Peptide-based nanoprobes for molecular imaging and disease diagnostics

Pengcheng Zhang a, Yonggang Cui b, Caleb F. Anderson c, Chunli Zhang b, Yaping Li *a, Rongfu Wang *b and Honggang Cui *cde
aState Key Laboratory of Drug Research & Center for Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China. E-mail: ypli@simm.ac.cn
bDepartment of Nuclear Medicine, Peking University First Hospital, 8 Xishiku Dajie, Xicheng District, Beijing, 100034, China. E-mail: rongfu_wang@163.com
cDepartment of Chemical and Biomolecular Engineering, and Institute for NanoBiotechnology, The Johns Hopkins University, 3400 North Charles Street, Baltimore, Maryland 21218, USA. E-mail: hcui6@jhu.edu
dDepartment of Materials Science and Engineering, The Johns Hopkins University, 3400 North Charles Street, Baltimore, Maryland 21218, USA
eDepartment of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA

Received 16th November 2017

First published on 2nd March 2018


Pathological changes in a diseased site are often accompanied by abnormal activities of various biomolecules in and around the involved cells. Identifying the location and expression levels of these biomolecules could enable early-stage diagnosis of the related disease, the design of an appropriate treatment strategy, and the accurate assessment of the treatment outcomes. Over the past two decades, a great diversity of peptide-based nanoprobes (PBNs) have been developed, aiming to improve the in vitro and in vivo performances of water-soluble molecular probes through engineering of their primary chemical structures as well as the physicochemical properties of their resultant assemblies. In this review, we introduce strategies and approaches adopted for the identification of functional peptides in the context of molecular imaging and disease diagnostics, and then focus our discussion on the design and construction of PBNs capable of navigating through physiological barriers for targeted delivery and improved specificity and sensitivity in recognizing target biomolecules. We highlight the biological and structural roles that low-molecular-weight peptides play in PBN design and provide our perspectives on the future development of PBNs for clinical translation.


image file: c7cs00793k-p1.tif

Pengcheng Zhang

Pengcheng Zhang received a Bachelor's degree in Life Sciences from Fudan University in 2006, and a PhD degree in Pharmaceutics from the Shanghai Institute of Materia Medica, Chinese Academy of Sciences in 2011. He was a Postdoctoral Fellow between 2011 and 2014 in the Chemical and Biomolecular Engineering department at Johns Hopkins University. He joined the Shanghai Institute of Materia Medica, Chinese Academy of Sciences in 2014 as an Associate Professor. He is a recipient of the Shanghai Pujiang Talent program and a member of Youth Innovation Promotion Association of Chinese Academy of Sciences. His research interests lie in the development of biomimetic nanostructures for disease diagnosis and treatment.

image file: c7cs00793k-p2.tif

Yonggang Cui

Yonggang Cui received his Medical Master's degree in molecular imaging and nuclear medicine in 2009 from Peking University First Hospital and has ever since practiced nulear medicine in the same institution. He is currently an Attending Doctor in the Department of Nuclear Medicine at Peking University First Hospital and mainly engages in the clinical research of nuclear medicine for tumor imaging, diagnosis and treatment. He specializes in endocrine radionuclide diagnosis and therapy. He received the Outstanding Young Physician Paper Award of the 2nd Yanjing Cancer Clinical and PET/CT Conference in 2012 and the 2016 Teaching Award of Peking University First Hospital.

image file: c7cs00793k-p3.tif

Caleb F. Anderson

Caleb F. Anderson received his bachelor's degree in Chemical and Biomolecular Engineering from the Georgia Institute of Technology in 2016. He is currently a PhD student in Dr Honggang Cui's laboratory in the department of Chemical and Biomolecular Engineering and the Institute for NanoBiotechnology at the Johns Hopkins University. His research interests include theranostics, inhalable therapeutics, peptide-based nanomaterials, and innovative drug delivery systems.

image file: c7cs00793k-p4.tif

Chunli Zhang

Chunli Zhang received a Bachelor's degree in Materials Science from East China University of Science and Technology in 1982, a Master's degree in Chemistry from Beijing Normal University in 1998 and a PhD degree in Pharmaceutics from Peking University in 2010. She joined the Peking University First Hospital in 1998 and was promoted to Researcher in 2002. She currently serves as the Vice Director of the Nuclear Medicine Department. She is a recipient of the Beijing Science and Technology Progress Award and Excellent Achievement Award for Scientific Research in the Ministry of Education. Her research focuses on the development of new radiopharmaceuticals for targeted diagnosis and therapy of tumors.

image file: c7cs00793k-p5.tif

Yaping Li

Yaping Li received a Master's degree in Pharmaceutics from the Shengyang Pharmaceutical University in 1996, and a PhD degree in Pharmaceutics from Fudan University in 2001. He was a postdoctoral Fellow between 2001 and 2002 in Ludwig Maximilian Muenchen Unitversitaet. He joined the Shanghai Institute of Materia Medica, Chinese Academy of Sciences in 1996 and was promoted to Professor in 2006. He currently serves as the director of the Center for Pharmaceutics. Dr Li is a recipient of the National Science Foundation for Distinguished Young Scholars of China, The national key talent project, The Hundred Talents Program of Chinese Academy of Sciences, and Wuxi Life Chemistry Scholar Award. His research focuses on nanotechnology-based targeting drug delivery systems for the treatment of multidrug resistant and metastasized cancer.

image file: c7cs00793k-p6.tif

Rongfu Wang

Rongfu Wang received a Bachelor's degree in Medicine from Fujian Medical College in 1982. He then obtained a Master's degree in Nuclear medicine & Biological physics and his first doctoral degree in Nuclear Medicine at the Fifth University of Paris in 1993, followed by a second PhD degree in Radiopharmacology from the Third University of Toulouse in 1995. He is currently the Director of the Department of Nuclear Medicine, Peking University Health Science Center, Peking University First Hospital & Peking University International Hospital. He has published more than 500 scientific papers, and received 3 ministerial level Science and Technology Achievement Awards and 1 ministerial Teaching Achievement Award. His research focuses on clinical radionuclide diagnosis and therapy, and molecular nuclear medicine, with broad interests in radiopharmaceuticals and cancer.

image file: c7cs00793k-p7.tif

Honggang Cui

Honggang Cui received a Bachelor's degree in Polymer Materials Science and Engineering from the Beijing University of Chemical Technology in 1999, a Master's degree in Materialogy/Chemical Engineering from Tsinghua University in 2002, and a PhD degree in Materials Science and Engineering from the University of Delaware in 2007. He was a Postdoctoral Fellow between 2007 and 2010 in the department of Materials Science and Engineering and the Institute for BioNanotechnology in Medicine at Northwestern University. He is currently an Associate Professor of Chemical and Biomolecular Engineering at the Johns Hopkins University, with joint appointments in the Institute for NanoBiotechnology, the Department of Materials Science and Engineering, and the Department of Oncology and Sidney Kimmel Comprehensive Cancer Center. His research focuses on the design, synthesis and functional assembly of peptidic molecules and therapeutic agents for applications in drug delivery, cancer diagnosis and imaging, and tumor microenvironment mimicking.


1. Introduction

1.1 The emergence and development of molecular imaging

The incidence, progression, and treatment susceptibility of a disease, as revealed by recent omics studies, can be indicated by altered molecular profiles of the diseased sites before discernible anatomic and physiological changes are present. To translate this knowledge into clinical benefits, real-time visualization of biological processes at the cellular and molecular level within living cells, tissues, and intact subjects is therefore necessary (Fig. 1).1–3 This unmet desire resulted in the emergence of molecular imaging about two decades ago. Since then, it has been broadly applied to the study of fundamental biological processes, diagnosis, treatment design, and therapeutic outcome assessment for many diseases, including cardiovascular disease and cancer, and has proven to be advantageous for these purposes.4–12 For instance, somatostatin receptor scintigraphy with 111In-Octreoscan, the first peptide probe for somatostatin receptor-positive tumors (approved in 1994 by FDA),13 can reveal additional metastases not visualized by conventional imaging in 30–50% of various neuroendocrine tumors, helping to optimize the surgical strategy and management in 25% and 47% of patients, respectively.14–17 One key for successful molecular imaging is the development of imaging probes (contrast agents).
image file: c7cs00793k-f1.tif
Fig. 1 Biomarker identification and the application of biomarker imaging in disease diagnosis, staging, treatment plan, and outcome assessment.

A typical probe, such as 111In-Octreoscan, includes two major parts: (1) a sensing moiety that has a high affinity for the targeted molecule (e.g. targeting ligands, substrates, or bio-responsive materials); and (2) a reporter module that emanates a detectable signal. Most reporter modules utilize clinically-established imaging techniques such as computed tomography (CT),18 positron emission tomography (PET),19–21 magnetic resonance imaging (MRI),22,23 and ultrasound (US).24 Others, such as fluorescence imaging (FI),4,25–27 photoacoustics (PA),28–31 photothermal imaging (PTI),32 and Raman spectroscopy,33 are currently only available for small animal models. Each imaging modality has its strengths and limitations, and thus it is necessary to choose an appropriate imaging modality, depending on the type of biological information one wishes to collect when designing imaging probes.2

Many probes for molecular imaging are being tested in preclinical and clinical studies with the aim to accurately probe the metabolism, angiogenesis, gene expression, enzyme expression, and hypoxia of diseased sites.34,35 One excellent example is the imaging probe that utilizes an activatable cell penetrating peptide (ACPP) to detect matrix metalloproteinase (MMP) activity.36–40 The ACPP-based probe is a peptide backbone (succinyl-e9-XPLGLAG-r9-Xk, where X denotes 6-aminohexanoyl, the sensing moiety) with a reporter fluorophore (such as Cy5) or gadolinium chelator on its C-terminus and can be activated by MMPs overexpressed in tumors to induce cellular entry and retention of the probe. The probe is highly tunable and has been tailored for imaging of different enzymes using fluorescence resonance energy transfer (FRET) effect-based FI and PA agents.39,41,42 One ACPP-based probe, AVB-620, is currently undergoing an initial pilot study in patients with breast cancer (NCT02391194). Other peptide-based probes designed for the imaging of the somatostatin receptor (68Ga-DOTA-NOC,43 and 68Ga-DOTATOC44), cyclophilin A (ASYNYDAGGGSK-FITC45), colonic dysplasia related receptor (VRPMPLQ-FITC46), Annexin A2 (BLZ-10047), tyrosine kinase c-Met (GE-13748), and cathepsin (LUM01549) have been or are currently being tested in the clinic, as detailed in Table 1.

Table 1 Various peptide-based probes in clinical use or trials
Probe Functional peptide Target Contrast generator Disease Status Trial identifier
111In-Octreoscan fC1FwKTC1T Somatostatin receptor 111In Neuroendocrine tumors, mammary cancer, and small cell lung cancer Approved
68Ga-DOTA-NOC fC1-1Nal-wKTC1T Somatostatin receptor 68Ga Gastroenteropancreatic neuroendocrine tumors II/III NCT02608203
99mTc-EDDA/HYNIC-TOC fC1YwKTC1T Somatostatin receptor 99mT Neuroendocrine tumors II NCT02691078
68Ga-DOTATOC fC1YwKTC1T Somatostatin receptor 68Ga Neuroendocrine tumors III NCT03136328
68Ga-DOTATATE fC1YwKTC1T Somatostatin receptor 68Ga Neuroendocrine Carcinoma I/II NCT01879657
68Ga-HA-DOTATATE fC1IYwKTC1T Somatostatin receptor 68Ga Neuroendocrine Carcinoma I/II NCT03145857
BLZ-100 MC1MPC2FTTDHQMARKC3DDC1C4GGKGRGKC2YGPQC3LC4R (numbers indicate the formation of disulfide bonds between the residues) Annexin A2 ICG Soft-tissue sarcoma I NCT02464332
Solid tumors I NCT02496065
Paediatric CNS tumors I NCT02462629
Adult glioma I NCT02234297
GE-137 AGSC1YC2SGPPRFEC2WC1YETEGT Tyrosine kinase c-Met Cy5 Colon polyps and cancer I 2010-019197-33
LUM015 GGRK Cathepsin Cy5/QSY21 Soft-tissue sarcoma, breast cancer I NCT01626066
AVB-620 e9-XPLGLAG-r9 MMPs Cy5, Cy7 Breast cancer I NCT02391194
18[thin space (1/6-em)]F-Alfatide II c(RGDfk) Integrin 18F Brain metastasis I NCT02441972
Breast cancer I NCT02582801
99mTc-3PRGD2 c(RGDfk) Integrin 99mTc Esophageal cancer I NCT02744729
99mTc-NC100692 K1C2RGDC2FC1 Integrin 99mTc Breast cancer NCT00888589
18F-RGD-K5 c(RGDfk) Integrin 18F Diffuse large B cell lymphoma II NCT02490891
18F-Galacto-RGD c(RGDfK(SAA)) Integrin 18F Glioblastoma III NCT01939574
18F-AH-111585 K1C2RGDC2FC1 Integrin 18F High-grade glioma, lung cancer, head & neck cancer, sarcoma, melanoma II NCT00565721
68Ga-RM2 fQWAVGH-Sta-L Gastrin-releasing peptide receptor 68Ga Prostate cancer II NCT02559115
68Ga-NOTA-RM26 fQWAVGH-Sta-L Gastrin-releasing peptide receptor 68Ga Prostate cancer I NCT03164837
68Ga-NOTA-Aca-BBN QWAVGHLM Gastrin-releasing peptide receptor 68Ga Glioma I NCT02520882
68Ga-NOTA-exendin-4 HGEGTFTSDLSKGMEEEAVRLFIEWLKNGGPSSGAPPPSC Glucagon-like peptide-1 receptor 68Ga Insulinoma nesidioblastosis I NCT02560376


1.2 The biological, structural, and auxiliary roles of peptides in peptide-based nanoprobes

Although small molecular weight peptide-based probes have achieved great successes in the area of molecular imaging, they suffer from limitations such as fast renal clearance, poor stability, and low delivery efficiency, preventing the establishment of sufficient imaging contrast at disease sites.50 In addition, tedious chemistry is necessary to optimize the physiochemical properties of peptide-based probes, which is particularly challenging when developing multi-functional probes. In consideration of these issues, nanoparticle-based contrast agents have been developed,51–54 because nanoparticles can: (1) enhance the signal by establishing a microenvironment to accommodate multiple reporter moieties;55–60 (2) improve the pharmacokinetics of probes taking advantage of their unique morphology and surface chemistry;61–72 (3) bring in new and/or better reporter modules;73–101 (4) offer additional strategies for contrast generation via the introduction of status-change synchronized signal transitions (from monomer-to-nanoparticle-to-nanocluster);102–109 and (5) feasibly realize multi-modality molecular imaging and theranostics.110–124

To be clinically useful, the nanoprobes must first be molecularly specific, and exclusive recognition between the nanoprobes and targeted molecules is thus of the utmost importance. Given that most essential functions in living organisms are carried out by proteins, protein expression and activity profiles are some of the most valuable biomarkers for molecular imaging and disease diagnosis, such as certain enzymes or surface receptors. Since proteins exert their functions through protein–protein interactions between functional domains defined by a few amino acid residues, peptide-based materials are a natural choice to create nanoprobes and have shown broad applications in many biomedical areas.125 This is mainly because peptides can exert numerous functions by varying their sequences, secondary structures, and side chain modifications (e.g., acetylation, phosphorylation, glycosylation), which can be borrowed and screened from the enormous library of both natural and artificial proteins and peptides. Their easy chemical synthesis and superior biocompatibility are additional reasons that warrant their use in biomedical applications. Peptide-based nanoprobes (PBNs) provide efficient molecular imaging (specific signal-to-noise ratio) in two major aspects (Fig. 2). First, peptides are used to maximize target-to-background selection of nanoprobes via specifically improving their accessibility, affinity, and retention in targeted molecules, as exemplified by targeting peptides, immune evasion peptides, and cell/tissue penetrating peptides.35,126–128 Second, enzymatic-mediated peptide cleavage and modification can be used to turn nanoprobes ON/OFF specifically for sensing the activity of enzymes mainly through inducing a structure change of the nanoprobes,129–132 the specificity of which could be optimized via tuning of the sequence, structure, and local environment of the substrate peptides to achieve high contrast.133 In rare cases, self-assembled peptides have also been used as a reporter module.134 Collectively, the utility of peptides provides an effective means of achieving molecular imaging with great accuracy and precision, making PBNs optimal tools for diagnostics and other biomedical applications.


image file: c7cs00793k-f2.tif
Fig. 2 The molecular roles that peptides may play in the design and construction of nanoprobes.

In this review article, we discuss in detail methods to discover functional peptides for the development of PBNs. We then provide an overview of recent applications of PBNs in molecular imaging and disease diagnosis, based on the roles that the peptides play in the nanoprobe design. We conclude with a brief discussion on the current challenges and possible directions of the area for future development.

2. Peptide Identification

2.1 Nature-derived peptides

As we discussed above, specific protein–protein interactions are usually mediated by peptide domains. Therefore, it is logical and promising to start with the natural ligand/substrate of a receptor/enzyme of interest when trying to develop a contrast agent for the receptor/enzyme.135 Many targeting ligands used in the design of nanoprobes are derived from natural peptides and proteins. For example, the naturally derived peptide RGD, which was discovered by Pierschbacher & Ruoslahti from fibronectin in 1984, has been heavily explored for its molecular recognition capabilities.136 The validity of the peptide in integrin recognition was confirmed later, as the binding affinity also relies on auxiliary segments and surrounding domains.137 The use of RGD for molecular imaging has been extensively explored, some cases of which are already in clinical trials, as shown in Table 1. The somatostatin analog is another example of a nature-derived peptide used for molecular recognition; the labeling of this peptide with radioisotope resulted in the development of 111In-Octreoscan for somatostatin receptor mapping in neuroendocrine patients.138 In addition to natural ligands, substrates of enzymes,139 venoms of animals,140,141 antibodies,142–144 and natural β-sheet-forming peptides145–149 are also useful motifs to improve the accuracy and sensitivity of PBNs.

To identify a minimal functional peptide sequence from a natural protein, it typically takes at least four steps (Fig. 3). Taking angiopep as an example, the first step is screening out a natural protein with the highest transcytosis efficiency across the blood–brain-barrier (BBB) through the low-density lipoprotein receptor-related protein (LRP150,151)-mediated pathway. Then, the sequence of the chosen aprotinin was aligned with other related proteins to locate a minimal peptide sequence (angiopep-1) interacting with LRP.152 A library of 96 peptides was further created and evaluated for their efficiency in penetrating bovine brain capillary endothelial cell (BBCEC) monolayers and in vivo on mouse brains, finally identifying angiopep-2 (TFFYGGSRGKRNNFKTEEY) with higher transcytosis efficacy and parenchyma accumulation than the parental aprotinin. Methods such as the one described serve as an effective means of identifying the minimal peptide sequence to interact with different biomarkers and thus produce optimal PBN systems.


image file: c7cs00793k-f3.tif
Fig. 3 Identification of angiopep-2 for BBB penetration. (a) Transcytosis efficiency evaluation of natural proteins. (b) Alignment of aprotinin with related proteins to give a leading peptide, angiopep-1. (c) Peptide library construction and screening. (d) Transcytosis efficiency evaluation of identified peptides. Angiopep-2 was identified with high parenchyma accumulation and low capillary adhesion. Reproduced from ref. 152 with permission from the American Society for Pharmacology and Experimental Therapeutics, copyright 2008.

2.2 Peptide libraries and combinational chemistry in peptide screening

2.2.1 Phage display. Phage display is a powerful strategy to screen functional peptides and proteins for specific biological function, and the detailed principles and practices have been excellently reviewed by Smith and Perenko.153 Briefly, the whole process includes 3 steps: the creation of a peptide library, selection (negative then positive screening), and identification of the peptide (Fig. 4). To create a peptide library, random DNA sequences are usually inserted into genes encoding minor coat protein 3 (cPIII) or major coat protein 8 (cPVIII) of the filamentous phages. The library will be first screened negatively against non-specific ligands and then positively against the desired target in vitro and in vivo.154 The identified peptides will be chemically synthesized and then their targeting capability will be validated. The key advantage of this strategy is that a large population of peptides can be screened at one time, and since the peptide sequence is directly related to the DNA sequence coding the protein, this allows the functional peptide to be easily identified and sequenced. Many peptides for molecular imaging and diagnosis of cancer have been developed through phage display, including those targeting different endothelial markers (termed vascular zip codes) and neuropilin-1 (NRP-1) for specific recognition and tissue penetration.155–158 The screening of large targeting ligands (such as affibodies) and enzyme substrates using phage display has also been reported, as well as simultaneous identification of targeting peptides and their receptors.159–161 For a more comprehensive summary, the readers are referred to the review by Deutscher.162
image file: c7cs00793k-f4.tif
Fig. 4 Phage display-based peptide screen. (a) Construction of plasmids containing random peptide-coding sequences. (b) Screening and identification of functional peptides.
2.2.2 Combinational chemistry and simulation. In addition to phage display, combinatorial peptide libraries offer an alternative strategy to generate functional peptides.163 With the advances in peptide synthesis techniques, huge libraries (>34 million hexapeptides) can now be established for the screening of new ligands.164–166 Although still not as large as phage display-generated libraries, combinatorial peptide libraries are advantageous because non-peptidic moieties (β-amino acids or other non-natural residues) and modified peptidic residues (phosphorylated or glycosylated) can be incorporated into the analyzed sequences.167 Thus, signal generation groups (e.g., carbamoylmethylcoumarin, ACC) can be easily incorporated into the peptides during the creation of a combinatorial library, making it ideal for screening protease substrates.168 Typically, two rounds of selection are necessary to identify both the N- and C-terminus of the enzymatically cleavable peptide (Fig. 5).169 First, one needs to create a library of peptides with the N-terminus acetylated, and then the peptides are treated with proteases. The generated peptide segments with a free N-terminus are then sequenced to identify the C-terminus of the cleavage site. The secondary library is then created containing the same biotin-labeled C-terminus sequence. The cleavage of the peptides is performed, and the mixture is treated with immobilized avidin. The N-terminus sequence of the cleavage site can thus be identified through N-terminal sequencing. To simplify the screening protocol, a FRET-based strategy for the screening of protease substrates was introduced.170 In this system, the fluorescence from fluorescent π-conjugated polyelectrolytes (CPEs) can be switched ON or OFF via enzymatic cleavage to remove the quencher or caged quencher that is complexed with CPEs through electrostatic interactions.
image file: c7cs00793k-f5.tif
Fig. 5 Schematic illustration of the peptide library strategy for protease substrate identification. Reproduced from ref. 169 with permission from the Nature Publishing Group, copyright 2001.

Although the efficiency of peptide synthesis has been significantly improved due to the introduction of automated robotic machines, the establishment of a peptide library requires tedious chemical synthesis. Even for the screening of a tripeptide hydrogelator, there will be 203 = 8000 different sequences requiring a sufficient amount of materials for a gelation property test. To overcome this challenge, Ulijn and colleagues utilized the CG MARTINI force field simulation, which uses both the propensity to aggregate and hydrophilicity of the tripeptides considered to rank their output (Fig. 6).171 Fifteen tripeptides out of the whole peptide space were then synthesized by the group, and their gelation properties were evaluated and compared, showing significant agreement between predicted scores and experimental behavior. These results suggested that both aromatic amino acids (in positions 2 and 3) and positive and hydrogen-bonding residues (N-terminus) are preferred for hydrogel formation, yielding a motif that can be used in further rational design of peptide hygrogelators.


image file: c7cs00793k-f6.tif
Fig. 6 Screening for self-assembling tripeptides. (a) Representation of all 20 amino acids in the MARTINI force field. (b) MD simulation results (50 ns) for KYF, KFD, PFF and GGG tripeptides, (c) aggregation propensity (AP) as a function of hydrophobicity for all 8000 tripeptides. (d) TEM images of KYF, KFD and PFF. (e) DLS autocorrelation functions for KYF, KFD, PFF and GGG (10 mM at pH 7). Reproduced from ref. 171 with permission from the Nature Publishing Group, copyright 2015.

2.3 Rational peptide design

Natural peptides sometimes suffer from decreased affinity, limited function, and fast degradation, which hinder their application to in vivo settings. Therefore, rational peptide design and optimization are necessary to overcome these barriers. It has generally been accepted that decreased activity of peptides is usually due to the absence of auxiliary domains and conformation, and thus one commonly adopted strategy in rational peptide design is the reconstruction of their environment and conformation. For instance, the integrin binding affinity of knottin peptide (GCPQGRGDWAPTSCCSQDSDCLAGCVCGPNGFCG) was doubled compared with c[RGDfK] due to its formation of a 3D structure,172 while the efficacy of VEGFR-1 antagonist peptide F56 (WHSDMEWWYLLG) and cell penetrating peptides (CPPs) could be improved by recapturing the hydrophilic and hydrophobic domains of the original protein with auxiliary moieties, respectively.173–176 The conformation of selected peptide sequences is thus important to consider when designing and developing PBN systems.

Another strategy commonly used in rational peptide design is the creation of chimeric peptides by integrating peptides of different functions into one, as exemplified by the rational design of iRGD (CRGDK/RGPDC) based on the discovery of RGD and C-end rule peptides (peptides with R/KXXR/K sequence158) for tumor accumulation and tissue penetration (Fig. 7).177 The iRGD peptide contains an RGD domain that can recognize integrins overexpressed by the neovasculature of a tumor and can be cleaved in tumors to expose RGDK/R for further NRP-1 binding and tissue penetration. Thus, cancer cells that are located deep within the tumor could, for example, be reached and labeled with iRGD-decorated magnetic nanoworms for effective molecular imaging.177 Integrating CPPs with intracellular functional peptides, such as caspase 3 substrate (EPD) and mitochondria-targeting sequence (MLRAALSTARRGPRLSRLL178), is also commonly adopted.179,180 Chimeric peptides can thus improve the overall targeting efficacy and selectivity to markers of diseased states and yield improved signal-to-noise ratios for PBN systems.


image file: c7cs00793k-f7.tif
Fig. 7 Rational design of iRGD from cyclic RGD-containing peptides and CendR peptides. Reproduced from ref. 177 with permission from Elsevier, copyright 2009.

The third strategy used in rational peptide design is the de novo design of the whole sequence based on the understanding of the properties of individual residues. This strategy is usually adopted in the design of short and simple sequences, where knowledge of the propensity of different amino acids to participate in specific intermolecular interactions drives the design process. Some β-sheet forming peptides and peptide hydrogelators have been successfully designed,181–187 being based mainly on the knowledge of the hydrogen bond forming propensity of specific amino acids.188 Collagen mimetic peptides have also been created that can recapture almost all the features of natural collagen.189 One recent example of de novo peptide design is the creation of fluorescent peptide nanoparticles, in which Fan et al. designed a dipeptide YF (two residues frequently found in fluorescence proteins) and co-assembled it with Zn2+ to confine the conformation of the peptides (Fig. 8).134 This design successfully created a 160 nm nanoparticle, showing emission at 423 nm with superior photo-, temperature-, and pH-stability and biocompatibility. Understanding the characteristics of different residues can help develop the ideal peptides to tune PBN systems for a specific function.


image file: c7cs00793k-f8.tif
Fig. 8 Design of the self-assembled fluorescent dipeptide nanoparticles (DNPs). (a) Schematic illustration of the π–π stacking-induced fluorescence red shift in YFP. (b) Schematic illustration of Zn(II)-induced fluorescence intensity enhancement. (c) Rational design of DNPs based on π–π stacking and structure rigidification. Reproduced from ref. 134 with permission from the Nature Publishing Group, copyright 2016.

These strategies provide peptides with significantly improved efficiency, selectivity, and function. However, further improvement of their biostability by using D-amino acids or designing cyclic peptides is sometimes necessary to realize their applications in molecular imaging in vivo.190,191 Overall, however, peptides can be designed to assist PBN systems to improve target-site accumulation, yield more precise and accurate signals, and mitigate non-specific degradation and off-target signaling.

3. Biology interfacing

During in vivo molecular imaging, nanoprobes constantly interact with biological barriers before reaching their targeted biomolecules, and thus they must achieve selective molecular recognition in the microenvironment where their targets reside. Surface modification of nanoprobes with functional moieties can fulfill this requirement, and some commonly used moieties include small molecules,192,193 peptides,127 proteins,194,195 protein scaffolds,196 antibodies,197 aptamers,198 and saccharides.199 Among various ligands, peptides receive additional interest because of their physiological barrier-penetrating and receptor-targeting capabilities with low cost and high biocompatibility.128,200 We recently reviewed the peptides that have been used in targeted delivery, with a focus on tumor targeting.127 In this section, we mainly focus on their application in modulating the nanoprobe-biology interface for improved molecular recognition and signaling.

3.1 Physiological barrier penetrating PBNs

3.1.1 Immune system-evading PBNs. The immune system actively monitors and cleans foreign particles in the body and thus presents an immunological barrier that prevents sufficient accumulation of nanoprobes in the diseased sites. This function is mainly executed by organ mononuclear phagocytes, which fulfill their task by checking the surface of encountered particles for both non-self and self-markers.201 Particles with exogenous antigens or surface-adsorbed endogenous proteins (such as antibodies and complements) will be phagocytized unless membrane protein CD47 (self-marker) is detected by SIRPα (CD172a) expressed on phagocytes.202 Based on this principle, two strategies have been developed using functional peptides. The first strategy attempts to evade immune clearance of nanoparticles by reducing their susceptibility toward protein fouling and thus making them invisible to phagocytes. Jiang's group first demonstrated this by grafting zwitterionic peptide EKEKEKE onto a gold surface, significantly inhibiting the adsorption of both positively charged lysozyme (Lyz) and negatively charged fibrinogen (Fib) in buffers of normal ionic strength (150 mM NaCl) and various pH values (5.7, 7.4, and 8) (Fig. 9a).203 The presence of EKEKEKE on the surface of gold nanoparticles (AuNPs) prevented the significant adsorption of serum protein onto AuNPs, maintained colloidal stability, and inhibited the phagocytosis of the nanoparticles by RAW264.7 macrophages (Fig. 9b).204 The second strategy involves the development of CD47-mimic peptides, which inhibit phagocytosis by activating the CD47-SIRPα axis. To achieve this, Discher and colleagues recently identified a minimal self-peptide (GNYTCEVTELTREGETIIELK) from CD47 using computer-aided design (Fig. 10a).205 To prove this concept, nanobeads decorated with the minimal self-peptides were prepared and their clearance by phagocytes was tested in vitro and in vivo. Improved circulation stability and intratumoral accumulation were observed for self-peptide-decorated nanobeads compared to those without decoration (Fig. 10b), and the clearance rate of the nanobeads in vivo depended on the density and binding affinity of the self-peptide to its target receptor (Fig. 10c). The histological analysis found that self-peptides inhibited the phagocytosis of nanobeads by activating SIRPα's cytoplasmic tail hyperphosphorylation and then SHP1 phosphatase that dephosphorylated myosin-II (Fig. 10d). These results demonstrated that the immune system serves as an important barrier to overcome to ensure the efficacy of PBNs, and the incorporation of specific peptides can help to mitigate immunological responses elicited from PBN systems.
image file: c7cs00793k-f9.tif
Fig. 9 Zwitterionic peptide-modified gold surface with ultralow protein adsorption. (a) Protein absorption of a gold surface decorated with EK mixed (1[thin space (1/6-em)]:[thin space (1/6-em)]1, mol/mol) polypeptide thin films at pH 7.4 and different NaCl ionic strengths (reproduced from ref. 203 with permission from Elsevier, copyright 2009). (b) Design of EK-containing peptide-modified gold nanoparticles and their uptake by bovine aortic endothelial cells (BAEC) and mouse macrophage (RAW 264.7) cells. Citric acid stabilized gold nanoparticles were used as a control (reproduced from ref. 204 with permission from the American Chemical Society, copyright 2014).

image file: c7cs00793k-f10.tif
Fig. 10 Minimal “self” peptides that inhibit phagocytic clearance. (a) Schematic illustration of the “self” peptide strategy. (b) Prolonged circulation and improved tumor accumulation of nanobeads through hCD47 or “self” peptide modification. (c) Modification density-dependent nanobead clearance from circulation. (d) SIRPα hyperphosphorylation induced by hCD47 or “self” peptide binding. Reproduced from ref. 205 with permission from The American Association for the Advancement of Science, copyright 2013.
3.1.2 Tissue barrier penetrating PBNs. Despite benefiting from prolonged circulation, in most cases nanoprobes in blood circulation still have to penetrate blood–tissue barriers to access their targeted biomarkers, even for the imaging of tumors with leaky vasculature.206 Leaky neovasculature can be heterogeneous and transient,207 thereby limiting the sensitivity of tumor diagnosis and treatment outcome assessment with MRI and other imaging modalities with low sensitivity. To address this issue, Sugahara et al. developed a cyclic tumor-homing peptide, iRGD (CRGDK/RGPDC) and incorporated the peptides onto magnetic nanoworms (Fig. 7).177 After intravenous injection, the integrin and neuropilin expressing tumor was visually marked with a substantial decrease in the T2-weighted MRI signal throughout the tumor and an increase in tumor-localized fluorescence (Fig. 11a). In sharp contrast, CRGDC-decorated nanoworms only showed hypointense vascular signals, while no detectable signal was recorded in non-targeted nanoworm-treated tumors. The tumor penetrating capability of iRGD was further confirmed with fluorescent silicon nanoparticle-based FI, and the contrast could be improved 100-fold for fluorescence imaging when used in combination with gated luminescence imaging techniques (Fig. 11b).208
image file: c7cs00793k-f11.tif
Fig. 11 Improved tumor imaging with iRGD modified contrast agents. (a) Improved contrast of tumor imaging with iRGD-modified magnetic nanoworms in T2-weighted MRI and fluorescence imaging (reproduced from ref. 177 with permission from Elsevier, copyright 2009). (b) Improved FI contrast using iRGD-modified silicon nanoparticles and the gated luminescence imaging technique (reproduced from ref. 208 with permission from the American Chemical Society, copyright 2015).

Another important physiological barrier that impedes tissue accumulation of nanoprobes is the blood–brain barrier (BBB), which is characterized by the overexpression of lipoprotein receptor-related proteins (LRPs).151 To address this issue, Li and colleagues developed a dual-modality nanoprobe by functionalizing PAMAM dendrimers (G5) with two peptides (c[RGDyK] and angiopep-2) and contrast agents (Cy5.5 and Gd3+–DOTA) (Fig. 12a).209 In this design, c[RGDyK] improved nanoprobe binding to the BBB in the brain tumor lesion, while angiopep-2 facilitated transcytosis of the probes across the BBB, resulting in significant contrast in orthotopic brain tumors during MRI and FI (Fig. 12b). Recently, Frigell et al. explored the BBB penetration efficacy of a neuropeptide derivative Enk (YGGFL) modified AuNPs (2 nm, stabilized with glucose and 68Gd–NOTA–lipoic acid), and found that the brain accumulation of AuNPs increased 3-fold in vivo.210 In addition to the above-used peptides, peptides derived from viruses (such as Tat peptide (GRKKRRQR), rabies virus glycoprotein-derived peptide (WMPENPRPGTPCDIFTNSRGKRASNGGGGGGC)) or a screening from phage display (NGR peptide) were also found to be able to deliver nanoprobes across the BBB.211–215


image file: c7cs00793k-f12.tif
Fig. 12 Brain tumor imaging with integrin and LRP targeting dual-modality dendrimers. (a) Nanoprobe design. (b) Intracranial brain tumor imaging with MRI (upper) and FI (lower) in vivo. Normal mice were used as a control. Reproduced from ref. 209 with permission from the American Chemical Society, copyright 2012.

Although less explored, peptides are also able to help the penetration of nanoprobes through other physiological barriers. For instance, R9 has been used to increase the retention and accumulation of nanogels (20–40 nm in diameter) in alveolar macrophages and epithelial cells,216 while CSK peptide (CSKSSDYQC) increased penetration of nanoprobes across the intestinal epithelium.217 These studies showed that PBN systems can be facilely designed to overcome tissue barriers and therefore reach their relevant targets for signaling.

3.1.3 Cell-penetrating peptides. Cells, as the basic unit of life, can act either like a therapeutic drug themselves or as the target of a drug. Intracellular drug tracking allows their efficacy to be assessed at an early stage but requires nanoprobes to be delivered intracellularly. Surface modification of nanoprobes with cell-penetrating peptides is a feasible strategy to achieve cytosol access.218–220 Based on this principle, intracellular cell labeling and siRNA tracking have been successfully realized using Tat, F3 (KDEPQRRSARLSAKPAPPKPEPKPKKAPAKK), oligoarginine (R7), or HIV gp41 glycoprotein-derived peptide (LGRRGWEVLKYWWNLLQYWSQELC) functionalized CLIOs, and quantum dots (QDs).221–227 The real-time monitoring of mesenchymal stem cell (MSC) differentiation has been proven to be possible via tracking of the intracellular biochemical changes with surface-enhanced Raman scattering (SERS) using intracellularly-located gold nanostars.228 Therefore, CPPs can be advantageous for many different biomedical applications and are of utmost importance for visualizing intracellular activity.

Recent efforts in this area mainly focus on the development of nanoprobes with better sensitivity, specificity, and biocompatibility.229–232 Non-selective and strong adsorption of positively charged CPPs to a negatively charged cell surface is the major cause of low specificity. To overcome the non-specific binding of nanoprobes to their encountering cells, Yang and colleagues created an extracellular acidity-activatable Tat peptide by blocking the ε-amine of lysine in the Tat peptide (GRKKRRQRRR) with dimethylmaleic anhydride.231 The resulting nanoprobes thus showed preferential accumulation in the tumors (due to their slightly acidic microenvironment) when compared with Tat-modified probes, as evidenced by NIR fluorescence imaging and MRI. In addition to reducing the non-specific binding, elimination of the extracellular nanoprobe is another strategy to improve its contrast. Ruoslahti and colleagues developed an etchable plasmonic nanoprobe, using a Ag nanoparticle as its core, which was further decorated with NeutrAvidin, an amine-reactive fluorophore and biotin-RPARPAR (or other biotinylated C-end rule peptides such as iRGD) (Fig. 13a).232 The unique property of the nanoprobe is that the Ag nanoparticles deposited outside of the cell membrane could be etched with hexacyanoferrate (HCF, Fe(III)(CN)63−) and thiosulfate (TS, S2O32−), allowing imaging and quantification of the endocytosed nanoprobes in vitro and in vivo (Fig. 13b). Thu et al. prepared an MRI nanoprobe solely using FDA approved materials (heparin, protamine, and ferumoxytol, HPF) with the aim of producing a clinically translatable nanoprobe.229 The HPF complexes successfully labeled a spectrum of primary cells, including hematopoietic stem cells, bone marrow stromal cells, neural stem cells, and T cells, without disturbing the cellular physiology or function of the cells (Fig. 14a). As little as 1000 labeled cells can be detected in vivo after being injected into the brain of a rat (Fig. 14b). The decoration of PBNs with CPPs can improve their targeting efficacy and accumulation inside diseased cells, allowing for improved signal-to-noise ratios and thus more precise diagnostics.


image file: c7cs00793k-f13.tif
Fig. 13 Etchable plasmonic nanoparticles for intracellular labeling of cells. (a) Schematic illustration of the mechanism of etchable plasmonic nanoparticles for intracellular labeling. (b) In vivo imaging of endocytosis using etchable plasmonic nanoparticles. Reproduced from ref. 232 with permission from the Nature Publishing Group, copyright 2014.

image file: c7cs00793k-f14.tif
Fig. 14 Live cell labeling with HFP. (a) TEM images of intact and endocytosed HPF and their effect on cell apoptosis and ROS production. (b) HPF labeled cell imaging in vivo with MRI and ex vivo with FI. 1: 1000 labeled cells; 2: 10[thin space (1/6-em)]000 labeled cells; 3: 5000 labeled cells; and 4: no labeled cells. Reproduced from ref. 229 with permission from the Nature Publishing Group, copyright 2012.
3.1.4 Subcellular organelle targeting PBNs. Many important biomolecules, such as DNA, are localized in subcellular organelles, like the nucleus, and their imaging requires targeted delivery of contrast agents into the corresponding organelles. The same challenge exists when cells transport newly synthesized proteins to their distinct target, which is resolved by signal peptides, including nuclear localization signals (NLSs) and mitochondrial targeting peptides (MTPs), to allow for the transport of proteins in or out of the organelle.233 Many peptides that are able to target subcellular organelles have been reported and have been thoroughly reviewed by Medintz and colleagues.234 In this section, we mainly focus on the unique application of PBNs in the imaging of subcellularly-located biomolecules.

The nucleus is the subcellular organelle where replication and transcription of DNA occurs. The change in mRNA transcription of cells is, therefore, a good predictor of a shift in their functions and physiological state. Previous studies have shown that AuNPs modified with the SV-40 NLS (KGGGPKKKRKVED) can target the nucleus and detect nucleus-specific chemical information through SERS.235,236 Based on this discovery, Mahajan and coworkers developed similar NLS-peptide decorated AuNPs (AuNP-CGTGPKKKRKVGGK-Flu) and imaged nucleus-based DNA and RNA with SERS using principal component analysis (Fig. 15a).237 They found that differentiated cells can be distinguished from their progenitor cells by analyzing their increased DNA/RNA ratio and protein peaks, which reflects the DNA condensation and improved transcription in the differentiated cells (Fig. 15b). In addition to their role as probes for endogenous biomolecules, nucleus-targeted nanoprobes have also been used as a tracker for the efficiency of nucleic acid drugs.238,239 The mitochondrion is another organelle that plays a pivotal role in cells because it generates the most biofuel (ATP) for the cell and also controls apoptosis. Metabolism in the mitochondria will generate heat locally and, therefore, the temperature of mitochondria can be a good indicator of cell activity.240 For this purpose, Huang and colleagues created mitochondrial-targeting AuNPs by using an MTP (CCYMLRAALSTARRGPRLSRLL) as a stabilizer of the AuNPs (1.5 ± 0.4 nm) (Fig. 16a).241 After incubating with cells, the MTP-AuNPs were primarily found in the mitochondria of the cells and showed strong temperature-dependent red fluorescence (Fig. 16b).


image file: c7cs00793k-f15.tif
Fig. 15 Nucleus SERS imaging with NLS-decorated nanoprobes for cell differentiation monitoring. (a) Schematic illustration of the design of the nucleus-targeting nanoprobe and SERS images of sugar phosphate, DNA and proteins. (b) Principal component analysis of nuclear spectra. PC1 score (Tyr and O–P–O RNA) enables distinguishing between differentiated and undifferentiated cells. Reproduced from ref. 237 with permission from the American Chemical Society, copyright 2013.

image file: c7cs00793k-f16.tif
Fig. 16 Mitochondrial imaging with MTP-decorated AuNPs. (a) Schematic illustration of the design of the mitochondrial temperature imaging nanoprobe. (b) Mitochondrial targeting and temperature dependent fluorescence of MTP-AuNPs. Reproduced from ref. 241 with permission from Springer, copyright 2017.

Given that many diseases are a collective result of changes in two or more biomarkers, multiplexing imaging with SERS was explored. Lim and colleagues developed a series of small spherical AuNPs with an intraparticle nanogap (Au-NNPs) filled with different Raman active molecules, and the surface of each type of Au-NNP was modified with a MTP (MLALLGWWWFFSRKKC), RGD peptide, or NLS (CGGGPKKKRKVGG) (Fig. 17a).242 Each type of Au-NNP showed strong and uniform Raman intensity under transient exposure time (10 ms) at low input power (785 nm, 200 μW), and subcellular organelle imaging in live cells was achieved with high speed and resolution (Fig. 17b). Since activity within different organelles can be used as an indicator of a diseased state, PBN systems that utilize CPPs in conjunction with peptides responsive to these activities can effectively produce signals for diagnostics at the subcellular level.


image file: c7cs00793k-f17.tif
Fig. 17 Multiplexing imaging of subcellular organelles with Au-NNPs. (a) The preparation of subcellular organelle-targeting Au-NNPs with different dyes in the nanogap. (b) Cytoplasm, mitochondria and nucleus imaging with Au-NNPs. Reproduced from ref. 242 with permission from the American Chemical Society, copyright 2015.

3.2 Membrane protein targeted PBNs

A typical mammalian cell is estimated to have 1010 proteins, 20–30% of which are membrane proteins.243,244 The types and numbers of membrane proteins vary among cells, distinguishing one cell from another. Under different pathological conditions, the expression profile of surface proteins will change as well. Therefore, molecular imaging of these proteins with nanoprobes will aid the diagnosis and staging of different diseases, and help to predict the efficacy of targeted nanomedicines,245 and monitor the outcome of treatments.246 Peptide ligands have been commonly used to guide nanoprobes to their specific receptors,127 and combinatorial use of two or more peptides has also been explored to improve the selectivity further.247
3.2.1 Integrin imaging with PBNs. Integrins play a vital role in many diseases via regulating the survival and migration of cells, where the detection in vivo at high resolution can benefit the diagnosis and treatment of diseases.248,249 In addition to their natural ligands, such as fibronectin, synthetic RGD-containing peptides are able to recognize and bind to integrins, allowing for the efficient detection and prolonged imaging of integrin expressing cells,137,250 which has been reported to occur as early as 10 min after injection without significant variation among tumor models used.251 RGD-containing peptides have been used to guide the accumulation of nanoprobes, including superparamagnetic iron oxide nanoparticles (SPIONs),252 MOFs,253–255 QDs,256 upconversion nanoparticles (UCNPs), liposomes, AuNPs,257–259 zinc oxide nanowires,260 and other biomimetic particles,261 and realize integrin imaging with various imaging modalities.

Integrin nanoprobes for MRI have been the most investigated application, because MRI allows integrin imaging with high spatiotemporal resolution and can be easily translated into the clinic. With a c[RGDyE]-decorated nanoprobe, tumors with high (HaCaT-ras-A-5RT3) and low (A431) area fractions of αvβ3 integrin-positive vessels can be distinguished with MRI.252 IONPs modified with c[RGDyK] are also able to bind integrin,262 and intracranial brain tumors that overexpress integrin could be imaged with the nanoprobes, as evidenced by a reduced T2-weighted signal (Fig. 18a).246 Four days after anti-VEGFR treatment, reduced contrast was noticed using the same nanoprobe, indicating a decrease in the integrin expression (Fig. 18b). However, MRI imaging of integrins usually suffers from low sensitivity. Therefore, other integrin-targeting nanoprobes that can be imaged with other clinically-relevant techniques have been developed. For instance, ultra-sensitive and quantitative integrin imaging was achieved with RGD-modified PET nanoprobes (76Br-labeled dendritic nanoprobes50 and 64Cu–DOTA–CN263) developed by Almutairi et al. and Liu et al., respectively.


image file: c7cs00793k-f18.tif
Fig. 18 Integrin imaging with c[RGDyK]-modified IONPs. (a) Design of c[RGDyK]-modified IONPs (RGD-IONPs). (b) Integrin expressing intracranial brain tumor imaging with RGD-IONPs before or after injection in mice with or without anti-VEGF treatment. Reproduced from ref. 246 with permission from Elsevier, copyright 2012.

Optical imaging has recently attracted much attention because it is compatible with clinical facilities used in disease diagnosis and surgery, and images of cellular resolution can be captured with specialized devices. Fluorescence imaging of integrins in vivo has been explored using RGD-modified QDs.80,264–267 Although proven to be promising, conventional FI of integrins still suffers from low contrast. Fluorescence from non-specifically accumulated nanoprobes is one major contributor to low contrast in FI. To minimize background fluorescence, Gao and coworkers recently developed a c[RGDfK]-modified ultra pH-sensitive intracellular (UPSi) nanoprobe self-assembled from Cy5.5-conjugated poly(ethylene glycol)-b-poly(2-(diisopropyl amino)ethyl methacrylate) copolymer, which can exhibit a signal switch (OFF–ON) with a decrease of only 0.25 pH units (Fig. 19a).108In vivo application of the cRGD-UPSi probe in tumor-bearing mice showed that the nanoprobe specifically imaged the integrins overexpressed in angiogenic tumor vasculature with a >300 S/N ratio (Fig. 19b).


image file: c7cs00793k-f19.tif
Fig. 19 Integrin-expressing tumor imaging with cRGD-UPSi. (a) Design and working mechanism of cRGD-UPSi for imaging of integrin-overexpressing tumors. (b) Imaging of integrin-expressing tumors with cRGD-UPSi in vivo which could be blocked by free cRGD. UPSi was used as a control. Reproduced from ref. 108 with permission from the Nature Publishing Group, copyright 2014.

Irradiation-induced autofluorescence is another factor responsible for low contrast. To address this issue, RGD-modified nanoprobes that can emit fluorescence in vivo without excitation have been developed. One such nanoprobe is near-infrared (NIR)-emitting long-persistent luminescent nanoparticles (LPLNPs), which could emit 695 nm light for more than 6 h in integrin-overexpressing tumors after a charging process (254 nm, 5–10 min irradiation) (Fig. 20a).268 Another nanoprobe of this type is c[RGDfK]-modified self-luminescing bioluminescence resonance energy transfer (BRET)-fluorescence resonance energy transfer (FRET) dots (RET1IR@cRGD), mainly composed of semiconductor polymer nanoparticles MEH-PPV (poly[2-methoxy-5-((2-ethylhexyl)oxy)-p-phenylenevinylene]), NIR775, and luciferases (Fig. 20b). The self-luminescing feature (initiated by the luciferase substrate coelenterazine) realized excellent tumor-to-background ratios (>100) for imaging small integrin expressing tumors (4[thin space (1/6-em)]mm in diameter) just 5 min after a coelenterazine injection (Fig. 20c).269 Photoacoustic imaging, as opposed to monitoring laser-induced fluorescence, records laser-generated ultrasound. Therefore, deep integrin imaging is obtainable. Indeed, Gambhir and colleagues showed that PA imaging with RGD-modified single-walled carbon nanotubes (SWCNTs), when compared with QD-based fluorescence imaging, illustrated the depth-information of integrin with greater spatial resolution (Fig. 21a).270 By adsorbing indocyanine green (ICG) onto the surface of SWCNTs, a 300-fold increase in sensitivity was achieved (sub-nanomolar) (Fig. 21b).271 Moreover, this technique enables multiplex molecular imaging with PA,272,273 as dyes other than ICG (such as QSY21) could also be adsorbed onto SWCNTs for molecular imaging. Based on the difference in absorbance spectrum, signals from SWCNT-ICG-RGD and SWCNT-QSY21-RGD could be separated in vivo (Fig. 21c).274 Other than SWCNTs, RGDfC modified nanogold tripods could also serve as nanoprobes for PA imaging of integrin,275 where the PA data collected from the probes correlated well with PET data for integrin imaging in the U87MG tumor.


image file: c7cs00793k-f20.tif
Fig. 20 Self-illuminated fluorescence nanoprobes. (a) Design of long persistent luminescent nanoparticles (LPLNPs) and their application in ex vivo NIR luminescence imaging of isolated organs and tumor tissue from a U87MG tumor-bearing mouse at 6 h post intravenous injection of RGD-LPLNPs. 1: heart, 2: lung, 3: liver, 4: spleen, 5: kidney, 6: stomach, 7: pancreas, 8: intestine, 9: bladder, 10: bone, 11: tumor (reproduced from ref. 268 with permission from the American Chemical Society, copyright 2013). (b) Design of RET1IR@cRGD and its BRET and FRET effect in a solution containing coelenterazine. (c) Time-dependent fluorescence imaging of a U87MG tumor-bearing mouse injected with RET1IR@cRGD or RET1IR NPs. Reproduced from ref. 269 with permission from the American Chemical Society, copyright 2012.

image file: c7cs00793k-f21.tif
Fig. 21 Imaging of integrin expression with RGD-modified SWCNTs. (a) Design of RGD-modified SWCNTs and their capability for integrin imaging with high spatiotemporal specificity. RGD-QDs were used as a control (reproduced from ref. 270 with permission from the Nature Publishing Group, copyright 2008). (b) Design of SWCNT-ICG-RGD and its potential for ultrasensitive imaging of integrin (reproduced from ref. 271 with permission from the American Chemical Society, copyright 2010). (c) Design of SWCNT–dye–RGD and its potential for multiplexing imaging (reproduced from ref. 274 with permission from the American Chemical Society, copyright 2012).

As we discussed above, each imaging modality has its pros and cons. Therefore, multimodality nanoprobes have been investigated for integrin imaging. Combination of an MRI modality with an FI/PET modality is a commonly explored strategy to gain anatomic information on integrins quantitatively with high sensitivity. In these cases, functional inorganic nanoparticles such as QDs and IONs are used as cores that are then surface-functionalized with Gd/64Cu-chelators and RGD-containing peptides to achieve dual-modality integrin imaging.276–278 Biomimetic nanoparticles, such as reconstituted high-density lipoprotein (rHDL), have also been used, because of the feasibility of incorporating fluorophores and chelators into their formulation.279 Integrin imaging with RGD peptide-modified FI and PET dual-modality nanoprobes has also been investigated, mainly aiming to obtain quantitative information at different levels of resolution. In addition to QD-based nanoprobes,280 natural nanoparticles such as ferritin and melanin have been used. Ferritin is a natural protein with a hollow cavity that is self-assembled from 24 subunits,281 and has been used as a nanoprobe for MRI.282,283 The assembly and disassembly of the 24 subunits of ferritin are pH dependent, and thus Chen and coworkers prepared an integrin targeting dual-modality nanoprobe via disassembling RGD4C peptide (CDCRGDCFC) or Cy5.5-modified ferritin mixtures at pH 2, followed by subsequent reassembly of the subunits with 64Cu into a dual-modal nanoprobe at pH 7.4 (Fig. 22a).284 After intravenous injection, the nanoprobes accumulated to a great extent in integrin-overexpressing U87MG gliomas in mice with prolonged retention, which could be attenuated by the presence of free c[RGDyK], as evidenced by both PET and fluorescence imaging (Fig. 22b). Fan and colleagues used another type of bio-derived material, melanin, that can absorb light and chelate ions such as Fe3+ and 64Cu2+ (Fig. 22c).285 When decorated with PEG and c[RGDfK], the nanoprobes were able to accumulate in subcutaneous U87MG tumors with high integrin expression after intravenous injection, allowing for the imaging of tumors with PA, MRI, and PET (Fig. 22d).


image file: c7cs00793k-f22.tif
Fig. 22 Multi-modality imaging of integrin. (a) Design of the ferritin-based RGD-modified dual-modality nanoprobe. (b) Imaging of integrin overexpressing tumors with PET and FI. Reproduced from ref. 284 with permission from the American Chemical Society, copyright 2011. (c) Design of RGD-modified tri-modality melanin nanoparticles (MNPs). (d) Imaging of integrin overexpressing tumors with MRI, PA and PET. Reproduced from ref. 285 with permission from the American Chemical Society, copyright 2014.

Arising from extensive studies on integrin imaging using RGD-decorated nanoprobes, the first clinical trial of ultrasmall inorganic hybrid nanoparticles, “C dots” (Cornell dots, 6–7 nm in diameter), in patients with metastatic melanoma was undertaken.286 The C dots are composed of a silica core loaded with Cy5 and decorated with PEG and 124I-labeled c[RGDyC] (Fig. 23a).287 Serial whole-body PET and PET-CT were performed on patients to understand the PK profiles of intravenously injected C dots and it was found that the C dots were cleared mainly through urine with minimal retained activity by 72 hours in major organs (Fig. 23b).286 Accumulation of C dots in known metastasis sites was observed, reflecting the exploitation of integrin biology, which is different from metabolism biology obtained by 18F-FDG-based imaging (Fig. 23c), with no treatment-related toxicity recorded. This nanoprobe is thus ideal for tumor diagnosis and identifying patients for further integrin-targeted therapy.


image file: c7cs00793k-f23.tif
Fig. 23 Integrin imaging with C dots in humans. (a) Design of the C dots (reproduced from ref. 287 with permission from the American Society for Clinical Investigation, copyright 2011). (b) Biodistribution and clearance of the C dots in patients. (c) Comparison of integrin expression and glucose metabolism information in one patient collected from PET imaging with C dots or 18F-FDG, respectively. Reproduced from ref. 286 with permission from the American Association for the Advancement of Science, copyright 2014.

Recently, the combination of integrin imaging and photo-triggered therapy in a single nanoparticle is being actively explored, as exemplified by gold nanoparticle-based nanoprobes that are naturally photothermal therapeutic agents and other co-delivery nanoparticles.257,288–290 The readers are referred to recently published reviews on theranostic nanoparticles for detailed information.53 In summary, these results showed that integrins are effective and easily-targeted markers of disease, and PBN systems for integrin imaging have promising potential in clinical utility for accurate diagnostics.

3.2.2 Chemokine receptor imaging with PBNs. Chemokine receptors (CCRs) and their ligands regulate cell motility, invasiveness, and survival.291 Normally, CCRs are expressed on lymphatic cells and have been successfully imaged with an Ac-TZ14011 (Ac-RR-Nal-C1Y-Cit-RKPYR-Cit-C1R)-decorated nanoprobe (99mTc-HAS).292 However, some CCRs are overexpressed on cancer cells, such as CCR5 which is upregulated in primary tumors and their metastases in triple negative breast cancer (TNBC) patients. Its expression is associated with promoted cancer progression and, therefore, CCR5 can be a useful biomarker for the disease. To image CCR5, Xia and colleagues developed DAPTA (aSTTTNYT) modified PdCu@Au core–shell tripods (Fig. 24a).293 The CCR5 expression in 4T1 tumors can be visualized using a radio-labeled version of the nanoprobe, DAPTA-Pd64Cu@Au, with higher uptake and tumor-to-muscle accumulation ratios compared to non-targeted nanoprobes and free DAPTA-blocked nanoprobes (Fig. 24b). Since the tripod itself is a photothermal agent, treatment can be easily performed via laser irradiation, resulting in a reduced tumor burden in the treated mice (Fig. 24c), yielding both therapeutic and imaging functions in a single probe. It is obvious that CCR can serve as a useful biomarker for PBN systems to help accurately diagnose disease.
image file: c7cs00793k-f24.tif
Fig. 24 Chemokine receptor 5 imaging with 64Cu-doped PdCu@Au tripods. (a) Schematic illustration, TEM image and photothermal effect of DAPTA-modified PdCu@Au tripods. (b) Application of DAPTA-modified PdCu@Au tripods in the diagnosis of CCR5 expressing tumors. (c) Photothermal tumor ablation effect of DAPTA-modified PdCu@Au tripods probed with 18F-FDG-based PET. Reproduced from ref. 293 with permission from the American Chemical Society, copyright 2016.
3.2.3 ECM imaging with PBNs. The extracellular matrix (ECM) is usually shielded from circulation via vascular endothelial cells and exposure of the ECM is thus associated with disintegration of the vasculature.294 Peptides of specific sequences can recognize ECM proteins, such as fibrin and collagen, allowing for specific molecular imaging. For instance, 153Gd-labeled EP-1873 (153Gd-DTPA-FHCHypY(3-CI)DLCHIL) and GPRPPGGSKGC-labeled CLIO have been successfully used for targeted imaging of exposed fibrin in plaque ruptures.295,296 Similarly, collagen exposure to blood is usually associated with fibrosis and cancer.297 To realize collagen targeted delivery, Farokhzad and colleagues identified a collagen binding peptide (KLWVLPK) through five rounds of biopanning against human collagen IV with a phage display library (Fig. 25a).298 The peptide was then conjugated to the surface of a nanoparticle composed of Alexa Fluor 647 fluorescent dye–poly(lactic-co-glycolic acid) (A647–PLGA), and the formed nanoprobes specifically accumulated in angioplasty sites in sharp contrast with the bare nanoprobe or scrambled peptide-decorated nanoprobes (Fig. 25b). The collagen-targeting peptide was then used by Stupp and colleagues in their design of peptide amphiphiles (KLWVLPK-CKKAAVVK(amide)-C12, KLWVLPK-CKKK(amide)-C12/C22) for collagen targeted imaging (Fig. 26a). They found that only targeted nanofibers self-assembled from KLWVLPK-CKKAAVVK(amide)-C12 stained injured carotid arteries, suggesting that the shape of nanoprobes is critical for their targeting efficiency (Fig. 26b).299 The integrity of the ECM can evidently serve as an effective target for PBN systems for diagnosing a wide variety of diseases.
image file: c7cs00793k-f25.tif
Fig. 25 Collagen targeting nanoprobes for angioplasty imaging. (a) The sequences of 4 peptides identified through phage display and their binding affinity with Matrigel. (b) Design of C-11 modified nanoprobes and their capability for angioplasty imaging after intraarticular and intravenous injection. Reproduced from ref. 298 with permission from the National Academy of Sciences of the United States of American, copyright 2009.

image file: c7cs00793k-f26.tif
Fig. 26 Shape effect on the collagen targeting efficiency of nanoprobes. (a) The chemical structures and morphologies of 3 different peptide amphiphiles. (b) Binding efficiency of targeted nanospheres and nanofibers to injured arteries. Reproduced from ref. 299 with permission from Wiley, copyright 2015.
3.2.4 Prostate-associated antigen imaging with PBNs. Prostate cancer is of high incidence,300 affecting many people around the world. Prostate-specific antigen (PSA) is now used as a biomarker for the clinical screening of prostate cancer,301 while urea-based peptide-mimetic ligands are under extensive investigation for the imaging of prostate-specific membrane antigen (PSMA), which is believed to be a better indicator than PSA.302 Other than PSMA, the expression of hepsin, natriuretic peptide clearance receptor (NPRC), and gastrin-releasing peptide receptors on prostate cancer cells are also elevated.303,304 Molecular imaging of these receptors has been achieved with nanoprobes modified with IPL-F (IPLVVPL),305 c[RSSCPGGRIDRIGAC],306 and bombesin-like peptides (QWAVGHLM, or β-Ala-QWAV-β-Ala-HF-Nle),307–309 respectively, helping the diagnosis of prostate cancer in vivo. In these cases, several receptors are typically required to bind with one nanoprobe, which limits the sensitivity of receptor imaging. To improve the sensitivity, Kelly and co-workers engineered SPPTGIN peptides in the PIII protein of M13 bacteriophage for the targeting of prostate-specific overexpression of SPARC (secreted protein, acidic and rich in cysteine)310 and inserted triglutamate into PVIII proteins for magnetic nanoparticle (MNP) binding (Fig. 27a).311 This design significantly improved the targeted delivery efficiency of MNPs (one peptide per 5 SPIONs) to SPARC-overexpressing tumors, resulting in an enhanced contrast in T2 imaging (Fig. 27b). These biomarkers serve as a means of improving prostate cancer diagnosis with PBN systems and present the notion of using similar markers for other cancer types.
image file: c7cs00793k-f27.tif
Fig. 27 SPARC targeted imaging with an SPPTGIN-modified magnetic nanoprobe. (a) Schematic illustration and TEM image of M13-templated magnetic nanoparticles. (b) MRI of tumors with high (C4-2B) and low (DU145) SPARC expression with the SPPTGIN-modified magnetic nanoprobe. Reproduced from ref. 311 with permission from the Nature Publishing Group, copyright 2012.
3.2.5 Scavenger receptor imaging with PBNs. Scavenger receptors (SRs) regulate the homeostatic balance of cholesterol312 and are expressed on endothelial cells of the heart. These receptors have been used as a target for heart PET imaging, such as with SR binding peptide (CRPPR) modified radioactive liposomes.313 Among many SRs, SR class B type I (SR-BI) is overexpressed in many cancers, including prostate cancer.314 Zheng's group has developed a reconstructed high-density lipoprotein (rHDL) using synthetic lipoprotein A mimics (Ac-FAEKFKEAVKDYFAKFWD), 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC), cholesterol oleate ester, and NIR dye (1,1′-dioctadecyl-3,3,3′,3′-tetramethylindotricarbocyanine iodide bis-oleate) (DiR-BOA), obtaining an HDL-mimicking peptide phospholipid nanoscafold (NIR) (HPPS (NIR)) (Fig. 28a).315 Intravenous injection of the HPPS (NIR) into mice bearing PC3 orthotropic prostate tumors specifically identified SR-B1-overexpressing tumors both in vivo and ex vivo (Fig. 28b). One advantage of rHDL is that multi-modality imaging can be easily achieved by incorporating chelators into the formulation, yielding highly effective PBN systems.316
image file: c7cs00793k-f28.tif
Fig. 28 HDL mimicking nanoprobe (HPPS (NIR)) for SR-B1 imaging. (a) Design of multifunctional recombinant rHDL. (b) SR-B1 mediated internalization of HPPS (NIR) by PC3-lu6 prostate cancer cells and specific internalization-based PC3-lu6 tumor imaging. Reproduced from ref. 315 with permission from Wiley, copyright 2014.
3.2.6 VEGFR-1 imaging with PBNs. Vascular endothelial growth factor receptor 1 (VEGFR-1) is a tumor-specific vascular endothelial cell surface protein that was determined by subtractive proteomic mapping,317 and thus its specific imaging is beneficial for tumor diagnosis and treatment. To image the expression of VEGFR-1 in tumors, Dawson and colleagues created a VEGFR-1 specific nanoprobe (FP3) by decorating cowpea mosaic virus (CPMV) with F56f peptide (K(fluorescein)RRGWHSDMEWWYLLGRR) (Fig. 29a).174 Specific binding of the nanoparticles to human endothelial cells (AE.hy926) was observed, and an in vivo study showed that F56f peptide modified CPMV stained VEGFR-1 expressing tumors, but that without F56f peptide could not (Fig. 29b). While this is only one example, VEGFR-1 shows promise as a relevant target for a PBN system for cancer diagnostics.
image file: c7cs00793k-f29.tif
Fig. 29 VEGFR-1 imaging with FP3 in vitro and in vivo. (a) Schematic illustration of the design of FP3. (b) Ligand-dependent binding of FP3 to VEGFR-1 expressing endothelial cells (EA.hy926) in vitro and blood vessels in vivo. Reproduced from ref. 174 with permission from the American Chemical Society, copyright 2010.
3.2.7 uMUC-1 imaging with PBNs. Underglycosylated mucin-1 tumor antigen (uMUC-1) is overexpressed in breast cancer cells and is a useful biomarker in breast cancer diagnosis and treatment outcome assessment.318 Nanoprobes (cross-linked iron oxide labeled with Cy5.5) modified with EPPT1 peptide (YCAREPPTRTFAYWG) can bind uMUC-1 with prolonged retention evidenced by both MRI and FI,319 and the therapeutic effect of chemotherapy can be assessed in real-time by monitoring the change in the expression of uMUC-1.143 When a therapeutic modality was introduced, tumor diagnosis, treatment, and outcome monitoring were realized with one single nanoparticle.144,320 Since breast cancer is of very high incidence, its early and accurate diagnosis is critical, and uMUC-1-targeting with PBN systems can make that a reality.
3.2.8 Other molecules imaged with PBNs. In addition to the above discussed biomolecules, many more membrane proteins have been imaged with specially crafted nanoprobes using peptides as targeting ligands. For instance, nanoprobes modified with LyP-1 (C1GNKRTRGC1),321,322 YLFFVFER (H6), and KLRLEWNR (H10)166 have been used to image gC1q receptor p32 protein and human epidermal growth factor receptor 2 (HER-2) overexpressed on breast cancer cells. Prostate cancer was successfully diagnosed with bombesin-like peptide (QRLGNQWAVGHLM)-grafted CLIO(Cy5.5) and EphB4-binding peptide (TNYLFSPNGPIARAW) decorated core(Cy7)–shell (111In-DTPA) nanoprobes targeting to bombesin and EphB4 receptors, respectively.323,324 Similarly, molecular imaging of melanocortin receptors and membrane-bound matrix metalloproteinase 2 (MMP-2) with [Nle4D-Phe7] peptide (Ac-SYS-Nle-EHfRWGKPV-NH2) or chlorotoxin-grafted nanoprobes yielded the diagnosis of melanoma and glioma, respectively.325,326 Other applications of PBNs include the early diagnosis of type-I diabetes, atherosclerosis, and cell differentiation based on the affinity of NRP-A7 antigen (KYNKANAFL) for pancreatic islet toxic CD8 + T cells,327 a peptide (VHPKQHR) for VCAM-1,328,329 and a peptide (THRPPMWSPVWP) for transferrin receptor (TfR).330 We envision that more membrane-associated receptors and proteins will become available as valuable biomarkers of many diseases, and their easy imaging with targeted PBNs allows for quick and precise diagnosis of a diseased state.

3.3 New approaches in biology interfacing molecular imaging with PBNs

Conventional molecular imaging at biological interfaces typically involves only one peptide–receptor pair and focuses on identifying peptides that specifically recognize targeted receptors. A recent study explored the potential of synergistic targeting strategies and found that combinatorial use of c[RGDfK] and Anginex (ANIKLSVQMKLFKRHLKWKJIVKLNDGRELSLD) can significantly improve the imaging of endothelium (expressing αvβ3 and galectin-1) using a liposomal contrast agent,331 indicating that the targeting of multiple receptors can potentially increase the accuracy of PBN systems and yield more useful diagnostic information than detecting one biomarker alone.

The specificity of molecular imaging is limited by the off-target phenomenon, due to non-specific molecular recognition. Introducing a non-naturally existing recognition moiety to targeted biomolecules could significantly improve the specificity of PBNs, a concept that is under investigation. In a strategy developed by the Weissleder and Sainlos groups, either biotin acceptor peptide (BAP) or an AP-tag (GLNDIFEAQKIEWHE) was engineered into a targeted protein. The fused proteins can be biotinylated by endogenous mammalian biotin ligase and can be labeled by avidin-coated magnetic nanoparticles, fluorochromes, or horseradish peroxidase to achieve molecular imaging with super-resolution (Fig. 30).332,333 Another strategy developed by Cheng and colleagues focused on a glycosylation process of peptides, where they engineered a 1-((4-(2,6-diacetamidohexanamido)phenyl) (phenyl)methoxy)-3,4,6-triacetyl-N-azidoacetylmannosamine (DCL-AAM) that can be internalized, processed specifically in cancer cells by histone deacetylase and cathepsin L, and displayed on the extracellular domains of membrane proteins. After intravenous injection of dibenzocyclooctyne-Cy5, tumor-specific labeling was achieved (Fig. 31).334 Very recently, Kelly and co-workers developed a peptide probe to detect misfolded transthyretin (TTR) oligomers in the plasma of hereditary amyloidosis patients, pushing the specificity of molecular imaging to a new level (Fig. 32a).335 The peptide probe (VINVAVHVFR) was derived from the native TTR, and could co-assemble with misfolded TTR from either mutated recombinant TTR or serum of patients with predominant neuropathic phenotype or serum of patients with predominant neuropathic phenotype (V30M FAP patients), while no staining was observed in native TTR and mutated TTR with a different secondary structure (Fig. 32b). With this peptide probe, it is possible to differentiate patients with a specific point mutation in TTR from healthy subjects, which could not be achieved with an antibody against TTR (Fig. 32c). Minimizing the off-target activity of PBN systems will further help to improve signal-to-noise ratios and yield more accurate disease diagnoses.


image file: c7cs00793k-f30.tif
Fig. 30 Schematic illustration of the cell labeling strategy using protein engineering. This strategy first requires introduction of a biotin acceptor peptide (AP) into the protein of interest. The engineered protein is then processed by an endogenous protein that adds a biotin to the protein AP-tag (1). The protein is then exported onto the membrane (2), and could then be recognized with streptavidin-bonded dye or other multimodal imaging agents that could be further amplified (3). Reproduced from ref. 332 and 333 with permission from the Nature Publishing Group, copyright 2006 and 2017.

image file: c7cs00793k-f31.tif
Fig. 31 Schematic illustration of the working mechanism of DCL-AAM and its potential for tumor-specific fluorescence labeling. Reproduced from ref. 334 with permission from the Nature Publishing Group, copyright 2017.

image file: c7cs00793k-f32.tif
Fig. 32 Imaging of misfolded transthyretin (TTR) oligomers in the plasma of hereditary amyloidosis patients. (a) Schematic illustration of the design of a nano-native TTR binding peptide probe. (b) Specificity of the designed peptide probe against disease-related TTR mutants revealed by native PAGE. Only TTR aggregates of a specific secondary structure were stained. (c) Comparison of total protein staining, peptide probe (B-2-Rhod) staining, and antibody staining in their capability to differentiate native and mutated TTR. Reproduced from ref. 335 with permission from the American Association for the Advancement of Science, copyright 2017.

The sensitivity of conventional molecular imaging is limited by the number of biomolecules available for detection. Inspired by the signal amplification cascades during cellular apoptosis and thrombi formation, a few efforts have been devoted to creating such biomimetic nanoprobes. One such effort was made by Ruoslahti and coworkers, who conjugated fluorescein/Cy7 labeled CREKA onto the surface of SPIONs to create CREKA-SPIONs for fibrin imaging (Fig. 33a).336 The resulting nanoparticles were found to bind to fibrin in MDA-MB-435 tumors and, more importantly, they further promoted platelet activation and clotting locally in tumors (Fig. 33b). More fibrin is exposed in tumors, thereby achieving significant enhancement of the fluorescence signal in tumors and not healthy tissue. Improved accumulation was further achieved when filamentous nanoprobes were used, likely due to their enhanced capability in fibrin binding and platelet activation.337,338 Another recent example was reported by Gu, Mao, and colleagues, where they designed a living contrast agent (T7 phage encoding GFP and gold binding peptide VSGSSPDS) for miRNA detection at low abundance in cells (Fig. 34a).339 The T7 phage was bound to a gold nanoparticle decorated with miRNA capture oligonucleotide 1 and mixed with magnetic microparticles decorated with miRNA capture oligonucleotide 2. In the presence of targeting miRNA, the two types of particles would form complexes that could be captured with a magnetic field, and the fluorescent T7 phages are plated on a host bacterial medium in a Petri dish to develop fluorescent plaques, allowing for the quantification of miRNA with both fluorescence microscopy and the naked eye at attomolar concentrations (Fig. 34b). Developing new ways for PBN systems to interact with and detect changes at various biological interfaces will open the door toward more precise and accurate molecular imaging and yield quantifiable information for disease diagnostics.


image file: c7cs00793k-f33.tif
Fig. 33 Imaging fibrin with CREKA-modified SPIO. (a) Schematic illustration of the structure of CREKA-modified SPIO and its fibrin-targeting capability. (b) Co-localization of CREKA-SPIO with CD31, fibrin and platelets. The results indicate that CREKA-SPIO can recognize exposed fibrin and trigger clotting for signal amplification in the platelet independent pathway. Reproduced from ref. 336 with permission from the National Academy of Sciences of the United States of American, copyright 2007.

image file: c7cs00793k-f34.tif
Fig. 34 Imaging of miRNA with engineered T7 phage. (a) The working mechanism of the T7 phage-based nanoprobe for miRNA imaging. (b) miRNA quantification at attomolar concentrations with the naked eye by counting the number of phage plaques. Reproduced from ref. 339 with permission from the Nature Publishing Group, copyright 2015.

4. Probing enzymatic activities with PBNs

In the previous section, we discussed molecular imaging of biomolecules using PBNs based on ligand–receptor affinity. Other than receptors, enzymatic activity is also closely related to the status and progression of a disease, and imaging the activity of certain enzymes in living subjects will greatly facilitate disease detection and monitoring.340 Compared with molecular imaging at biological interfaces, imaging of enzymatic activity offers a means of active targeting and is advantageous due to the intrinsic capability of enzymes to amplify signals.341,342 The challenge in this area is thus to translate enzymatic activity into detectable signals in vivo with high specificity. Fortunately, taking advantage of the substrate specificity of enzymes, specific activatable PBNs can be designed using substrate peptides.343 The PBNs are mainly established via one of the four following mechanisms, including enzymatic processing induced by (1) binding and retention of PBNs; (2) molecular beacon cleavage in PBNs; (3) molecular self-assembly and disassembly; and (4) PBN assembly and disassembly.

4.1 Enzymatic activity-induced binding and retention

Enzymatic processing of the peptide shell of PBNs will significantly alter its surface chemistry, which affects its interaction with proteins and other biomolecules in a physiological environment.344 Early efforts in this area were reported by Nivorozhkin et al., who designed a trilysine protected Gd3+–DTPA complex that would have enhanced binding to human serum albumin (HSA) after carboxypeptidase B (thrombin-activatable fibrinolysis inhibitor)-mediated cleavage of lysine.345 The binding of the Gd3+–DTPA complex to HSA induced a two-fold increase in R1 relaxivity, enabling the imaging of carboxypeptidase B by MRI. Recently, Tsien and colleagues described an ACPPD (activatable cell penetrating peptide modified dendrimer) for MMP-2/9 imaging, where both enzymes have been shown to be overexpressed by various cancer types.346 The ACPPD is composed of a dendrimer-based core conjugated with Cy5, Gd–DOTA, and ACPPs (Fig. 35a).55 The MMP-2/9 overexpressed in the tumor microenvironment converts ACPPD into CPPD (CPP modified dendrimer) by removing the blocking sequence (e8) of CPP, which subsequently improved the cellular entry and retention of the nanoprobes, resulting in an increased T1 contrast and fluorescence signal in MMP-2/9 overexpressing tumors (Fig. 35b).
image file: c7cs00793k-f35.tif
Fig. 35 ACPPD for dual-modality MMP-2/9 imaging. (a) The design and working mechanism of ACPPD. (b) FI and MRI imaging of MMP-2/9 overexpressing HT-1080 tumors with ACPPD. Reproduced from ref. 55 with permission from the National Academy of Sciences of the United States of American, copyright 2010.

In addition to physiological binding, PBNs can also be chemically retained in the diseased sites by enzymes, generating contrasts for activity assessment. For instance, the activation of FVIII into FVIIIa is characteristic and necessary for thrombus formation. McCarthy et al. imaged the transglutaminase activity of FVIIIa using CLIOs decorated with a peptide (GN13QEQVSPLTLLK24C) derived from the N-terminus of α2-antiplasmin.296 The PBNs could be specifically cross-linked with the ε-amino group of a lysine residing within fibrin by FXIIIa at the N14 residue, resulting in enhanced retention of the PBN and therefore a strong T2 decrease in the thrombi (Fig. 36). To overcome the artefact of 1H-MRI in heterogeneous tissue, Temme and colleagues thus decorated the same peptide onto perfluorocarbon nanoemulsions, and successfully visualized the activation of FXIIIa in early venous thrombosis.347 Enzymatic activity is not only an effective means of producing detectable signals but can also function to increase the retention of the probes in diseased tissues to maximize the detection of relevant biomarkers.


image file: c7cs00793k-f36.tif
Fig. 36 Thrombi imaging with fibrin and FXIIIa targeting nanoprobes. (a) The design of the nanoprobes. (b) Thrombi specific imaging with CLIO-FXIII using MRI and FI. Reproduced from ref. 296 with permission from the American Chemical Society, copyright 2009.

4.2 Molecular beacons in PBNs

As we noted previously, molecular beacons provide excellent contrast and capability for enzymatic activity imaging but suffer from fast clearance and poor tissue penetration. Nanoparticles can either serve as carriers for molecular beacons or as a component of the beacon, yielding improved contrast, higher loading capacity, and greater retention of the probes.348 Nanomaterial-based molecular probes improve the accuracy and precision of molecular probes by mitigating nonspecific degradation and off-target signals, thereby improving signal-to-noise ratios.135,349,350 These systems can be easily tuned via changes in shape, size, and surface chemistry to influence how the probes interact with cells and relevant biomarkers.63–72
4.2.1 Nanoparticles as carriers of peptide-based molecular beacons. Nanoparticles can facilitate the transportation of peptide-based molecular beacons to the site of disease. For instance, nanoparticles self-assembled from 5β-cholanic acid-modified glycol chitosan were used by Kim and colleagues to enhance the delivery of an MMP-activatable molecular beacon (Cy5.5-G[P with combining low line][L with combining low line][G with combining low line][V with combining low line][R with combining low line][G with combining low line]L(BHQ3)GG), achieving specific MMP activity imaging in both subcutaneous SCC7 tumors and orthotopic colon tumors induced by azoxymethane (AOM).351 In their following work, AuNPs were further introduced as the core of the glycol chitosan nanoparticles to provide contrast for CT, allowing for MMP activity imaging with anatomic resolution (Fig. 37).352 Mesoporous silica nanoparticles (MSNs) have also been used for tumor-targeted delivery of an MMP sensor (TAMRA-GPLGVRGK-(Dabcyl)K-N3).353 In addition to MMP imaging, in vivo imaging of the urokinase-type plasminogen activator system (uPA, activity overexpressed in certain cancer types354) has also been achieved using PBNs composed of AuNPs, Quasar 670-HSSKLQC, and BHQ-2-HSSKLQC, based on signal generation after liberation of fluorophores from the AuNPs.355 Other enzymes, such as caspases and cathepsins, are located in the cytosol, and therefore imaging their activity with peptide-based molecular beacons requires nanocarriers for cytosol access.356–359 Early efforts to detect these proteases involved the use of branched poly(ethyleneimine) (PEI) as a carrier for covalently conjugated Cy5.5-GG[D with combining low line][E with combining low line][V with combining low line][D with combining low line]GGC.356 Recently, Chen and colleagues used PLUSin® for the delivery of C8 (Cy5.5-GDEVDAPK-BHQ-3, for caspase 8 imaging) and C3 (FGP465-GIETDAPK-BHQ-1, for caspase 3 imaging).357 The two molecular beacons could be released after cellular entry and are subsequently activated by caspase 8 and caspase 3, respectively, after TRAIL activated apoptosis (Fig. 38a). A Pt prodrug-loaded UCNP has also been used as a carrier for a caspase-3 molecular probe (Cy5-acp-CGDEVDAK-Qsy21) to realize apoptosis triggering and efficacy monitoring with one nanoparticle (Fig. 38b).358
image file: c7cs00793k-f37.tif
Fig. 37 MMP activity imaging with a tri-modality nanoprobe. The nanoprobe enables the imaging of MMP expressing tumors with MRI, CT and FI. Reproduced from ref. 352 with permission from Wiley, copyright 2011.

image file: c7cs00793k-f38.tif
Fig. 38 Caspase activity imaging in apoptotic cells. (a) Schematic illustration of sequential caspase-3 and caspase-7 activation (reproduced from ref. 357 with permission from Elsevier, copyright 2012). (b) Design of NIR-responsive theranostic nanoprobes and its capability to trigger and monitoring caspase-3 activation in drug sensitive cancer cells (reproduced from ref. 358 with permission from Wiley, copyright 2014).

In most cases, the in situ protease activity monitored in vivo requires specialized facilities. Bhatia and colleagues developed a novel nanoprobe for the imaging of proteinase activity by measuring the cleaved biomarkers in urine with paper microfluidics (Fig. 39).360 The nanoprobe includes three parts: (1) poly(ethylene glycol)-coated iron oxide nanoworms (NWs), (2) protease-cleavable linkers PLGLRSW (thrombi-sensitive) and PLGVRGK (MMP-9 sensitive), and (3) reporter molecules (fluorophore-GluFib-biotin). The fluorescence from the reporter molecules is quenched due to high grafting density but will be recovered after being cleaved from the NWs. The cleaved reporter molecules accumulated in the urine due to renal clearance and were easily detected by a paper lateral flow assay using paper strips embedded with anti-fluorophore antibodies and streptavidin-colloidal gold. While this is only one example, the additional development of other novel detection systems can simplify the means of analysing signals generated from PBN systems and allow for high fidelity diagnostics without specialized equipment.


image file: c7cs00793k-f39.tif
Fig. 39 Schematic illustration of protease activity detection with synthetic urinary biomarkers and paper microfluidics. After intravenous injection of the NWs (I), the MMPs will cleave the peptide substrate and liberate reporter molecules from the surface of the NWs (II). Due to their small molecular weight, the released reporter molecules can be excreted from the body through urine (III). The collected urine sample is then applied to a paper strip, and the analytes in urine will bind to antibodies adsorbed on the surface of specific lanes that can be further visualized using detection nanoparticles (IV). Reproduced from ref. 360 with permission from the National Academy of Sciences of the United States of America, copyright 2014.
4.2.2 Nanoparticles as a component of the peptide-based beacon. In addition to serving as carriers, nanoparticles can also be the generator or quencher of an imaging signal, especially a number of inorganic nanoparticles. For example, gold nanorods have been used as a quencher of pyropheophorbide-a in a PBN for MMP-2 imaging.361 Another nano-quencher is graphene oxide (GO), which when conjugated to DEVD-FAM and a Tat peptide forms a PBN for caspase-3 imaging (Fig. 40a).362 The Tat peptides help improve cellular delivery of the quenched PBNs. The PBNs are turned ON by caspase-3-mediated DEVD cleavage during staurosporine-induced apoptosis, achieving real-time imaging of caspase-3 activity (Fig. 40b). The luminescence resonance energy transfer effect has also been used to construct nanoprobes for the imaging of caspase-3 activity by Zheng and colleagues.363 In their design, a green fluorescence-emitting UCNP was tethered to (H)6G[D with combining low line][E with combining low line][V with combining low line][D with combining low line]AK-TAMRA to create a caspase-3 responsive beacon. Doxorubicin-treatment-induced caspase-3 activation cleaved TAMRA off the UCNP, resulting in a red to green shift of the fluorescence.
image file: c7cs00793k-f40.tif
Fig. 40 Caspase-3 activity imaging with a graphene oxide (GO)-based nanoprobe. (a) Schematic illustration of the preparation and working mechanism of a graphene-based nanoprobe. (b) Caspase-3 activity imaging with GO-based nanoprobes. Reproduced from ref. 362 with permission from Wiley, copyright 2011.

In the above examples, the design of PBNs was focused on FRET interactions between fluorophores and nanoparticles. Since the efficiency of FRET is distance-dependent, it is useful for the imaging of proteins with peptide cleavage activity. However, there are also other enzymes that can be utilized as biomarkers but are responsible for regulating the post-translation modification of peptides, including histone deacetylase 1 (HDAC 1, an enzyme involved in the epigenetic modification of DNA-binding histones364) and protein kinase A (PKA, a cAMP effector365) both of which are associated with cancer incidence and progression. To image the activity of these enzymes, PBNs of different activating mechanisms should be developed. Gold nanoclusters have strong and durable fluorescence due to the quantum effect, and their fluorescence is influenced by oxidation status. Based on this principle, Wen and colleagues prepared peptide-templated gold nanoclusters for the detection of HDAC 1 and PKA using CCIHK(Ac)/CCGGK(Ac)/CCLIK(Ac) or CCLRRASLG as templates, respectively (Fig. 41).366 Indeed, the resulting highly fluorescent gold nanoclusters were quenched dramatically after DHAC 1 and PKA induced peptide modification, allowing the detection of the two enzymes at 5 and 6 pM, respectively.


image file: c7cs00793k-f41.tif
Fig. 41 Histone deacetylase 1 activity imaging using peptide-protected AuNCs. Deacetylation of the peptide increases the solubility of the peptides and results in the oxidation of AuNCs, leading to a drop in fluorescence emission. Reproduced from ref. 366 with permission from the American Chemical Society, copyright 2013.

4.3 Supramolecular assembly and disassembly based signal transition of PBNs

Peptides can be engineered with the capability to switch between the monomeric form and an assembled nanostructure responding to enzyme activity.130,131 This property allows for the creation of PBNs for enzymatic activity imaging by conjugating aggregation status-sensitive contrast agents to the peptides. Fluorophores are one type of such contrast agent that produce signals through this mechanism. Most aromatic fluorophores show aggregation-caused quenching (ACQ) because of the formation of excimers through π–π stacking under concentrated conditions.367 As opposed to ACQ, some fluorophores show aggregation-induced emission (AIE), which is mainly due to the restriction of intramolecular motions of fluorophores in their aggregated state.368 MRI probes are another class of such contrast agents and, therefore, can also be used to monitor the activity of the corresponding enzymes.22 There are mainly two categories of PBNs that are sensitive to supramolecular assembly and disassembly: one shows increased signal after disassembly, while the other shows increased signal after self-assembly.
4.3.1 PBNs with disassembly activated signals. A feasible strategy to create PBNs with disassembly activated signals is to conjugate ACQ fluorophores to peptides that are substrates of enzymes. For instance, Weissleder and coworkers created a series of VivoTag-S680 conjugated polylysine-based nanoparticles of 5, 25, and 40 nm, and found that the 40 nm nanoparticles were the most sensitive in detecting cathepsin B in the atherosclerotic plaques of apoE−/− mice.369 The therapeutic benefit of atorvastatin treatment can also be monitored with the same nanoprobe, indicated by a decrease in the fluorescence signal. When using hydrophobic chlorin e6 (Ce6) instead, molecular imaging of cathepsin B could also be achieved but with the added benefit of simultaneous photodynamic therapy.370 In these designs, the fluorescence was statically quenched. To further increase the quenching efficiency, Cui and coworkers developed a nanobeacon self-assembled from Tat-5-FAM-BHQ-1 (TFB), using both static and dynamic quenching mechanisms (Fig. 42a).371 Conjugation of a fluorophore (5-FAM) and quencher (BHQ-1) pair to one Tat peptide quenched 98% of the 5-FAM fluorescence (dynamic quenching), and their self-assembly into nanospheres inhibited the fluorescence further (static quenching). The activity and location of cathepsin B in the cells were successfully imaged when the 5-FAM was cleaved from the beacon at the GFLG site (Fig. 42b). Under some pathological conditions, both the activity of intracellular and extracellular enzymes should be determined. The same group thus recently developed a class of nanobeacons based on Sup35 (GNNQQNY-X3) peptide, the shape and surface charge of which could be tuned through molecular engineering (Fig. 42c).372 The positively charged nanospheres were able to enter the cells, while the filaments showed negligible intracellular accumulation (Fig. 42d). This study provided a useful strategy to distinguish extracellular and intracellular cathepsin B activity in vitro. Yang and coworkers similarly incorporated phenylalanine into their design of dabcyl-GFnG(3−n)[D with combining low line][E with combining low line][V with combining low line][D with combining low line]GK(FITC) molecular beacons to obtain a nanobeacon through self-assembly. Their results further confirmed that the self-assembly lowered the background fluorescence by more than five-fold compared with the phenylalanine-free beacon, while at the same time it did not affect its activation during caspase 3 imaging.373
image file: c7cs00793k-f42.tif
Fig. 42 Cathepsin B activity imaging with peptide-based nanobeacons. (a) Schematic illustration of the expected cleavage and detection mechanism. (b) TEM image of TFB nanobeacons and their cathepsin B activated fluorescence regeneration. Reproduced from ref. 371 with permission from the American Chemical Society, copyright 2013. (c) Schematic illustration of the self-assembly of SFB-K/E into either spheres or filaments. (d) TEM image of SFB nanobeacons and the shape effect on intracellular cathepsin B activity determination. Reproduced from ref. 372 with permission from the American Chemical Society, copyright 2016.

In addition to ACQ fluorophores, some magnetic probes also exert a signal that can be turned on after disassembly. Chemical exchange saturation transfer (CEST) is an MRI contrast mechanism that selectively detects exchangeable protons at their unique chemical shift and is thus sensitive to the aggregation status.374–376 CEST signals of endogenous cellular proteins and peptides have been successfully used to distinguish tumor recurrence from radiation necrosis,377 but they suffer from poor contrast. Cui and colleagues recently reported a drug amphiphile-based label-free MRI contrast agent, and the CEST MRI signal was weaker in comparison to free drugs.378 These results indicate that the enzymatic activity can be imaged by conjugating a CEST MRI contrast agent to an enzyme cleavable peptide.

4.3.2 PBNs with self-assembly activated signals. One disadvantage of disassembly activated PBNs is that their signals will decay quickly due to the fast clearance of small molecular contrast agents. To achieve a prolonged signal, self-assembly activated PBNs have been investigated. AIE fluorophores are frequently used to create such PBNs. PBNs for MRI imaging have also been explored, as the relaxivity of an MRI contrast agent depends on both aggregation status and adopted morphology.379–381 Based on the mechanism of self-assembly, peptides mainly promote the formation of PBNs via three different mechanisms: (1) by binding with target biomolecules, (2) by modifying side chains with target biomolecules, and (3) by cleavage from target biomolecules.

Firstly, peptide-based monomers can self-assemble to form signal-emitting PBNs when binding with target biomolecules. For instance, Yang and coworkers developed a self-assembly-ON PBN by conjugating tax-interacting protein-1 (TIP-1, a protein that promotes infiltrative growth of glioblastoma382)-specific ligands (EEGWRESAI) with a 4,7-di(thiophen-2-yl)-2,1,3-benzothiadiazole (DBT) molecule whose fluorescence is low in aqueous environments but enhanced in hydrophobic circumstances (Fig. 43a).383 The resulting DBT-2EEGWRESAI molecule can self-assemble with soluble TIP-1 forming spheres or networks with strong fluorescence, yielding the possible detection of TIP-1 as low as 11 μg mL−1 and effectively reflecting β-catenin transcription activity (Fig. 43b). This design is not practical for the imaging of membrane-immobilized biomolecules (such as the aa moiety on the cell wall of Gram-positive bacteria), and therefore Xu and coworkers developed peptide-based monomers containing vancomycin (to recognize the aa moiety), rhodamine, and a peptide (FFYEGK) that links the two moieties together, creating Rho-FF-Van (Fig. 44a).384 The monomers self-assembled into filaments with enhanced fluorescence after accumulation in a Gram-positive bacteria-infected site in a mouse model, while no signal was observed in a Gram-negative bacteria-infected site or from the control molecule (Rho-GG-Van) (Fig. 44b).


image file: c7cs00793k-f43.tif
Fig. 43 TIP-1 imaging with self-assembly ON nanoprobes. (a) Chemical structure of the DBT-2EEGWRESAI. (b) Schematic illustration of the self-assembly turning on mechanism during TIP-1 imaging. Reproduced from ref. 383 with permission from the American Chemical Society, copyright 2014.

image file: c7cs00793k-f44.tif
Fig. 44 Gram negative bacteria imaging with Rho-FF-Van. (a) Schematic illustration of the design and working mechanism of Rho-FF-Van. (b) Gram negative bacterial imaging with 125I-Rho-FF-Van using FI and isotope imaging. Reproduced from ref. 384 with permission from Wiley, copyright 2017.

Secondly, peptide-based monomers can self-assemble to form signal-emitting PBNs when modified with target biomolecules in their side chains. Using this strategy, Xu and colleagues first realized the imaging of intracellular alkaline phosphatase (ALP, a biomarker of poor prognosis in patients with metastatic prostate cancer385) activity using their peptide progelator NapFFK(NBD)Yp (Fig. 45a).386 The 4-nitro-2,1,3-benzoxadiazole (NBD) here is a fluorophore that yields more intense fluorescence signals in hydrophobic environments.387 The progelator could enter cells efficiently at high incubation concentrations, and then be converted into the gelator NapFFK(NBD)Y via ALP-induced cleavage of the phosphoester. The gelator self-assembles into filaments with NBD buried in the hydrophobic core, resulting in a strong yellow fluorescence in the cells. The site and rate of fluorescence generation provide information about the location and activity of intracellular ALP (Fig. 45b). Since the L-peptides are susceptible to protease degradation, the same group further developed the D-peptide version of the gelator (Napffk(NBD)yp) and found that Napffk(NBD)yp can also be dephosphorylated by ALP for intracellular imaging, and thus it functions as a biostable and biocompatible nanoprobe (Fig. 45c).190 Further optimization of the probe was performed, producing a probe capable of imaging ALP activity at different locations (both intracellular and extracellular) and for different cell types with high spatiotemporal resolution.388,389 The obtained ALP activity profiles can be used as a biomarker to differentiate cancer cells from stromal cells, to discern drug resistant cells from sensitive ones, and to identify hormone-stimulated cells.389 Similar to ALP, the activity of myeloperoxidase (a key biomarker of inflammation) was imaged with two MRI molecular probes (Gd-5-HT-DOTA and Gd-bis-5-HT-DTPA), which will undergo oligomerization post-myeloperoxidase exposure.390


image file: c7cs00793k-f45.tif
Fig. 45 Alkaline phosphatase activity imaging with AIE nanoprobes. (a) Structure of and schematic illustration of the working mechanism of NapFFK(NBD)Yp. (b) ALP activity imaging in living cells with activated yellow fluorescence from NapFFK(NBD)Y nanofibers. Reproduced from ref. 386 with permission from the Nature Publishing Group, copyright 2012. (c) Structure of Napffk(NBD)yp and its capability in ALP activity imaging (reproduced from ref. 190 with permission from the American Chemical Society, copyright 2013).

Thirdly, peptide-based monomers can self-assemble to form signal-emitting PBNs when their backbones are cleaved by the targeted biomolecules. Caspases are the most commonly used targets, as their activity is involved in inflammation and apoptosis.391 Liu and colleagues explored the imaging of caspase-3/7 activity with DEVD-conjugated hydrophobic tetraphenylethene (TPE) that can undergo AIE upon cleavage of hydrophilic DEVD (Fig. 46a).392 Treating cells with chemotherapeutics, such as cisplatin and staurosporine, induces the apoptosis of cells which can be imaged in real time 1 h after treatment (Fig. 46b). Imaging of intracellular thiols was also realized using a similar molecular design, where TPE was conjugated to an FFYE-ss-EE peptide.393 The fluorescence emitted by aggregated TPE is blue, however, hindering its in vivo applicability. The same group then improved their design by using tetraphenylethenethiophene (TPETP) that emits red fluorescence (665 nm) in its aggregated form, allowing the in vitro visualization of caspase-3/7 activation (Fig. 46c).394 Rao and colleagues developed a caspase-sensitive nano-aggregation fluorescent probe (C-SNAF) that undergoes an optimized first-order bioorthogonal cyclization reaction-controlled self-assembly for in vivo imaging of caspase activity (Fig. 47a).395 The C-SNAF contains three major components: (1) the cyclizable section containing D-cysteine and 2-cyano-6-hydroxyquinoline (CHQ) moieties linked to an amino luciferin scaffold, (2) the capping section containing an L-DEVD caspase-3/7-cleavable sequence and a GSH-responsive disulfide bond, and (3) the Cy5.5 fluorophore. After their accumulation in apoptotic cells, the capping part of the C-SNAF molecules is removed to expose the free amine and thiol group of D-cysteine that can react with CHQ, forming a macrocyclic product (C-SNAF-cycl). The C-SNAF-cycl aggregates into nanoparticles of 174 ± 44 nm in diameter, and elicits a 1.6-fold stronger fluorescence in doxorubicin-treated tumors compared to saline-treated ones, corresponding to a 1.9-fold increase in the caspase-3/7 activity (Fig. 47b). Other than caspases, gelatinases overexpressed in tumor microenvironments by cancer cells for continuous ECM remodelling are another target. To image the activity of gelatinase, Wang and colleagues developed a purpurin18–peptide conjugate (purpurin18-PLG/VRG-RGD) (Fig. 48a).396 The RGD peptide facilitates the retention of the purpurin18–peptide conjugate within the tumor, and the cleavage of the conjugate gives rise to purpurin18-PLG, which can self-assemble into nanofibers with red-shifted absorbance at 730 nm, enabling long-term imaging of gelatinase activity in the tumor with PA (Fig. 48b).


image file: c7cs00793k-f46.tif
Fig. 46 Caspase-3/7 activity imaging with an AIE-based nanoprobe. (a) The design of the TPE-based probe for caspase-3/7 imaging. (b) Dose-dependence of chemically induced caspase-3/7 imaging with the TPE-based probe. Na asb and STS are sodium ascorbate and staurosporine, respectively. Reproduced from ref. 392 with permission from the American Chemical Society, copyright 2012. (c) Design of a TPETP-based probe for caspase-3 imaging and its efficacy in imaging caspase-3 activity in chemical-treated cells (reproduced from ref. 394 with permission from the Royal Society of Chemistry, copyright 2016).

image file: c7cs00793k-f47.tif
Fig. 47 Caspase-3/7 activity imaging with C-SNAF. (a) Schematic illustration of the design of C-SNAP and its activation mechanism. (b) TEM image of nanoaggregates formed by activated C-SNAF (C-SNAF-cycl) and its application in imaging caspase-3/7 activation in Dox-treated tumors. Reproduced from ref. 395 with permission from the Nature Publishing Group, copyright 2014.

image file: c7cs00793k-f48.tif
Fig. 48 Imaging gelatinase activity with nanoprobes. (a) The design and working mechanism of gelatinase activatable nanoprobe purpurin18-PLGVRGRGD. (b) PA imaging of gelatinase in tumors treated with purpurin18-PLGVRGRGD, purpurin18-PMGMRGRGD, purpurin18-PLGVRGRDG, and PBS. Reproduced from ref. 396 with permission from Wiley, copyright 2015.

4.4 Nanoparticle assembly and disassembly based signal transition of PBNs

The assembly and disassembly of nanoparticles can induce signal transition as well. For example, the relaxivity of MRI probes is size-dependent and high relaxivity is usually associated with a larger particle size.397,398 Taking advantage of this, Atanasijevic et al. developed a calcium (Ca2+, an important player in cancer399)-sensitive MRI contrast agent with SPIONs decorated with either calmodulin (CaM) or M13 peptide, which binds to CaM in the presence of Ca2+.400 The two types of nanoparticles form binary aggregates in the presence of >1 μM Ca2+, resulting in a five-fold increase in T2 relaxivity. During MRI-based MMP-2 activity imaging, the size transition was realized by cleaving PEG from the alkyl chain-derived Gd–DOTA at the MMP-2 cleavable peptide site (SPAYYTAA), which resulted in an increase in longitudinal relaxivity after reaching a temporary plateau.401 Recently, Long and colleagues developed a new type of PBN for MMP activity imaging, based on a chemical reaction between azide or alkyne modified SPIONs after the cleavage of C–X–C chemokine receptor type 4 (CXCR4)-targeted peptide ligands (c[R-2-Nal-GyK]) with tethered PEG layers by MMPs (Fig. 49a).402 A T2 signal enhancement of around 160% was observed when the two types of SPIONs were incubated with cells expressing both CXCR4 and MMP-2/9, resulting in enhanced contrast in CXCR4-expressing U87.CD4.CXCR4 tumors in vivo after intravenous injection (Fig. 49b).
image file: c7cs00793k-f49.tif
Fig. 49 MMP-2/9 activity imaging with clicking nanoprobes. (a) The design of clicking nanoprobes. (b) The T2 mapping of MMP-2/9 activity in the tumor before and 4 h after clicking nanoprobe administration. Reproduced from ref. 402 with permission from Wiley, copyright 2014.

Biomolecule-induced disassembly of nano-clusters has also been used in the imaging of their activities. One such PBN is the “crown nanoparticle plasmon ruler” created by Alivisatos and coworkers for caspase-3 imaging, where an avidin-coated AuNP is enveloped by biotin-peptide (biotin-GSEGGSESE[D with combining low line][E with combining low line][V with combining low line][D with combining low line]GGSNSGGRLC)-linked AuNP (40 nm) satellites (Fig. 50a).403 The AuNP clusters scatter light ∼44 times more intensely than a single particle with a significant redshift (75 nm). A caspase-3 incubation time-dependent shift and diminishment of scattering light were observed, corresponding to the cleavage of the satellite nanoparticles (Fig. 50b). After surface modification with Tat peptides, the AuNP clusters accumulated within the cells and showed a gradual diminishing of the scattering light in SW620 colon cancer cells pretreated with TNF-α and CHX (Fig. 50c). Recently, Yang and coworkers created fluorescent short nanofibers self-assembled from NBD-FFF[D with combining low line][E with combining low line][V with combining low line][D with combining low line]GGH or NBD-FFFEE-ss-EGGH, and further grew them into quenched long nanofibers by adding Cu2+.404 Caspase-3 and GSH cleaved Cu2+-binding GGH segments off the peptides, resulting in the fragmentation of long nanofibers and a recovery in fluorescence that can be correlated with the activity of caspase 3 or GSH.


image file: c7cs00793k-f50.tif
Fig. 50 Caspase-3 activity imaging with a crown nanoparticle plasmon ruler. (a) The design of the crown nanoparticle plasmon ruler. (b) Caspase-3 mediated disassembly and the corresponding blue-shift and diminishment of scattering light. (c) Real-time imaging of caspase-3 activation in living cells. The scattering light diminishment needed to be triggered with tumor necrosis factor-α (TNF-α) and cycloheximide (CHX), and could be blocked with caspase-3 inhibitor z-DEVD-fmk. Reproduced from ref. 403 with permission from the National Academy of Sciences of the United States of American, copyright 2009.

5. Conclusions and perspectives

During the past two decades, important achievements have been made in both fields of chemistry and biology, pushing the limit from being able to identify late-stage anatomic changes to being capable of detecting early-stage molecular changes. The area of molecular imaging has been largely expanded by the creation of numerous types of molecular, supramolecular, and nanostructured probes. Of particular interest is peptide-based nanoprobes, mainly because functional peptides can be easily derived from natural proteins, screened from peptide libraries, and conceived through rational design to target different biomarkers of various diseases, and also because peptides can serve as excellent molecular building units to construct well-defined nanoscale objects with tunable physicochemical characteristics to overcome physiological and pathological barriers. In addition, the ease of peptide synthesis in a cost-effective manner also contributes to the development and clinical translation of peptide-based imaging agents. A chosen peptide of a particular sequence can be facilely incorporated into the PBN design for identifying the spatiotemporal expression of biomolecules and enzymatic activities that are implicated in the incidence and progression of many human diseases.

Looking to the future, PBNs will likely continue their rapid growth in several key areas. First of all, PBNs with multi-modality and multiplexing imaging capability represent a subject of heavy fundamental and clinical interest, aiming to understand a complex and dynamic biological process involving multiple biomolecules in spatiotemporal resolution.405–407 Clinical technology such as PET/CT is an elegant example to illustrate the significance of dual-modality imaging for clinical applications. Incorporation of a multiplexing imaging modality, such as SERS, will surely provide more valuable information.408 Second, efforts should be devoted to further improving the specificity and sensitivity of PBNs. Recent advances in molecular biology have revealed that tissue in diseased sites such as tumors, is highly heterogeneous thereby presenting several different types of relevant biomarkers.409,410 Moreover, cells that are restricted in quantity or availability, such as cancer stem cells and cancer-associated immune cells, play a vital role in drug response and resistance, highlighting the importance of their imaging with nanoprobes of high sensitivity and specificity.411 Third, the development of PBNs with new activation mechanisms could expand the range of biomolecules to be imaged and detected. PBNs developed thus far mainly focus on imaging of cell receptors and enzymes, however, many other non-enzymatic biomolecules, such as transcription factors and cyclins that exert essential biological functions,412,413 cannot be imaged using the current activation mechanisms. Fourth, it is imperative to develop new data analysis and processing methodologies for PBN-based assays and in vivo applications. Currently, the data acquisition and analysis methodologies used in the clinic are optimized for monomeric molecular probes. Nanoprobes that function as aggregates/assemblies of molecular probes, provide an additional layer of information by reflecting upon the status of nanoparticles. For instance, it has been reported that cellular compartmentalization of internalized paramagnetic liposomes has a strong influence on both the T1 and T2 relaxivity.414 As such, detailed data analyses discerning extracellular signals from intracellular signals may provide more valuable information on the targeted receptors. Lastly, interest in PBNs with combined modules of molecular imaging and targeted therapeutics is rapidly growing. With the advances in peptide engineering and nanotechnology, PBNs can be easily engineered into scaffolds that are compatible with both imaging contrast agents and therapeutic agents, exemplified by the recent development of theranostic nanoparticles.122,415 Molecular imaging and radiotherapy of somatostatin receptors overexpressed in neuroendocrine tumors has been explored in the clinic with radiolabeled peptides, providing valuable information for clinical applications of theranostic PBNs.416 With these continued efforts, PBNs are poised to broaden the applications of molecular imaging from understanding the molecular basis of disease, to disease diagnosis and staging, treatment plan design, and therapeutic outcome assessment, which will eventually result in improved clinical benefits and personalized medicine.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

We acknowledge financial support from the National Natural Science Foundation of China (81521005, 81690265, 81671808, 30870729, and 81071183), and Youth Innovation Promotion Association of CAS (2017335), National Major Scientific Equipment Special Grant (2011YQ03011409) and Twelfth “Five-Year” Plan for Science & Technology Support (2014BAA03B03), W. W. Smith Charitable Trust, and Johns Hopkins Discovery Award.

References

  1. R. Weissleder and U. Mahmood, Radiology, 2001, 219, 316–333 CrossRef CAS PubMed.
  2. M. L. James and S. S. Gambhir, Physiol. Rev., 2012, 92, 897–965 CrossRef CAS PubMed.
  3. R. R. Zhang, A. B. Schroeder, J. J. Grudzinski, E. L. Rosenthal, J. M. Warram, A. N. Pinchuk, K. W. Eliceiri, J. S. Kuo and J. P. Weichert, Nat. Rev. Clin. Oncol., 2017, 14, 347–364 CrossRef CAS PubMed.
  4. R. Weissleder and M. J. Pittet, Nature, 2008, 452, 580–589 CrossRef CAS PubMed.
  5. T. Hussain and Q. T. Nguyen, Adv. Drug Delivery Rev., 2014, 66, 90–100 CrossRef CAS PubMed.
  6. M. A. Whitney, J. L. Crisp, L. T. Nguyen, B. Friedman, L. A. Gross, P. Steinbach, R. Y. Tsien and Q. T. Nguyen, Nat. Biotechnol., 2011, 29, 352–356 CrossRef CAS PubMed.
  7. A. L. Vahrmeijer, M. Hutteman, J. R. van der Vorst, C. J. H. van de Velde and J. V. Frangioni, Nat. Rev. Clin. Oncol., 2013, 10, 507–518 CrossRef CAS PubMed.
  8. R. Weissleder, M. C. Schwaiger, S. S. Gambhir and H. Hricak, Sci. Transl. Med., 2016, 8, 355ps316 Search PubMed.
  9. M. F. Kircher, H. Hricak and S. M. Larson, Mol. Oncol., 2012, 6, 182–195 CrossRef CAS PubMed.
  10. J. K. Willmann, N. van Bruggen, L. M. Dinkelborg and S. S. Gambhir, Nat. Rev. Drug Discovery, 2008, 7, 591–607 CrossRef CAS PubMed.
  11. S. Singhal, S. M. Nie and M. D. Wang, Annu. Rev. Med., 2010, 61, 359–373 CrossRef CAS PubMed.
  12. T. E. Yankeelov, D. A. Mankoff, L. H. Schwartz, F. S. Lieberman, J. M. Buatti, J. M. Mountz, B. J. Erickson, F. M. Fennessy, W. Huang, J. Kalpathy-Cramer, R. L. Wahl, H. M. Linden, P. E. Kinahan, B. Zhao, N. M. Hylton, R. J. Gillies, L. Clarke, R. Nordstrom and D. L. Rubin, Clin. Cancer Res., 2016, 22, 284–290 CrossRef PubMed.
  13. V. Rufini, M. L. Calcagni and R. P. Baum, Semin. Nucl. Med., 2006, 36, 228–247 CrossRef PubMed.
  14. E. P. Krenning, D. J. Kwekkeboom, H. Y. Oei, R. J. de Jong, F. J. Dop, J. C. Reubi and S. W. Lamberts, Ann. N. Y. Acad. Sci., 1994, 733, 416–424 CrossRef CAS PubMed.
  15. W. Shi, C. F. Johnston, K. D. Buchanan, W. R. Ferguson, J. D. Laird, J. G. Crothers and E. M. McIlrath, QJM, 1998, 91, 295–301 CrossRef CAS PubMed.
  16. R. Lebtahi, G. Cadiot, L. Sarda, D. Daou, M. Faraggi, Y. Petegnief, M. Mignon and D. le Guludec, J. Nucl. Med., 1997, 38, 853–858 CAS.
  17. B. Termanini, F. Gibril, J. C. Reynolds, J. L. Doppman, C. C. Chen, C. A. Stewart, V. E. Sutliff and R. T. Jensen, Gastroenterology, 1997, 112, 335–347 CrossRef CAS.
  18. S. R. Meikle, P. Kench, M. Kassiou and R. B. Banati, Phys. Med. Biol., 2005, 50, R45–R61 CrossRef CAS PubMed.
  19. S. S. Gambhir, Nat. Rev. Cancer, 2002, 2, 683–693 CrossRef CAS PubMed.
  20. M. Shokeen and C. J. Anderson, Acc. Chem. Res., 2009, 42, 832–841 CrossRef CAS PubMed.
  21. X. L. Sun, W. B. Cai and X. Y. Chen, Acc. Chem. Res., 2015, 48, 286–294 CrossRef CAS PubMed.
  22. E. L. Que and C. J. Chang, Chem. Soc. Rev., 2010, 39, 51–60 RSC.
  23. D. E. Sosnovik and R. Weissleder, Curr. Opin. Biotechnol., 2007, 18, 4–10 CrossRef CAS PubMed.
  24. K. W. Ferrara, M. A. Borden and H. Zhang, Acc. Chem. Res., 2009, 42, 881–892 CrossRef CAS PubMed.
  25. S. A. Hilderbrand and R. Weissleder, Curr. Opin. Chem. Biol., 2010, 14, 71–79 CrossRef CAS PubMed.
  26. B. N. Giepmans, S. R. Adams, M. H. Ellisman and R. Y. Tsien, Science, 2006, 312, 217–224 CrossRef CAS PubMed.
  27. Q. Yang, Z. Ma, H. Wang, B. Zhou, S. Zhu, Y. Zhong, J. Wang, H. Wan, A. Antaris, R. Ma, X. Zhang, J. Yang, X. Zhang, H. Sun, W. Liu, Y. Liang and H. Dai, Adv. Mater., 2017, 29, 1605497 CrossRef PubMed.
  28. X. L. Dean-Ben, S. Gottschalk, B. Mc Larney, S. Shoham and D. Razansky, Chem. Soc. Rev., 2017, 46, 2158–2198 RSC.
  29. L. H. V. Wang and S. Hu, Science, 2012, 335, 1458–1462 CrossRef CAS PubMed.
  30. J. Weber, P. C. Beard and S. E. Bohndiek, Nat. Methods, 2016, 13, 639–650 CrossRef CAS PubMed.
  31. S. Zackrisson, S. M. W. Y. van de Ven and S. S. Gambhir, Cancer Res., 2014, 74, 979–1004 CrossRef CAS PubMed.
  32. J. F. Lovell, T. W. Liu, J. Chen and G. Zheng, Chem. Rev., 2010, 110, 2839–2857 CrossRef CAS PubMed.
  33. C. Krafft, M. Schmitt, I. W. Schie, D. Cialla-May, C. Matthaus, T. Bocklitz and J. Popp, Angew. Chem., Int. Ed., 2017, 56, 4392–4430 CrossRef CAS PubMed.
  34. A. Mahajan, V. Goh, S. Basu, R. Vaish, A. J. Weeks, M. H. Thakur and G. J. Cook, Clin. Radiol., 2015, 70, 1060–1082 CrossRef CAS PubMed.
  35. S. Lee, J. Xie and X. Y. Chen, Chem. Rev., 2010, 110, 3087–3111 CrossRef CAS PubMed.
  36. T. Jiang, E. S. Olson, Q. T. Nguyen, M. Roy, P. A. Jennings and R. Y. Tsien, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 17867–17872 CrossRef CAS PubMed.
  37. Q. T. Nguyen, E. S. Olson, T. A. Aguilera, T. Jiang, M. Scadeng, L. G. Ellies and R. Y. Tsien, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 4317–4322 CrossRef CAS PubMed.
  38. E. S. Olson, T. A. Aguilera, T. Jiang, L. G. Ellies, Q. T. Nguyen, E. H. Wong, L. A. Gross and R. Y. Tsien, Integr. Biol., 2009, 1, 382–393 RSC.
  39. M. Whitney, E. N. Savariar, B. Friedman, R. A. Levin, J. L. Crisp, H. L. Glasgow, R. Lefkowitz, S. R. Adams, P. Steinbach, N. Nashi, Q. T. Nguyen and R. Y. Tsien, Angew. Chem., Int. Ed., 2013, 52, 325–330 CrossRef CAS PubMed.
  40. R. Weinstain, E. N. Sayariar, C. N. Felsen and R. Y. Tsien, J. Am. Chem. Soc., 2014, 136, 874–877 CrossRef CAS PubMed.
  41. J. Levi, S. R. Kothapalli, T. J. Ma, K. Hartman, B. T. Khuri-Yakub and S. S. Gambhir, J. Am. Chem. Soc., 2010, 132, 11264–11269 CrossRef CAS PubMed.
  42. J. Levi, S. R. Kothapalli, S. Bohndiek, J. K. Yoon, A. Dragulescu-Andrasi, C. Nielsen, A. Tisma, S. Bodapati, G. Gowrishankar, X. Yan, C. Chan, D. Starcevic and S. S. Gambhir, Clin. Cancer Res., 2013, 19, 1494–1502 CrossRef CAS PubMed.
  43. N. Naswa, P. Sharma, A. Kumar, A. H. Nazar, R. Kumar, S. Chumber and C. Bal, AJR, Am. J. Roentgenol., 2011, 197, 1221–1228 CrossRef PubMed.
  44. S. Koukouraki, L. G. Strauss, V. Georgoulias, J. Schuhmacher, U. Haberkorn, N. Karkavitsas and A. Dimitrakopoulou-Strauss, Eur. J. Nucl. Med. Mol. Imaging, 2006, 33, 460–466 CrossRef CAS PubMed.
  45. M. B. Sturm, B. P. Joshi, S. Lu, C. Piraka, S. Khondee, B. J. Elmunzer, R. S. Kwon, D. G. Beer, H. D. Appelman, D. K. Turgeon and T. D. Wang, Sci. Transl. Med., 2013, 5, 184ra161 Search PubMed.
  46. 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 and T. D. Wang, Nat. Med., 2008, 14, 454–458 CrossRef CAS PubMed.
  47. M. R. Stroud, S. J. Hansen and J. M. Olson, Curr. Pharm. Des., 2011, 17, 4362–4371 CrossRef CAS PubMed.
  48. J. Burggraaf, I. M. Kamerling, P. B. Gordon, L. Schrier, M. L. de Kam, A. J. Kales, R. Bendiksen, B. Indrevoll, R. M. Bjerke, S. A. Moestue, S. Yazdanfar, A. M. Langers, M. Swaerd-Nordmo, G. Torheim, M. V. Warren, H. Morreau, P. W. Voorneveld, T. Buckle, F. W. van Leeuwen, L. I. Odegardstuen, G. T. Dalsgaard, A. Healey and J. C. Hardwick, Nat. Med., 2015, 21, 955–961 CrossRef CAS PubMed.
  49. M. J. Whitley, D. M. Cardona, A. L. Lazarides, I. Spasojevic, J. M. Ferrer, J. Cahill, C. L. Lee, M. Snuderl, D. G. Blazer, E. S. Hwang, R. A. Greenup, P. J. Mosca, J. K. Mito, K. C. Cuneo, N. A. Larrier, E. K. O'Reilly, R. F. Riedel, W. C. Eward, D. B. Strasfeld, D. Fukumura, R. K. Jain, W. D. Lee, L. G. Griffith, M. G. Bawendi, D. G. Kirsch and B. E. Brigman, Sci. Transl. Med., 2016, 8, 320ra4 CrossRef PubMed.
  50. A. Almutairi, R. Rossin, M. Shokeen, A. Hagooly, A. Ananth, B. Capoccia, S. Guillaudeu, D. Abendschein, C. J. Anderson, M. J. Welch and J. M. Frechet, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 685–690 CrossRef CAS PubMed.
  51. H. Koo, M. S. Huh, J. H. Ryu, D. E. Lee, I. C. Sun, K. Choi, K. Kim and I. C. Kwon, Nano Today, 2011, 6, 204–220 CrossRef CAS.
  52. A. S. Thakor and S. S. Gambhir, Ca-Cancer J. Clin., 2013, 63, 395–418 CrossRef PubMed.
  53. P. Zhang, C. Hu, W. Ran, J. Meng, Q. Yin and Y. Li, Theranostics, 2016, 6, 948–968 CrossRef CAS PubMed.
  54. L. K. Bogart, G. Pourroy, C. J. Murphy, V. Puntes, T. Pellegrino, D. Rosenblum, D. Peer and R. Levy, ACS Nano, 2014, 8, 3107–3122 CrossRef CAS PubMed.
  55. E. S. Olson, T. Jiang, T. A. Aguilera, Q. T. Nguyen, L. G. Ellies, M. Scadeng and R. Y. Tsien, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 4311–4316 CrossRef CAS PubMed.
  56. S. Aime, D. D. Castelli, S. G. Crich, E. Gianolio and E. Terreno, Acc. Chem. Res., 2009, 42, 822–831 CrossRef CAS PubMed.
  57. J. S. Ananta, B. Godin, R. Sethi, L. Moriggi, X. Liu, R. E. Serda, R. Krishnamurthy, R. Muthupillai, R. D. Bolskar, L. Helm, M. Ferrari, L. J. Wilson and P. Decuzzi, Nat. Nanotechnol., 2010, 5, 815–821 CrossRef CAS PubMed.
  58. J. Della Rocca, D. Liu and W. Lin, Acc. Chem. Res., 2011, 44, 957–968 CrossRef CAS PubMed.
  59. Y. Lyu, X. Zhen, Y. S. Miao and K. Y. Pu, ACS Nano, 2017, 11, 358–367 CrossRef CAS PubMed.
  60. D. Wang, J. Qian, S. L. He, J. S. Park, K. S. Lee, S. H. Han and Y. Mu, Biomaterials, 2011, 32, 5880–5888 CrossRef CAS PubMed.
  61. Y. J. Liu and M. J. Welch, Bioconjugate Chem., 2012, 23, 671–682 CrossRef CAS PubMed.
  62. V. J. Yao, S. D'Angelo, K. S. Butler, C. Theron, T. L. Smith, S. Marchiò, J. G. Gelovani, R. L. Sidman, A. S. Dobroff and C. J. Brinker, J. Controlled Release, 2016, 240, 267 CrossRef CAS PubMed.
  63. Y. Geng, P. Dalhaimer, S. Cai, R. Tsai, M. Tewari, T. Minko and D. E. Discher, Nat. Nanotechnol., 2007, 2, 249–255 CrossRef CAS PubMed.
  64. H. S. Choi, W. Liu, P. Misra, E. Tanaka, J. P. Zimmer, B. Itty Ipe, M. G. Bawendi and J. V. Frangioni, Nat. Biotechnol., 2007, 25, 1165–1170 CrossRef CAS PubMed.
  65. B. D. Chithrani and W. C. Chan, Nano Lett., 2007, 7, 1542–1550 CrossRef CAS PubMed.
  66. J. H. Park, G. von Maltzahn, L. Zhang, M. P. Schwartz, E. Ruoslahti, S. N. Bhatia and M. J. Sailor, Adv. Mater., 2008, 20, 1630–1635 CrossRef CAS PubMed.
  67. S. E. Gratton, P. A. Ropp, P. D. Pohlhaus, J. C. Luft, V. J. Madden, M. E. Napier and J. M. DeSimone, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 11613–11618 CrossRef CAS PubMed.
  68. H. Hong, K. Yang, Y. Zhang, J. W. Engle, L. Z. Feng, Y. A. Yang, T. R. Nayak, S. Goel, J. Bean, C. P. Theuer, T. E. Barnhart, Z. Liu and W. B. Cai, ACS Nano, 2012, 6, 2361–2370 CrossRef CAS PubMed.
  69. J. W. Kim, E. I. Galanzha, E. V. Shashkov, H. M. Moon and V. P. Zharov, Nat. Nanotechnol., 2009, 4, 688–694 CrossRef CAS PubMed.
  70. T. Chen, C. S. Wu, E. Jimenez, Z. Zhu, J. G. Dajac, M. X. You, D. Han, X. B. Zhang and W. H. Tan, Angew. Chem., Int. Ed., 2013, 52, 2012–2016 CrossRef CAS PubMed.
  71. E. Kim, J. Yang, J. Park, S. Kim, N. H. Kim, J. I. Yook, J. S. Suh, S. Haam and Y. M. Huh, ACS Nano, 2012, 6, 8525–8535 CrossRef CAS PubMed.
  72. C. C. Wu, T. Chen, D. Han, M. X. You, L. Peng, S. Cansiz, G. Z. Zhu, C. M. Li, X. L. Xiong, E. Jimenez, C. J. Yang and W. H. Tan, ACS Nano, 2013, 7, 5724–5731 CrossRef CAS PubMed.
  73. E. C. Cho, C. Glaus, J. Chen, M. J. Welch and Y. Xia, Trends Mol. Med., 2010, 16, 561–573 CrossRef CAS PubMed.
  74. A. P. Alivisatos, Science, 1996, 271, 933–937 CAS.
  75. X. Michalet, F. F. Pinaud, L. A. Bentolila, J. M. Tsay, S. Doose, J. J. Li, G. Sundaresan, A. M. Wu, S. S. Gambhir and S. Weiss, Science, 2005, 307, 538–544 CrossRef CAS PubMed.
  76. A. M. Smith and S. M. Nie, Acc. Chem. Res., 2010, 43, 190–200 CrossRef CAS PubMed.
  77. I. L. Medintz, H. T. Uyeda, E. R. Goldman and H. Mattoussi, Nat. Mater., 2005, 4, 435–446 CrossRef CAS PubMed.
  78. G. S. Hong, J. T. Robinson, Y. J. Zhang, S. Diao, A. L. Antaris, Q. B. Wang and H. J. Dai, Angew. Chem., Int. Ed., 2012, 51, 9818–9821 CrossRef CAS PubMed.
  79. G. Xu, S. Zeng, B. Zhang, M. T. Swihart, K. T. Yong and P. N. Prasad, Chem. Rev., 2016, 116, 12234–12327 CrossRef CAS PubMed.
  80. R. Tang, J. P. Xue, B. G. Xu, D. W. Shen, G. P. Sudlow and S. Achilefu, ACS Nano, 2015, 9, 220–230 CrossRef CAS PubMed.
  81. L. H. Reddy, J. L. Arias, J. Nicolas and P. Couvreur, Chem. Rev., 2012, 112, 5818–5878 CrossRef CAS PubMed.
  82. J. Gao, H. Gu and B. Xu, Acc. Chem. Res., 2009, 42, 1097–1107 CrossRef CAS PubMed.
  83. C. Xu and S. Sun, Adv. Drug Delivery Rev., 2013, 65, 732–743 CrossRef CAS PubMed.
  84. J. Xie, G. Liu, H. S. Eden, H. Ai and X. Chen, Acc. Chem. Res., 2011, 44, 883–892 CrossRef CAS PubMed.
  85. C. Tassa, S. Y. Shaw and R. Weissleder, Acc. Chem. Res., 2011, 44, 842–852 CrossRef CAS PubMed.
  86. F. Wang and X. Liu, Chem. Soc. Rev., 2009, 38, 976–989 RSC.
  87. J. Zhou, Z. Liu and F. Li, Chem. Soc. Rev., 2012, 41, 1323–1349 RSC.
  88. K. Y. Pu, A. J. Shuhendler, J. V. Jokerst, J. G. Mei, S. S. Gambhir, Z. N. Bao and J. H. Rao, Nat. Nanotechnol., 2014, 9, 233–239 CrossRef CAS PubMed.
  89. K. Pu, N. Chattopadhyay and J. Rao, J. Controlled Release, 2016, 240, 312–322 CrossRef CAS PubMed.
  90. L. Y. Cui and J. H. Rao, Wiley Interdiscip. Rev.: Nanomed. Nanobiotechnol., 2017, 9, 233–239 Search PubMed.
  91. S. Goel, F. Chen and W. B. Cai, Small, 2014, 10, 631–645 CrossRef CAS PubMed.
  92. G. Ku, M. Zhou, S. L. Song, Q. Huang, J. Hazle and C. Li, ACS Nano, 2012, 6, 7489–7496 CrossRef CAS PubMed.
  93. M. Zhou, R. Zhang, M. Huang, W. Lu, S. Song, M. P. Melancon, M. Tian, D. Liang and C. Li, J. Am. Chem. Soc., 2010, 132, 15351–15358 CrossRef CAS PubMed.
  94. M. Hu, J. Y. Chen, Z. Y. Li, L. Au, G. V. Hartland, X. D. Li, M. Marquez and Y. N. Xia, Chem. Soc. Rev., 2006, 35, 1084–1094 RSC.
  95. L. Nie, S. Wang, X. Wang, P. Rong, Y. Ma, G. Liu, P. Huang, G. Lu and X. Chen, Small, 2014, 10(1585–1593), 1441 CrossRef.
  96. K. Kostarelos, A. Bianco and M. Prato, Nat. Nanotechnol., 2009, 4, 627–633 CrossRef CAS PubMed.
  97. K. Yang, L. Feng, X. Shi and Z. Liu, Chem. Soc. Rev., 2013, 42, 530–547 RSC.
  98. C. Chung, Y. K. Kim, D. Shin, S. R. Ryoo, B. H. Hong and D. H. Min, Acc. Chem. Res., 2013, 46, 2211–2224 CrossRef CAS PubMed.
  99. A. E. Prigodich, P. S. Randeria, W. E. Briley, N. J. Kim, W. L. Daniel, D. A. Giljohann and C. A. Mirkin, Anal. Chem., 2012, 84, 2062–2066 CrossRef CAS PubMed.
  100. D. Lee, S. Khaja, J. C. Velasquez-Castano, M. Dasari, C. Sun, J. Petros, W. R. Taylor and N. Murthy, Nat. Mater., 2007, 6, 765–769 CrossRef CAS PubMed.
  101. L. Rodriguez-Lorenzo, R. de la Rica, R. A. Alvarez-Puebla, L. M. Liz-Marzan and M. M. Stevens, Nat. Mater., 2012, 11, 604–607 CrossRef CAS PubMed.
  102. J. Hu, G. Zhang and S. Liu, Chem. Soc. Rev., 2012, 41, 5933–5949 RSC.
  103. F. Y. Li, J. X. Lu, X. Q. Kong, T. Hyeon and D. S. Ling, Adv. Mater., 2017, 29, 1605897 CrossRef PubMed.
  104. J. Zhou, J. Li, X. W. Du and B. Xu, Biomaterials, 2017, 129, 1–27 CrossRef CAS PubMed.
  105. X. Xu, X. Liu, Z. Nie, Y. Pan, M. Guo and S. Yao, Anal. Chem., 2011, 83, 52–59 CrossRef CAS PubMed.
  106. M. L. Viger, J. Sankaranarayanan, C. de Gracia Lux, M. Chan and A. Almutairi, J. Am. Chem. Soc., 2013, 135, 7847–7850 CrossRef CAS PubMed.
  107. A. Almutairi, S. J. Guillaudeu, M. Y. Berezin, S. Achilefu and J. M. Frechet, J. Am. Chem. Soc., 2008, 130, 444–445 CrossRef CAS PubMed.
  108. Y. Wang, K. Zhou, G. Huang, C. Hensley, X. Huang, X. Ma, T. Zhao, B. D. Sumer, R. J. DeBerardinis and J. Gao, Nat. Mater., 2014, 13, 204–212 CrossRef CAS PubMed.
  109. Y. Takaoka, T. Sakamoto, S. Tsukiji, M. Narazaki, T. Matsuda, H. Tochio, M. Shirakawa and I. Hamachi, Nat. Chem., 2009, 1, 557–561 CrossRef CAS PubMed.
  110. G. Bao, S. Mitragotri and S. Tong, Annu. Rev. Biomed. Eng., 2013, 15, 253–282 CrossRef CAS PubMed.
  111. S. Y. Lee, S. I. Jeon, S. Jung, I. J. Chung and C. H. Ahn, Adv. Drug Delivery Rev., 2014, 76, 60–78 CrossRef CAS PubMed.
  112. J. Rieffel, U. Chitgupi and J. F. Lovell, Small, 2015, 11, 4445–4461 CrossRef CAS PubMed.
  113. H. Koo, M. S. Huh, I. C. Sun, S. H. Yuk, K. Choi, K. Kim and I. C. Kwon, Acc. Chem. Res., 2011, 44, 1018–1028 CrossRef CAS PubMed.
  114. K. Park, S. Lee, E. Kang, K. Kim, K. Choi and I. C. Kwon, Adv. Funct. Mater., 2009, 19, 1553–1566 CrossRef CAS.
  115. L. Tang and J. Cheng, Nano Today, 2013, 8, 290–312 CrossRef CAS PubMed.
  116. B. Godin, E. Tasciotti, X. W. Liu, R. E. Serda and M. Ferrari, Acc. Chem. Res., 2011, 44, 979–989 CrossRef CAS PubMed.
  117. A. Gianella, P. A. Jarzyna, V. Mani, S. Ramachandran, C. Calcagno, J. Tang, B. Kann, W. J. R. Dijk, V. L. Thijssen, A. W. Griffioen, G. Storm, Z. A. Fayad and W. J. M. Mulder, ACS Nano, 2011, 5, 4422–4433 CrossRef CAS PubMed.
  118. M. F. Kircher, A. de la Zerda, J. V. Jokerst, C. L. Zavaleta, P. J. Kempen, E. Mittra, K. Pitter, R. M. Huang, C. Campos, F. Habte, R. Sinclair, C. W. Brennan, I. K. Mellinghoff, E. C. Holland and S. S. Gambhir, Nat. Med., 2012, 18, 829–834 CrossRef CAS PubMed.
  119. M. Nahrendorf, E. Keliher, B. Marinelli, P. Waterman, P. F. Feruglio, L. Fexon, M. Pivovarov, F. K. Swirski, M. J. Pittet, C. Vinegoni and R. Weissleder, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 7910–7915 CrossRef PubMed.
  120. J. S. Kim, W. J. Rieter, K. M. Taylor, H. An, W. Lin and W. Lin, J. Am. Chem. Soc., 2007, 129, 8962–8963 CrossRef CAS PubMed.
  121. J. Hassoun, H. G. Jung, D. J. Lee, J. B. Park, K. Amine, Y. K. Sun and B. Scrosati, Nano Lett., 2012, 12, 5775 CrossRef CAS PubMed.
  122. T. Lammers, S. Aime, W. E. Hennink, G. Storm and F. Kiessling, Acc. Chem. Res., 2011, 44, 1029–1038 CrossRef CAS PubMed.
  123. J. F. Lovell, C. S. Jin, E. Huynh, H. L. Jin, C. Kim, J. L. Rubinstein, W. C. W. Chan, W. G. Cao, L. V. Wang and G. Zheng, Nat. Mater., 2011, 10, 324–332 CrossRef CAS PubMed.
  124. J. H. Lee, K. Lee, S. H. Moon, Y. Lee, T. G. Park and J. Cheon, Angew. Chem., Int. Ed., 2009, 48, 4174–4179 CrossRef CAS PubMed.
  125. R. de la Rica and H. Matsui, Chem. Soc. Rev., 2010, 39, 3499–3509 RSC.
  126. M. Fani, H. R. Maecke and S. M. Okarvi, Theranostics, 2012, 2, 481–501 CrossRef CAS PubMed.
  127. A. G. Cheetham, D. Keith, P. Zhang, R. Lin, H. Su and H. Cui, Curr. Cancer Drug Targets, 2016, 16, 489–508 CrossRef CAS PubMed.
  128. E. Ruoslahti, Adv. Mater., 2012, 24, 3747–3756 CrossRef CAS PubMed.
  129. M. R. Pinto and K. S. Schanze, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 7505–7510 CrossRef CAS PubMed.
  130. R. J. Williams, R. J. Mart and R. V. Ulijn, Biopolymers, 2010, 94, 107–117 CrossRef CAS PubMed.
  131. Y. Gao, Z. Yang, Y. Kuang, M. L. Ma, J. Li, F. Zhao and B. Xu, Biopolymers, 2010, 94, 19–31 CrossRef CAS PubMed.
  132. J. P. Schneider, D. J. Pochan, B. Ozbas, K. Rajagopal, L. Pakstis and J. Kretsinger, J. Am. Chem. Soc., 2002, 124, 15030–15037 CrossRef CAS PubMed.
  133. D. K. Nagler, W. Tam, A. C. Storer, J. C. Krupa, J. S. Mort and R. Menard, Biochemistry, 1999, 38, 4868–4874 CrossRef CAS PubMed.
  134. Z. Fan, L. M. Sun, Y. J. Huang, Y. Z. Wang and M. J. Zhang, Nat. Nanotechnol., 2016, 11, 388–394 CrossRef CAS PubMed.
  135. J. C. Reubi and H. R. Maecke, J. Nucl. Med., 2008, 49, 1735–1738 CrossRef CAS PubMed.
  136. M. D. Pierschbacher and E. Ruoslahti, Nature, 1984, 309, 30–33 CrossRef CAS PubMed.
  137. E. Ruoslahti, Annu. Rev. Cell Dev. Biol., 1996, 12, 697–715 CrossRef CAS PubMed.
  138. A. Becker, C. Hessenius, K. Licha, B. Ebert, U. Sukowski, W. Semmler, B. Wiedenmann and C. Grotzinger, Nat. Biotechnol., 2001, 19, 327–331 CrossRef CAS PubMed.
  139. S. C. Drew, C. L. Haigh, H. M. Klemm, C. L. Masters, S. J. Collins, K. J. Barnham and V. A. Lawson, J. Neuropathol. Exp. Neurol., 2011, 70, 143–150 CrossRef PubMed.
  140. J. Deshane, C. C. Garner and H. Sontheimer, J. Biol. Chem., 2003, 278, 4135–4144 CrossRef CAS PubMed.
  141. Z. R. Stephen, F. M. Kievit, O. Veiseh, P. A. Chiarelli, C. Fang, K. Wang, S. J. Hatzinger, R. G. Ellenbogen, J. R. Silber and M. Q. Zhang, ACS Nano, 2014, 8, 10383–10395 CrossRef CAS PubMed.
  142. R. Hussain, N. S. Courtenay-Luck and G. Siligardi, Biomed. Pept., Proteins Nucleic Acids, 1996, 2, 67–70 CAS.
  143. Z. Medarova, W. Pham, Y. Kim, G. P. Dai and A. Moore, Int. J. Cancer, 2006, 118, 2796–2802 CrossRef CAS PubMed.
  144. Z. Medarova, L. Rashkovetsky, P. Pantazopoulos and A. Moore, Cancer Res., 2009, 69, 1182–1189 CrossRef CAS PubMed.
  145. W. J. Goux, L. Kopplin, A. D. Nguyen, K. Leak, M. Rutkofsky, V. D. Shanmuganandam, D. Sharma, H. Inouye and D. A. Kirschner, J. Biol. Chem., 2004, 279, 26868–26875 CrossRef CAS PubMed.
  146. A. G. Cheetham, P. Zhang, Y. A. Lin, L. L. Lock and H. Cui, J. Am. Chem. Soc., 2013, 135, 2907–2910 CrossRef CAS PubMed.
  147. C. Liang, R. Ni, J. E. Smith, W. S. Childers, A. K. Mehta and D. G. Lynn, J. Am. Chem. Soc., 2014, 136, 15146–15149 CrossRef CAS PubMed.
  148. Y. A. Lin, A. G. Cheetham, P. Zhang, Y. C. Ou, Y. Li, G. Liu, D. Hermida-Merino, I. W. Hamley and H. Cui, ACS Nano, 2014, 8, 12690–12700 CrossRef CAS PubMed.
  149. R. Nelson, M. R. Sawaya, M. Balbirnie, A. O. Madsen, C. Riekel, R. Grothe and D. Eisenberg, Nature, 2005, 435, 773–778 CrossRef CAS PubMed.
  150. C. Fillebeen, L. Descamps, M. P. Dehouck, L. Fenart, M. Benaissa, G. Spik, R. Cecchelli and A. Pierce, J. Biol. Chem., 1999, 274, 7011–7017 CrossRef CAS PubMed.
  151. M. M. Hussain, D. K. Strickland and A. Bakillah, Annu. Rev. Nutr., 1999, 19, 141–172 CrossRef CAS PubMed.
  152. M. Demeule, A. Regina, C. Che, J. Poirier, T. Nguyen, R. Gabathuler, J. P. Castaigne and R. Beliveau, J. Pharmacol. Exp. Ther., 2008, 324, 1064–1072 CrossRef CAS PubMed.
  153. G. P. Smith and V. A. Petrenko, Chem. Rev., 1997, 97, 391–410 CrossRef CAS PubMed.
  154. D. N. Krag, G. S. Shukla, G. P. Shen, S. Pero, T. Ashikaga, S. Fuller, D. L. Weaver, S. Burdette-Radoux and C. Thomas, Cancer Res., 2006, 66, 7724–7733 CrossRef CAS PubMed.
  155. R. Pasqualini and E. Ruoslahti, Nature, 1996, 380, 364–366 CrossRef CAS PubMed.
  156. E. Ruoslahti and D. Rajotte, Annu. Rev. Immunol., 2000, 18, 813–827 CrossRef CAS PubMed.
  157. E. Ruoslahti, Biochem. Soc. Trans., 2004, 32, 397–402 CrossRef CAS PubMed.
  158. T. Teesalu, K. N. Sugahara, V. R. Kotamraju and E. Ruoslahti, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 16157–16162 CrossRef CAS PubMed.
  159. K. Nord, E. Gunneriusson, J. Ringdahl, S. Stahl, M. Uhlen and P. A. Nygren, Nat. Biotechnol., 1997, 15, 772–777 CrossRef CAS PubMed.
  160. Y. Sugimura, M. Hosono, F. Wada, T. Yoshimura, M. Maki and K. Hitomi, J. Biol. Chem., 2006, 281, 17699–17706 CrossRef CAS PubMed.
  161. L. L. Zhang, J. A. Hoffman and E. Ruoslahti, Circulation, 2005, 112, 1601–1611 CrossRef CAS PubMed.
  162. S. L. Deutscher, Chem. Rev., 2010, 110, 3196–3211 CrossRef CAS PubMed.
  163. K. S. Lam, M. Lebl and V. Krchnak, Chem. Rev., 1997, 97, 411–448 CrossRef CAS PubMed.
  164. R. A. Houghten, C. Pinilla, S. E. Blondelle, J. R. Appel, C. T. Dooley and J. H. Cuervo, Nature, 1991, 354, 84–86 CrossRef CAS PubMed.
  165. C. F. Cho, G. A. Amadei, D. Breadner, L. G. Luyt and J. D. Lewis, Nano Lett., 2012, 12, 5957–5965 CrossRef CAS PubMed.
  166. Z. H. Wang, W. Z. Wang, X. L. Bu, Z. W. Wei, L. L. Geng, Y. Wu, C. Y. Dong, L. Q. Li, D. Zhang, S. Yang, F. Wang, C. Lausted, L. Hood and Z. Y. Hu, Anal. Chem., 2015, 87, 8367–8372 CrossRef CAS PubMed.
  167. S. P. Rohrer, E. T. Birzin, R. T. Mosley, S. C. Berk, S. M. Hutchins, D. M. Shen, Y. S. Xiong, E. C. Hayes, R. M. Parmar, F. Foor, S. W. Mitra, S. J. Degrado, M. Shu, J. M. Klopp, S. J. Cai, A. Blake, W. W. S. Chan, A. Pasternak, L. H. Yang, A. A. Patchett, R. G. Smith, K. T. Chapman and J. M. Schaeffer, Science, 1998, 282, 737–740 CrossRef CAS PubMed.
  168. J. L. Harris, B. J. Backes, F. Leonetti, S. Mahrus, J. A. Ellman and C. S. Craik, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 7754–7759 CrossRef CAS PubMed.
  169. B. E. Turk, L. L. Huang, E. T. Piro and L. C. Cantley, Nat. Biotechnol., 2001, 19, 661–667 CrossRef CAS PubMed.
  170. M. R. Pinto and K. S. Schanze, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 7505–7510 CrossRef CAS PubMed.
  171. P. W. Frederix, G. G. Scott, Y. M. Abul-Haija, D. Kalafatovic, C. G. Pappas, N. Javid, N. T. Hunt, R. V. Ulijn and T. Tuttle, Nat. Chem., 2015, 7, 30–37 CrossRef CAS PubMed.
  172. J. K. Willmann, R. H. Kimura, N. Deshpande, A. M. Lutz, J. R. Cochran and S. S. Gambhir, J. Nucl. Med., 2010, 51, 433–440 CrossRef CAS PubMed.
  173. P. An, H. Lei, J. Zhang, S. Song, L. He, G. Jin, X. Liu, J. Wu, L. Meng, M. Liu and C. Shou, Int. J. Cancer, 2004, 111, 165–173 CrossRef CAS PubMed.
  174. F. M. Brunel, J. D. Lewis, G. Destito, N. F. Steinmetz, M. Manchester, H. Stuhlmann and P. E. Dawson, Nano Lett., 2010, 10, 1093–1097 CrossRef CAS PubMed.
  175. P. Zhang, L. L. Lock, A. G. Cheetham and H. Cui, Mol. Pharmaceutics, 2014, 11, 964–973 CrossRef CAS PubMed.
  176. P. Zhang, A. G. Cheetham, L. L. Lock and H. Cui, Bioconjugate Chem., 2013, 24, 604–613 CrossRef CAS PubMed.
  177. K. N. Sugahara, T. Teesalu, P. P. Karmali, V. R. Kotamraju, L. Agemy, O. M. Girard, D. Hanahan, R. F. Mattrey and E. Ruoslahti, Cancer Cell, 2009, 16, 510–520 CrossRef CAS PubMed.
  178. Y. Abe, T. Shodai, T. Muto, K. Mihara, H. Torii, S. Nishikawa, T. Endo and D. Kohda, Cell, 2000, 100, 551–560 CrossRef CAS PubMed.
  179. L. E. Edgington, A. B. Berger, G. Blum, V. E. Albrow, M. G. Paulick, N. Lineberry and M. Bogyo, Nat. Med., 2009, 15, 967–973 CrossRef CAS PubMed.
  180. R. Lin, P. Zhang, A. G. Cheetham, J. Walston, P. Abadir and H. Cui, Bioconjugate Chem., 2015, 26, 71–77 CrossRef CAS PubMed.
  181. F. Tantakitti, J. Boekhoven, X. Wang, R. V. Kazantsev, T. Yu, J. Li, E. Zhuang, R. Zandi, J. H. Ortony, C. J. Newcomb, L. C. Palmer, G. S. Shekhawat, M. O. de la Cruz, G. C. Schatz and S. I. Stupp, Nat. Mater., 2016, 15, 469–476 CrossRef CAS PubMed.
  182. S. S. Lee, T. Fyrner, F. Chen, Z. Alvarez, E. Sleep, D. S. Chun, J. A. Weiner, R. W. Cook, R. D. Freshman, M. S. Schallmo, K. M. Katchko, A. D. Schneider, J. T. Smith, C. Yun, G. Singh, S. Z. Hashmi, M. T. McClendon, Z. Yu, S. R. Stock, W. K. Hsu, E. L. Hsu and S. I. Stupp, Nat. Nanotechnol., 2017, 12, 821–829 CrossRef CAS PubMed.
  183. D. J. Smith, G. A. Brat, S. H. Medina, D. Tong, Y. Huang, J. Grahammer, G. J. Furtmuller, B. C. Oh, K. J. Nagy-Smith, P. Walczak, G. Brandacher and J. P. Schneider, Nat. Nanotechnol., 2016, 11, 95–102 CrossRef CAS PubMed.
  184. F. Zhao, M. L. Ma and B. Xu, Chem. Soc. Rev., 2009, 38, 883–891 RSC.
  185. H. Wang, Z. Feng and B. Xu, Chem. Soc. Rev., 2017, 46, 2421–2436 RSC.
  186. L. Adler-Abramovich and E. Gazit, Chem. Soc. Rev., 2014, 43, 6881–6893 RSC.
  187. Y. Hu, R. Lin, P. Zhang, J. Fern, A. G. Cheetham, K. Patel, R. Schulman, C. Kan and H. Cui, ACS Nano, 2016, 10, 880–888 CrossRef CAS PubMed.
  188. D. L. Minor, Jr. and P. S. Kim, Nature, 1994, 367, 660–663 CrossRef PubMed.
  189. L. E. O'Leary, J. A. Fallas, E. L. Bakota, M. K. Kang and J. D. Hartgerink, Nat. Chem., 2011, 3, 821–828 CrossRef PubMed.
  190. J. Y. Li, Y. Gao, Y. Kuang, J. F. Shi, X. W. Du, J. Zhou, H. M. Wang, Z. M. Yang and B. Xu, J. Am. Chem. Soc., 2013, 135, 9907–9914 CrossRef CAS PubMed.
  191. O. V. Maltsev, U. K. Marelli, T. G. Kapp, F. S. Di Leva, S. Di Maro, M. Nieberler, U. Reuning, M. Schwaiger, E. Novellino and L. Marinelli, Angew. Chem., Int. Ed., 2016, 55, 1535 CrossRef CAS PubMed.
  192. P. S. Low, W. A. Henne and D. D. Doorneweerd, Acc. Chem. Res., 2008, 41, 120–129 CrossRef CAS PubMed.
  193. R. Kikkeri, B. Lepenies, A. Adibekian, P. Laurino and P. H. Seeberger, J. Am. Chem. Soc., 2009, 131, 2110–2112 CrossRef CAS PubMed.
  194. J. P. Holland, M. J. Evans, S. L. Rice, J. Wongvipat, C. L. Sawyers and J. S. Lewis, Nat. Med., 2012, 18, 1586–1591 CrossRef CAS PubMed.
  195. G. Y. Lee, W. P. Qian, L. Y. Wang, Y. A. Wang, C. A. Staley, M. Satpathy, S. M. Nie, H. Mao and L. L. Yang, ACS Nano, 2013, 7, 2078–2089 CrossRef CAS PubMed.
  196. J. Lofblom, J. Feldwisch, V. Tolmachev, J. Carlsson, S. Stahl and F. Y. Frejd, FEBS Lett., 2010, 584, 2670–2680 CrossRef CAS PubMed.
  197. J. H. Lee, Y. M. Huh, Y. W. Jun, J. W. Seo, J. T. Jang, H. T. Song, S. Kim, E. J. Cho, H. G. Yoon, J. S. Suh and J. Cheon, Nat. Med., 2007, 13, 95–99 CrossRef CAS PubMed.
  198. D. Shangguan, Y. Li, Z. W. Tang, Z. H. C. Cao, H. W. Chen, P. Mallikaratchy, K. Sefah, C. Y. J. Yang and W. H. Tan, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 11838–11843 CrossRef CAS PubMed.
  199. Y. H. Lee, H. Lee, Y. B. Kim, J. Y. Kim, T. Hyeon, H. Park, P. B. Messersmith and T. G. Park, Adv. Mater., 2008, 20, 4154–4157 CAS.
  200. M. E. Akerman, W. C. W. Chan, P. Laakkonen, S. N. Bhatia and E. Ruoslahti, Proc. Natl. Acad. Sci. U. S. A., 2002, 99, 12617–12621 CrossRef CAS PubMed.
  201. R. Medzhitov and C. A. Janeway, Jr., Science, 2002, 296, 298–300 CrossRef CAS PubMed.
  202. E. J. Brown and W. A. Frazier, Trends Cell Biol., 2001, 11, 130–135 CrossRef CAS PubMed.
  203. S. Chen, Z. Cao and S. Jiang, Biomaterials, 2009, 30, 5892–5896 CrossRef CAS PubMed.
  204. A. K. Nowinski, A. D. White, A. J. Keefe and S. Jiang, Langmuir, 2014, 30, 1864–1870 CrossRef CAS PubMed.
  205. P. L. Rodriguez, T. Harada, D. A. Christian, D. A. Pantano, R. K. Tsai and D. E. Discher, Science, 2013, 339, 971–975 CrossRef CAS PubMed.
  206. J. Fang, H. Nakamura and H. Maeda, Adv. Drug Delivery Rev., 2011, 63, 136–151 CrossRef CAS PubMed.
  207. Y. Matsumoto, J. W. Nichols, K. Toh, T. Nomoto, H. Cabral, Y. Miura, R. J. Christie, N. Yamada, T. Ogura, M. R. Kano, Y. Matsumura, N. Nishiyama, T. Yamasoba, Y. H. Bae and K. Kataoka, Nat. Nanotechnol., 2016, 11, 533–538 CrossRef CAS PubMed.
  208. J. Joo, X. Liu, V. R. Kotamraju, E. Ruoslahti, Y. Nam and M. J. Sailor, ACS Nano, 2015, 9, 6233 CrossRef CAS PubMed.
  209. H. H. Yan, L. Wang, J. Y. Wang, X. F. Weng, H. Lei, X. X. Wang, L. Jiang, J. H. Zhu, W. Y. Lu, X. B. Wei and C. Li, ACS Nano, 2012, 6, 410–420 CrossRef CAS PubMed.
  210. J. Frigell, I. Garcia, V. Gomez-Vallejo, J. Llop and S. Penades, J. Am. Chem. Soc., 2014, 136, 449–457 CrossRef CAS PubMed.
  211. J. Lee, H. S. Min, D. G. You, K. Kim, I. C. Kwon, T. Rhim and K. Y. Lee, J. Controlled Release, 2015, 223, 197–206 CrossRef PubMed.
  212. Y. Cheng, Q. Dai, R. A. Morshed, X. B. Fan, M. L. Wegscheid, D. A. Wainwright, Y. Han, L. J. Zhang, B. Auffinger, A. L. Tobias, E. Rincon, B. Thaci, A. U. Ahmed, P. C. Warnke, C. He and M. S. Lesniak, Small, 2014, 10, 5137–5150 CAS.
  213. N. Huang, S. Cheng, X. Zhang, Q. Tian, J. L. Pi, J. Tang, Q. Huang, F. Wang, J. Chen, Z. Y. Xie, Z. Y. Xu, W. F. Chen, H. Z. Zheng and Y. Cheng, Nanomedicine, 2017, 13, 83–93 CrossRef CAS PubMed.
  214. S. Santra, H. Yang, J. T. Stanley, P. H. Holloway, B. M. Moudgil, G. Walter and R. A. Mericle, Chem. Commun., 2005, 3144–3146 RSC.
  215. J. Y. Kim, W. I. Choi, Y. H. Kim and G. Tae, Biomaterials, 2013, 34, 1170–1178 CrossRef CAS PubMed.
  216. Y. Liu, A. Ibricevic, J. A. Cohen, J. L. Cohen, S. P. Gunsten, J. M. Frechet, M. J. Walter, M. J. Welch and S. L. Brody, Mol. Pharmaceutics, 2009, 6, 1891–1902 CrossRef CAS PubMed.
  217. X. Li, C. Wang, R. Liang, F. Sun, Y. Shi, A. Wang, W. Liu, K. Sun and Y. Li, Pharm. Res., 2015, 32, 1017–1027 CrossRef CAS PubMed.
  218. D. M. Copolovici, K. Langel, E. Eriste and U. Langel, ACS Nano, 2014, 8, 1972–1994 CrossRef CAS PubMed.
  219. J. B. Delehanty, C. E. Bradburne, K. Susumu, K. Boeneman, B. C. Mei, D. Farrell, J. B. Blanco-Canosa, P. E. Dawson, H. Mattoussi and I. L. Medintz, J. Am. Chem. Soc., 2011, 133, 10482–10489 CrossRef CAS PubMed.
  220. J. H. Lee, A. Q. Zhang, S. S. You and C. M. Lieber, Nano Lett., 2016, 16, 1509–1513 CrossRef CAS PubMed.
  221. M. Lewin, N. Carlesso, C. H. Tung, X. W. Tang, D. Cory, D. T. Scadden and R. Weissleder, Nat. Biotechnol., 2000, 18, 410–414 CrossRef CAS PubMed.
  222. X. H. Gao, Y. Y. Cui, R. M. Levenson, L. W. K. Chung and S. M. Nie, Nat. Biotechnol., 2004, 22, 969–976 CrossRef CAS PubMed.
  223. G. R. Reddy, M. S. Bhojani, P. McConville, J. Moody, B. A. Moffat, D. E. Hall, G. Kim, Y. E. Koo, M. J. Woolliscroft, J. V. Sugai, T. D. Johnson, M. A. Philbert, R. Kopelman, A. Rehemtulla and B. D. Ross, Clin. Cancer Res., 2006, 12, 6677–6686 CrossRef CAS PubMed.
  224. G. B. Braun, A. Pallaoro, G. Wu, D. Missirlis, J. A. Zasadzinski, M. Tirrell and N. O. Reich, ACS Nano, 2009, 3, 2007–2015 CrossRef CAS PubMed.
  225. A. M. Derfus, A. A. Chen, D. H. Min, E. Ruoslahti and S. N. Bhatia, Bioconjugate Chem., 2007, 18, 1391–1396 CrossRef CAS PubMed.
  226. Z. Medarova, W. Pham, C. Farrar, V. Petkova and A. Moore, Nat. Med., 2007, 13, 372–377 CrossRef CAS PubMed.
  227. E. J. Kwon, J. M. Bergen and S. H. Pun, Bioconjugate Chem., 2008, 19, 920–927 CrossRef CAS PubMed.
  228. C. Shi, X. Cao, X. Chen, Z. Sun, Z. Xiang, H. Zhao, W. Qian and X. Han, Biomaterials, 2015, 58, 10–25 CrossRef CAS PubMed.
  229. M. S. Thu, L. H. Bryant, T. Coppola, E. K. Jordan, M. D. Budde, B. K. Lewis, A. Chaudhry, J. Ren, N. R. Varma, A. S. Arbab and J. A. Frank, Nat. Med., 2012, 18, 463–467 CrossRef CAS PubMed.
  230. G. C. Chen, F. Tian, Y. Zhang, Y. J. Zhang, C. Y. Li and Q. B. Wang, Adv. Funct. Mater., 2014, 24, 2481–2488 CrossRef CAS.
  231. M. Gao, F. Fan, D. D. Li, Y. Yu, K. R. Mao, T. M. Sun, H. S. Qian, W. Tao and X. Z. Yang, Biomaterials, 2017, 133, 165–175 CrossRef CAS PubMed.
  232. G. B. Braun, T. Friman, H. B. Pang, A. Pallaoro, T. H. de Mendoza, A. M. A. Willmore, V. R. Kotamraju, A. P. Mann, Z. G. She, K. N. Sugahara, N. O. Reich, T. Teesalu and E. Ruoslahti, Nat. Mater., 2014, 13, 904–911 CrossRef CAS PubMed.
  233. J. G. Huang, T. Leshuk and F. X. Gu, Nano Today, 2011, 6, 478–492 CrossRef CAS.
  234. L. D. Field, J. B. Delehanty, Y. Chen and I. L. Medintz, Acc. Chem. Res., 2015, 48, 1380–1390 CrossRef CAS PubMed.
  235. W. Xie, L. Wang, Y. Y. Zhang, L. Su, A. G. Shen, J. Q. Tan and J. M. Hu, Bioconjugate Chem., 2009, 20, 768–773 CrossRef CAS PubMed.
  236. A. K. Oyelere, P. C. Chen, X. H. Huang, I. H. El-Sayed and M. A. El-Sayed, Bioconjugate Chem., 2007, 18, 1490–1497 CrossRef CAS PubMed.
  237. A. Huefner, W. L. Kuan, R. A. Barker and S. Mahajan, Nano Lett., 2013, 13, 2463–2470 CrossRef CAS PubMed.
  238. R. Vankayala, C. L. Kuo, K. Nuthalapati, C. S. Chiang and K. C. Hwang, Adv. Funct. Mater., 2015, 25, 5934–5945 CrossRef CAS.
  239. S. Y. Lin, N. T. Chen, S. P. Sum, L. W. Lo and C. S. Yang, Chem. Commun., 2008, 4762–4764 RSC.
  240. S. Arai, M. Suzuki, S. J. Park, J. S. Yoo, L. Wang, N. Y. Kang, H. H. Ha and Y. T. Chang, Chem. Commun., 2015, 51, 8044–8047 RSC.
  241. H. Huang, H. Li, J. J. Feng and A. J. Wang, Microchim. Acta, 2017, 184, 1215–1221 CrossRef CAS.
  242. J. W. Kang, P. T. C. So, R. R. Dasari and D. K. Lim, Nano Lett., 2015, 15, 1766–1772 CrossRef CAS PubMed.
  243. R. Milo, BioEssays, 2013, 35, 1050–1055 CrossRef CAS PubMed.
  244. S. Wagner, M. L. Bader, D. Drew and J. W. de Gier, Trends Biotechnol., 2006, 24, 364–371 CrossRef CAS PubMed.
  245. S. Kunjachan, R. Pola, F. Gremse, B. Theek, J. Ehling, D. Moeckel, B. Hermanns-Sachweh, M. Pechar, K. Ulbrich, W. E. Hennink, G. Storm, W. Lederle, F. Kiessling and T. Lammers, Nano Lett., 2014, 14, 972–981 CrossRef CAS PubMed.
  246. F. Zhang, X. L. Huang, L. Zhu, N. Guo, G. Niu, M. Swierczewska, S. Lee, H. Xu, A. Y. Wang, K. A. Mohamedali, M. G. Rosenblum, G. M. Lu and X. Y. Chen, Biomaterials, 2012, 33, 5414–5422 CrossRef CAS PubMed.
  247. E. Kluza, D. W. J. van der Schaft, P. A. I. Hautvast, W. J. M. Mulder, K. H. Mayo, A. W. Griffioen, G. J. Strijkers and K. Nicolay, Nano Lett., 2010, 10, 52–58 CrossRef CAS PubMed.
  248. B. H. Luo, C. V. Carman and T. A. Springer, Annu. Rev. Immunol., 2007, 25, 619–647 CrossRef CAS PubMed.
  249. D. M. McDonald and P. L. Choyke, Nat. Med., 2003, 9, 713–725 CrossRef CAS PubMed.
  250. J. S. Guthi, S. G. Yang, G. Huang, S. Z. Li, C. Khemtong, C. W. Kessinger, M. Peyton, J. D. Minna, K. C. Brown and J. M. Gao, Mol. Pharmaceutics, 2010, 7, 32–40 CrossRef CAS PubMed.
  251. C. W. Kessinger, O. Togao, C. Khemtong, G. Huang, M. Takahashi and J. M. Gao, Theranostics, 2011, 1, 263–273 CrossRef CAS PubMed.
  252. C. F. Zhang, M. Jugold, E. C. Woenne, T. Lammers, B. Morgenstern, M. M. Mueller, H. Zentgraf, M. Bock, M. Eisenhut, W. Semmler and F. Kiessling, Cancer Res., 2007, 67, 1555–1562 CrossRef CAS PubMed.
  253. K. M. Taylor, W. J. Rieter and W. Lin, J. Am. Chem. Soc., 2008, 130, 14358–14359 CrossRef CAS PubMed.
  254. J. S. Kim, W. J. Rieter, K. M. L. Taylor, H. An, W. L. Lin and W. B. Lin, J. Am. Chem. Soc., 2007, 129, 8962–8963 CrossRef CAS PubMed.
  255. H. Yang, C. Qin, C. Yu, L. Yang, H. Zhang, F. Xue, D. Wu, Z. Zhou and S. Yang, Adv. Funct. Mater., 2014, 24, 1738–1747 CrossRef CAS.
  256. K. T. Yong, R. Hu, I. Roy, H. Ding, L. A. Vathy, E. J. Bergey, M. Mizuma, A. Maitra and P. N. Prasad, ACS Appl. Mater. Interfaces, 2009, 1, 710–719 CAS.
  257. L. M. Nie, S. J. Wang, X. Y. Wang, P. F. Rong, A. Bhirde, Y. Ma, G. Liu, P. Huang, G. M. Lu and X. Y. Chen, Small, 2014, 10, 1585–1593 CrossRef CAS PubMed.
  258. Y. H. Kim, J. Jeon, S. H. Hong, W. K. Rhim, Y. S. Lee, H. Youn, J. K. Chung, M. C. Lee, D. S. Lee, K. W. Kang and J. M. Nam, Small, 2011, 7, 2052–2060 CrossRef CAS PubMed.
  259. R. Huang, S. Harmsen, J. M. Samii, H. Karabeber, K. L. Pitter, E. C. Holland and M. F. Kircher, Theranostics, 2016, 6, 1075–1084 CrossRef CAS PubMed.
  260. H. Hong, J. Shi, Y. A. Yang, Y. Zhang, J. W. Engle, R. J. Nickles, X. D. Wang and W. B. Cai, Nano Lett., 2011, 11, 3744–3750 CrossRef CAS PubMed.
  261. M. Boucher, F. Geffroy, S. Preveral, L. Bellanger, E. Selingue, G. Adryanczyk-Perrier, M. Pean, C. T. Lefevre, D. Pignol, N. Ginet and S. Meriaux, Biomaterials, 2017, 121, 167–178 CrossRef CAS PubMed.
  262. J. Xie, K. Chen, H. Y. Lee, C. J. Xu, A. R. Hsu, S. Peng, X. Y. Chen and S. H. Sun, J. Am. Chem. Soc., 2008, 130, 7542–7543 CrossRef CAS PubMed.
  263. Z. Liu, W. B. Cai, L. N. He, N. Nakayama, K. Chen, X. M. Sun, X. Y. Chen and H. J. Dai, Nat. Nanotechnol., 2007, 2, 47–52 CrossRef CAS PubMed.
  264. W. Cai, D. W. Shin, K. Chen, O. Gheysens, Q. Cao, S. X. Wang, S. S. Gambhir and X. Chen, Nano Lett., 2006, 6, 669–676 CrossRef CAS PubMed.
  265. D. Deng, L. Qu, J. Zhang, Y. Ma and Y. Gu, ACS Appl. Mater. Interfaces, 2013, 5, 10858–10865 CAS.
  266. J. H. Gao, K. Chen, R. G. Xie, J. Xie, Y. J. Yan, Z. Cheng, X. G. Peng and X. Y. Chen, Bioconjugate Chem., 2010, 21, 604–609 CrossRef CAS PubMed.
  267. B. R. Smith, Z. Cheng, A. De, A. L. Koh, R. Sinclair and S. S. Gambhir, Nano Lett., 2008, 8, 2599–2606 CrossRef CAS PubMed.
  268. A. Abdukayum, J. T. Chen, Q. Zhao and X. P. Yan, J. Am. Chem. Soc., 2013, 135, 14125–14133 CrossRef CAS PubMed.
  269. L. Q. Xiong, A. J. Shuhendler and J. H. Rao, Nat. Commun., 2012, 3, 1193 CrossRef PubMed.
  270. A. De La Zerda, C. Zavaleta, S. Keren, S. Vaithilingam, S. Bodapati, Z. Liu, J. Levi, B. R. Smith, T. J. Ma, O. Oralkan, Z. Cheng, X. Y. Chen, H. J. Dai, B. T. Khuri-Yakub and S. S. Gambhir, Nat. Nanotechnol., 2008, 3, 557–562 CrossRef CAS PubMed.
  271. A. de la Zerda, Z. Liu, S. Bodapati, R. Teed, S. Vaithilingam, B. T. Khuri-Yakub, X. Chen, H. Dai and S. S. Gambhir, Nano Lett., 2010, 10, 2168–2172 CrossRef CAS PubMed.
  272. V. Ntziachristos and D. Razansky, Chem. Rev., 2010, 110, 2783–2794 CrossRef CAS PubMed.
  273. H. D. Lu, B. K. Wilson, T. L. Lim, A. Heinmiller and R. K. Prud'homme, ACS Biomater. Sci. Eng., 2017, 3, 443–451 CrossRef CAS.
  274. A. de la Zerda, S. Bodapati, R. Teed, S. Y. May, S. M. Tabakman, Z. Liu, B. T. Khuri-Yakub, X. Chen, H. Dai and S. S. Gambhir, ACS Nano, 2012, 6, 4694–4701 CrossRef CAS PubMed.
  275. K. Cheng, S. R. Kothapalli, H. G. Liu, A. L. Koh, J. V. Jokerst, H. Jiang, M. Yang, J. B. Li, J. Levi, J. C. Wu, S. S. Gambhir and Z. Cheng, J. Am. Chem. Soc., 2014, 136, 3560–3571 CrossRef CAS PubMed.
  276. W. J. M. Mulder, R. Koole, R. J. Brandwijk, G. Storm, P. T. K. Chin, G. J. Strijkers, C. D. Donega, K. Nicolay and A. W. Griffioen, Nano Lett., 2006, 6, 1–6 CrossRef CAS PubMed.
  277. W. J. M. Mulder, K. Castermans, J. R. van Beijnum, M. G. A. O. Egbrink, P. T. K. Chin, Z. A. Fayad, C. W. G. M. Lowik, E. L. Kaijzel, I. Que, G. Storm, G. J. Strijkers, A. W. Griffioen and K. Nicolay, Angiogenesis, 2009, 12, 17–24 CrossRef CAS PubMed.
  278. H. Y. Lee, Z. Li, K. Chen, A. R. Hsu, C. J. Xu, J. Xie, S. H. Sun and X. Y. Chen, J. Nucl. Med., 2008, 49, 1371–1379 CrossRef CAS PubMed.
  279. W. Chen, P. A. Jarzyna, G. A. F. van Tilborg, V. A. Nguyen, D. P. Cormode, A. Klink, A. W. Griffioen, G. J. Randolph, E. A. Fisher, W. J. M. Mulder and Z. A. Fayad, FASEB J., 2010, 24, 1689–1699 CrossRef CAS PubMed.
  280. W. Cai, K. Chen, Z. B. Li, S. S. Gambhir and X. Chen, J. Nucl. Med., 2007, 48, 1862–1870 CrossRef CAS PubMed.
  281. P. M. Harrison and P. Arosio, Biochim. Biophys. Acta, 1996, 1275, 161–203 CrossRef.
  282. M. Uchida, M. L. Flenniken, M. Allen, D. A. Willits, B. E. Crowley, S. Brumfield, A. F. Willis, L. Jackiw, M. Jutila, M. J. Young and T. Douglas, J. Am. Chem. Soc., 2006, 128, 16626–16633 CrossRef CAS PubMed.
  283. T. Kitagawa, H. Kosuge, M. Uchida, Y. Iida, R. L. Dalman, T. Douglas and M. V. McConnell, J. Magn. Reson. Imaging, 2017, 45, 1144–1153 CrossRef PubMed.
  284. X. Lin, J. Xie, G. Niu, F. Zhang, H. Gao, M. Yang, Q. Quan, M. A. Aronova, G. Zhang, S. Lee, R. Leapman and X. Chen, Nano Lett., 2011, 11, 814–819 CrossRef CAS PubMed.
  285. Q. L. Fan, K. Cheng, X. Hu, X. W. Ma, R. P. Zhang, M. Yang, X. M. Lu, L. Xing, W. Huang, S. S. Gambhir and Z. Cheng, J. Am. Chem. Soc., 2014, 136, 15185–15194 CrossRef CAS PubMed.
  286. E. Phillips, O. Penate-Medina, P. B. Zanzonico, R. D. Carvajal, P. Mohan, Y. P. Ye, J. Humm, M. Gonen, H. Kalaigian, H. Schoder, H. W. Strauss, S. M. Larson, U. Wiesner and M. S. Bradbury, Sci. Transl. Med., 2014, 6, 9 Search PubMed.
  287. M. Benezra, O. Penate-Medina, P. B. Zanzonico, D. Schaer, H. Ow, A. Burns, E. DeStanchina, V. Longo, E. Herz, S. Iyer, J. Wolchok, S. M. Larson, U. Wiesner and M. S. Bradbury, J. Clin. Invest., 2011, 121, 2768–2780 CAS.
  288. X. L. Sun, X. L. Huang, X. F. Yan, Y. Wang, J. X. Guo, O. Jacobson, D. B. Liu, L. P. Szajek, W. L. Zhu, G. Niu, D. O. Kiesewetter, S. H. Sun and X. Y. Chen, ACS Nano, 2014, 8, 8438–8446 CrossRef CAS PubMed.
  289. D. S. Wang, B. W. Fei, L. V. Halig, X. L. Qin, Z. L. Hu, H. Xu, Y. A. Wang, Z. J. Chen, S. Kim, D. M. Shin and Z. Chen, ACS Nano, 2014, 8, 6620–6632 CrossRef CAS PubMed.
  290. Y. Y. Li, C. H. Jiang, D. W. Zhang, Y. Wang, X. Y. Ren, K. L. Ai, X. S. Chen and L. H. Lu, Acta Biomater., 2017, 47, 124–134 CrossRef CAS PubMed.
  291. F. Balkwill, Nat. Rev. Cancer, 2004, 4, 540–550 CrossRef CAS PubMed.
  292. A. Bunschoten, T. Buckle, J. Kuil, G. D. Luker, K. E. Luker, O. E. Nieweg and F. W. B. van Leeuwen, Biomaterials, 2012, 33, 867–875 CrossRef CAS PubMed.
  293. B. Pang, Y. F. Zhao, H. Luehmann, X. Yang, L. Detering, M. You, C. Zhang, L. Zhang, Z. Y. Li, Q. S. Ren, Y. J. Liu and Y. N. Xia, ACS Nano, 2016, 10, 3121–3131 CrossRef CAS PubMed.
  294. J. Xu and G. P. Shi, Biochim. Biophys. Acta, 2014, 1842, 2106–2119 CrossRef CAS PubMed.
  295. R. M. Botnar, A. S. Perez, S. Witte, A. J. Wiethoff, J. Laredo, J. Hamilton, W. Quist, E. C. Parsons, A. Vaidya, A. Kolodziej, J. A. Barrett, P. B. Graham, R. M. Weisskoff, W. J. Manning and M. T. Johnstone, Circulation, 2004, 109, 2023–2029 CrossRef CAS PubMed.
  296. J. R. McCarthy, P. Patel, I. Botnaru, P. Haghayeghi, R. Weissleder and F. A. Jaffer, Bioconjugate Chem., 2009, 20, 1251–1255 CrossRef CAS PubMed.
  297. H. Wahyudi, A. A. Reynolds, Y. Li, S. C. Owen and S. M. Yu, J. Controlled Release, 2016, 240, 323–331 CrossRef CAS PubMed.
  298. J. M. Chan, L. Zhang, R. Tong, D. Ghosh, W. Gao, G. Liao, K. P. Yuet, D. Gray, J. W. Rhee, J. Cheng, G. Golomb, P. Libby, R. Langer and O. C. Farokhzad, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 2213–2218 CrossRef CAS PubMed.
  299. T. J. Moyer, H. A. Kassam, E. S. M. Bahnson, C. E. Morgan, F. Tantakitti, T. L. Chew, M. R. Kibbe and S. I. Stupp, Small, 2015, 11, 2750–2755 CrossRef CAS PubMed.
  300. R. L. Siegel, K. D. Miller and A. Jemal, Ca-Cancer J. Clin., 2017, 67, 7–30 CrossRef PubMed.
  301. F. H. Schroder, J. Hugosson, M. J. Roobol, T. L. Tammela, S. Ciatto, V. Nelen, M. Kwiatkowski, M. Lujan, H. Lilja, M. Zappa, L. J. Denis, F. Recker, A. Berenguer, L. Maattanen, C. H. Bangma, G. Aus, A. Villers, X. Rebillard, T. van der Kwast, B. G. Blijenberg, S. M. Moss, H. J. de Koning, A. Auvinen and E. Investigators, N. Engl. J. Med., 2009, 360, 1320–1328 CrossRef PubMed.
  302. R. C. Mease, C. A. Foss and M. G. Pomper, Curr. Top. Med. Chem., 2013, 13, 951–962 CrossRef CAS PubMed.
  303. S. M. Dhanasekaran, T. R. Barrette, D. Ghosh, R. Shah, S. Varambally, K. Kurachi, K. J. Pienta, M. A. Rubin and A. M. Chinnaiyan, Nature, 2001, 412, 822–826 CrossRef CAS PubMed.
  304. B. A. Vesely, A. A. Alli, S. J. Song, W. R. Gower, Jr., J. Sanchez-Ramos and D. L. Vesely, Eur. J. Clin. Invest., 2005, 35, 700–710 CrossRef CAS PubMed.
  305. K. A. Kelly, S. R. Setlur, R. Ross, R. Anbazhagan, P. Waterman, M. A. Rubin and R. Weissleder, Cancer Res., 2008, 68, 2286–2291 CrossRef CAS PubMed.
  306. E. D. Pressly, R. A. Pierce, L. A. Connal, C. J. Hawker and Y. Liu, Bioconjugate Chem., 2013, 24, 196–204 CrossRef CAS PubMed.
  307. N. Chanda, R. Shukla, K. V. Katti and R. Kannan, Nano Lett., 2009, 9, 1798–1805 CrossRef CAS PubMed.
  308. N. Chanda, V. Kattumuri, R. Shukla, A. Zambre, K. Katti, A. Upendran, R. R. Kulkarni, P. Kan, G. M. Fent, S. W. Casteel, C. J. Smith, E. Boote, J. D. Robertson, C. Cutler, J. R. Lever, K. V. Katti and R. Kannan, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 8760–8765 CrossRef CAS PubMed.
  309. N. F. Steinmetz, A. L. Ablack, J. L. Hickey, J. Ablack, B. Manocha, J. S. Mymryk, L. G. Luyt and J. D. Lewis, Small, 2011, 7, 1664–1672 CrossRef CAS PubMed.
  310. K. A. Kelly, P. Waterman and R. Weissleder, Neoplasia, 2006, 8, 1011–1018 CrossRef CAS PubMed.
  311. D. Ghosh, Y. Lee, S. Thomas, A. G. Kohli, D. S. Yun, A. M. Belcher and K. A. Kelly, Nat. Nanotechnol., 2012, 7, 677–682 CrossRef CAS PubMed.
  312. S. Acton, A. Rigotti, K. T. Landschulz, S. Z. Xu, H. H. Hobbs and M. Krieger, Science, 1996, 271, 518–520 CAS.
  313. H. Zhang, J. Kusunose, A. Kheirolomoom, J. W. Seo, J. Qi, K. D. Watson, H. A. Lindfors, E. Ruoslahti, J. L. Sutcliffe and K. W. Ferrara, Biomaterials, 2008, 29, 1976–1988 CrossRef CAS PubMed.
  314. W. J. McConathy, M. P. Nair, S. Paranjape, L. Mooberry and A. G. Lacko, Anti-Cancer Drugs, 2008, 19, 183–188 CrossRef CAS PubMed.
  315. Q. Y. Lin, C. S. Jin, H. Huang, L. L. Ding, Z. H. Zhang, J. Chen and G. Zheng, Small, 2014, 10, 3072–3082 CrossRef CAS PubMed.
  316. L. Cui, Q. Lin, C. S. Jin, W. Jiang, H. Huang, L. Ding, N. Muhanna, J. C. Irish, F. Wang, J. Chen and G. Zheng, ACS Nano, 2015, 9, 4484–4495 CrossRef CAS PubMed.
  317. P. Oh, Y. Li, J. Y. Yu, E. Durr, K. M. Krasinska, L. A. Carver, J. E. Testa and J. E. Schnitzer, Nature, 2004, 429, 629–635 CrossRef CAS PubMed.
  318. S. Nath and P. Mukherjee, Trends Mol. Med., 2014, 20, 332–342 CrossRef CAS PubMed.
  319. A. Moore, Z. Medarova, A. Potthast and G. P. Dai, Cancer Res., 2004, 64, 1821–1827 CrossRef CAS PubMed.
  320. M. Kumar, M. Yigit, G. P. Dai, A. Moore and Z. Medarova, Cancer Res., 2010, 70, 7553–7561 CrossRef CAS PubMed.
  321. V. Fogal, L. Zhang, S. Krajewski and E. Ruoslahti, Cancer Res., 2008, 68, 7210–7218 CrossRef CAS PubMed.
  322. J. M. Kinsella, R. E. Jimenez, P. P. Karmali, A. M. Rush, V. R. Kotamraju, N. C. Gianneschi, E. Ruoslahti, D. Stupack and M. J. Sailor, Angew. Chem., Int. Ed., 2011, 50, 12308–12311 CrossRef CAS PubMed.
  323. X. Montet, R. Weissleder and L. Josephson, Bioconjugate Chem., 2006, 17, 905–911 CrossRef CAS PubMed.
  324. R. Zhang, C. Y. Xiong, M. Huang, M. Zhou, Q. Huang, X. X. Wen, D. Liang and C. Li, Biomaterials, 2011, 32, 5872–5879 CrossRef CAS PubMed.
  325. C. Kim, E. C. Cho, J. Chen, K. H. Song, L. Au, C. Favazza, Q. Zhang, C. M. Cobley, F. Gao, Y. Xia and L. V. Wang, ACS Nano, 2010, 4, 4559–4564 CrossRef CAS PubMed.
  326. O. Veiseh, C. Sun, J. Gunn, N. Kohler, P. Gabikian, D. Lee, N. Bhattarai, R. Ellenbogen, R. Sze, A. Hallahan, J. Olson and M. Zhang, Nano Lett., 2005, 5, 1003–1008 CrossRef CAS PubMed.
  327. A. Moore, J. Grimm, B. Y. Han and P. Santamaria, Diabetes, 2004, 53, 1459–1466 CrossRef CAS PubMed.
  328. M. Nahrendorf, F. A. Jaffer, K. A. Kelly, D. E. Sosnovik, E. Aikawa, P. Libby and R. Weissleder, Circulation, 2006, 114, 1504–1511 CrossRef CAS PubMed.
  329. R. Weissleder, K. Kelly, E. Y. Sun, T. Shtatland and L. Josephson, Nat. Biotechnol., 2005, 23, 1418–1423 CrossRef CAS PubMed.
  330. M. H. Jo, B. A. Ali, A. A. Al-Khedhairy, C. H. Lee, B. Kim, S. Haam, Y. M. Huh, H. Y. Ko and S. Kim, Biomaterials, 2012, 33, 6456–6467 CrossRef CAS PubMed.
  331. E. Kluza, I. Jacobs, S. Hectors, K. H. Mayo, A. W. Griffioen, G. J. Strijkers and K. Nicolay, J. Controlled Release, 2012, 158, 207–214 CrossRef CAS PubMed.
  332. B. A. Tannous, J. Grimm, K. F. Perry, J. W. Chen, R. Weissleder and X. O. Breakefield, Nat. Methods, 2006, 3, 391–396 CrossRef CAS PubMed.
  333. I. Chamma, O. Rossier, G. Giannone, O. Thoumine and M. Sainlos, Nat. Protoc., 2017, 12, 748–763 CrossRef CAS PubMed.
  334. H. Wang, R. Wang, K. Cai, H. He, Y. Liu, J. Yen, Z. Wang, M. Xu, Y. Sun, X. Zhou, Q. Yin, L. Tang, I. T. Dobrucki, L. W. Dobrucki, E. J. Chaney, S. A. Boppart, T. M. Fan, S. Lezmi, X. Chen, L. Yin and J. Cheng, Nat. Chem. Biol., 2017, 13, 415–424 CrossRef CAS PubMed.
  335. J. D. Schonhoft, C. Monteiro, L. Plate, Y. S. Eisele, J. M. Kelly, D. Boland, C. G. Parker, B. F. Cravatt, S. Teruya, S. Helmke, M. Maurer, J. Berk, Y. Sekijima, M. Novais, T. Coelho, E. T. Powers and J. W. Kelly, Sci. Transl. Med., 2017, 9, eaam7621 CrossRef PubMed.
  336. D. Simberg, T. Duza, J. H. Park, M. Essler, J. Pilch, L. Zhang, A. M. Derfus, M. Yang, R. M. Hoffman, S. Bhatia, M. J. Sailor and E. Ruoslahti, Proc. Natl. Acad. Sci. U. S. A., 2007, 104, 932–936 CrossRef PubMed.
  337. J. H. Park, G. von Maltzahn, L. L. Zhang, A. M. Derfus, D. Simberg, T. J. Harris, E. Ruoslahti, S. N. Bhatia and M. J. Sailor, Small, 2009, 5, 694–700 CrossRef CAS PubMed.
  338. E. J. Chung, Y. Cheng, R. Morshed, K. Nord, Y. Han, M. L. Wegscheid, B. Auffinger, D. A. Wainwright, M. S. Lesniak and M. V. Tirrell, Biomaterials, 2014, 35, 1249–1256 CrossRef CAS PubMed.
  339. X. Zhou, P. Cao, Y. Zhu, W. Lu, N. Gu and C. Mao, Nat. Mater., 2015, 14, 1058–1064 CrossRef CAS PubMed.
  340. A. Razgulin, N. Ma and J. H. Rao, Chem. Soc. Rev., 2011, 40, 4186–4216 RSC.
  341. C. F. Anderson and H. Cui, Ind. Eng. Chem. Res., 2017, 56, 5761–5777 CrossRef CAS PubMed.
  342. R. de la Rica, D. Aili and M. M. Stevens, Adv. Drug Delivery Rev., 2012, 64, 967–978 CrossRef CAS PubMed.
  343. P. Zhang, A. G. Cheetham, L. L. Lock, Y. Li and H. Cui, Curr. Opin. Biotechnol., 2015, 34, 171–179 CrossRef CAS PubMed.
  344. C. D. Walkey and W. C. Chan, Chem. Soc. Rev., 2012, 41, 2780–2799 RSC.
  345. A. L. Nivorozhkin, A. F. Kolodziej, P. Caravan, M. T. Greenfield, R. B. Lauffer and T. J. McMurry, Angew. Chem., Int. Ed., 2001, 40, 2903–2906 CrossRef CAS PubMed.
  346. C. Gialeli, A. D. Theocharis and N. K. Karamanos, FEBS J., 2011, 278, 16–27 CrossRef CAS PubMed.
  347. S. Temme, C. Grapentin, C. Quast, C. Jacoby, M. Grandoch, Z. P. Ding, C. Owenier, F. Mayenfels, J. W. Fischer, R. Schubert, J. Schrader and U. Flogel, Circulation, 2015, 131, 1405–1414 CrossRef CAS PubMed.
  348. H. S. Choi, W. Liu, F. Liu, K. Nasr, P. Misra, M. G. Bawendi and J. V. Frangioni, Nat. Nanotechnol., 2010, 5, 42–47 CrossRef CAS PubMed.
  349. T. W. Liu, J. Chen and G. Zheng, Amino Acids, 2011, 41, 1123–1134 CrossRef CAS PubMed.
  350. S. Lee, J. Xie and X. Y. Chen, Biochemistry, 2010, 49, 1364–1376 CrossRef CAS PubMed.
  351. S. Lee, J. H. Ryu, K. Park, A. Lee, S. Y. Lee, I. C. Youn, C. H. Ahn, S. M. Yoon, S. J. Myung, D. H. Moon, X. Chen, K. Choi, I. C. Kwon and K. Kim, Nano Lett., 2009, 9, 4412–4416 CrossRef CAS PubMed.
  352. I. C. Sun, D. K. Eun, H. Koo, C. Y. Ko, H. S. Kim, D. K. Yi, K. Choi, I. C. Kwon, K. Kim and C. H. Ahn, Angew. Chem., Int. Ed., 2011, 50, 9348–9351 CrossRef CAS PubMed.
  353. J. J. Hu, L. H. Liu, Z. Y. Li, R. X. Zhuo and X. Z. Zhang, J. Mater. Chem. B, 2016, 4, 1932–1940 RSC.
  354. P. A. Andreasen, L. Kjoller, L. Christensen and M. J. Duffy, Int. J. Cancer, 1997, 72, 1–22 CrossRef CAS PubMed.
  355. C. J. Mu, D. A. Lavan, R. S. Langer and B. R. Zetter, ACS Nano, 2010, 4, 1511–1520 CrossRef CAS PubMed.
  356. K. Kim, M. Lee, H. Park, J. H. Kim, S. Kim, H. Chung, K. Choi, I. S. Kim, B. L. Seong and I. C. Kwon, J. Am. Chem. Soc., 2006, 128, 3490–3491 CrossRef CAS PubMed.
  357. L. Zhu, X. Huang, K. Y. Choi, Y. Ma, F. Zhang, G. Liu, S. Lee and X. Chen, J. Controlled Release, 2012, 163, 55–62 CrossRef CAS PubMed.
  358. Y. Z. Min, J. M. Li, F. Liu, E. K. L. Yeow and B. G. Xing, Angew. Chem., Int. Ed., 2014, 53, 1012–1016 CrossRef CAS PubMed.
  359. X. Chen, D. Lee, S. Yu, G. Kim, S. Lee, Y. Cho, H. Jeong, K. T. Nam and J. Yoon, Biomaterials, 2017, 122, 130–140 CrossRef CAS PubMed.
  360. A. D. Warren, G. A. Kwong, D. K. Wood, K. Y. Lin and S. N. Bhatia, Proc. Natl. Acad. Sci. U. S. A., 2014, 111, 3671–3676 CrossRef CAS PubMed.
  361. B. Jang and Y. Choi, Theranostics, 2012, 2, 190–197 CrossRef CAS PubMed.
  362. H. B. Wang, Q. Zhang, X. Chu, T. T. Chen, J. Ge and R. Q. Yu, Angew. Chem., Int. Ed., 2011, 50, 7065–7069 CrossRef CAS PubMed.
  363. Z. Tao, Z. Tao, W. Wei, L. Zhi, W. Dan, W. Li, J. Guo, X. He and M. Nan, ACS Appl. Mater. Interfaces, 2015, 7, 11849 Search PubMed.
  364. S. Ropero and M. Esteller, Mol. Oncol., 2007, 1, 19–25 CrossRef CAS PubMed.
  365. A. Caretta and C. Mucignat-Caretta, Cancers, 2011, 3, 913–926 CrossRef CAS PubMed.
  366. Q. Wen, Y. Gu, L. J. Tang, R. Q. Yu and J. H. Jiang, Anal. Chem., 2013, 85, 11681–11685 CrossRef CAS PubMed.
  367. J. B. Birks, Photophysics of Aromatic Molecules, London, 1970 Search PubMed.
  368. J. Mei, Y. N. Hong, J. W. Y. Lam, A. J. Qin, Y. H. Tang and B. Z. Tang, Adv. Mater., 2014, 26, 5429–5479 CrossRef CAS PubMed.
  369. M. Nahrendorf, P. Waterman, G. Thurber, K. Groves, M. Rajopadhye, P. Panizzi, B. Marinelli, E. Aikawa, M. J. Pittet, F. K. Swirski and R. Weissleder, Arterioscler., Thromb., Vasc. Biol., 2009, 29, 1444–1451 CrossRef CAS PubMed.
  370. Y. Choi, R. Weissleder and C. H. Tung, Cancer Res., 2006, 66, 7225–7229 CrossRef CAS PubMed.
  371. L. L. Lock, A. G. Cheetham, P. C. Zhang and H. G. Cui, ACS Nano, 2013, 7, 4924–4932 CrossRef CAS PubMed.
  372. L. L. Lock, C. D. Reyes, P. C. Zhang and H. G. Cui, J. Am. Chem. Soc., 2016, 138, 3533–3540 CrossRef CAS PubMed.
  373. C. H. Ren, H. M. Wang, D. Mao, X. L. Zhang, Q. Q. Fengzhao, Y. Shi, D. Ding, D. L. Kong, L. Wang and Z. M. Yang, Angew. Chem., Int. Ed., 2015, 54, 4823–4827 CrossRef CAS PubMed.
  374. P. C. van Zijl and N. N. Yadav, Magn. Reson. Med., 2011, 65, 927–948 CrossRef CAS PubMed.
  375. G. Liu, X. Song, K. W. Chan and M. T. McMahon, NMR Biomed., 2013, 26, 810–828 CrossRef CAS PubMed.
  376. M. T. McMahon, A. A. Gilad, M. A. DeLiso, S. M. Berman, J. W. Bulte and P. C. van Zijl, Magn. Reson. Med., 2008, 60, 803–812 CrossRef CAS PubMed.
  377. J. Y. Zhou, E. Tryggestad, Z. B. Wen, B. Lal, T. T. Zhou, R. Grossman, S. L. Wang, K. Yan, D. X. Fu, E. Ford, B. Tyler, J. Blakeley, J. Laterra and P. C. M. van Zijl, Nat. Med., 2011, 17, 130–134 CrossRef CAS PubMed.
  378. L. L. Lock, Y. G. Lo, X. P. Mao, H. W. Chen, V. Staedtke, R. Y. Bai, W. Ma, R. Lin, Y. Li, G. S. Liu and H. G. Cui, ACS Nano, 2017, 11, 797–805 CrossRef CAS PubMed.
  379. S. Liu, P. Zhang, S. R. Banerjee, J. Xu, M. G. Pomper and H. Cui, Nanoscale, 2015, 7, 9462–9466 RSC.
  380. A. Ghosh, M. Haverick, K. Stump, X. Y. Yang, M. F. Tweedle and J. E. Goldberger, J. Am. Chem. Soc., 2012, 134, 3647–3650 CrossRef CAS PubMed.
  381. A. T. Preslar, G. Parigi, M. T. McClendon, S. S. Sefick, T. J. Moyer, C. R. Haney, E. A. Waters, K. W. MacRenaris, C. Luchinat, S. I. Stupp and T. J. Meade, ACS Nano, 2014, 8, 7325–7332 CrossRef CAS PubMed.
  382. H. Wang, M. Han, W. Whetsell, Jr., J. Wang, J. Rich, D. Hallahan and Z. Han, Oncogene, 2014, 33, 1558–1569 CrossRef CAS PubMed.
  383. H. Wang, J. Liu, A. Han, N. Xiao, Z. Xue, G. Wang, J. Long, D. Kong, B. Liu, Z. Yang and D. Ding, ACS Nano, 2014, 8, 1475–1484 CrossRef CAS PubMed.
  384. C. H. Yang, C. H. Ren, J. Zhou, J. J. Liu, Y. M. Zhang, F. Huang, D. Ding, B. Xu and J. Liu, Angew. Chem., Int. Ed., 2017, 56, 2356–2360 CrossRef CAS PubMed.
  385. S. R. Rao, A. E. Snaith, D. Marino, X. Cheng, S. T. Lwin, I. R. Orriss, F. C. Hamdy and C. M. Edwards, Br. J. Cancer, 2017, 116, 227–236 CrossRef CAS PubMed.
  386. Y. Gao, J. F. Shi, D. Yuan and B. Xu, Nat. Commun., 2012, 3, 1033 CrossRef PubMed.
  387. T. Hiratsuka and T. Kato, J. Biol. Chem., 1987, 262, 6318–6322 CAS.
  388. Y. Gao, Y. Kuang, X. Du, J. Zhou, P. Chandran, F. Horkay and B. Xu, Langmuir, 2013, 29, 15191–15200 CrossRef CAS PubMed.
  389. J. Zhou, X. Du, C. Berciu, H. He, J. Shi, D. Nicastro and B. Xu, Chem, 2016, 1, 246–263 CAS.
  390. J. W. Chen, M. Q. Sans, A. Bogdanov and R. Weissleder, Radiology, 2006, 240, 473–481 CrossRef PubMed.
  391. B. A. Smith and B. D. Smith, Bioconjugate Chem., 2012, 23, 1989–2006 CrossRef CAS PubMed.
  392. H. B. Shi, R. T. K. Kwok, J. Z. Liu, B. G. Xing, B. Z. Tang and B. Liu, J. Am. Chem. Soc., 2012, 134, 17972–17981 CrossRef CAS PubMed.
  393. A. Han, H. Wang, R. T. Kwok, S. Ji, J. Li, D. Kong, B. Z. Tang, B. Liu, Z. Yang and D. Ding, Anal. Chem., 2016, 88, 3872–3878 CrossRef CAS PubMed.
  394. Y. Yuan, R. Zhang, X. Cheng, S. Xu and B. Liu, Chem. Sci., 2016, 7, 4245–4250 RSC.
  395. D. J. Ye, A. J. Shuhendler, L. N. Cui, L. Tong, S. S. Tee, G. Tikhomirov, D. W. Felsher and J. H. Rao, Nat. Chem., 2014, 6, 519–526 CrossRef CAS PubMed.
  396. D. Zhang, G. B. Qi, Y. X. Zhao, S. L. Qiao, C. Yang and H. Wang, Adv. Mater., 2015, 27, 6125–6130 CrossRef CAS PubMed.
  397. M. Zhao, L. Josephson, Y. Tang and R. Weissleder, Angew. Chem., Int. Ed., 2003, 42, 1375–1378 CrossRef CAS PubMed.
  398. J. M. Perez, L. Josephson, T. O'Loughlin, D. Hogemann and R. Weissleder, Nat. Biotechnol., 2002, 20, 816–820 CrossRef CAS PubMed.
  399. G. R. Monteith, D. McAndrew, H. M. Faddy and S. J. Roberts-Thomson, Nat. Rev. Cancer, 2007, 7, 519–530 CrossRef CAS PubMed.
  400. T. Atanasijevic, M. Shusteff, P. Fam and A. Jasanoff, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 14707–14712 CrossRef CAS PubMed.
  401. B. Jastrzebska, R. Lebel, H. Therriault, J. O. McIntyre, E. Escher, B. Guerin, B. Paquette, W. A. Neugebauer and M. Lepage, J. Med. Chem., 2009, 52, 1576–1581 CrossRef CAS PubMed.
  402. J. Gallo, N. Kamaly, I. Lavdas, E. Stevens, Q. D. Nguyen, M. Wylezinska-Arridge, E. O. Aboagye and N. J. Long, Angew. Chem., Int. Ed., 2014, 53, 9550–9554 CrossRef CAS PubMed.
  403. Y. W. Jun, S. Sheikholeslami, D. R. Hostetter, C. Tajon, C. S. Craik and A. P. Alivisatos, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 17735–17740 CrossRef CAS PubMed.
  404. Y. Cai, Y. Shi, H. Wang, J. Wang, D. Ding, L. Wang and Z. Yang, Anal. Chem., 2014, 86, 2193–2199 CrossRef CAS PubMed.
  405. A. Louie, Chem. Rev., 2010, 110, 3146–3195 CrossRef CAS PubMed.
  406. H. Kobayashi, M. R. Longmire, M. Ogawa and P. L. Choyke, Chem. Soc. Rev., 2011, 40, 4626–4648 RSC.
  407. S. Y. Shaw, E. C. Westly, M. J. Pittet, A. Subramanian, S. L. Schreiber and R. Weissleder, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 7387–7392 CrossRef CAS PubMed.
  408. C. L. Zavaleta, B. R. Smith, I. Walton, W. Doering, G. Davis, B. Shojaei, M. J. Natan and S. S. Gambhir, Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 13511–13516 CrossRef CAS PubMed.
  409. J. P. B. O'Connor, C. J. Rose, J. C. Waterton, R. A. D. Carano, G. J. M. Parker and A. Jackson, Clin. Cancer Res., 2015, 21, 249–257 CrossRef PubMed.
  410. K. Schwamborn and R. M. Caprioli, Nat. Rev. Cancer, 2010, 10, 639–646 CrossRef CAS PubMed.
  411. M. A. Miller and R. Weissleder, Nat. Rev. Cancer, 2017, 17, 399–414 CrossRef CAS PubMed.
  412. J. P. Thiery, H. Acloque, R. Y. J. Huang and M. A. Nieto, Cell, 2009, 139, 871–890 CrossRef CAS PubMed.
  413. E. A. Musgrove, C. E. Caldon, J. Barraclough, A. Stone and R. L. Sutherland, Nat. Rev. Cancer, 2011, 11, 558–572 CrossRef CAS PubMed.
  414. M. B. Kok, S. Hak, W. J. M. Mulder, D. W. J. van der Schaft, G. J. Strijkers and K. Nicolay, Magn. Reson. Med., 2009, 61, 1022–1032 CrossRef CAS PubMed.
  415. J. V. Jokerst and S. S. Gambhir, Acc. Chem. Res., 2011, 44, 1050–1060 CrossRef CAS PubMed.
  416. S. Froidevaux and A. N. Eberle, Biopolymers, 2002, 66, 161–183 CrossRef CAS PubMed.

Footnote

These authors contributed equally to this work.

This journal is © The Royal Society of Chemistry 2018