Mapping Transient Protein Interactions at the Nanoscale in Living

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Mapping Transient Protein Interactions at the Nanoscale in Living Mammalian Cells Herlinde De Keersmaecker, Rafael Camacho, David Manuel Rantasa, Eduard Fron, Hiroshi Uji-i, Hideaki Mizuno, and Susana Rocha ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.8b01227 • Publication Date (Web): 07 Sep 2018 Downloaded from http://pubs.acs.org on September 8, 2018

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Mapping Transient Protein Interactions at the Nanoscale in Living Mammalian Cells

Herlinde De Keersmaeckera,b, Rafael Camachob, David Manuel Rantasaa, Eduard Fronb, Hiroshi Uji-ic, Hideaki Mizunoa and Susana Rochab,*

a

Molecular and Structural Biology Section, Department of Chemistry, KU Leuven, 3001 Heverlee, Belgium b

Molecular Imaging and Photonics, Department of Chemistry, KU Leuven, 3001 Heverlee, Belgium c

Research Institute for Electronic Science,

Hokkaido University, N20W10, Kita-Ward Sapporo 001-0020, Japan

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ABSTRACT Protein-protein interactions (PPIs) form the basis of cellular processes, regulating cell behavior and fate. PPIs can be extremely transient in nature, which hinders their detection. In addition, traditional biochemical methods provided limited information on the spatial distribution and temporal dynamics of PPIs, crucial for their regulation in the crowed cellular environment. Given the pivotal role of membrane micro- and nano-domains in the regulation of PPIs at the plasma membrane, the development of methods to visualize PPIs with a high spatial resolution is imperative. Here we present a super-resolution fluorescence microscopy technique that can detect and map short-lived transient protein-protein interactions on a nanometer scale in the cellular environment. This imaging method is based on single molecule fluorescence microscopy and exploits the effect of the difference in the mobility between cytosolic and membrane-bound proteins in the recorded fluorescence signals. After a proof-of-concept using a model system based on membrane-bound modular protein domains and fluorescently labeled peptides, we applied this imaging approach to investigate the interactions of cytosolic proteins involved in the epidermal growth factor signaling pathway, namely Grb2, c-Raf and PLCγ1. The detected clusters of Grb2 and c-Raf were correlated with the distribution of the receptor at the plasma membrane. Additionally, the interactions of wild type PLCγ1 were compared to those detected with truncated mutants, which provided important information regarding the role played by specific domains in the interaction with the membrane. The results presented here demonstrate the potential of this technique to unravel the role of membrane heterogeneity in the spatio-temporal regulation of cell signaling.

Keywords: protein-protein interactions, single molecule localization microscopy, fluorescence microscopy, live cell imaging, interaction map, epidermal growth factor, cell signaling

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Protein-protein interactions (PPIs) are intrinsic to cellular processes, driving both metabolic and regulatory pathways. Depending on the lifetime (or stability) of the complex, PPIs can be classified as stable or transient interactions. For example, large macromolecular complexes, such as the ribosome, are highly stable and permanent whereas dynamic and transient interactions are key components in signaling and regulatory networks.1-3 PPIs can also be classified based on their folds as domain-domain or domain-peptide interactions. The complexes belonging to the latter group involve a globular domain, a short linear motif (LM) and the small interface on which the interaction takes place. Special interaction domains (such as PDZ, SH2, SH3, WW) provide an elegant mechanism in cell signaling as they usually recognize and bind specific motifs of peptides (often within intrinsically disordered regions of partner proteins) with no need to undergo large conformational changes.4,5 Identification of fast transient PPIs is technically challenging because at any given moment there is a small amount of complexes (compared to the free proteins). The techniques of isolation and purification that are traditionally used in biochemistry tend to pick up the most robust complexes, whereas weaker interacting and short-lived transient complexes are overlooked. Identification and analysis of transient PPIs require sensitive techniques (reviewed in reference 5). Some of the methods for the identification and analysis of transient interactions are based on fluorescence microscopy. For instance, fluorescence resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC) have been successfully applied to the investigation of a wide range of transient PPIs.6-8 Both techniques detect the interaction between a pair of labelled molecules with high specificity and can be used to image PPIs in living cells. By acquiring fluorescence images, the presence of interacting molecules is linked to a higher intensity signal or FRET efficiency, for BiFC and FRET imaging, respectively. Although highly informative, in the majority of the applications the spatial resolution of the acquired images is limited to about 200 nm by light diffraction, hindering mapping the localization of these interactions at the nanometer scale. Despite the possibility to combine BiFC and FRET with super-resolution microscopy, the limited number of suitable fluorescent probes renders such studies sparse. 9-12 Accumulating results suggest that spatial distribution and temporal dynamics are crucial for the outcome and precise regulation of PPIs.13-17 PPIs involved in signal transduction take place in the highly compartmentalized cellular membrane, where lipid rafts and protein domains create an organization at the nanometer-scale. In order to fully understand the role of membrane compartmentalization in cell signaling, there is a demand for new methods that detects short-lived transient PPIs in living cells with nanometer resolution. In the last few decades, various fluorescence microscopic techniques circumventing the diffraction limit of light have been developed. These so called super-resolution microscopy methods have provided new insights on a wide range of biological processes occurring at the nanometer-scale.18-21 A subset of these microscopic techniques makes use of spatio-temporal separation of fluorescence emission for isolating the signals of single emitters, followed by precise localization of respective molecules by fitting the detected fluorescence signal. A super-resolution image is rendered by plotting the coordinates of the large number of molecules that appeared in thousands of consecutive frames. Well known examples of single molecule localization based super-resolution techniques are (direct) stochastic optical reconstruction microscopy ((d)STORM),22,23 photoactivated localization microscopy (PALM)24,25 and point 2


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accumulation for imaging in nanoscale topography (PAINT).26 In the last years, single molecule based super-resolution has been used to visualize biological structures at the nanometer scale. The extension of super-resolution imaging to multicolor enabled the investigation of the co-localization of different proteins in a cellular context, providing clues for possible interactions27-36. Despite being highly informative, multi-color super-resolution images must be carefully interpreted, as co-localizing molecules are not necessarily interacting. Here we propose a variant of PAINT that enables detection and mapping of the interactions between a specific cytosolic protein and membrane-bound molecules, at the nanometer-scale. While PALM and (d)STORM rely on the stochastic switching between two different states of the fluorophore to isolate the signals of single fluorescent molecules, PAINT achieves a temporal separation of fluorescent signals by imaging fluorescent probes as they reversibly bind to the object. Depending on the nature of the interaction between the labeled probe molecule and the structure of interest, a number of variants have been reported.28,29,37,38 To the best of our knowledge, all PAINT-based approaches reported in literature are used to image a structure taking advantage of a well-known interaction such as complementary DNA strands, antibody-antigen and biotin-streptavidin interactions.28,29,37,39,40 In these, one interaction partner is irreversibly coupled to the protein of interest while the interacting counterpart is fluorescently labeled and added to the sample. The imaging approach suggested here takes advantage of the difference in the diffusion coefficient of proteins freely moving in the cytosol and proteins interacting with membrane-bound molecules to selectively image the interacting fraction, thereby providing insight in the spatial distribution of PPIs. As discussed in the results section, molecular motion during image acquisition induces blurring of the detected signal. Consequently, unbound molecules diffuse fast and are undetectable whereas interacting molecules diffuse slower and are detected as a spot in the fluorescence images. The exploitation of motion blurring to detect molecular interactions has been successfully demonstrated for proteins binding to the large macromolecular DNA/chromatin structure41-43 or enzymes binding to lipid bilayers.37 Here this concept was adapted and combined with super-resolution imaging for mapping the PPI at the membrane interface of a living cell with nanometer resolution. Using this approach, we mapped the interactions at the plasma membrane of several cytosolic proteins involved in a well-studied signaling cascade: the epidermal growth factor (EGF) pathway. The obtained interaction maps display short-lived transient interactions that are overlooked with other techniques. Additionally, we correlated the detected interactions with the membrane distribution of the EGF receptor and we examined the effect of individual domains on the interaction maps for PLCγ1. The imaging method here presented offers the possibility to detect and visualized short-lived PPIs in live cells.

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RESULTS AND DISCUSSION Detection of protein-protein interactions at the plasma membrane In single molecule fluorescence imaging, each molecule is detected through an optical system and therefore the recorded fluorescence signal will be spread, following a spatial intensity distribution known as the point spread function (PSF). It is well known that particle movement during image acquisition alters the shape of the observed intensity distribution.44 As a consequence, in wide-field fluorescence microscopy (acquisition times of tens of milliseconds) labeled proteins diffusing through the cytosol appear as elongated and blurred signals, while the intensity distribution of membrane-bound proteins approximates the stationary PSF (diffraction limited). The blurring of the PSF depends on both the diffusion coefficient of the molecule and the rate used to acquire the images (Supporting Information, Figure S1). Weak adsorption of protein molecules on lipid layers induces the diffusion coefficient of the protein to decrease almost ten-fold, from 20-50 µm2/s in the cytosol to 4-6 µm2/s on the membrane.37,45 In spite of the reduced diffusion, when using acquisition rates lower than 62.5 Hz (16 ms), the fluorescence signal of proteins adsorbed on the lipid membrane cannot be discriminated from the background (Supporting Information, Figure S1). On the other hand, the detection of fluorescently labelled lipid anchored proteins and membrane proteins is easily achievable (diffusion coefficients of 0.1-1 or 0.001-0.1 µm2/s, respectively).46 We therefore reasoned that the interaction of cytosolic proteins with membrane-bound proteins and the subsequent decrease in the mobility of the cytosolic protein alters the observed intensity distribution and this difference can be used to selectively detect the fraction of cytosolic molecules interacting with proteins at the plasma membrane. We first tested this concept using a model system where membrane-bound modular protein domains and interacting peptides were co-expressed in HeLa cells. Figure 1 shows the results acquired using the PDZ domain of SH3 and multiple ankyrin repeat domains 3 (Shank3),1,47 and the Dlgap1/2/3 peptide (KD of 0.2 µM). The modular protein domain was artificially targeted to the plasma membrane with the N-terminal residues of the Lyn kinase (lyn11) and fused to the SNAP-tag for imaging their distribution on the plasma membrane (Figure 1c, f and m). The interacting peptides were fused to a photo-convertible fluorescent protein, mEos3.2 (details in Methods) and imaged under total internal reflection illumination (TIRF). The use of TIRF microscopy and photo-convertible fluorescent proteins enabled the selective photo-conversion and detection of molecules in close proximity to the basal membrane, which greatly decreased the background and allowed the discrimination of single peptide molecules (Supporting Information, Text and Figure S2). Moreover, the use of a light controllable fluorescent protein allowed tuning the number of fluorophores detected at each image, crucial for super-resolution imaging. As depicted in Figure 1, in cells expressing only labeled Dlgap1/2/3 peptides, the signal of photo-converted molecules freely diffusing in the cytosol just above the basal membrane was smeared (Figure 1d). It was possible to discriminate single molecules but the majority of the localized fluorescence signals appeared as a blurred dim signal. In contrast, in the presence of the membrane-targeted PDZ domain, most of the fluorescence signals of the Dlgap1/2/3 molecules showed as clear spots (Figure 1e, comparison shown in Supporting Information, Video 1).

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In single molecule based super-resolution microscopy, the reconstructed super-resolved fluorescence images are typically obtained by plotting the calculated position of the detected molecules, after fitting the intensity distribution of each molecule to a 2D-Gaussian function. As exemplified in the intensity profiles shown in Figure 1g and h, the observed intensity distributions of the individual labeled peptides in the presence of the membrane-bound domain were narrower and brighter. This was a consequence of the decrease in the diffusion coefficient of the peptide when interacting with the protein domain. Simulations showed that by setting strict parameters for the detection of single fluorophores, only molecules diffusing slower that 2.5 µm2/s were detected (more details in Methods and Supporting Information, Text and Figure S1). Moreover, molecules with a diffusion coefficient of 1 µm2/s are barely detected (for acquisition times higher than 30 ms less than 7% of the molecules in the illumination area are detected). As proteins interacting weakly with (phospho-)lipids present a diffusion coefficient of 1.7-2.3 µm2/s,37 they are practically undetected at these acquisition rates. Hence, the cytosolic molecules detected at the plasma membrane are either strongly interacting with confined (phospho-)lipid molecules, e.g. via a pleckstrin homology (PH) domain, or with proteins localized at the membrane, e.g. transmembrane receptors. For the example presented in Figure 1, selective detection of only the slow diffusing Dlgap1/2/3 peptides rendered the super-resolved images shown in Figure 1i-l. The superresolved image of the Dlgap1/2/3 locations in the presence of the PDZ domain corresponded to the diffraction limited distribution of the labeled PDZ domain targeted to the plasma membrane (Figure 1l and 1m). This result proves that by selectively detecting molecules with decreased mobility, it was possible to segregate interacting from non-interacting molecules. Furthermore, as the calculated position of the interacting molecules corresponded to the localization of the membrane-bound interaction partners, we can conclude that the reconstructed image corresponded to a map of the interactions of the Dlgap1/2/3 peptide with membrane-bound PDZ domain. Importantly, since the position of the molecules could be localized with high precision (ca. 35 nm, see Methods), the positions of the interacting molecules were mapped at the nanometer scale. As depicting in Figure 1i-l, the image reconstructed from the detected positions of interaction peptides has a higher spatial resolution than the diffraction limit fluorescence image of the membrane bound protein domains (representative line profiles are shown in Supporting Information, Figure S3).

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Figure 1. Super-resolution imaging based on the interaction between cytosolic and membrane-bound proteins. HeLa cells expressing mEos3.2-Dlgap1/2/3 peptides alone (left column) or together with expression of SNAP-PDZ (PDZ domain from Shank3, middle and right column) were imaged under TIRF illumination. (a, b, c) Schematic drawing depicting the molecules expressed in each set of experiments. The molecules imaged are highlighted in color: Dlgap1/2/3 peptides (blue), mEos3.2 (green), PDZ domain (orange) and SNAP (red). (d, e) Representative fluorescence images acquired for mEos3.2-Dlgap1/2/3 alone (d) or mEos3.2-Dlgap1/2/3 coexpressed with SNAP-PDZ (e). The insets show the intensity distribution of the fluorescence signal from the molecules indicated by the arrowheads. (f) Fluorescence image of SNAP-PDZ labeled with SNAP-SiR dye showing the membrane distribution of the domain. (g, h) Fluorescence intensity profiles along the white line in panels d and e (for g and h, respectively). The line was shifted downwards for clarity. (i-l) Super-resolution image of mEos3.2-Dlgap1/2/3 in the absence (I,k) and presence (j,l) of the membrane-bound SNAP-PDZ. This images were reconstructed using the positions calculated with an accurate fit. (k, l, m) Magnifications of the region indicted by the white square in i, j and f, respectively. Scale bars indicate 5 µm.

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The influence of binding affinities and rate of data acquisition Transient protein-protein interactions play an important role in signal transduction, with dissociation constants (KD) values typically in the µM range.48,49 KD values are determined by the dissociation and association rate constants (koff and kon, respectively). Since koff represents the number of dissociation events per interacting molecule per second, the time that a cytosolic protein remains bound to its membrane associated partner is 1/koff and is further designated by ‘on-time’. Knowing that the dissociation constant, KD, equals to koff/kon, the on-time can also be written as 1/(KD×kon). Assuming the kon is between 105 and 108 M-1s-1 (the range reported for transient protein-protein interactions)49,50 the on-time for an interaction with a KD between 1 and 100 µM is in the range of 10 s to 0.1 ms (Supporting Information, Table S1). This time interval overlaps with the image acquisition rate typically used in single molecule fluorescence microscopy (10 ms to 200 ms). As bounded molecules can be detected in several frames, the quality of the reconstructed interaction maps is influenced by both the binding affinity of the interacting molecules and the rate used for image acquisition. Moreover, when fast acquisition rates are used, higher excitation powers are applied to enhance the signal and subsequent detection of single fluorophores. Faster acquisition rates are therefore linked to faster photobleaching of the fluorophore. In short, the number of interacting molecules detected depends on the time period the molecule stay at the plasma membrane (binding kinetics), the time period it can be detected (lifetime of the fluorescent probe) and the number of frames where the molecule is detected (higher contrast in the reconstructed image). In order to investigate the influence of binding affinities on the number of molecules detected, five different protein domain/peptide combinations were tested, with a KD ranging from 0.2 to 170 µM (Supporting Information, Table S2). Figure 2 shows some representative reconstructed images obtained using the PDZ domain of Shank3 with 3 different peptides, Dlgap1/2/3, Cnksr2 and p1, with KD of 0.2, 16 and 24 µM, respectively (images obtained with other protein domain/peptide combinations are shown in Supporting Information, Figure S4). For the slower acquisition rate (50 ms exposure time), co-expression of interaction pairs with a higher KD yielded a decreased number of localized molecules (first column, figure 2 and Supporting Information, Figure S4). This is a consequence of shorter on-time at the membrane, which results in a reduced number of detected localizations. Correspondingly, when higher acquisition rates are used, an increase in the number of detections is excepted. For instance, if a molecule remains bound to the membrane for 150 ms, it will be detected roughly 3, 5 or 9 times, for images acquires with 50, 30 or 16 ms of exposure time, respectively. The reconstructed interaction maps of Cnksr2 and p1 peptides show this trend (the number of localizations is shown in Supporting Information, Table S3 and Figure S5g). However, in the case of Dlgap1/2/3, a more stable interaction with a lower KD, the number of localizations decreases with the decrease of the exposure time. Since higher excitation powers were used, we hypothesized that the fluorophore bleached before the unbinding of the cytosolic protein. As the binding spot remained occupied, no new molecules were able to bind, which reduced the amount of detections. In order to confirm this hypothesis, we investigated the effect of on-time, acquisition rate and photobleaching using simulated fluorescence images (details of the simulations in Supporting Information). When bleaching of the fluorescence probe is omitted, simulations showed that (i) for a given acquisition rate, interactions with lower KD resulted in a higher number of 7


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detected molecules and (ii) shorter acquisition times increase the number of localizations (Supporting Information, Figure S5a, c and e). However, when the cytosolic molecule is at the plasma membrane, the fluorescent probe can be quenched or photobleached. More importantly, the time that the probe remains in an emissive state depends of the excitation powers used, which are higher for shorter acquisition times. Simulations performed using a quenching time dependent on the acquisition time show two trends: (i) for short interactions (high KD), the effect of bleaching is negligible – most molecules unbind before the fluorescent probe goes to a non-emissive state; (ii) for stable interactions (low KD), when high excitation powers are used (short acquisition times), the probe bleaches and the interacting protein is no longer detected; since no new molecules can bind, there is a decrease in the total number of detections (Supporting Information, Figure S5b, d and f).

Figure 2. Super-resolved interaction maps of domain/peptide pairs using different acquisition times. Reconstructed images using the localization of single interacting peptide molecules, Dlgap1/2/3 (a, b, c), Cnksr2 (d, e, f) and p1 (g, h, i) labeled with mEos3.2, using 50 ms (a, d, g), 30 ms (b, e, h) or 16 ms (c, f, i) of integration time. The data was acquired during 4 minutes (4800, 8000 and 15000 frames for 50, 30 and 16 ms, respectively). For comparison purposes the images of the same peptide are displayed using the same lookup table. Scale bar indicate 5 µm.

This implies that by tuning experimental conditions, fast transient interactions with different binding kinetics can be imaged. While shorter interactions can be detected by increasing the

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excitation power and reducing the acquisition time, more stable interactions are more efficiently detected when low excitation powers and longer acquisition times are applied. It is important to mention that a difference in the KD cannot be directly linked to a difference in the on-time of the interaction. In general, protein-protein interaction is a two-step process: a fast-collisional encounter is followed by a rate-limiting conformational change. In the case of the interactions between a protein domain and a small peptide, the crucial conformational change is expected to occur on the protein side, and consequently, different protein domains can have different kon values. This implies that interactions between different proteins with similar KD values can display very different on-times. Here we compared the interaction between the same protein domain and different peptides, and can therefore assume that different KD values reflect different on-times. The density of available binding proteins at the membrane also affects the detection of transient interactions, in addition to the binding kinetics, acquisition rate and fluorescence lifetime. According to the Nyquist criterion, it is possible to obtain a reconstructed image when there are at least two data points per resolution unit. This implies that for a final resolution unit of 35 nm, the specimen must contain an emitter every 17.5 nm (which corresponds to a molecular density of approximately 4160 molecules/µm2). Considering a volume of 2000 µm3 per cell and a cellular membrane area of 1000 µm2, an expression level of at least 3.4 µM is required. For the model system here presented, where the membrane bound proteins are over-expressed, we estimate an average expression level of 10-100 µM per cell.51 This implies that the density of potential labelling sites at the plasma membrane is high enough to reconstruct super-resolved images. Consequently, for over-expressed proteins, this factor does not play an important role on the quality of the maps obtained. Taken together these results show that interaction pairs with a wide range of binding affinities can be imaged. However, the effective range detectable is influenced by the ratio between kon and koff, acquisition rate, protein expression levels and photo-stability of the fluorescent tag. Protein localization versus interaction mapping Super-resolution fluorescence microscopy has been mostly applied to image the cellular distribution and organization of a protein of interest or cellular structures (by fluorescently labelling structural proteins or proteins that interact strongly with the structure of interest). To avoid artifacts arising from cellular or molecular movement, imaging is performed after chemically fixing the specimen. Proteins strongly interacting with other proteins or lipids are crosslinked to their surroundings and (super-resolution) fluorescence images display the localization of the protein. However, for weak transiently interacting proteins the cellular localization of the proteins and the localization of interacting subpopulations might be substantially different. For instance, after chemically fixing HeLa cells co-expressing the membrane targeted PDZ domain of Shank3 and labeled Dlgap1/2/3 peptide, the reconstructed image of Dlgap1/2/3 did not resemble the distribution of the PDZ domain, whereas the distribution of the domain itself was maintained after fixation (Figure 3). Instead of colocalizing with the PDZ domain of Shank3, the detected Dlgap1/2/3 were dispersed throughout the plasma membrane. It is important to mention that the Dlgap1/2/3 peptides were localized mainly in the cytoplasm and the molecules detected at the plasma membrane represent a small fraction of the total number of molecules present in the cell. This is in stark contrast with the 9


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super-resolution reconstructed images of HeLa cells expressing mEos3.2 directly fused to the Lyn11 tag. In this case there is a strong interaction between the protein and the lipid bilayer, and after fixation it is still possible to reconstruct the distribution of the protein at the plasma membrane (Supporting Information, Text and Figures S6g-h). These results indicate that living conditions are an important requirement to image the shortlived transient interactions. However, when performing super-resolution imaging in living cells the high power density of the excitation generates reactive oxygen species (ROS) through excited-state reactions of endogenous and exogenous chromophores that have a high potential to damage cellular components.52 To tackle this, we limited the acquisition time and laser intensity to ensure that the cell viability was not affected (see Supporting Information, Text and Figure S7). Furthermore, for the experimental conditions used, cellular movement has a negligible effect on the obtained reconstructed images (see Supporting Information, Text and Figure S8).

Figure 3. Influence of chemical fixation. Membrane distribution and interaction maps of Shank3 and Dlgap1/2/3 over-expressed in HeLa cells, under living conditions and after chemical fixation. (a-d) Membrane distribution of membrane targeted Shank3 protein domain, fused to the SNAP tag. Similar to HeLa cells expressing Lyn11-mEos3.2 (Supporting Information, Figure S6a-h), chemical fixation does not alter the distribution of the Shank3 domain. (e-h) Super-resolution reconstructed images of Dlgap1/2/3 in HeLa cells expressing membrane targeted Shank3 domain. For comparison purposes the reconstructed super-resolution images are displayed using the same lookup table. Scale bar: 5 µm

Case study: the EGFR signaling pathway The epidermal growth factor receptor (EGFR) is one of the most studied receptors and lies ahead of a complex and highly conserved signaling pathway.53,54 The EGFR pathway is triggered by the binding of epidermal growth factor (EGF), which leads to dimerization of the receptor and subsequent phosphorylation of tyrosine residues on the C-terminal tail. These phosphorylated residues serve as docking sites, recruiting downstream signaling proteins 10


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containing a Src homology 2 (SH2) domain or a phosphotyrosine binding domain (PTB) domain, such as phospholipase Cγ (PLCγ), growth-factor-receptor-bound protein 2 (Grb2) and SH2-containing protein (Shc).55 To explore the potential of the imaging method presented here, we investigated the interaction of cytosolic molecules involved in the EGF pathway with proteins located at the plasma membrane. More specifically, we acquired nanoscale interaction maps of the adaptor protein, growth factor receptor-bound protein-2 (Grb2) and the enzyme, phospholipase γ1 (PLCγ1), both binding to the phosphorylated C-terminal tail of the EGF receptor (EGFR), and of the rapidly accelerated fibrosarcoma kinase (c-Raf), which binds towards activated Ras proteins, downstream of the EGFR. Mapping and analysis of Grb2 interactions One of the key downstream pathways of the EGFR is the Grb2-Sos-Ras/MAP kinase pathway, which regulates fundamental cellular processes such as proliferation, survival, differentiation, apoptosis and migration.50 The first step of this pathway is binding of the small adaptor protein Grb2 to phosphorylated tyrosine residues of the receptor’s tail. In addition to its role in downstream signaling, binding of Grb2 to the EGF receptor (EGFR) is essential for clathrin mediated endocytosis of the receptor.56 Addition of EGF to HeLa cells lead to the translocation of Grb2 to the cell membrane and accumulation in spot-like structures enriched in EGFR (Figure 4 and Supporting Information, Video 2). These EGFR enriched domains are the starting point for the down-regulation of the signal via endocytosis. However, in the first 5 minutes after stimulation with EGF, the majority of these domains are localized at the plasma membrane (only 15% of the receptors are endocytosed).57 Due to the short-lived transient nature of the Grb2-EGFR interactions, after chemical fixation these structures were concealed in the reconstructed fluorescence images (Supporting Information, Figure S9).

Figure 4: Dual color diffraction limited TIRF imaging of labeled EGFR and Grb2. False colored expression pattern of eGFP-EGFR (green) and mCherry-Grb2 (magenta) before EGF addition (a-d) and after 1.6 nM EGF addition (e-h) detected under TIRF illumination mode. (c-h) Overlaid images of EGFR and Grb2. (d, h)

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Magnifications indicated in the white boxes in the image on the left. Time point relative to EGF addition is indicated on panels a and e. Scale bars indicate 5 µm.

Figure 5. Reconstructed super-resolution images of Grb2 molecules interacting with membrane proteins. (a) Nanoscale interaction map of mEos3.2-Grb2 molecules expressed in HeLa cells after addition of EGF (to a final concentration of 1.6 nM). The panels b*, b** are magnifications of the white squares indicated in panel a. (c) Overlay of the nanoscale interaction map of Grb2 (blue) with the diffraction limited image of eGFP-EGFR (gray). (d) Density of calculated Grb2 positions relative to the center of the closest EGFR-enriched domain (Drel) as a function of the distance between the Grb2 molecule and the center of the closest EGFR cluster. The dotted lines represent the 95 % confidence interval using 10 simulations with a random distribution of EGFR clusters. (e) Expansion of the region indicated by the white box in (c), showing the overlap between the diffraction limited image of eGFP-EGFR (gray, f) with the nanoscale interaction map of Grb2 (blue, g). Scale bars indicate 5 µm.

Using the microscopic approach here presented, we were able to map the location of the Grb2 molecules interacting at the level of the cellular membrane, at the nanometer scale (Figure 5ab and Supporting Information, Video 3). As depicted in Figure 5, the reconstructed images showed small circular clusters that match the spot-like structures observed in the diffraction limited fluorescence images of the labeled EGFR. In order to correlate the presence of interacting Gbr2 molecules with the EGFR enriched domains, HeLa co-expressing labelled EGFR and mEos3.2-Grb2 were imaged (details in Methods). Figure 5c shows the reconstructed image of the detected Grb2 interactions super-imposed on the diffraction limited fluorescence image of EGFR. Correlation between the membrane interacting Grb2 molecules and the EGFR 12


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enriched membrane domains was evaluated using an analysis method where the local density of the detected interactions was calculated relatively to the position of the closest EGFRenriched domain (similar to 58-60, details in Methods). In this analysis method the normalized deviation between the detected number of interactions in a circle around each domain and the expected number of interactions based on the overall density is calculated (Drel). Thereafter, Drel was calculated for interactions located in circles with an increasing radius around the centre of the domain. When the interactions are not randomly distributed in relation towards the domain, it is expected that Drel is smaller (negative correlation) or larger (positive correlation) than the one calculated for randomly distributed clusters (95% confidence interval calculated from simulated data based on a random distribution of EGFR domains). This analysis showed that Grb2 interactions are preferentially located around the centre of an EGFR-enriched domain (Figure 5d). In addition to the small circular clusters, other structures were also observed in the reconstructed interaction maps, specifically a fine structure that resembled the ridges in a fingerprint (Figure 5b**). Presumably, due to the background signal from the cytosolic Grb2 and the limited resolution, this fingerprint-like pattern was completely masked in the conventional TIRF images (Supporting Information, Figure S10). Mapping and analysis of c-Raf interactions Translocation of Grb2 to the plasma membrane brings along the guanine exchange factor son of sevenless (SOS) thereby bringing SOS in close proximity to its substrate, rat sarcoma (Ras), located at the cellular membrane. Binding of SOS induces the activation of Ras molecules (exchange of GDP for GTP) and the consequent recruitment of c-Raf to the cellular membrane.61 The interaction between activated Ras molecules and the cytosolic c-Raf connects the stimulation of EGF receptors to the cytosolic MAPK pathway. It has been suggested that lipid rafts and protein enriched domains might work as signalling platforms, bringing together different molecules of a specific pathway.62 We tested this hypothesis by imaging EGFR enriched domains and c-Raf molecules interacting at the plasma membrane. Plotting of the localization of the detected c-Raf molecules rendered the interaction map shown in Figure 6a-b. The reconstructed image displayed very small and bright clusters dispersed on randomly distributed interactions. In stark contrast with Grb2, the distribution of interacting c-Raf molecules was not correlated to the location of EGFR-enriched domains, as shown by Drel (Figure 6d, analysis performed as described in the Methods for Grb2). This suggests that molecules downstream of Grb2 do not bind preferentially to EGFR enriched domains, dismissing their role as signaling platforms.

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Figure 6. Reconstructed super-resolution images of c-Raf molecules interacting with membrane proteins. (a) Nanoscale interaction map of mEos3.2-c-Raf molecules expressed in CHO cells after addition of EGF (to a final concentration of 16 nM). The panel b is a magnification of the white square indicated in panel a. (c) Overlay of the nanoscale interaction map of c-Raf (blue) with the diffraction limited image of eGFP-EGFR (gray). (d) Density of calculated c-Raf positions relative to the center of the closest EGFR-enriched domain (Drel) as a function of the distance between the c-Raf molecule and the center of the closest EGFR cluster. The dotted lines represent the 95 % confidence interval using 10 simulations with a random distribution of EGFR clusters. (e) Expansion of the region indicated by the white box in (c), showing the overlap between the diffraction limited image of eGFP-EGFR (gray, f) with the nanoscale interaction map of c-Raf (blue, g). Scale bars indicate 5 µm.

Mapping and analysis of PLCγ1 interactions Phospholipase Cγ1 (PLCγ1) is another effector of the EGFR. The most prominent role of PLC enzymes is severing the polar head of phosphoinositol, more specifically phosphatidylinositol4,5-bisphosphate (PIP2) generating diacylglycerol (DAG) and inositol-1,4,5-trisphosphate (IP3). These molecules are important signaling messengers that regulate the cytosolic Ca2+ concentration and the activation of kinases such as PKC, among other processes.63 All members of the PLC family share a common core constituted of an N-terminal pleckstrin homology (PH) domain, one or more EF hands, a catalytic TIM barrel and a C-terminal C2 domain.64 The PH domain can bind to phosphatidylinositol lipids within biological membranes. However, while isolated PH domains are localized to the plasma membrane, the majority of

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PLC isoforms are primarily found in the cytosol. In the specific case of PLC γ1 the TIM barrel is split up into two domains, termed X-box and Y-box, responsible for the enzymatic reaction and substrate recognition, respectively. Whereas other PLC isoforms have short linkers between the catalytic domains X and Y, PLCγ1 exhibits a complex highly structured loop flanked by the X and Y catalytic domains. This structure plays an important role in the regulation of PLCγ1. It contains two SH2 domains, an nSH2 and a cSH2 domain, and a SH3 domain. The presence of two SH2 domains offers additional affinity and specificity due to the binary interaction and spatial constrains. The SH2 domains of PLCγ1 are required to bind to tyrosine-autophosphorylated receptors and subsequent phosphorylation and activation of PLCγ1. It has been shown that both SH2 domains display different binding preferences. The C-terminal SH2 (cSH2) has an auto-inhibitory role through an intramolecular interaction that is released after tyrosine phosphorylation.65,66 The N-terminal SH2 (nSH2) binds preferentially to the C-terminal tails of receptor tyrosine kinases,67 which triggers PLCγ1 translocation to the plasma membrane upon cellular stimulation with EGF.68 Here we mapped the interactions of PLCγ1 towards the plasma membrane (Figure 7a-c). Under these conditions, clear single molecule fluorescent signals were observed. As PLCγ1 has a PH domain that binds to phosphatidylinositol 3,4,5-triphosphate (PIP3) molecules, the reduced diffusion of the molecules detected at the plasma membrane might be a consequence of interactions with membrane bound proteins or with lipids confined in membrane domains. Even though both Grb2 and PLCγ1 are known to interact with EGFR at the plasma membrane, the reconstructed super-resolution images of PLCγ1 molecules slowly diffusing on the plasma membrane were different than the ones acquired with labeled Grb2. While Grb2 molecules interacted with membrane proteins located in spot- and fingerprint-like structures, interacting PLCγ1 molecules were uniformly distributed throughout the whole plasma membrane (Figure 7b and c). The role of each protein domain can be evaluated by measuring different mutants. For instance, while the interaction maps of labeled nSH2 domains were comparable with the ones of wild type PLCγ1 (Figure 7h and i), the interactions detected for isolated cSH2 were distributed in fingerprint-like structures (Figure 7e and f). This implies that expression of the full-length protein either inhibits the interactions of the cSH2 domain or induces changes in the binding kinetics of the interaction, rendering it undetectable under these experimental conditions.

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Figure 7. Reconstructed super-resolution images of PLCg1 molecules interacting with membrane proteins. Typical fluorescence images of HeLa cells expressing full length PLCg1 (a), cSH2 (d) and nSH2 (g) fused to mEos3.2 under resting conditions. The detected single molecule localizations were plotted, yielding the interaction maps shown in panels b, e and h, for full length PLCg1, cSH2 and nSH2, respectively. (c, f, i) Magnifications of the areas indicated by the white boxes in b, e, and h. Scale bars indicate 5 µm.

CONCLUSIONS

In conclusion, we have developed a approach to image (short-lived) transient interactions of cytosolic molecules with membrane-bound proteins. To the best of our knowledge this approach is the first to provide a nano-scale map of transient interactions for a wide range of proteins at the plasma membrane. This approach is an easy-to-implement extension of today’s single molecule localization imaging toolbox as the underlying principles are based on PAINT. Similar to other single molecule localization techniques, this method is extendable to multicolor imaging. For example, interaction of two different cytosolic molecules can be detected by labelling one with mEos3.2 and the other with a far-red dye conjugated with a selflabelling protein tag, e.g. Halo, CLIP or SNAP tag. This protein tag can then be labelled with a permeable dye, such as the newly developed JF 647.69 Excitation and detection of these two dyes can be spectrally separated. While the density of activated mEos3.2 molecules is controlled by the power of the UV light source, the density of the detected JF 647 molecules can be tuned during the labelling procedure. Recent developments in 3D imaging such as PSF modulation, 70-73 multifocal imaging 74,75 or light sheet,76,77 will allow to use this method for 16


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3D imaging, which will extend the applicability to intracellular membranes and structures. Furthermore, as exemplified here for the Grb2 and c-Raf clusters, the distribution of the PPIs at the plasma membrane can be correlated to other cellular structures (e.g. membrane domains, focal adhesions, cytoskeleton). Last, by imaging of truncated or mutated proteins the role of specific/modified regions on the distribution of the interactions can be studied with a nanoscale resolution, which can highlight their cellular function. As accumulating results suggest that spatial distribution and temporal dynamics are crucial for precise regulation of PPIs,13-16 this super-resolution imaging method will be crucial to explore the role of membrane heterogeneity in the regulation of cell signalling events. METHODS Plasmids

Plasmids for mammalian expression of fusion protein were constructed using standard molecular cloning techniques by enzymatic restriction digestion. Point mutations were introduced using a modified quick-change site-directed mutagenesis protocol.78 DNA encoding the SNAP protein79 was amplified from the pSNAPf vector (NEB) and inserted in an expression vector (pcDNA3, Invitrogen, Carlsbad, CA). The Lyn11 targeting signal was added in the forward primer, yielding the Lyn-SNAP/pcDNA3 construct. The DNA sequences for the protein domains Shank3,1 PDZ-RGS31 and WW80 were ordered as a g-block from Integrated DNA technologies (IDT, Belgium). Each domain was sub-cloned in the LynSNAP/pcDNA3 vector. mEos3.2 was generated by introducing point mutations on the mEos2 sequence (a kind gift from Loren Looger, Addgene plasmid #20341).81,82 Fluorescent proteins, eGFP (gift from R. Tsien), mCherry (gift from R. Tsien) and mEos3.2, were cloned into pcDNA3 or pcDNA6-v5 (Invitrogen), generating eGFP/pcDNA3, mCherry/pcDNA3, mEos3.2/pcDNA6-v5 and mEos3.2/pcDNA3. Subsequently, DNA sequences for the short peptides (Supporting Information, Table S4) were added by PCR. A small spacer (sp), GLAGSGSGSGGS, separates the peptide and fluorescent protein. The gene encoding Grb2 (DNAform, clone ID 100009383) was subcloned into mCherry/pcDNA3 and mEos3.2/pcDNA3. The gene encoding c-Raf (a kind gift of Matsuda 83 ) was subcloned into a mammalian expression vector mEos3.2/pcDNA6v5. On the Nterminus of c-Raf a small linker (GSGSGSGGS) was added. The gene of human PLCg1 was amplified from pCR-XL-TOPO plasmid (Clone ID: 9052656, Thermo Scientific, Rockford, IL) and subcloned into pcDNA3. Subsequently, mEos3.2 was added to the C-terminus. For the nSH2 and cSH2 fragments, the domains were amplified by PCR and subcloned in mammalian vectors expressing mEos3.2. The plasmid encoding eGFP-EGFR was previously described.84 The sequence of LifeAct 85 was amplified based on two overlapping primers and subcloned into a mammalian expression vector eGFP/pcDNA3 (LifeAct-eGFP). For mitochondrial targeting, a tandem duplication of the N-terminal 36 amino acids of human cytochrome c oxidase subunit VIII 86 was fused to the N-terminus of eGFP (2mt8-eGFP/pcDNA3).87 pEYFPTub was from Clontech (Mountain View, CA). Venus (a kind gift from A. Miyawaki) was targeted to the endoplasmic reticulum (ER) by adding a calreticulin signal sequence

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(MLLSVPLLLGLLGLAAAD) and a ER retention signal (KDEL) to the N-terminus and Cterminus, respectively (calr-Venus-KDEL/pcDNA3). Simplified vector maps are listed in Supporting Information, Table S5. Cell culture and sample preparation HeLa cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) without phenol red (Life Technologies, Carlsbad, CA) and supplemented with 10% fetal bovine serum (FBS) (Life Technologies) and 50 µg/ml gentamycin (Life Technologies) at 37°C under a humidified 5% CO2 atmosphere. CHO cells were maintained in Ham's F-12 Nutrient Mixture without phenol red (Sigma-Aldrich, St. Louis, MO) and supplemented with 10% FBS and 50 µg/ml gentamycin at 37°C under a humidified 5% CO2 atmosphere. For imaging, cells were seeded in 35-mm glass bottom dishes (MatTeK), and transfected with the expression plasmids using Extreme 9 DNA transfection reagent (Sigma Aldrich) 36 to 48 hours before imaging, according to the manufacturer's instructions. Before imaging, 0.1 mM Trolox (Fluka) mixed with 1% chremophor and 0.1 mM ascorbic acid (Merck) in DMEM (with or without FBS) was added. The cells were incubated at 37°C under a humidified 5% CO2 atmosphere for one hour. Cells expressing SNAP tag labeled protein domains were incubated at 37°C with 3 nM SiRSNAP tag ligand (NEB, Ipswich, MA) in DMEM supplemented with FBS for 15 minutes. Thereafter the sample was washed for 3 times with Hank's balanced salt solution supplemented with magnesium and calcium (HBSS, Invitrogen) and incubated for 1 hour with DMEM supplemented with FBS at 37°C. Before imaging the sample was washed three times more with HBSS. Imaging was performed in HBSS at room temperature unless specified differently. EGF was purchased from Invitrogen and reconstituted in phosphate-buffered saline (PBS; Invitrogen) containing 0.35% bovine serum albumin (BSA). When indicated, cells where stimulated with EGF to a final concentration of 1.6 nM. Chemical fixation was performed with 4% formaldehyde (ThermoFisher) in PBS, pH7.4 at room temperature for 30 min. Instrumentation A home build setup based on an inverted microscope (IX83, Olympus) was used to detect single molecules in TIRF and wide field mode. The setup was equipped with two Electron Multiplying-CCD cameras (ImagEM C9100-13; Hamamatsu Photonics, Hamamatsu, Japan) and an APON 60XOTIRF objective lens (NA 1.49, Olympus). Fluorescence images in wide field mode were obtained by trans-illumination with a mercury lamp (SHI-130 OL, Olympus). The red form of mEos3.2 and mCherry proteins were excited with a 561-nm line from a DPSS laser (200 mW; Coherent Inc., Santa Clara, California). A 488-nm line from a DPSS laser (100 mW; Spectra-Physics, Irvine, California) was used to excite the eGFP and the green form of mEos3.2. mEos3.2 was converted with a 405-nm line from a diode laser (Cube, 100 mW; Coherent Inc., Santa Clara, California). A 644-nm line from a diode laser (100mW, SpectraPhysics) was used for excitation of SiR dye. Before being expanded, the laser lines

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were combined using appropriate dichroic mirrors (405bcm, 488bcm and 561bcm). The laser lines were guided onto the sample by a dichroic mirror z488/561/633rpc. The fluorescence of the red of mEos3.2 form was detected through a long pass filter 572, in combination with a band pass filter HQ590M40-2P, while the fluorescence images for the green form passed through two bandpass filters, HQ525M40-2P and z488/561m. The fluorescence emission of eGFP was detected through a 500 nm long pass filter and a band pass filter HQ525M50-2P. For SiR a 655 long pass filter was combined with a HQ700/75m band pass filter to select the correct emission range. Fluorescence signals from the red form of mEos3.2 and the far red emission of the SiR was separated by using a 630dcsp short pass filter. Fluorescence signals from the eGFP and the red mEos3.2 form were separated by a dichroic mirror 550dclp. All the filters were purchased from Chroma Inc. Image recording Time-lapse fluorescence images were recorded with continuous illumination and 20Hz acquisition rate. To monitor the eGFP-EGFR distribution and detect single mEos3.2-Grb2 molecules, the fluorescence signals of the red form of mEos3.2 were recorded with 46 ms integration time at 10 Hz acquisition rate and eGFP signals were acquired every 3 s with 46 ms integration time. Alternate excitation and detection of the mEos3.2 fluorescence and eGFP was performed using two fast mechanical shutters (Oriel, Stratford, Connecticut) synchronized with two separate CCD cameras by a DAQ card NI USB-6343 (National Instruments, Austin, Texas). A home-developed LabVIEW software (National Instruments) was used to precisely control the timing. When imaging live cells, a DIC image of the cell was taken every 4 minutes to follow cell viability (Supporting Information, Text and Figure S7). When the SNAP tag was labeled with SiR, a fluorescence image was acquired before single molecule data acquisition. The intensity of the different laser lines was measured after the objective lens in epi-mode. A power density of 290 W/cm2 of 561 nm and 0-12.5 mW/cm2 405 nm was used for single molecule data acquisition. Image simulations All simulations were implemented in Matlab (version R2017b, MathWorks, Natick, MA). A detailed description is included in the Supporting Information. The source code is freely available at: https://github.com/CamachoDejay/diffusion_binding_simulations Calculation and representation of interaction maps: For calculation of single molecule coordinates the program 'Localizer' 88 running from Igor Pro (version 6.3.7.2) or Matlab (version R2017b, MathWorks, Natick, MA) was used. Particle detection was achieved using the Generalized Likelihood Ratio Test (GLRT) method. Selective detection of immobile molecules was achieved by using a sensitivity of 25. Lower values resulted in the detection of a higher fraction of mobile/non-interacting molecules (Supporting Information, Figure S1 and S11). After localization, the coordinates are plotted in a binned image with a pixel size 4 times smaller as the original pixel size of the CCD camera (26.75 nm = 107/4 nm). Thereafter a 19


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Gaussian blur is applied to the image. In the reconstructed images shown, brightness and contrast was adjusted using Fiji.89 Calculation and representation of trajectories: The positions of a molecule detected in consecutive frames are connected to reconstruct a trajectory using home-developed software in Matlab. Coordinates presented in consecutive frames are linked to form a single trajectory when they uniquely appear in a distance smaller than 535 nm (corresponding to 5 pixels, which avoids misconnection given the signal density while connecting molecules with a maximal mobility of 10.7 µm/s) Calculation of the localization precision: The precision of the measurements is calculated by averaging the precision of position of each detected molecule. The precision of each position was calculated as described by Mortensen et al.,90 using the equation (1): !

" $% & )* '

(+ +

-.$% & /& '0 &

1 (1)

"

with 𝜎0 " = 𝜎 " + 𝑎 712 , where 𝜎 is the width of the Gaussian function, N is the total number of detected photons, σ is the width of the fitted Gaussian function, b is the standard deviation of the background and a is the pixel size. In the experiments here presented, we had a precision of 34.8 ± 12.9 nm (mean ± std). Cluster identification: Analysis of the EGFR distribution in the diffraction limited fluorescence images was performed by pre-treating the fluorescent images by an SRRF analysis 91 (to improve signal to noise ratio and image resolution). However, this analysis decreased time resolution. Therefore we minimized correlation in time to only 3 frames leaving a time resolution of 15 s. Temporal analysis was calculated by a pixel-by-pixel average projection of the intensity of the radiality map (Temporal Radiality Average). To minimize peaks in radiality caused by noise, the radiality map was calculated based on a ring radius of 2 with 6 axis in the ring, a radiality magnification of 3 and intensity weighted. Based on these pre-treated image sequences, the periphery of clusters was determined using a local threshold algorithm 92 implemented in home-developed software on Matlab (version R2017b, MathWorks, Natick, MA) (Supporting Information, Figure 12). Correlation between Grb2/c-Raf interactions and EGFR distribution: Registration of the two channels was performed using a calibration sample with multicolor fluorescent beads (0.2 µm, TetraSpek beads, Invitrogen). Images were transformed using a local weight mean algorithm (software developed in house using Matlab). Then, correlation between the Grb2 interactions and the distribution of EGFR was based on a home-build analysis written in Matlab. The center position of each previously detected cluster was determined. The distribution of the interactions around these centers was examined. Therefore, the deviation between the detected number of interactions and the expected number of interactions based on the overall density, was calculated in a ring around the center of the bright region. This was repeated with an increasing

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distance of the ring towards the center of the region and for all regions. Values were normalized with the number of centers and interactions in each plane. This can be described with equation (2): 𝐷9:; (𝑟) , 𝑟" ) = '

)

@ 'A

A ∑' I (

C@ (9D,9& ) .(9& & E 9D &)

− 𝜆H 1 , 𝑟) , < 𝑟" (2)

where NA and NB are the total number of single molecule localizations and detected EGFR clusters, respectively, ri is the radius of circles centered at cluster positions, nA is the number of localizations inside the ring determined by the circles with radius r1 and r2. λA is the density of single molecule locations considering the total analysis region. The 95 % confidence interval was calculated based on 10 simulations, keeping the distribution of the interactions while randomly positioning the cluster centers. This approach was tested using a set of simulations of clusters and interacting molecules (Supporting Information, Figure S13). Data acquisition took up to 15 min for a single cell. During the total acquisition time, clusters of EGFR might move, appear and disappear. Therefore the acquired data of a single cell was analyzed in time bins of 15 s. To combine results, the mean value of Drel and the confidence interval is shown. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

SUPPORTING INFORMATION Supporting notes related to single molecule detection, photo-toxicity/cell viability, influence of chemical fixation, the influence of molecular and cellular movement and simulation of microscopic images are included in the supporting information. Figures of single molecule detection at the plasma membrane, the effect of interaction on the single molecule intensity profile, super-resolved interaction maps of different domain/peptide pairs, lines profiles for the images shown in Figure 1, effect of binding kinetics, cluster selection using an adaptive intensity threshold and simulations on the correlation between clusters and molecular positions can also be found in the supporting information. This section also includes supporting tables with the aminoacid sequences of the interacting peptides, the on-time for different binding kinetics, the number of molecules detected in Figure 2, and simplified vectors maps. The videos showing the detection of interacting molecules, cluster formation after the addition of EGF and fluorescence imaging of interacting Gbr2 molecules are also included in the supporting data. This material is available free of charge at http://pubs.acs.org. CORRESPONDING AUTHOR * To whom correspondence should be addressed. Phone: +32 16 32 6640. E-mail: [email protected]

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ACKNOWLEDGEMENTS We thank E. Deridder, M. Kluba, E. Liekens, A. Garcia for technical assistance. This work was partly supported by the Research Foundation-Flanders (FWO Onderzoeksproject G0B5514N, G0A5817N and 1529418N) and by KU Leuven (C14/16/053). D.M.R. and H.D.K were supported by a PhD grant from the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen). R.C and S.R. are postdoctoral fellows of the Research Foundation Flanders (FWO Vlaanderen).

REFERENCES (1) Gavin, A.-C.; Aloy, P.; Grandi, P.; Krause, R.; Boesche, M.; Marzioch, M.; Rau, C.; Jensen, L.J.; Bastuck, S.; Dümpelfeld, B.; Edelmann, A.; Heurtier, M.A.; Hoffman, V.; Hoefert, C.; Klein, K.; Hudak, M.; Michon, A.M.; Schelder, M.; Schirle, M.; Remor, M.; et al., Proteome Survey Reveals Modularity of the Yeast Cell Machinery. Nature 2006, 440, 631–636. (2) Nooren, I. M. A.; Thornton, J. M. Diversity of Protein-Protein Interactions. EMBO J. 2003, 22, 3486–3492. (3) Jansen, R.; Greenbaum, D.; Gerstein, M. Relating Whole-Genome Expression Data with Protein-Protein Interactions. Genome Res. 2002, 12, 37–46. (4) Tompa, P.; Davey, N. E.; Gibson, T. J.; Babu, M. M. A Million Peptide Motifs for the Molecular Biologist. Mol. Cell 2014, 55, 161–169. (5) Acuner Ozbabacan, S. E.; Engin, H. B.; Gursoy, A.; Keskin, O. Transient ProteinProtein Interactions. Protein Eng. Des. Sel. 2011, 24, 635–648. (6) Kerppola, T. K. Bimolecular Fluorescence Complementation (BiFC) Analysis as a Probe of Protein Interactions in Living Cells. Annu. Rev. Biophys. 2008, 37, 465–487. (7) Gonçalves, S. A.; Matos, J. E.; Outeiro, T. F. Zooming Into Protein Oligomerization in Neurodegeneration Using BiFC. Trends Biochem. Sci. 2010, 35, 643–651. (8) Miller, K. E.; Kim, Y.; Huh, W.-K.; Park, H.-O. Bimolecular Fluorescence Complementation (BiFC) Analysis: Advances and Recent Applications for GenomeWideInteraction Studies. J. Mol. Biol. 2015, 427, 2039–2055. (9) Chang, L.; Ding, M.; Wang, S.; Chen, X.; Sun, Y. Development of Bimolecular Fluorescence Complementation Using rsEGFP2 for Detection and Super-Resolution Imaging of Protein-Protein Interactions in Live Cells. Biomed Opt Express 2017, 8, 3119–3131. (10) Hertel, F.; Mo, G. C. H.; Duwé, S.; Dedecker, P.; Zhang, J. RefSOFI for Mapping Nanoscale Organization of Protein-Protein Interactions in Living Cells. Cell Reports 2016, 14, 390–400. (11) Nickerson, A.; Huang, T.; Lin, L.-J.; Nan, X. Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM) for Nanoscale Imaging of Protein-Protein Interactions in Cells. PLoS ONE 2014, 9, e100589. (12) Winckler, P.; Lartigue, L.; Giannone, G.; De Giorgi, F.; Ichas, F.; Sibarita, J.-B.; Lounis, B.; Cognet, L. Identification and Super-Resolution Imaging of LigandActivated Receptor Dimers in Live Cells. Sci. Rep. 2013, 3, 2387. (13) Zhou, Y.; Hancock, J. F. Ras Nanoclusters: Versatile Lipid-Based Signaling Platforms. Biochim. Biophys. Acta 2015, 1853, 841–849. (14) Kholodenko, B. N.; Hancock, J. F.; Kolch, W. Signalling Ballet in Space and Time. Nat. Rev. Mol. Cell Biol. 2010, 11, 414–426. 22


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Mapping Transient Protein Interactions at the Nanoscale in Living

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