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.

Fluctuations in attentional states, such as mind-wandering (MW), are associated with critical variability in task performance. While fMRI studies highlight the opposing roles of task-positive (e.g., dorsal attention network) and task-negative (e.g., default mode network) systems, the electrophysiological mechanisms underlying these dynamics remain poorly understood. Using intracranial electrocorticography in humans performing a sustained attention task, we identified global oscillatory dynamics linked to attentional shifts. MW was characterized by (i) reduced theta ({theta}) and alpha ({square}) power, (ii) decreased aperiodic signal components, indicating a shift toward cortical inhibition, (iii) enhanced phase synchronization across networks, and (iv) strengthened {theta} phase-behavior correlations ({rho}). These features support a non-network-specific framework in which low-frequency {theta} dynamics--captured by both {theta} power and {rho}--are associated with attentional fluctuations, while aperiodic offset relates to attentional state indirectly through its association with {rho} (Structural Equation Modeling: power[-&gt;]state {beta} = -0.118, p = 0.002; {rho}[-&gt;]state {beta} = 0.246, p < 0.001; offset[-&gt;]{rho} {beta} = -0.222, p < 0.001). Our study provides a unified neurophysiological framework for understanding how spontaneous neural activity can drive attentional fluctuations and performance variability, with implications for research on attention, learning, and neuropsychiatric disorders.

Simultaneous EEG-fMRI offers a powerful way to study brain dynamics, but combining the two modalities in a common whole-brain model remains challenging. Here, I developed a fusion modeling framework for resting-state simultaneous EEG-fMRI that emphasized careful multimodal alignment, construction of a stable shared feature space, and cross-validated, reproducibility-based model selection. Using 15 eyes-open resting-state runs from 12 healthy adults in an open simultaneous EEG-fMRI dataset, I constructed a no-lag, 15-TR-minimum fusion dataset comprising 3550 retained TRs and 124.25 min of usable data. A leave-one-subject-out cross-validation sweep supported a parsimonious three-state fusion hidden Markov model. In the final full-data solution, one state emerged as a dominant backbone state with the highest occupancy, strongest persistence, and clearest canonical BOLD network organization. Two lower-occupancy states behaved as transient alternatives: one appeared as a broadly attenuated version of the backbone state, whereas the other showed more selective network reweighting. The states also differed in their descriptive cross-modal BOLD-EEG structure, suggesting that electrophysiological and hemodynamic network expression may align differently across latent brain states. These results provide both a practical whole-brain EEG-fMRI fusion workflow and a biologically interpretable account of low-order resting-state brain dynamics.

Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised softwares and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved 93{+/-}2% accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and readily adaptable to new labelling schemes, classification targets, cell lines, and microscopy modalities, enabling efficient integration into automated microscopes.

Insects body barriers rely on specialized extracellular matrices that protect against harmful environmental influences. The outer barrier is the cuticle, which is composed of chitin, cuticle proteins and lipids. The peritrophic matrix (PM) serves as an inner barrier lining the midgut epithelium. It is composed of chitin fibers that are organized by PM proteins. While cuticle and PM proteins have received considerable attention in the past, supramolecular organization and physicochemical properties of the chitin component - particularly of the PM - remain poorly understood. Here, we combine synchrotron-based X-ray diffraction data from the PMs of lepidopteran and coleopteran insects with RNA interference (RNAi), mass spectrometric and histochemical analyses of the PM from Tribolium castaneum to determine chitins allomorphic state and degree of acetylation. The chitin of the PM exhibits signatures characteristic of dihydrate {beta}-chitin along the entire midgut. In contrast, the cuticle is made of tightly packed -chitin nanofibrils. Mass spectrometry revealed that the PMs chitin is highly acetylated (>95%). RNAi silencing of gut-specific genes encoding chitin deacetylasesTcCDA6-9 further increases the degree of acetylation. Histochemical analyses staining chitin with different degrees of acetylation confirm the predominance of highly acetylated chitin in the PM. Notably, the larval cuticle has a layered organization with deacetylated chitin present in exo- and highly acetylated chitin in endocuticles. Depletion of both TcCDA1 or TcCDA2 impairs chitin deacetylation, which indicates that both proteins cooperate in their activity in the integument. These results establish fundamental principles of polysaccharide-based extracellular matrices, with broad implications for insect biology.

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.

The transcription factor FOXP2 is the most well-known language-related gene in humans, yet its role in primate vocalization remains poorly understood. Here we report that knockdown of FOXP2 in the striatum markedly disrupts vocalization stability in the marmoset monkey, a valuable non-human primate model for studying vocal behavior. FOXP2 exhibited high expression in the marmoset striatum, especially during early development. Using the CRISPR-Cas12 system, we achieved specific in vivo editing of the FOXP2 gene and effective knockdown of FOXP2 protein expression in the marmoset striatum. Two neonatal marmosets received bilateral striatal injections of the gene-editing and control virus, respectively, and were raised together in the same family. In three such marmoset pairs, analysis of vocalizations recorded during 6-15 weeks post-injection revealed that striatal FOXP2 knockdown significantly altered vocal features and increased intra-individual variability in phee syllables--the most common marmoset vocalization, often produced repetitively as multi-syllable phee calls. Notably, in FOXP2-edited marmosets, acoustic alterations were minimal in the first syllable of phee calls but became progressively more pronounced in subsequent syllables, which exhibited a marked upward shift in the frequency spectrum over time with progressively steeper slopes. These temporal dynamics in vocal features reflect a reduction in the stability of continuous vocal production. In line with the known striatal functions in motor control, our findings provide the first evidence of FOXP2 in controlling vocalization in non-human primates, thereby opening new avenues for investigating the neural mechanisms underlying FOXP2 function.

Multicellular organisms comprise various types of cells, which are characterized by gene expression through interactions between chromosomal DNA and nuclear proteins. Many cutting-edge methods have been developed to reveal the three-dimensional organization of chromosomes. The detailed analyses of whole chromosomes have begun to uncover structural features specific to several cell types. Here, we show that cell types are instantly and highly accurately classified using conventional DNA staining and a convolutional neural network (CNN). A high-resolution single slice image of the nucleus is sufficient for the accurate classification of both live and fixed cells, including neurons and non-neural cells. These findings suggest that there may be cell-type-specific features decipherable by deep learning in a thin two-dimensional slice of the nucleus.

Human brain assembloids offer a powerful platform for modeling neurological diseases, yet comprehensive methods for analyzing their complex network dynamics are lacking. Here, we developed a time-resolved network analysis pipeline that extracts quantitative biomarkers from two-photon calcium imaging, enabling the detection of subtle differences between disease and control models. We applied this pipeline to assembloids containing a pathogenic MAPT p.R406W variant--clinically associated with an Alzheimers disease-like phenotype--and their isogenic controls. Our analysis revealed that mutant networks exhibit significantly increased degree variance and clustering. This indicates a "hub-like", interconnected topology prone to hypersynchrony, a finding that parallels the network hyperexcitability and seizure-like features observed in in-vivo models of Alzheimers disease. Furthermore, a Random Forest classifier trained on these dynamic network features distinguished between diseased and control states with high accuracy (F1 score = 0.90). These results establish that dynamic network properties can serve as potent biomarkers for identifying pathological states in assembloid models, providing a quantitative framework to investigate disease mechanisms and potential therapeutic interventions.

Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor "motifs" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.

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.

Acquiring nutrients is a fundamental biological process of all organisms, playing crucial roles in ecological sustainability. Diplonemids are highly abundant heterotrophic unicellular flagellates that are widespread in the worlds ocean. They have a highly complex microtubule-based feeding apparatus (cytostome-cytopharynx complex) located adjacent to the deep flagellar pocket from which two flagella emerge from parallel basal bodies. The apical papilla is a tongue-shaped structure unique to diplonemids that connects the cytopharynx and the flagellar pocket, the latter of which is formed by reinforcing microtubules (MTR) and two flagellar roots called intermediate and dorsal roots. Here we report identification of 17 proteins that localize at the feeding apparatus or flagellar apparatus in Diplonema papillatum. Using ultrastructure expansion microscopy, we show that Mad2 and its interaction partner MBP65 localize at the MTR, intermediate root, and dorsal root. Homologs of proteins that associate with the flagellar apparatus in Trypanosoma brucei (PFR2, KMP11, BILBO1) localize at the feeding apparatus in D. papillatum. We also identify proteins that localize at the apical papilla, MTR, parallel microtubule loop, or cytopharynx. By discovering components of the feeding apparatus for the first time in diplonemids, this work forms the foundation to understand molecular mechanisms of the feeding apparatus in these highly abundant marine plankton.

Synapses are the basic unit of information transfer between neurons. Their dysfunction is a common trigger of cognitive diseases and disorders. However, high-throughput analysis methods to assess synaptic function and dysfunction are lacking. Calcium imaging in cultured neurons in the absence of Mg2+ and presence of TTX allows visualization of NMDAR-dependent spontaneous synaptic calcium transients, which report pre and postsynaptic function. Here, we introduce a high-throughput automated analysis pipeline that combines Suite2p ROI detection and Python scripts to analyze tens of thousands of synapses and quantify changes in presynaptic vesicle fusion rates (frequency), postsynaptic function (amplitude), and the number of functional synapses. We use this pipeline to test known NMDAR agonists (glycine) and antagonists (ketamine, memantine, APV), presynaptic function modulating compounds (PDBu), and encephalitis patient-derived NMDAR auto-antibodies, where our pipeline proved more sensitive in detecting dysfunction at the single-synapse level than other methods. The ability to detect, track, and quantify activity across tens of thousands of synapses and millions of synaptic calcium transients using this pipeline will aid drug discovery of compounds that protect synapse function.

Validation of somatic mutation burden assays is fundamentally constrained by the absence of a robust ground truth, limiting the interpretability of performance metrics. To address this, we propose a framework based primarily on relative validation, complemented by a suite of secondary metrics aligned to common failure modes. We implement this approach in SomaticCODEC, a ready-to-run assay for quantifying SNV burden in primary human samples, demonstrating strong linearity across mixtures of sperm and blood samples (R2 = 0.91) and high intra-batch precision (CV = 3.3%). This framework provides a practical approach for validating somatic mutation burden assays without requiring a ground truth.

Recoverin is a key calcium sensor that controls the desensitization of the visual rhodopsin by GRK1. Previous studies have traditionally been conducted on bovine protein (bRec), while data on human ortholog (hRec) remain scarce. Here, we combine X-ray crystallography, X-ray absorption spectroscopy (XANES), quantum mechanical calculations, molecular dynamics, and functional assays to provide an integrated characterization of hRec. The 2Ca2+-bound hRec structure was solved at 1.60 [A], showing that, unlike bRec, hRec interacts with ROS membranes at physiologically relevant submicromolar Ca2+ levels, due to a species-specific charge distribution that might influence membrane interactions. Both recoverins form a set of Ca2+/Zn2+-bound conformers with improved functional performance. X-ray crystallography (1.85 [A]) and XANES revealed a specific tetrahedral Zn2+ site in 1Ca2+-bound hRec, the first such site reported in the NCS family. In 1Ca2+-bound hRec, zinc promotes the formation of active state, whereas in 2Ca2+-state of bRec, it significantly enhances GRK1 binding, as the latter can complement the Zn2+ coordination. These data refine our understanding of recoverin function in humans and highlight its role as a key link between calcium and zinc signaling in mammalian photoreceptors under normal and pathological conditions.