Bioorthogonal chemistry amplifies nanoparticle binding and enhances the sensitivity of cell detection

doi:10.1038/nnano.2010.148

Nat Nanotechnol. Author manuscript; available in PMC 2010 September 7.

Published in final edited form as:

Nat Nanotechnol. 2010 September; 5(9): 660–665.

Published online 2010 August 1. doi: 10.1038/nnano.2010.148

PMCID: PMC2934903

NIHMSID: NIHMS228830

Bioorthogonal chemistry amplifies nanoparticle binding and enhances the sensitivity of cell detection

Jered B. Haun,¹ Neal K. Devaraj,¹ Scott A. Hilderbrand,¹ Hakho Lee,¹ and Ralph Weissleder^1,²

¹ Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, Massachusetts 02114, USA

² Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts 02115, USA

Correspondence and requests for materials should be addressed to R.W., Email: rweissleder/at/mgh.harvard.edu

See other articles in PMC that cite the published article.

Abstract

Nanoparticles have emerged as key materials for biomedical applications because of their unique and tunable physical properties, multivalent targeting capability, and high cargo capacity¹^,². Motivated by these properties and by current clinical needs, numerous diagnostic³^–¹⁰ and therapeutic¹¹^–¹³ nanomaterials have recently emerged. Here we describe a novel nanoparticle targeting platform that uses a rapid, catalyst-free cycloaddition as the coupling mechanism. Antibodies against biomarkers of interest were modified with trans-cyclooctene and used as scaffolds to couple tetrazine-modified nanoparticles onto live cells. We show that the technique is fast, chemoselective, adaptable to metal nanomaterials, and scalable for biomedical use. This method also supports amplification of biomarker signals, making it superior to alternative targeting techniques including avidin/biotin.

It is often the case that affinity ligands and bioconjugation strategies must be optimized for each new preparation to maximize the binding properties of the targeted conjugates¹⁴^,¹⁵. To streamline these efforts, there remains a critical need to develop advanced conjugation techniques that simplify operation, as well as extend detection limits by improving the efficiency of targeting and amplifying marker signals. Moreover, successful translation into clinical settings will require simple scale-up to successfully process tens or hundreds of samples. Here, we explore a modular and broadly applicable targeting platform based on bioorthogonal chemistry. We were particularly interested in (i) using a biocompatible chemistry with a fast reaction rate, (ii) using reaction partners with very small ‘footprints’ to maximize the number of covalent binding sites, and (iii) developing universal labelling approaches that build upon the vast array of available monoclonal antibodies. We reasoned that such a strategy would be valuable in further improving nanoparticle targeting.

We and others have recently described a covalent, bioorthogonal reaction between a 1,2,4,5-tetrazine (Tz) and a trans-cyclooctene (TCO) and used it for small molecule labelling¹⁶^–¹⁸. The [4 + 2] cycloaddition is fast, chemoselective, does not require a catalyst, and proceeds in serum. We hypothesized that this chemistry could be adapted to targeting nanoparticle sensors in different configurations to improve binding efficiency and detection sensitivity. We have named this technique ‘bioorthogonal nanoparticle detection’ (BOND).

Figure 1 summarizes the chemistry, comparative molecular species dimensions, and experimental approaches of the different BOND strategies. We used magneto-fluorescent nanoparticles (MFNPs) to assess the performance of BOND using established fluorescence techniques and a novel miniaturized diagnostic magnetic resonance detector system developed for clinical point-of-care use¹⁹^,²⁰. To explore BOND in a biologically relevant system, we chose monoclonal antibodies as the scaffold for nanoparticle attachment due to their large size and the availability of numerous primary amine functional groups. For example, the monoclonal antibody trastuzumab, which is used clinically to treat breast cancers expressing HER2²¹, has approximately 90 lysine residues that could be converted into nanoparticle reaction sites (Fig. 1b). We then comparatively tested BOND for targeting extracellular receptors on cancer cells using two assay types. In one setting, we directly coupled MFNPs to antibodies before cell exposure (BOND-1). In another setting, we used a two-step strategy (BOND-2) in which TCO-modified antibodies were used for primary target binding followed by covalent reaction with Tz–MFNP (Fig. 1c).

Figure 1

Overview of BOND

We first determined the extent to which TCO modification of antibodies promotes nanoparticle binding under the BOND-2 format. Figure 2 summarizes the results for three antibodies that were separately used to target HER2, EpCAM (CD326) and EGFR on cancer cell lines. For each antibody, TCO loading was modulated using various concentrations of an amine-reactive TCO. This resulted in a range of TCO valencies between approximately one and 30 per antibody (see Supplementary Methods and Figs S1–S3). Following sequential incubations with TCO–antibody and Tz–MFNP, we found that nanoparticle binding increased with successive TCO loading until saturating around at 20 TCO per antibody for HER2. TCO modifications had little effect on anti-HER2 affinity until loading levels reached 30 TCO per antibody (Supplementary Fig. S4). Furthermore, TCO modification did not affect the level of non-specific binding of MFNPs to control NIH/3T3 fibroblasts (Supplementary Fig. S4).

Figure 2

Effect of TCO loading on nanoparticle binding using BOND-2

To further confirm the above results and to determine the spatial distribution of targeted nanoparticles, we performed confocal microscopy on live cancer cells (Fig. 2b). In these experiments, the cells were similarly incubated with TCO–antibody followed by Tz–MFNP. In all cases, a strong fluorescence signal was detected at the cell membranes; this was not observed for a TCO-modified control antibody. These data establish that the Tz/TCO cyclo-addition used for BOND-2 is sufficiently rapid and chemoselective to effectively target nanoparticles to live cells.

We next determined the comparative performance of BOND-2 relative to direct labelling with MFNP immuno-conjugates produced via maleimide/thiol chemistry and BOND-1 (Fig. 3a). For BOND-2, the optimized preparations determined in the above experiments were used. We found that BOND-2 consistently yielded higher nanoparticle binding to cells compared to either of the immuno-conjugates, higher by more than a factor of 15 for HER2 and approaching 10 for the other cases. The BOND-1 and maleimide/thiol immuno-conjugates bound to a similar level, but tended to vary across the different markers depending on antibody affinity (5 nM for cetuximab, 9 nM for trastuzumab²²^,²³). These observations suggest that TCO-decorated antibodies can serve as scaffolds for subsequent nanoparticle attachment. This strategy effectively amplifies the achievable signal, the extent of which increases with the number of available TCO reaction sites.

Figure 3

Comparison of different nanoparticle targeting strategies

To determine whether the amplification was unique to the bioorthogonal chemistry applied or simply a consequence of using a two-step labelling strategy, we tested avidin/biotin as the secondary coupling mechanism. We used avidin/biotin for this purpose because it is the gold standard for biological, non-covalent binding interactions²⁴. However, it is known that the large molecular size of avidin (~6 nm, ~67 kDa) and its potential for eliciting immune responses in vivo limit its use for many clinical applications²⁵. We biotinylated antibodies using similar procedures to achieve a range of loadings (see Supplementary Methods and Figs S1–S3). Although nanoparticle binding using avidin/biotin exceeded the direct conjugates and could be improved by increasing biotin valency, the overall signal remained considerably lower compared to BOND-2, despite higher biotin loadings (Fig. 3b). We believe that this finding can be attributed to the large size of avidin, which could potentially mask adjacent biotin sites. Second, biotin must associate within a deep cleft inside the avidin protein, which could physically or spatially constrain certain binding configurations. Conversely, Tz is a small molecule and can interact with TCO on the surface of the antibody without physical limitation or encroachment on neighbouring TCO sites. Finally, the Tz valency (84) was considerably higher than the avidin valency (8) on the nanoparticle due to its smaller size, increasing binding probability. The above arguments are supported by the fact that nanoparticle binding saturated at a lower TCO valency (~20) in comparison to biotin (>30). Thus, relatively higher quantities and more diversely spaced reaction sites are required to further increase nanoparticle binding for avidin/biotin. The net result is that the small-molecule bioorthogonal chemistry allows nanoparticles to pack more densely onto the antibody scaffolds, yielding greater signal amplification. It should be noted that the MFNP, although larger than avidin (~28 nm versus ~6 nm hydrodynamic diameter), is not limiting because the bulk of the size can be attributed to a dextran matrix, which can promote binding at a longer range through the presentation of extended reactive linkages. Table 1 summarizes the results from the various labelling techniques used in this study.

Table 1

Comparison of nanoparticle targeting strategies

The ability to rapidly profile cancer cells in peripheral blood²⁶^,²⁷ or fine needle aspirates²⁰ has important clinical applications for early cancer detection and in devising treatment decisions²⁸. We therefore adapted BOND-2 to molecular profiling of small populations of cancer cells by diagnostic magnetic resonance (Fig. 4). MFNPs were targeted to tumour cells using BOND-2, and the transverse relaxation rate (R₂) was measured for ~1,000 cells using a miniaturized diagnostic magnetic resonance device²⁰. At these scant sample sizes, which are in line with clinical specimens, fluorescent signal detection was difficult. However, parallel magnetic resonance measurements could be performed rapidly and at good signal-to-noise levels (Fig. 4a). As expected, markers signals were near normal levels for benign fibroblasts and leukocytes (with the exception of CD45, naturally expressed in the latter). Tumour cells showed considerable heterogeneity in the expression of the different markers, a finding that correlated well with the actual expression levels that were independently determined by flow cytometry using larger sample sizes (Fig. 4b). (Marker expression levels are listed in Supplementary Table S1.) The sensitivity of magnetic detection, including both diagnostic magnetic resonance and magnetic resonance imaging, using BOND-2 and the other targeting techniques are presented in Supplementary Fig. S5. Collectively, these data demonstrate the feasibility of profiling scant cell populations using the efficient and modular nanoparticle targeting strategy of BOND-2.

Figure 4

Profiling cancer cells using diagnostic magnetic resonance

The bioorthogonal [4 + 2] cycloaddition chemistry between TCO and Tz results in higher nanoparticle binding to mammalian cells compared to other standard techniques. This was achieved because the high valencies and small sizes of the reactants promoted attachment of multiple nanoparticles to each antibody scaffold, amplifying the signal per marker. In contrast, direct nanoparticle immuno-conjugates are limited to at most one nanoparticle per marker, and potentially less than one due to multivalent binding. Because the TCO–antibody is applied in excess before nanoparticle exposure and contains numerous TCO moieties, most marker sites should be occupied by separate antibody scaffolds (Supplementary Fig. S4), and crosslinking of neighbouring antibodies by a nanoparticle consumes an additional TCO rather than an entire marker. We therefore speculate that a portion of the amplification observed for BOND-2 (and avidin/biotin) can be attributed to more efficient antigen recognition. BOND-2 also outperformed a similar two-step targeting strategy using avidin/biotin, most likely as a result of biotin masking or steric constraints that are imposed by the large footprint of avidin. Consequently, the covalent Tz/TCO reaction permits more nanoparticles to bind per antibody scaffold. The increased detection sensitivity resulting from amplification and the modular nature of the BOND-2 technique make it ideally suited for clinically oriented molecular profiling applications. We have demonstrated such an application here with magnetic detection of tumour cells using a miniaturized magnetic resonance detector that was designed for point-of-care clinical use.

We expect that the described BOND platform will have widespread use in diverse nanoparticle targeting applications, including alternative bioorthogonal small-molecule chemistries, affinity molecule scaffolds (proteins, peptides, aptamers, natural products, engineered hybrids) and nanoparticle sensors such as quantum dots, carbon nanotubes, gold nanoshells and polymer matrices/vesicles.

Methods

Preparation of MFNPs

We used crosslinked iron oxide (CLIO) as our model nanoparticle. The synthesis of CLIO bearing 89 primary amine functional groups is described in the Supplementary Methods. Amine-terminated MFNP (amino-MFNP) was created by treating the CLIO with a limiting quantity of an amine-reactive cyanine dye (VivoTag 680, VT680, VisEn Biomedical). This reaction was performed in phosphate buffered saline (PBS) at pH 8.0 for 1 h, followed by purification using gel filtration (Sephadex G-50, GE Healthcare). Approximately 4.7 VT680 molecules were conjugated per MFNP based on absorbance measurements, leaving ~84 available primary amine sites.

Tetrazine modification of MFNPs

Amino–MFNP was modified with 2,5-dioxopyrrolidin-1-yl 5-(4-(1,2,4,5-tetrazin-3-yl)benzylamino)-5-oxopentanoate (Tz-NHS, see Supplementary Methods) to create tetrazine–MNFP (Tz–MFNP). The reaction was performed using 500 equiv. of Tz–NHS relative to amino–MFNP, and proceeded in PBS containing 5% dimethylformamide (DMF) for 3 h at room temperature. Excess Tz–NHS was removed using Sephadex G-50. No primary amine groups were detectable at this point using the N-succinimidyl-3-(2-pyridyldithio)propionate (SPDP) method (see Supplementary Methods), and thus the tetrazine valency was assumed to be 84.

Antibody modifications

Monoclonal antibodies were modified with (E)-cyclooct-4-enyl 2,5-dioxopyrrolidin-1-yl carbonate (TCO-NHS) that was synthesized as previously reported by our group¹⁶. For each case, 0.5 mg of antibody was buffer exchanged into PBS (pH 8.0) using 2 ml Zeba desalting columns (Thermo Fisher). TCO–NHS was then reacted in 10% DMF for 3 h at room temperature. Anti-HER2 antibody (trastuzumab, Genentech) was reacted with 30, 100, 300, 1,000 and 3,000 equiv. of TCO–NHS. Equivalents were 10, 30, 100, 300 and 1,000 for the anti-EpCAM antibody (clone 158206, R&D Systems), and 10, 100, 300 and 1,000 for the anti-EGFR antibody (cetuximab, Imclone Systems). Control mouse IgG₁ (clone MOPC-21, BioLegend), anti-Mucin1 (clone M01102909, Fitzgerald Industries) and anti-CD45 (clone HI30, BioLegend) antibodies were reacted with 1,000 equiv. of TCO–NHS exclusively. Samples were purified using Zeba columns, and concentration was determined by absorbance measurement. Biotinylation was performed for the anti-HER2 and anti-EpCAM antibodies using EZ-Link NHS–biotin (Thermo Fisher) at 10, 100 and 300 equiv. in a similar fashion. TCO and biotin valencies were determined based on changes in molecular weight using MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry (see Supplementary Methods). Functional TCO and biotin loadings were also measured using a Tz-VT680 probe and the HABA assay, respectively (see Supplementary Methods).

Cell labelling and detection

The human cancer cell lines SK-BR-3, HCT 116, A549, NCI-H1650, A431 and SK-OV-3 and the mouse embryonic fibroblast cell line NIH/3T3 were all obtained from ATCC and maintained in DMEM media supplemented with 10% fetal bovine serum (FBS) and 5% penicillin/streptomycin. Before experiments, cells were grown to ~90% confluency, released using 0.05% Tryspin/0.53 mM EDTA, and prepared by washing twice with PBS containing 2% FBS (PBS + ). Cells (250,000/sample) were then labelled with TCO- or biotin- modified monoclonal antibody (10 μg ml⁻¹) in 0.1 ml PBS+ for 10 min at room temperature. Following centrifugation and aspiration of the antibody solution, cells were directly resuspended with Tz–MFNP (0.2–200 nM) or avidin–MFNP (100 nM), incubated for 30 min at room temperature, and washed twice by centrifugation with ice-cold PBS +. Antibodies were omitted for control samples. For direct antibody–MFNP conjugates, only the MFNP binding period (0.2–100 nM) was used. Avidin– and antibody–MFNP conjugates were prepared as described in the Supplementary Methods. MFNP molar concentration was determined based on an estimated 447 kDa molecular weight for CLIO (8,000 Fe atoms per core crystal, 55.85 Da each²⁹). At the conclusion of the labelling procedures, VT680 fluorescence was assessed using an LSRII flow cytometer (Becton Dickinson) and mean fluorescence intensity was determined using FlowJo software. All measurements were performed in triplicate, and the data are presented as the mean ± standard error. For confocal microscopy studies, cells were grown on glass slides with removable chamber wells (Lab-Tek; Thermo Fisher). Cell labelling was performed as described above, and VT680 fluorescence was imaged using a multichannel upright laser-scanning confocal microscope (FV1000; Olympus) with a ×40 water immersion objective lens. Images were acquired with Fluoview software (version 4.3; Olympus) and analysed using ImageJ software (version 1.41; Bethesda MD). For magnetic resonance detection, human tumour cells lines, control NIH/3T3 fibroblasts and fresh human peripheral blood leukocytes were labelled with a TCO–antibody (control; anti-HER2, EpCAM, EGFR, Mucin1 or CD45) and Tz–MFNP as described above. Magnetic resonance measurements were performed using a previously described miniaturized nuclear magnetic resonance device (see Supplementary Methods)²⁰.

Supplementary Material

Suppl

Figure S1: Mass spectrometry to quantitate antibody modifications with TCO or biotin for anti-HER2 antibody. TCO and biotin loadings on the anti-HER2 antibody were determined by MALDI-TOF mass spectrometry based on changes in molecular weight. a, Representative molecular weight/ioniziation (M/z) histograms for the unmodified anti-HER2 antibody, and following reaction with 1000-fold excess of TCO-NHS. The singly ionised peak (z=1) has an average molecular weight of ~148,000 dalton for the unmodified antibody, with a doubly ionised peak (z=2) also detectable at ~75,000 dalton. The approximately 3500 dalton change in molecular weight with addition of the TCO corresponds to ~23 TCO/antibody based on 152.2 dalton mass added. b, Merged data for TCO and biotin modifications to the anti-HER2 antibody. Biotin valencies were determined based on 226.1 dalton mass added. Average molecular weight and data merges were performed using MatLab.

Figure S2: Mass spectrometry to quantitate antibody modifications with TCO or biotin for other antibodies. A similar analysis to Supplementary Fig. S1 was perfomed for the a, TCO and biotin modifications of the anti-EpCAM antibody and b, TCO modification of the anti-EGFR, control, anti-CD45, and anti-Mucin1 antibodies. The anti-EpCAM antibody and -EGFR antibody (at high TCO loadings) did not ionise well, resulting in histograms with significantly more noise.

Figure S3: Correlation of TCO and biotin valencies between the MALDI-MS measurements and functional assays. a, TCO valencies correlated well between mass measurements and functional assays performed using at Tz-fluorophore (VT680) for each of the antibodies tested. However, the functional values were considerably lower than the mass values, presumably because certain TCO sites could not be accessed or due to physical interaction between the large, highly-charged VT680 molecules. b, Similar results were obtained for biotin valencies, this time where the functional metric was avidin binding using the HABA assay. TCO and biotin valencies used throughout the text reflect the MALDI-MS results so that the TCO and biotin species can be compared directly.

Figure S4: Effect of TCO loading on antibody affinity and background nanoparticle adhesion. a, Modification of the anti-HER2 antibody with TCO had negligible effect on binding affinity at low loadings, consistently exhibiting a K_D = ~1–2 nM up to approximately 10 TCO/Ab. Affinity decreased by approximately 3-fold at 23 TCO/Ab (K_D = ~4 nM) and 10-fold at 30 TCO/Ab (K_D = ~10 nM). These results also indicate that antibody binding is saturated at the concentration employed for cell labelling studies (10 μg/mL, or ~67 nM). b, Modification of the anti-HER2 antibody with TCO did not affect the level of MFNP binding to control NIH/3T3 fibroblast cells under the BOND-2 scheme. This result demonstrates that the TCO does not make the antibody more “sticky” to cells, even at high TCO loadings, and further confirms the specificity of the BOND-2 technique. Fluorescence values are comparable to controls for HER2-positive SK-BR-3 cells (see Fig. 2).

Figure S5: Diagnostic magnetic resonance detection of the different nanoparticle targeting strategies. (a) Magnetic resonance detection sensitivity for cancer cells based on HER2. BOND-2 outperformed the other approaches, with a detection threshold of approximately 200 cells in comparison to > 1000 cells for direct immuno-conjugates. (b) Detection thresholds for each technique, which was determined based on the number of cells required to register a 2.5% change in transverse relaxation time (T₂). Note that using more magnetic nanoparticles can significantly improve detection thresholds to near single cell levels⁶.

Figure S6: Magnetic resonance imaging of the different nanoparticle targeting strategies. Magnetic resonance imaging of targeted cells. Cancer (SK-BR-3) and normal (NIH/3T3) cells were labelled using the different targeting strategies and imaged by a magnetic resonance scanner (4.7 T; Bruker Pharmascan). Consistent with fluorescence and diagnostic magnetic resonance measurements, BOND-2 yielded the most pronounced T₂ changes due to higher nanoparticle binding. Prior to imaging, labelled cells (10⁶) were lysed and embedded in agar.

Supplementary Table S1: Biomarker expression levels for the cell lines used for magnetic resonance profiling (Fig. 4). The number of biomarkers per cell was determined by flow cytometry using fluorescent antibody staining and calibration beads (see Supplementary Methods).

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Acknowledgments

The authors gratefully acknowledge N. Sergeyev for synthesizing CLIO and C. Wang for assistance with MALDI-TOF measurements. We especially thank G. Thurber, M. Pittet, F. Swirski and M. Nahrendorf for their many helpful suggestions. We also thank Y. Fisher-Jeffes for reviewing the manuscript. This work was funded in part by NCI grant P50CA86355 and RO1 EB004626.

Footnotes

Author contributions

J.B.H. designed and performed the experiments, analysed the data and wrote the manuscript. N.K.D. and S.A.H. developed and synthesized the bioorthogonal chemistries. H.L. performed the magnetic resonance measurements. R.W. provided overall guidance, designed experiments, reviewed the data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Additional information

The authors declare no competing financial interests. Supplementary information accompanies this paper at www.nature.com/naturenanotechnology. Reprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/.

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