Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal de-repression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use MERFISH (Multiplexed Error Robust Fluorescent In Situ Hybridization) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated control, isogenic D4Z4 contraction mutant and FSHD patient myotubes and unfused mononuclear cells (MNCs), as well as the individual nuclei within them. We find myocyte nuclei segregate into 2 clusters defined by expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, while MNCs cluster based on developmental state. Patient/mutant myotubes are found in "FSHD-hi" and "FSHD-lo" states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared to control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development.

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.

Immunolabeling-compatible PEGASOS tissue clearing for high-resolution whole mouse brain imaging.

Novel brain clearing methods revolutionize imaging by increasing visualization throughout the brain at high resolution. However, combining the standard tool of immunostaining targets of interest with clearing methods has lagged behind. We integrate whole-mount immunostaining with PEGASOS tissue clearing, referred to as iPEGASOS (immunostaining-compatible PEGASOS), to address the challenge of signal quenching during clearing processes. iPEGASOS effectively enhances molecular-genetically targeted fluorescent signals that are otherwise compromised during conventional clearing procedures. Additionally, we demonstrate the utility of iPEGASOS for visualizing neurochemical markers or viral labels to augment visualization that transgenic mouse lines cannot provide. Our study encompasses three distinct applications, each showcasing the versatility and efficacy of this approach. We employ whole-mount immunostaining to enhance molecular signals in transgenic reporter mouse lines to visualize the whole-brain spatial distribution of specific cellular populations. We also significantly improve the visualization of neural circuit connections by enhancing signals from viral tracers injected into the brain. Last, we show immunostaining without genetic markers to selectively label beta-amyloid deposits in a mouse model of Alzheimer's disease, facilitating the comprehensive whole-brain study of pathological features.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

EBMGC-GNF: Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion.

Exploiting consistent structure from multiple graphs is vital for multi-view graph clustering. To achieve this goal, we propose an Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion (EBMGC-GNF) model which comprehensively extracts credible consistent neighbor information from multiple views by designing a Cross-view Good Neighbors Voting module. Moreover, a novel balanced regularization term based on p-power function is introduced to adjust the balance property of clusters, which helps the model adapt to data with different distributions. To solve the optimization problem of EBMGC-GNF, we transform EBMGC-GNF into an efficient form with graph coarsening method and optimize it based on accelareted coordinate descent algorithm. In experiments, extensive results demonstrate that, in the majority of scenarios, our proposals outperform state-of-the-art methods in terms of both effectiveness and efficiency.

Flexible Piezoresistive Sensors Based on PPy Granule-Anchored Multilayer Fibrous Membranes with a Wide Operating Range and High Sensitivity.

The employment of flexible piezoresistive sensors has sparked growing interest within the realm of wearable electronic devices, specifically in the fields of health detection and e-skin. Nevertheless, the advancement of piezoresistive sensors has been impeded by their limited sensitivity and restricted operating ranges. Consequently, it is imperative to fabricate sensors with heightened sensitivity and expanded operating ranges through the utilization of the appropriate methodologies. In this paper, piezoresistive sensors were fabricated utilizing electrospun polyvinylidene fluoride/polyacrylonitrile/polyethylene-polypropylene glycol multilayer fibrous membranes anchored with polypyrrole granules as the sensing layer, while electrospun thermoplastic polyurethane (TPU) fibers were employed as the flexible substrate. The sensitivity of the sensor is investigated by varying the fiber diameter of the sensing layer. The experimental findings reveal that a concentration of 14 wt % in the spinning solution exhibits high sensitivity (996.7 kPa-1) within a wide working range (0-10 kPa). This is attributed to the favorable diameter of the fibers prepared at this concentration, which facilitates the uniform in situ growth of pyrrole. The highly deformable TPU flexible fibers and multilayer sensing layer structure enable different linear responses across a broad pressure range (0-1 MPa). Furthermore, the sensor demonstrates good cyclic stability and can detect human movements under different pressures. These results suggest that the piezoresistive sensor with a wide operating range and high sensitivity has significant potential for future health monitoring and artificial intelligence applications.

Subicular neurons encode concave and convex geometries.

Animals in the natural world constantly encounter geometrically complex landscapes. Successful navigation requires that they understand geometric features of these landscapes, including boundaries, landmarks, corners and curved areas, all of which collectively define the geometry of the environment1-12. Crucial to the reconstruction of the geometric layout of natural environments are concave and convex features, such as corners and protrusions. However, the neural substrates that could underlie the perception of concavity and convexity in the environment remain elusive. Here we show that the dorsal subiculum contains neurons that encode corners across environmental geometries in an allocentric reference frame. Using longitudinal calcium imaging in freely behaving mice, we find that corner cells tune their activity to reflect the geometric properties of corners, including corner angles, wall height and the degree of wall intersection. A separate population of subicular neurons encode convex corners of both larger environments and discrete objects. Both corner cells are non-overlapping with the population of subicular neurons that encode environmental boundaries. Furthermore, corner cells that encode concave or convex corners generalize their activity such that they respond, respectively, to concave or convex curvatures within an environment. Together, our findings suggest that the subiculum contains the geometric information needed to reconstruct the shape and layout of naturalistic spatial environments.

How the forebrain transitions to adulthood: developmental plasticity markers in a long-lived rodent reveal region diversity and the uniqueness of adolescence.

Maturation of the forebrain involves transitions from higher to lower levels of synaptic plasticity. The timecourse of these changes likely differs between regions, with the stabilization of some networks scaffolding the development of others. To gain better insight into neuroplasticity changes associated with maturation to adulthood, we examined the distribution of two molecular markers for developmental plasticity. We conducted the examination on male and female degus (Octodon degus), a rodent species with a relatively long developmental timecourse that offers a promising model for studying both development and age-related neuropathology. Immunofluorescent staining was used to measure perineuronal nets (PNNs), an extracellular matrix structure that emerges during the closure of critical plasticity periods, as well as microglia, resident immune cells that play a crucial role in synapse remodeling during development. PNNs (putatively restricting plasticity) were found to be higher in non-juvenile (>3 month) degus, while levels of microglia (putatively mediating plasticity) decreased across ages more gradually, and with varying timecourses between regions. Degus also showed notable variation in PNN levels between cortical layers and hippocampal subdivisions that have not been previously reported in other species. These results offer a glimpse into neuroplasticity changes occurring during degu maturation and highlight adolescence as a unique phase of neuroplasticity, in which PNNs have been established but microglia remain relatively high.

Monosynaptic Rabies Tracing Reveals Sex- and Age-Dependent Dorsal Subiculum Connectivity Alterations in an Alzheimer's Disease Mouse Model.

The subiculum (SUB), a hippocampal formation structure, is among the earliest brain regions impacted in Alzheimer's disease (AD). Toward a better understanding of AD circuit-based mechanisms, we mapped synaptic circuit inputs to dorsal SUB using monosynaptic rabies tracing in the 5xFAD mouse model by quantitatively comparing the circuit connectivity of SUB excitatory neurons in age-matched controls and 5xFAD mice at different ages for both sexes. Input-mapped brain regions include the hippocampal subregions (CA1, CA2, CA3), medial septum and diagonal band, retrosplenial cortex, SUB, postsubiculum (postSUB), visual cortex, auditory cortex, somatosensory cortex, entorhinal cortex, thalamus, perirhinal cortex (Prh), ectorhinal cortex, and temporal association cortex. We find sex- and age-dependent changes in connectivity strengths and patterns of SUB presynaptic inputs from hippocampal subregions and other brain regions in 5xFAD mice compared with control mice. Significant sex differences for SUB inputs are found in 5xFAD mice for CA1, CA2, CA3, postSUB, Prh, lateral entorhinal cortex, and medial entorhinal cortex: all of these areas are critical for learning and memory. Notably, we find significant changes at different ages for visual cortical inputs to SUB. While the visual function is not ordinarily considered defective in AD, these specific connectivity changes reflect that altered visual circuitry contributes to learning and memory deficits. Our work provides new insights into SUB-directed neural circuit mechanisms during AD progression and supports the idea that neural circuit disruptions are a prominent feature of AD.

Multichannel bridges and NSC synergize to enhance axon regeneration, myelination, synaptic reconnection, and recovery after SCI.

Regeneration in the injured spinal cord is limited by physical and chemical barriers. Acute implantation of a multichannel poly(lactide-co-glycolide) (PLG) bridge mechanically stabilizes the injury, modulates inflammation, and provides a permissive environment for rapid cellularization and robust axonal regrowth through this otherwise inhibitory milieu. However, without additional intervention, regenerated axons remain largely unmyelinated (<10%), limiting functional repair. While transplanted human neural stem cells (hNSC) myelinate axons after spinal cord injury (SCI), hNSC fate is highly influenced by the SCI inflammatory microenvironment, also limiting functional repair. Accordingly, we investigated the combination of PLG scaffold bridges with hNSC to improve histological and functional outcome after SCI. In vitro, hNSC culture on a PLG scaffold increased oligodendroglial lineage selection after inflammatory challenge. In vivo, acute PLG bridge implantation followed by chronic hNSC transplantation demonstrated a robust capacity of donor human cells to migrate into PLG bridge channels along regenerating axons and integrate into the host spinal cord as myelinating oligodendrocytes and synaptically integrated neurons. Axons that regenerated through the PLG bridge formed synaptic circuits that connected the ipsilateral forelimb muscle to contralateral motor cortex. hNSC transplantation significantly enhanced the total number of regenerating and myelinated axons identified within the PLG bridge. Finally, the combination of acute bridge implantation and hNSC transplantation exhibited robust improvement in locomotor recovery. These data identify a successful strategy to enhance neurorepair through a temporally layered approach using acute bridge implantation and chronic cell transplantation to spare tissue, promote regeneration, and maximize the function of new axonal connections.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

First International Conference on Unconventional Animal Models of Alzheimer's Disease and Aging.

The first International Conference on Unconventional Animal Models of Alzheimer's Disease and Aging (UAMAA) took place on December 13-16, 2023, in Santiago, Chile. The Alzheimer's disease (AD) research field is currently in search for new and unconventional models that could hold greater translational potential than transgenic mouse models. Thus this UAMAA conference is timely and significant. The event consisted of 6 sessions with talks from 28 world-class scientists from all over the world. These animal models of interest include the degu (Octodon degu), the dog (Canis familiaris), and certain species of nonhuman primates that may better recapitulate neuropathology and cognitive impairments in human AD. Our conference has provided a formal forum to discuss and highlight new research directions, alternative animal models, and innovative approaches for the AD and aging research field.

Identification and characterization of CHD4-associated eRNA as a novel modulator of fetal hemoglobin levels in ß-thalassemia.

Fetal-to-adult hemoglobin switching is controlled by programmed silencing of γ-globin while the re-activation of fetal hemoglobin (HbF) is an effective strategy for ameliorating the clinical severity of ß-thalassemia and sickle cell disease. The identification of enhancer RNAs (eRNAs) related to the fetal (α2γ2) to adult hemoglobin (α2ß2) switching remains incomplete. In this study, the transcriptomes of GYPA+ cells from six ß-thalassemia patients with extreme HbF levels were sequenced to identify differences in patterns of noncoding RNA expression. It is interesting that an enhancer upstream of CHD4, an HbF-related core subunit of the NuRD complex, was differentially transcribed. We found a significantly positive correlation of eRNA-CHD4 enhancer-gene interaction using the public database of FANTOM5. Specifically, the eRNA-CHD4 expression was found to be significantly higher in both CD34+ HSPCs and HUDEP-2 than those in K562 cells which commonly expressed high level of HbF, suggesting a correlation between eRNA and HbF expression. Furthermore, prediction of transcription binding sites of cis-eQTLs and the CHD4 genomic region revealed a putative interaction site between rs73264846 and ZNF410, a known transcription factor regulating HbF expression. Moreover, in-vitro validation showed that the inhibition of eRNA could reduce the expression of HBG expression in HUDEP-2 cells. Taken together, the findings of this study demonstrate that a distal enhancer contributes to stage-specific silencing of γ-globin genes through direct modulation of CHD4 expression and provide insights into the epigenetic mechanisms of NuRD-mediated hemoglobin switching.

New rabies viral resources for multi-scale neural circuit mapping.

Comparisons and linkage between multiple imaging scales are essential for neural circuit connectomics. Here, we report 20 new recombinant rabies virus (RV) vectors that we have developed for multi-scale and multi-modal neural circuit mapping tools. Our new RV tools for mesoscale imaging express a range of improved fluorescent proteins. Further refinements target specific neuronal subcellular locations of interest. We demonstrate the discovery power of these new tools including the detection of detailed microstructural changes of rabies-labeled neurons in aging and Alzheimer's disease mouse models, live imaging of neuronal activities using calcium indicators, and automated measurement of infected neurons. RVs that encode GFP and ferritin as electron microscopy (EM) and fluorescence microscopy reporters are used for dual EM and mesoscale imaging. These new viral variants significantly expand the scale and power of rabies virus-mediated neural labeling and circuit mapping across multiple imaging scales in health and disease.

Activation of γ-globin expression by LncRNA-mediated ERF promoter hypermethylation in ß-thalassemia.

The mechanism that drives the switch from fetal to adult hemoglobin (Hb) provides a therapeutic target for ß-thalassemia. We have previously identified that hypermethylation of transcription factor ERF promoter reactivated γ-globin expression. To uncover the mechanism underlying the hypermethylation of ERF promoter, we performed RNA sequencing in ß0/ß0-thalassemia patients and identified an upregulated long noncoding RNA (RP11-196G18.23) associated with HbF production. RP11-196G18.23 bound to the ERF promoter and recruited DNA methyltransferase 3A to promote DNA hypermethylation-mediated ERF downregulation, thereby ameliorating ERF-induced γ-globin inactivation. The identification of RP11-196G18.23 provides an epigenetic mechanism for the reactivation of fetal γ-globin expression for ß-hemoglobinopathies.

Intranasal Delivery of Ketamine Induces Cortical Disinhibition.

Our previous studies find that subcutaneously administered (s.c.) subanesthetic ketamine promotes sustained cortical disinhibition and plasticity in adult mouse binocular visual cortex (bV1). We hypothesized that intranasal delivery (i.n.) of subanesthetic ketamine may have similar actions. To test this, we delivered ketamine (10 mg/kg, i.n.) to adult mice and then recorded excitatory pyramidal neurons or PV+ interneurons in L2/3 of bV1 slices. In pyramidal neurons the baseline IPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of neuregulin-1 (NRG1) to cortical slices increases these IPSC amplitudes in mice treated with ketamine but not in controls. In PV+ interneurons, the baseline EPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of NRG1 to cortical slices increases these EPSC amplitudes in mice treated with ketamine but not in controls. We also found that mice treated with ketamine exhibit increased pCREB staining in L2/3 of bV1. Together, our results show that a single intranasal delivery of ketamine reduces PV+ interneuron excitation and reduces pyramidal neuron inhibition and that these effects are acutely reversed by NRG1. These results are significant as they show that intranasal delivery of ketamine induces cortical disinhibition, which has implications for the treatment of psychiatric, neurologic, and ophthalmic disorders.

Single cell spatial transcriptomics reveals distinct patterns of dysregulation in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer's risk gene mutation.

INTRODUCTION: The R47H missense mutation of the TREM2 gene is a strong risk factor for development of Alzheimer's Disease. We investigate cell-type-specific spatial transcriptomic changes induced by the Trem2R47H mutation to determine the impacts of this mutation on transcriptional dysregulation. METHODS: We profiled 15 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H; 5xFAD genotypes using MERFISH spatial transcriptomics. Single-cell spatial transcriptomics and neuropathology data were analyzed using our custom pipeline to identify plaque and Trem2R47H induced transcriptomic dysregulation. RESULTS: The Trem2R47H mutation induced consistent upregulation of Bdnf and Ntrk2 across many cortical excitatory neuron types, independent of amyloid pathology. Spatial investigation of genotype enriched subclusters identified spatially localized neuronal subpopulations reduced in 5xFAD and Trem2R47H; 5xFAD mice. CONCLUSION: Spatial transcriptomics analysis identifies glial and neuronal transcriptomic alterations induced independently by 5xFAD and Trem2R47H mutations, impacting inflammatory responses in microglia and astrocytes, and activity and BDNF signaling in neurons.

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment.

In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.