442 Int. J. Nanotechnol., Vol.

Copyright © 2009 Inderscience Enterprises Ltd.

Molecular imaging with targeted quantum dot bioconjugates: the need for contrast optimisation studies

Mathieu Roy and Brian C. Wilson* Department of Medical Biophysics, Ontario Cancer Institute, University of Toronto, 610 University Ave., Toronto, ON, M5G2M9, Canada Fax: +1-416-946-6529 E-mail: mroy@uhnres.utoronto.ca E-mail: wilson@unhres.utoronto.ca *Corresponding author

Abstract: Quantum dots have the potential to be used as contrast agents for molecular imaging in vivo, but there are many challenges in optimising such procedures, both in pre-clinical animal models and in the potential clinical applications. In particular, it is critical to obtain the optimal target-to-background contrast to achieve maximum diagnostic accuracy. However, available data are insufficient to determine the optimal parameters for this objective. Here, we explore the factors that affect imaging contrast with quantum dots, including time, dosage, emission and excitation wavelengths, and instrumentation. This includes a brief review of the published work. In addition, we present initial studies with tissue-simulating phantoms that allow the optimum excitation wavelength to be determined for visible-emitting quantum dots used to image surface or near-surface lesions, as found in endoscopic diagnosis of early cancer. We also suggest needed areas for future research for optimisation of quantum dot-based bio-imaging applications.

Keywords: quantum dots; fluorescence imaging; medical imaging; contrast optimisation; contrast agent; tissue phantoms; molecular imaging; autofluorescence; signal-to-background; excitation and emission spectra.

Reference to this paper should be made as follows: Roy, M. and Wilson, B.C. (2009) ‘Molecular imaging with targeted quantum dot bioconjugates: the need for contrast optimisation studies’, Int. J. Nanotechnol., Vol.

Biographical notes: Mathieu Roy is a 2004 graduate from the engineering physics program at Université Laval, Québec, Canada. He joined Dr. Wilson’s biophotonics laboratories in 2004 as a graduate (MSc) candidate in the Department of Medical Biophysics at the University of Toronto. He then reclassified as a PhD candidate in 2006. He specialises in image analysis, spectroscopy and tissue optics, and his research focuses primarily on contrast optimisation of quantum dots as novel contrast agents for in vivo fluorescence imaging.

Brian C. Wilson received his PhD Degree from University of Glasgow, Scotland in 1971. He is currently Head of the Division of Biophysics and Bioimaging at the Ontario Cancer Institute and Professor of Medical Biophysics at the University of Toronto. His research interests cover various aspects of biophotonics, including medical diagnosis, monitoring,

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and treatment. He has made pioneering contributions to the fields of tissue optics and photodynamic therapy and now leads several research projects in fluorescence endoscopy, Raman spectroscopy, imaged-guided surgery, and nanoparticle-assisted cancer diagnosis and treatment. He has contributed to over 400 scientific publications and several patents, supervised over 50 graduate students and post-doctoral fellows, and serves on the scientific advisory and editorial boards of numerous companies and scientific journals. He is co-founder and serves as the Biophotonics Theme Director of the Canadian Institute for Photonics Innovations. In 2004, he received a Mark Award from the American Society for Laser Medicine and Surgery for contributions to medical laser science. In 2006, he was awarded with a Lifetime Achievement Award for “extraordinary pioneering contributions to the translation of optical technologies from blackboard to benchtop to bedside”.

1 Introduction

Optical spectroscopy and imaging techniques are important tools for biomedical applications [1]. Photons are ideal probes for minimally-invasive observation of cells, tissues and organs for diagnostic and treatment monitoring purposes. Intrinsic differences in the way light interacts with cells and tissues can provide relevant biological information. For example, structural optical coherence tomography (OCT) images are based on variations in tissue scattering. Likewise, fluorescence spectroscopy can differentiate benign and malignant cancer lesions based on variations in tissue endogenous fluorescent molecules. However, the intrinsic optical signals are not always sufficient, and several biomedical applications require the use of contrast agents.

Recent advances in nanotechnology provide a platform for a new realm of possibilities for biomedical optical imaging [2]. Nanoengineered, biocompatible, optically-active probes are emerging as a novel class of reporter and contrast enhancing particles. The impact of nanotechnology is expected to be particularly high for fluorescence-based techniques such as fluorescence-guided surgery, tissue immuno-pathology and in vivo molecular imaging, since quantum dots exhibit unique advantageous optical properties and enhanced photostability in comparison to traditional fluorophores [3,4].

Quantum dots (QDs) have broad excitation and narrow, size-tunable emission spectra, which makes them ideal candidates for multiplexed molecular imaging [5]. The transfer of this novel technology as a clinical imaging tool could have a significant impact on early cancer diagnosis, since molecular imaging offers the possibility of detecting lesions early, as well as differentiating between lesions of similar morphology based on their molecular signature (Figure 1). Moreover, the diagnostic potential increases with the number of different molecular markers reported [6] (multiplexing).

In vivo fluorescence imaging of molecular-targeted QD probes has been demonstrated in small animal models [7,8], but several challenges must be overcome before QDs may be used as contrast agents in the clinic. Efforts are needed to improve our understanding in several aspects, from the molecular biology and the pharmacokinetics to the surface chemistry and probe toxicity. Even when all aspects of the molecular targeting process are optimised, detection of QD signal from the target

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is not guaranteed. Competing background fluorescence can mask the relevant signal, especially if the imaging parameters are not optimised.

It is thus important to maximise target contrast by selecting the best:

• imaging time window

• probe dosage

• QD emission wavelength

• illumination spectrum

• imaging instrumentation.

Most researchers in the field base their selection on related published studies, not knowing whether the published results were fully optimised. Although optimising image contrast is a complex task since the ideal settings will change with QD optical properties, instrumentation, animal model and molecular target, quantitative contrast optimisation studies could help identify general trends on how these settings behave in various conditions. In this paper, we review and discuss existing contrast optimisation strategies and highlight potential areas of future research.

Figure 1 Illustration of multiplexed cancer molecular imaging using immuno-targeted QD bioconjugates. Simultaneous imaging of multiple disease-related molecular markers has the potential to improve sensitivity and specificity of current optical imaging techniques (see online version for colours)

2 Maximum contrast phase

After intravenous injection of a targeted contrast agent, there is an initial circulation phase, during which all perfused tissues fluoresce. The circulation phase is characterised by a low target-to-background ratio (TBR) and so is generally unsuited for imaging. A relative accumulation phase follows, during which the contrast agent clears from both background and target tissue. However, due to preferential binding, clearance from the target is slower than from background tissue, resulting in an increase in

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TBR. Eventually the TBR reaches a maximum, and imaging should ideally be done during this maximum-contrast phase.

The post-injection delay and duration of the maximum contrast phase varies with circulation half-life, probe-target binding affinity, probe dosage and target nature. Few studies have reported successful in vivo molecular imaging of QD-probes supported with detailed pharmacokinetics data. Gao et al. [8] targeted prostate cancer xenograft tumours using Anti-PSMA antibody conjugated QDs and obtained measurable contrast 2 h post-injection. However, they did not monitor the TBR, so it is not clear whether the selected imaging window was optimal. Diagaradjane et al. [9,10] monitored the TBR through several time points over a 24 h post-injection period as QD-EGF probes accumulated in colorectal xenograft tumours. They obtained measurable tumour contrast from 3 h to 18 h post injection, with a maximum at 6 h (Figure 2).

Figure 2 Progression of the tumour-to-background ratio (TBR) provided by untargeted QD800 probes (left) and EGF-QD800 probes (right) as a function of time post-injection. Untargeted QDs do not accumulate significantly, while EGF-QDs provide optimal contrast 6 h post-injection. Reproduced with permission from Diagaradjane et al. [10] (see online version for colours)

Cai et al. [11] used 705 nm QDs conjugated to an angiogenesis peptide marker to image glioblastoma xenograft tumours and monitored the TBR over four time points. They obtained maximum contrast six hours post-injection and also reported measurable contrast at 4 and 27 h. As a comparison, using an identical animal model and targeting peptide but with Cy5.5 instead of QDs, Chen et al. [12] obtained maximum contrast 4, 3 and 24 h post-injection, for different injected doses of 0.1, 0.5 and 3 nmol. Interestingly, the intermediate dose of 0.5 nmol resulted in both the earliest maximum contrast phase and the highest TBR. Unfortunately, no dose escalation study has yet been reported for QD-based probes.

Intravenous injection is not the only possible route for delivery of targeted contrast agents. Local administration is particularly relevant to endoscopic imaging. The technique consists of spraying the contrast agent directly on the mucosal tissue surface, letting the probes infiltrate the tissue and bind to their targets, and then washing off the unbound probe prior to imaging. Recently, Karwa et al. [13] showed molecular imaging of inflammation markers in a mouse model of colitis using topically administered QD probes. The QD solution was put in contact with the target tissue for 15 minutes before washing. These data suggest that imaging could be done much faster

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with topical delivery compared to intravenous injection. However, the effect of the administration method on the obtained TBR is not known. Comparative in vivo studies between these two delivery routes would be extremely useful for contrast optimisation purposes.

Research efforts would also be accelerated by pharmacokinetic models adapted specifically to targeted QD imaging, e.g., to estimate the time delay and duration of the maximum contrast phase. Although no such models of QD tumour targeting studies have yet been reported, Riviere and coworkers [14] recently published an interesting modelling study involving QDs, and validated this experimentally in an isolated perfused porcine skin flap model [15]. Since the target tissue is isolated, the probe uptake could be precisely measured by monitoring its concentration in the circulating serum. These authors reported a significant difference in the rate at which skin takes up PEG-QDs and COOH-QDs.

Another recent modelling study is that of Fibich et al. [16]. Based on the binding affinity of molecular probes to their receptors, time-domain equations were developed to describe the specific accumulation of fluorescent contrast agents in tumours. Although this model assumed intra-tumoural injection, it could be adapted to systemic administration case by combining it to a blood-clearance model similar to that of Riviere and coworkers.

3 Concentration and dosage

Although single QD tracking [17,18] has been performed under ideal conditions in vitro, detection of QD signals in the context of in vivo fluorescence imaging can be confounded by competing signals, such as instrumental noise and tissue autofluorescence. In the latter case, we can assume that there exists a threshold QD concentration, that generates enough signal to locally overcome the competing signals. If this concept is extended to in vivo administration of QDs, there also exists a minimum administered dose that raises the local QD concentration above the detectable threshold.

Although these basic concepts are important for contrast optimisation, studies that assess threshold QD concentrations and in vivo dose escalation experiments are still missing from the literature. Troy et al. [19] published an interesting study that attempted to quantify and compare the threshold number of fluorescent or bioluminescent cells that could be imaged in vitro and in vivo (sub-cutaneously). Their data showed that although cells labelled with fluorescent dyes or transfected with fluorescent proteins provided more signal than bioluminescent cells, the number of cells that could be imaged, both in vitro and in vivo, was much lower for bioluminescent cells due to the lower background signal of bioluminescence imaging (Figure 3). This study also quantified the effects of animal diet, fur, spectral filtering (emission wavelength) and background subtraction techniques on the measured threshold.

The emerging QD imaging community would benefit from a similar study to set the groundwork for accurate dose estimates. Gao et al. [8] compared the contrast provided by identical numbers of cells labelled with QDs or transfected with green fluorescent protein (GFP) in vivo (sub-cutaneous) and showed that red QD emission could be spectrally shifted away from the green autofluorescence, thereby providing better in vivo contrast than GFP. However, their results were not quantitative. The relevance of detailed quantification studies could be contested by arguing that QDs should not, in principle,

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behave differently from fluorescent dyes. In both cases, similar fluorescence background signals are involved. However, QDs and organic fluorophores differ in their excitation spectra, and additional studies could quantify the effect of the excitation wavelength on the detection threshold (further discussed below).

Figure 3 Target Signal to background ratio (SBR) and threshold signal comparison between fluorescence (A-C) and bioluminescence (D) imaging. Bioluminescence yields better SBR because of lower background signal. This study also illustrates how image processing (background subtraction) can improve both the SBR and the threshold signal. Reproduced with permission from Troy et al. [19] (see online version for colours)

Another significant challenge is the experimental determination of the concentration of quantum dots in solution. In general, gravimetric methods are not reliable of because of the unknown exact proportion of surface ligands. Hence, determining the concentration of QDs based on extinction coefficient measurements [20] or by atomic emission spectroscopy [21] of QD core heavy metal atoms (e.g., Cd or Pb) are often considered more accurate and reliable. However, Zhang and Johnson [22] recently introduced single particle counting as an advantageous way of evaluating the concentration of QD solutions. The method produces accurate measurements regardless of surface coating, core materials or buffer, and may also be used to characterise mixtures of QDs.

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4 Emission and excitation wavelengths

The broad excitation spectrum is often stated as an important advantage of QDs over standard fluorophores, since it allows imaging of different QDs with a single excitation source. While this advantage holds for in vitro imaging, the effective excitation spectrum can be significantly narrowed in vivo due to light absorption and scattering by tissue. Lim et al. [23] investigated the effects of tissue attenuation on the detectable QD fluorescence. This modelling study led to several important findings. First, four spectral windows (Figure 4) in the near and mid infrared were identified, corresponding to minima in whole (combined oxy- and deoxy- haemoglobin with equal proportions) haemoglobin (Hbtot) and water absorption peaks (Figure 5), suggesting that QDs should be used that emit at these particular wavelengths. Second, they showed that near- and mid-infrared emitting QDs offer better performance in tissues with high water-to-haemoglobin and high haemoglobin-to-water ratios, respectively. Overall, the results suggest that 1320 nm QDs offer the best compromise for all tissue compositions analysed. Third, the excitation spectrum of QDs narrowed significantly (Figure 6) due to tissue attenuation and was constrained to a band close to the emission wavelength range, similar to the case of organic fluorophores. However, the narrowing effect was not as pronounced in high water-to-haemoglobin ratio tissues, and QDs thus maintained their broad excitation spectrum in these tissue types.

Figure 4 Calculated tissue transmission for high water-to-haemoglobin (left) and high haemoglobin-to-water (top right) ratio tissues. The four ideal spectral bands are illustrated underneath. Adapted with permission from Lim et al. [23]

Figure 5 Normalised water (solid line) and whole haemoglobin (dashed line) absorption spectra (see online version for colours)

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Figure 6 Calculated effective excitation spectra for QD800 (band 1) and QD1320 (band 3) through 5 mm tissue. The effective excitation spectra are significantly narrowed in tissue (thin, dashed lines) compared to the baseline (thick line). Adapted with permission from Lim et al. [23]

This work focussed on deep (>1 mm) tissue imaging and so does not address optimisation in the visible spectral window, which is appropriate for surface/shallow (<100 µm) imaging. Using visible excitation, the detected fluorescence has a strong background due to tissue autofluorescence. In the context of fluorescence endoscopy, important lesions such as pre-malignant dysplasias and early cancers arise in the thin mucosal layer of hollow organs (lung, esophagus, colon, stomach, cervix, bladder), so that imaging through several millimetres of tissue is not necessary. Indeed, use of long wavelengths reduces the contrast because of the increased contribution from deeper tissues.

The use of visible QDs provides several fundamental advantages over NIR QDs:

• commercially available QDs offer a better selection of emission wavelengths and are better characterised

• visible QDs are usually smaller and more photostable

• visible light may be detected with video cameras, colour CCD chips and the naked eye.

However, in vivo detection of visible QDs is limited by tissue autofluorescence (AF), which varies with both excitation and emission wavelength. While a broadband excitation source maximises the QD signal in autofluorescence-free conditions, this strategy may significantly increase the background signal, leading to sub-optimal contrast in autofluorescence-limited conditions. For visible QD imaging, the spectrum of the excitation source should be selected carefully to optimise the QD signal to autofluorescence background ratio (SBR).

As part of a study to investigate this in detail, we have developed a tissue phantom based on homogenised tissues [24,25] to determine whether specific regions of the visible spectrum should be avoided or prioritised to maximise contrast. The initial results suggest that the 380–420 nm excitation band is optimal for surface visible QD imaging in high haemoglobin-to-water ratio tissues (Figure 7). This window is advantageous, since it corresponds to an haemoglobin absorption peak and thus yields lower autofluorescence background signal. However, for sub-surface imaging (depth >~25 µm), the measured SBR spectra are relatively flat, and broad-band excitation then becomes advantageous. We are currently extending these studies to low blood content tissues and developing mathematical models to support the experimental data.

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Figure 7 Homogenised tissue phantom method to evaluate the effect of the excitation wavelength on QD signal-to-background ratio (SBR). Schematic of the experimental setup (top) and measured SBR for kidney samples (bottom). For surface QD imaging, we measured an optimal excitation window at 380–420 nm. As the imaging depth (z) increases, the SBR spectra flatten and a broadband excitation is ideal. Adapted with permission from Roy and Wilson [25] (see online version for colours)

It is interesting to note that while Lim et al. [23] recommend using QD emission wavelengths that correspond to minima in the tissue attenuation spectra, our data suggests that it is advantageous to excite QDs at wavelengths corresponding to tissue attenuation maxima. However, these two studies are only valid within their respective domains: autofluorescence-limited, visible and surface imaging for our work [25] and tissue attenuation-limited, infrared, depth imaging for the studies of Lim et al. [23].

5 Imaging instrumentation

Instrumentation plays a role in molecular imaging, particularly when the relevant signal is competing with strong background fluorescence. Several advanced fluorescence imaging techniques take advantage of the information added by time-domain, spectral and spatial measurements to enhance the molecular probe signal. Dahan et al. [26] showed that QDs have a fluorescence lifetime significantly longer than tissue autofluorescence, and used a time-gated imaging system to extract relevant QD signals from the background fluorescence in vitro. Similarly, Bloch et al. [27] used a

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time-resolved system to image Cypate dye-GRD peptide conjugates in vivo and showed that the fluorescence lifetime of the conjugate increased in the tumour environment, thereby enhancing specificity.

Gao et al. [8] and Mansfield et al. [28] showed that a hyperspectral imaging system can clearly separate subcutaneous QD signal from mouse skin autofluorescence. Hyperspectral imaging consists of measuring the full fluorescence emission spectrum at each pixel of the image and storing the information as a 3D(x, y, λ) data cube. Post-processing algorithms such as real or principal component analysis (RCA or PCA) may be used to spectrally un-mix different fluorophores based on their specific spectral signatures. Mansfield et al. successfully unmixed images containing five different QDs, each labelling different cellular organelles (Figure 8). Recently, acousto-optic tuneable filters [29] and liquid crystal tuneable filters [30] were used to design and implement hyperspectral endoscopes. These novel endoscopy techniques pave the way towards multiplexed QD-based in vivo cancer diagnostics. Another approach to spectral imaging is to combine a colour CCD camera and bandpass filters [31]. This approach can be a cost-saving alternative if the useful data is known to lie within certain spectral bands.

Figure 8 Hyperspectral imaging of cells labelled with five different QD bioconjugates. Each QD signal was spectrally unmixed to yield separate images representing maps of respective molecular markers. The QD emission spectra are also shown (bottom right). Adapted with permission from Mansfield et al. [28] (see online version for colours)

When very high spatial resolution is needed, confocal fluorescence microscopy is the modality of choice. Confocal microscopy offers depth resolved images at sub-cellular spatial resolution, and can be performed in vivo using a microendoscope. Recently, Hsiung et al. [32] used a confocal fluorescence microendoscope to image, in patients, a fluorescein-peptide conjugate that binds specifically to colonic dysplasia. However, standard confocal microscopy suffers from the low penetration of visible excitation light in tissue, and is therefore not suited for deep tissue microscopy.

Multiphoton excitation allows deeper imaging, improved depth resolution and is increasingly used for intravital confocal microendoscopy [33,34]. Recently, multiphoton excitation has been used for vascular [35,36] and brain [37] imaging in mice.

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Moreover, it has been shown that QDs have a higher two-photon cross-section [35] and slower photobleaching rate than most endogenous and exogenous fluorophores, which makes them ideal multiphoton probes.

Finally, the techniques mentioned above produce images that are either projections or slices. Epifluorescence and transillumination imaging produce projection images, so that the depth information is lost, while confocal microscopy produces slices but the surrounding information is lost. None of these techniques enable accurate 3D reconstruction of fluorophore distribution. One could attempt collecting a stack of depth-resolved slices with confocal microscopy, but this process is generally time consuming and the sampled volume is relatively small. Fluorescence molecular tomography (FMT), is a technique consisting of taking fluorescence images from different angles around the object (e.g., mouse model) and applying reconstruction algorithms to obtain a quantitative 3D dataset of fluorophore location and concentration. FMT has been used successfully to quantify known distributions of fluorophores in phantoms and in animals [38]. It was also demonstrated that FMT could monitor tumour progression and effects of anti-angiogenic treatments in vivo [39,40]. However, FMT requires the animal to be submerged in index-matching fluid. It is therefore better suited for small animal imaging than for endoscopy.

6 Discussion

Before QDs may be used as molecular imaging contrast agents in the clinic, challenges need to be addressed in every stage of the process, from the molecular biology to the instrumentation. In the past decade, significant advances have been made in QD synthesis protocols and bioconjugation techniques but there is still an important gap in the optimisation of the image contrast. Here, we have examined existing strategies for contrast optimisation and highlighted areas where further investigation is most urgently needed.

Recently, QD pharmacokinetic data in animal cancer models have been reported that provide clues to achieving maximum target accumulation of QDs and the duration of this maximum-contrast phase. However, further studies are required to increase our ability to predict the dynamic behaviour of these novel contrast agents. Another important area for research is to establish a quantitative comparison between systemic and topical administration routes, and to identify the conditions that favour each.

Very little work has been done towards identifying optimal wavelengths and dosage for in vivo QD imaging. Ideal spectral windows have been identified in the infrared for QD emission, and we have introduced new experimental methods to evaluate the optimal excitation wavelength in the autofluorescence-limited regime. Similar methods could also be used to estimate threshold QD concentration for tissue imaging. These imaging parameters have a crucial impact on target contrast and thus deserve further investigation.

Fluorescence imaging instrumentation is an advanced field and a variety of different devices (for both clinical and pre-clinical use), acquisition techniques, and post-processing software already exist. Moreover, advanced fluorescence imaging techniques such as confocal microscopy and multiphoton excitation have already been implemented in endoscopes, and can be used for QD imaging without major modification. It is important that potential clinical users of molecular imaging

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are aware of the various imaging technologies and advanced processing techniques in order to select the technology that is best suited to their specific applications.

Contrast optimisation is a complex process and it is important to note that the different optimal imaging parameters are often inter-dependant and application-sensitive. For example, excitation with blue light is common practice for cell and tissue section fluorescence imaging, where autofluorescence and attenuation are not limiting factors, but should be avoided for depth–imaging applications such as fluorescence-guided surgery and whole animal cancer imaging. Likewise, since both QD emission and pharmacokinetics are size-dependent, the image contrast phase may vary with the QD emission wavelength. Future contrast optimisation studies should, therefore, provide general optimisation guidelines, while acknowledging these complex interdependencies.

Acknowledgements

This work was supported by the Canadian Institutes of Health Research (CIHR #RMF-72551). M. Roy was supported by a postgraduate scholarship from the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors wish to thank Drs. Warren Chan and Ralph DaCosta for continuing insight and guidance.

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