T cell activation and especially trafficking of T cell receptor microclusters during immunological synapse formation are widely thought to rely on cytoskeletal remodeling. However, important details on the involvement of actin in the latter transport processes are missing. Using a suite of advanced optical microscopes to analyze resting and activated T cells, we show that, following contact formation with activating surfaces, these cells sequentially rearrange their cortical actin across the entire cell, creating a previously unreported ramifying actin network above the immunological synapse. This network shows all the characteristics of an inward-growing transportation network and its dynamics correlating with T cell receptor rearrangements. This actin reorganization is accompanied by an increase in the nanoscale actin meshwork size and the dynamic adjustment of the turnover times and filament lengths of two differently sized filamentous actin populations, wherein formin-mediated long actin filaments support a very flat and stiff contact at the immunological synapse interface. The initiation of immunological synapse formation, as highlighted by calcium release, requires markedly little contact with activating surfaces and no cytoskeletal rearrangements. Our work suggests that incipient signaling in T cells initiates global cytoskeletal rearrangements across the whole cell, including a stiffening process for possibly mechanically supporting contact formation at the immunological synapse interface as well as a central ramified transportation network apparently directed at the consolidation of the contact and the delivery of effector functions.

Activation of immune cells relies on a dynamic actin cytoskeleton. Despite detailed knowledge of molecular actin assembly, the exact processes governing actin organization during activation remain elusive. Using advanced microscopy, we here show that Rat Basophilic Leukemia (RBL) cells, a model mast cell line, employ an orchestrated series of reorganization events within the cortical actin network during activation. In response to IgE antigen-stimulation of FCε receptors (FCεR) at the RBL cell surface, we observed symmetry breaking of the F-actin network and subsequent rapid disassembly of the actin cortex. This was followed by a reassembly process that may be driven by the coordinated transformation of distinct nanoscale F-actin architectures, reminiscent of self-organizing actin patterns. Actin patterns co-localized with zones of Arp2/3 nucleation, while network reassembly was accompanied by myosin-II activity. Strikingly, cortical actin disassembly coincided with zones of granule secretion, suggesting that cytoskeletal actin patterns contribute to orchestrate RBL cell activation.

Cytoskeletal actin dynamics is essential for T cell activation. Here, we show evidence that the binding kinetics of the antigen engaging the T cell receptor influences the nanoscale actin organization and mechanics of the immune synapse. Using an engineered T cell system expressing a specific T cell receptor and stimulated by a range of antigens, we found that the peak force experienced by the T cell receptor during activation was independent of the unbinding kinetics of the stimulating antigen. Conversely, quantification of the actin retrograde flow velocity at the synapse revealed a striking dependence on the antigen unbinding kinetics. These findings suggest that the dynamics of the actin cytoskeleton actively adjusted to normalize the force experienced by the T cell receptor in an antigen-specific manner. Consequently, tuning actin dynamics in response to antigen kinetics may thus be a mechanism that allows T cells to adjust the lengthscale and timescale of T cell receptor signaling.

Cells tightly regulate their contents. Still, nonspecific Coulombic interactions between cationic molecules and anionic membrane components can lead to adventitious endocytosis. Here, we characterize this process in a natural system. To do so, we create variants of human pancreatic ribonuclease (RNase 1) that differ in net molecular charge. By conjugating a small-molecule latent fluorophore to these variants and using flow cytometry, we are able to determine the kinetic mechanism for RNase 1 internalization into live human cells. We find that internalization increases with solution concentration and is not saturable. Internalization also increases with time to a steady-state level, which varies linearly with molecular charge. In contrast, the rate constant for internalization (t1/2 = 2 h) is independent of charge. We conclude that internalization involves an extracellular equilibrium complex between the cationic proteins and abundant anionic cell-surface molecules, followed by rate-limiting internalization. The enhanced internalization of more cationic variants of RNase 1 is, however, countered by their increased affinity for the cytosolic ribonuclease inhibitor protein, which is anionic. Thus, Coulombic forces mediate extracellular and intracellular equilibria in a dichotomous manner that both endangers cells and defends them from the potentially lethal enzymatic activity of ribonucleases.

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

Ultrafast diffraction at X-ray free-electron lasers (XFELs) has the potential to yield new insights into important biological systems that produce radiation-sensitive crystals. An unavoidable feature of the `diffraction before destruction' nature of these experiments is that images are obtained from many distinct crystals and/or different regions of the same crystal. Combined with other sources of XFEL shot-to-shot variation, this introduces significant heterogeneity into the diffraction data, complicating processing and interpretation. To enable researchers to get the most from their collected data, a toolkit is presented that provides insights into the quality of, and the variation present in, serial crystallography data sets. These tools operate on the unmerged, partial intensity integration results from many individual crystals, and can be used on two levels: firstly to guide the experimental strategy during data collection, and secondly to help users make informed choices during data processing.

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues—pancreas, liver, kidney, heart, thymus, hippocampus, and skin—from a single mouse. The detailed datasets are openly accessible on OpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.

This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix. Two real world applications are studied: (1) the holistic recognition (i.e., recognition without segmentation) of touching handwritten numeral pairs and (2) the classification of cancer tissue types based on microarray gene expression data. The results show that the proposed DECOC is able to deliver competitive accuracy compared with other ECOC methods, using parsimonious base learners than the pairwise coupling (one-vs-one) decomposition scheme. With a rejection scheme defined by a simple robustness measure, high reliabilities of around 98% are achieved in both applications.