Photonics West 2008 Proceedings

An accurate homogenized tissue phantom for broad spectrum autofluorescence studies: a tool for optimizing quantum dot-based

contrast agents

Mathieu Roya, Brian C. Wilsonab* aDepartment of Medical Biophysics, University of Toronto, Toronto, ON

bOntario Cancer Institute, University Health Network, Toronto, ON

ABSTRACT

We are investigating the use of ZnS-capped CdSe quantum dot (QD) bioconjugates combined with fluorescence endoscopy for improved early cancer detection in the esophagus, colon and lung. A major challenge in using fluorescent contrast agents in vivo is to extract the relevant signal from the tissue autofluorescence (AF). The present studies are aimed at maximizing the QD signal to AF background ratio (SBR) to facilitate detection. These contrast optimization studies require optical phantoms that simulate tissue autofluorescence, absorption and scattering over the entire visible spectrum, while allowing us to control the optical thickness. We present an optical phantom made of fresh homogenized tissue diluted in water. The homogenized tissue is poured into a clear polymer tank designed to hold a QD-loaded silica capillary in its center. Because of the non-linear effects of absorption and scattering on measured autofluorescence, direct comparison between results obtained using tissue phantoms of different concentration is not possible. We introduce mathematical models that make it possible to perform measurements on diluted tissue homogenates and subsequently extrapolate the results to intact (non-diluted) tissue. Finally, we present preliminary QD contrast data showing that the 380-420 nm spectral window is optimal for surface QD imaging. Keywords: quantum dots, optimization, contrast, autofluorescence, homogenized tissue, microfluidics, optical

properties, extrapolation, optical phantom, signal-to-background ratio

1. INTRODUCTION In the context of on-going efforts to improve early cancer detection in the gastro-intestinal tract [1,2], we are investigating the use of novel quantum dot (QD) bioconjugates as contrast agents for fluorescence endoscopy. Although QD-based molecular imaging has great potential [3,4], a number of important issues need to be addressed. Major challenges arise in finding relevant cancer biomarkers, solving QD toxicity problems [5], understanding bioconjugate pharmacokinetics [6], optimizing the QD bioconjugation chemistry [7] and optimizing imaging contrast. In this paper, we focus on the imaging contrast issues. One key parameter for contrast optimization is selection of the excitation and emission wavelengths. While Lim et al.[8] have identified a number of ideal QD emission spectral windows, little work has been done to date to understand the effect of the excitation wavelength on the image contrast. Moreover, the results of Lim et al. suggest the use of infrared emitting QDs, which are still under development. Most commercially available QDs emit in the visible, where tissue autofluorescence is the major source of background signal. Hence, we have developed experimental and mathematical models to identify the optimal excitation wavelength for QD imaging in autofluorescence-dominated tissues [9]. In the context of endoscopy applications, the current investigation is restricted to surface and sub-surface (< 500 µm) measurements.

* [email protected]; phone 416-946-2952; fax 416-946-6529

Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurements of Tissue, edited byRobert J. Nordstrom, Proc. of SPIE Vol. 6870, 68700E, (2008) · 1605-7422/08/$18 · doi: 10.1117/12.764639

Proc. of SPIE Vol. 6870 68700E-1

Optical tissue phantoms are crucial for the optimization studies. In particular, to address the optimal excitation, we need to image QDs embedded in a spatially homogenous material of known optical absorption, scattering and autofluorescence across the excitation wavelengths of interest (here 360-550 nm). It is also necessary to know accurately the QD depth below the tissue surface. Finding a combination of artificial materials having the correct spectral properties to simulate biological tissues over a broad range of wavelengths is challenging if not impossible. An alternative is to use fresh ex vivo tissue samples in the required spatial configuration. Our first phantom consisted of QDs located in a polymer microchannel imaged through frozen tissue sections [9]. This approach has several limitations. First, slicing fresh or frozen tissue to thicknesses in the 50-500 µm range is non trivial. Most microtomes capable of making thick (>20 µm) sections require fixed samples, which exhibit altered scattering properties. One option is to stack multiple 10 µm frozen sections. However, these samples tend to dehydrate rapidly, resulting in tissue shrinkage and altered autofluorescence. Measuring the thickness of frozen tissue sections to the required accuracy is also problematic. To avoid these problems, we have used the alternative approach to homogenize the tissue and dilute it to obtain an aqueous suspension. Micro-pipetting then allows precise adjustment of the sample thickness, which is scaled relative to intact (non-diluted) tissue. The main problem with this method is that, unlike the absorption and scattering properties, the autofluorescence does not scale linearly with dilution. Modeling is therefore required to determine the equivalent intact-tissue behaviour. This paper describes how diluted tissue homogenates can be used as optical phantoms to evaluate the optimal excitation wavelength for QD-based fluorescence endoscopy. Details on the phantom preparation are given, together with initial results showing that tissue autofluorescence has a significant influence on the optimal excitation wavelength for visible QD imaging. Moreover, we demonstrate how results obtained with diluted tissue homogenates can be extrapolated to homogenates of lower dilution and intact tissues.

2. METHODS

2.1. Quantum dot preparation Two 600 nm peak emission ZnS/CdSe QD solutions were used: a lysine cross-linked water-soluble type (LM-QD600) and a TOPO capped chloroform-soluble type (TOPO-QD600). The TOPO-QDs are ideal for phantom experiments due to their high quantum yield and long-term photostability. However, they are not biocompatible, so that water-soluble QDs must be used for bioconjugation, cell or tissue experiments. All the figures and data presented here were obtained using the LM-QD600, except for Figure 5 and Figure 13. Water soluble quantum dots were prepared as described by Jiang et al. [10]. QD absorbance and fluorescence spectra (Figure 1) were acquired using a spectrometer (Cary 300, Varian Inc., Palo Alto, CA, USA) and a scanning spectrofluorometer (Fluorolog 3, Horiba Jobin-Yvon, Edison, NJ, USA), respectively.

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Figure 1. Normalized absorption (dashed curve) and fluorescence emission (solid curve) spectra of the LM-QD600 solution (left)

and the TOPO-QD600 solution (right).


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2.2. Capillary design A 3m long, 100x100um (inner dimensions), 300x300um (outer dimensions) square silica capillary was purchased from Polymicro, Phoenix, AZ. For each experiment, a 6cm length was cleaved using a scalpel. The opaque polyimide coating was removed by exposing it to a flame and then wiping off the carbonized residues with ethanol. The capillary was loaded with QDs by capillary action by dipping one end in a vial containing the QD solution. To prevent evaporation of the solution during imaging, both ends were gently sealed with a small amount of grease.

Figure 2. Schematic of the silica square capillary.

2.3. Homogenized tissue preparation Fresh whole beef liver and pork kidney cortex were homogenized using a commercial blender and then diluted with tap water to 5%, 10%, 25%, 50% or used undiluted (100%). The first three suspensions were passed through a sieve to remove fragments > ~ 100um. All the data were obtained using samples from the same kidney and liver, except for Figure 5 and Figure 13. For the QD contrast measurements, the homogenized tissue was poured into a custom-made clear polymer tank comprising equal size upper and lower sections that can be screwed together, with rubber seals to ensure water tightness. The QD-loaded capillary can then be held in the center, with both ends lying outside the tank. The volume of homogenized tissue can be adjusted with a pipette (for tissue concentrations < 50% only) to control the optical thickness, z, above the capillary.

Figure 3. 3D rendering (left) and cross-section schematic (right) of the custom-made tank used to make the homogenized tissue measurements. The holes in the four corners are used to screw the two parts together, which compresses the capillary in the middle. Images are taken from the top, in reflectance geometry.


2.4. Tissue optical properties The tissue optical absorption and transport scattering spectra were determined by measuring the diffuse reflectance and transmittance using an integrating sphere setup. A mercury/xenon lamp (Spectral Energy, Washingtonville, NY, USA) and collimating optics were used to illuminate the sample and the transmitted and reflected spectra were measured with a 15 cm diameter integrating sphere (SphereOptics, Contoocook, NH, USA) using a portable spectrometer (SD2000, Ocean Optics, Dunedin, FL, USA). Optical properties were retrieved using an inverse calculation based on Monte Carlo-generated look-up tables [11]. The accuracy of the technique was determined by measuring mixtures of Intralipid-20% and methylene blue of known scattering and absorption spectra, respectively. 16 different solutions (4 concentrations of Intralipid-20% x 4 concentrations of methylene blue) were prepared and measured.

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Figure 4. Measured (thin solid lines) absorption (left) and scattering (right) coefficient spectra of 16 standard Intralipid-MB solutions calculated from diffuse reflectance and transmittance measurements. The expected spectra for µa and µ’s (thick dashed lines) were obtained from a calibrated spectrophotometer measurement and from Mie scattering calculations [12], respectively.

The measurements matched the expected spectra relatively well over a broad range of wavelengths, except for discrepancies at low µa values. However, this is not critical for the tissue measurements here, since most tissues are highly absorbing below 600 nm.

2.5. Tissue autofluorescence Tissue autofluorescence (AF) spectra were obtained by imaging thick tissue slices with an epifluorescence stereomicroscope (MZFLIII, Leica Microsystems, Richmond Hill, ON, Canada) using 12 different excitation filters and one emission filter. Excitation filters had a 20 nm bandwidth and ranged from 365 nm to 546 nm center wavelength. The emission filter, 600 nm center wavelength and 50 nm bandwidth, was selected according to the QD emission profile. A long-pass filter (500 nm) was used in combination with the emission filters to block residual excitation light. For each excitation filter, a fluorescence image was captured with a CCD camera (Coolsnap K4, Photometrics, Tucson, AZ, USA), and the average pixel intensity and standard deviation were computed offline (Matlab 7, The Mathworks Inc., Natick, MA, USA). The standard deviation values were used as an indicator of tissue AF heterogeneity and as a contribution to the estimated errors. Each spectrum was corrected for the excitation lamp profile, which was measured with a power meter (Gentec Electro-Optics, Quebec City, QC, Canada). Spectra were also corrected for acquisition time, to yield units of pixel intensity per mW·s. Autofluorescence excitation spectra were also collected for the homogenized tissue samples using a Fluorolog 3 spectrofluorometer. For these measurements, the tissue homogenates were poured into a 80 ml plastic container. The autofluorescence was collected with a fiber-optic probe consisting of one excitation fiber surrounded by 6 emission fibers. The probe was brought in gentle contact with the homogenates for each measurement.


2.6. Signal-to-background ratio measurements The SBR measurements (SBRm) were done as previously described [9]. The signal-to-background ratio was calculated by dividing the average pixel intensity of the microchannel region (XCH) by the average pixel intensity of the autofluorescence region (XAF):

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Figure 5. Imaging layout used to measure the SBRm. This example shows TOPO-QD600 imaged through a 625µm layer of 10% diluted kidney homogenate, using a 510 nm excitation filter and a 600 nm emission filter. The SBRm value obtained from this image from Equation 1 is 2.88 ± 0.02.

2.7. Extrapolation of SBR data The extrapolation algorithm is based on the following assumptions: 1) the proportion of excitation light that reaches the QDs decays exponentially as a function of tissue thickness; 2) the attenuation coefficient varies with excitation wavelength; 3) in the XCH region, the proportion of autofluorescence shielded by the QDs is negligible; 4) the variation of autofluorescence spectra as a function of dilution can be evaluated empirically from spectrofluorometer measurements. Assumptions 1) and 2) leads to the following equation:

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( )( ) eff ex zQD QDX z X e µ λ−= (2)

, where XQD0 is the QD signal measured when the capillary lies on the surface of the homogenate (z = 0) and µeff (λex) is the wavelength-dependent attenuation coefficient. Assumptions 3) lead to the following equations: ( ) ( )CH QD AFX z X z X= + (3)

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( 0)QD CH AFX X z X= = − (4) Substitution of Equation 2 and 3 into Equation 1 leads to a new expression for the SBR, which will be used throughout to extrapolate the measured SBR data. The extrapolated SBR data is denoted by SBRe:

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Note that, as z increases SBR → 1 (no contrast). Also, Equation 5 indicates that, if the SBR is known when the QDs are located at the surface (z = 0), then the value at any depth can be extrapolated as long as the attenuation coefficient µeff is known. This means that knowledge of the main optical properties of the tissue (µa, µs and AF(λex)) should be sufficient to predict the SBR for QDs located at any depth. Several attempts were made to find a relationship between the autofluorescence spectrum and the dilution factor through intrinsic fluorescence spectroscopy. The best results were obtained by using empirically-derived transfer functions to estimate how autofluorescence varies with dilution. We used the fluorometer measurements as a reference to get these functions. The autofluorescence at concentration C% derived from a measurement made with a 10% tissue homogenate sample is given by

%% 10%

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, where AF represent autofluorescence measurements made with the microscope method and Fl represents measurement made with the fluorometer.

3. RESULTS AND DISCUSSION

3.1. Optical properties Figure 6 shows the optical properties of 25% kidney and liver homogenates measured using the method described in section 2.4. Both the absorption and scattering coefficients scale linearly with dilution (data not shown). The features in the absorption spectra correspond to those of hemoglobin. The effective attenuation coefficient was calculated with the following approximation:

( ' )eff a a sµ µ µ µ= + (7)

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Figure 6. Measured kidney (left) and liver (right) absorption (solid curves), reduced scattering (dashed curves) and calculated effective attenuation coefficients (dotted curves).

3.2. Tissue autofluorescence The success of the tissue homogenate method lies in the ability to transfer measured SBR data back to intact tissues (100%). However, this requires knowledge of the autofluorescence spectrum as a function of dilution. Figure 9 shows AF excitation spectrofluorometer data using homogenate samples of increasing dilution. The AF spectra vary non-linearly with dilution. As the concentration approaches 100%, the spectra tend to red-shift and drop in intensity.


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Figure 7. Kidney (left) and liver (right) homogenate autofluorescence excitation spectra measured with the spectrofluorometer. Each curve represents a different dilution factor (5%, 10%, 25%, 50% and 100%).

This non-linear behaviour is due to the effects of tissue absorption and scattering, which increase with concentration of absorbing and scattering components since the effective volume of fluorophores reached by the excitation light decreases. Since the tissues absorb and scatter more at shorter wavelengths (Figure 6), the measured AF shifts to the red at high concentration. Ideally, this non-linear behaviour would be modeled with simple mathematical expressions, but this is not trivial. Müller et al. [13] successfully removed the effects of absorption and scattering from measured AF spectra using intrinsic fluorescence spectroscopy. The technique consists of collecting both diffuse reflectance and fluorescence spectra with the same probe. Using light diffusion models, the diffuse reflectance measurements provide estimates for µa and µ’s values, which are used to process the measured fluorescence spectrum and retrieve the intrinsic fluorescence. Unfortunately, we have not been able to reproduce their results to date, due to inconsistencies in our reflectance measurements. We opted instead for an extrapolation technique described below (Figure 8).

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Figure 8. Example of the extrapolation technique used to estimate the AF spectra at 100% from measurements made using a lower concentration liver homogenate. Following Equation 6, the AF spectrum measured from 10% homogenate with the imaging method (circles) was multiplied by the ratio of the 100% (dashed curve) and the 10% (solid curve) AF spectra measured with the spectrofluorometer. The result is an extrapolated 100% liver AF spectrum (Figure 9d, circles).


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Figure 9. a) Measured homogenized kidney and b) liver autofluorescence excitation spectra at 600 nm emission for 10% (circles) and 25% (squares) dilution. These data were then extrapolated (Figure 8) to obtain 100% AF estimates (c,d).

Figure 7 shows that 10% liver AF measurements made using the imaging technique and the spectrofluorometer differ significantly. Possible reasons for this discrepancy are the difference in excitation-collection geometry between the two methods and the fact that spectral response of the instrument has not yet been corrected for (spectrofluorometer method). Despite this, the extrapolation of 100% AF spectra from 10% and 25% dilutions leads to similar results (Figure 9c,d), indicating that the respective correction factors for both methods approximately cancel when applying Equation 6.

3.3. QD contrast measurements Figure 10 demonstrates graphically how Equations 2-5 are used to estimate the pure QD fluorescence signal (Figure 10b) and the effective attenuation coefficient µeff (Figure 10d) from a series of SBR spectra measured from a diluted tissue homogenate. First, note that the SBR amplitude drops gradually, for all wavelengths, as the QD depth increases (Figure 10a). The SBR amplitude also tends toward a value of 1 (no contrast), as described by Equation 5. The pure QD fluorescence spectra (Figure 10b) were obtained by multiplying each SBR curve by the 10% liver AF spectrum and then subtracting the same 10% liver AF spectrum (Figure 9b), as described by Equation 5. Equation 2 indicates that the QD fluorescence signal should decay exponentially with depth at a rate µeff. The QD signal series plotted as a function of depth (Figure 10c) could be fitted to single exponentials. The decay rate (log slope) was then calculated at each wavelength. Since µeff scales linearly with tissue concentration, division by the dilution factor yields an estimated µeff value for non-diluted tissue (100%). Figure 10d shows a comparison between the 100% µeff spectra obtained from 10% and 25% dilutions, which are similar in spectral shape and amplitude. Their spectral shape also resembles that of µeff measured with the integrating sphere technique.

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Figure 10. Estimation of pure QD spectra and effective attenuation coefficients from SBR series measured from 10% liver homogenate. a) Measured SBR spectra at increasing QD depth (from 0 to 625µm) in 10% liver homogenate. Each curve corresponds to a distinct QD depth value. b) Estimated pure QD spectra at corresponding depths. c) The decrease in QD fluorescence as a function of depth is accurately modeled by decaying exponentials (4 distinct wavelengths shown). d) The effective attenuation coefficient (µeff) spectra estimated from 10% (circles) and 25% (squares) liver SBR series are displayed in comparison with data obtained from the integrating sphere technique (Figure 6).

So far, we have demonstrated extrapolation of 100% AF spectrum and µeff spectrum from 10% or 25% diluted homogenate measurements. Substitution into Equation 5 then yields complete SBR series at any given QD depth. Figure 11 and Figure 12 show all the SBR spectra measured and extrapolated from kidney and liver diluted homogenates, respectively.

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Figure 11. Kidney SBR spectra measured from a) 10% and b) 25% diluted homogenates using the imaging method described in section 2.6. c) and d) shows non-diluted (100%) SBR spectra extrapolated from 10% and 25% kidney dilutions, respectively, using the method described by Equations 2-5. The overlapping of the curves for z = 0 and z = 125 µm in (a) is caused by an experimental error that sometimes occurs due to the difficulty of setting the volume of tissue homogenate so that the capillary lies right at the tissue surface.

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Figure 12. Liver SBR spectra measured from a) 10% and b) 25% diluted homogenates using the imaging method described in section 2.6. c) and d) shows non-diluted (100%) SBR spectra extrapolated from 10% and 25% liver dilutions, respectively, using the method described by Equations 3 to 6.

Observation of these results leads to significant conclusions concerning contrast optimization of QDs in tissue. First, note that the SBR at z = 0 only depends on QD0 and on the tissue AF spectra. Since the QD0 spectrum is relatively flat between 360 and 550 nm (slight negative slope with increasing wavelength), most of the spectral features in the SBR spectrum come from the autofluorescence. Hence, most SBR spectra (circles) at z = 0 resemble an inverse of the corresponding AF spectrum. For both kidney and liver, all AF spectra have minima around 400 nm and 550 nm, and maxima between 450 and 500 nm, which translates into maxima and minima on the SBR spectra, respectively. However, since µeff is maximal around 400 nm, the 400 nm maximum on the SBR spectra tends to decay faster as a function of QD depth than the 550 nm maximum. For both kidney and liver, extrapolation of the 10% and 25% dilution measurements leads to almost identical 100% SBR spectra in terms of amplitude and spectral features. This supports the initial motivation to avoid the technical difficulties associated with manipulation of fresh ex vivo tissues by making measurements using physically thicker diluted tissue homogenates. These measurements can then be extrapolated to estimate the behavior of intact tissue samples. To further validate this novel technique, an extra experiment was made on kidney homogenates, comparing the extrapolated 100% SBR spectra with a real measurement made on a non-diluted homogenate at z = 0 (Figure 13).

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Figure 13. Direct measurement of 10% kidney homogenate SBR (left) and corresponding extrapolated 100% spectra (right). Although the tissues come from a different animal, these results are similar to those shown in Figure 11. A measured 100% SBR spectrum (black dots) is shown for comparison with the extrapolated data.

The extrapolated and measured 100% SBR curves at z = 0 were in good agreement. Moreover, this extra experiment suggests reproducibility of the method: similar results are obtained using similar tissue samples and similar QDs (in comparison with Figure 11). SBR measurements at increasing QD depth could not be performed since it is practically impossible to control the volume of 100% tissue homogenate to obtain accurate z values smaller than 50 µm, due to its texture. This general approach has some limitations. First, the variation of autofluorescence as a function of dilution factor is based on empirical measurements. The predictive potential of the method could be improved by incorporating a technique such as intrinsic fluorescence spectroscopy [13]. The autofluorescence could then be evaluated at any dilution factor as long as the tissue intrinsic fluorescence spectrum, absorption and scattering properties were known. Moreover, all our results are based on spatially-homogenous media. Further investigations are needed to evaluate the effect of heterogeneities on the SBR. Finally, since the QDs are located in a capillary, the light has to go through several interfaces. This could result in artifacts that were neglected here. Moreover, since the QDs are physically isolated from the tissue, no quenching or shielding from tissue endogenous molecules can occur.

4. CONCLUSIONS Three general conclusions are obtained form the present studies. First, we have demonstrated the use of an optical phantom that simulates tissue absorption, scattering and autofluorescence properties throughout the visible spectrum. Basing this on real tissue has the advantage that the intrinsic optical properties are obtained accurately across a wide spectral range, which is very difficult using composite artificial materials. The homogenized tissue approach allows flexibility in the simulated tissue geometry, which was particularly important for detailed surface/sub-surface studies. Second, we have shown that it is possible to perform measurements on diluted tissue homogenates and subsequently extrapolate the results to estimate the behavior of intact tissue. This strategy allowed us to use larger quantities of tissue homogenate to obtain equivalent optical thicknesses, resulting in a great improvement in precision and ease of manipulation. Finally, this approach allowed us to obtain reliable data towards solving an important contrast-optimization problem, namely identifying the excitation wavelength(s) that maximize QD-based image contrast under autofluorescence-dominated conditions. Our results suggest that the 380 to 420 nm window is optimal for surface QD imaging in tissues rich in blood like liver and kidney. For sub-surface imaging (z > 50 µm), the SBR spectra become relatively flat, so that white light illumination could be considered.


5. ACKNOWLEDGEMENTS This work was supported by the Canadian Institutes of Health Research. The authors would like to acknowledge Paul Constantinou and Ralph DaCosta for insightful discussions, Wen Jiang, Dawei Li and Warren Chan for providing QDs and QD-related information, and George Netchev, Robert Weersink and Sean Davidson for assistance with optical property measurements. M. Roy received partial financial support from the Natural Sciences and Engineering Research Council through a post-graduate scholarship.

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