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.

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.

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1 week after fertilization-a developmental stage where they have approximately 1% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for ∼1 min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1 week after fertilization-a developmental stage where they have approximately 1% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for ∼1 min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

The ability of naturally occurring proteins to change conformation in response to environmental changes is critical to biological function. Although there have been advances in the de novo design of stable proteins with a single, deep free-energy minimum, the design of conformational switches remains challenging. We present a general strategy to design pH-responsive protein conformational changes by precisely preorganizing histidine residues in buried hydrogen-bond networks. We design homotrimers and heterodimers that are stable above pH 6.5 but undergo cooperative, large-scale conformational changes when the pH is lowered and electrostatic and steric repulsion builds up as the network histidine residues become protonated. The transition pH and cooperativity can be controlled through the number of histidine-containing networks and the strength of the surrounding hydrophobic interactions. Upon disassembly, the designed proteins disrupt lipid membranes both in vitro and after being endocytosed in mammalian cells. Our results demonstrate that environmentally triggered conformational changes can now be programmed by de novo protein design.