Author response: High-throughput, single-particle tracking reveals nested membrane domains that dictate KRasG12D diffusion and trafficking (2019)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Membrane nanodomains have been implicated in Ras signaling, but what these domains are and how they interact with Ras remain obscure. Here, using single particle tracking with photoactivated localization microscopy (spt-PALM) and detailed trajectory analysis, we show that distinct membrane domains dictate KRasG12D (an active KRas mutant) diffusion and trafficking in U2OS cells. KRasG12D exhibits an immobile state in ~70 nm domains, each embedded in a larger domain (~200 nm) that confers intermediate mobility, while the rest of the membrane supports fast diffusion. Moreover, KRasG12D is continuously removed from the membrane via the immobile state and replenished to the fast state, reminiscent of Ras internalization and recycling. Importantly, both the diffusion and trafficking properties of KRasG12D remain invariant over a broad range of protein expression levels. Our results reveal how membrane organization dictates membrane diffusion and trafficking of Ras and offer new insight into the spatial regulation of Ras signaling. eLife digest The Ras family of proteins play an important role in relaying signals from the outside to the inside of the cell. Ras proteins are attached by a fatty tail to the inner surface of the cell membrane. When activated they transmit a burst of signal that controls critical behaviors like growth, survival and movement. It has been suggested that to prevent these signals from being accidently activated, Ras molecules must group together at specialized sites within the membrane before passing on their message. However, visualizing how Ras molecules cluster together at these domains has thus far been challenging. As a result, little is known about where these sites are located and how Ras molecules come to a stop at these domains. Now, Lee et al. have combined two microscopy techniques called 'single-particle tracking' and 'photoactivated localization microscopy' to track how individual molecules of activated Ras move in human cells grown in the lab. This revealed that Ras molecules quickly diffuse along the inside of the membrane until they arrive at certain locations that cause them to halt. However, computer models consisting of just the 'fast' and 'immobile' state could not correctly re-capture the way Ras molecules moved along the membrane. Lee et al. found that for these models to mimic the movement of Ras, a third 'intermediate' state of Ras mobility needed to be included. To investigate this further, Lee et al. created a fluorescent map that overlaid all the individual paths taken by each Ras molecule. The map showed regions in the membrane where the Ras molecules had stopped and possibly clustered together. Each of these 'immobilization domains' were then surrounded by an 'intermediate domain' where Ras molecules had begun to slow down their movement. Although the intermediate domains did not last long, they seemed to guide Ras molecules into the immobilization domains where they could cluster together with other molecules. From there, the cell constantly removed Ras molecules from these membrane domains and returned them back to their 'fast' diffusing state. Mutations in Ras proteins occur in around a third of all cancers, so a better understanding of their dynamics could help with future drug discovery. The methods used here could also be used to investigate the movement of other signaling molecules. Introduction The plasma membrane has a complex and dynamic landscape that helps shape how diverse membrane-localized signaling molecules behave (Grecco et al., 2011; Staubach and Hanisch, 2011; Simons and Toomre, 2000; Schmick and Bastiaens, 2014; Varma and Mayor, 1998; Garcia-Parajo et al., 2014). Among others, the Ras small GTPases are prototypical examples of signaling molecules whose biological activities are directly regulated by the membrane (Zhou and Hancock, 2015; Abankwa et al., 2010). While biochemical aspects of how Ras interacts with downstream effectors such as Raf have been well studied (Simanshu et al., 2017; Cox and Der, 2010), the mechanisms through which the biological membrane defines the signaling activity and specificity of Ras are still poorly understood. Recent studies by us and others suggest that Ras signaling may involve the formation of multimers (dimers and/or clusters) in a membrane-dependent manner (Güldenhaupt et al., 2012; Nan et al., 2015; Spencer-Smith et al., 2017; Ambrogio et al., 2018; Prior et al., 2003; Inouye et al., 2000; Plowman et al., 2005), and that partitioning of Ras into nanoscopic membrane domains and interactions with scaffold proteins or structures likely constitute critical steps to Ras multimer formation and signaling (Zhou et al., 2015; Chung et al., 2018; Belanis et al., 2008; Shalom-Feuerstein et al., 2008; Chen et al., 2016). While previous high-resolution imaging experiments using immuno-EM (Prior et al., 2003; Plowman et al., 2005) or quantitative superresolution microscopy (Nan et al., 2015; Huang et al., 2015) were instrumental to revealing the existence of Ras multimers, the resulting images were mostly static and provided limited information about the spatiotemporal dynamics of Ras – membrane domain interactions. Live-cell single-particle tracking (SPT) (Schmidt et al., 1996; Kusumi et al., 2005; Saxton and Jacobson, 1997) complements static imaging by providing information about molecular motions, and it has been used to study Ras dynamics on the membrane (Lommerse et al., 2006; Murakoshi et al., 2004; Lommerse et al., 2004). The underlying rationale is that interactions of Ras with different membrane domains and signaling partners would manifest as varied diffusion behavior. Indeed, using SPT, Murakoshi et al. observed transient events of Ras immobilization on the membrane, which became more frequent upon epidermal growth factor stimulation, potentially reflecting the formation of signaling complexes or interactions with raft domains (Murakoshi et al., 2004). Lommerse and colleagues also used SPT to probe Ras diffusion and similarly observed transient and context-dependent confinement of Ras in membrane regions not more than 200 nm in diameter (Lommerse et al., 2006). These prior studies offered important initial insight into the potential connections between Ras diffusion, function, and membrane organization, but the technical constraints of traditional SPT limited the imaging throughput and depth of analysis in these studies. Typically, only a few tens of trajectories could be obtained from each experiment, which precluded detailed and quantitative characterization of the heterogeneous and stochastic nature of molecular diffusion. In consequence, while the studies consistently reported two diffusion states – a 'free' diffusion state and another 'immobile' state, it remains to be seen whether a two-state model adequately recapitulates Ras membrane dynamics (Lommerse et al., 2006; Murakoshi et al., 2004; Lommerse et al., 2004). Thus, the nature of the membrane domains occupied by each of these states and how Ras molecules transition between the states in connection with multimer formation and signaling remain unclear. Recent years have seen significant advances in both experimental (Manley et al., 2008; Benke et al., 2012; Huang et al., 2018; Basu et al., 2018; English, 2015; Cutler et al., 2013) and data analysis strategies (Persson et al., 2013; Hansen et al., 2018; Liu et al., 2015; Jaqaman et al., 2008; Chenouard et al., 2014; Shen et al., 2017; Monnier et al., 2015; Ito et al., 2017; Lindén and Elf, 2018) of SPT, some of which have dramatically improved the information throughput. Among others, spt-PALM combines SPT with photoactivated localization microscopy (PALM) to enable single molecule tracking under dense labeling conditions through stochastic photoswitching (Manley et al., 2008). With spt-PALM, it is routine to acquire thousands of diffusion trajectories from a single cell. A growing list of software tools has also been developed to facilitate spt-PALM data analysis (Persson et al., 2013; Hansen et al., 2018; Jaqaman et al., 2008; Ito et al., 2017; Newby et al., 2018). For example, variational Bayes SPT (vbSPT) allows construction of a detailed diffusion model from spt-PALM data with parameters such as the number of states, the diffusion coefficient and the occupancy of each state, as well as the state transition rates even when the individual trajectories are short (Persson et al., 2013). Additional methods have also been introduced to quantitate various aspects of diffusion dynamics from SPT trajectories (Chenouard et al., 2014; Ito et al., 2017; Manzo and Garcia-Parajo, 2015). These advances help overcome the limitations of conventional SPT and make it possible to analyze Ras membrane dynamics in much greater depth. Here, we report our efforts on combining spt-PALM with detailed trajectory analysis to reveal previously unknown aspects of Ras diffusion on the cell membrane. With carefully controlled expression levels and photoactivation rate, spt-PALM trajectories of PAmCherry1-tagged KRasG12D (KRas with an activating mutation and thus primarily GTP-bound) consistently reported three diffusion states, including a fast diffusion state, an immobile state, and a previously unidentified diffusion state with intermediate mobility. Leveraging the large number of trajectories, we were able to spatially map the diffusion states to distinctive membrane domains, estimate the size and lifetime of each domain, and define the spatial relationship between the domains. Moreover, in analyzing how KRasG12D transitions from one diffusion state to another, we discovered that KRasG12D diffusion follows a non-equilibrium steady state (NESS) model with net mass flow from the fast state to the immobile state, potentially coupled to the endocytic trafficking and membrane recycling of KRasG12D. Based on these results, we propose a new model to describe the membrane dynamics of KRasG12D, where nested membrane nanodomains dictate the diffusion and trafficking, with implications in Ras multimer formation and signaling. Results KRasG12D diffuses on the membrane in three distinct states To investigate the lateral diffusion properties of KRasG12D under controlled expression levels, we established isogenic U2OS cells stably expressing PAmCherry1-KRasG12D under doxycycline (Dox) regulation (Nan et al., 2015). The expression level of PAmCherry1-KRasG12D could be tuned from a level below that of the endogenous KRas at <1 ng/mL Dox to highly over-expressed at 5–10 ng/mL Dox (Figure 1A). Initially data were collected from cells expressing KRasG12D at a moderate level by inducing at 2 ng/mL Dox. The photoactivatable fluorescent protein PAmCherry1 has been widely used for quantitative PALM and spt-PALM (Subach et al., 2009). Owing to the good single-molecule brightness of activated PAmCherry1, we were able to track individual PAmCherry1-KRasG12D molecules at frame rates up to ~83 Hz (i.e.,~12 ms/frame) with a low excitation dose (~400 W/cm2 at 561 nm). The low spontaneous photoactivation rate of PAmCherry1 also permits clean single-molecule imaging even at high expression levels, yielding as many as hundreds of thousands of trajectories per cell via spt-PALM (Figure 1B and Video 1). Under these conditions, the average trajectory lengths were ~4 and~5 frames for data acquired at 12 ms/frame and 35 ms/frame rates, respectively (Figure 1—figure supplement 1). Despite the faster frame rate, data acquired at 12 ms/frame had a lower signal-to-noise ratio, causing a more frequent loss of molecules during tracking to yield significantly shorter trajectories (~50 ms average duration) than data acquired at 35 ms/frame (~175 ms average duration). We therefore used both frame rates in this work for the benefit of better temporal resolution or spatial precision. Figure 1 with 6 supplements see all Download asset Open asset Defining the membrane diffusion model of KRasG12D using spt-PALM and vbSPT. (A) Western blot showing the increasing expression level of PAmCherry1-KRasG12D with increasing doxycycline (Dox) concentration; (B) Example trajectory map of membrane KRasG12D acquired at 12 ms frame rate using total internal reflection fluorescence (TIRF) illumination. Each line represents an individual Ras molecule coordinate over time acquired for the duration of the movie (20 min). Only a subset of all trajectories is plotted. Scale bar: 10 μm; (C) Expanded view of the boxed region in B). Only a subset of all of the trajectories in the boxed region is shown to allow unhindered view of individual Ras trajectories. Inset shows a KRasG12D trajectory displaying multiple diffusion states. Scale bars: main figure: 1 μm; inset: 200 nm; (D) Determining the optimal model size for KRasG12D membrane diffusion using CDF fitting, with smaller root mean squared error indicating a better model (n = 8); (E) Determining the optimal model size for KRasG12D membrane diffusion using vbSPT, with smaller absolute model score (i.e., score of zero being the best global model) indicating a better model (n = 5); (F) Comparing the model parameters obtained from CDF fit and vbSPT, both using a three-state model for KRasG12D membrane diffusion. State transition probabilities were not inferred from CDF fit and therefore not included in the comparison. Error bars are 95% confidence intervals (CIs). Figure 1—source data 1 Excel sheet for data used for generating panels D, E, and F. Data for individual panels are included as separate tabs in the excel file. https://cdn.elifesciences.org/articles/46393/elife-46393-fig1-data1-v2.xlsx Download elife-46393-fig1-data1-v2.xlsx Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Clip of a raw spt-PALM video showing PAmCherry1-KRasG12D diffusion on U2OS cell membrane. Data acquired at 35 ms/frame. Cells were induced with 5 ng/mL doxycycline before imaging. A close inspection of the individual trajectories clearly shows larger diffusive steps intermittent with moments of transient entrapment, indicating the presence of multiple diffusion states and frequent state transitions (Figure 1C and inset). Similar observations were reported for both HRas and KRas in previous low throughput SPT experiments, where two diffusion states – a 'fast' state and an 'immobile' diffusion state – were detected (Lommerse et al., 2006; Murakoshi et al., 2004). Since spt-PALM offers a much larger number of trajectories, we first asked whether KRasG12D diffusion on the cell membrane could indeed be described by a simple two-state model. To this end, we used two methods to analyze the Ras diffusion trajectories. The first approach fits cumulative distribution function (CDF) for Brownian motion to the squared displacements of Ras trajectories to extract diffusion coefficients and the respective occupancies of the diffusion states (Schütz et al., 1997). The second method, vbSPT, treats particle diffusion and the associated state transitions with a Hidden Markov Model and performs model selection through variational inference (Persson et al., 2013). Of note, vbSPT is well suited for analyzing large numbers of short trajectories such as those obtained via spt-PALM. We found that particle densities higher than 0.03 µm−2 per frame under our experimental conditions (12 ms/frame with the fastest diffusion rate at ~1 µm2/s) led to occasional misconnected trajectories, and that even a small fraction of such misconnected trajectories could lead to skewed model outputs with vbSPT (Figure 1—figure supplement 2). In addition, the threshold for maximum particle displacement between adjacent frames also had an impact on trajectory misconnection, although to a lesser extent as tested with simulated trajectories (Figure 1—figure supplements 2 and 3). Thus, for diffusion model construction, we chose to use a high frame rate (12 ms/frame) and a low particle density (<0.03 µm−2) to eliminate misconnected trajectories while maintaining a sufficient number of trajectories. Using trajectories acquired and analyzed with the above precautions, both CDF fitting and vbSPT yielded similar three-state models for KRasG12D diffusion on the membrane of live U2OS cells. Specifically, CDF fitting to a three-state model had significantly lower residual error compared to a single- or a two-state model and further increasing the model size did not decrease the error (Figure 1D), indicating that a three-state model is sufficient to describe the data. For vbSPT, a score equal to zero indicates the best model, a condition that was met with a three-state model but not with larger or smaller size models (Figure 1E). To rule out the possibility of imprecise single-molecule localizations causing vbSPT to misinterpret two-state spt-PALM data as three states, we performed vbSPT analysis on simulated trajectories based on a two-state model with varying localizations errors (0, 20, 40, 80, 100 nm) added. Even at the highest localization errors (100 nm), vbSPT correctly retrieved a two-state model (Figure 1—figure supplement 4) from the simulated data, suggesting that the three-state model derived from experimental spt-PALM data is unlikely a result of localization errors in SPT. The diffusion coefficient and the occupancy for each of the diffusion states were in good agreement between the two analysis methods and within each method when applied to different cells under the same conditions, as evidenced by the small errors (Figure 1F). Datasets with high particle densities can return models with different sizes, sometimes also with aberrant model parameters (Figure 1—figure supplement 2B–D and Figure 1—figure supplement 5A–B); even so, the histogram of all vbSPT-derived diffusion coefficients still showed three distinct clusters (Figure 1—figure supplement 3C) corresponding to the three states listed in Figure 1F. Thus, we concluded that the membrane diffusion of KRasG12D under our experimental conditions is best described by a three-state model, demonstrating the existence of an intermediate state not detected in previous studies. Between the two methods, vbSPT was used for most subsequent analyses in the remainder of this work because it infers the transition probabilities and state identities for every time step whereas CDF does not. The diffusion coefficient of the slowest state in Figure 1F is comparable to that expected from single-molecule localization error (~40 nm, Figure 1—figure supplement 6), which implied that the actual diffusion of KRasG12D in this state may be even slower than it appeared. To test this hypothesis, we acquired spt-PALM data at a slower frame rate (35 ms/frame) to improve the localization accuracy of slowly moving molecules since more photons could now be collected for each PAmCherry1 molecule in a single frame (Figure 1—figure supplement 6). Indeed, these reported a significantly smaller diffusion coefficient µm2/s) for the slowest state than that obtained µm2/s) using data taken at 12 ms/frame. This result that the slowest diffusion state of KRasG12D is an immobile state, with previous (Lommerse et al., 2006; Murakoshi et al., 2004). KRasG12D diffusion states to distinct membrane domains The diffusion model in Figure the results from the spt-PALM trajectory analyses using vbSPT. Each represents one of the diffusion states with indicating the transition probabilities between of states. A of this model is that to be a state transition KRasG12D molecules transition between the fast (F) and the immobile states by through the intermediate state, and transitions between the fast and the immobile states In to this transition we compared the distribution of step to the immobile state since different step different diffusion with the state transition observed in Figure the histogram of step adjacent to the immobile steps to the intermediate diffusion state (Figure while the distribution of the steps had a a of both fast and intermediate diffusion steps (Figure where the indicates a of As the step to the immobile states (Figure are even smaller compared to that of the other two states. The of these three step size the transition through the intermediate state. The in step the three states were even more on data taken at 35 which had better single-molecule localization (Figure supplement 1). Thus, the intermediate state is not a state with intermediate mobility but an between the immobile and the fast states of KRasG12D. Figure 2 with 2 supplements see all Download asset Open asset KRasG12D diffusion states are associated with distinct membrane domains. (A) The three-state model for KRasG12D diffusion with and the the and the immobile states, Model parameters were inferred using vbSPT on spt-PALM with at trajectories obtained on cells induced with 2 ng/mL Dox. The state transitions the of to a different state in the and the of the and the of the are both to their parameters were derived from data acquired at 12 ms frame for the diffusion coefficient of the immobile state, which was inferred from data taken at 35 ms frame Error bars are 95% (n = 8); (B) size for immobilization events one step before or the immobilization and all other steps A diffusion step was of an immobilization immobile state was to that trajectory by vbSPT (n = see Materials and (C) of the membrane locations where KRasG12D molecules diffusion states to as state within a duration from a spt-PALM of total duration). and locations of the the and the fast states, with each to the mean diffusion coefficient for the (D) analysis on the state of spt-PALM The same as in was used to the three states. For this analysis, molecules in the same diffusion state in frames only a state The average state of all molecules within a were used for analysis, and the results from multiple were to yield the The was through a and the resulting trajectories were analyzed the same as the Materials and (E) analysis between of diffusion states. The state were the same way as in prior to the analysis, that the was performed between two different diffusion states. The was through a and the resulting trajectories were analyzed the same as the Materials and (F) the lower size for the immobile and the intermediate domains. The was based on the maximum by the molecule while in the same diffusion state. The main shows results inferred from data taken at 35 ms frame intervals for improved localization precision. The shows the data taken at 12 ms/frame (n = for 12 ms and = for 35 ms Figure data 1 Excel sheet for data used for generating panels D, E, and F. Data for individual panels are included as separate tabs in the excel file. Download The observed state transition may from at two potential In the first fast diffusing KRasG12D may transition into the intermediate then the immobile state through spontaneous to slow or static membrane the immobile states could be by KRasG12D to molecules or structures 'immobilization or 'immobilization in membrane regions to as 'intermediate that intermediate mobility to KRasG12D. these intermediate domains would as transition between membrane regions where KRasG12D exhibits fast diffusion and the sites of KRasG12D yielding the observed state transition In the intermediate and the immobile states of KRasG12D would be and spatially It is only in the that we would multiple to the same intermediate or immobilization domains by different KRasG12D provided that both domains have than our temporal Of note, even in the second KRasG12D to the intermediate or the immobilization domains may be by in To between the two we performed and analysis on the locations of KRasG12D a certain diffusion state to as state We first the spatial of the states by each raw into time and the state on the same with each one of the states (Figure Figure supplement and Video 2). Each diffusion trajectory only a few to the as limited by short and the from multiple trajectories over time to 1 in this to a map of the membrane regions associated with each diffusion state. Despite yielding short trajectories, the of PAmCherry1 more of the membrane domains by KRasG12D in the of As shown in Figure the intermediate state locations and the immobile state locations not only but also each to Specifically, regions corresponding to the intermediate states to to nanoscopic domains a few nm in and the of the immobilization sites are surrounded by the intermediate domains. regions corresponding to the fast state the of the membrane While both the intermediate and the immobilization domains to be a domain map showed that at some of these domains could last a few be further below in Figure 3). Thus, spatial of the KRasG12D state provided for the presence of nanoscopic domains the distinct KRasG12D diffusion states. Figure Download asset Open asset properties of the immobile and intermediate domains. analysis of the state at different time intervals and min). The of the function at each time was in the main with the raw shown in the show of for the the and the fast states, respectively Materials and The in each was through a and the resulting trajectories were analyzed the same as the Materials and analysis on KRasG12D diffusion trajectories by diffusion states The was as the between two of the trajectory while the molecule was in the same diffusion state. shown for data acquired at 12 ms/frame (n = Figure data 1 Excel sheet for data used for generating all the Data for individual panels are included as separate tabs in the excel file. Download Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg video of the domain map from individual trajectories within each immobilization domains intermediate fast domains.