Biology subjects

Sun, R.

Publications and source records attributed to Sun, R..

At least 109 records · Page 6Linked to original sources

bioRxiv2020

Localization, proteomics and metabolite profiling reveal a putative vesicular transporter for UDP-glucose

Vesicular neurotransmitter transporters (VNTs) mediate the selective uptake and enrichment of small molecule neurotransmitters into synaptic vesicles (SVs) and are therefore a major determinant of the synaptic output of specific neurons. To identify novel VNTs expressed on SVs (thus identifying new neurotransmitters and/or neuromodulators), we conducted localization profiling of 361 solute carrier (SLC) transporters tagging with a fluorescent protein in neurons, which revealed 40 possible candidates through comparison with a known SV marker. We parallelly performed proteomics analysis of immunoisolated SVs and identified 7 transporters in overlap. Ultrastructural analysis confirmed one of the transporters, SLC35D3, localized to SVs. Finally, by combining metabolite profiling with a radiolabeled substrate transport assay, we identified UDP-glucose as the principal substrate for SLC35D3. These results provide new insights into the functional role of SLC transporters in neurotransmission and improve our understanding of the molecular diversity of chemical transmitters.

neuroscience↗

bioRxiv2020

Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis

Sample sizes vary substantially across tissues in the Genotype-Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as blood. This severely limits power for identifying tissue-specific expression quantitative trait loci (eQTL) in undersampled tissues. Here we propose Surrogate Phenotype Regression Analysis (SO_SCPLOWPRAYC_SCPLOW) for leveraging information from a correlated surrogate outcome (e.g. expression in blood) to improve inference on a partially missing target outcome (e.g. expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, SO_SCPLOWPRAYC_SCPLOW jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. SO_SCPLOWPRAYC_SCPLOW estimates the same association parameter estimated by standard eQTL mapping and controls the type I error even when the target and surrogate outcomes are truly uncorrelated. We demonstrate analytically and empirically, using simulations and GTEx data, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.

genetics↗

bioRxiv2020

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

bioinformatics↗

bioRxiv2020

Gene mutations in members of the PI3K/Akt signalling pathway are related to immune thrombocytopenia pathogenesis

PurposeImmune thrombocytopenic (ITP) is an autoimmune bleeding disease with genetic susceptibility. In this research, we conducted an in-depth genomic analysis of a cohort of patients and elucidated the molecular features associated with the pathogenesis of ITP. MethodHigh-molecular-weight genomic DNA was extracted from freshly frozen bone marrow blood mononuclear cells (BMBMCs) from 20 active ITP patients. Next, the samples were subjected to molecular genetic analysis by whole-exome sequencing (WES), and the results were confirmed by Sanger sequencing. The signalling pathways and cellular processes associated with the mutated genes were identified with gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. ResultsThe results of this study revealed 3,998 missense mutations involving 2,269 genes in more than 10 individuals. Some unique genetic variants, including phosphatase and tensin homologue (PTEN), insulin receptor (INSR) and coagulation factor C homology (COCH) variants, were the most associated with the pathogenesis of ITP. Functional analysis revealed that these gene mutations mainly affected the phosphoinositide 3 kinase (PI3K)/protein kinase B (Akt) signalling pathways (signal transduction) and platelet activation (immune system). ConclusionsOur findings demonstrate the functional connections between these gene variants and ITP. Although the underlying mechanisms and the impact of these genetic variants remain to be revealed through further investigation, the application of next-generation sequencing in ITP in this paper is valuable for revealing the genetic mechanisms of ITP. SummaryImmune thrombocytopenic (ITP) is an autoimmune bleeding disease with genetic susceptibility. DNA mutation profile of ITP patient bone marrow samples (n=20) were investigated by using next-generation sequencing (NGS), and then confirmed by sanger sequencing method. Our results showed PTEN, INSR and COCH were mutated in all ITP patients. Functional analysis revealed these mutation genes mainly participate PI3K/Akt signaling pathways and platelet activation. These results suggest that genetic alterations might be involved in the pathogenesis of ITP.

cell biology↗

bioRxiv2020

RBM39 alters phosphorylation of c-Jun and binds to viral RNA to promote PRRSV proliferation

ABTRASTAs transcriptional co-activator of AP-1/Jun, estrogen receptors and NF-{kappa}B, nuclear protein RBM39 also involves in precursor mRNA (pre-mRNA) splicing. Porcine reproductive and respiratory syndrome virus (PRRSV) causes sow reproductive disorders and piglet respiratory diseases, which resulted in serious economic losses worldwide. In this study, the up-regulated expression of RBM39 and down-regulated of inflammatory cytokines (TNF, IL-1{beta}) were determined in PRRSV-infected 3D4/21 cells, and accompanied with the PRRSV proliferation. The roles of RBM39 altering phosphorylation of c-Jun to inhibit the AP-1 pathway to promote PRRSV proliferation were further verified. In addition, the nucleocytoplasmic translocation of RBM39 and c-Jun from nucleus to cytoplasm were enhanced in PRRSV-infected cells. The three RRM domain of RBM39 are crucial to support the proliferation of PRRSV. several PRRSV RNA (nsp4, nsp5, nsp11 and N) binding with RBM39 were determined, which may also contribute to the PRRSV proliferation. Our results revealed a complex mechanism of RBM39 by altering c-Jun phosphorylation and nucleocytoplasmic translocation, and regulating binding of RBM39 with viral RNA to prompt PRRSV proliferation. The results provide new viewpoints to understand the immune escape mechanism of PRRSV infection.

immunology↗

bioRxiv2020

Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment

Linkage-Disequilibrium Score Regression (LDSC) is a popular framework for analyzing GWAS summary statistics that allows for estimating SNP heritability, confounding, and functional enrichment of genetic variants with different annotations. Recent work has highlighted the influence of implicit and explicit assumptions of the model on the biological interpretation of the results. In this work, we explored a formulation of LDSC that replaces the r2 measure of LD with a recently-proposed unbiased estimator of the D2 statistic. In addition to modest statistical difference across estimators, this derivation highlighted implicit and unrealistic assumptions about the relationship between allele frequency, effect size, and annotation status. We carry out a systematic comparison of alternative LDSC formulations by applying them to summary statistics from 47 GWAS traits. Our results show that commonly used models likely underestimate functional enrichment. These results highlight the importance of calibrating the LDSC model to achieve a more robust understanding of polygenic traits.

genetics↗

bioRxiv2020

R-spondins are BMP receptor antagonists in early embryonic development

BMP signalling plays key roles in development, stem cells, adult tissue homeostasis, and disease. How BMP receptors are extracellularly modulated and in which physiological context, is therefore of prime importance. R-spondins (RSPOs) are a small family of secreted proteins that co-activate WNT signalling and function as potent stem cell effectors and oncogenes. Evidence is mounting that RSPOs act WNT-independently but how and in which physiological processes remains enigmatic. Here we show that RSPO2 and RSPO3 also act as BMP antagonists. RSPO2 is a high affinity ligand for the type I BMP receptor BMPR1A/ALK3, and it engages ZNRF3 to trigger internalization and degradation of BMPR1A. In early Xenopus embryos, Rspo2 is a negative feedback inhibitor in the BMP4 synexpression group and regulates dorsoventral axis formation. We conclude that R-Spondins are bifunctional ligands, which activate WNT- and inhibit BMP signalling via ZNRF3, with implications for development and cancer.

developmental biology↗

bioRxiv2020

EEG Artifact to Signal: Predicting Horizontal Gaze Position from SOBI-DANS Identified Ocular Artifact Components

Ocular artifact in EEG has long been viewed as a problem for interpreting EEG data in basic and applied research. The removal of such artifacts has been an on-going effort over many decades. We have recently introduced a hybrid method combining second-order blind identification (SOBI) with DANS, a novel automatic identification method, to extract components containing specifically signals associated with horizontal and vertical saccadic eye movements (H and V Comps) and found that these components event-related potentials in response to saccadic eye movement are systematically modulated by movement directions and distances. Here in a case study, taking advantage of signals about gaze positions contained in the ocular artifact components, we introduced a novel concept of EEG-based virtual eye tracking (EVET) and presented its first prototype. Specifically, we determined (1) the amount of data needed for constructing models of horizontal gaze positions; (2) the asymptotic performance levels achieved with such models. We found that for the specific calibration task, 4 blocks of data (4 saccades per target position) are needed for reaching an asymptotic performance with a prediction accuracy of 0.44 and prediction reliability of 1.67. These results demonstrated that it is possible to track horizontal gaze position via EEG alone, ultimately enabling coregistration of eye movement and the neural signals.

neuroscience↗

bioRxiv2020

Elements and Evolutionary Determinants of Genomic Divergence Between Paired Primary and Metastatic Tumors

Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cells lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY HighlightsO_LIDepth of most recent detectable variant characterizes Metastatic-Primary divergence C_LIO_LIDecay in variant detectability determines the expected M-P divergence C_LIO_LIDissemination from late detectable subclone leads to an abrupt drop in M-P divergence C_LIO_LISpatial model verifies growth mode governs M-P divergence dependency on seeding time C_LI

genomics↗

bioRxiv2020

SIFNet: Electromagnetic Source Imaging Framework Using Deep Neural Networks

Electroencephalography (EEG) and magnetoencephalography (MEG) are used to measure brain activity, noninvasively, and are useful tools for brain research and clinical management of brain disorders. Tremendous effort has been made in solving the inverse source imaging problem from EEG/MEG measurements. This is a challenging ill-posed problem, since the number of measurements is much smaller than the number of possible sources in the brain. Various methods have been developed to estimate underlying brain sources from noninvasive EEG/MEG as this can offer insight about the underlying brain electrical activity with significantly improved spatial resolution. In this work, we propose a novel data-driven Source Imaging Framework using deep learning neural networks (SIFNet), where (1) a simulation pipeline is designed to model realistic brain activation and EEG/MEG signals to train generalizable neural networks, (2) and a residual convolutional neural network is trained using the simulated data, capable of estimating source distributions from EEG/MEG recordings. The performance of our proposed SIFNet approach is evaluated in a series of computer simulations, which indicates the excellent performance of SIFNet outperforming conventional weighted minimum norm algorithms that were tested in this work. The SIFNet is further tested by analyzing interictal EEG data recorded in a clinical setting from a focal epilepsy patient. The results of this clinical data analysis indicate accurate localization of epileptogenic activity as validated by the epileptogenic zone clinically determined in this patient. In sum, the proposed SIFNet approach promises to offer an alternative solution to the EEG/MEG inverse source imaging problem, shows promising signs of being robust against measurement noise, and is easy to implement, therefore, being translatable to clinical practice.

bioengineering↗

bioRxiv2020

A Large-scale Drug Repositioning Survey for SARS-CoV-2 Antivirals

The emergence of novel SARS coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of severe pneumonia-like disease designated as coronavirus disease 2019 (COVID-19). To date, more than 2.1 million confirmed cases and 139,500 deaths have been reported worldwide, and there are currently no medical countermeasures available to prevent or treat the disease. As the development of a vaccine could require at least 12-18 months, and the typical timeline from hit finding to drug registration of an antiviral is >10 years, repositioning of known drugs can significantly accelerate the development and deployment of therapies for COVID-19. To identify therapeutics that can be repurposed as SARS-CoV-2 antivirals, we profiled a library of known drugs encompassing approximately 12,000 clinical-stage or FDA-approved small molecules. Here, we report the identification of 30 known drugs that inhibit viral replication. Of these, six were characterized for cellular dose-activity relationships, and showed effective concentrations likely to be commensurate with therapeutic doses in patients. These include the PIKfyve kinase inhibitor Apilimod, cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825, and ONO 5334, and the CCR1 antagonist MLN-3897. Since many of these molecules have advanced into the clinic, the known pharmacological and human safety profiles of these compounds will accelerate their preclinical and clinical evaluation for COVID-19 treatment.

microbiology↗

bioRxiv2020

Deletion of immune evasion genes provides an effective vaccine design for tumor-associated herpesviruses

Vaccines based on live attenuated viruses often induce broad, multifaceted immune responses. However, they also usually sacrifice immunogenicity for attenuation. It is particularly difficult to elicit an effective vaccine for herpesviruses due to an armament of immune evasion genes and a latent phase. Here, to overcome the limitation of attenuation, we developed a rational herpesvirus vaccine in which viral immune evasion genes were deleted to enhance immunogenicity while also attaining safety. To test this vaccine strategy, we utilized murine gammaherpesvirus-68 (MHV-68) as a proof-of-concept model for the cancer-associated human {gamma}-herpesviruses, Epstein-Barr virus and Kaposi sarcoma-associated herpesvirus. We engineered a recombinant MHV-68 virus by targeted inactivation of viral antagonists of type I interferon (IFN-I) pathway and deletion of the latency locus responsible for persistent infection. This recombinant virus is highly attenuated with no measurable capacity for replication, latency, or persistence in immunocompetent hosts. It stimulates robust innate immunity, differentiates virus-specific memory T cells, and elicits neutralizing antibodies. A single vaccination affords durable protection that blocks the establishment of latency following challenge with the wild type MHV-68 for at least six months post-vaccination. These results provide a novel approach to effective vaccination against cancer-associated herpesviruses through the elimination of latency and key immune evasion mechanisms from the pathogen.

microbiology↗

bioRxiv2020

Programmable RNA Targeting using CasRx in Flies

CRISPR-Cas genome editing technologies have revolutionized the fields of functional genetics and genome engineering, but with the recent discovery and optimization of RNA-targeting Cas ribonucleases, we may soon see a similar revolution in the study of RNA function and transcriptome engineering. However, to date, successful proof-of-principle for Cas ribonuclease RNA targeting in eukaryotic systems has been limited. Only recently has successful modification of RNA expression by a Cas ribonuclease been demonstrated in animal embryos. This previous work, however, did not evaluate endogenous expression of Cas ribonucleases and only focused on Cas ribonuclease function in early developmental stages. A more comprehensive evaluation of this technology is needed to assess its potential impact in the field. Here we report on our efforts to develop a programmable platform for RNA-targeting using a Cas ribonuclease, CasRx, in the model organism Drosophila melanogaster. By genetically encoding CasRx in flies, we demonstrate moderate transcript targeting of known phenotypic genes in addition to unexpected toxicity and lethality. We also report on the off-target effects following on-target transcript cleavage by CasRx. Taken together, our results present the current state and limitations of a genetically encoded programmable RNA-targeting Cas system in Drosophila melanogaster, paving the way for future optimization of the system.

bioengineering↗

bioRxiv2020

BatchServer: a web server for batch effect evaluation, visualization and correction

Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualizion of batch effects. We demonstate its application in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

bioinformatics↗

bioRxiv2020

Aberrant pathogenic GM-CSF+ T cells and inflammatory CD14+CD16+ monocytes in severe pulmonary syndrome patients of a new coronavirus

Pathogenic human coronavirus infections, such as severe acute respiratory syndrome CoV (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV), cause high morbidity and mortality 1,2. Recently, a severe pneumonia-associated respiratory syndrome caused by a new coronavirus was reported at December 2019 (2019-nCoV) in the city Wuhan, Hubei province, China3-5, which was also named as pneumonia-associated respiratory syndrome (PARS)6. Up to 9th of February 2020, at least 37, 251 cases have been reported with 812 fatal cases according to the report from China CDC. However, the immune mechanism that potential orchestrated acute mortality from patients of 2019-nCoV is still unknown. Here we show that after the 2019-nCoV infection, CD4+T lymphocytes are rapidly activated to become pathogenic T helper (Th) 1 cells and generate GM-CSF etc. The cytokines environment induces inflammatory CD14+CD16+ monocytes with high expression of IL-6 and accelerates the inflammation. These aberrant and excessive immune cells may enter the pulmonary circulation in huge numbers and play an immune damaging role to causing lung functional disability and quick mortality. Our results demonstrate that excessive non-effective host immune responses by pathogenic T cells and inflammatory monocytes may associate with severe lung pathology. Therefore, we suggest that monoclonal antibody that targets the GM-CSF or interleukin 6 receptor may potentially curb immunopathology caused by 2019-nCoV and consequently win more time for virus clearance.

immunology↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2020

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

bioinformatics↗

bioRxiv2020

Mesophasic organization of GABAA receptors in hippocampal inhibitory synapse

Information processing in the brain depends on synaptic transmission and plasticity, which in turn require specialized organization of neurotransmitter receptors and scaffolding proteins within the postsynaptic density (PSD). However, how these molecules are organized in situ remains largely elusive, limiting our mechanistic understanding of synaptic formation and functions. Here, we have developed template-free classification of over-sampled sub-tomograms to analyze cryo-electron tomograms of hippocampal synapses, enabling us to identify type-A {gamma}-aminobutyric acid receptor (GABAAR) in inhibitory synapses and determine its in situ structure at 19 [A] resolution. We found that these receptors are organized hierarchically: from GABAAR super-complexes with a fixed 11-nm inter-receptor distance but variable relative angles, through semi-ordered two-dimensional receptor networks with reduced Voronoi entropy, to mesophasic assembly with a sharp phase boundary. This assembly aligns with condensates of postsynaptic scaffolding proteins and putative presynaptic vesicle release sites. Such mesophasic self-organization may allow synapses to achieve a "Goldilocks" state with a delicate balance between stability and flexibility, enabling both reliability and plasticity in information processing.

neuroscience↗