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A novel real-time proximity assay for characterizing multiple receptor interactions on living cells Sina Bondza, Hanna Björkelund, Marika Nestor, Karl Andersson, and Jos Buijs Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b02983 • Publication Date (Web): 21 Nov 2017 Downloaded from http://pubs.acs.org on November 22, 2017

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Analytical Chemistry

A novel real-time proximity assay for characterizing multiple receptor interactions on living cells Sina Bondza1,2*, Hanna Björkelund2, Marika Nestor1, Karl Andersson1,2 and Jos Buijs1,2 1 Department

of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden Ridgeview Instruments AB, Dag Hammarskjölds väg 28, 75237 Uppsala, Sweden * Corresponding author: [email protected]

2

ABSTRACT: Cellular receptor activity is often controlled through complex mechanisms involving interactions with multiple molecules, which can be soluble ligands and/or other cell surface molecules. In this study, we combine a fluorescence based technology for real-time interaction analysis with fluorescence quenching to create a novel time resolved proximity assay to study protein-receptor interactions on living cells. This assay extracts the binding kinetics and affinity for two proteins if they bind in proximity on the cell surface. One application of real-time proximity interaction analysis is to study relative levels of receptor dimerization. The method was primarily evaluated using the HER2 binding antibodies Trastuzumab and Pertuzumab and two EGFR binding antibodies including Cetuximab. Using Cetuximab and Trastuzumab, proximity of EGFR and HER2 was investigated before and after treatment of cells with the tyrosine-kinase inhibitor Gefitinib. Treated cells displayed 50% increased proximity signal, whereas the binding characteristics of the two antibodies were not significantly affected, implying an increase in EGFR-HER2 dimer level. These results demonstrate that real-time proximity interaction analysis enables determination of the interaction rate constants and affinity of two ligands while simultaneously quantifying their relative co-localization on living cells.

hampers dimerization of the human epidermal growth factor receptor 2 (HER2)6. As receptor regulation can involve various factors that influence each other, there are many cases where the exact functional mechanism is not completely elucidated, even for well-known and widely studied receptors such as the epidermal growth factor receptor (EGFR)8. To fully understand the complex mechanisms of receptor regulation and exploit this knowledge in drug-design, methods that enable studying of how the protein of interest interacts with a receptor in combination with dimerization and clustering processes would be highly beneficial. Detailed characterization of receptor binding is commonly achieved by the use of real-time interaction analysis as it allows to study interactions; not only in terms of their affinity or binding strength, but also quantifies their dynamic properties9. While the affinity yields information on the concentration dependent ratio between bound and free molecules at equilibrium, the association rate reflects how quick complexes are formed while the dissociation rate reflects the stability of the interaction.

Cellular receptors are essential for transferring signals from extracellular to intracellular space and thus control the cell’s response to its environment. The crucial role of receptors for an appropriate cellular response becomes apparent when noting that deregulation of receptor signaling can contribute to most of the hallmarks of cancer1. In particular proliferation is receptor driven, and it has been found that deregulation of growth signaling occurs preferably on the receptor level2. Receptor activity is often controlled through complex mechanisms involving ligand binding, receptor dimerization and clustering, as well as conformational changes. Due to their central role in regulating cell signaling, receptors are popular drug targets3. Common approaches in oncology for targeting receptor activity are to prevent the ligand from interacting with the receptor or to block intracellular enzymatic function of the receptor, as it is often done for receptor kinases4,5. Besides, efforts are undertaken to target receptor dimerization6 and clustering7. One successful example is the monoclonal antibody (mAb) Pertuzumab that

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Methods commonly used for real-time interaction analysis are surface plasmon resonance (SPR)10, Quartz-crystal microbalance (QCM)11 and biolayer interferometry (BLI)12. These biophysical techniques are sensitive to subtle changes in interaction characteristics but are more suited for studying interactions with isolated proteins or fixated cells rather than living cells13,14,15. Today, observing the proximity of two or more proteins in a cellular context and time-resolved manner is mainly done with imaging techniques. Various methods utilizing Förster-resonance energy transfer (FRET) to deduct proximity are applied for analyzing dimerization processes16,17,18,19. Fluorescence cross-correlation spectroscopy (FCCS) detects co-localization of molecules, which is used to analyze dimerization processes. Both techniques have also been applied to obtain the affinity of interacting proteins 20,21, however not while simultaneously studying dimerization patterns. Moreover, accurate affinity extraction requires a thorough analysis of the measurement system and is not trivial with either FCCS or FRET-based approaches22,23,24. A system designed for real-time interaction analysis on living cells is provided by LigandTracer®25. Currently, the binding of one protein to a cellular receptor is monitored directly, while the influence of other proteins is inferred from the interaction pattern without directly monitoring the second interaction or its proximity relative to the first interaction26. LigandTracer technology detects protein receptor binding using labeled compounds including those that are fluorescently labeled27. Fluorescence is sensitive to its direct environment and this property is commonly used to study co-localization with for example FRET28,29; a similar process is fluorescence quenching30. In this study, we combined real-time interaction analysis with fluorescence quenching to study antibody-receptor kinetics and proximity simultaneously on living cells. Realtime interaction analysis is performed by evaluating the relative signal change over time. Thus, by having one antibody, labeled with a quencher, that binds in proximity of another antibody, labeled with a fluorophore, both interactions can be quantified on their affinities and interaction rate constants without requiring knowledge on factors that influence the absolute signal such as receptor quantity, the fluorescence quantum yield or quenching efficiency. By analyzing the interaction curve for the fluorescently labeled antibody, also the signal corresponding to target saturation is obtained. In a similar manner, evaluation of the quenched signal over time reveals the fluorescence fraction that is quenched upon target saturation by both antibodies. When experiments are performed with the same labeling batches, this quenched fraction is a relative measure of the proximity of both antibodies. The quenched fraction is affected by both the portion of quencher-labeled antibodies that is in proximity to fluorescently labeled antibodies as well as the actual distance between the two antibodies. As a model system, the binding characteristics and proximity of the two therapeutic mAbs Trastuzumab and Pertuzumab were evaluated. Both antibodies bind to different epitopes on HER231,32, a receptor tyrosine kinase overexpressed in approximately 20% of breast cancers33. A combi-

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nation of the mAbs improves clinical outcome of HER2 positive breast cancer patients34. EGFR is another receptor tyrosine kinase that is frequently overexpressed in a variety of cancers and the FDA-approved mAb Cetuximab has been developed to block EGFR signaling35. Cetuximab was combined with Trastuzumab in order to detect the presence and relative levels of EGFR-HER2 heterodimers, while simultaneously evaluating the binding characteristics of these two antibodies. Materials and Methods Cell culture and seeding SKOV3 cells (HTB-77, ATCC, US) were maintained in RPMI 1640 (Biochrom AG, Germany) and A431 cells (CLR 1555, ATCC) in Ham’s F10 cell culture medium (Biochrom AG); both supplemented with 10% FBS (Merck Life Sciences, Germany) 2 mM L-Glutamine and 100 µg/ml Penicillin-Streptomycin (both Biochrom AG, Germany). For real-time measurements, cells were seeded in a local area of a cell culture dish (Nunc 150350, Thermo Fischer Scientific) as described previously36. For assays with Gefitinib (CAS 184475-35-2, Bioaffin GmbH, Germany) treatment, after seeding cells were treated with 1 µM Gefitinib dissolved in DMSO for at least 24 h before the measurement. Cell culture conditions are further detailed in the supplementary. Antibodies and labeling The therapeutic mAbs Cetuximab, Trastuzumab, and Pertuzumab were purchased from Apoteket AB (Sweden) and a rat anti-human EGFR mAb, clone ICR10 was obtained from Abcam (UK) as both unlabeled variant (ab231) and conjugated with Fluorescein isothiocyanate (=FITC) (ab11400). Unlabeled antibodies were labeled with either FITC (F3651, Merck Life Sciences) or the quencher Atto-Q450 (AD 540Q-31, Atto-Tech, Germany) as described previously36. For experiments that were used to deduct relative co-localizations of receptors, antibodies from the same labeling batch were used for all replicates. Real-time interaction measurements Binding kinetics of labeled antibodies to living cells was measured with LigandTracer Green (Ridgeview Instruments AB, Sweden)36. In brief, a cell dish with cells seeded in a local area and a cell-free reference area is placed on an inclined and rotating support. The fluorescence detector is mounted above the upper part of the dish so that fluorescence from bound ligand is monitored while the incubation solution is in the lower part of the dish. Signals from cell and reference areas are recorded during every rotation, resulting in a background subtracted binding curve. After recording a baseline with typically 3 ml cell culture medium, fluorescent antibody is added and incubated until clear curvature is visible in the binding trace. The incubation solution is then changed to fresh cell culture medium, not containing any antibody, to record dissociation of the bound antibody. For real-time interaction measurements with proximity evaluation, a defined concentration of fluorescent antibody was incubated until a clear signal increase was obtained followed by the addition of a second antibody labeled with a


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quencher. If the two antibodies bound in close proximity on the cell surface, a decrease in the slope of the binding curve was expected. To exclude competition between first and second antibody as a cause for signal decrease, control experiments with unlabeled second antibody were performed. These experiments also served as reference binding traces to evaluate the quenching effect.

the quenching efficiency. The latter depends on intrinsic properties of the fluorescent and quenching label used, their labeling degree, and the actual physical proximity of the labels. Thus, when the quenching efficiency can be assumed the same, as expected when performing experiments with the same labeling batches and composition of the interacting complex, the ratio between the fluorescence and quenching Bmax values is proportional to the number of binding sites in proximity. In this study, all interactions were analyzed according to the Langmuir 1:1 model. For real-time measurements of fluorescently labeled mAbs interacting with cellular receptors, TraceDrawer (Version 1.8, Ridgeview Instruments AB) was used to extract ka, kd and Bmax by applying the default starting guesses 105 M-1s-1 for ka and 10-3 s-1 for kd. For the second interaction, a 1:1 model with a negative signal contribution, whose magnitude was dependent on the target saturation of the first interaction, was fitted to the quenching phase of the curve using Excel. Starting guesses for ka and kd of the quenching interaction were obtained from separate measurements with the same antibody but a fluorescent label.

Real-time interaction analysis Interactions between two molecules, such as a target (T) and a ligand (L) is described as a fully reversible process and characterized by the rate of complex formation, ka, and the rate by which the complex dissociates, kd, that depends on the stability of the complex. [T] + [L]

[TL]

[1]

The affinity or binding strength of the interaction is defined as the ratio between dissociation and association rate constants. Applied to real-time interaction analysis, where the total number of receptors is kept at a constant level, Eq. 1 can be rewritten to express the change in the number of complexes as function of time and ligand concentration. = ∙ [ ] ∙ ( − ) − ∙ [2] In this equation, B is the signal, which is proportional to the number of complexes, and Bmax is the signal obtained with target saturation. Real-time interaction analysis derives ka, kd and the target saturation level (=B/Bmax) from the non-linearity of the binding signal as a function of ligand concentration and time. For real-time interaction measurements with proximity evaluation, a signal reduction caused by binding of the quenching ligand is only observed when a complex of the fluorescent interaction was formed in close proximity30. Thus, for the interaction derived from the quenched signal, the Bmax value depends on the maximum number of binding sites that are in proximity of the first interaction as well as

Data normalization All interaction curves depicted were normalized by setting the baseline levels to zero and the point when either quencher or unlabeled antibody was added was set to 100 to clearly visualize the quenching effect. This means that normalized signals are higher when a quencher was added earlier. Statistical analysis As a measure for the reproducibility, the coefficient of variation (CV) was calculated for the extracted kinetic parameters. Differences between treatments were tested for significance by using an un-paired two-tailed students Ttest.

Figure 1 Principle of time-resolved proximity assay. The binding traces for the individual fluorescent (yellow) and quenching (grey, dotted) interactions are depicted. If these two interactions occur in proximity, addition of the quenching ligand results in a signal decrease for the combined trace (yellow-grey).

Results and Discussion

thereby allowing to simultaneously characterize two interactions on living cells if they occur in proximity. As a general set-up, a fluorescently labeled ligand is added to the cell and a binding curve recorded with the fluorescent signal being

In this study, a new assay is presented that combines realtime interaction analysis with fluorescence quenching,



proportional to the number of bound ligands (Fig. 1). The shape of the binding curve contains information about the kinetic parameters of this first ligand. A second ligand labeled with a quencher is then added to the cells, resulting in a reduction of the fluorescent signal if the second ligand binds in close proximity. The shape of the second part of the curve over time contains information about the binding kinetics of both ligands and the signal reduction by quenching is proportional to the number of quenching ligands bound in proximity to a fluorescent ligand. As a first model system, the binding of the two therapeutic antibodies Trastuzumab and Pertuzumab to HER2 overexpressing cells was studied. It is known that Trastuzumab binds to region IV31 on HER2 whereas Pertuzumab binds to region II32, meaning that the antibodies bind in close proximity without competing for the same binding site37. The absence of competition was confirmed in our assay (S Fig. 1A&B). Individual binding profiles of the FITC labeled antibodies to SKOV3 cells were established with real-time measurements in LigandTracer Green. To study the proximity of two interaction processes in real-time, FITCTrastuzumab binding to SKOV3 cells was monitored for 2 h after which Pertuzumab either labeled with a quencher (QPertuzumab) (red) or unlabeled (blue) was added (Fig. 2A). Upon addition of Q-Pertuzumab, the slope for the recorded

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binding signal decreased and clearly deviated from the recorded binding trace for FITC-Trastuzumab. Kinetic parameters for the antibody interactions (Table 1) were calculated under the assumption that binding of the two antibodies is independent from each other. This independency was verified by first monitoring binding of FITC-Trastuzumab until it approached equilibrium and then adding Q-Pertuzumab (Fig. 2B). As the first interaction approached phase was expected to primarily reflect the second interaction process. To visualize this, the quenching phase of the curve was overlaid with an inverted binding curve recorded for FITCPertuzumab alone at the respective concentration (Fig 2B). The quenching phase overlapped well with the recorded binding curve for the individual FITC-Pertuzumab interaction, thereby confirming that Q-Pertuzumab binds very similar to the cells compared to FITC-Pertuzumab and is not influenced by the presence of Trastuzumab, as has been reported previously37. To ensure that evaluation of the proximity and binding characteristics is independent of the order in which antibodies are incubated with the cells, the labels for the antiHER2 antibodies were switched: first FITC-Pertuzumab was added to the cells, then Q-Trastuzumab (Fig. 2C). The addition of Q-Trastuzumab resulted in quenching which caused the fluorescent signal to deviate clearly from the

Figure 2 Assay validation on HER2 model system (A) FITC-Trastuzumab (Tmab) binding to SKOV3 cells. A decrease in binding slope was observed when adding Q-Pertuzumab (Pmab) (red: measured data, black: kinetic fit) compared to unlabeled Pmab(blue). (B) Real-time proximity assay with FITC-Tmab and Q-Pmab on SKOV3 cells (red). The quenching phase overlaps with an inverted binding curve for FITC-Pmab alone (blue). (C) Fitc-Pmab binding to SKOV3 cells, addition of Q-Tmab (red) results in clear deviation from the control with unlabeled Tmab (blue). Black depicts the fits for the respective curves. (D) The quenching effect, observed when adding Q-Tmab (red), can be blocked with excess of unlabeled Tmab (dark blue). The blocked curve (dark blue) displays the same binding profile as the control with unlabeled Tmab (light blue).


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Table 1 Summary of kinetic rate constants and affinity (± CV) for all interactions based on n replicates Ligand

Cell line

ka×104 (M-1s-1)

kd ×10-5(s-1)

KD (pM)

n

Fitc-Pertuzumab Q-Trastuzumab

SKOV3 SKOV3

5.5 ± 18% 5.7 ± 20%

0.7 ± 29% 0.4 ± 18%

129 ± 29% 70 ± 11%

8 7

Fitc-Trastuzumab Q-Pertuzumab

SKOV3 SKOV3

3.9 ± 24% 6.3 ± 23%

0.5 ± 26% 0.3 ± 31%

124 ± 34% 53 ± 42%

9 5

Fitc-ICR10 mAb Q-Cetuximab

A431 A431

6.0 ± 23% 14.7 ± 39%

1.3 ± 49% 1.0 ± 56%

235 ± 66% 68 ± 35%

5 5

Fitc-Cetuximab Q-ICR10 mAb

A431 A431

12.5 ± 8% 11.8 ± 26%

0.8 ± 14% 2.5 ± 25%

63 ± 7% 247 ± 36%

4 5

Fitc-Cetuximab Q-Trastuzumab

SKOV3 SKOV3

89.8 ± 42% 3.8 ± 46%

0.1 # 0.4 ± 51%

1.3 ± 39% 118 ± 73%

13 10

# global fit due to slow dissociation

observation that some interactions seem to slow down when taking place in a cellular environment, especially in their dissociation rate constants, has been described before44. Although the origin is not well understood, it has for example been demonstrated that binding of Trastuzumab to HER2 is affected by the presence of mucins on the cell surface45. Blocking experiments were preformed to confirm that the observed quenching effect was indeed due to the second ligand binding in close proximity to the first. For this purpose, SKOV3 cells were blocked with unlabeled Trastuzumab prior to real- time interaction analysis with proximity evaluation (Fig. 2D). Addition of Q-Trastuzumab to blocked SKOV3 cells did not result in any quenching, whereas on non-blocked cells the addition of QTrastuzumab resulted in visible quenching. Moreover, experiments with high concentrations of an FITC-labeled antiCD44 antibody and Q-Trastuzumab, did not result in any quenching effect (S Fig 2A), whereas picomolar concentrations of Q-Trastuzumab resulted in visible quenching in combination with FITC-Pertuzumab (S Fig 2B). Taken together, this demonstrates that quenching is not due to locally high concentrations of quencher in either the incubation solution or on the cell surface, but a specific signal

binding signal for FITC-Pertuzumab to which an equivalent amount of unlabeled Trastuzumab was added. The extracted kinetic rate constants for Q-Trastuzumab were similar to those obtained for FITC-Trastuzumab (Table 1). For Pertuzumab, the association rate constants were very similar between the fluorescent and quenching labeled variants, whereas kd deviated by a factor of two. Off-rates (kd) that are close to the limit of detection (1*10-6 s-1) are more difficult to estimate accurately from the curve shape due to the low signal variation over time during dissociation38. When measuring two ligands with slow off-rates simultaneously while one of the ligands has a negative signal contribution, the observed signal decrease over time is even smaller than for the individual interactions. Accurate estimations of the off-rates then become challenging, as is reflected here by the deviation in kd for the labeled variants of Pertuzumab. The affinity for Trastuzumab was similar to previously published values and has been shown to be cell-line dependent 39,27,40. The affinity for Pertuzumab observed in this study was stronger than reported values for isolated protein systems, which are in the range of 1.8-15.2 nM37,41, 42,43. Of note, the value for the on-rate (ka) obtained in this study was in the same range as those obtained by SPR whereas the offrate was remarkably slower in our cell-based assay37,41. The

Figure 3 Assay validation on EGFR model system (A) Fitc-ICR10 mAb binding to A431 cells. Addition of Q-Cetuximab (=Cmab) (red) clearly deviated from a control experiment with an equivalent amount of unlabeled Cmab (blue). (B) FITC-Cmab binding to A431 cells upon addition of Q-ICR10 (red) the signal slope decreases compared to a control with unlabeled ICR10 (blue). Kinetic fits are shown in black.



implying proximity of the bound antibodies. Another model system for real-time proximity interaction analysis was tested with Cetuximab and the commercially available rat anti-EGFR antibody ICR10. Binding of both antibodies in their respective fluorescent version to A431 cells was observed and addition of the respective other quencher-labeled antibody resulted in a decrease in the slope of the binding signal compared to control experiments (Fig. 3A&B). The observation that ICR10 bound in proximity to Cetuximab, which is a well-established EGFR binder, confirms that ICR10 indeed binds specifically to its intended target. The resulting kinetic parameters were very similar for FITC-Cetuximab compared to Q-Cetuximab and FITC ICR10 compared to Q-ICR10 (Table 1) indicating that the two antibodies did not affect each other’s binding characteristics. This was, however, observed for ICR10 binding when the epidermal growth factor (EGF) with and without a quencher was added (S Fig 3). The affinity value obtained for Cetuximab was in the same range as previously published data on living A431 cells27,46, whereas the affinity value for the ICR10 mAb was somewhat lower than previously reported47. However, these measurements were performed on a different cell line, and it has been shown that the affinity of mAbs can be cell line dependent40. To demonstrate broader applicability, the proximity assay was applied on a biologically unrelated system and demonstrated that an anti-IgM antibody targeting the B-cell receptor and the therapeutic mAb Rituximab targeting CD20 co-localize on B-cells (S Fig 4) as has been reported in the literature48. Kinetic analysis of the second interaction was performed by applying adopted starting guesses for ka and kd, which were obtained from binding measurements using the corresponding fluorescently labeled mAb. This was required to enhance robustness in the fitting process and prevent suboptimal solutions (local minima). It has been demonstrated that a robust curve fitting with a 1:1 Langmuir model requires an association phase with two concentrations giving strong curvature as well as a dissociation phase49. To test if the same applies to our data, one concentration of fluorescent ligand was incubated until equilibrium was approached, followed by two consecutive concentrations of quencher-labeled ligand and a dissociation phase (Fig. 4A).

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Evaluating this binding trace with default starting guesses (105 M-1s-1 for ka and 10-3 s-1 for kd,) resulted in the same solution as when using adopted starting guesses. This indicates that an optimized assay set-up, consisting preferably of two concentrations for each ligand and a dissociation phase, would make the data fitting more robust and thus circumvent the need to establish the rate constants of the interactions in individual experiments. Besides the rate constants and affinity, real-time interaction analysis also extracts the Bmax value for an interaction. For the first interaction, the Bmax value simply represents the signal obtained when all receptors are occupied by a fluorescently labeled ligand. For the interaction derived from the quenched signal, the Bmax value depends on the maximum number of binding sites that are in proximity of the first interaction and the quenching efficiency. Binding of the quencher visibly affects the binding curve when the fluorescently-labeled antibody already generates a clear signal and is bound in proximity. The required signal level depends on the biological system under investigation as it is affected by receptor expression levels, kinetic properties and concentration of the antibodies and the quenching efficiency. Extracting interaction parameters (ka, kd, KD and Bmax) from kinetic modeling, however, does not dependent on reaching a certain target saturation level before adding the quencherantibody, but relies on curvature and signal quality generated by the fluorescence and quencher labeled antibodies. When the same ligand labeling batches are used and when the relative number of targets in close proximity stays the same, the ratio of the Bmax values for the two interactions is expected to be the same, irrespective of the ligand concentrations used. This was confirmed experimentally by real-time proximity interaction analysis with FITCPertuzumab followed by addition of either 4 nM or 12 nM Q-Trastuzumab in independent experiments (Fig. 4B). Kinetic evaluation revealed a Bmax (Q)/Bmax (F) ratio of 0.40 and 0.39 for the binding trace with 4 nM and 12 nM QTrastuzumab, respectively. From this it can be deducted that, if the same labeling batches are used on the same cell line, variations in the Bmax ratio can be attributed to changes in relative number of targets that are in proximity and thus gives information on the relative co-localization.

Figure 4 Data evaluation of real-time proximity assay (A) FITC-Pertuzumab (Pmab) binding to SKOV3 cells followed by two consecutive concentrations of Q-Trastuzumab (Tmab) (measured data: red, kinetic fit: black,). (B) FITC-Pmab binding to SKOV3 cells with addition of either 4 nM (orange) or 12 nM (red) Q-Tmab.


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shown to increase the number of EGFR-HER2 heterodimers on cancer cells51. SKOV3 cells that were treated with 1 µM Gefitinib for at least 24 h exhibited on average a Bmax(Q)/Bmax(F) ratio of 0.33 ± 0.02 (n=4), which was significantly higher (p= 0.001) than for the control group (Fig. 5B&C). This implies that either the relative number of antibodies in proximity increased or that the distance between antibodies decreased. As no significant differences for the interaction characteristic were observed, it is not expected that the two antibodies bind closer to each other upon gefitinib treatment. The increase in proximity due to higher level of co-localization is a more plausible explanation as an increase in EGFR-HER2 dimers has been observed by others51. Another reported effect on EGFR-HER2 dimerization is that it is hampered by Pertuzumab32 which was verified with the presented proximity assay; binding of FITCCetuximab to SKOV3 cells was not quenched by the addition of Q-Pertuzumab (S Fig. 5). Conclusion Current real-time techniques for interaction analysis allow characterization of one ligand, while kinetic parameters of a second ligand can only be quantified when it is competing for the same binding epitope. By combining real-time measurements with fluorescent quenching, the kinetics of a second ligand can now also be extracted if bound in proximity to the first. This can confirm whether or not two compounds can be bound simultaneously to the same target structure while also extracting their respective binding characteristics. Moreover, real-time proximity interaction analysis enables to quantify the binding of two proteins, while assessing their relative co-localization on living cells. It thus allows for a more detailed characterization of complex molecular processes on the cell surface such as dimerization or clustering of receptors. As these processes are known to regulate receptor signaling, their modulation is of substantial pharmacological interest. In this context, providing an adequate in vitro model system to study the real-life complexity of molecular interactions has the potential to aid in a better understanding of how binding kinetics translate to biological function.

Figure 5 Quantification of EGFR-HER2 heterodimers (A) FITC-Cetuximab and Q-Trastuzumab (Tmab) (red) or unlabeled (unl) Tmab (blue) binding to SKOV3 cells expressing both EGFR and HER2. Kinetic fits are depicted in black. (B) SKOV3 cells treated with 1 µM Gefitinib (red) displayed higher Bmax(Q)/Bmax(F) ratios compared to untreated (DMSO control) cells (blue) (C) Quantification of the relative co-localization of EGFR and HER2 on treated and untreated (DMSO control) SKOV3 cells.

AUTHOR INFORMATION Corresponding Author

This was demonstrated by performing real-time proximity interaction analysis with FITC-Cetuximab and QTrastuzumab on SKOV3 cells which express both EGFR and HER2 that are known to form heterodimers (Fig. 5A). A quenching effect was observed, meaning that at least a portion of the antibodies bound in close proximity, and kinetic parameters for both antibodies could be extracted (summarized in Table 1). The affinity for the FITC-Cetuximab interaction on SKOV3 cells was higher than on A431 cells, likely due to cell-line dependent binding that has been observed previously for Cetuximab50. The average Bmax(Q)/Bmax(F) ratio was estimated to be 0.22 ± 0.02 (n=4) for untreated (DMSO control) cells (Fig. 5B). The kinase inhibitor Gefitinib that selectively binds to the kinase domain of EGFR has been

* [email protected]

Author Contributions SB, MN, KA and JB planned the work, SB, HB and JB participated in experimental procedures and/or analyzed data, SB and JB wrote the manuscript. All authors read, reviewed and approved the final version of the manuscript. Conflict of Interest Ridgeview Instruments AB (RIAB) develops and sells the device LigandTracer, as well as the software TraceDrawer that are described in the manuscript. SB, HB, KA, and JB are employed by RIAB. HB, KA and JB are shareholders of RIAB.



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Analytical Chemistry For TOC only:

ACS Paragon Plus Environment

9


Figure 2 Assay validation on HER2 model system (A) FITC-Trastuzumab (Tmab) binding to SKOV3 cells. A decrease in bind-ing slope was observed when adding Q-Pertuzumab (Pmab) (red: measured data, black: kinetic fit) compared to unlabeled Pmab(blue). (B) Real-time proximity assay with FITC-Tmab and Q-Pmab on SKOV3 cells (red). The quenching phase overlaps with an inverted binding curve for FITC-Pmab alone (blue). (C) Fitc-Pmab binding to SKOV3 cells, addition of Q-Tmab (red) results in clear deviation from the control with unlabeled Tmab (blue). Black depicts the fits for the respective curves. (D) The quenching effect, observed when adding Q-Tmab (red), can be blocked with excess of unlabeled Tmab (dark blue). The blocked curve (dark blue) displays the same binding profile as the control with unlabeled Tmab (light blue). 119x80mm (300 x 300 DPI)


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Figure 4 Data evaluation of real-time proximity assay (A) FITC-Pertuzumab (Pmab) binding to SKOV3 cells followed by two consecutive concentrations of Q-Trastuzumab (Tmab) (measured data: red, kinetic fit: black,). (B) FITC-Pmab binding to SKOV3 cells with addition of either 4 nM (orange) or 12 nM (red) QTmab. 64x23mm (300 x 300 DPI)


Novel Real-Time Proximity Assay for ... - ACS Publications

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