Nanomedicine development guided by FRET imaging

Nano Today 18 (2018) 124–136

Contents lists available at ScienceDirect

Nano Today journal homepage: www.elsevier.com/locate/nanotoday

Review

Nanomedicine development guided by FRET imaging Danielle M. Charron a,b , Gang Zheng a,b,c,∗ a

Princess Margaret Cancer Centre and Techna Institute, University Health Network, Toronto, Ontario M5G 1L7, Canada Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 1L7, Canada c Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada b

a r t i c l e

i n f o

Article history: Received 7 October 2017 Received in revised form 10 November 2017 Accepted 23 December 2017 Available online 10 January 2018 Keywords: Nanoparticle Drug delivery Self-assembly Theranostics Biophotonics Cancer

a b s t r a c t Nano-bio interactions are dynamic, interrelated, and overwhelmingly influence nanomedicine efficacy. Molecular imaging techniques such as fluorescence are essential for illuminating the nano-bio interface to guide nanomedicine development. With advances in reliable labelling of nanoparticles with fluorophores, integrated Förster resonance energy transfer (FRET) provides responsive fluorescence linked to nanomedicine integrity and function. In this review, we provide an overview of developments in the application of integrated FRET for imaging nanomedicines during nano-bio interactions. Furthermore, we discuss technical considerations and a general methodology for maximizing information from integrated FRET imaging and advancing its use as a routine tool in nanomedicine research. © 2018 Elsevier Ltd. All rights reserved.

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Nanomedicines in biological environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Integrated FRET imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Imaging the nano-bio interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Nanomedicine integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Drug delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Maximizing FRET information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Donor-acceptor selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Conflicts of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Introduction Nanotechnology is separate from other scientific fields because control of materials at the nanometer scale results in significantly changed properties [1]. With this distinction in mind, nanomedicines are nanoscale materials designed to meaningfully

∗ Corresponding author at: Princess Margaret Cancer Centre and Techna Institute, University Health Network, Toronto, Ontario M5G 1L7, Canada. E-mail address: [email protected] (G. Zheng). https://doi.org/10.1016/j.nantod.2017.12.006 1748-0132/© 2018 Elsevier Ltd. All rights reserved.

improve the biological behaviour of molecular therapeutics or imaging agents, as well as nanoparticles that possess new medically useful properties resulting from their size. It is clear from this definition that nanomedicine research encompasses both applied research in the engineering and application of new nanomedicines [2], and basic research seeking to understand nano-bio interactions [3]. Over the field’s short lifetime, the emphasis between applied and basic research has fluctuated, with current trends towards more fundamental studies, particularly in cancer nanomedicine [4–7]. Now, researchers seek to understand a beguilingly simple question: When a nanomedicine enters a biological system,

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

what happens to it? The in vivo fate of a nanoparticle depends on its context-dependent interactions with biological species such as proteins and cells within many microenvironments. Elucidating nano-bio interactions in a context-specific way requires non-invasive, real-time molecular imaging. Fortunately, applied nanomedicine research has already generated a toolkit of methods to label nanoparticles with probes for applications in imaging [8–11], theranostics [12–16], and sensing [17–19]. Fluorescence imaging in particular has been widely used to track nanomedicines across subcellular to whole-animal scales due to its accessibility, excellent resolution, and moderate sensitivity [17,20–24]. In this review, we highlight innovative ways the fluorescence technique Förster resonance energy transfer (FRET) is being used to study the nano-bio interface. FRET involves a transfer of energy from one fluorophore to another, shifting fluorescence emission [25,26]. FRET can be used to monitor, for example, interactions between fluorescent nanoparticles and fluorescently-labelled proteins or cells, providing valuable spatial and kinetic information [27]. Resonance energy transfer from bioluminescent proteins to fluorescent nanoparticles (BRET) is also seeing increased use to develop near-infrared systems with enhanced tissue penetration [28]. In this review, we focus on applications of integrated FRET, where the donor and acceptor fluorophores are both incorporated within the nanoparticle itself. For the purposes of this review, integrated FRET is here defined as FRET that takes place between fluorescent species that are structural components or payloads of a nanomedicine formulation. The donor and acceptor fluorophores can include, but are not limited to, fluorophore-labelled polymers, lipids, proteins, and small molecule therapeutics, intrinsically fluorescent drugs, fluorescent nanoparticles, and physically encapsulated fluorophores. Depending on the fluorescent species selected and the nanoparticle architecture, integrated FRET signal can be used to dynamically image various aspects of nanoparticle integrity and function during nano-bio interactions. This technique also offers many imaging advantages compared with more conventional single wavelength off-on (also known as “activatable”) fluorescence labelling techniques since FRET is insensitive to fluorophore/nanoparticle concentration, can be isolated from other photophysical processes that quench fluorescence, and can distinguish nanomedicine accumulation from desired signal changes [17]. While integrated FRET is only newly being applied in vivo, we expect that due to its versatility and ease of use, FRET-based imaging will become routine for guiding nanomedicine development in future.

Nanomedicines in biological environments As nanomedicines traverse the body from the point of administration to the target site, they encounter many biological species, local microenvironments, and barriers to delivery. Nano-bio interactions along the delivery route ultimately determine whether nanomedicines reach their targets and remain functional [3,29,30]. A few important nano-bio interactions are depicted in Fig. 1 and will be briefly introduced in this section to illustrate the value of molecular imaging in nanomedicine development. To fulfill their therapeutic or imaging functions, nanomedicines are engineered with specific material properties collectively called their “synthetic identity”. The synthetic identity includes a nanoparticle’s physical properties (e.g., size, morphology, surface charge, porosity, flexibility) and chemical composition (e.g., lipids, polymers, proteins, inorganic materials, surface ligands). When a nanomedicine enters the bloodstream, its synthetic identity and overall stability will first determine whether it immediately disassembles or burst releases its payload (Fig. 1-1). Next, serum proteins rapidly adsorb to the nanoparticle surface, forming the

125

protein corona (Fig. 1-2) [31–33]. This protein interaction can destabilize the nanoparticle, mask targeting ligands and surface properties, and overall change the engineered synthetic identity into a “biological identity” seen by cells. As a result, it can be difficult to attribute engineered physicochemical properties to a specific physiological response. Some of the proteins that form the corona are opsonins, which flag the nanoparticles for clearance by the mononuclear phagocyte system (MPS). The biological identity is dynamic and depends on the local biological milieu; therefore, nano-bio interactions are context-dependent. In addition to burst release, nanoparticles can progressively disaggregate or leak their payloads into the bloodstream, i.e., premature drug release (Fig. 13). Free nanoparticle components can then be renally cleared, which reduces their circulation time, or bind to serum proteins and lipoproteins, which may extend their circulation and change their biodistribution. Very small nanoparticles (<6 nm) and freed components are renally excreted to the urine (Fig. 1-4), while larger nanoparticles can undergo hepatobiliary clearance in the liver to the feces (Fig. 1-5) [34,35]. Nanomedicines are well recognized to be primarily cleared from circulation by resident macrophages in the liver and spleen that are part of the MPS, and reducing MPS clearance is an ongoing challenge [36,37]. Treatment of neurological diseases requires nanomedicines to further cross the blood-brainbarrier—and reach the other side intact (Fig. 1-6). In the context of cancer treatment, once nanomedicines reach the tumour, they must extravasate from circulation into the interstitial space (Fig. 17) [38]. This is the rate-limiting step for cancer nanomedicine, and is dependent on tumour physiological properties that permit the enhanced permeability and retention (EPR) of nanoscale materials [39–41]. Extravasation does not automatically lead to interaction with tumour cells: once in the tumour interstitium, nanoparticles encounter a dense, proteinaceous milieu that can again change its biological identity, impede transport, and cause in situ dissociation and drug release (Fig. 1-8) [42]; tumour-associated macrophages and other non-tumour cells in the microenvironment take-up a large fraction of accumulated nanoparticles (Fig. 1-9) [43,44]; and, nanoparticles can transit to tumour-draining lymph tissue (Fig. 1-10). Nanomedicines that reach the target tumour cells are internalized with an efficiency dependent on both their synthetic and biological identities (Fig. 1-11) [45]. Further, cell uptake mechanisms, intracellular distribution, drug bioavailability, and treatment response are all dependent on interactions between the nanomedicine and the cell, which are influenced by the nanomedicine’s biological history [46]. In addition to on-target delivery, nanomedicines or their individual components may nonspecifically accumulate in off-target tissues, negatively impacting therapy, imaging, and toxicity (Fig. 1-12). In summary, nano-bio interactions are complex, dynamic, and interrelated. Generating a full picture of nanomedicine in vivo behaviour requires noninvasive, real-time imaging.

Integrated FRET imaging FRET is a powerful imaging technique that provides spatial information two orders of magnitude below the optical diffraction limit [47]. When combined with nanomedicine, FRET imaging can provide visual information about nanoparticle integrity, function, and behaviour during nano-bio interactions. In this section, we introduce FRET theory in the context of integrated FRET imaging, where responsive FRET systems are incorporated within the nanomedicine formulation. FRET is the through-space, non-radiative energy transfer from an excited donor (D) to a ground state acceptor (A) (Fig. 2A). When a pair of fluorophores satisfy the conditions of FRET, excitation of the donor results in emission by the acceptor (FFRET ) and simulta-

126

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

Fig. 1. Schematic illustration of nano-bio interactions influencing nanomedicine behaviour along its delivery route. Nanomedicine fate is initially dictated by nano-bio interactions in the blood stream that lead to nanoparticle disassembly and burst release of payloads (1), formation of a biological identity through serum protein adsorption (2), and continuous leakage of payloads and nanoparticle components that can then be bound by serum proteins and lipoproteins (3). The dual influence of a nanomedicine’s engineered properties and acquired biological identity influence its renal (4) and hepatobiliary clearance (5) and sequestration by the mononuclear phagocyte system. In addition to clearance mechanisms, barriers such as the blood-brain barrier (6) can impede nanomedicine delivery. In cancer treatment specifically, nanomedicines must additionally extravasate out of the blood stream (7), transport through the interstitial space (8), avoid uptake by tumour-associated macrophages (9) and wash-out by the lymphatic system (10), and finally be internalized by the target cell (11), hopefully without non-specific tissue accumulation (12).

Fig. 2. Integrated FRET imaging. (A) Simplified Jablonski diagram illustrating the FRET photophysical process and its dependence on the separation (r) between the donor and acceptor fluorophores. ExD : donor excitation; FD : donor emission; FFRET : acceptor emission with donor excitation (B) FRET efficiency is dependent on the overlap (J) between donor emission (solid blue curve) and acceptor excitation (dashed purple curve) spectra. In imaging applications, FRET is commonly assessed by the FFRET /FD ratio. (C) A schematic example of how integrated FRET can be used to monitor nanoparticle integrity by co-encapsulating donor (blue) and acceptor (purple) fluorophores in the nanoparticle core, and monitoring the ratiometric FRET signal.

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

neous quenching of the donor fluorescence (FD ). For FRET to occur, emission from the donor must be able to excite the acceptor, i.e., FD overlaps the acceptor excitation spectrum (εA ), as demonstrated in Fig. 2B. This overlap is quantified by the spectral overlap integral J (in units M−1 cm−1 nm4 ):



J () =

∞ 0

FD () εA () 4 d



∞ 0

FD () d

(1)

As a through-space energy transfer, FRET efficiency (E) strongly depends on the distance between the acceptor and donor (r), making FRET a highly spatially-responsive optical technique: E=

R06 R06

(2)

+ r6

For example, if a donor-acceptor FRET pair is encapsulated in the core of a nanoparticle, FFRET can be used to monitor nanoparticle stability, with loss of FFRET and recovery of FD indicating disruption of the nanoparticle and spatial separation of the fluorophores (Fig. 2C). R0 in Eq. (2) is the Förster distance, which represents the donor-acceptor distance at which E = 50%; for most FRET pairs 1 nm < R0 < 10 nm R0 depends on J and the donor quantum yield (˚D ):



R0 = 0.0211 JD 2 n−4

1/6

(3)

Therefore, for efficient FRET, donor-acceptor pairs with large spectral overlap should be chosen, and bright donor fluorophores are preferred. 2 in Eq. (3) is the orientation factor, which depends on the relative orientation of the donor and acceptor transition dipole moments. 2 is commonly approximated as 2/3 for free dyes; however, for immobilized or encapsulated FRET pairs this approximation fails, making reliable estimation of R0 difficult for nanoparticle systems. The parameter n is the refractive index between the donor-acceptor pair, which depends on the nanoparticle composition and structure and where the donor and acceptor are located. In practice, R0 is not calculated for nanoparticle systems, and E is maximized by donor-acceptor selection based on their optical properties. Because E of a nanoparticle system is averaged across many donor-acceptor pairs, each having variable R0 values, integrated FRET cannot be used as a “nanoscopic ruler” to measure nanoscale distances between donors and acceptors [47]; instead, integrated FRET can only be considered as “on” or “off”. For a detailed explanation of FRET, derivation of Eqs. (1)–(3), and additional considerations for selecting donor-acceptor pairs and calculating R0 , refer to [48]. Imaging the nano-bio interface Many innovative strategies have been developed for engineering nanomedicines with integrated FRET, and proof-of-concept studies have demonstrated feasibility for in vitro and in vivo applications [17,27]. In this section, we highlight innovative ways integrated FRET imaging is being used to elucidate nanomedicine behaviour in biological environments and guide nanomedicine development. The two themes we will cover are monitoring nanomedicine integrity, and drug delivery, which are by far the dominant applications reported in the literature. Additional current and future applications are discussed in Concluding Remarks. Nanomedicine integrity For nanomedicines formed by self-assembly (e.g., liposomes, polymer micelles, lipoprotein mimetics, nanoemulsions, nanobubbles, inorganic nanoparticles with self-assembled coatings), structural integrity is particularly important for maintaining functionality. Zhao and colleagues recently demonstrated the power of

127

integrated FRET for measuring the dissociation kinetics and fate of self-assembled nanomedicines in vivo [49]. The authors developed a near-infrared (NIR) FRET imaging technique to monitor the stability of quantum dot (QD) core self-assembled lipidic nanoparticles (SALNPs), using the NIR QD core as the donor and Cy7-lipid in the self-assembled polyethylene glycol (PEG)-ylated lipid coating as the acceptor (Fig. 3A). FRET was confirmed by increasing the Cy7lipid concentration in the nanoparticle formulation and observing the generation of FFRET with corresponding loss of FD . A key advantage of this FRET design for in vivo imaging lies in the large spectral separation between QD and Cy7-lipid, making channel cross-talk correction unnecessary and allowing for simultaneous imaging of FFRET , FD , and FA . Following i.v. administration of the nanoparticle in a mouse subcutaneous colon cancer model, FFRET , FD , and FA whole-animal reflectance images revealed different dynamics and distributions for the QD and Cy7-lipid components. Within the tumour, FFRET rapidly increased, indicating fast accumulation of the intact nanoparticle, followed by a gradual reduction in FFRET and corresponding increase in FD from 2 h post injection, signifying nanoparticle dissociation and retention of QD (Fig. 3B). Since FA signal decreased during this time, Cy7-lipid was not retained within the tumour following dissociation. Similar distribution dynamics were observed in the tumour-draining lymph nodes by ex vivo imaging following peritumoral injection (Fig. 3C), and were confirmed using intravital confocal laser scanning microscopy (CLSM) in a dorsal window chamber model (Fig. 3D). Using this setup, vascular dissociation of Cy7-lipid from QD was also observed, and the lipid dissociation constant was calculated to be 2.7 × 10−4 s−1 with a half-life of ∼42 min based on the ratio FFRET /FD . Cy7-lipid trafficking was further investigated using ex vivo fluorescence imaging of the clearance organs (Fig. 3E). Fluorescence signal trends in the liver were similar to those observed in the tumour, showing that QD was cleared by the liver, whereas FA in the kidneys indicated additional renal clearance of Cy7-lipid. Further to these imaging experiments, the authors conducted fast protein liquid chromatography (FPLC) of the plasma to investigate Cy7-lipid association with serum proteins and lipid exchange with lipoproteins. These experiments collectively present a comprehensive picture of the in vivo fate of the nanoparticle, involving SALNP dissociation within the vasculature, tumour, lymphatics, and liver; Cy7-lipid association with serum components and trafficking to the kidneys; and QD retention in tumour and lymph nodes (Fig. 3F). While reflectance fluorescence imaging is not a quantitative modality, FRET can be used to semi-quantitatively measure absolute nanoparticle integrity in tissues. Bouchaala and colleagues were the first to demonstrate reliable correlation of in vivo FRET signal with nanoparticle integrity using a standard curve [50]. The authors physically co-encapsulated lipophilic variants of the NIR dyes Cy5.5 and Cy7.5 as a donor-acceptor FRET pair within the highly hydrophobic core of a lipid nanoemulsion droplet (Fig. 4A). By using two NIR emitting fluorophores, FRET signal contamination from tissue autofluorescence and wavelength-dependent optical scattering was minimized. The in vivo FRET capabilities of this nanoparticle following i.v. administration in healthy mice was assessed by the ratio FFRET /FD . At 15 min post injection, FFRET /FD signal clearly highlighted vasculature in the tail vein and hind legs as well as the liver and lungs (Fig. 4B). Over 24 h, FFRET /FD signal decreased ∼30-fold due to particle disassociation. This large dynamic range in FFRET /FD signal is attributed to efficient FRET between Cy5.5 and Cy7.5 resulting from their large spectral overlap, and implies that this FRET system can enable highly sensitive imaging of nanoparticle integrity. To correlate FRET imaging with nanoparticle integrity, a standard curve was generated by measuring FFRET and FD from mixtures of FRET nanoparticles and FRET-inactive nanoparticles encapsulating either the donor or acceptor individually (Fig. 4C). Importantly, the reflectance

128

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

Fig. 3. Monitoring the in vivo dissociation kinetics of self-assembled nanoparticles by integrated FRET imaging. (A) FRET between the quantum dot (QD) core and Cy7-lipid shell component of a self-assembled lipid nanoparticle (SALN) was used to monitor nanoparticle integrity. FRET imaging demonstrated dissociation of Cy7-lipid from the QD within (B) tumour tissue, (C) tumour-draining lymph nodes, and (D) vasculature (scale bar 100 ␮m), and preferential retention of QD within those spaces. (E) Due to the large spectral separation between QD and Cy7-lipid, Cy7-lipid trafficking could be unambiguously followed to the kidneys, while QD was cleared by the liver. Li: liver; Sp: spleen; Ki: kidney; Sk: skin; Lu: lung: Tu: tumour. (F) FRET imaging revealed nanoparticle integrity kinetics and the differential fate of individual components of this self-assembled system. Adapted with permission from reference [49], copyright 2013 American Chemical Society.

Fig. 4. Semi-quantitative calibration of ratiometric FRET images to determine absolute nanoparticle integrity. (A) Hydrophobic variants of the Cy3.3-Cy5.5 FRET pair were coencapsulated in a lipid nanoemulsion droplet to generate a robustly-emissive FRET system. (B) High-contrast FFRET /FD signal highlighted the vasculature of healthy mice and demonstrated a large dynamic range suitable for sensitive image analysis. (C) Nanoparticle integrity was modelled using mixtures of nanoparticles co- or singly-encapsulating the donor and acceptor. (D) Application of the calibration curve to the images in (B) enabled reliable estimation of nanoparticle integrity within the vasculature and liver noninvasively and longitudinally. Adapted with permission from reference [50] under the Creative Commons Attribution License.

imaging system and acquisition parameters used were identical to those used in the in vivo studies. Calibration of the FFRET /FD images in Fig. 4B confirmed the reliability of this method, with nanoparticle integrity calculated to be 93% ± 2% in the vascula-

ture 15 min post injection, remaining stable until the nanoparticles cleared (nanoparticle half-life was 3 h ± 1 h) (Fig. 4D). In comparison, nanoparticle integrity in the liver was only 66% ± 2% at 6 h post injection, and the nanoparticle integrity half-life in the

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

129

Fig. 5. FRET imaging reveals the stability-dependent cell uptake routes of polymer micelles. (A) A hydrophobic DiO and DiI donor-acceptor FRET pair was co-encapsulated in the hydrophobic core of self-assembled and cross-linked polymer micelles to monitor nanoparticle integrity in vitro. (B) Obvious differences in FFRET (red) and FD (green) signal distributions were observed in cells incubated for 2 h with SA or DS micelles (scale bar, 10 ␮m). (C) By combining integrated FRET imaging with conventional inhibition studies, the cell uptake routes of SA and DS micelles were elucidated. Adapted with permission from reference [51], copyright 2013 American Chemical Society.

liver was calculated to be 8.2 h ± 0.4 h. Similar measurements were performed in a D2A1 subcutaneous tumour model, with strong intratumor FFRET /FD signal and nanoparticle integrity of 77% ± 1% at 2 h demonstrating accumulation of intact nanoparticles. In summary, this in vivo calibration of FRET images for nanoparticle integrity was reliable due to the combination of excellent FRET nanoparticle qualities (efficient donor-acceptor pair, NIR emission, stable dye encapsulation) and careful image collection and analysis (consistent optical setup, autofluorescence subtraction, FFRET /FD normalization). Complementary to the impact of nano-bio interactions on nanoparticle integrity, a nanoparticle’s intrinsic stability has also been shown to influence how it interacts with biological systems. Using a FRET-based microscopy strategy, Lee and colleagues investigated the influence of self-assembly on cellular uptake of polymer micelles [51]. The authors physically co-encapsulated the hydrophobic dyes DiO and DiI as a donor-acceptor FRET pair in the hydrophobic core of self-assembled (SA) polymer micelles (Fig. 5A). To compare self-assembled and structurally stable nanoarchitectures, the polymer micelles were subsequently cross-linked using disulfide bonds (DS). When cells were incubated with SA or DS micelles, dramatically different FFRET and FD signal distributions were observed (Fig. 5B). After 2 h incubation with SA micelles, the relative FRET efficiency (Erel ), experimentally estimated as FFRET /(FFRET + FD ), remained high (Erel = 0.72) in the extracellular media due to robust FRET between co-encapsulated DiO and DiI. In the same images, bright FD outlined the cells, indicating micellemembrane fusion and incorporation of lipophilic DiO in the cell membrane. Punctate FFRET was observed inside the cells due to subsequent internalization of membrane-bound DiO and DiI by clathrin-mediated endocytosis, wherein FRET was partially recovered due to concentration in the endocytic membrane (Erel = 0.32). In comparison, no membrane fluorescence was observed following 2 h incubation with DS micelles, while punctate FFRET signal was observed inside the cells (Erel = 0.49). This result suggests that DS micelles are internalized intact, and are subsequently degraded by the reductive conditions of early endosomes. These FRET imaging results were supported by conventional inhibition studies to iden-

tify the mechanisms involved in cell uptake of SA and DS micelles (Fig. 5C). The results from this study demonstrate the importance of nanoparticle integrity during nano-cell interactions and the dramatic impact it can have on cell uptake mechanisms, payload release kinetics, and intracellular distribution. In addition, the study introduces an important consideration for interpreting data from integrated FRET imaging studies: FRET between lipophilic donoracceptor pairs can be recovered in intracellular membranes, in which case FFRET /FD and Erel are no longer proportional to nanoparticle integrity. An example imaging strategy that takes advantage of this in situ FRET generation is discussed next. Drug delivery Closely related to nanoparticle stability is the technical challenge of achieving both stable payload encapsulation during circulation and selective, controlled release at the target site. To address this issue, Zou and colleagues developed a FRET-based imaging strategy to screen prospective polymer nanomedicines for premature drug release [52]. As a first step, the lipophilic donoracceptor FRET pair DiO-DiI was physically co-encapsulated within the hydrophobic core of a poly(ethylene oxide)-b-polystyrene nanoparticle. In vitro FRET imaging confirmed the confounding recovery of FRET signal within cell membranes following DiO and DiI release that was observed in reference [51] (Fig. 6A). In an innovative shift, the authors used this FRET turn-on signal as a selective response to nanoparticle disruption and payload release. In situ generation of FRET was observed within the membranes of cells incubated with a mixture of polymer nanoparticles individually encapsulating either DiO or DiI (Fig. 6B). The two FRET techniques were further compared in vivo using the NIR donor-acceptor analogs DiD and DiR (Fig. 6C). Following i.v. administration of nanoparticles co-encapsulating the donor-acceptor pair in healthy mice, the whole-animal average FFRET /FD signal by reflectance imaging decreased from 0.60 ± 0.03 at 10 min to 0.48 ± 0.03 at 2 h. In comparison, FFRET /FD signal for a mixture of nanoparticles individually encapsulating donor or acceptor increased from 0.29 ± 0.04 at 10 min to 0.48 ± 0.02 at 2 h post injection. For the

130

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

Fig. 6. Taking advantage of in situ FRET generation for monitoring payload release. (A) When lipophilic Di dyes are used as donor-acceptor FRET pairs to monitor payload release, FRET is recovered in cell membranes due to the high local concentration of released dye. (B) This in situ FFRET signal is also generated when the dyes are delivered individually encapsulated in separate nanoparticles. (C) Comparing the in vivo FFRET /FD signal from both encapsulation methods over time is a useful strategy to semiquantitatively measure drug release kinetics. Adapted with permission from reference [52], copyright 2013 American Chemical Society.

two FRET approaches, the final FFRET /FD signal was similar, as was expected for complete nanoparticle disruption and incorporation of the donor-acceptor FRET pair at similar concentrations within the in vivo cell membrane sink. Consequently, the in vivo drug release rate could be measured using a four-step process: 1) Monitor FFRET /FD signal following administration of nanoparticles co-encapsulating the donor-acceptor pair; 2) Repeat for a mixture of nanoparticles individually encapsulating the donor or acceptor; 3) Calculate the difference in FFRET /FD signals between the two systems; 4) Fit the difference over time to a first-order exponential decay. The authors used this multi-step process to compare the drug loading stability of the original polymer nanoparticle with nanoparticles core-loaded with oleic acid-coated iron

oxide nanoparticles (IONP), theorizing that dye affinity for oleic acid would slow its release. By incorporating IONP in the polymer nanoparticle, the in vivo dye release half-life increased from 9.2 min to 50.8 min, demonstrating the feasibility of this FRET approach that uses fluorophores as model drugs for screening polymer nanoparticles for stable drug loading. In situ FRET can also be generated upon exchange of donoracceptor pairs when self-assembled nanoparticles normalize their composition. Using this phenomenon, the Raymo group demonstrated intracellular dynamic exchange of structural components and payloads between polymer micelles [53,54]. Self-assembled polymer micelles were labelled with the hydrophobic fluorophores anthracene and BODIPY (Fig. 7A) as donor-acceptor FRET pair

Fig. 7. Dynamic exchange of structural and payload components between self-assembled nanoparticles elucidated using FRET imaging. (A) The hydrophobic donor-acceptor FRET pair of anthracene (1) and BODIPY (2) was physically encapsulated in polymer micelles or conjugated to the amphiphilic polymer backbone (3). (B) Nanoparticles covalently loaded with donor (a), acceptor (b), or both (c), imaged using their respective channels, showed the nanoparticles are internalized intact. (C) Relative to background ICG signal (a), in vitro incubation of acceptor-loaded nanoparticles (b) followed by donor-loaded nanoparticles (c) generated in situ FRET. (D) Similar in situ FRET generation was observed when the dyes were covalently conjugated, demonstrating their dynamic exchange between micelles. (A, D) adapted with permission from reference [54], copyright 2015 American Chemical Society. (B, C) adapted with permission from reference [53], copyright 2014 American Chemical Society.

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

131

Fig. 8. Use of a fluorescent model drug to assess nanocarrier-payload compatibility and its influence on in vivo release. (A) Cy7 (or Cy5) model drug was systematically modified to alter its hydrophobicity and polymer miscibility, then encapsulated in Cy5.5- (or Cy3.5-) labelled polymer micelles. (B) FRET from the nanocarrier to model drug was monitored to assess intravascular drug release by intravital microscopy and (C) biodistribution and tumour accumulation in vivo. (D) Premature drug release was lowest and tumour drug delivery highest for model drugs with high hydrophobicity and polymer miscibility. (E) Doxorubicin prodrug (Dox-X) was modified following these guidelines to improve nanotherapy in a breast cancer model. Adapted with permission from reference [55] under the Creative Commons Attribution License

through physical encapsulation in the hydrophobic core or by conjugation to the amphiphilic polymer backbone. Nanoparticles conjugated with the acceptor and encapsulating the donor in the core showed bright FFRET following 3 h incubation in HeLa cells, indicating the nanoparticles were internalized intact (Fig. 7B). Intracellular exchange of core-encapsulated dyes was investigated by incubating cells with nanoparticles loaded with the acceptor, washing, incubating with nanoparticles loaded with the donor, and washing again (Fig. 7C). In situ FRET generation was observed, with an ∼4-fold increase in FFRET signal from baseline. Since the anthracene and BODIPY dyes have low water solubility, their release and subsequent incorporation within other micelles is unlikely. To further investigate the mechanism, this experiment was repeated using nanoparticles singly-conjugated with either the donor or acceptor in the polymer backbone (Fig. 7D); strong in situ FRET generation was similarly observed. These results suggest dynamic exchange of the polymer component between micelles, bringing the hydrophobic encapsulated dyes with them. Further spectroscopy experiments of mixed micelle solutions demonstrated that this process has fast kinetics. It should be noted, however, that this normalization process may not be generalizable to molecular therapeutics with different physicochemical properties than the studied core-encapsulated fluorophores. As this study shows, the biological behaviour of nanomedicine structural components influences the fate of their payloads and should be considered and imaged when investigating nano-bio interactions. Drug leakage during circulation reduces the efficacy of nanotherapies and increases the risk of off-target effects. The influence of nanocarrier-drug compatibility on loading stability in biological environments was recently systematically assessed by Zhao and colleagues [55]. The authors covalently labelled polymer micelles with Cy5.5 donor and physically encapsulated Cy7-X acceptor as

a model drug (Fig. 8A); Cy3.5 and Cy5-X were used for intravital microscopy, respectively. Here, “X” refers to a series of tail components (C12, OLA, PLGA2k) conjugated to Cy7 or Cy5 that alter its hydrophobicity (log D) and polymer miscibility (␹), factors that impact how stably drugs are associated with polymeric materials. By using an imageable, easily modified fluorophore as a model drug, general trends in drug physicochemical properties and polymer micelle loading stability could be identified and extrapolated to real drugs. The FFRET /FD signal during circulation was monitored by intravital microscopy to assess model drug leakage (Fig. 8B). Burst release of Cy5-X was observed, followed by a plateau in FFRET /FD signal, with substantially higher retention of Cy5-PLGA2k compared with the other model drugs over 40 min imaging. Similar trends were observed by whole animal reflectance imaging following i.v. administration in a MDA-MB231 subcutaneous breast cancer model (Fig. 8C). Cy7-X model drugs with higher polymer miscibility (Cy7-PLGA2k) or hydrophobicity (Cy7-OLA) showed longer maintenance of FFRET /FD signal in circulation and lower kidney accumulation of released Cy7-X; as a result, Cy7-X tumour accumulation was highest for Cy7PLGA2k and Cy7-OLA. These imaging results were complimented by serum and protein incubation studies monitored by fluorescence spectroscopy, and computer simulations of polymer-Cy7-X interactions. Based on these experimental results using a model drug, the authors presented a guideline for modifying molecular therapeutics to improve their loading stability in polymer micelles by altering either their hydrophobicity or polymer miscibility (Fig. 8D). As proof-of-principle, the authors generated a series of doxorubicin prodrugs (Dox-X) modified with C4, C18, and PLA2k. The tail component was conjugated to Dox with a pH-cleavable linker so that free Dox is produced upon cellular uptake. Following i.v. administration in a 4T1 orthotopic breast cancer model, tumour

132

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

Fig. 9. Monitoring siRNA unpackaging from nanocarriers using FRET imaging demonstrates increased bioavailability with high molecular weight PEGylation. (A) Cationic micelles encapsulating siRNA for gene delivery were coated with PEG of varied content and molecular weight. (B) Increasing PEG molecular weight improved intracellular bioavailability, as demonstrated by loss in FRET efficiency between co-encapsulated siRNA-donor and siRNA-acceptor FRET pairs. Adapted with permission from reference [56], copyright 2015 Elsevier.

accumulation and response followed the trends predicted from the model drug studies (Fig. 8E). While this study presented specific guidelines for enhancing drug loading stability in polymer micelles, the FRET-based approach using fluorophores as model drugs is adaptable to different nanoparticle architectures and may be used to rapidly identify key variables influencing drug delivery in vivo. In the previous examples, fluorophores were used as model drugs to elucidate drug loading stability and drug delivery efficacy. This approach is advantageous because few therapeutics, even fluorescent drugs such as doxorubicin, have adequate optical properties for FRET in vivo imaging (i.e., bright, stable, NIR emission), and fluorescently labelling drugs can affect their functionality. However, this approach assumes that the fluorophore model drug is a suitable substitute for the therapeutic, which in the context of nanomedicine may not be the case. The previous example [55] showed how drug properties directly influence loading stability, biodistribution, clearance, and therapeutic success of the nanomedicine. Gene delivery is an application where direct fluorophore labelling of the therapeutic is routine. As an example, Miteva and colleagues used fluorophore-labelled small interfering RNA (siRNA) to assess the impact of nanocarrier composition on gene delivery [56]. siRNA is commonly delivered using cationic nanocarriers, which condense the anionic siRNA, protect it from degradation, and increase cell delivery efficiency [57]. However, serum protein binding interferes with the electrostatic interactions holding the complex together, leading to burst release or aggregation. Therefore, PEGylation is frequently used to reduce protein adsorption and improve stability, even though it shields the cationic surface charge that enhances cell uptake. In this study, the composition and molecular weight of PEG in the corona of cationic polymer micelles was systematically varied, and its influence on siRNA loading stability, circulation time, blood toxicity, biodistribution, cell uptake, bioavailability, and gene knockdown were investigated (Fig. 9A). As expected, high molecular weight PEG decreased in vitro cell uptake of the nanoparticles; however, gene knockdown remained high for all PEG molecular weights studied. The authors hypothesized that for high molecular weight PEG, increased bioavailability compensated for lower cell uptake. To test this hypothesis, integrated FRET imaging was used to monitor siRNA unpackaging from the polymer micelles (Fig. 9B). For this experiment, siRNA was labelled with either Alexa Fluor 488 or Alexa Fluor 546 as donor-acceptor FRET pair, and the two siRNA populations were co-encapsulated. Following cell incubation, FFRET /FD signal decreased with increasing PEG molecular weight, indicating siRNA release to the cytoplasm, confirming the hypothesis. This study is an excellent example of how integrated FRET imaging can

be combined with more conventional experimental methods to provide visual evidence for guiding nanomedicine development. Maximizing FRET information In the above discussion, examples from the literature were highlighted that make use of integrated FRET information to reveal nanomedicine biological behaviour that could not otherwise be imaged with conventional non-responsive fluorescence techniques. These examples also demonstrate that while fluorescence imaging is not a quantitative modality, FRET imaging can be semiquantitative if appropriate measures are taken. In this section, we provide an overview of two important considerations for maximizing FRET information and minimizing ambiguity in imaging results. Donor-acceptor selection The first step to using integrated FRET is to select the donor and acceptor fluorophores with which to label the nanomedicine. Examples of classic fluorophores used for FRET imaging are provided in Table 1. These fluorophores are all well-studied, commercially-available cyanines, with Cy3, Cy5, and Cy7 being the prototypical Cy series, and DiO to DiR lipophilic variants commonly used as membrane tracers. Sulfonated variants of the Cy dyes have also been developed to improve water solubility, as well as variants with reactive subunits for conjugation. FRET pairings of these dyes are provided in Table 2. All donor-acceptor combinations have large Table 1 Classical fluorophores for FRET. Fluorophore Cy3 Cy3.5 Cy5 Cy5.5 Cy7 Cy7.5 DiO DiI DiD DiR a b c d e f

ex a (nm) 555 591 646 684 750 788 484 549 644 748

εb (M−1 cm−1 ) e

150,000 116,000e 150,000e 209,000e 199,000e 223,000e 154,000f 148,000f 264,000f 270,000f

Excitation maximum. Peak extinction coefficient. Emission maximum. Quantum yield, in MeOH. Values from https://www.lumiprobe.com. Values from https://www.thermofisher.com.

em c (nm)

˚d

570 604 662 710 773 808 501 565 665 780

0.31e 0.35e 0.2e 0.2e 0.3e 0.3e 0.04 [52] 0.07 [52] 0.33 [52] 0.28 [52]

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

133

Table 2 Common donor-acceptor FRET pairs.a Donor Cy3 Cy3 Cy3.5 Cy3.5 Cy5 Cy5 Cy5.5 Cy5.5 DiO DiD a b c d e f

Acceptor

Jb (M−1 cm−1 nm4 )

R0 c (nm)

BR Ratiod

Ex SBTe (%)

Em SBTf (%)

Cy5 Cy5.5 Cy5 Cy5.5 Cy7 Cy7.5 Cy7 Cy7.5 DiI DiR

4.68 × 10 4.64 × 1015 9.21 × 1015 1.10 × 1016 1.52 × 1016 6.60 × 1015 2.57 × 1016 1.63 × 1015 5.60 × 1015 2.42 × 1016

5.49 5.48 6.27 6.45 6.21 5.40 6.78 6.28 4.02 7.29

1.6:1 1.1:1 1.4:1 1:1 1:2 1:2.2 1:1.4 1:1.6 1:1.7 1.2:1

6 3 11 6 12 4 17 7 11 12

4 2 15 8 3 1 10 8 12 5

15

Spectral data for calculations from https://searchlight.semrock.com. Spectral overlap integral, calculated using Eq. (1). Förster distance, calculated using Eq. (3) with 2 = 2/3 and n = 1.33. Brightness, product of ε and ˚. Spectral bleed-though between donor and acceptor excitation. Spectral bleed-through between donor and acceptor emission.

spectral overlap integrals (J) and Förster distances (R0 ) between 4.0 nm and 7.3 nm, making them efficient FRET pairs. A large J is the most important consideration for maximizing FRET stability and imaging signal-to-noise [58]. However, highly efficient FRET pairs also likely have non-negligible spectral bleed-through (SBT) in their excitation (Ex SBT) or emission (Em SBT) spectra. SBT makes selecting appropriate excitation and emission filters difficult, and results in channel cross-talk that must be corrected for; therefore, when selecting donor-acceptor FRET pairs, balance efficient FRET with SBT. For example, the donor-acceptor pair Cy5-Cy7 has an R0 of 6.21 nm and Ex SBT of 12%; in comparison, Cy5-Cy7.5, while being slightly less efficient with an R0 of 5.40 nm, has an Ex SBT of only 4%. Another source of ambiguity in FRET imaging is poor fluorescence stability, which encompasses environmental stability and photostability. While the Cy dyes have improved fluorescence stability compared to other common dyes such as fluorescein, tetramethylrhodamine (TRITC), and indocyanine green (ICG; the only clinically-approved NIR dye), their fluorescence spectra and quantum yields are still impacted by aggregation in aqueous solution, protein binding, and photobleaching [22,59,60]. Differential changes in donor and acceptor optical properties obviously interfere with FRET imaging, and are uncontrollable in vivo. Many interchangeable substitutes to the Cy dyes have been commercially developed (e.g., Alexa Fluor, DyLight) that claim increased solubility, environmental stability, brightness—also important for decreasing R0 , see Eq. (3)—and photostability. In addition to small molecule fluorophores, fluorescent nanoparticles such as QD can also be used as donor or acceptor, and offer many optical advantages including excellent photostability, brightness, and sharp spectral profiles [61]. However, it can be technically difficult to label nanomedicines with relatively large fluorescent nanoparticles without impacting their properties and thus changing their biological fate; integrated FRET using fluorescent nanoparticles is best performed when the fluorescent nanoparticle is already a key functional or structural component of the nanomedicine formulation.

Image analysis The following is a general strategy for maximizing the semiquantitative information gained from FRET imaging through relatively simple image analysis. First, remove fluorescence signals that interfere with FRET analysis. For in vitro fluorescence microscopy images, remove autofluorescence by background subtraction using fluorescence images collected prior to nanomedicine administration. Then, correct for SBT and channel cross-talk. To perform this cor-

rection, label nanoparticles with the donor fluorophore only and measure the correction coefficient ␣ = FFRET /FD . Similarly, for nanoparticles labelled with acceptor only, measure ˇ = FFRET /FA . The corrected FRET image is calculated from the equation FFRET net = FFRET − ␣ × FD − ˇ × FA . In vivo and ex vivo fluorescence images cannot be corrected in this way due to tissue- and wavelength-dependent optical properties [62,63]. Therefore, for in vivo applications it is particularly important to select donoracceptor pairs with well-separated emission and absorption spectra to minimize SBT, and carefully select excitation and emission filter sets to minimize channel cross-talk. If excitation cross-talk is negligible, spectral unmixing can be used to distinguish donor and acceptor fluorescence in vivo. To perform spectral unmixing, instead of collecting separate FD and FFRET images, collect a spectral imaging cube (lambda stack) spanning the collective donor and acceptor emission window. Most in vivo fluorescence imaging systems have automated unmixing capabilities that can identify autofluorescence, donor, and acceptor signals without reference spectra [64]. Next, convert the corrected fluorescence imaging data into a FRET index. FRET indices enable comparisons among FFRET signals collected longitudinally and in different subjects, although their values are unique to the nanomedicine, donor-acceptor FRET pair, optical setup, and other experimental conditions. Many FRET indices have been used for FRET microscopy [58,65], but most are not applicable for in vivo imaging or the specific application of nanomedicine. For monitoring nano-bio interactions, the simple FRET ratio FFRET /FD is most commonly used. FRET ratio is comparable to the relative FRET efficiency (Erel ): Erel =

FFRET FFRET + FD

(4)

These FRET indices are generally considered interchangeable, although the dynamic range of FRET ratio is larger than that of Erel , and so FRET ratio is more commonly used for imaging data to visually enhance differences in FRET signal distributions. As with any relative measurement, appropriate experimental controls are necessary to ensure FRET imaging data is not overinterpreted or overstated. For each experimental subject (e.g., cell culture well, mouse) normalize FRET index data to an initial value collected immediately post nanomedicine administration to account for variations in parameters such as fluorophore dose, injection success, and tissue-specific optical properties. Both FRET ratio and Erel are qualitative values that cannot replace the absolute efficiency E, but are useful metrics for monitoring fold changes in FRET signal. For example, nanoparticle stability in circulation can be assessed by

134

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

measuring the average Erel in a region-of-interest (ROI) along the tail vein over time and fitting the data to an exponential decay: Erel (t) = Erel (t0 ) × e−kt

(5)

From Eq. (5), the nanoparticle dissociation rate (k) and half-life (t1/2 = ln(2)/k) can be calculated. Note that a change in FRET index does not automatically indicate a comparable change in FRET efficiency or donor-acceptor separation if the donor and acceptor have different environmental sensitivities or photobleaching rates; therefore, as discussed above, optically stable fluorophores are preferable. For some nanoparticles and applications, it is possible to calibrate FRET indices as nanoparticle integrity using a standard curve. To generate a standard curve, it must be possible to formulate nanoparticles with only the donor or only the acceptor to model nanoparticle integrity as the percentage of FRET-active donoracceptor pairs relative to the total amount of donors and acceptors in the system. Furthermore, donor or acceptor exchange between individual nanoparticles must be negligible to preserve the engineered FRET-active and FRET-inactive nanoparticles upon mixing (e.g., select hydrophobic donor-acceptor fluorophores for encapsulation in a highly hydrophobic nanoparticle core). While this approach can be very useful for in vitro experiments, care must be exercised in applying a standard curve necessarily measured in solution to in vivo images. For an example of how to reliably calibrate in vivo Erel images, refer to the discussion of reference [50] in Imaging the Nano-bio Interface: Nanomedicine Integrity. Concluding remarks In this review, we introduced the concept of integrated FRET for imaging the nano-bio interface in nanomedicine applications. As highlighted above, integrated FRET has so far successfully been applied in vivo to monitor nanomedicine integrity and drug release during circulation, clearance, interstitial transport, and cell uptake. However, as Fig. 1 demonstrates, nanomedicine behaviour is influenced by many nano-bio interactions, and integrated FRET could provide additional spatial and functional information at these interfaces. For example, integrated FRET has been used to monitor nanomedicine transport and integrity across the blood-brain barrier in trans-well in vitro assays and zebrafish [66–68], but has not yet been applied for in vivo imaging in murine or diseased models. Additionally, while this review focused on nanomedicine behaviour following i.v. administration, other delivery routes (e.g., oral, cutaneous) have inherent barriers [69]. Recent proof-ofconcept studies have demonstrated that integrated FRET may be useful for monitoring nanoparticle penetrance and functionality on its path to the bloodstream [70,71]. Once in circulation, protein adsorption is rapid, dynamic, and highly influential not only on nanoparticle stability but its subsequent interactions with cells. FRET between fluorescent nanomedicines and fluorescentlylabelled isolated serum proteins has been used to compare protein adsorption in solution [72]. Integrated FRET imaging strategies for monitoring the formation and dynamics of the protein corona in vivo would be highly impactful. Further, many advanced targeted, stimuli-responsive, and smart nanomedicines have been developed to overcome delivery barriers and improve efficacy and safety profiles [73–76]. Integrated FRET could confirm whether these highly engineered systems are performing as intended. One example application is activatable photodynamic therapy (PDT) of cancer, wherein initially quenched photosensitizers accumulate in the tumour, their PDT activity is unquenched following a tumour-specific biological process, and then laser light is administered to stimulate the photosensitizers to produce cytotoxic singlet oxygen [77]. FRET from photosensitizers to acceptor fluorophores

has been shown to quench both photosensitizer fluorescence and singlet oxygen generation [78]; therefore, integrated FRET can be used both to engineer and monitor this activation process [79]. This technique has been well-studied for molecular constructs, but its extension to nanoparticles encapsulating large numbers of photosensitizers is more challenging due to photosensitizer concentration-dependent quenching mechanisms such as selfquenching and exciton coupling [17,80,81]. Finally, there are many integrated FRET spectroscopy-based studies of nanomedicines in the literature that could be translated to imaging for additional spatial and temporal information [82–87]. The goal of studying the nano-bio interface is to improve nanomedicine efficacy; however, apart from reference [55], integrated FRET imaging information has not been translated back to nanomedicine development and application. For integrated FRET to become a routine tool in guiding nanomedicine development, some technical advancements are required along with standardization of methods. For example, the needs of in vivo FRET imaging are more rigorous than those of microscopy: photostable and environmentally insensitive dyes should be used in place of the classical cyanine dyes; spectral unmixing should be used to separate tissue, acceptor, and donor emissions since channel cross-talk cannot be corrected. Additionally, while whole-animal reflectance imaging systems are advantageous for quickly imaging multiple animals at once, they measure overall surface fluorescence and so cannot be used to image deep-seated tissues and are not quantitative. FRET imaging accuracy and quantitation can be maximized by using fluorescence molecular tomography (FMT) systems, which measure tissue optical properties to generate a 3D map of quantifiable fluorescence signal [88,89]. Use of FMT for quantitative integrated FRET imaging, however, still relies on the general guidelines discussed in Maximizing FRET Information. As a volumetric imaging technique, FMT is frequently used in cancer applications requiring deep tissue imaging of orthotopic and metastatic models. Recently, FMT coregistration with computed tomography (CT) has been demonstrated to further improve anatomical accuracy [90,91]. The primary disadvantage of FMT is its poor temporal resolution resulting from long scan times; therefore, FMT for integrated FRET imaging is most appropriate for monitoring phenomena that occur on time scales of hours to days, such as nanoparticle biodistribution and metabolism. Combination of integrated FRET imaging with quantitative modalities such as positron emission tomography (PET) is also likely to elucidate potential ambiguities associated with FRET that result from fluorescence instability by providing a secondary independent, sensitive, and quantitative signal to track nanomedicine components [8,92]. In addition to providing quantitative PET contrast, many clinical isotopes also emit Cerenkov luminescence, which can potentially enable background-free fluorescence imaging. Recently, integrated FRET from blue-emitting 89 Zr to NIR-emitting QDs was demonstrated in co-labelled nanomedicines in order to shift the Cerenkov luminescence into the biological optical window and enable deeptissue imaging [93]. Use of Cerenkov-emitting radioisotopes as FRET donors may prove to be a useful technique to balance the advantages and limitations of wide-field fluorescence imaging and FMT for integrated FRET. As a further technical consideration, most of the studies highlighted in this review utilize physical encapsulation or conjugation of hydrophobic fluorophores in the nanoparticle core. Strategies for labelling the nanoparticle surface still need to be developed and verified. This is more difficult than core encapsulation, since solvent differences, relative fluorophore orientation (2 ), and protein adsorption all influence FRET efficiency and the reliability of FRET imaging [94]. In conclusion, integrated FRET imaging is a budding tool for visualizing nanomedicine behaviour in biological contexts. With rapid technological advances and demonstrations

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

of its utility, integrated FRET is fast becoming routine for guiding nanomedicine development. Conflicts of interest None. Acknowledgements The authors gratefully acknowledge support from Terry Fox Research Institute, Princess Margaret Cancer Foundation, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Canadian Foundation for Innovation and Joey and Toby Tanenbaum/Brazilian Ball Chair in Prostate Cancer Research. References [1] T.J. Webster, Int. J. Nanomed. 1 (2006) 115–116. [2] B. Pelaz, C. Alexiou, R.A. Alvarez-Puebla, F. Alves, A.M. Andrews, S. Ashraf, et al., ACS Nano 11 (2017) 2313–2381. [3] L.A. Lane, X. Qian, A.M. Smith, S. Nie, Annu. Rev. Phys. Chem. 66 (2015) 521–547. [4] J. Shi, P.W. Kantoff, R. Wooster, O.C. Farokhzad, Nat. Rev. Cancer 17 (2017) 20–37. [5] C.Y. Tay, M.I. Setyawati, J. Xie, W.J. Parak, D.T. Leong, Adv. Funct. Mater. 24 (2014) 5936–5955. [6] W.C.W. Chan, Acc. Chem. Res. 50 (2017) 627–632. [7] A. Albanese, P.S. Tang, W.C. Chan, Annu. Rev. Biomed. Eng. 14 (2012) 1–16. [8] S. Kunjachan, J. Ehling, G. Storm, F. Kiessling, T. Lammers, Chem. Rev. 115 (2015) 10907–10937. [9] R. Toy, L. Bauer, C. Hoimes, K.B. Ghaghada, E. Karathanasis, Adv. Drug Deliv. Rev. 76 (2014) 79–97. [10] B.R. Smith, S.S. Gambhir, Chem. Rev. 117 (2017) 901–986. [11] A.L. Petersen, A.E. Hansen, A. Gabizon, T.L. Andresen, Adv. Drug Deliv. Rev. 64 (2012) 1417–1435. [12] S. Mura, P. Couvreur, Adv. Drug Deliv. Rev. 64 (2012) 1394–1416. [13] E.K. Lim, T. Kim, S. Paik, S. Haam, Y.M. Huh, K. Lee, Chem. Rev. 115 (2015) 327–394. [14] D.M. Charron, J. Chen, G. Zheng, Theranostic lipid nanoparticles for cancer medicine, in: C.A. Mirkin, T.J. Meade, S. Hurst Petrosko, A.H. Stegh (Eds.), Nanotechnology-Based Precision Tools for the Detection and Treatment of Cancer, Springer, Switzerland, 2015, pp. 103–127. [15] H. Huang, J.F. Lovell, Adv. Funct. Mater. 27 (2017) 1603524. [16] H. Chen, W. Zhang, G. Zhu, J. Xie, X. Chen, Nat. Rev. Mater. 2 (2017) 17024. [17] B.M. Luby, D.M. Charron, C.M. MacLaughlin, G. Zheng, Adv. Drug Deliv. Rev. 113 (2017) 97–121. [18] G. Chen, I. Roy, C. Yang, P.N. Prasad, Chem. Rev. 116 (2016) 2826–2885. [19] W. Zhong, Anal. Bioanal. Bioanal. Chem. 394 (2009) 47–59. [20] M.L. James, S.S. Gambhir, Physiol. Rev. 92 (2012) 897–965. [21] B. Priem, C. Tian, J. Tang, Y. Zhao, W.J.M. Mulder, Expert Opin. Drug Deliv. 12 (2015) 1881–1894. [22] V.J. Pansare, S. Hejazi, W.J. Faenza, R.K. Prud’Homme, Chem. Mater. 24 (2012) 812–827. [23] J. Yao, M. Yang, Y. Duan, Chem. Rev. 114 (2014) 6130–6178. [24] J. Mérian, J. Gravier, F. Navarro, I. Texier, Molecules 17 (2012) 5564–5591. [25] R.M. Clegg, in: T.W.J. Gadella (Ed.), FRET and FLIM Techniques, Elsevier Science, Amsterdam, 2009, pp. 1–57. [26] Y. Sun, H. Wallrabe, S.A. Seo, A. Periasamy, ChemPhysChem 12 (2011) 462–474. [27] N.T. Chen, S.H. Cheng, C.P. Liu, J.S. Souris, C.T. Chen, C.Y. Mou, et al., Int. J. Mol. Sci. 13 (2012) 16598–16623. [28] J.O. Lee, B. Lim, Y.P. Kim, IEEE J. Sel. Top. Quantum Electron. 20 (2014) 6801410. [29] B. Fadeel, N. Feliu, C. Vogt, A.M. Abdelmonem, W.J. Parak, Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 5 (2013) 111–129. [30] E. Blanco, H. Shen, M. Ferrari, Nat. Biotechnol. 33 (2015) 941–951. [31] C.D. Walkey, W.C.W. Chan, Chem. Soc. Rev. 41 (2012) 2780–2799. [32] G. Caracciolo, Nanomed. Nanotechnol. Biol. Med. 11 (2015) 543–557. [33] S. Tenzer, D. Docter, J. Kuharev, A. Musyanovych, V. Fetz, R. Hecht, et al., Nat. Nano 8 (2013) 772–781. [34] Y.N. Zhang, W. Poon, A.J. Tavares, I.D. McGilvray, W.C.W. Chan, J. Control. Release 240 (2016) 332–348. [35] M. Yu, J. Zheng, ACS Nano 9 (2015) 6655–6674. [36] K.M. Tsoi, S.A. Macparland, X.Z. Ma, V.N. Spetzler, J. Echeverri, B. Ouyang, et al., Nat. Mater. 15 (2016) 1212–1221. [37] D.E. Owens Iii, N.A. Peppas, Int J. Pharm. 307 (2006) 93–102. [38] M.I. Setyawati, C.Y. Tay, D. Docter, R.H. Stauber, D.T. Leong, Chem. Soc. Rev. 44 (2015) 8174–8199.

135

[39] V.P. Chauhan, T. Stylianopoulos, Y. Boucher, R.K. Jain, Annu. Rev. Chem. Biomol. Eng. 2 (2011) 281–298. [40] Y. Nakamura, A. Mochida, P.L. Choyke, H. Kobayashi, Bioconjugate Chem. 27 (2016) 2225–2238. [41] N. Bertrand, J. Wu, X. Xu, N. Kamaly, O.C. Farokhzad, Adv. Drug Deliv. Rev. 66 (2014) 2–25. [42] B. Goins, W.T. Phillips, A. Bao, Expert Opin. Drug Deliv. 13 (2016) 873–889. [43] S. Vinogradov, G. Warren, X. Wei, Nanomedicine 9 (2014) 695–707. [44] L. Miao, J.M. Newby, C.M. Lin, L. Zhang, F. Xu, W.Y. Kim, et al., ACS Nano 10 (2016) 9243–9258. [45] N. Oh, J.H. Park, Int. J. Nanomed. 9 (Suppl. 1) (2014) 51–63. [46] J. Lazarovits, Y.Y. Chen, E.A. Sykes, W.C. Chan, Chem. Commun. 51 (2015) 2756–2767. [47] P.C. Ray, Z. Fan, R.A. Crouch, S.S. Sinha, A. Pramanik, Chem. Soc. Rev. 43 (2014) 6370–6404. [48] J.R. Lakowicz, Principles of Fluorescence Spectroscopy, Springer Science, New York, 2006, pp. 443–475. [49] Y. Zhao, I. Van Rooy, S. Hak, F. Fay, J. Tang, C.D.L. Davies, et al., ACS Nano 7 (2013) 10362–10370. [50] R. Bouchaala, L. Mercier, B. Andreiuk, Y. Mély, T. Vandamme, N. Anton, et al., J. Control. Release 236 (2016) 57–67. [51] S.Y. Lee, J.Y. Tyler, S. Kim, K. Park, J.X. Cheng, Mol. Pharm. 10 (2013) 3497–3506. [52] P. Zou, H. Chen, H.J. Paholak, D. Sun, Mol. Pharm. 10 (2013) 4185–4194. [53] S. Swaminathan, C. Fowley, B. McCaughan, J. Cusido, J.F. Callan, F.M. Raymo, J. Am. Chem. Soc. 136 (2014) 7907–7913. [54] E.R. Thapaliya, C. Fowley, B. Callan, S. Tang, Y. Zhang, J.F. Callan, et al., Langmuir 31 (2015) 9557–9565. [55] Y. Zhao, F. Fay, S. Hak, J. Manuel Perez-Aguilar, B.L. Sanchez-Gaytan, B. Goode, et al., Nat. Commun. 7 (2016) 11221. [56] M. Miteva, K.C. Kirkbride, K.V. Kilchrist, T.A. Werfel, H. Li, C.E. Nelson, et al., Biomaterials 38 (2015) 97–107. [57] J. Wang, Z. Lu, M.G. Wientjes, J.L.S. Au, AAPS J. 12 (2010) 492–503. [58] C. Berney, G. Danuser, Biophys. J. 84 (2003) 3992–4010. [59] M. Levitus, S. Ranjit, Q. Rev. Biophys. 44 (2011) 123–151. [60] R. Roy, S. Hohng, T. Ha, Nat. Meth. 5 (2008) 507–516. [61] U. Resch-Genger, M. Grabolle, S. Cavaliere-Jaricot, R. Nitschke, T. Nann, Nat. Meth. 5 (2008) 763–775. [62] J. Gravier, L. Sancey, S. Hirsjärvi, E. Rustique, C. Passirani, J.P. Benoît, et al., Mol. Pharm. 11 (2014) 3133–3144. [63] S.L. Jacques, Phys. Med. Biol. 58 (2013) R37–R61. [64] H. Xu, B.W. Rice, J. Biomed. Opt. 14 (2009) 064011. [65] E.A. Jares-Erijman, T.M. Jovin, Nat. Biotechnol. 21 (2003) 1387–1395. [66] A. Gaudin, O. Tagit, D. Sobot, S. Lepetre-Mouelhi, J. Mougin, T.F. Martens, et al., Chem. Mater. 27 (2015) 3636–3647. [67] T. Chen, C. Li, Y. Li, X. Yi, R. Wang, S.M.Y. Lee, et al., ACS Appl. Mater. Interfaces 9 (2017) 9516–9527. [68] F. Meng, S. Asghar, S. Gao, Z. Su, J. Song, M. Huo, et al., Colloids Surf. B Biointerfaces 134 (2015) 88–97. [69] S. Barua, S. Mitragotri, Nano Today 9 (2014) 223–243. [70] H. Li, M. Chen, Z. Su, M. Sun, Q. Ping, Int. J. Pharm. 511 (2016) 524–537. [71] H. Li, N.E.I. Guissi, Z. Su, Q. Ping, M. Sun, RSC Adv. 6 (2016) 84164–84176. [72] Z. Ma, J. Bai, X. Jiang, ACS Appl. Mater. Interfaces 7 (2015) 17614–17622. [73] S. Mura, J. Nicolas, P. Couvreur, Nat. Mater. 12 (2013) 991–1003. [74] S. Wang, P. Huang, X. Chen, ACS Nano 10 (2016) 2991–2994. [75] J. Du, L.A. Lane, S. Nie, J. Control. Release 219 (2015) 205–214. [76] E. Fleige, M.A. Quadir, R. Haag, Adv. Drug Deliv. Rev. 64 (2012) 866–884. [77] J.F. Lovell, T.W. Liu, J. Chen, G. Zheng, Chem. Rev. 110 (2010) 2839–2857. [78] J.F. Lovell, J. Chen, M.T. Jarvi, W.G. Cao, A.D. Allen, Y. Liu, et al., J. Phys. Chem. B 113 (2009) 3203–3211. [79] W. Wu, X. Shao, J. Zhao, M. Wu, Adv. Sci. 4 (2017) 1700113. [80] D. Charron, J. Chen, G. Zheng, ChemNanoMat 2 (2016) 430–436. [81] J.F. Lovell, C.S. Jin, E. Huynh, H. Jin, C. Kim, J.L. Rubinstein, et al., Nat. Mater. 10 (2011) 324–332. [82] T. Skajaa, Y. Zhao, D.J. van den Heuvel, H.C. Gerritsen, D.P. Cormode, R. Koole, et al., Nano Lett. 10 (2010) 5131–5138. [83] Y. Zhao, P. Schapotschnikow, T. Skajaa, T.J.H. Vlugt, W.J.M. Mulder, C. De Mello Donegá, et al., Small 10 (2014) 1163–1170. ˜ [84] L. Grana-Suárez, W. Verboom, J. Huskens, Chem. Commun. 52 (2016) 2597–2600. [85] E. Roger, J.C. Gimel, C. Bensley, A.S. Klymchenko, J.P. Benoit, J. Control. Release 253 (2017) 11–18. [86] Y. Li, M.S. Budamagunta, J. Luo, W. Xiao, J.C. Voss, K.S. Lam, ACS Nano 6 (2012) 9485–9495. [87] J.E. Tengood, I.S. Alferiev, K. Zhang, I. Fishbein, R.J. Levy, M. Chorny, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 4245–4250. [88] V. Ntziachristos, Nat. Methods 7 (2010) 603–614. [89] V. Ntziachristos, Annu. Rev. Biomed. Eng. 8 (2006) 1–33. [90] C. Darne, Y. Lu, E.M. Sevick-Muraca, Phys. Med. Biol. 59 (2014) R1–R64. [91] S. Kunjachan, F. Gremse, B. Theek, P. Koczera, R. Pola, M. Pechar, et al., ACS Nano 7 (2013) 252–262. [92] F. Stuker, J. Ripoll, M. Rudin, Pharmacy 3 (2011) 229–274. [93] Y. Zhao, T.M. Shaffer, S. Das, C. Pérez-Medina, W.J.M. Mulder, J. Grimm, Bioconjugate Chem. 28 (2017) 600–608. [94] K.K. Ng, M. Takada, C.C.S. Jin, G. Zheng, Bioconjugate Chem. 26 (2015) 345–351.

136

D.M. Charron, G. Zheng / Nano Today 18 (2018) 124–136

Danielle M Charron received a BASc in Nanotechnology Engineering from the University of Waterloo in 2013. She is currently a PhD candidate in the Institute of Biomaterials and Biomedical Engineering at the University of Toronto under the supervision of Dr. Gang Zheng. Her research focuses on developing biologically-inspired photonic nanosystems for cancer theranostics. She serves as a Managing Editor for Theranostics.

Dr. Gang Zheng is a Professor at the University of Toronto in the Departments of Medical Biophysics, Biomaterials and Biomedical Engineering, and Pharmacy. He is also a Senior Scientist and the Joey and Toby Tanenbaum/Brazilian Ball Chair in Prostate Cancer Research at the Princess Margaret Cancer Centre, and Scientific Lead for Nanotechnology and Radiochemistry at Techna Institute, University Health Network. He obtained his PhD from SUNY Buffalo and postdoctoral training from Roswell Park Cancer Institute, joined the faculty of the University of Pennsylvania in 2001 before moving to Canada in 2006. His research focuses on nanomedicine, molecular imaging, and photodynamic therapy. He is an AIMBE Fellow and an Associate Editor for Bioconjugate Chemistry.