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Keywords:

  • fluorescence microscopy;
  • MET receptor;
  • protein–protein interactions;
  • single-molecule studies;
  • TNF receptor 1

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

Protein–ligand interactions play an important role in many biological processes. Notably, membrane receptors are the starting point for a huge variety of cellular signal transduction pathways. Quantifying the binding affinity of a ligand for its transmembrane receptor is of great importance as it provides information on the potency of the ligand. We developed a new experimental procedure to determine binding affinities of ligands for their membrane receptors directly on intact single cells using super-resolution imaging. Dissociation constants were determined by titrating fluorophore-labelled ligand against cells expressing the target protein and applying single-molecule imaging.


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

Interactions between receptors and ligands are key steps in many fundamental biological processes. Binding affinities are one important quantity governing these interactions and they provide valuable mechanistic insights into signalling pathways. Dissociation constants (KD) of ligand–protein interactions are often in the nM range, but weaker and stronger affinities are also observed. For example, the catecholamine epinephrine binds with low affinity (2 μM) to β-adrenergic receptors.1, 2 In contrast, the interaction of biotin and avidin exhibits an exceptionally high affinity of around 1 fM.3 The binding affinity relates to the range of ligand concentration needed to activate the receptor, produce a biological signal and may play a role in receptor desensitization.

Several established methods are available to determine binding affinities. Enzyme-linked immunosorbent assays (ELISA)4, 5 and surface plasmon resonance (SPR)6 are widely used. Both methods are based on the immobilization of one binding partner, and the detection of a change in a read-out signal following ligand binding (e.g. enzyme-based colorimetric reaction, change in resonance). Radiometric ligand binding assays use a ligand labelled with a radioactive isotope (often 125I) and can monitor equilibrium binding to a receptor directly on living cells or tissue. Here, the radioactivity of cells is detected with respect to the ligand concentration.7 Thermodynamics of protein–ligand interactions can be studied by isothermal titration calorimetry (ITC) where the heat change upon ligand addition is recorded and a binding constant can be determined.8 Fluorescence correlation spectroscopy (FCS) is a more recent but meanwhile well-established method to study molecular interactions.9, 10 FCS is typically carried out in solution and monitors size dependent diffusion times from which dissociation constants of biomolecules and their complexes can be obtained. More recently, FCS measurements were also demonstrated on cell membranes.11 Finally, microscale thermophoresis constitutes another recent technique that is also based on in vitro measurements in solution.12

All the above mentioned techniques are averaging measurements and are mostly carried out in vitro. ELISA and SPR are surface-based methods, which might lead to artefacts due to immobilization. Conventional FCS, microscale thermophoresis and ITC experiments are typically carried out with isolated biomolecules in vitro, such that physiological interactions of the receptor and/or ligand with other membrane proteins are not taken into account, which might affect the results. In addition, these techniques often require a recombinant, soluble form of the receptor (e.g. ectodomain), which may alter binding affinities. FCS can also be used to investigate molecular processes directly on the membranes of living cells, but as a point-measurement technique it only probes local binding affinities. ITC experiments have the advantage that no labelling is needed but they require huge amounts of protein. Radioligand binding assays average over a large number of cells, thus also giving bulk information rather than giving information on the single cell level.

Spatial resolution in conventional fluorescence imaging is limited by diffraction to approximately 200 nm. In the last years, several sub-diffraction imaging techniques emerged.13 One prominent example is single-molecule localization microscopy, including photoactivated-localization microscopy (PALM),14 fluorescence photoactivation localization microscopy (FPALM),15 stochastic optical reconstruction microscopy (STORM)16 and directSTORM (dSTORM).17 These techniques are based on photoswitchable or photoactivatable fluorescent proteins or organic dyes. By separating nearby fluorophore emission in time, accurate position determination of single fluorophores with nanometer precision is achieved. Super-resolution microscopy is an evolving field with great potential. Recent biological applications include single-molecule counting, cluster and structural analysis and high-density single-particle tracking.18

Here, we use single-molecule and super-resolution microscopy to determine copy numbers of receptors and ligands as well as binding affinities from single-cell measurements. Typically, receptor densities are too high for conventional microscopy methods so that individual receptors cannot be discerned due to the diffraction limit of light microscopy. In a first step, we use immunofluorescence labelling and single-molecule localization microscopy to determine receptor copy numbers on cells. In a second step, we use single-molecule imaging of fluorophore-labelled ligands at different concentrations, and determine binding constants on intact cells. In addition, this approach allows discerning binding constants of ligand-induced and uninduced cells, which may give insight into the impact of co-receptors. Finally, we compare the number of receptor sites available at the cell membrane with the maximal number of ligands bound, and determine the occupation at saturating ligand concentration. We apply this approach to two different plasma membrane receptors. The receptor tyrosine kinase MET is our first target. MET acts as receptor for hepatocyte growth factor (HGF) and is essential in vertebrate development and tissue regeneration.19 MET is also targeted by the bacterial pathogen Listeria monocytogenes through internalin B (InlB),20 which we investigate as MET ligand in this study. Our second target is the factor 1 (TNF-R1), which is involved in cell death as well as inflammation and proliferation.21 We examined the binding between TNF-R1 and the cytokine TNFα.

2. Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

2.1. Receptor Densities Determined by dSTORM

We developed a novel and simple approach to determine receptor densities and ligand-binding affinities to membrane receptors on single cells using single-molecule super-resolution imaging. Most membrane receptors are present at copy numbers too high to allow discerning single receptors with conventional diffraction-limited imaging such as confocal laser scanning microscopy. To get an idea on the molecular density of receptors at the plasma membrane, we immunolabelled the target proteins with the photoswitchable dye Alexa Fluor 647, performed dSTORM measurements and counted single spots (in whole cells or large sections of cells imaged) which represent single receptor sites. dSTORM images of MET and TNF-R1 are shown in Figure 1. Our experiments yielded membrane receptor densities in HeLa cells of 6.5±0.6 (0.6=standard deviation, s.d.) receptors per μm2 for MET and 1.3±0.3 (s.d.) receptors per μm2 for TNF-R1. We determined the membrane surface of HeLa cells using confocal microscopy to about 1600±380 (s.d.) μm2 (N=13). With that estimate, we calculated the average number of receptors per cell to be between 7900 and 12 900 (MET) and 1500 to 2900 (TNF-R1), respectively. The number of MET determined here is in reasonable agreement with published data from other cell lines.2224 For TNF-R1, receptor copy numbers derived from TNFα-binding sites were reported in the range of 128±29 on neuroblastoma cells25 and about 7500 for FS-4 cells.26 In addition, receptor densities determined from super-resolution images can resolve variations between different cells. We observed that the receptor density ranged between 5.5 and 7.1 molecules per μm2 for MET (N=7), whereas we found a range of 0.8 to 1.8 molecules per μm2 for TNF-R1 (N=9). However, we note that our measurements cannot resolve oligomers, for example, dimers in the case of MET27 or dimers and trimers for TNF-R1.28 In this sense, the resulting receptor densities are to be considered as a lower estimate. Receptor densities from these experiments can complement other experimental data, such as from photobleaching experiments27, 29 or quantitative PALM,3032 where the oligomeric state (dimers, trimers, etc.) and the heterogeneity of oligomeric states can be determined.

thumbnail image

Figure 1. dSTORM images of Alexa Fluor 647 immunolabelled membrane receptors. Super-resolution images are shown for a) MET receptor and b) TNF-R1 distribution on the cell membrane of HeLa cells, respectively. The insets show the wide-field images (scale bar 2 μm).

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In a second experiment, we determined molecular densities of receptor-specific ligands by quantifying the number of fluorophore-labelled ligands (InlB-ATTO647N, TNFα-ATTO647N) bound to the respective receptor at the single-cell level. Serum-starved HeLa cells were incubated with a fluorophore-labelled ligand and single-molecule imaging was performed. We observed a high degree of glass adsorption for both ligands, which interfered with single-molecule imaging on the cell membrane. We adapted our experimental protocol by staining cells with the respective ligands. After fixation, we gently scraped cells from the surface, followed by washing and seeding cells on a new glass slide (for details, see the Experimental Section). This approach reduced the background fluorescence signal significantly. Cells were incubated at different concentrations of labelled ligand. With single-molecule imaging, we counted the corresponding ligand number bound to receptors on single cells and determined ligand densities at saturating condition by fitting with a model for binding (see the Experimental Section). In the case of InlB binding to MET, we observed ligand densities between 1.25±0.24 (s.d.) (no scraping) and 2.52±0.26 (s.d.) molecules per μm2 (with scraping) (Table 1) under ligand saturation conditions. The lower ligand density in the case of cells that were not scraped can be explained by the higher background fluorescence due to the surface adsorption of the ligand, which interferes with the signal from ligand bound to the receptor. Thus, accurate fitting of fluorescent spots during the data analysis is impaired. For InlB only the MET receptor was found as receptor.20 The low receptor occupancy by InlB-ATTO647N can also be partly explained by preferential binding of the unlabelled ligand species to the receptor.

Table 1. Receptor and ligand densities on cells. For the determination of ligand densities, the number of InlB-ATTO647N and TNFα-ATTO647N bound to induced cells was determined. Standard deviations are given for each value.
ReceptorReceptor density [μm−2]Ligand density [μm−2]Max. receptor occupation [%]
  1. [a] Calculated in consideration of the degree of labelling of InlB (DOL=0.5). [b] Cells were scraped before imaging.

MET6.5±0.61.25±0.24[a] 2.52±0.26[a,b]19±7 39±5
TNF-R11.3±0.30.45±0.0004[b]35±8

In order to determine the biological activity of TNFα-ATTO647N, a NF-κB reporter gene assay was performed (see the Supporting Information). Stimulation of U251 astroglioma cells with both native and ATTO647N-labelled TNFα resulted in significantly increased NF-κB-activity compared to untreated controls (Figure S1). No significant differences in biological activity between ATTO647N-labelled and native TNFα were observed. For TNF-R1, we found a ligand density of 0.45±0.0004 (s.d.) molecules per μm2 (with scraping) at saturation levels. We note that TNFα also binds to TNF-R2, however, the number of TNF-R2 receptors (compared to TNF-R1) in HeLa cells is negligibly small,33, 34 such that we do not further consider it in the following analysis.

We compared the numbers of receptors to the numbers of ligands bound under saturating conditions, which should give information on the average maximal occupation density. We found that the receptor occupation ranges between 19±7 (s.d.) % (no scraping) and 39±5 (s.d.) % (with scraping) for MET. TNF-R1 is occupied with 35±8 (s.d.) % by TNFα-ATTO647N. The low occupation levels could be accounted for by the use of polyclonal antibodies, which might have more flexibility in binding to the receptor than a corresponding ligand. Other reasons might be that a fraction of fluorophores (attached to the ligands) already resides in a non-fluorescent dark state or photobleaches before the beginning of the measurement. Also, a special receptor conformation or oligomerization state might be a prerequisite for ligand binding. However, this requires more experimental evidence and careful controls and is beyond the scope of this study. The receptor and ligand densities as well as resulting receptor occupation levels are summarized in Table 1.

2.2. Super-Resolution Imaging Reveals High-Affinity Binding of Ligands to Membrane Receptors on Intact Cells

We established a new method to determine binding affinities directly on cells using single-molecule imaging. Fluorophore-labelled ligand was titrated against cells expressing the target protein, single-molecule imaging was applied and ligand binding sites were counted. By plotting the number of labelled ligand bound to receptor against the total ligand concentration surrounding the cells, we derived binding curves. We applied a 1:1 binding model to determine the dissociation constants of ligand–receptor interactions on intact cells (Figure 2). This is legitimate, as for our studied receptor–ligand interactions, a 1:1 stoichiometry is predicted. For MET-InlB a 2:2 binding was found.35, 36 While TNF-R1-TNFα has a 3:3 stoichiometry, one TNFα trimer exhibits three binding sites for the receptor.28, 37, 38 In our experiments, plotting the total ligand concentration is justified, because the reduction of the ligand concentration caused by receptor binding is negligible (<0.01 %) and the dissociation constants determined are significantly larger than the receptor concentration. To test whether our method yields reliable data, we compared this approach to data obtained from fluorescence correlation spectroscopy (FCS).27 Specifically, we compared single-molecule data on binding of InlB-ATTO647N to the uninduced MET receptor (Figure S2, Table 2) to FCS data on binding of ligand to the MET ectodomain in vitro. For the uninduced case, receptors were fixed with formaldehyde prior to ligand incubation, that is, a biological reaction, for example receptor dimerization or co-receptor recruitment, cannot be induced. This approach rather shows the in vitro behaviour of the receptor than reflecting the in vivo system. We obtained similar dissociation constants for FCS and single-molecule imaging, that is, KD=5.0±0.8 (s.d.) nM (FCS)27 and KD=5.2±1.4 (s.d.) nM (single-molecule imaging). This good agreement of experimental results confirms the validity of the single-molecule approach. However, we note that the standard deviation is higher for the ex vivo measurements on cells, which is expected, as we deal with individual cells that naturally show a heterogeneous behaviour.

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Figure 2. Binding study on ligand-induced HeLa cells via single-molecule imaging. Representative localisation images at different ligand concentration and the resulting titration data are shown for a) InlB-ATTO647N binding to MET (no scraping) and b) TNFα-ATTO647N binding to TNF-R1 (with scraping). Here, the x-axis represents the total ligand concentration. The y-axis corresponds to the number of counted ligand–receptor pairs. In the case of InlB-ATTO647N the bound ligand density was corrected for the DOL of 0.5. The binding curves were determined by least-square-fitting with a Langmuir-binding model. The single-molecule images on the left correspond to the data points with green and blue circles in the binding curves. For each ligand concentration, several cells (N=4–10) were analysed (scale bar 1 μm).

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Table 2. Dissociation constants of different receptor–ligand systems.
ReceptorLigandKD [nM]
MET, uninducedInlB-ATTO647N5.2±1.4
MET, inducedInlB-ATTO647N3.1±1.3
MET, induced, scrapedInlB-ATTO647N3.3±0.8
TNF-R1, induced, scrapedTNFα-ATTO647N15.69±0.03

We now make use of our approach to determine ligand–receptor binding constants on intact cells and investigated how the dissociation constant changes when looking at ligand binding in living cells (induced receptor). We mimicked this interaction by first incubating live HeLa cells with endogenous MET/TNF-R1 expression with a fluorophore-labelled ligand, and fixing cells afterwards, prior to single-molecule imaging. Representative binding curves for the MET receptor and TNF-R1 are shown in Figure 2. Notably, we determined the same dissociation constant for induced MET for both cells measured without (Figure 2 A) and with cell scraping (Figure S3), which indicates that cell scraping and additional washing steps do not alter the results (see also Table 2). A possible explanation for the higher ligand density is the decrease in background fluorescence in the case of scraped cells which might overlap with the ligand signal. Accurate localisation of the fluorescent signal of labelled ligands can therefore be impaired.

The binding affinity of InlB for its receptor MET slightly increases in live cells, which is expressed in a decrease of the dissociation constant from 5.2±1.4 nM in uninduced cells (Figure S2) to 3.1±1.3 nM after induction (Figure 2 a). This observation could be explained for example by the presence of co-receptors involved in the binding reaction or co-receptors recruiting the ligand in the proximity of the receptor or by the additional contact formed upon dimerization of two InlB molecules.36 The binding curves show that the number of bound ligand varies significantly at higher ligand concentrations, again showing cell-to-cell variability. In the past, other techniques were used to determine the binding affinity of InlB to MET. Surface plasmon resonance yielded dissociation constants between 20 and 150 nM39 and ELISA yielded values of 1 nM and 5 nM.3941

We also determined TNF-R1/TNFα binding affinity (Figure 2 b), and found a value of 15.69±0.03 nM in induced cells. Notably, literature reports a vast range of binding affinities for TNF-R1/TNFα in different cell lines, ranging from 3 to 920 pM using radioligand binding assays4244 to 0.59 to 290 nM using SPR.4547 We again point out that these large variations may in part be explained by the different techniques. It is known that TNF-R1 undergoes reorganization upon TNFα binding which may affect further ligand–receptor interactions; a receptor pre-assembly for ligand binding is discussed.48 This binding assay is performed on living cells in the case of ligand induction, thus taking effects of receptor organization on the cell membrane into consideration. In contrast, in vitro binding assays reflect the single-site ligand-binding affinity, excluding avidity effects.

In summary, the presented approach allows measuring dissociation constants directly on cells and under varying experimental conditions. Compared to other techniques, for example in vitro FCS, the results obtained for the MET receptor are in good agreement, but show a higher standard deviation. This standard deviation must partly be attributed to cell-to-cell variability, as expression levels of receptors differ. Other cell-based approaches like radiometric ligand binding assays average over a large number of cells so that information on individual cells are lost. Also, no differentiation between intact and damaged cells is made. In contrast, single-molecule binding studies are performed at the single-cell level. Simultaneously, by averaging over several cells information on average ligand numbers bound to receptors is gained. In this way, we could determine binding curves revealing moderate- to high-affinity binding of different ligand–receptor pairs.

3. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

Single-molecule imaging offers a versatile toolbox to investigate receptor–ligand interactions. Our approach provides an assay to determine binding affinities directly on the membrane of cells. In combination with single-receptor counting, information on effective ligand-binding sites compared to average receptor copy numbers on intact cells is accessible. Single-molecule binding studies can also reveal mechanistic aspects of receptor–ligand interactions directly on cells, such as cooperativity or co-receptor recruitment. Furthermore, competitive or kinetic binding experiments can be implemented. It can also be combined with other single-molecule techniques, for example, photobleaching and quantitative super-resolution microscopy.

Experimental Section

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

Cell Culture

HeLa cells were cultured on chamber slides or 24-well plates and grown for 24 h in DMEM containing 100 U mL−1 penicillin, 100 μg mL−1 streptomycin, 1 % GlutaMAX (Gibco) and 10 % fetal bovine serum (FBS) at 37 °C in 5 % CO2. Before the experiment, the cells were serum starved for 12–24 h to reduce background fluorescence and avoid activation of the target proteins by growth factors or other possible ligands present in the serum.

Receptor Immunostaining

Cells were fixed with 4 % formaldehyde for 10–15 min. After washing with phosphate-buffered saline (PBS) (pH 7.4), cells were blocked with blocking buffer (2–5 % BSA in PBS) for 1 h at room temperature. The polyclonal primary antibody (MET: AF276, R&D Systems; TNF-R1: ab19139, Abcam) was added to the cells at a concentration of 2 μg mL−1 in blocking buffer. Cells were incubated for 2 h at room temperature and washed three times with PBS. Secondary antibody labelled with Alexa Fluor 647 (MET: rabbit anti-goat IgG, Invitrogen; TNF-R1: goat anti-rabbit F(ab′)2, A21246, Invitrogen) was added at a concentration of 2 μg mL−1 in blocking buffer. After 1 h incubation, cells were washed three times with PBS. The sample was post-fixated with 4 % formaldehyde for 10 min to stabilize antibody staining.

Labelling of Ligands

Labelling of InlB with ATTO647N maleimide is described in Dietz et al.27 A degree of labelling (DOL) of 0.5 was determined by absorption spectroscopy.

TNFα was labelled with ATTO647N NHS-ester. Recombinant human TNFα was resuspended according to the manufacturer’s recommendation. For fluorescent coupling with ATTO647N, 50 μg TNFα (Immunotools, 11343017) was diluted in PBS (pH 7.4) to a final concentration of 0.25 μg μL−1. ATTO647N NHS-ester (ATTO-TEC) was dissolved in dimethyl sulfoxide and a 10-fold molar excess of dye:protein was added. The dye–protein solution was incubated for 2 h at room temperature in the dark. Unbound dye was removed using Illustra NAP-5 columns (GE Healthcare) with PBS as equilibration buffer. The final protein concentration and the DOL were determined via absorption spectroscopy. A DOL of 1.3 was obtained.

Receptor–Ligand Binding

For the MET receptor, uninduced and induced HeLa cells were studied. For the uninduced case, adherent HeLa cells were fixed with 4 % formaldehyde in PBS (pH 7.4) for 10 min. After washing with PBS, the cells were treated with a dilution series (0.01 nM–500 nM) of InlB-ATTO647N in PBS for 1 h at 4 °C. For InlB-induced MET receptors, living HeLa cells were cooled for at least 10 min on ice to prevent internalization of MET upon ligand binding. Afterwards, different concentrations of InlB-Atto647N in DMEM (-FBS) were added and the cells were incubated for 1 h at 4 °C. After washing with ice-cold PBS, cells were fixed with 4 % formaldehyde for 10 min.

For TNFα induction, HeLa cells, seeded in 24-well plates, were serum-starved for 12 h prior to ligand addition (as described above). Labelled TNFα was added in serum-free DMEM to pre-chilled HeLa cells (concentration range: 0.01 nM–50 nM). Cells were incubated for 1 h at 4 °C to prevent ligand internalisation, washed with ice-cold PBS and fixed with 4 % formaldehyde in PBS for 15 min.

To reduce background fluorescence due to protein adsorption to the glass surface, ligand-labelled and fixed cells were gently scraped off the 24-well plates with a cell scraper. After washing twice with PBS and spinning the cells down with 200 x g (Heraeus Multifuge X1R, Thermo Scientific), labelled cells were resuspended in PBS, put on chamber slides (Sarstedt) and allowed to settle down over night.

Binding Affinities: Imaging and Data Analysis

Super-resolution measurements were carried out on two different setups. The first experimental setup consisted of an inverted microscope (Olympus IX-71) with an oil-immersion objective (60×, NA 1.45, Olympus). For excitation the 647 nm laser line from an argon krypton laser (Coherent) was selected by an acousto-optic tunable filter (AAOptics), passed a dichroic beamsplitter (FF560/659, Sembrock) and focused onto the back focal plane of the microscope lens. Total internal reflection fluorescence (TIRF) configuration is used for near-surface illumination of the cell membrane. The emission light is filtered in the detection path by a bandpass (ET700/75, AHF Analysentechnik) and a longpass filter (LP647RU, AHF Analysentechnik) and detected on an electron-multiplying charge-coupled device (EMCCD) camera (iXon DU-897 or iXon Ultra 897, Andor). For the second setup an Olympus IX-71 inverted microscope equipped with a 100× oil-immersion objective lens (PLAPO 100x TIRFM, NA≥1.45, Olympus) was used. The setup was operated in TIRF geometry via a translation mirror. A 405 nm diode laser (CUBE 405-100C, Coherent) and a 643 nm diode laser (iBeam smart, Toptica) were coupled into the microscope by a quad-band dichroic mirror (F73-888, AHF). Fluorescence light was spectrally separated from excitation light with a bandpass filter (F47-700, AHF) and projected on the 512×512 pixel chip of an EMCCD (iXon3, Andor).

For imaging 100 mM β-mercaptoethylamine (MEA) in PBS (pH 8.0) was added to the samples. Super-resolution imaging was performed in TIRF mode. Movies were recorded with 33 Hz using an irradiation intensity of about 0.3 kW cm−2. For fluorescence recovery, the 405 nm laser was used.

For data analysis, movies were evaluated with rapidSTORM.49 The resulting super-resolution images were further analysed with Image J using the 3D object counter plugin.50 Plotting the ligand number N(labelled ligand) bound to the receptor against the total added ligand concentration yielded a binding curve. Fitting with a 1:1 binding model [Eq. (1)] gives the dissociation constant of the ligand-receptor interaction.(1)

  • equation image(1)

Here, Nsat is the ligand density bound to the receptor on the cell membrane at saturation concentration and [ligand] is the concentration of the added ligand. c represents the number of background counts which was close to zero.

Acknowledgements

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

<~ErrTag=I>We thank Daniel Haße for providing labelled InlB constructs and Darius Widera for support with the TNFα activity assay. This work was supported by German Science Foundation (6166/2-1 and NI 694/3-1) and the cluster of excellence “Macromolecular Complexes” (CEF, DFG-Cluster of Excellence (EXC115)).<~ErrTag=/I>

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Results and Discussion
  5. 3. Conclusions
  6. Experimental Section
  7. Acknowledgements
  8. Supporting Information

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