitivity by three orders of magnitude, and new implementations of the technique currently underway promise further increases. Such improvements will enable future studies to determine the tumor detection sensitivity of CARS imaging systems.

Fig. 9. Raman spectrum acquired from the center of a large astrocytoma in a fresh coronal slice of a SCID mouse brain tissue immersed in PBS. There is minimal Raman intensity at 2852 cm-1 and 2880 cm-1, indicating the tissue is lipid deficient relative to normal brain tissue. The peaks at 2920 cm-1 and 2960 cm-1 arise due to the CH3 symmetric and anti-symmetric stretching vibrations. The large signal increase after 3000 cm-1 is due to the water stretching vibration of PBS solution. The spectrum was integrated for 200s and averaged twice.

Potential clinical applications of CARS microscopy would initially complement and perhaps ultimately replace traditional histopathology. These may include minimally invasive microendoscopy for neuropathologic diagnosis of neoplastic and non-neoplastic brain diseases. Recent reports of in vivo optical microendoscopy in living mice have demonstrated that minimally invasive optical imaging of deep brain tissues can be performed through a fiber optic microendoscope [27

27. A. Mehta, J. Jung, B. Flusberg, and M. Schnitzer, “Fiber optic in vivo imaging in the mammalian nervous system,” Curr. Opin. Neurobiol. 14, 11 (2004). [CrossRef]

, 28

28. B. A. Flusberg, J. C. Jung, E. D. Cocker, E. P. Anderson, and M. J. Schnitzer, “In vivo brain imaging using a portable 3.9 gram two-photon fluorescence microendoscope,” Opt. Lett. 30, 3 (2005). [CrossRef]

]. A CARS endoscope, based on similar miniaturized equipment, is technologically feasible and could be used to reduce or eliminate the need for brain tissue resection in definitive neurosurgical diagnosis of brain mass lesions [29

29. F. Légaré, C. L. Evans, F. Ganikhanov, and X. S. Xie, “Towards CARS Endoscopy,” Opt. Express 14, 4427–4432 (2006). [CrossRef] [PubMed]

]. A CARS-based microendoscope would also overcome any limitations in optical penetration depth. In addition to reducing the need for brain tissue biopsy in situ, CARS imaging may allow for more definitive diagnosis compared to the small fragmented samples obtained with needle biopsy, as images of intact tissue provide information about tissue architecture, vascularity, cellularity, inflammatory infiltration, mix of cell types and tissue invasion. This information, of great diagnostic significance in neuropathology, cannot be derived from the tissue samples obtained by current minimally invasive needle biopsy or suction extraction neurosurgical techniques. Moreover, because microendoscopy is minimally damaging to tissue, multiple samples could be acquired with greater safety than using current techniques, reducing the rate of non-diagnostic biopsies.

One example of an important neuropathologic problem that a CARS-based microendoscope could help to address would be the distinction of primary brain tumor from non-glial neoplasms, such as metastasis and lymphoma, and non-neoplastic mimics, such as infection, demyelination and ischemia. Differentiation of such tumor before resection of tissue would improve the safety and efficacy of definitive operative diagnosis of masses detected on conventional brain MRI. Furthermore, during definitive resection of glioma, CARS microendoscopy could be used to safely probe beyond the resection margin, allowing more complete tumor resection. Finally, in the neuropathology laboratory, CARS may also be able to augment traditional neuropathologic techniques by allowing non-destructive chemically specific analysis of small samples of excised tissue obtained at brain biopsy, as a complement to traditional light microscopy. This may improve the specificity of diagnosis and allow adequate tissue to be reserved for special stains and microarray analysis without the need for resection of larger tissue samples.

5. Conclusion

We demonstrate that CARS microscopy of fresh unfixed ex vivo mouse brain can be used to identify normal gray and white matter structures on several scales, to distinguish glioma from normal brain tissue, and to clearly identify the tumor margin. This data establishes the feasibility of CARS microscopy for tissue diagnosis of unstained brain tissue and provides the preliminary proof of principle needed to justify developing CARS-based techniques for imaging brain tissue in vivo. Although this dataset was intended only as a proof of principle, we found good agreements between the CARS microscopic images and H&E stained light microscopic images of the same specimen.

Acknowledgments

We thank Brian G. Saar for his help and contributions throughout the project. The work of X. Xu, S.T.C. Wong, and G. S. Young is partly supported by a program grant awarded to the Functional and Molecular Imaging Center of the Department of Radiology of Brigham and Women’s Hospital, Boston, MA. S. Kesari is supported by a Hagerty Award, Department of Neurosurgery, Brigham and Women’s Hospital, and the Leonard Florence Family and John Kenney Memorial Funds. The CARS microscopy development was funded by the NIH Director’s Pioneer Award to X.S. Xie.

§These two authors contributed equally to this work

References and links

1.

D. D. Langleben and G. M. Segall, “PET in differentiation of recurrent brain tumor from radiation injury,” J. Nucl. Med. 41, 1861–1867 (2000). [PubMed]

2.

M. C. Preul, R. Leblanc, Z. Caramanos, R. Kasrai, S. Narayanan, and D. L. Arnold, “Magnetic resonance spectroscopy guided brain tumor resection: differentiation between recurrent glioma and radiation change in two diagnostically difficult cases,” Can. J. Neurol. Sci. 25, 13–22 (1998). [PubMed]

3.

Y. Ge, M. Law, and R. I. Grossman, “Applications of diffusion tensor MR imaging in multiple sclerosis,” Ann. N Y Acad. Sci. 1064, 202–219 (2005). [CrossRef]

4.

D. Goldberg-Zimring, A. U. Mewes, M. Maddah, and S. K. Warfield, “Diffusion tensor magnetic resonance imaging in multiple sclerosis,” J. Neuroimaging 15, 68S–81S (2005). [CrossRef] [PubMed]

5.

W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, “Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation,” Proc. Natl. Acad. Sci. U S A 100, 7075–7080 (2003). [CrossRef] [PubMed]

6.

P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Threedimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J. 81, 493–508 (2002). [CrossRef]

7.

W. R. Zipfel, R. M. Williams, and W. W. Webb, “Nonlinear magic: multiphoton microscopy in the biosciences,” Nat. Biotechnol. 21, 1369–1377 (2003). [CrossRef] [PubMed]

8.

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Am. J. of Physiol. 268, H802 (1995).

9.

J. G. Fujimoto, “Optical coherence tomography - a review of the principles and contemporary uses in retinal investigation,” Nat. Biotechnol. 21, 1361–1367 (2003). [CrossRef] [PubMed]

10.

J. X. Cheng and X. S. Xie, “Coherent anti-Stokes Raman scattering microscopy: instrumentation, theory, and applications,” J. Phys. Chem. B 108, 827–840 (2004). [CrossRef]

11.

F. Ganikhanov, C. L. Evans, B. G. Saar, and X. S. Xie, “High sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy,” Opt. Lett. 31, 1872–1874 (2006). [CrossRef] [PubMed]

12.

C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U S A 102, 16807–16812 (2005). [CrossRef] [PubMed]

13.

E. Potma, W. P. de Boeij, P. J. van Haastert, and D. A. Wiersma, “Real-time visualization of intracellular hydrodynamics in single living cells,” Proc. Natl. Acad. Sci. U S A 98, 1577–1582 (2001). [CrossRef] [PubMed]

14.

X. Nan, E. O. Potma, and X. S. Xie, “Nonperturbative chemical imaging of organelle transport in living cells with coherent anti-stokes Raman scattering microscopy,” Biophys. J. 91, 728–735 (2006). [CrossRef] [PubMed]

15.

F. Ganikhanov, S. Carrasco, X. Sunney Xie, M. Katz, W. Seitz, and D. Kopf, “Broadly tunable dualwavelength light source for coherent anti-Stokes Raman scattering microscopy,” Opt. Lett. 31, 1292–1294 (2006). [CrossRef] [PubMed]

16.

H. Wang, Y. Fu, P. Zickmund, R. Shi, and J. X. Cheng, “Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues,” Biophys. J. 89, 581–591 (2005). [CrossRef] [PubMed]

17.

F. Helmchen and W. Denk, “Deep tissue two-photon microscopy,” Nature 200, 5 (2002).

18.

M. Rueckel, J. A. Mack-Bucher, and W. Denk, “Adaptive wavefront correction in two-photon microscopy using coherence-gated wavefront sensing,” Proc. Natl. Acad. Sci. U S A 103, 17137 (2006). [CrossRef] [PubMed]

19.

P. Marsh, D. Burns, and J. Girkin, “Practical implementation of adaptive optics in multiphoton microscopy,” Opt. Express 11, 1123–1130 (2003). [CrossRef] [PubMed]

20.

M. Müller and J. M. Schins, “Imaging the thermodynamics state of lipid membranes with multiplex CARS microscopy,” J. Phys. Chem. B 106, 3715–3723 (2002). [CrossRef]

21.

J. X. Cheng, A. Volkmer, L. D. Book, and X. S. Xie, “Multiplex coherent anti-Stokes Raman scattering microspectroscopy and study of lipid vesicles,” J. Phys. Chem. B 106, 8493–8498 (2002). [CrossRef]

22.

E. O. Potma, C. Evans, X. S. Xie, R. J. Jones, and J. Ye, “Picosecond-pulse amplification with an external passive optical cavity,” Opt. Lett. 28, 1835–1837 (2003). [CrossRef] [PubMed]

23.

A. Mizuno, T. Hayashi, K. Tashibu, S. Maraishi, K. Kawauchi, and Y. Ozaki, “Near-infrared FT-Raman spectra of the rat brain tissues,” Neurosci. Lett. 141, 47–52 (1992). [CrossRef] [PubMed]

24.

C. W. Ong, Z. X. Shen, Y. He, T. Lee, and S. H. Tang, “Raman Microspectroscopy of the brain tissues in the substantia nigra and MPRP-induced Parkinson’s disease,” J. Raman Spectrosc. 30, 91–96 (1999). [CrossRef]

25.

S. C. Gebhart, W. C. Lin, and A. Mahadevan-Jansen, “In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling,” Phys. Med. Biol. 51, 2011–2027 (2006). [CrossRef] [PubMed]

26.

E. O. Potma, C. L. Evans, and X. S. Xie, “Heterodyne coherent anti-Stokes Raman scattering (CARS) imaging,” Opt Lett 31, 241–243 (2006). [CrossRef] [PubMed]

27.

A. Mehta, J. Jung, B. Flusberg, and M. Schnitzer, “Fiber optic in vivo imaging in the mammalian nervous system,” Curr. Opin. Neurobiol. 14, 11 (2004). [CrossRef]

28.

B. A. Flusberg, J. C. Jung, E. D. Cocker, E. P. Anderson, and M. J. Schnitzer, “In vivo brain imaging using a portable 3.9 gram two-photon fluorescence microendoscope,” Opt. Lett. 30, 3 (2005). [CrossRef]

29.

F. Légaré, C. L. Evans, F. Ganikhanov, and X. S. Xie, “Towards CARS Endoscopy,” Opt. Express 14, 4427–4432 (2006). [CrossRef] [PubMed]

OCIS Codes
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(170.4730) Medical optics and biotechnology : Optical pathology
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(180.6900) Microscopy : Three-dimensional microscopy
(300.6230) Spectroscopy : Spectroscopy, coherent anti-Stokes Raman scattering
(300.6450) Spectroscopy : Spectroscopy, Raman

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: June 25, 2007
Revised Manuscript: August 24, 2007
Manuscript Accepted: August 31, 2007
Published: September 6, 2007

Virtual Issues
Vol. 2, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Conor L. Evans, Xiaoyin Xu, Santosh Kesari, X. Sunney Xie, Steven T. C. Wong, and Geoffrey S. Young, "Chemically-selective imaging of brain structures with CARS microscopy," Opt. Express 15, 12076-12087 (2007)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-15-19-12076


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References

  1. D. D. Langleben, and G. M. Segall, "PET in differentiation of recurrent brain tumor from radiation injury," J. Nucl. Med. 41, 1861-1867 (2000). [PubMed]
  2. M. C. Preul, R. Leblanc, Z. Caramanos, R. Kasrai, S. Narayanan, and D. L. Arnold, "Magnetic resonance spectroscopy guided brain tumor resection: differentiation between recurrent glioma and radiation change in two diagnostically difficult cases," Can. J. Neurol. Sci. 25, 13-22 (1998). [PubMed]
  3. Y. Ge, M. Law, and R. I. Grossman, "Applications of diffusion tensor MR imaging in multiple sclerosis," Ann. N Y Acad. Sci. 1064, 202-219 (2005). [CrossRef]
  4. D. Goldberg-Zimring, A. U. Mewes, M. Maddah, and S. K. Warfield, "Diffusion tensor magnetic resonance imaging in multiple sclerosis," J. Neuroimaging 15, 68S-81S (2005). [CrossRef] [PubMed]
  5. W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, "Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation," Proc. Natl. Acad. Sci. U S A 100, 7075-7080 (2003). [CrossRef] [PubMed]
  6. P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, "Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues," Biophys. J. 81, 493-508 (2002). [CrossRef]
  7. W. R. Zipfel, R. M. Williams, and W. W. Webb, "Nonlinear magic: multiphoton microscopy in the biosciences," Nat. Biotechnol. 21, 1369-1377 (2003). [CrossRef] [PubMed]
  8. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, "In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography," Am. J. of Physiol. 268, H802 (1995).
  9. J. G. Fujimoto, "Optical coherence tomography - a review of the principles and contemporary uses in retinal investigation," Nat. Biotechnol. 21, 1361--1367 (2003). [CrossRef] [PubMed]
  10. J. X. Cheng, and X. S. Xie, "Coherent anti-Stokes Raman scattering microscopy: instrumentation, theory, and applications," J. Phys. Chem. B 108, 827-840 (2004). [CrossRef]
  11. F. Ganikhanov, C. L. Evans, B. G. Saar, and X. S. Xie, "High sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy," Opt. Lett. 31, 1872-1874 (2006). [CrossRef] [PubMed]
  12. C. L. Evans, E. O. Potma, M. Puoris'haag, D. Côté, C. P. Lin, and X. S. Xie, "Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy," Proc. Natl. Acad. Sci. U S A 102, 16807-16812 (2005). [CrossRef] [PubMed]
  13. E. Potma, W. P. de Boeij, P. J. van Haastert, and D. A. Wiersma, "Real-time visualization of intracellular hydrodynamics in single living cells," Proc. Natl. Acad. Sci. U S A 98, 1577-1582 (2001). [CrossRef] [PubMed]
  14. X. Nan, E. O. Potma, and X. S. Xie, "Nonperturbative chemical imaging of organelle transport in living cells with coherent anti-stokes Raman scattering microscopy," Biophys. J. 91, 728-735 (2006). [CrossRef] [PubMed]
  15. F. Ganikhanov, S. Carrasco, X. Sunney Xie, M. Katz, W. Seitz, and D. Kopf, "Broadly tunable dual-wavelength light source for coherent anti-Stokes Raman scattering microscopy," Opt. Lett. 31, 1292-1294 (2006). [CrossRef] [PubMed]
  16. H. Wang, Y. Fu, P. Zickmund, R. Shi, and J. X. Cheng, "Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues," Biophys. J. 89, 581-591 (2005). [CrossRef] [PubMed]
  17. F. Helmchen, and W. Denk, "Deep tissue two-photon microscopy," Nature 200, 5 (2002).
  18. M. Rueckel, J. A. Mack-Bucher, and W. Denk, "Adaptive wavefront correction in two-photon microscopy using coherence-gated wavefront sensing," Proc. Natl. Acad. Sci. U S A 103, 17137 (2006). [CrossRef] [PubMed]
  19. P. Marsh, D. Burns, and J. Girkin, "Practical implementation of adaptive optics in multiphoton microscopy," Opt. Express 11, 1123--1130 (2003). [CrossRef] [PubMed]
  20. M. Müller, and J. M. Schins, "Imaging the thermodynamics state of lipid membranes with multiplex CARS microscopy," J. Phys. Chem. B 106, 3715-3723 (2002). [CrossRef]
  21. J. X. Cheng, A. Volkmer, L. D. Book, and X. S. Xie, "Multiplex coherent anti-Stokes Raman scattering microspectroscopy and study of lipid vesicles," J. Phys. Chem. B 106, 8493-8498 (2002). [CrossRef]
  22. E. O. Potma, C. Evans, X. S. Xie, R. J. Jones, and J. Ye, "Picosecond-pulse amplification with an external passive optical cavity," Opt. Lett. 28, 1835-1837 (2003). [CrossRef] [PubMed]
  23. A. Mizuno, T. Hayashi, K. Tashibu, S. Maraishi, K. Kawauchi, and Y. Ozaki, "Near-infrared FT-Raman spectra of the rat brain tissues," Neurosci. Lett. 141, 47-52 (1992). [CrossRef] [PubMed]
  24. C. W. Ong, Z. X. Shen, Y. He, T. Lee, and S. H. Tang, "Raman Microspectroscopy of the brain tissues in the substantia nigra and MPRP-induced Parkinson's disease," J. Raman Spectrosc. 30, 91-96 (1999). [CrossRef]
  25. S. C. Gebhart, W. C. Lin, and A. Mahadevan-Jansen, "In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling," Phys. Med. Biol. 51, 2011-2027 (2006). [CrossRef] [PubMed]
  26. E. O. Potma, C. L. Evans, and X. S. Xie, "Heterodyne coherent anti-Stokes Raman scattering (CARS) imaging," Opt Lett 31, 241-243 (2006). [CrossRef] [PubMed]
  27. A. Mehta, J. Jung, B. Flusberg, and M. Schnitzer, "Fiber optic in vivo imaging in the mammalian nervous system," Curr. Opin. Neurobiol. 14, 11 (2004). [CrossRef]
  28. B. A. Flusberg, J. C. Jung, E. D. Cocker, E. P. Anderson, and M. J. Schnitzer, "In vivo brain imaging using a portable 3.9 gram two-photon fluorescence microendoscope," Opt. Lett. 30, 3 (2005). [CrossRef]
  29. F. Légaré, C. L. Evans, F. Ganikhanov, and X. S. Xie, "Towards CARS Endoscopy," Opt. Express 14, 4427-4432 (2006). [CrossRef] [PubMed]

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