Abstract Plasma membrane homeostasis requires balanced exocytosis and endocytosis, yet their coordination at the single-event level in non-neuronal cells is unresolved. We present Shape2Fate, a morphology-aware deep-learning pipeline that detects, tracks, and classifies individual exocytic and endocytic carriers in live-cell total internal reflection fluorescence structured illumination microscopy (TIRF-SIM) movies at ∼100 nm resolution. Trained on synthetic data and exploiting carrier shape evolution rather than fluorescence intensity, Shape2Fate achieves expert-level tracking and outcome classification across diverse cell types, imaging conditions, and microscope platforms. Applying Shape2Fate to constitutive secretion and insulin-stimulated GLUT4 exocytosis in adipocytes, we uncover two opposing exo-endocytic coupling architectures: exocytic fusion locally nucleates de novo clathrin-coated pits, whereas GLUT4 vesicles target pre-existing pits for rapid cargo capture. These findings establish that the spatial rules governing exo-endocytic coordination are not universal but are pathway-specific. Shape2Fate is openly available, enabling direct event-level mechanistic dissection of exo-endocytic coordination across pathways in living cells.

AAK1 and BMP2K are serine/threonine kinases traditionally known for phosphorylating AP2 during clathrin-mediated endocytosis (CME), but their broader roles remained incompletely defined. Here, using motif-guided in silico, biochemical, and phosphoproteomic screens, we identify PDLIM5 and Talin1 as direct AAK1/BMP2K substrates. Despite high kinase-domain similarity, only AAK1 promotes cell migration and potentiates focal adhesion (FA) turnover. Live-cell imaging shows that AAK1 recruitment to FAs peaks as disassembly begins. The conserved AAK1 C-terminal PDZ-binding motif mediates direct, low-affinity binding to PDLIM5, providing a plausible mechanism for localized substrate access. Dynamic analyses of phospho-mimetic and phospho-null mutants support a model in which AAK1-dependent phosphorylation promotes timely release of PDLIM5 and Talin1 during FA disassembly. These findings reveal a kinase-driven contribution to FA turnover distinct from protease- and phosphatase-based mechanisms and suggest that functional divergence between AAK1 and BMP2K may provide a strategy to modulate cell migration with reduced impact on CME.

During endocytosis a dynamic protein network is responsible for assembly of the clathrin coat. Early in the process the Eps15 protein is thought to undergo liquid-liquid phase-separation to form biomolecular condensates. These condensates facilitate clathrin assembly, producing clathrin-coated vesicles that are ultimately excluded from the condensate as the vesicles depart from the plasma membrane. An array of distinct clathrin accessory proteins drive this process, yet their interactions with protein condensates have not been explored. Here we show that clathrin accessory proteins promote the assembly of the clathrin lattice by simultaneously destabilizing protein condensates. By adding diverse accessory proteins to Eps15 condensates, we observed that accessory proteins which cross-linked Eps15 proteins stabilized condensates, while accessory proteins that compete with Eps15-Eps15 interactions destabilized condensates. Interestingly, accessory proteins that destabilized condensates also promoted the assembly of clathrin and its exclusion from condensates. In contrast, accessory proteins that stabilized condensates opposed exclusion of clathrin. Finally, we demonstrated that decreasing condensate stability enhanced clathrin assembly and exclusion, even when no additional clathrin assembly motifs were introduced. Taken together, these results help to explain how condensates can catalyze the assembly of coated vesicles by providing both a substrate for their initiation and a driving force for their exclusion and ultimate departure.

Since the outbreak of SARS-CoV-2 in recent years, our society has become more aware that zoonotic diseases pose a real threat. Therefore, the demand for small molecules that target host proteins, essential for viral entry and replication, has increased as an interesting strategy for the development of antiviral agents, as these agents may be effective against several different pathogens. NAK kinases is one such potential target family because they are involved in a variety of cellular functions, hijacked by viruses to invade host cells, such as clathrin-mediated endocytosis. A large number of different inhibitors have already been reported targeting NAK kinases, but there are still no compounds that selectively target AAK1 over other NAK family members, in particular the closely related family member BIKE. Here, we developed a series of pyrazolo[1,5-a]pyrimidine-based macrocyclic NAK inhibitors, starting from the acyclic AAK1 inhibitor LP-935509. Through a structure-guided activity relationship study within the NAK family, we identified potent AAK1 inhibitors 16, 18 and 27, which show promising selectivity within the NAK family. The inhibitors showed a potent inhibition of the phosphorylation of the AP-2 complex and the antiviral activity of the compounds was evaluated against various RNA viruses.

Mutation of the ATL1 gene is one of the most common causes of hereditary spastic paraplegia (HSP), a group of genetic neurodegenerative conditions characterised by distal axonal degeneration of the corticospinal tract axons. Atlastin-1, the protein encoded by ATL1 , is one of three mammalian atlastins, which are homologous dynamin-like GTPases that control endoplasmic reticulum (ER) morphology by fusing tubules to form the three-way junctions that characterise ER networks. However, it is not clear whether atlastin-1 is required for correct ER morphology in human neurons and if so what the functional consequences of lack of atlastin-1 are. Using CRISPR-inhibition we generated human cortical neurons lacking atlastin-1. We demonstrate that ER morphology was altered in these neurons, with a reduced number of three-way junctions. Neurons lacking atlastin-1 had longer endosomal tubules, suggestive of defective tubule fission. This was accompanied by reduced lysosomal proteolytic capacity. As well as demonstrating that atlastin-1 is required for correct ER morphology in human neurons, our results indicate that lack of a classical ER-shaping protein such as atlastin-1 may cause altered endosomal tubulation and lysosomal proteolytic dysfunction. Furthermore, they strengthen the idea that defective lysosome function contributes to the pathogenesis of a broad group of HSPs, including those where the primary localisation of the protein involved is not at the endolysosomal system.

Fat grafting, a key regenerative medicine technique, often requires repeat procedures due to high-fat reabsorption and volume loss. Addressing this, a novel drug delivery system uniquely combines a thermosensitive, FDA-approved hydrogel (itaconic acid-modified PLGA-PEG-PLGA copolymer) with FGF2-STAB, a stable fibroblast growth factor 2 with a 21-day stability, far exceeding a few hours of wild-type FGF2's stability. Additionally, the growth factor was encapsulated in "green" liposomes prepared via the Mozafari method, ensuring pH protection. The system, characterized by first-order FGF2-STAB release, employs green chemistry for biocompatibility, bioactivity, and eco-friendliness. The liposomes, with diameters of 85.73 ± 3.85 nm and 68.6 ± 2.2% encapsulation efficiency, allowed controlled FGF2-STAB release from the hydrogel compared to the unencapsulated FGF2-STAB. Yet, the protein compromised the carrier's hydrolytic stability. Prior tests were conducted on model proteins human albumin (efficiency 80.8 ± 3.2%) and lysozyme (efficiency 81.0 ± 2.7%). This injectable thermosensitive system could advance reconstructive medicine and cosmetic procedures.

Clathrin-mediated endocytosis (CME) is the main mechanism by which mammalian cells control their cell surface proteome. Proper operation of the pivotal CME cargo-adaptor AP2 requires membrane-localised FCHO. Here, live-cell eTIRF-SIM shows that FCHO marks sites of clathrin- coated pit (CCP) initiation, which mature into uniform sized CCPs comprising a central patch of AP2 and clathrin corralled by an FCHO/Eps15 ring. We dissect the network of interactions between the FCHO interdomain-linker and AP2, which concentrates, orients, tethers and partially destabilizes closed AP2 at the plasma membrane. AP2’s subsequent membrane deposition drives its opening, which triggers FCHO displacement through steric competition with PtdIns4,5P2, clathrin, cargo and CME accessory factors. FCHO can now relocate toward a CCP’s outer edge to engage and activate further AP2s to drive CCP growth/maturation.125 character summary FCHO primes AP2 for CCV incorporation, a process that triggers FCHO release to enable activation/recruitment of further AP2s### Competing Interest StatementThe authors have declared no competing interest.

Structured illumination microscopy is a widely popular super-resolution technique for live cell imaging capable of surpassing the diffraction limit. Its temporal resolution is limited by the need to capture multiple low-resolution images to reconstruct a single high-resolution image. When observing rapid biological processes, the local movement between frames leads to the formation of reconstruction artifacts, which subsequently impair the data interpretation. We propose to include this type of movement in the definition of the image formation forward problem. The motion can then be estimated from the original data using optical flow, and the optimization problem is solved using the alternating direction method of multipliers. Our approach is tested against other reconstruction techniques on both synthetic and real biological data.

Clathrin-mediated endocytosis (CME) is the main mechanism by which mammalian cells control their cell surface proteome. Proper operation of the pivotal CME cargo adaptor AP2 requires membrane-localized Fer/Cip4 homology domain-only proteins (FCHO). Here, live-cell enhanced total internal reflection fluorescence–structured illumination microscopy shows that FCHO marks sites of clathrin-coated pit (CCP) initiation, which mature into uniform-sized CCPs comprising a central patch of AP2 and clathrin corralled by an FCHO/Epidermal growth factor potential receptor substrate number 15 (Eps15) ring. We dissect the network of interactions between the FCHO interdomain linker and AP2, which concentrates, orients, tethers, and partially destabilizes closed AP2 at the plasma membrane. AP2’s subsequent membrane deposition drives its opening, which triggers FCHO displacement through steric competition with phosphatidylinositol 4,5-bisphosphate, clathrin, cargo, and CME accessory factors. FCHO can now relocate toward a CCP’s outer edge to engage and activate further AP2s to drive CCP growth/maturation.

Integrin-mediated cell adhesion and signaling are critical for many physiological processes. The dynamic turnover of integrins and their associated adhesion complexes through endocytic and recycling pathways has emerged as an important mechanism for controlling cell migration and invasion in cancer. Thus, the regulation of integrin trafficking and how this may be altered by disease-specific molecular mechanisms has generated considerable interest. However, current tools available to study integrin trafficking may cause artifacts and/or do not provide adequate kinetic information. Here, we report the generation of a functionally neutral and monovalent single chain antibody to quantitatively and qualitatively measure β1 integrin trafficking in cells. Our novel probe can be used in a variety of assays and allows for the biochemical characterization of rapid recycling of endogenous integrins. We also demonstrate its potential utility in live cell imaging, providing proof of principle to guide future integrin probe design.

(Developmental Cell 50, 494–508.e1–e11; August 19, 2019) As a result of an author oversight in the originally published version of this article, the author Susanne Salomon was omitted from the author list. This error has now been corrected in the article online. The authors apologize for the error and any inconvenience that may have resulted. Temporal Ordering in Endocytic Clathrin-Coated Vesicle Formation via AP2 PhosphorylationWrobel et al.Developmental CellAugust 19, 2019In BriefWrobel et al. show that phosphorylation of the mammalian endocytic AP2 adaptor causes a conformational change that allows it to efficiently bind the protein NECAP. This, in turn, recruits membrane-remodeling proteins into clathrin-coated pits, which drive their formation toward final scission from the parent membrane. Full-Text PDF Open Access

Clathrin-mediated endocytosis (CME) is key to maintaining the transmembrane protein composition of cells' limiting membranes. During mammalian CME, a reversible phosphorylation event occurs on Thr156 of the μ2 subunit of the main endocytic clathrin adaptor, AP2. We show that this phosphorylation event starts during clathrin-coated pit (CCP) initiation and increases throughout CCP lifetime. μ2Thr156 phosphorylation favors a new, cargo-bound conformation of AP2 and simultaneously creates a binding platform for the endocytic NECAP proteins but without significantly altering AP2's cargo affinity in vitro. We describe the structural bases of both. NECAP arrival at CCPs parallels that of clathrin and increases with μ2Thr156 phosphorylation. In turn, NECAP recruits drivers of late stages of CCP formation, including SNX9, via a site distinct from where NECAP binds AP2. Disruption of the different modules of this phosphorylation-based temporal regulatory system results in CCP maturation being delayed and/or stalled, hence impairing global rates of CME.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Appendix 3 Appendix 4 References Decision letter Author response Article and author information Metrics Abstract Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic insight into cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu. https://doi.org/10.7554/eLife.22323.001 eLife digest Cell biologists often use microscopes to look closely at cells and see what is happening during an experiment. Cell biology experiments typically involve measuring more than one aspect of the cells, for example, the forces a cell is experiencing and the direction it is moving, or the locations of two different components in the cell. The task is then to decipher the interactions between these independent variables to better understand the inner workings of a living cell. This task, however, can be challenging because other variables can mask the interactions between the pairs of variables being studied. For example, it is difficult to know if two components of cells overlap in microscopy images because they directly interact or simply because the overall organization of the cell makes it more likely they will end up in the same place. Several statistical methods have been developed to estimate and eliminate such confounding effects to reveal specific interactions. However, these confounding effects – termed “global biases” – may themselves contain valuable information about how cells work. Ignoring the possible roles of global biases may limit our understanding of biological processes. Zaritsky et al. have now addressed this issue by developing an algorithm called DeBias that takes global bias into account by decoupling it from true interactions between two variables. DeBias uses global bias as a second measurement to understand how two biological variables interact via confounding variables. Zaritsky et al. then used DeBias with data from four different cell biology experiments to show that the algorithm works. For example, animal cells contain several proteins that form filaments, which give them their shape and help them move. So-called vimentin filaments and microtubules had previously been seen to occur in the same place within cells but it was not clear whether their alignment was due to local interactions or determined by the overall shape of the cell. DeBias was used to analyze cells that were either still or moving in a particular direction. The algorithm could tease apart the effect of this movement and showed that co-alignment of vimentin filaments and microtubules was caused more by the movement and shape of the cell and less by specific interactions. Overall, DeBias is a new mathematical tool for cell biologists that is freely accessible online. Other researchers can now use this tool in future studies to identify local interactions and global biases in a wide range of cell biology experiments and interpret the data in a meaningful way. https://doi.org/10.7554/eLife.22323.002 Introduction Interpretation of the relations among coupled variables is a classic problem in many areas of cell biology. One example is the spatiotemporal co-localization of molecules – a critical clue to interactions between molecular components; another example is alignment of molecular structures, such as filamentous networks. However, co-localization or alignment may also occur because the observed components are associated with external effectors. For example, the internal components of a polarized cell are organized along the polarization axis, making it difficult to quantify how much of the observed alignment between two filamentous networks is related to common organizational constraints, and how much of it is indeed caused by direct interaction between filaments. Another example is introduced with protein co-localization, where their intensity distributions may be heavily biased to specific levels regulated by the cell state. The combined effects of global bias with local interactions are manifest in the joint distribution of the spatially coupled variables. The contribution of global bias to this joint distribution can be recognized from the deviation of the marginal distributions of each of the two variables from an (un-biased) uniform distribution. Although global bias can significantly mislead the interpretation of co-localization and co-orientation measurements, most studies do not account for this effect (Adler and Parmryd, 2010; Bolte and Cordelières, 2006; Costes et al., 2004; Das et al., 2015; Dunn et al., 2011; Kalaidzidis et al., 2015; Rizk et al., 2014; Serra-Picamal et al., 2012; Tambe et al., 2011). Previous approaches indirectly assessed spatial correlations (e.g., [Drew et al., 2015; Karlon et al., 1999]), variants of mutual information (e.g., [Krishnaswamy et al., 2014; Reshef et al., 2011]) or spatial biases (Helmuth et al., 2010) but did not explicitly quantify the contribution of the global bias to the observed joint distribution. These methods approach the global bias as a confounding factor (VanderWeele and Shpitser, 2013) that must be eliminated for more accurate assessment of the true local interaction, but ignore the possibility that the global bias contains by-itself valuable mechanistic information to cell behavior. Here, we present DeBias as an algorithm to decouple the global bias (represented by a global index) from the bona fide local interaction (represented by a local index) in co-localization and co-orientation of two independently-measured spatial variables. The decoupling enables simultaneous investigation of processes that drive global bias and local interactions between spatially-matched variables. Our method is dubbed DeBias because it Decouples the global Bias from local interactions between two variables. To highlight its capabilities, DeBias was applied to data from four different areas in cell biology, ranging in scale from macromolecular to multicellular: (1) alignment of vimentin fibers and microtubules in the context of polarized cells; (2) alignment of cell velocity and traction stress during collective migration; (3) fluorescence resonance energy transfer of Protein Kinase C; and (4) recruitment of transmembrane receptors to clathrin-coated pits during endocytosis. These examples demonstrate the generalization of the method and underline the potential of extracting global bias as an independent functional measurement in the analysis of multiplex biological variables. Results Similarity of observed co-orientation originating from different mechanisms The issue of separating contributions from global bias and local interactions is best illustrated with the alignment of two sets of variables that carry orientational information. Examples of co-orientation include the alignment of two filament networks (Drew et al., 2015; Gan et al., 2016; Nieuwenhuizen et al., 2015), or the alignment of cell velocity and traction stress, a phenomenon referred to as plithotaxis (Das et al., 2015; Tambe et al., 2011; Trepat and Fredberg, 2011). In these systems, global bias imposes a preferred axis of orientation on the two variables, which is independent of the local interactions between the two variables (Figure 1A). Figure 1 Download asset Open asset Illustration of global bias and local interaction using the alignment of two orientational variables. (A) The relation between two variables X, Y can be explained from a combination of direct interactions (orange) and a common effector. (B) Simulation. Given two distributions X, Y, pairs of coupled variables are constructed by drawing sample pairs (xi,yi) and transforming them to (xi’,yi’) by a correction parameter ζi = αθi, which represents the effect of a local interaction. α is constant for each of these simulations. (C) Simulated joint distributions. X, Y truncated normal distributions with mean 0 and σX = σY. Shown are the joint distributions of 4 simulations with reduced global bias (i.e., increased standard deviation σX, σY) and increased local interaction (left-to-right). All scenarios have similar observed mean alignment of ~19°. (D) Example of 100 draws of coupled orientational variables from the two most extreme scenarios in panel C. Most orientations are aligned with the x-axis when the global bias is high and no local interaction exists (left), while the orientations are less aligned with the x-axis but maintain the mean alignment between (xi’,yi’) pairs for reduced global bias and increased local interaction (right). https://doi.org/10.7554/eLife.22323.003 Similar observed alignments may arise from different levels of global bias and local interactions. This is demonstrated by simulation of two independent random variables X and Y, representing orientations (Figure 1B, left), from which pairs of samples xi and yi are drawn to form an alignment angle θi (Figure 1B, middle). Then, a local interaction between the two variables is modeled by co-aligning θi by ζi degrees, resulting in two variables xi’ and yi’ with an observed alignment θi - ζi (Figure 1B, right). We show the joint distribution of X, Y for four simulations (Figure 1C) where X and Y are normally distributed with identical means but different standard deviations (σ), truncated to [−90∘,90∘], and different magnitudes of local interactions (ζ). The latter is defined as ζ = αθ (Figure 1B, α = 1 for perfect alignment). Throughout the simulations both σ and α are gradually increased (Figure 1C, left-to-right), implying that the global bias in the orientational variables is reduced while their local interactions increase. As a result, all simulations display similar observed alignments (mean values, 18.9°−19.5°). Figure 1D visualizes 100 samples from each of the two most distinct scenarios: low σ and no local interaction (σ = 17°, α = 0) leads to tendency of X and Y to align independently to one direction (left); higher variance together with increased interaction (σ = 40°, α = 0.5) leads to more diverse orientations of X and Y (right), while maintaining similar mean alignment. This simple example highlights the possibility of observing similar alignments arising from different mechanisms. While the described properties are well known and many others have used statistical post-processing to eliminate confounding factors for accurate assessment of local interactions (Drew et al., 2015; Helmuth et al., 2010; Karlon et al., 1999; Krishnaswamy et al., 2014; Reshef et al., 2011), we aim at directly quantifying the global bias, with the goal of extracting encapsulated information that is fundamental to the biological question. DeBias: a method to assess the global and local contribution to observed co-alignment DeBias models the observed marginal distributions X’ and Y’ as the sum of contributions by a common effector, i.e., the global bias, and by local interactions that effect the co-alignment of the two variables in every data point (Figure 2A). Figure 2 with 1 supplement see all Download asset Open asset DeBias algorithm. (A) Underlying assumption: the observed relation between two variables is a cumulative process of a global bias and a local interaction component. (B) Quantifying local and global indices: sample from the marginal distributions X, Y to construct the resampled distribution. The global index (GI) is calculated as the Earth Movers Distance (EMD) between the uniform and the resampled distributions. The local index (LI) is calculated as the subtraction of the GI from the EMD between the uniform and the observed distribution. (C) Local and global indices calculated for the examples from Figure 1C. Black circles represent the (GI,LI) value for the corresponding example in Figure 1C, bars represent the relative contribution of the local (green) and global (red) index to the observed alignment. (D-E) Simulation using a constant interaction parameter α = 0.2 and varying standard deviations of X, Y, σ = 50° to 5°. (D) Joint distributions. Correlation between X and Y is (subjectively) becoming less obvious for increasing global bias (decreasing σ). (E) GI and LI are negatively correlated: decreased σ enhances GI and reduces LI. The change in GI is ~4 fold larger compared to the change in LI indicating that the GI has a limited effect on LI values. Inset: stretched LI emphasizes the negative correlation. (F) Both LI and GI are needed to discriminate between simulations with different interaction parameters. α = 0.2 (red) or 0.25 (cyan), σ is drawn from a normal distribution (mean = 25°, standard deviation = 4°). Number of simulations = 40, for each parameter setting. Inset: stretched LI emphasizes the discrimination. Number of histogram bins, K = 15, for all simulations. https://doi.org/10.7554/eLife.22323.004 In a scenario without any global bias or local interaction between X’ and Y’, the observed alignment would be uniformly distributed (denoted uniform). Hence any deviation from the uniform distribution would reflect contributions from both the global bias and the local interactions. To extract the contribution of the global bias we constructed a resampled alignment distribution (denoted resampled) from independent samples of the marginal distributions X’ and Y’, which decouples matched pairs (xi’, yi’), and thus excludes their local interactions. The global bias is defined as the dissimilarity between the uniform and resampled distributions and accordingly describes to what extent elements of X’ and Y’ are aligned without local interaction (Figure 2B). If a local interaction exists then the distribution of the observed alignment angles will differ from the independently resampled alignment distribution. Hence, the uniform distribution will be less similar to the experimentally observed alignment distribution (denoted observed) than to the resampled distribution. Accordingly, the local interaction is defined by the difference of dissimilarity between the observed and uniform distributions and dissimilarity between the resampled and uniform distributions (Figure 2B). The Earth Mover's Distance (EMD) (Peleg et al., 1989; Rubner et al., 2000) was used to calculate dissimilarities between distributions. The EMD of 1-dimensional distributions is defined as the minimal 'cost' to transform one distribution into the other (Kantorovich and Rubinstein, 1958). This cost is proportional to the minimal accumulated number of moving observations to adjacent histogram bins needed to complete the transformation. Formally, we calculate EMD(A,B)=∑i=1,…,K|∑j=1,…,iaj−∑j=1,…,ibj|, with K - number of histogram bins, aj and bj - fraction of observations in bin j for distributions A and B correspondingly. Introducing the EMD defines scalar values for the dissimilarities and allows us to define the EMD between resampled and uniform alignment distributions as the global index (GI) and the local index (LI) as the difference of EMD between observed and uniform and the GI (Figure 2B).Figure 2C, demonstrates how the GI and LI recognize the global bias and local interactions between the matched variable pairs (xi’, yi’) established in Figure 1C. For a scenario with no local interaction (α = 0) DeBias correctly reports LI~0 and GI~3. For a scenario with gradually wider distributions X,Y, i.e., less global bias, and gradually stronger local interactions (α > 0), the LI increases while the GI decreases. Additional simulations showed that similar properties apply for negative local interactions ζ = αθ (Figure 1B) were α < 0 (Figure 2—figure supplement 1). In the previous illustrations, changes in spread of the distributions X and Y were compensated by changes in the local interactions. When leaving the interaction parameter α constant while changing the spread of X and Y, a weak, but intrinsically negative correlation between LI and GI becomes apparent (Figure 2D–E). Thus, while DeBias can correctly distinguish scenarios with substantial shifts from global bias to local interactions, the precise numerical values estimating the contribution of LI varies between scenarios with a low versus high global bias. To address this issue we propose to exploit the variation between experiments for modeling the relation between LI and GI. This is demonstrated by comparing two distinct values of the interaction parameter, α, emulating different experimental settings (Figure 2F). Within experiments variation was obtained by drawing multiple values of σ from a normal distribution. Due to the negative correlation between LI and GI the experimental patterns can only be discriminated by combining LI and GI into a two-dimensional descriptor (Figure 2F). This point will be further demonstrated in one of the following case studies and in the Discussion. Theory and limiting cases of DeBias To characterize the properties of DeBias we used theoretical statistical reasoning. The first limiting case is set by the scenario in which observations from X and Y are independent. The expected values of the observed and resampled alignments are identical; accordingly, LI converges to 0 for large N (Appendix 1, Theorem 1). The second limiting case is set by the scenario in which X and Y are both uniform. The corresponding resampled alignment is also uniform; accordingly, GI converges to 0 for a large N (Appendix 1, Theorem 2). The third limiting case occurs with perfect alignment, i.e., xi = yi for all i. In this case the observed alignment distribution is concentrated in the bin containing θ = 0. We examine two scenarios of perfect alignment: (1) When all the locally matched measurements are identical (xi = yj for all i, j), the resampled distribution is also concentrated in the bin θ = 0 implying that LI = 0 and GI assumes the maximal possible value: GI=1K∑i=1,..,K(i−1)=K−12, where K is the number of quantization bins (Appendix 1, Theorem 3.I). (2) When X, Y are uniform (and xi = yi for all i), the resampled distribution is uniform, thus GI = 0 and LI reaches its maximum value: LI=1K∑i=1,..,K(i−1)=K−12, (Appendix 1, Theorem 3.II). Generalizing this case, we prove that LI is a lower bound for the actual contribution of the local interaction to the observed alignment (Appendix 1, Theorem 4). Complementarily, GI is an upper bound for the contribution of the global bias to the observed alignment. Last, we show that when X and Y are truncated normal distributions, or when the alignment distribution is truncated normal, GI reduces to a limit of 0 as σ → ∞, when σ is the standard deviation of the normal distribution before truncation (Appendix 1, Theorem 5). Simulations complement this result demonstrating that σ and GI are negatively associated, i.e., GI decreases with increasing σ (Figure 2E). This final property is intuitive, because resampling from more biased distributions (smaller σ) tends to generate high agreement between (xi, yi) leading to reduced alignment angles and increased GI. The modeling of the observed alignment as the sum of GI and LI allowed us to assess the performance of DeBias from synthetic data. By using a constant local interaction parameter ζ (ζ = c), we were able to retrieve the portion of the observed alignment that is attributed to the local interaction and to compare it with the true predefined ζ (Appendix 2, Appendix 2—figure 1). These simulations demonstrated again the need for a GI-dependent interpretation of LI (first shown in Figure 2E–F). Simulations were also performed to assess how the choice of the quantization parameter K (i.e., number of histogram bins) and number of observations N affect GI and LI (Appendix 2, Appendix 2—figures 2–3), and this was also verified in our experimental data (Figure 3—figure supplement 1). In summary, by combining theoretical considerations and simulations we demonstrated the properties and limiting cases of DeBias in decoupling paired matching variables from orientation data. Local alignment of vimentin and microtubule filaments We applied DeBias to investigate the degree of alignment between vimentin intermediate filaments and microtubules in polarized cells. Recent work using genome-edited Retinal Pigment Epithelial (RPE) cells with endogenous levels of fluorescently tagged vimentin and α-tubulin showed that vimentin provides a structural template for microtubule growth, and through this maintains cell polarity (Gan et al., 2016). The effect was strongest in cells at the wound front where both vimentin and microtubule networks collaboratively align with the direction of migration (Figure 3A–C). An open question remains as to how much of this alignment is caused by the extrinsic directional bias associated with the collective migration of cells into the wound as opposed to a local interaction between the two cytoskeleton systems. Figure 3 with 1 supplement see all Download asset Open asset : Alignment of microtubule and vimentin intermediate filaments in the context of cell polarity. (A) RPE cells expressing TagRFP α-tubulin (MT) and mEmerald-vimentin (VIM) at endogenous levels during a wound healing assay. Scale bar 100 μm. (B) Zooming in on cells in different locations in respect to the wound edge. Right-most column, computer segmented filaments of both cytoskeleton systems. Top row, cells located at the wound edge (‘Front’); Middle row, cells located 2–3 rows away from the wound edge (’Back’); Bottom row, cells located at the wound edge partially with shRNA knock-down of vimentin. Scale bar 10 μm. (C) Orientation distribution of microtubules (left column) and vimentin filaments (middle columns) for the cells outlined in B. Vimentin-microtubule alignment distributions (right column). (D) Scatterplot of GI versus LI derived by DeBias. The GI is significantly higher in WT cells at the wound edge (‘Front’, n = 12) compared to cells inside the monolayer (‘Back’, n = 12, fold change = 4.8, p-value < 0.0001); or compared to vimentin-depleted cells at the wound edge (‘VIM KD’, n = 7, fold change = 5.2, p-value < 0.0001). Statistics based on Wilcoxon rank-sum test. All DeBias analyses performed with K = 15. (E) Polarization of RPE cells at the wound edge at different time points after scratching. Scale bar 10 μm. (F) Representative experiment showing the progression of LI and GI as a function of time after scratching (see color code). Correlation between GI and time ~0.90, p-value < 10−30 (n time points = 83). N = 5 independent experiments were conducted of which four experiments showed a gradual increase in GI with increased observed polarity. All DeBias analyses performed with K = 15. https://doi.org/10.7554/eLife.22323.006 Analysis of the GI and LI revealed that most of the discrepancy in vimentin-microtubule alignment originated from a shift in the global bias (Figure 3D), suggesting that the local interaction between the two cytoskeletons is unaffected by the cell position or knock-down of vimentin. Instead, the reduced alignment between the two cytoskeletons is caused by a loss of cell polarity in cells away from the wound edge, probably associated with the reduced geometric constraints imposed by the wound edge. In a similar fashion, reduction of vimentin expression relaxes global cell polarity cues that tend to impose alignment. To corroborate our conclusion that the global state of cell polarity is encoded by the GI, we performed a live cell imaging experiment, in which single cells at the edge of a freshly inflicted wound in a RPE monolayer were monitored for 80 min after scratching. DeBias was applied to calculate a time sequence of LI and GI. Cells at the wound edge tended to gradually increase their polarity and started migrating during the imaging time frame (Figure 3E, Video 1). Accordingly, the GI increased over time (Figure 3F). We also used this data set to verify that the reported shifts in GI are independent of the number of data points in and the binning of the distribution (Figure 3—figure supplement 1). This demonstrates the capacity of DeBias to distinguish fundamentally different effectors of cytoskeleton alignment. 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 Polarization of RPE cells at the monolayer edge over time. Please note several occasions (44 and 46 min, 65 and 67 min, 73 and 75 min) of focus drift followed by automated focus correction. https://doi.org/10.7554/eLife.22323.008 Identifying molecular factors in alignment of cell velocity and mechanical forces during collective cell migration Collective cell migration requires intercellular coordination, achieved by mechanical and chemical information transfer between cells. One mechanism for cell-cell communication is plithotaxis, the tendency of individual cells to align their velocity with the maximum principal stress orientation (He et al., 2015; Tambe et al., 2011; Zaritsky et al., 2015). As in the previous example of vimentin and microtubule interaction, much of this alignment is associated with a general directionality of velocity and stress field parallel to the axis of collective migration (Zaritsky et al., 2015). Using a wound healing assay, Das et al. (Das et al., 2015) screened 11 tight-junction proteins to identify pathways that promote motion-stress alignment (Figure 4A). Knockdown of Merlin, Claudin1, Patj and Angiomotin (Amot) reduced the alignment of velocity direction and stress orientation (Das et al., 2015). Further inspection of these hits showed that the stress orientation remained stable upon depletion of these proteins, but the velocity direction distribution was much less biased towards the wound edge (Zaritsky et al., 2015). Here, we further analyze this data to demonstrate the capacity of DeBias to pinpoint tight-junction proteins that alter specifically the global or local components that induce velocity-stress alignment. Figure 4 Download asset Open asset : Alignment of stress orientation and velocity direction during collective cell migration. (A) Assay illustration. Wound healing assay of MDCK cells. Particle image velocimetry was applied to calculate velocity vectors (red) and monolayer stress microscopy to reconstruct stresses (blue). Alignment of velocity direction and stress orientation was assessed. (B) Mini-screen that includes depletion of 11 tight-junction proteins and Merlin. Data from (Das et al., 2015), where effective depletion was demonstrated. Shown are GI and LI values; molecular conditions are sorted by the LI values (control is ranked sixth, pointed by the black arrow). Each dot was calculated from accumulation of 3 independent experiments (N = 925–1539 for each condition). Three groups of tight junction proteins are highlighted by dashed rectangles: red - low LI and GI compared to control, purple – different GI but similar LI, orange – high LI. All DeBias analyses were performed with K = 15. (C) Pair-wise statistical significance for LI values. P-values were calculated via a permutation-test on the velocity and stress data (Materials and methods). Red – no significant (p ≥ 0.05) change in LI values, blue – highly significant (< 0.01) change in LI values. (D) Highlighted hits: Claudin1, Claudin2, Merlin and ZO1. Top: Distribution of stress orientation (top), velocity direction (middle) and motion-stress alignment (bottom). Bottom: table of mean alignment angle, LI and GI. Claudin1 and Claudin2 have similar mechanisms for transforming stress to aligned velocity. ZO1 depletion enhances alignment of velocity by stress. https://doi.org/10.7554/eLife.22323.009 By distinguishing GI and LI we generated a refined annotation of the functional alteration that depletion of these tight-junction components caused in mechanical coordination of collectively migrating cells (Figure 4B–C). First, we confirmed that the four hits reported by (Das et al., 2015) massively reduced the GI, consistent with the notion that absence of these proteins diminished the general alignment of velocity to the direction induced by the migrating sheet (Figure 4B, red dashed rectangle). Merlin, Patj and Angiomotin reduced the LI to values close to 0, suggesting that the local dependency between stress orientation and velocity direction was lost. Depletion of Claudin1, or of its paralog Claudin2, which was not reported as a hit in the Das et al. screen, reduced the LI to a lesser extent, similarly for both proteins, but had very different effects on the GI (Figure 4B, purple dashed rectangle). This suggested that the analysis by (Das et al., 2015) missed effects that do not alter the general alignment of stress or motion, and implied the existence of a local velocity-st

The critical initiation phase of clathrin-mediated endocytosis (CME) determines where and when endocytosis occurs. Heterotetrameric adaptor protein 2 (AP2) complexes, which initiate clathrin-coated pit (CCP) assembly, are activated by conformational changes in response to phosphatidylinositol-4,5-bisphosphate (PIP2) and cargo binding at multiple sites. However, the functional hierarchy of interactions and how these conformational changes relate to distinct steps in CCP formation in living cells remains unknown. We used quantitative live-cell analyses to measure discrete early stages of CME and show how sequential, allosterically regulated conformational changes activate AP2 to drive both nucleation and subsequent stabilization of nascent CCPs. Our data establish that cargoes containing Yxxφ motif, but not dileucine motif, play a critical role in the earliest stages of AP2 activation and CCP nucleation. Interestingly, these cargo and PIP2 interactions are not conserved in yeast. Thus, we speculate that AP2 has evolved as a key regulatory node to coordinate CCP formation and cargo sorting and ensure high spatial and temporal regulation of CME.

PEI and polylysine are among the most investigated synthetic polymeric carriers for DNA delivery. Apart from their practical use, these 2 classes of polymers are also of interest from a fundamental point of view as they both can be prepared in different architectures (linear and branched/dendritic) and in a wide range of molecular weights, which is attractive to establish basic structure–activity relationships. This manuscript reports the results of an extensive study on the influence of molecular weight and architecture of a library of polylysine variants that includes linear, dendritic and hyperbranched polylysine. Hyperbranched polylysine is a new polylysine-based carrier that is structurally related to dendritic polylysine but possesses a randomly branched structure. Hyperbranched polylysine is attractive as it can be prepared in a one-step process on a large scale. The performance of these 3 classes of polylysine analogs was evaluated by assessing eGFP and IgG production in transient gene expression experiments with CHO DG44 cells, which revealed that protein production generally increased with increasing molecular weight and that at comparable molecular weight, the hyperbranched analogs were superior as compared to the dendritic and linear polylysines. To understand the differences between the gene delivery properties of the hyperbranched polylysine analogs on the one hand and the dendritic and linear polylysines on the other hand, the uptake and trafficking of the corresponding polyplexes were investigated. These experiments allowed us to identify (i) polyplex–external cell membrane binding, (ii) free, unbound polylysine coexisting with polyplexes as well as (iii) polymer buffer capacity as three possible factors that may contribute to the superior transfection properties of the hyperbranched polylysines as compared to their linear and dendritic analogs. Altogether, the results of this study indicate that hyperbranched polylysine is an interesting, alternative synthetic gene carrier. Hyperbranched polylysine can be produced at low costs and in large quantities, is partially biodegradable, which may help to prevent cumulative cytotoxicity, and possesses transfection properties that can approach those of PEI.

Three synthesis lots of linear poly(ethyleneimine) (PEI) are compared to a fully hydrolyzed linear PEI (commercially available as PEI "Max") regarding structure, polyplex formation with plasmid DNA, and transfection of suspension-adapted HEK-293E cells. PEI "Max" binds DNA more efficiently than the other PEIs, but it is the least effective in terms of transient recombinant protein yield. One PEI lot is fractionated by means of SEC. The fractions of high-M(n) PEI are the most efficient for complex formation and transfection. Nevertheless, the highest transient recombinant protein yields are achieved with unfractionated PEI. The results demonstrate that the polydispersity and charge density of linear PEI are important parameters for gene delivery to suspension-adapted HEK-293E cells.

The feasibility of a new transfection agent, HBPL, for the production of recombinant IgG antibody via TGE as well as for the transfection of primary cells is studied. Under the conditions investigated, transfection of CHO-DG44 cells using HBPL results in IgG yields that are comparable to those obtained with PEI. In experiments with CHO-K1 cells and MEFs the use of HPBL allows to achieve transfection efficiencies comparable to or better than those obtained with PEI or Fugene®. HBPL-mediated transfection does not require complex pre-formation, works well in serum-containing media and is biodegradable, which may prevent cumulative cytotoxicity and facilitates downstream processing.