Communications Biology

The retinal nerve fiber layer, composed of axon bundles converging toward the optic nerve, is a key biomarker for diagnosing and monitoring glaucoma and other neurodegenerative diseases. High-resolution en face imaging of individual nerve fiber bundles offers morphological information beyond what conventional optical coherence tomography provides, yet clinical integration remains limited by the lack of automated analysis tools and normative data. Here, we imaged 14 healthy volunteers using time-domain full-field optical coherence tomography and adaptive optics scanning laser ophthalmoscopy, and developed automated pipelines to quantify bundle width, trajectory, tortuosity, and orientation. Bundles were on average 25% wider at shallower retinal depths, width measurements were consistent across imaging modalities, and estimated axon count per bundle decreased significantly with age. Global trajectory analysis revealed systematic deviations of high resolution data from existing mathematical models, particularly in the temporal sector, leading us to propose two refined trajectory models. These normative results provide a foundation for high resolution biomarkers for use in investigations of retinal neurodegeneration.

Cryopreservation offers an option for long-term storage and global distribution of complex in vitro models, yet protocols for multicellular microphysiolgocial systems (MPS) such as brain organoids/spheroids remain limited. Here, we systematically compared three commercially available cryopreservation (mFreSR, CryoStorCS10, and 3dGRO) and two freezing time points, and established a robust workflow for freezing and recovering brain organoids. After defrosting, we assessed morphology and metabolic activity. We also evaluated electrophysiology, calcium transients, and neurite outgrowth. In addition, we measured astrocyte migration, apoptosis, mitochondrial integrity, microglia survival, and neural marker expression. We found that organoids require a 4-week recovery period to regain structural and functional stability. Although organoids frozen at week 6 showed higher metabolic activity after recovery, organoids cryopreserved at week 2 had clearly better functional outcomes. They exhibited stronger spontaneous network firing and maintained calcium transients. Finally, incorporated microglia-like cells survived the freezing and displayed comparable morphology to unfrozen controls. Across the endpoints measured here, 3dGRO showed the most favorable overall performance; formal ranking across media awaits harmonized normalization, single-organoid electrophysiology, and prespecified QC thresholds. Together, these results define a practical and reproducible cryopreservation strategy that preserves key physiological features of brain organoids and supports the establishment of ready-to-use organoid banks. The ability to reliably store and distribute complex brain-like tissues represents an essential step toward global standardization, scalable experimentation, and wider adoption of human-relevant microphysiological systems. Together, these results demonstrate recovery of key physiological features in the subset of organoids that remain viable after thaw and support the feasibility of brain organoid banking.

Postoperative cognitive decline (POCD) after coronary artery bypass grafting (CABG) is increasingly conceptualized as a system-level disturbance of large-scale brain coordination rather than focal dysfunction. Here, we propose a multiscale neural engineering framework that combines static and dynamic information-theoretic connectivity with graph-theoretical analysis to characterize postoperative network vulnerability and its association with cognitive outcome. Resting-state fMRI was acquired in 14 male CABG patients at an early postoperative baseline (BL) and at 3-month follow-up (FU). Cognitive outcome at follow-up was assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), classifying 7 patients as POCD (RBANS < 80) and 7 as NO POCD. Functional connectivity between 32 brain regions, grouped in 8 resting-state networks (RSN), was estimated using mutual information (MI; static dependence) and mutual information rate (MIR; dynamic information exchange), each computed with parametric Gaussian (linear) and model-free k-nearest neighbor estimators. Pairwise connections were validated via surrogate testing, and group differences in longitudinal connectivity change ({Delta} = FU-BL) were assessed with permutation tests at global, intra- and inter-RSN scales. Graph metrics were computed on statistically thresholded weighted networks and related to RBANS using permutation-based Spearman correlations. POCD was not associated with a uniform reduction in connectivity but with a structured pattern of network reorganization. Static connectivity showed widespread alterations, particularly within higher-order associative systems, including salience, dorsal attention, and default mode networks. Dynamic connectivity did not exhibit global group differences but revealed selective, network-specific alterations in temporal information exchange. Longitudinal analyses showed that better cognitive outcomes were associated with increased global efficiency and density and reduced modularity and small-worldness, indicating a greater brain integration. In contrast, poorer outcomes were associated with increased segregation and higher betweenness centrality, suggesting greater reliance on hub-mediated communication. Linear measures captured more widespread connectivity changes, whereas nonlinear estimators revealed more selective alterations in dynamic information flow. Combining static and dynamic information measures with complementary estimators and surrogate-validated graph analysis reveals dissociable signatures of postoperative network dysfunction. POCD is characterized by impaired restoration of distributed integration and a progressive shift toward hub-dependent communication, suggesting that large-scale integrative vulnerability may constitute a candidate biomarker of cognitive resilience after cardiac surgery.

Rhesus macaques (Macaca mulatta) are the most widely used non-human primate model for translational research relevant to human health and disease. Although several genetically distinct populations have been recognized across the species extensive habitat range in Asia, the majority of biomedical studies in the United States and abroad focuses on individuals of either Chinese or Indian descent. Notably, phenotypic differences exist between these two populations which can influence biomedical research outcomes; however, the genetic basis and molecular mechanisms underlying these differences are generally not well understood. Based on novel PacBio HiFi long-read sequencing data from 20 rhesus macaques -- ten of Chinese origin and ten of Indian origin -- we here characterize the genome-wide landscape of structural variation in these two biomedically-relevant populations. Our results highlight differences in the structural variant landscape affecting genes involved in neural communication and signaling pathways, in line with the known differences in temperament between the two populations. Furthermore, while the majority of discovered structural variants were located in intergenic and non-coding regions of the genome, 15 of the discovered population-specific structural variants were predicted to exhibit a high functional effect on genes associated with human disease, indicating that they may play an important role in shaping the differences in disease susceptibility between the populations. Taken together, by providing detailed insights into population-specific structural variation, this genomic resource will aid the design and interpretation of future studies aiming to link genotype, phenotype, and fitness in the context of human health and disease, and facilitate broader comparative analyses of structural variation as a force shaping genome evolution across primates.

Standardized biological specimens are essential for optimizing cryoEM workflows and benchmarking instrument performance. While apoferritin fulfills this role for single-particle analysis, no equivalent exists for cryo-electron tomography. Ribosomes are frequently used but require large datasets due to C1 symmetry and structural heterogeneity, limiting rapid optimization and standardized comparison of workflows. Here, we present PP7 virus-like particles (VLPs) overexpressed in E. coli as a scalable in situ benchmark. VLPs have high orders of symmetry enabling rapid, high-resolution validation of tomographic pipelines from minimal datasets, while their distinct structural features across low to high resolutions provide a practical resolution metric.

Linear state-space models have been shown to effectively reproduce large-scale brain dynamics. We applied this approach to resting-state fMRI data acquired from 20 mice, focusing on the systems Jacobian matrix, i.e. the effective connectivity, and specifically on its component encoding nonzero-lag interactions: the differential covariance matrix. Within this matrix, we concentrated on the off-diagonal component (dC-Cov), which reflect endogenous time-lagged correlations. Our aim was to identify a decomposition of the Jacobian matrix that facilitates its interpretation from a mechanistic perspective. Since the dC-Cov captures the rotational component of signal trajectories, we employed Schur decomposition to extract 2D rotational modes, each characterized by a pair of orthogonal vectors, and an associated angular frequency. This provides a more generative formulation of the modeling framework, thereby reducing the interpretability gap between this approach and connectome-based network models of coupled neural masses. Within this framework, the precision matrix governs the coupling between different Schur modes, while we hypothesize that the dC-Cov reflects spatial constraints imposed by inter-regional distances. By examining the relationship between dC-Cov and structural constraints imposed by the spatial placement of brain areas, we found a consistent alignment between the faster Schur modes across mice and the leading eigenvectors of the structural distance matrix.

High-quality chromosome-scale assemblies are scarce in Papilionidae. This limits comparative genomics to model species, Lepidoptera, Bombyx mori. Here, we present a phased, chromosome-level genome assembly of Parides eurimedes mylotes. We generated this assembly using 125[x] PacBio HiFi sequencing and assembled it with hifiasm. The final haplotype assemblies (Hap1 and Hap2) span 274 Mb and 270 Mb, respectively. These assemblies are organized into 31 near-telo-mere-to-telomere chromosomes, with scaffold N50 values of 9.72 Mb and 9.22 Mb, respectively. BUSCO analysis revealed assembly completeness of 96.6 % and 96.4 % for Hap1 and Hap2, respectively. Repeats annotation identified 18-19 % repetitive content, with Helitron elements being the dominant class of transposable elements. We identified the W and Z sex chromosomes and completely assembled the mitochondrial genome. Compared to the previously available Parides photinus draft assembly, our genome exhibits an 11,000-fold reduction in scaffold fragmentation and nearly complete gene assembly. This assembly provides a robust genomic reference for functional, evolutionary, and multi-omics investigation in Papilionidae. In addition to serving as a high-quality genomic reference for Papilionidae, this assembly is essential for linking the genetic architecture of butterfly wings to the hierarchical nanostructures underlying structural coloration. By identifying the genes and regulatory networks involved in scale morphogenesis, we can correlate the butterflys genotype with its photonic function. This insight into the evolutionary origin of the biological photonic systems informs the design of biomimetic, structurally colored materials.

Archaellum-associated motility has been viewed as solely archaeal, yet new findings in Chloroflexota prompt a broader perspective. By analysing a curated [~]22,000 NCBI reference genomes alongside 2,397 archaeal and 226 archaellum-encoding Chloroflexota genomes, this study systematically characterises the co-distribution of archaellum loci with chemosensory system (CSS) classes. Maximum-likelihood phylogeny of 3,727 F1-type CheA proteins reveals three major clades, with Clade 1 comprising [~]80% monoderm representation, uniting archaeal and monoderm bacterial lineages in a shared evolutionary grouping. Overall, this work shows that not only archaeal-type motility, but also F1-CSS based sensing system, might have been gained from Archaea to Chloroflexota via horizontal gene transfer and both systems shared an evolutionary trajectory altogether.

Understanding how the brain operates in naturalistic settings requires methods that go beyond conventional repeated-measurement approaches, necessitating the development of single-trial neural activity analysis. Recent advances in machine learning offer new opportunities for analyzing brain electrophysiological signals. Here, we recorded surface electrocorticography (ECoG) and intracranial local field potentials (LFPs) from emotion-related brain regions in a monkey performing a Pavlovian conditioning task, in which sensory cues predicting reward or punishment were presented randomly, followed by the actual unconditioned outcome. We evaluated the performance of two machine learning algorithms, a Convolutional neural network (CNN) model and a Transformer-based model (EEG-Conformer), in classifying raw ECoG/LFP traces. Both models successfully classified valence type during conditioned and unconditioned stimulus presentation. Furthermore, the Transformer achieved significantly superior classification performance compared to the CNN, particularly in multi-state classification including baseline periods. By optimizing the training dataset for the Transformer model, we could detect dynamic fluctuations in emotional valence consistent with task type from continuously evolving ECoG/LFP patterns recorded throughout the task. These results demonstrate the utility of Transformer-based models for decoding emotional valence from neurophysiological signals in non-human primates.

The choroid is critical for maintaining vision and implicated in several ocular diseases, being the sole source of nutrients and waste removal for the outer retina. Genetic discovery can help elucidate the pathways through which choroidal features influence disease risk. Our meta-analysis of genome-wide association studies (n= 78,682 participants) identified 30 genomic regions, including 20 novel loci, associated with choroidal thickness. Findings suggest inflammatory and vascular processes drive choroidal thickness, with overlapping mechanisms shared with refractive error. Genome-wide independently significant SNPs accounted for 18.7% of the genetic variance in choroidal thickness. Mendelian randomisation analyses showed a causal effect of age-related macular degeneration on choroidal thickness, and suggest a bidirectional causal effect between choroidal thickness and primary angle-closure glaucoma. These findings provide insight into the shared genetic architecture and biological pathways linking choroidal thickness and related diseases.

Microtubules are dynamic filaments of tubulin heterodimers that comprise an essential part of the eukaryotic cytoskeleton1. The nucleotide state of tubulin controls microtubule dynamics: stable GTP-microtubules favor polymerization, whereas unstable GDP-microtubules drive depolymerization2. Anticancer compounds such as Taxol (paclitaxel) target microtubule dynamicity by preventing microtubule depolymerization3,4. Despite decades of work, the molecular basis of microtubule dynamics remains poorly defined. Using cryo-EM, we determined [~]2.2 [A] structures of human microtubules in GTP-like (GMPCPP) and GDP states. Comparison of these two states revealed switch-like structural changes as tubulins transition from the pre-hydrolysis (GMPCPP) to the post-hydrolysis (GDP) state. Additional structure determination of Taxol-bound microtubules at [~]2.2 [A] showed that Taxol binding converts the microtubule lattice into a pre-hydrolysis state by reversing the structural switches flipped during GTP hydrolysis. Focusing our analysis on the microtubule seam shows that the pre-hydrolysis conformation of GMPCPP or Taxol-GDP exhibits favorable lateral interactions at the seam, with lattice deformations clearly visible at the GDP seam. Together, our data show the existence of structural switches in tubulin that are coupled to the nucleotide state and are exploited by Taxol to stabilize microtubules into a pre-hydrolysis-like state. (191 words)

The tissue-resident immune system involves complex 3D assemblies that interact with extended structures such as blood vessels and nerves. These interactions are difficult to study using conventional 2D profiling because they span many tissue sections. In animal tissues, volumetric imaging approaches such as light-sheet fluorescence microscopy (LSFM) are widely used to study 3D tissue organization, with labelling often aided by genetically encoded reporters and vascular dyes. In contrast, LSFM of human specimens remains underdeveloped because most clinical samples are available only as formalin-fixed paraffin-embedded (FFPE) tissue, limiting labeling strategies primarily to dyes and antibodies. Here, we present a volumetric cyclic immunofluorescence (v-CyCIF) and virtual H&E toolbox that overcomes key barriers to multiplexed imaging of immune cells and nerves in human specimens up to 1 mm thick. We use v-CyCIF to study neuroimmune interactions in normal and cancer tissues and to immunoprofile intact secondary and tertiary lymphoid structures. Re-embedding and sectioning of specimens following volumetric imaging enables high-plex high-resolution analysis of subcellular structures and cell-cell interactions associated with immune cell activity. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

Unsupervised deep-learning image phenotypes derived from brain MRI are propelling imaging genetics to link brain structure to genetic variation. However, their replicability across data sets has not been sufficiently evaluated, raising questions about whether they capture robust biological structure or reflect training-specific artifacts. Here, we assess the replicability of unsupervised deep-learning image phenotypes under variation in model initialization, data partitioning, and cohort, directly evaluating their stability across experimental conditions. We trained multiple models under (i) different training batch random seeds, (ii) cross-validation splits, and (iii) independent datasets (UKB and ADNI), across CNN and ViT architectures. We then derived representations from a separate UKB discovery cohort (N = 22,985) for both trained models and random initialized models without training. The representation stability was assessed using centered kernel alignment (CKA; mean ViT 0.74 vs random 0.27) and kernel canonical correlation analysis (KCCA; mean ViT 0.84 vs random 0.60), as well as genetic discovery stability using loci overlap ratio (mean ViT 0.45 vs random 0.08). We further applied weighted MAXVAR generalized CCA to 12 embeddings to extract a shared 30-dimensional subspace. Our result showed that UDIPs exhibit statistically significant stability (CKA, KCCA t test p < 0.001) across training perturbations and preserve biologically meaningful structure (loci overlap ratio t test p <0.001) across cohorts, supporting their use in imaging genetics.

Conversation is a fundamental human behaviour that requires rapid coordination between speaking, listening, and turn-taking, yet datasets capturing its neural dynamics in natural interaction remain scarce. Hyperscanning EEG is particularly valuable for this purpose because it records both interlocutors simultaneously, enabling the study of speaker-listener coupling, response timing, and dyadic coordination during live exchange. Here we present DUET (Dyadic Understanding, EEG and Turn-taking), a hyperscanning dataset for studying natural French face-to-face conversation. The dataset comprises recordings from 18 dyads, or 36 French-speaking adults, performing the Diapix collaborative spot-the-difference task across eight 4-minute face-to-face conversation blocks. For each participant, EEG was recorded from 36 participants; most recordings used 64-channel EEG, with one pilot dyad recorded using 32 electrodes. The public release includes raw EEG recordings, precomputed ICA decompositions for reuse in downstream preprocessing as well as various features derived from the audio and manually corrected transcripts.

Multiplexed imaging approaches of various molecular modalities in tissues are becoming increasingly adopted in discovery and translational studies. For clinical implementation, novel instrumentation, complex analysis workflows and high costs per sample are bottlenecks that hinders broader introduction in the clinical setting. Here, we demonstrate a cost efficient integrated workflow that combines multiplexed immunofluorescence of a handful of protein markers, with in situ proximity ligation assay, to detect direct protein interactions between neighboring cells. As a proof of concept case of relevance for clinical adaptation, we target the major immunotherapy signalling axis of programmed death receptor 1 (PD-1) and its ligand PD-L1, to demonstrate the interaction between immune cells in germinal centers of tonsil tissue and in a tertiary lymphoid structure in bladder cancer tissue, respectively, from a patient treated with immunotherapy.

Alzheimers disease (AD) progression involves systemic network transitions. To capture these using ROSMAP bulk RNA-seq (n=624), we focused on the geometry of the covariance structure, performing a Riemannian (Log-Euclidean) analysis of stage-wise covariance matrices as points on the manifold of symmetric positive-definite (SPD) matrices. On the SPD manifold the three stages were non-collinear: geodesic distances were non-uniform and MCI was displaced from the NCI-AD chord, while the von Neumann entropy of the covariance structure dipped at MCI (S = 2.760, 2.639, 2.647 for normal cognitive intact NCI, mild cognitive impairment MCI, AD) and the path-curvature profile reached a minimum there -- together identifying MCI as a saddle/bifurcation state. The differential covariance spectrum (CAD - CNCI) separated AD-amplified ("structural collapse") from AD-suppressed ("protective loss") modes. Ultimately, second-order statistics analyzed through Riemannian geometry, rather than Euclidean summaries, reveal AD progression structure invisible to mean-level analysis.

During forebrain development, inhibitory interneurons and oligodendrocyte progenitor cells migrate long distances into the developing dorsal cortex. Human induced pluripotent stem cell-derived forebrain assembloids (FAs) provide direct experimental access to this migratory process in vitro. Using viral labeling to express yellow fluorescent protein (EYFP) and tandem-dimer tomato (tdTomato) driven by EF1 or SOX10 promoters, respectively, we tracked cells in FAs over 15-17h using spinning disk confocal microscopy. We developed an end-to-end processing pipeline for 4D volumetric imaging data, consisting of background subtraction and drift correction, manual cell coordinate tracking, and an analysis workflow to describe migratory cell behavior. Image preprocessing significantly improved data quality for subsequent manual tracking in datasets with heterogeneous labeling density and brightness. Trajectory analysis of 336 EYFP- and 337 tdTomato-labeled cells from twelve FAs indicates that most cells show super-diffusive directed motility. Our pipeline represents a key resource for cell tracking in FAs and similar three-dimensional platforms. This pipeline represents the first open tracking resource for iPSC-derived FAs and can be used as a ground-truth resource for the development of automated cell detection and tracking algorithms.

The role of chemodiversity in plant-insect interactions is widely recognised. However, our understanding of the extent to which chemodiversity connects both partners remains limited. Here, we investigated how aphid chemistry is linked to their plant diet and whether aphids capture plant inter- and intraspecific chemodiversity. Up to 93% of aphid chemical features were detected in plants. Untargeted metabolomics of aphids feeding on diets composed of distinct species or chemotypes within species unveiled the aphid capacity to capture inter- and intraspecific chemodiversity. Multiple chemodiversity indices and metabolic features significantly tracked diet variation and plant chemotypes were reflected in aphid metabolites. These features included phenolics and amino acids, likely ingested with the phloem sap, and fatty acids and terpenoids, potentially captured from the leaf surface. Overall, these findings expand our knowledge of the aphid plant-derived chemical repertoire and highlight that plant chemodiversity can be transmitted, supporting the need for chemodiversity preservation programs.

Neural oscillations are thought to play a central role in encoding and transmitting cognitive information across large-scale brain networks, yet the relative contributions of phase synchrony and amplitude co-modulations to distributed coding remain unclear. A key obstacle is the absence of tools that can simultaneously quantify task-relevant information in the frequency domain and disentangle its phase and amplitude components across pairwise and higher-order interactions. Here, we introduce a spectral partial information decomposition framework (named NeOPID) for quantifying information about cognitive variables in power and phase contributions, and to quantify redundant and synergistic information in brain relations, from pairwise to higher-order interactions. We validated the approach on Kuramoto and Stuart-Landau oscillator networks, including a whole-brain model constrained by macaque anatomical connectivity. NeOPID accurately recovers ground-truth encoding schemes and reveals that phase relations and amplitude co-modulations act as complementary coding channels with both redundant and synergistic components. NeOPID further extends this decomposition to higher-order functional interactions enabling the characterization of how cognitive information is collectively distributed across multiple oscillatory edges via redundant and synergistic encoding. To illustrate biological applicability, we applied NeOPID to local field potentials (LFPs) recorded from the macaque fronto-parietal network during a working memory task. In this dataset, NeOPID identified beta-band amplitude co-modulations as the primary carrier of stimulus information, and revealed that higher-order phase interactions exhibit both redundant and synergistic structure during the memory delay. These results establish NeOPID as a principled tool for dissecting the informational architecture about cognitive processes of oscillatory brain networks.

Vitellogenins are essential transport lipoproteins and precursors to egg-yolk proteins in oviparous species. Several molecular structure studies have elucidated most of their multi-domain organization, yet the structure and function of their C-terminal cysteine knot (CTCK) domain have remained largely elusive. In this study, we present the 1.2 [A] resolution crystal structure of the recombinant CTCK domain from a vitellogenin isoform of the mosquito Aedes albopictus (Vg-CTCK). The molecular architecture reveals a CTCK fold defined by two antiparallel beta sheets stabilized by three intramolecular disulfide bonds, featuring a unique 12-amino acid insertion shaping an alpha helix positioned between the two main beta sheets. Analysis of the crystal packing and biophysical characterization in solution consistently confirms that recombinant Vg-CTCK is monomeric. To validate these findings in a native context, we employed ex vivo cross-linking mass spectrometry (XL-MS) on intact mosquito ovaries, which corroborated the molecular architecture of the Vg-CTCK observed in the crystal structure and highlighted the absence of inter-molecular cross-links. Collectively, our data highlight an evolutionarily divergent, monomeric assembly for the mosquito Vg-CTCK domain, challenging previous hypotheses that suggested this domain might facilitate vitellogenin oligomerization.