estimation of minimum doses for optimized quantum dot contrast-enhanced vascular imaging in vivo

Imaging

Estimation of Minimum Doses for Optimized Quantum Dot Contrast-Enhanced Vascular Imaging In Vivo

Mathieu Roy , Carolyn J. Niu , Yonghong Chen , Patrick Z. McVeigh , Adam J. Shuhendler , Michael K. Leung , Adrian Mariampillai , Ralph S. DaCosta , and Brian C. Wilson *

© 2012 Wiley-VCH Verlag Gm

Quantum dot (QD) contrast-enhanced molecular imaging has potential for early cancer detection and image guided treatment, but there is a lack of quantitative image contrast data to determine optimum QD administered doses, affecting the feasibility, risk and cost of such procedures, especially in vivo. Vascular fl uorescence contrast-enhanced imaging is performed on nude mice bearing dorsal skinfold window chambers, injected with 4 different QD solutions emitting in the visible and near infrared. Linear relationships are observed among the vascular contrast, injected contrast agent volume, and QD concentration in blood. Due primarily to differential light absorption by blood, the vasculature is optimally visualized when exciting in the 435–480 nm region in 81% of the cases (89 out of 110 regions of interest in 22 window chambers). The threshold dose, defi ned here as the quantity of injected nanoparticles required to yield a vascular target-to-autofl uorescence ratio of 2, varies from 10.6 to 0.15 pmol g − 1 depending on the QD emission wavelength. The wavelength optimization maximum and broadband gain, defi ned as the ratio of threshold doses estimated for optimal and suboptimal (worst wavelength or broadband) spectral illumination, has average values of 4.5 and 1.9, respectively. This study demonstrates, for the fi rst time, optimized QD imaging in vivo. It also proposes and validates a theoretical framework for QD dose estimation and quantifi es the effects of blood absorption, QD emission wavelength, and vessel diameter relative to the threshold dose.

DOI: 10.1002/smll.201102105

Dr. M. Roy , P. Z. McVeigh , M. K. Leung , Dr. A. Mariampillai , Prof. B. C. Wilson Department of Medical Biophysics University of Toronto 610 University Ave., Toronto, ON, M5G 2M9, Canada E-mail: [email protected]

C. J. Niu , Y. Chen , R. S. DaCosta , Prof. B. C. Wilson Ontario Cancer Institute University Health Network 610 University Ave., Toronto, ON, M5G 2M9, Canada

Dr. A. J. Shuhendler Department of Pharmaceutical Sciences University of Toronto 144 College St, Toronto, ON, M5S 3M2, Canada

small 2012, DOI: 10.1002/smll.201102105

1. Introduction

Quantum dots (QDs) are luminescent semiconductor nano-

crystals that are often considered as ideal components for

nano-engineered molecular imaging probes due to their

broad excitation spectrum, size-tunable emission and photo-

stability. [ 1 ] Molecular imaging, using exogenous contrast

agents that bind specifi cally to target cells/tissues according

to their molecular signature, [ 2 ] has potential for improved

early cancer detection [ 3 , 4 ] and image-guided interventions. [ 5 ]

Although label-free fl uorescence imaging [ 6 ] is sometimes

preferable to using exogenous contrast agents, nanoparticles

have the potential to also serve as vehicles for drug delivery [ 7 ]

and other targeted therapeutics, [ 8 ] thus opening a realm of

possibilities for combined detection and treatment.

1bH & Co. KGaA, Weinheim wileyonlinelibrary.com

ttp://doi.wiley.com/10.1002/smll.201102105

M. Roy et al.full papers
However, achieving nanoparticle-based optical diagnosis
and treatment in vivo is a complex task, requiring multi-

disciplinary expertise to address several signifi cant chal-

lenges. One major challenge is to design delivery vehicles

for the QDs, and signifi cant progress has been made in this

area since QDs were fi rst introduced for biomedical imaging

by Chan et al. [ 9 ] and Bruchez et al. [ 10 ] in 1998. For example,

Akerman et al. [ 3 ] showed that thiolated molecules, such as

peptides and poly(ethylene glycol) (PEG), could be attached

to QDs in order to target specifi c tissues in vivo. In an alter-

native approach, Dubertret et al. [ 3 , 11 ] demonstrated that QDs

could be encapsulated in phospholipid micelles as a delivery

vehicle for in vivo imaging. Later, amphiphillic polymer and

triblock copolymer coatings were used by Ballou et al. [ 12 ] and

Gao et al., [ 13 ] respectively, to improve the stability of the QDs

in vivo and increase their vascular circulation time. However,

the former group found intact QDs in the liver, lymph nodes

and bone marrow even 4 months after injection, which raised

concerns about the potential long-term toxicity of QDs,

particularly those based on heavy metals such as cadmium.

Understanding the clearance and non-specifi c accumulation

mechanisms of QD bioconjugates became an important area

of research and Fischer et al. [ 14 ] were the fi rst to publish an

exhaustive study on the subject. Their work revealed that

QDs injected intravenously were not excreted through the

urine or feces, thus confi rming the potential long-term toxicity

concerns. Similar results were also obtained by Yang et al. [ 15 ]

The potential long term toxicity concern associated with

CdSe core QDs remain a major roadblock for the clinical

applications of this novel technology, although it should be

noted that while signifi cant progress has been made towards

understanding the mechanisms of QD cytotoxicity in vitro,

actual evidence of direct toxicity in vivo are lacking. [ 16 , 17 ] One

potential solution is to reduce the size of the QDs, since it has

been demonstrated [ 18 ] that QDs having a hydrodynamic diam-

eter smaller than ∼ 5 nm are almost entirely cleared through the

kidneys with corresponding short circulation times. However,

this particle size range constrains the possible targeting and

delivery strategies, so that an alternative is to use heavy metal-

free QDs. [ 19–21 ] Finally, it may be possible to work with larger

cadmium-based QD bioconjuages if the dose is kept beneath

a certain toxicity threshold. While this threshold remains to

be quantifi ed, studies to optimize the dose and timing of QD

contrast agent delivery are important, not only to minimize

potential toxicity risks but also to evaluate the likely technical

feasibility and cost of QD-based clinical imaging procedures.

While efforts were made by Diagaradjane et al. [ 22 ] to

better characterize QD pharmacokinetics and identify the

best time windows for imaging, to our knowledge studies

that address the photophysical aspects of QD dosimetry have

been largely lacking. [ 23 ] Indeed, to achieve robust dosimetry

guidelines for fl uorescence imaging, one must consider a

plethora of photophysical parameters, such as the illumina-

tion spectrum, the target size and depth, the tissue optical

properties (absorption and scattering) and background

autofl uorescence spectra, and the contrast agent concentra-

tion and excitation/emission spectra. Lim et al. [ 24 ] were the

fi rst to report wavelength optimization for QD imaging in tis-

sues. They modeled the depth-dependent tissue attenuation

2 www.small-journal.com © 2012 Wiley-VCH V

analytically and identifi ed optimal excitation and emission

wavelength windows that inspired the design of NIR QDs,

later used for vascular [ 25 ] and lymph node [ 5 ] imaging in vivo.

However, the work of Lim et al. [ 24 ] focused primarily on

deep-tissue imaging in the near-infrared, and so neglected the

background autofl uorescence signal, in addition to lacking

quantitative experimental validation of the modeling. To

address these limitations, we recently presented a numer-

ical model predicting the effects of tissue attenuation and

autofl uorescence on the contrast of QD-based surface and

subsurface fl uorescence imaging, together with quantitative

experimental validation in novel homogenized-tissue phan-

toms with known and well-controlled optical properties. [ 26 ] In

a follow up study, [ 27 ] we extended this approach to a variety

of both intact and homogenized ex vivo tissues having a

range of optical absorption and scattering properties, using

QD solutions emitting at 5 different visible and near-infrared

wavelengths. In both studies, the target was a fi ne glass capil-

lary tube placed on or below the tissue surface, representing

a blood vessel. The contrast-optimization was expressed in

terms of specifi c performance metrics, namely the threshold

QD concentration and the wavelength optimization gain.

These refer, respectively, to the minimum QD concentration

to achieve a target-to-autofl uorescence contrast ratio of 2 and

the ratio between the threshold concentration on and off the

optimum excitation wavelength.

Although these studies established a rigorous theoret-

ical and experimental framework for fl uorescence contrast-

enhanced imaging dosimetry, questions remain concerning

the applicability of the results in vivo, as related to the QD

pharmacokinetics, the effects of blood absorption on vascular

contrast, the effect of target size on contrast, and the unknown

relationship between injected QD volume and detected signal.

Hence, we report here on QD contrast-enhanced in vivo

imaging using a well-established dorsal skinfold window

chamber model in nude mice, in which an optically trans-

parent window is placed into a skin fl ap, thereby allowing

direct high-resolution intravital and longitudinal visualization

of the vasculature. [ 28 , 29 ] The geometry of this model is sim-

ilar to that of our previous ex vivo experiments where QDs

were imaged in a surface/subsurface glass capillary tube on

a thin tissue sample. To our knowledge, this is the fi rst study

of quantitative and wavelength-optimized imaging of QDs in

vivo. As will be discussed further after presenting the results,

this is certainly an oversimplifi ed model compared to the

eventual clinical (e.g., endoscopic imaging) situation. How-

ever, using live tissue does provide added value by addressing

some of the limitations of ex vivo tissue experiments, such

as preserving realistic blood perfusion and blood oxygena-

tion levels and maintaining tissue temperature. Moreover,

the simple near-2D geometry of the window chamber model

provides distinct technical advantages for accurate quantita-

tive studies that can then inform and guide future studies in

more complex 3D model systems and in patients. This same

model has been used by ourselves [ 30 , 31 ] and others [ 32–34 ] in a

variety of in vivo fl uorescence imaging studies with different

exogenous fl uorophores.

A total of 22 mice were injected systemically with one of

4 different visible or near infrared emitting QD solutions.

erlag GmbH & Co. KGaA, Weinheim small 2012, DOI: 10.1002/smll.201102105

Estimation of Minimum Doses for Quantum Dot Contrast-Enhanced Vascular Imaging

Figure 2 . Image analysis: a) view of a window chamber, with the 4 mm × 4 mm fl uorescence image fi eld of view indicated. Fluorescence images (465 nm excitation) taken b) before and c) 12 min after injecting 3.44 pmol g − 1 of QTracker655. d) The fl uorescence excitation spectra corresponding to the background and target ROIs. Each spectrum was obtained by averaging the pixel intensities of 5 different ROIs at each excitation wavelength. The error bars represent the standard deviation between ROIs. The grayscale of b and c represents the measured fl uorescence intensity in counts per μ J of excitation light.

400 450 500 550 6000

0.5

1

1.5

2

Excitation Wavelength (nm)

)Ju/stnuoc( ecnecseroulF

XT

XB

FT

FB

4x4 mm

a)

0.05

0.1

0.15

0.2

XTXBFTFB

before afterb) c)

0.5

1

1.5

2

2.5

d)

In addition to pharmacokinetics data, this

allowed the effects of blood absorption on

the vascular contrast to be quantifi ed, as well

as the quantitative relationships between the

vascular contrast, the injected QD dose and

its concentration in blood to be established.

From these data, the threshold doses and

wavelength optimization metrics were esti-

mated, as developed and validated previously

in phantoms and ex vivo tissues. This provides

guidelines to achieve optimized QD contrast-

enhanced imaging in vivo and, together with

our previous work, represents an essential

step in developing a complete dosimetry

framework for QD fl uorescence molecular

imaging. Additionally, the proposed approach

may be used to establish quantitative compar-

ison between different fl uorescent labels and

so should be of interest both to researchers

designing and synthesizing fl uorescent probes,

as well as those using such probes for bio-

medical and in vivo studies.

2. Results 2.1. Quantum Dot Characterization

Four different aqueous QD solutions, emit-

ting at λ em = 565, 655, 705, and 800 nm, were used. The nominal

concentration was 2 μ m for all stock solutions, and the meas-

ured concentrations agreed with the nominal values within

± 3%. For reference purposes, the fl uorescence excitation and

emission spectra of each QD solution, along with their respec-

tive quantum yields, are presented below ( Figure 1 ).

2.2. Image Analysis

A total of 22 mice were injected with QDs via the tail vein

and the vascular contrast was visualized with epi-fl uorescence

© 2012 Wiley-VCH Verlag Gm

Figure 1 . Fluorescence excitation spectra (dashed) and emission spectra (solid lines) for the 4 QD solutions. Each curve was normalized to the corresponding solution’s absorption value at 385 nm. The calculated quantum yield values were 0.28, 0.30, 0.27, and 0.04 for the QTracker565, 655, 705, and 800, respectively.

400 500 600 700 8000

0.5

1

1.5

Wavelength (nm)

Nor

mal

ized

Flu

ores

cenc

e

QTracker565QTracker655QTracker705QD800

small 2012, DOI: 10.1002/smll.201102105

multi-spectral imaging through a dorsal skinfold window

chamber. Image sets were processed using a Matlab routine,

allowing the user to identify regions of interest (ROI) corre-

sponding to blood vessels and background tissue, both before

and after injection of the contrast agent. The important image

metrics are defi ned as follows:

ST = XT − FT (1)

SB = XB − FB (2)

where in Equation (1) the target signal, S T , is given by sub-

tracting the tissue autofl uorescence, F T , measured at the

target ROI (average pixel intensity) prior to injecting the

contrast agent, from the fl uorescence signal, X T , measured at

the same location after injection, and analogously in Equa-

tion (2) for the (non-vessel) background ROI. The target-to-

autofl uorescence ratio (TAR) and target-to-background ratio

(TBR) are defi ned as follows:

TAR = XT/FB (3)

TBR = XT/XB (4)

Typical fl uorescence images and their corresponding fl uores-

cence excitation spectra are shown in Figure 2 . The vascular

( X T ) spectrum is typical of the entire data set, with a max-

imum at 465 nm and dips at 420 and 550 nm corresponding

to blood absorption maxima. This particular mouse exhib-

ited low levels of autofl uorescence and received a high dose

(3.44 pmol g − 1 ) of QTracker 655, yielding high vascular

3www.small-journal.combH & Co. KGaA, Weinheim


Figure 3 . Typical QTracker655 vascular (blue squares) and background (red dots) normalized signal as a function of time after injection. Since longer-time kinetics were not collected for QTracker655, QTracker705 vascular (black circles) and background (black asterisks) circulation were also plotted. The signal is relatively stable for the fi rst 2 h post-injection, and almost identical for both probes. Thus, it is expected that the longer-time circulation shown for the QTracker705 is also representative of the QTracker655. From this data, the circulation half-life was estimated to ∼ 5 h. Note that the QTracker655 dose (2.11 pmol g − 1 ) was ∼ 30% higher than the QTracker705 dose (1.60 pmol g − 1 ), resulting in higher contrast and lower relative uncertainty. The error bars represent the standard deviation for 5 different ROIs. The excitation wavelength was set to 465 nm to maximize contrast. Each curve was normalized to its corresponding S T value 20 minutes after injection.

0 0.5 1 1.5 2 2.50

0.20.40.60.8

11.2

Time after injection (h)

Norm

aliz

ed S

igna

l (a.

u.) ST,655

SB,655ST,705SB,705

5 10 15Time after injection (h)

ST,655SB,655ST,705SB,705

contrast. The resulting QD concentration in the blood was

34.4 n m , which is ∼ 0 times higher than the threshold, esti-

mated to 3.1 n m at 465 nm below (Section 2.8).

2.3. Pharmacokinetics

Given the relatively long time needed to acquire a mul-

tispectral image set (up to 2 min), fast clearance of the

contrast agent from the bloodstream could have been a

complicating factor in the analysis of the vessel contrast.

Hence, time-series measurements (before, during and after

QD injection) were made on 2 animals for each QD solu-

tion, yielding relatively long circulation times, with clear-

ance half-lives of approximately 5 h for QTracker565,

QTracker655 and QTracker705 and 2 h for Qdot800. Typi-

cally, the vascular signal was visible immediately after injec-

tion (within 10 s) and did not vary signifi cantly during the

fi rst hour, as shown in Figure 3 . This was observed for all

QDs. We note that the primary objective of these meas-

urements was to show that the circulation kinetics of the

nanoparticles was not fast enough to have impact on the

acquisition of multispectral image sets, typically requiring

1 to 5 min. Clearly, more animals would be required for a

full pharmacokinetics study, although approximate blood

circulation half-lives can still be estimated here.

The exact length of the PEG chains for the specifi c parti-

cles is not disclosed by the manufacturer, but it was possible

to confi rm that the chain length on the Cetuximab-QDot800

particles is slightly less than on the QTracker series. How-

ever, the observed difference in clearance times is more

likely attributable to an increase in opsonization and clear-

ance by the reticuloendothelial system due to the higher bio-

activity of reduced antibody fragments in serum as compared

to PEG.

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2.4. Effects of Blood Absorption

Not unexpectedly, in our previous study

using ex vivo tissues [ 27 ] light absorption

by blood was a signifi cant factor in opti-

mizing the excitation wavelength. Thus, for

shallow targets, high light absorption in the

tissue itself (due to blood in the capillary

network) could actually increase the QD

image contrast, since it reduces the back-

ground autofl uorescence signal. However,

in those experiments the glass capillaries

used to simulate larger vessels contained

only quantum dots and it is possible that

the presence of blood in the vessels being

imaged would cancel this positive effect.

In order to test this, the fl uorescence exci-

tation spectra of the 4 QD solutions in

glass capillary tubes were calculated and

measured before and after mixing with

(mouse) blood. The following 1D expres-

sions were used to quantify the effect of

blood on the fl uorescence excitation spectra:

ϕ(z) = e−(μa, λex +μa,λem )z (5)

Fv (λex, λem) = c

zcc0F0 (λex, λem)

zv∫

0

ϕ(z)dz

(6)

where φ ( z ) represents the depth dependence of the emission

fl uence, F v is the expected fl uorescence signal of blood con-

taining QD concentration c , F 0 is the measured fl uorescence

signal of a QD-only (no blood) solution at concentration c 0 ,

z v is the thickness of the blood vessel of interest, and z c =

100 μ m is the glass capillary thickness. μa,λex and μa,λem are

the blood absorption coeffi cients at the excitation and emis-

sion wavelength, respectively, measured using a UV–vis spec-

trometer, as shown in Figure 4 a.

The analysis of the blood samples revealed that mixing

the QDs with blood preserved the linearity of the target

fl uorescence signal as a function of QD concentration at all

wavelengths, as would be expected from a purely absorbing

medium. This has two relevant implications: 1) measuring

the fl uorescence signal of samples in a glass capillary tube

is a valid approach for evaluating the QD concentration in

blood, and 2) linear approximations should also be valid for

in vivo dosimetry calculations, as further discussed in Sec-

tion 2.7.

Another important aspect of the blood absorption is its

infl uence on the detected QD signal as a function of vessel

diameter. Since φ ( z ≅ 0) = 1, Equation (6) predicts that F v ini-

tially increases linearly with z v , but eventually reaches a pla-

teau as z v increases. Moreover, this plateau occurs earlier as

the absorption coeffi cient increases. This was verifi ed experi-

mentally, as shown in Figure 5 . In order to minimize the effect

of vascular QD concentration and isolate the effect of vessel

diameter, each measurement was normalized by the predicted

, Weinheim small 2012, DOI: 10.1002/smll.201102105


Figure 4 . a) Average measured mouse blood absorption spectrum used as input to Equation (5) . The absorption coeffi cients at specifi c excitation and emission wavelengths (used for imaging) are represented by green squares and red circles, respectively. Error bars represent the intersubject standard deviation for a group of 5 animals. b) Measured (solid lines) fl uorescence excitation spectra ( F 0 ) for the various QDs and the predicted (dashed lines) effect of adding whole blood. The concentrations are matched to c 0 = 50 n M . c) Measured (symbols) and predicted (dashed lines) excitation fl uorescence spectra obtained from whole blood extracted from 3 mice injected with QTracker655. In both b and c, the thin dashed lines represent the upper and lower boundaries of the predictions due to the uncertainty on the blood absorption spectrum.

300 400 500 600 700 800 90010

0

101

102

103

Wavelength (nm)

a (cm

-1)

SpectrometerExcitation filtersEmission filters

350 400 450 500 550 600 650 70010

-2

10-1

100

101


)Ju/stnuoc( ecnecseroulF

565655705800

a)

b)

350 400 450 500 550 600 6500

0.5

1

1.5


Flu

ores

cenc

e (c

ount

s/s)

N06N08N19

c)

Figure 5 . Measured (symbols) and predicted (dashed lines) relationship between normalized QD fl uorescence and vessel diameter for 2 different excitation wavelengths. The red dashed line represents the model prediction for a blood-free glass capillary, and the thin dashed lines represent the upper and lower boundaries of the model due to the uncertainty on the blood absorption measurements. The data includes all QD emission wavelengths. The uncertainty on the measurements was estimated to ∼ ± 50% but the error bars were not included for ease of viewing. We estimated the goodness of fi t using the reduced χ 2 coeffi cient and obtained values of 1.85 and 1.00 at 405 and 465 nm, respectively. Both curves plateau due to the increasing effect of light absorption by blood, with the effect being much more pronounced at the shorter wavelength, which is close to the Soret-band maximum absorption peak of hemoglobin.

0 200 400 6000

1

2

3

Vessel diameter ( m)

ecnecseroulf dezil amro

N

465 nm405 nmNo BloodModel

value of a 100 μ m diameter blood-free glass capillary loaded

with a QD solution at the corresponding concentration. Since

the normalization is not perfect, the experimental data are

noisy, but the general trend confi rms the prediction of Equa-

tion (6) .

In summary, since the excitation and emitted light are

both strongly absorbed by blood, there must come a depth

beyond which there will be no signifi cant increase in signal

by addition of more quantum dots. This limited depth of

penetration is clearly refl ected in the blood vessel diameter

results, and the observed QD fl uorescence intensity is ade-

quately predicted from absorbance measurements on blood

at the appropriate wavelengths.

© 2012 Wiley-VCH Verlag Gmbsmall 2012, DOI: 10.1002/smll.201102105

2.5. Contrast Versus Excitation Wavelength

Because of the effects of blood absorption indicated above,

the vasculature was better visualized at blood absorption

minima (435–480 nm) for most animals, as shown in Figure 6 .

These spectra are representative of the whole data set: in total,

110 blood vessels were measured in 5 ROIs selected in each

of 22 animals. In 90 of the 110 cases (81%), the optimal exci-

tation wavelength was within the 435–480 nm region. The

worst excitation wavelength range was 510–600 nm in 65%

of cases and 380–420 nm in 30% of cases, with no obvious

systematic split between the two groups. For most cases, both

of these spectral windows showed similarly low contrast, and

the exact location of the absolute minimum showed no sys-

tematic trend, and so was most likely due to the inter-animal

variations in the autofl uorescence.

Although the confi rmed effect of the blood absorption adds

a level of complexity to the wavelength optimization challenge,

it does not invalidate our previous conclusions. In fact, blood

absorption peaks still correspond to advantageous excitation

wavelengths for shallow, blood-free targets. This was verifi ed

in the present study in vivo by visualizing an exceptional vessel

that gave optimal contrast when excited in the 385–420 nm

region (Figure 6 ). The identity of this vessel is unclear, but if,

for example, it is lymphatic, then this would have signifi cant

diagnostic implications: clearly this merits future detailed

investigation beyond the scope of the present work.

2.6. Vascular QD Concentration Versus Injected Dose

The major objective of this study is to provide guidelines

for QD dose optimization. Hence, we compared the injected

contrast agent volume, V inj , to the blood QD concentration

5www.small-journal.comH & Co. KGaA, Weinheim

M. Roy et al.

6

full papers

Figure 6 . Examples of fl uorescence contrast images after injection of QTracker655. The fi rst case (a) shows slightly higher contrast when the tissue is excited at 465 nm (right) rather than at 405 nm (left). In a different mouse (b), where both blood-rich (V2) and blood-free (V3) targets are visible. The contrast of the blood-free target (V3) is much higher at 405 than at 465 nm, as confi rmed by the fl uorescence excitation spectra (c), but the vascular contrast has similar spectral features for both cases (V1 and V2). Images in a and b were acquired 12 and 6 min after injecting 3.44 and 3.43 pmol g − 1 of QTracker655, respectively. Each spectrum and its error bars correspond to the average pixel intensities and their standard deviation for a single ROI. Each image has a fi eld of view of 4 mm × 4 mm.

measured by inductively coupled plasma mass spectrometry

(ICP-MS), c blood , and hypothesized that the following simple

relationship would accurately predict the measured blood

concentration as a function of injected volume:

cblood = Vinjc inj

Vblood (7)

where V blood is the total blood volume, estimated as 10% of

body weight and c inj is the concentration of the injected QD

solution. The measured and expected concentrations were

strongly correlated ( r = 0.981), as shown in Figure 7 .

www.small-journal.com © 2012 Wiley-VCH Verlag GmbH & Co. KGaA

2.7. Contrast Versus Vascular QD Concentration

As with the glass capillary measurements

in Section 2.4, the vascular signal, S T , was

well correlated to the blood QD concen-

tration, with r > 0.85 for all excitation and

emission wavelengths. Equation (6) was

also used to predict the vascular signal and

target-to-autofl uorescence ratio as a func-

tion of blood QD concentration, as shown

in Figure 8 .

The discrepancies between the meas-

ured and predicted S T values in Figure 8 a

are mainly due to the use of group-mean

values for the vessel diameters and blood

absorption coeffi cients in Equation (6) .

The large intersubject autofl uorescence

variation also greatly contributes to the

uncertainty of the predicted TAR values

shown in Figure 8 b. Nevertheless, the data

indicate that vascular contrast does scale

linearly with injected dose, at least for

the range of doses and vessel diameters

tested, which facilitates dosimetry calcula-

tions. This behavior was as expected, since

optical self-absorption should be negligible

at the range of vascular concentrations

required to achieve detectable contrast.

However, the linearity also shows that

other potential complications that could

occur in vivo, such as QD aggregation and

self-quenching, are not signifi cant at these

concentrations.

2.8. Minimum QD Concentration

The methodology developed previously [ 27 ]

was used to estimate the threshold vas-

cular concentration for the different

QTracker solutions, defi ned as the concen-

tration required to obtain a threshold con-

trast ( TAR th ) value of ≥ 2, and described by

the following expression:

c th = (T ARth − 1)

FB

fQD (8)

where f QD = S T / c blood is the detected QD fl uorescence per unit

concentration. To quantify the improvements made by opti-

mizing the excitation wavelength, c th was evaluated for the

optimal, worst and broadband illumination cases. The optimal

and worst cases are characterized by the excitation fi lter that

minimizes or maximizes c th , respectively. The broadband illu-

mination was simulated by averaging the contributions from

all excitation fi lters shorter than the emission wavelength.

The wavelength optimization gain was calculated by taking

the ratios of the c th values for each illumination condition:

, Weinheim small 2012, DOI: 10.1002/smll.201102105


Figure 7 . Relationship between the expected vascular QD concentration calculated from the injected volumes and measured from blood samples collected immediately after imaging, typically 2 h post-injection of QDs, using ICP-MS. Each point represents a single animal, with a different symbol for each subgroup. Note that 1 data point is missing from the QD705 group, and 3 from the QD800 group, since longer-time imaging was performed and blood could not be collected within 2 h post-injection. The dashed line is the line of identity.

0 20 40 60 80 100 1200

20

40

60

80

100

120

Measured Concentration (nMolar)

)raloMn( noitartne cno

C detcep xE

r = 0.981

QD565QD655QD705QD800

Gbroadbrand = c th,broadbrand

c th,optimal, Gmax = c th,worst

c th,optimal(9)

Since the autofl uorescence varied greatly between subjects,

the advantages of wavelength optimization are masked by

large uncertainties when using population-average values

of cth . Hence, the threshold concentration and optimization

gain were calculated on a case-by-case basis, as shown below

in Figure 9 a–c. Despite the large intersubject variation in cth

values, the wavelength optimization yields substantial gains

(signifi cantly greater than 1) in most cases. The statistical

signifi cance of the c th reductions was verifi ed by one-sided

Student’s t-tests. These results are also representative of the

other QD solutions, as summarized in Table 1 below. Note

that the intersubject autofl uorescence variation was much

lower at λ em = 800 nm, resulting in less variation for the

gain values. As expected, the Gmax values were consistently

© 2012 Wiley-VCH Verlag Gmb

Figure 8 . a) Measured (green squares) and predicted (green dashed linein blood. Also shown are the corresponding measured (blue circles) skinstandard deviation (solid blue lines). The black inverted triangle at the jurepresents the threshold concentration. b) Measured (green squares) anconcentration at λ ex = 465 nm.

0 10 20 30 40 500

1

2

3

Blood QD Concentration (nMolar)

Fluo

resc

ence

(cou

nts/

uJ)

XBSTAvg XBModel

a)

small 2012, DOI: 10.1002/smll.201102105

greater than G broadband , with overall average values of 4.5 and

1.9, respectively.

Figure 9 d presents the vascular threshold QD concentra-

tion values estimated from each region of interest in com-

parison with an alternative method that uses, as inputs, the

expected QD signal given by Equation (6) and the intersub-

ject averaged autofl uorescence. In fact, when the autofl uores-

cence ( F B ) and the QD signal ( S T ) are equal, Equation (8)

reduces to c th = c blood . This corresponds to the intersection of

the modeled signal (green) and average tissue autofl uores-

cence (blue dashed line) in Figure 8 a.

The average optimal threshold concentrations obtained

using the experimental and linear regression approaches,

respectively, are summarized in Table 1 . Notice the good

agreement between the predicted and experimentally derived

values, suggesting that, with a priori knowledge of the QD

fl uorescence and blood absorption properties ( Equation (6) )

and of the autofl uorescence of the tissue of interest, accu-

rate dosimetry predictions may be achieved. Moreover, the

threshold concentration measurements can be readily con-

verted to threshold injection volume using Equation (7) . The

average mouse body weight (22.2 g) was used to convert the

vascular concentrations to minimum QD doses (in pmol g − 1 ).

3. Discussion

3.1. Experimental

The dorsal skinfold window chamber was a suitable in vivo

model to investigate the basic principles of optimized QD

imaging. The skin provides a relatively homogenous autofl uo-

rescence background, while the blood vessels serve as discrete

surface and subsurface targets such as might be encountered

during endoscopic examination of early lesions. The speckle

variance optical coherence tomography (OCT) measure-

ments revealed that the upper boundary of most vessels is

50–150 μ m below the surface (window chamber cover slip).

This small depth range did not result in signifi cant changes in

the detected QD signal, likely due to the low attenuation of

the hypodermis that comprises mostly of adipose tissue. [ 35 ]

The relative simplicity of this model allowed the focus to be


s) QD vascular signal at λ ex = 465 nm versus measured QD concentration fold autofl uorescence, together with its average (dashed blue line) and

nction of the average autofl uorescence and the extrapolated signal curve d predicted (blue lines) target-to-autofl uorescence ratio versus QD blood

0 10 20 30 40 500

5

10

15

20

25

Blood QD Concentration (nMolar)

TAR

(a.u

.)

PredictedMeasuredUncertainty

b)


Figure 9 . a) Estimated QTracker655 threshold concentrations in individual mice estimated for worse (red), broadband (green), and optimal (blue) illumination and the corresponding b) maximum and c) broadband gain values. In most cases, the concentrations for the worst and broadband illumination conditions were signifi cantly higher than for the optimal case, with p < 0.05 ( ∗ ) and p < 0.01 ( ∗ ∗ ), yielding gain values signifi cantly greater than 1. The error bars in a represent the standard deviation of the threshold concentration estimated from 5 different regions of interest. d) Predicted and measured threshold QD concentration as a function of emission wavelength. The model uncertainty was estimated from the intersection of the modeled signal curve and the upper and lower boundaries of the estimated autofl uorescence group averages (see Figure 8a).

550 600 650 700 750 80010

-1

100

101

102

103

Emission Wavelength (nm)

)Mn( noitartnecno

C dlohserhT

ModelUncertaintyExperimental

*

*

**

*

** **

**

* ***

**

**0

10

20

30

40

50

60

70

80

90

100

A B C D E F G H

QD

Co

nce

ntr

atio

n (n

M)

Individual mice

WorstBroadbandOptimal

0

1

2

3

4

A B C D E F G H

Gai

n

Individual mice

Optimal/Broadband

0

5

10

15

Gai

n

Optimal/Worst

a)

b)

c)

d)

Table 1. Average vascular threshold QD concentrations and gain values.

QTracker565 QTracker655 QTracker705 Qdot800

c th,optimal [n M ] a) 106 ± 26 5.7 ± 4.8 5.1 ± 3.7 1.5 ± 0.4

c th,optimal [n M ] b) 110 ± 30 6.5 ± 4.1 5.2 ± 3.5 1.7 ± 0.4

G max range, mean 1.8 to 3.6, 3.0 1.8 to 8.3, 5.2 2.2 to 9.2, 4.6 3.1 to 4.2, 3.7

G broadband range,

mean 1.5 to 2.6, 2.2 1.2 to 2.2, 1.7 1.5 to 3.4, 2.1 1.6 to 1.8, 1.8

Minimum Dose

[pmol g − 1 ] a) 10.6 ± 2.6 0.57 ± 0.48 0.51 ± 0.37 0.15 ± 0.04

a) Experimental approach; b) linear regression approach.

on challenges that are specifi c to in vivo applications, namely

the pharmacokinetics, blood absorption, and dosimetry

aspects, without the complexity of the optical attenuation in

8 www.small-journal.com © 2012 Wiley-VCH V

thick tissues. However, one limitation was the large intersub-

ject variations in the autofl uorescence intensity, which could

be due to several factors. For example, there were different

degrees of skin infl ammation noted between mice, related to

the healing responses to the surgical trauma of the window

implantation. A large proportion of the autofl uorescence

signal also came from the external surface contamination of

the skin, due to food pellets, cage bedding, hydration gels,

and animal excrements and urine. Care was taken to clean

both the external side of the skinfl ap and the window using

water and alcohol prior to imaging but it is likely that some

contaminants from the mouse cages were leftover. It would

likely be possible to reduce this intersubject variability in

skin autofl uorescence, but it may better represent the clinical

situation, since the mucosal autofl uorescence (e.g., in the gas-

trointestinal tract) is also known to have very large point-to-

point and patient-to-patient variability. [ 36 , 37 ] This variability

will tend to confound the diagnostic interpretation unless the

QD signal is markedly brighter. Hence, one possible approach

would be to image the autofl uorescence of the tissue prior to

administration of the QD and subtract the average value. The

QD dose could also be adjusted to ensure adequate signal-to-

background based on the highest autofl uorescence level. Since

we envisage topical application of the QD contrast as being

the most likely clinical technique, this would be practical and

could be done in a single endoscopic procedure as long as the

uptake and binding of the QD agent is fairly rapid.

The work presented above could have additional clinical

relevance if transferred to more complex animal models. For

example, on-going work includes both active and passive tar-

geting of QD bioconjugates to tumors grown in the window

chambers to study how tumor-specifi c accumulation affects

the threshold injection dose. As shown in Figure 10 , our pre-

liminary results suggest that the optimal excitation wavelength

shifts from 450 to 385 nm when the QDs escape the vasculature

and accumulate in the tumor vicinity, as would be expected

from the data presented previously (Section 2.5). Moreover,

the estimated c th,optimal value decreased with time as the QD

accumulated in the tumor, and at the optimum time point (75

min), the dose was ∼ 3 times lower than for vascular imaging.

This suggests that the minimum dose could be further reduced

by optimizing the contrast accumulation kinetics.

Although Figure 10 gives an outlook of how our

approach could be used to optimize tumor targeting in vivo,

erlag GmbH & Co. KGaA, Weinheim small 2012, DOI: 10.1002/smll.201102105


© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Figure 10 . Passive accumulation of QTracker705 in the vicinity of a ME-180 cervical carcinoma tumor imaged a) before, and b) 15 min, c) 45 min, and d) 75 min after injection of QDs. Images a to d were all acquired at λ ex = 385 nm and λ em = 700 nm and share the same grayscale, displayed in c. TAR spectra measured at different time points after injecting the QDs for e) vascular (V) and f) tumor (T) regions of interest, as indicated by the arrows in b to d. The fact that the contrast increases with time in f but not in e clearly indicates accumulation in the tumor vicinity. Note that this particular mouse had very high levels of autofl uorescence at λ em = 700 nm, explaining the much higher threshold doses compared to the data presented in Section 2.8. The fi eld of view was 4 mm × 4 mm for all images.

small 2012, DOI: 10.1002/smll.201102105

it is important to note that the dorsal

skinfold model differs from mucosal

(e.g., gastrointestinal) tissues in terms of

morphology and optical properties. Thus,

although the skin shares certain similari-

ties with, say, colon tissue (low attenu-

ation, comparable layer thicknesses),

differences remain in the distribution of

endogenous fl uorophores. In the present

experiments, epi-illumination was used as

a good representation of the endoscopic

imaging geometry. However, since the

main blood vessels that perfuse the skin

are beneath the epidermis, the window

chamber places these vessels to the fore-

front of the imaging plane. This is in con-

trast to endoscopic imaging of hollow

organs, where the main vessels lie below

the mucosal and muscular layers and

so lie in the background. Hence, true

mucosal tissue models will be required

to draw direct endoscopically relevant

conclusions. Since endoscopic imaging

in small animal models is technically

very challenging, work is ongoing using

chemically induced tumors in the ham-

ster cheek pouch. [ 38 ] This will also enable

comparison of the threshold doses for

intravascular versus topical delivery of

quantum dots: the latter is likely to be the

preferred route of endoscopic administra-

tion in patients.

3.2. Modeling

The simple 1D approach used to model the

effect of blood absorption on the detected

QD fl uorescence signal yielded satisfac-

tory agreement with the experimental

data across all the parameters tested:

vessel diameter, blood QD concentra-

tion, excitation and emission wavelengths.

Moreover, only a few experimental meas-

urements were required as inputs, namely

1) the average blood absorption spectrum,

2) the fl uorescence excitation spectrum of

a known concentration of the QD solution

measured using the imaging system, and

3) the average autofl uorescence spectrum

of the tissue of interest. Assuming line-

arity of the detected QD signal with con-

centration, this simple model accurately

predicted both the behavior of the target-

to-autofl uorescence ratio as a function of

the QD vascular concentration, as well as

the threshold QD concentration versus

emission wavelength, at least within the

9www.small-journal.com

limitations imposed by the intersubject autofl uorescence var-
iations. Finally, a simple stoichiometric relationship allowed

prediction of the vascular QD concentration based upon the

injected volume and an estimate of the animal’s total blood

volume.

This study was not designed to observe additional effects

such as secondary excitation of the target from back-scattered

photons or depth-dependent tissue attenuation of the exci-

tation and emission light, which would be present in thicker

and more pigmented tissues, for which more complex photo-

physical models would be required. These effects were inves-

tigated in our recent homogenized-tissue phantom work. [ 26 ]

3.3. Optimization

Although in principle, one would expect a decrease in the

required QD dose when using longer wavelength-emitting

nanoparticles, determining the real magnitude of this effect

is important and, experimentally, was found to be large. For

example, the average threshold concentration was ∼ 70 times

lower for Qdot800 than QTracker565. Considering that the

quantum yield is 7 times lower and the emission fi lter band-

width is double for the former (AF and QD signal reduced

by a factor 3 and 1.4, respectively), the effective difference

would be over a thousandfold if these two parameters were

matched. Hence, the emission wavelength is a dominant

factor in optimizing contrast-based in vivo imaging. This

was also observed ex vivo, where the improvement between

500 and 700 emitting QDs was 3 to 4 orders of magnitude,

depending on the tissue type, for both surface and subsurface

imaging. [ 27 ]

The results also show quantitatively that optimization of

the excitation wavelength is also generally worthwhile, with

average maximum and broadband gain values of 4.5 and

1.9, respectively. These are similar to the previous fi ndings

in a range of tissues ex vivo. [ 27 ] It is important to reiterate

that these values translate directly into reduced volumes of

injected contrast agents, and thus contribute to lower cost

and potential toxicity.

Finally, the QTracker565 results show how adjusting

the fi lter bandwidth can improve the contrast and keep

the required QD dose down. For example, using a 25 nm

bandwidth fi lter centered at 562 nm instead of a 50 nm

bandwidth emission fi lter centered at 550 nm reduced the

detected autofl uorescence by a factor 3 and the QD signal

by a factor 1.4, for a net gain of 2.1. Without this fi lter sub-

stitution, visualization of QTracker565 contrast would have

been impractical, since the threshold concentration would

have been ∼ 210 nM instead of ∼ 100 n m , which would have

required injecting 200 μ L (at 2.0 μ m QD concentration) in a

20 g mouse, at a current cost of $450. It is worth noting that,

with the perspective given by this study, the dosimetry guide-

lines provided by the manufacturer may require modifi cation,

at least for surface/sub-surface imaging applications in which

the tissue autofl uorescence is signifi cant. For example, Invit-

rogen recommends injecting 20 to 40 μ L per mouse (equiv-

alent to 40 to 80 n m blood concentration) regardless of the

QD emission wavelength, which seems to underestimate the

10 www.small-journal.com © 2012 Wiley-VCH

required dose for QTracker565 and overestimate it for the

other wavelengths.

3.4. Target-to-Background Ratio

As shown in Figure 2 , the fl uorescence of the background

areas surrounding the blood vessels increased signifi cantly

after injecting the QDs, with the background spectra having

features resembling that of the target vessels. This background

is likely related to microvasculature perfusion of the tissue.

However, the dips in the fl uorescence spectra corresponding

to hemoglobin absorption peaks were proportionally less pro-

nounced for the background ROIs, likely due to the weakened

blood saturation effects for smaller vessels. Further investiga-

tions would be required to confi rm this. However, since the

background signal appears to scale with the vascular signal,

it would be counter-productive to inject doses that are much

greater than the threshold. This gives yet another incentive to

follow contrast-optimization guidelines. Similarly, if the goal

is to use, for example, an immuno-targeted contrast agent

to highlight a suspicious lesion such as an early tumor, then

the dose should be adjusted so that the signal is signifi cantly

higher ( ≥ 2-fold) than the autofl uorescence background, as

demonstrated above. However, the actual tumor-to-normal

tissue contrast will normally be determined by the affi nity

of the targeting moiety to the target cells and by the tumor

accumulation and normal tissue clearance kinetics, rather

than by photophysical considerations.

4. Conclusion

This study extends our previous work in tissue-simulating

phantoms [ 26 ] and ex vivo tissues [ 27 ] to show that quantita-

tive biophysical modeling can be applied accurately to the in

vivo case, at least for the simplifi ed geometry of the window

chamber model. We note that it is very labor-intensive to sur-

gically install the window chamber for this in vivo model. In

addition, particularly for the shortest wavelength quantum

dots, the cost of the high dose required severely reduced the

number of animals that could be used. Despite the limited

numbers of animals, statistically valid conclusions could be

drawn in most cases.

The results demonstrate how quantum-dot based fl uo-

rescence image contrast could be optimized in vivo for

applications involving surface/subsurface imaging. We also

introduced a simple modeling approach that allows the

minimum dose to be calculated with reasonable accuracy.

Given the linearity of the QD signal with blood concentra-

tion, it is relatively simple to estimate the threshold dose and,

thereby, minimize the use of contrast agent, by using a-priori

knowledge of the average tissue autofl uorescence and blood

absorption spectra, the measured signal from a QD target of

known geometry and concentration, and an estimate of the

total blood volume.

Several quantitative conclusions have been reached.

Firstly, it was demonstrated that blood-free targets are opti-

mally excited at 380–420 nm, in contrast to blood-rich targets

Verlag GmbH & Co. KGaA, Weinheim small 2012, DOI: 10.1002/smll.201102105


that are better excited using the 435–480 region. Analysis

from more than 100 blood vessels revealed that optimization

of the excitation wavelength results in substantial savings

in the contrast agent dose, with average peak and broad-

band gains of 4.5 and 1.9, respectively. Secondly, there were

substantial improvements when using longer wavelength

QDs: the average injection doses required to achieve the

minimum target-to-background contrast were estimated to

∼ 10.6, 0.51, and 0.15 pmol g − 1 for QTracker565, QTracker705,

and Qdot800, respectively. These results were accurately

predicted by the photophysical modeling. Finally, observa-

tions of the detected signal with respect to vessel diameter

showed that the signal saturated for larger vessels, especially

at peak blood absorption wavelengths. This effect was also

accurately quantifi ed by the photophysical model.

This study provides initial guidelines, validated in vivo,

towards establishing a more complete framework for QD-

based quantitative imaging and dose optimization. The

simple geometry of the dorsal window chamber model avoids

most of the complex light propagation effects, such as depth

and wavelength-dependent light attenuation and scattering

in tissue, allowing the focus to be on the issues specifi c to in

vivo imaging. We recognize that the results from the window

chamber model do not necessarily translate directly into

providing optimal imaging parameters in more complex 3D

tumor models or clinical imaging. Rather, the approach repre-

sents a step towards a more complete description of quantita-

tive quantum dot imaging. While some of the fi ndings may be

considered somewhat self-evident, their direct experimental

validation indicates that we have reasonable understanding

of the underlying optical biophysics. In addition, the magni-

tude of the optimization effects, as summarized in Table 1 , is

surprising and demonstrates that it will be critical to optimize

QD imaging properly if reliable results are to be achieved at

acceptable cost. Work is in progress, as indicated by Figure 10 ,

on extending the studies to tumor-bearing window chamber

models in which both targeted and untargeted QD contrast

agents can be evaluated and future efforts will extend these

studies to a true endoscopic tumor model.

5. Experimental Section

QD Preparation : QTracker565, QTracker655, QTracker705 and Qdot800 were purchased from Invitrogen (Q21031MP, Q21021MP, Q21061MP, Q22071MP, Carlsbad, CA, USA), where the number denotes the peak emission wavelength. The hydrodynamic diam-eter for the QTracker products varies between ∼ 15 (QTracker565) to 20.5 nm (QTracker705), as reported by Invitrogen (Carlsbad, CA, USA). The concentration of each solution was verifi ed experi-mentally by measuring the optical absorption of serial dilutions using a UV–vis spectrometer (Cary 300, Varian Inc., Palo Alto, CA, USA) and dividing by the known extinction coeffi cients: 0.3, 0.8, 1.7, and 1.7 × 10 6 M − 1 cm − 1 at 545, 642, 550, and 550 nm for the QTracker565, QTracker655, QTracker705, and Qdot800, respec-tively (Invitrogen). The QD excitation and emission spectra were measured using a scanning spectrofl uorometer (Fluorolog 3, Horiba Jobin-Yvon, Edison, NJ, USA). The quantum yield for each QD was evaluated at 385 nm relative to the dye AlexaFluor488 (A-20000,

© 2012 Wiley-VCH Verlag Gmbsmall 2012, DOI: 10.1002/smll.201102105

Invitrogen, Carlsbad, CA, USA), which has a known fl uorescence quantum yield of 0.92. The Qdot800 was conjugated to Cetuximab, a monoclonal antibody that binds to the epidermal growth factor receptor (EGFR), according to the protocol included with Invitrogen’s antibody conjugation kit. The purpose of Cetuximab is indicated below. The labeling density was ∼ 2.5–3 antibody molecules per QD, and the conjugation contributed to an additional ∼ 2% absorp-tion at 280 nm. The effects of the conjugation on the QD optical properties in the visible and NIR were thus negligible. Note that the spectra shown in Figure 1 were acquired after the conjugation.

Animal Care: A total of 22 female NCr nude mice (NCRNU-F, Taconic, Hudson, NY, USA) were used, with institutional animal care approval (University Health Network, Toronto, protocol #1582.2). These were separated into 4 groups: I ( n = 2), II ( n = 8), III ( n = 7), and IV ( n = 5), injected with QTracker565, QTracker655, QTracker705, and Cetuximab-Qdot800, respectively. The average weight was 22.2 g (range 19.5–25.3 g). Two mice in group III and all mice in group IV had a human cervical cancer cell line ME-180 tumor growing under the fascia of the dermis. These mice and the Cetuximab-Qdot800 conjugate were originally intended for a tumor-targeting experiment, but since no specifi c QD tumor accumulation was observed in most cases, they were included in this vascular contrast optimization study: the tumors had no signifi cant effect on the vascular contrast in this case. The mice were anesthetized by intraperitoneal injection of a mixture of ketamine and xylazine (80 and 13 mg/(kg body weight), respectively) prior to the surgical installation of the dorsal skinfold window chamber, as described by Algire and Legallai [ 28 ] and Palmer et al. [ 29 ] As shown previously (Figure 2 a), the window chamber exposes a skin fl ap and its cuta-neous microvasculature, while but providing protection with a 1cm diameter thin glass coverslip. After chamber implantation, the mice were returned to their original cages and allowed to recover for at least 48 h before imaging. Unlike many fl uorescence studies in rodents, these animals were not maintained on a low-fl uorophore diet, in order to avoid positively biasing the results by having arti-fi cially low tissue autofl uoresence background. This provided a worst-case scenario approach to better prepare for the high patient-to-patient variability expected in clinical endoscopic imaging.

Blood Analysis: For blood analysis, immediately upon comple-tion of the imaging, blood was collected by cardiac puncture under deep anesthesia, and the mice were then sacrifi ced. To determine the QD blood concentration, a blood sample (0.3 μ L) was imaged in a square silica capillary tube (WWP100375, Polymicro Technolo-gies, Phoenix, AZ, USA) and compared to capillaries loaded with known dilutions of the QD solutions. ICP-MS was also performed on the blood samples, after digestion and dilution in concentrated hydrochloric acid. Thus, for each blood sample, all cadmium iso-topes were monitored and normalized to a Cd standard solution (10 ppb, N9300176, PerkinElmer, Waltham, MA, USA). The cad-mium content of blood samples was then compared to that of the stock QD solutions to determine the QD concentrations.

Analysis of Vascular Morphology: Speckle variance optical coherence tomography (sv-OCT) was performed on each window chamber to estimate the depth and diameter of the main vessels. The OCT system and the specifi c technique used for speckle vari-ance analysis are described in detail by Mariampillai et al. [ 39 ] The OCT imaging showed that most vessels were confi ned within a small depth margin (50 to 150 μ m), which did not have a measur-able impact on the contrast measurements.


M. Roy et al.

1

full papers
Fluorescence Imaging: Fluorescence imaging was performed
using a custom-made multispectral imaging system consisting of an epifl uorescence stereomicroscope (MZFLIII, Leica Microsys-tems, Richmond Hill, ON, Canada), cooled CCD camera (CoolSnap K4, Photometrics, Tucson, AZ, USA) and automated excitation and emission fi lter wheels (AB304-T, Spectral Products, Putnam, CT), as previously described in detail. [ 26 ] Twelve excitation fi lters between 385 and 620 nm, with an average bandwidth of 20 nm, were used (Chroma Technology, Bellows Falls, VT, USA). This spectral range was selected a) to avoid the UV, which is not commonly used in endoscopic imaging because of safety concerns, and b) to match commercially available quantum dots, but restricting these to vis-ible excitation since this is where the blood absorption is strongest and most wavelength dependent, thereby rigorously testing the biophysical modeling. The chosen fi lter bandwidth was adequate to provide uniform sampling across that range, given the 12 fi lter limit imposed by the excitation fi lter wheel. For QTracker655, QTracker705, and Qdot800 emission fi lters centered at 650, 700, and 800 nm (50 nm bandwidth) were used, respectively, and for the QTracker565 a combination of two 50 nm bandwidth fi lters centered at 550 and 575 nm was used, effectively simulating a 25 nm bandwidth fi lter centered at 562.5 nm. Filter sequences were automated via a custom-made Labview program, which also allowed automatic scaling of the exposure time (0.1 to 15 s) to the detected fl uorescence intensity, in order to maintain a high signal-to-noise ratio ( μ sig / σ noise > 6). Images had a fi eld of view of 4 mm × 4 mm and were digitized to 256 × 256 binned pixels (16 μ m spatial resolution). For time-series imaging, a catheter (MTV-01, Braintree Scientifi c, Braintree, MA, USA) was inserted in the tail vein, allowing intravenous injection of QDs while the window chamber was continuously imaged. This also allowed intravenous injection of anesthetics for up to 3 h of continuous imaging post-QD-injec-tion. Direct tail vein injection of QDs using a 0.5 mL insulin syringe was preferred as technically simpler when pharmacokinetic data were not required. Multispectral image sets were taken approx. 10 min before and 10 min after direct tail vein injection.

Image Analysis: For each window chamber, the imaging fi eld of view was selected to insure that at least one large vessel (250–500 μ m diameter) was visible, along with smaller branches. Using a Matlab routine, 5 rectangular target ROIs were manu-ally drawn in each window chamber. Each rectangle covered the largest vessel area possible without touching its edges. Likewise, the rectangular background ROIs were drawn as close as pos-sible to their corresponding target ROI, and as large as possible without touching other vessels. The smallest vessels that were analyzed were ∼ 45 μ m in diameter, corresponding to 3 pixels for our imaging system. The target ROIs were distributed as dispers-edly as possible but the edges of the image were avoided to mini-mize artifacts related to spatial illumination discrepancies. For each window chamber, the ROI selection process was repeated 3 times to estimate the uncertainty related to the manual selection process itself. In all cases, this uncertainty was comparable to the pixel-to-pixel variability within a single ROI, and typically an order of magnitude less than the vessel-to-vessel variability. All images were corrected for the camera noise, excitation lamp spectrum and spatial illumination profi le, as previously described. [ 26 ] For time-series analysis, a simple image registration algorithm was used to allow the ROIs to adapt to small motion artifacts when required.

2 www.small-journal.com © 2012 Wiley-VCH

[ 1 ] W. C. Chan , D. J. Maxwell , X. Gao , R. E. Bailey , M. Han , S. Nie , Curr. Opin. Biotechnol. 2002 , 13 , 40–46 .

[ 2 ] W. Cai , X. Chen , Small 2007 , 3 , 1840-1854 . [ 3 ] M. E. Akerman , W. C. Chan , P. Laakkonen , S. N. Bhatia ,

E. Ruoslahti , Proc. Natl. Acad. Sci. USA 2002 , 99 , 12617–12621 . [ 4 ] P. L. Hsiung , J. Hardy , S. Friedland , R. Soetikno , C. B. Du , A. P. Wu ,

P. Sahbaie , J. M. Crawford , A. W. Lowe , C. H. Contag , T. D. Wang , Nat. Med. 2008 , 14 , 454–458 .

[ 5 ] S. Kim , Y. T. Lim , E. G. Soltesz , A. M. De Grand , J. Lee , A. Nakayama , J. A. Parker , T. Mihaljevic , R. G. Laurence , D. M. Dor , L. H. Cohn , M. G. Bawendi , J. V. Frangioni , Nat. Biotechnol. 2004 , 22 , 93–97 .

[ 6 ] H. Makhlouf , A. F. Gmitro , A. A. Tanbakuchi , J. A. Udovich , A. R. Rouse , J. Biomed. Opt. 2008 , 13 , 044016 .

[ 7 ] M. Ferrari , Nat. Rev. Cancer 2005 , 5 , 161-171 . [ 8 ] K. Steffl ova , J. Chen , G. Zheng , Curr. Med. Chem. 2007 , 14 ,

2110–2125 . [ 9 ] W. C. Chan , S. Nie , Science 1998 , 281 , 2016–2018 . [ 10 ] M. Bruchez Jr. , M. Moronne , P. Gin , S. Weiss , A. P. Alivisatos , Sci-

ence 1998 , 281 , 2013–2016 . [ 11 ] B. Dubertret , P. Skourides , D. J. Norris , V. Noireaux , A. H. Brivanlou ,

A. Libchaber , Science 2002 , 298 , 1759–1762 . [ 12 ] B. Ballou , B. C. Lagerholm , L. A. Ernst , M. P. Bruchez ,

A. S. Waggoner , Bioconjug. Chem. 2004 , 15 , 79–86 . [ 13 ] X. Gao , Y. Cui , R. M. Levenson , L. W. Chung , S. Nie , Nat. Bio-

technol. 2004 , 22 , 969–976 . [ 14 ] H. C. Fischer , L. C. Liu , K. S. Pang , W. C. W. Chan , Adv. Funct.

Materials 2006 , 16 , 1299–1305 . [ 15 ] R. S. Yang , L. W. Chang , J. P. Wu , M. H. Tsai , H. J. Wang , Y. C. Kuo ,

T. K. Yeh , C. S. Yang , P. Lin , Environ. Health Perspect. 2007 , 115 , 1339–1343 .

[ 16 ] R. Hardman , Environ. Health Perspect. 2006 , 114 , 165–172 . [ 17 ] H. C. Fischer , W. C. Chan , Curr. Opin. Biotechnol. 2007 , 18 ,

565–571 . [ 18 ] H. S. Choi , W. Liu , P. Misra , E. Tanaka , J. P. Zimmer , B. Itty Ipe ,

M. G. Bawendi , J. V. Frangioni , Nat. Biotechnol. 2007 , 25 , 1165–1170 .

[ 19 ] N. Pradhan , D. M. Battaglia , Y. Liu , X. Peng , Nano Lett. 2007 , 7 , 312–317 .

[ 20 ] C. Yingchuan , P. Leah , T. Rahul , T. S. Preston , Proc. SPIE 2008 , 6866 , 686603 .

[ 21 ] R. Xie , X. Peng , J. Am. Chem. Soc. 2009 , 131 , 10645–10651 . [ 22 ] P. Diagaradjane , J. M. Orenstein-Cardona , N. E. Colon-Casasnovas ,

A. Deorukhkar , S. Shentu , N. Kuno , D. L. Schwartz , J. G. Gelovani , S. Krishnan , Clin. Cancer Res. 2008 , 14 , 731–741 .

This was done manually for the cases where the images before and after injection were done in separate imaging sessions.

Acknowledgements

This work was supported by the Canadian Institutes of Health Research (CIHR #RMF-72551). M.R. also received fi nancial support from the Natural Sciences and Engineering Research Council of Canada. Infrastructure support was provided by the Ontario Min-istry of Health and Long Term Care: the opinions expressed do not necessarily represent those of OMHLTC. The authors would like to thank Debbie Squires, Jean Flanagan, and Sadiya Yousef for assist-ance with the animal work and Leigh Conroy and Kenneth Lee for help with the OCT speckle variance image analysis.

Verlag GmbH & Co. KGaA, Weinheim small 2012, DOI: 10.1002/smll.201102105


[ 23 ] M. Roy , B. C. Wilson , Int. J. Nanotechnology 2009 , 6 , 442–455 . [ 24 ] Y. T. Lim , S. Kim , A. Nakayama , N. E. Stott , M. G. Bawendi ,

J. V. Frangioni , Mol. Imaging 2003 , 2 , 50-64 . [ 25 ] N. Y. Morgan , S. English , W. Chen , V. Chernomordik , A. Russo ,

P. D. Smith , A. Gandjbakhche , Acad. Radiol. 2005 , 12 , 313–323 . [ 26 ] M. Roy , A. Kim , F. Dadani , B. C. Wilson , J. Biomed. Opt. 2011 , 16 ,

016013 . [ 27 ] M. Roy , F. Dadani , C. J. Niu , A. Kim , B. C. Wilson , J. Biomed. Opt.

2012 , 17 , 026002. [ 28 ] G. H. Algire , F. Y. Legallais , J. Natl. Cancer Inst. 1949 , 10 , 225–253 ,

incl 228 pl. [ 29 ] G. M. Palmer , A. N. Fontanella , S. Shan , G. Hanna , G. Zhang ,

C. L. Fraser , M. W. Dewhirst , Nat. Protocols 2011 , 6 , 1355–1366 . [ 30 ] M. Khurana , E. H. Moriyama , A. Mariampillai , K. Samkoe , D. Cramb ,

B. C. Wilson , J. Biomed. Opt. 2009 , 14 , 064006–064014 . [ 31 ] A. Mariampillai , M. K. Leung , M. Jarvi , B. A. Standish , K. Lee ,

B. C. Wilson , A. Vitkin , V. X. Yang , Opt. Lett. 2010 , 35 , 1257–1259 .

[ 32 ] A. Erten , W. Wrasidlo , M. Scadeng , S. Esener , R. M. Hoffman , M. Bouvet , M. Makale , Nanomed. 2010 , 6 , 797–807 .

© 2012 Wiley-VCH Verlag Gmsmall 2012, DOI: 10.1002/smll.201102105

[ 33 ] S. A. de Visscher , S. Kascakova , H. S. de Bruijn , A. P. van den Heuvel , A. Amelink , H. J. Sterenborg , D. J. Robinson , J. L. Roodenburg , M.J. Witjes , Lasers Surg. Med. 2011 , 43 , 528–536 .

[ 34 ] M. W. Dewhirst , S. Shan , Y. Cao , B. Moeller , F. Yuan , C. Y. Li , Dis. Markers 2002 , 18 , 293–311 .

[ 35 ] P. Urso , M. Lualdi , A. Colombo , M. Carrara , S. Tomatis , R. Marchesini , Phys. Med. Biol. 2007 , 52 , N229–239 .

[ 36 ] R. S. DaCosta , B. C. Wilson , N. E. Marcon , Digest. Endoscopy 2003 , 15 , 153–173 .

[ 37 ] A. L. McCallum , J. T. Jenkins , D. Gillen , R. G. Molloy , Gastrointest. Endosc. 2008 , 68 , 283–290 .

[ 38 ] N. S. Soukos , M. R. Hamblin , S. Keel , R. L. Fabian , T. F. Deutsch , T. Hasan , Cancer Res. 2001 , 61 , 4490–4496 .

[ 39 ] A. Mariampillai , B. A. Standish , E. H. Moriyama , M. Khurana , N. R. Munce , M. K. Leung , J. Jiang , A. Cable , B. C. Wilson , I. A. Vitkin , V. X. Yang , Opt. Lett. 2008 , 33 , 1530-1532 .

Received: October 4, 2011 Revised: December 12, 2011 Published online:

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estimation of minimum doses for optimized quantum dot contrast-enhanced vascular imaging in vivo

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