The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

The point spread function (PSF) is fundamental to any type of microscopy, most importantly so for single-molecule localization techniques, where the exact PSF shape is crucial for precise molecule localization at the nanoscale. Optical aberrations and fixed fluorophore dipoles often result in non-isotropic and distorted PSFs, impairing and biasing conventional fitting approaches. Further, PSF shapes are deliberately modified in PSF engineering approaches for providing improved sensitivity, e.g., for 3D localization or determination of dipole orientation. As this can lead to highly complex PSF shapes, a tool for visualizing expected PSFs would facilitate the interpretation of obtained data and the design of experimental approaches. To this end, we introduce a comprehensive and accessible computer application that allows for the simulation of realistic PSFs based on the full vectorial PSF model. Our tool incorporates a wide range of microscope and fluorophore parameters, including orientationally constrained fluorophores, as well as custom aberrations, transmission and phase masks, thus enabling an accurate representation of various imaging conditions. An additional feature is the simulation of crowded molecular environments with overlapping PSFs. Further, our app directly provides the Cramér–Rao bound for assessing the best achievable localization precision under given conditions. Finally, our software allows for the fitting of custom aberrations directly from experimental data, as well as the generation of a large dataset with randomized simulation parameters, effectively bridging the gap between simulated and experimental scenarios, and enhancing experimental design and result validation.

Transfer of autoantibodies targeting ionotropic N-methyl-D-aspartate receptors in autoimmune encephalitis patients into mice leads to typical disease signs. Long-term effects of the pathogenic antibodies consist of immunoglobulin G-induced crosslinking and receptor internalization. We focused on the direct and immediate impact of a specific pathogenic patient-derived monoclonal autoantibody (immunoglobulin G #003-102) on receptor function.We performed cell-attached recordings in cells transfected with the GluN1 and GluN2A subunit of the N-methyl-D-aspartate receptor. Immunoglobulin G #003-102 binds to the amino-terminal domain of the glycine-binding GluN1 subunit. It reduced simultaneous receptor openings significantly compared to controls at both low and high glutamate and glycine concentrations. Closer examination of our data in 50-second to 2-second intervals revealed, that Immunoglobulin G #003-102 rapidly decreases the number of open receptors. However, antigen-binding fragments of immunoglobulin G #003-102 did not reduce the receptor openings.In conclusion, patient-derived immunoglobulin G #003-102 inhibits N-methyl-D-aspartate receptors rapidly and directly before receptor internalization occurs and the entire immunoglobulin G is necessary for this acute inhibitory effect. This suggests an application of the antigen-binding fragment-like constructs of #003-102 as a potential new treatment strategy for shielding the pathogenic epitopes on the N-methyl-D-aspartate receptors.

Histone-lysine N-methyltransferase 2 (KMT2) methyltransferases play critical roles in gene regulation, cell differentiation, animal development, and human diseases. KMT2 biological roles are often attributed to their methyltransferase activities on lysine 4 of histone H3 (H3K4). However, recent data indicate that KMT2 proteins also possess non-enzymatic functions. In this review, we discuss the current understanding of KMT2 family, with a focus on their enzymatic activity-dependent and -independent functions. Six mammalian KMT2 proteins of three subgroups, KMT2A/B (MLL1/2), KMT2C/D (MLL3/4), and KMT2F/G (SETD1A/B or SET1A/B), have shared and distinct protein domains, catalytic substrates, genomic localizations, and associated complex subunits. Recent studies have revealed the central role of KMT2C/D in enhancer regulation, differentiation, and development and have highlighted KMT2C/D enzymatic activity-dependent and independent roles in mouse embryonic development and cell differentiation. Catalytic dependent and independent roles for KMT2A/B and KMT2F/G in gene regulation, differentiation, and development are less understood. Finally, we provide our perspectives and lay out future research directions that may help advance the investigation on enzymatic activity-dependent and -independent biological roles and working mechanisms of KMT2 methyltransferases.

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism1. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons2. However, while AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3–5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that suppresses appetite on a rapid time scale. Here, we report the discovery of a novel population of leptin-target neurons expressing basonuclin 2 (Bnc2) that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is finely tuned by leptin, sensory food cues, and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Stereocilia are unidirectional F-actin-based cylindrical protrusions on the apical surface of inner ear hair cells and function as biological mechanosensors of sound and acceleration. Development of functional stereocilia requires motor activities of unconventional myosins to transport proteins necessary for elongating the F-actin cores and to assemble the mechanoelectrical transduction (MET) channel complex. However, how each myosin localizes in stereocilia using the energy from ATP hydrolysis is only partially understood. In this study, we develop a methodology for live-cell single-molecule fluorescence microscopy of organelles protruding from the apical surface using a dual-view light-sheet microscope, diSPIM. We demonstrate that MYO7A, a component of the MET machinery, traffics as a dimer in stereocilia. Movements of MYO7A are restricted when scaffolded by the plasma membrane and F-actin as mediated by MYO7A’s interacting partners. Here, we discuss the technical details of our methodology and its future applications including analyses of cargo transportation in various organelles.

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.

Analyzing immune cell interactions in the bone marrow is vital for understanding hematopoiesis and bone homeostasis. Three-dimensional analysis of the complete, intact bone marrow within the cortex of whole long bones remains a challenge, especially at subcellular resolution. We present a method that stabilizes the marrow and provides subcellular resolution of fluorescent signals throughout the murine femur, enabling identification and spatial characterization of hematopoietic and stromal cell subsets. By combining a pre-processing algorithm for stripe artifact removal with a machine-learning approach, we demonstrate reliable cell segmentation down to the deepest bone marrow regions. This reveals age-related changes in the marrow. It highlights the interaction between CXCR1 cells and the vascular system in homeostasis, in contrast to other myeloid cell types, and reveals their spatial characteristics after injury. The broad applicability of this method will contribute to a better understanding of bone marrow biology.