Search for: All records

Abstract The actin cortex plays a large role in regulating the dynamic organization of cell surface receptors, which in turn regulates their signaling. However, many receptors have short intracellular domains and no known link to cortical actin. In this work, we identified the β₁-integrin subunit and several tetraspanins – CD9, CD81 and CD151 – as part of the hitherto unknown molecular link between the surface receptor CD36 and cortical actin. We found that CD36 in vascular endothelial cells is recruited into complexes/nanodomains containing these proteins, with stronger recruitment near the cell edge. Perturbing this recruitment via the mutation G12V in the N-terminal transmembrane domain of CD36 alters the dynamic organization of CD36 on the vascular endothelial cell surface and weakens its coupling to cortical actin dynamics. Moreover, perturbing this recruitment abolishes thrombospondin-1-induced CD36 signaling through the Src family kinase Fyn. Given their many interactions with other transmembrane proteins, tetraspanins and integrins may provide a ubiquitous mechanism for plasma membrane-cortical actin coupling. 

The spatiotemporal organization of cell surface receptors is important for cell signaling. Cortical actin (CA), the subset of the actin cytoskeleton subjacent to the plasma membrane (PM), plays a large role in cell surface receptor organization. However, this has been shown largely through actin perturbation experiments, which raise concerns of nonspecific effects and preclude quantification of actin architecture and dynamics under unperturbed conditions. These limitations make it challenging to predict how changes in CA properties can affect receptor organization. To derive direct relationships between the architecture and dynamics of CA and the spatiotemporal organization of PM proteins, including cell surface receptors, we developed a multiscale imaging and computational analysis framework based on the integration of single-molecule imaging (SMI) of PM proteins and fluorescent speckle microscopy (FSM) of CA (combined: SMI-FSM) in the same live cell. SMI-FSM revealed differential relationships between PM proteins and CA based on the PM proteins’ actin binding ability, diffusion type, and local CA density. Combining SMI-FSM with subcellular region analysis revealed differences in CA dynamics that were predictive of differences in PM protein mobility near ruffly cell edges versus closer to the cell center. SMI-FSM also highlighted the complexity of cellwide actin perturbation, where we found that global changes in actin properties caused by perturbation were not necessarily reflected in the CA properties near PM proteins, and that the changes in PM protein properties upon perturbation varied based on the local CA environment. Given the widespread use of SMI as a method to study the spatiotemporal organization of PM proteins and the versatility of SMI-FSM, we expect it to be widely applicable to enable future investigation of the influence of CA architecture and dynamics on different PM proteins, especially in the context of actin-dependent cellular processes. 

Colocalization analysis of multicolor microscopy images is a cornerstone approach in cell biology. It provides information on the localization of molecules within subcellular compartments and allows the interrogation of known molecular interactions in their cellular context. However, almost all colocalization analyses are designed for two-color images, limiting the type of information that they reveal. Here, we describe an approach, termed “conditional colocalization analysis,” for analyzing the colocalization relationships between three molecular entities in three-color microscopy images. Going beyond the question of whether colocalization is present or not, it addresses the question of whether the colocalization between two entities is influenced, positively or negatively, by their colocalization with a third entity. We benchmark the approach and showcase its application to investigate receptor-downstream adaptor colocalization relationships in the context of functionally relevant plasma membrane locations. The software for conditional colocalization analysis is available at https://github.com/kjaqaman/conditionalColoc. 

This paper presents a new approach for predicting thermodynamic properties of perovskites that harnesses deep learning and crystal structure fingerprinting based on Hirshfeld surface analysis. It is demonstrated that convolutional neural network methods capture critical features embedded in two-dimensional Hirshfeld surface fingerprints that enable a quantitative assessment of the formation energy of perovskites. Building on our recent work on lattice parameter prediction from Hirshfeld surface calculations, we show how transfer learning can be used to speed up the training of the neural network, allowing multiple properties to be trained using the same feature extraction layers. We also predict formation energies for various perovskite polymorphs, and our predictions are found to give generally improved performance over a well-established graph network method, but with the methods better suited to different types of datasets. Analysis of the structure types within the dataset reveals the Hirshfeld surface-based method to excel for the less symmetric and similar structures, while the graph network performs better for very symmetric and similar structures. 

Abstract The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation. 

We present a study on automated analysis of phase diagrams to aid the materials science community that attempts to lay the groundwork for a large-scale, searchable, digitized database of phases of a wide variety of materials at different physical conditions and compositions. For this work, we concentrate on around 80 thermodynamic phase diagrams of binary metallic alloy systems which give phase information of alloys at varied temperatures and mixture ratios. We use image processing techniques to isolate phase boundaries and subsequently extract areas of the same phase. Simultaneously, document analysis techniques are employed to recognize and group the text used to label the phases; text present along the axes is identified so as to map image coordinates (x, y) to physical coordinates. Labels of unlabeled phases are inferred using standard rules. Once a phase diagram is thus digitized we are able to provide the phase of all materials present in our database at any given temperature and alloy mixture ratio. Using the digitized data, more complex queries may also be supported in the future. We evaluate our system by measuring the correctness of labeling of phase regions.