To elicit voluntary movements and integrative reflexes underlying behavior, the brain sends command signals to the spinal cord via specialized long-range descending neurons, known as spinal-projecting neurons (SPNs). The vast and widespread distribution of SPNs, combined with their complex long-distance connectivity, poses a significant challenge for mapping their anatomical organization and associating specific populations with distinct functions. Here we took advantage of the transparency and genetic accessibility of larval zebrafish to uncover the fundamental principles of SPN anatomical organization in a Teleost. Using an optical backfilling method relying on photoactivable GFP, we generate a whole-brain map of all neurons sending axons towards the spinal cord. This approach reveals far more SPNs than previously described through conventional strategies, offering an unparalleled opportunity to revisit distinct spinal-projecting nuclei distributed across hindbrain, midbrain, and diencephalic structures. Combining information on cell location, morphology, and projection patterns, we propose tentative homological designations for zebrafish of SPN nuclei based on established descriptions in mammals and other vertebrates.

Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling tradeoffs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution and multiphoton, all equipped with adaptive optics. MOSAIC enables noninvasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.

RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. Unexpectedly, AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by a RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.

Sensory neurons must extract behaviorally relevant features from dynamic environments while maintaining sensitivity across wide stimulus ranges. To understand how sensory encoding adapts to experience during behavior, we combine long-duration calcium imaging in freely moving C. elegans with a temperature-trajectory playback paradigm to determine how the thermosensory neuron AFD extracts behaviorally relevant sensory features during navigation. We observe that AFD functions as a leaky integrator of recently experienced temperature changes, accumulating thermal inputs over a rolling window of tens of seconds, resulting in calcium levels that represent recent temperature dynamics during runs. Importantly, we determine that AFD selectively amplifies responses to temperature changes near its learned preferred temperature. This experience-dependent gain control aligns encoding with the navigational goal, providing a mechanism for representing temperature preference within a derivative-based sensory system. A minimal mathematical model incorporating derivative detection, leaky integration, and temperature-dependent gain captures the calcium dynamics over a range of stimuli, and a simulation based on the mathematical model predicts goal-oriented locomotor strategies across stimulus regimes. Together, these findings show how gain control allows a derivative-based sensory code to represent an absolute goal and guide locomotory strategies during navigation.

Standard animal learning studies minimize individual reward magnitudes to maximize the repetitions of reinforced behaviors. We investigated how reward magnitude influences initial learning across five behavioral paradigms in naïve mice. Especially large rewards could substantially improve learning efficiency through dissociable effects on within- and across-session learning and task engagement. The duration and magnitude of ventral striatal dopamine release scaled with reward sizes, and prolonged optogenetic enhancement of dopamine reward responses also reproduced much, but not all, of the benefits to learning produced by outsized rewards. These findings indicate that the reinforcement learning efficiency of animals has traditionally been underestimated and that dopamine signaling of rewards mediates task engagement in proportion to absolute reward magnitude.

Direct identification of macromolecular complexes in their native context remains a major barrier to unbiased biological discovery. This challenge is particularly acute in mammalian sperm nuclei, in which condensed chromatin is interspersed with poorly understood phase-separated compartments termed nuclear vacuoles. Vacuoles are associated with reduced fertilization efficiency, yet their composition remains unclear. Here we combine high-resolution in situ cryo-electron tomography (cryo-ET) with AlphaFold docking to identify vacuole components as proteasomes, the proteasome activator PA200, and ferritin. In situ structures at resolutions up to 3.8 Å reveal distinct proteasome-PA200 associations and gating states, consistent with a stepwise activation mechanism. Ferritin assemblies exhibit heterogeneous mineralization states and directly contact chromatin. Together, these findings establish the molecular organization of sperm nuclear vacuoles and implicate protein turnover and metal homeostasis in shaping the nuclear landscape, while demonstrating the power of in situ cryo-ET to resolve protein identity and conformational dynamics in native cellular environments.

Theoretical models can explain how network structure shapes neural computation, but they typically assume idealized connectivity that is inconsistent with the heterogeneous wiring of biological circuits. We address this issue in the Drosophila head-direction system, a recurrent network with ring-attractor dynamics that enable angular velocity integration. The network’s symmetric wiring motifs are reminiscent of classical models, but with additional heterogeneity that should, in principle, destabilize attractor dynamics. Inspired by novel architectures discovered through machine-learning-based optimization, we develop an algorithm that transforms attractor models with symmetric connectivity into functionally equivalent models with heterogeneous connectivity. By replacing each unit with multiple clones that preserve its output, the algorithm embeds hidden symmetries in heterogeneous connectivity, maintaining ring-attractor dynamics and accurate integration. Analysis of multiple fly connectomes provides evidence for duplicated units whose connectivity reflects hidden symmetries, consistent with our theory. Our framework helps reconcile idealized models of neural computation with heterogeneous biological circuits.

Proteins annotated as localizing inside membrane-bound organelles are accepted as residing there. Thus, encountering a mitochondrial cytochrome in the cytoplasm would be unexpected. Yet, a mitochondrial cytochrome does relocalize to the cytoplasm during apoptotic cell death. In fact, a growing list of annotated luminal proteins has demonstrable cytoplasmic functions. It is with this perspective of bi-compartmental proteins that I encourage you to read the new study from Zhu and Fu in this issue of The FEBS Journal. The authors investigate how stress inside the ER lumen influences cytoplasmic energy regulation and uncover a surprising mechanism in fission yeast.

Access to rigorous optical microscopy education remains unevenly distributed across the globe despite widespread use of optical methods. While lower-resourced settings certainly feel this burden, it is far from a foregone conclusion that such opportunities are ubiquitous, even at well-funded institutions. Despite a growing number of online educational resources and other tools, many biomedical researchers learn microscopy in a task-specific manner, and without the conceptual foundation to maximise the potential of its capabilities, robustly interpret data, avoid bias, and/or troubleshoot effectively. Given microscopy's significant impact in the discovery process and status as a cornerstone scientific tool, we developed a remotely accessible, global, and highly interactive program to help address the education gap with respect to the fundamentals of microscopy, irrespective of one's location or ability to access high-end platforms. Here we reflect on the design and evolution of the resulting 'Widening the Lens' (WtL) program: what worked, what didn't, and how the course adapted in response, with the intent of enabling others to leverage our experience to develop programs of their own. We also describe our partnership with critical network organisations, including the African BioImaging Consortium (ABIC) to implement a WtL based train-the-trainer model across the African continent. WtL demonstrates that equitable microscopy education is not only achievable, but that closing education gaps involves building a community in concert with likeminded educational opportunities and networks across the globe.

We introduce a workflow to identify oligomeric structures that are recorded with single-molecule localization microscopy (SMLM) under cryogenic conditions. Typically, these oligomers are assumed to consist of protomers arranged as equilateral two-dimensional polygons and every protomer is labeled with a dye molecule for visualization. Unlike previous work, we consider scenarios in which the sample plane has an unknown orientation relative to the focal plane. Our contribution is a high-precision plane-fitting algorithm to determine the sample plane, combined with geometrical transformations and two circle-fitting algorithms to identify the oligomeric structures. Our simulations on synthetic data demonstrate that the proposed workflow achieves high accuracy in estimating both the unknown tilted plane and the oligomer size.