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Results for “molecular biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

PRISM: A Plasmid-based Reporter for Intracellular Spectral Microscopy

Organelles form an interconnected network whose morphology, positioning and interactions reflect cellular state. However, reproducibly quantifying these organelle phenotypes across large cell populations and diverse cell types remains a significant challenge. Here we present PRISM (Plasmid-based Reporter for Intracellular Spectral Microscopy), a PiggyBac-integrable construct encoding five unique fluorescent organelle reporters for spectral microscopy, with an accompanying modular analysis pipeline. PRISM stably labels the Golgi, peroxisomes, endoplasmic reticulum, mitochondria and lysosomes in multiple cell types while remaining compatible with additional molecular or functional probes. The workflow extracts over 500 metrics per cell, describing organelle morphology and distribution alongside pairwise and higher-order contacts. We use PRISM to characterise organelle responses to cytoskeletal perturbation, map PI(4)P redistribution during lysosomal damage, and reveal how Zika virus remodels the organelle landscape during infection. PRISM provides a reproducible approach for investigating organelle network remodelling across biological contexts

cell biology↗

bioRxiv2026

Rate of meristem initiation driven by the MADS-WUS axis contributes to floral survival and inflorescence evolution in grasses

Crop domestication has repeatedly shaped inflorescence architecture to improve floral production, but mechanisms coordinating the rate of floral initiation, maturation and survival remain unclear. Combining morphometry, modelling and molecular genetic analyses, we show that floral production in the indeterminate barley (Hordeum vulgare L.) inflorescence follows an "initiate fast-die young" strategy orchestrated by a main MADS-box gene, SPIKELET INITIATION AND FERTILITY (SIF). SIF accomplishes this duality by coordinately terminating the inflorescence meristem via WUSCHEL and activating the floral meristem via APETALA1 (Vrn-H1). Hereby, the ancestral SIF "slow" allele promotes a timely commitment to floral maturation, whereas the derived "fast" allele permits more floral initiations. Postdomestication selection of SIF alleles thus enables diversified reproductive strategies in barley populations to maintain yield traits in the field. Finally, we show that a lineage-specific SIF duplication contributed to meristem fate transition and inflorescence evolution during Triticeae cold adaptation. Our results establish developmental rate as a key driver of architectural innovation and reproductive success.

plant biology↗

bioRxiv2026

A Microneurosurgical Survival Platform for Elucidating Mechanisms of Brain Tumor Recurrence and Metastasis

Brain tumor recurrence remains the leading cause of mortality in neuro-oncology, and there is a lack of preclinical models replicating the clinical cycle of surgical resection and relapse. To bridge this gap, we developed a novel microneurosurgical survival platform in mice using the NICO Myriad system. We orthotopically implanted pediatric medulloblastoma cells into the mouse cerebral cortex or cerebellum, followed by longitudinal microneurosurgical resection. Bioluminescence imaging and gross fluorescence verified successful resection, local and distal recurrence and metastasis. Comparative bulk RNA sequencing revealed extensive stage-specific transcriptomic divergence alongside conserved core gene sets (2,702 genes in the cerebral cortex and 3,240 genes in the cerebellum) across primary, locally recurrent, and distally recurrent stages. Pathway analysis shows activation of cellular growth, second messenger signaling, and cellular stress adaptation pathways. Targeted qPCR validation demonstrated that post-surgical relapse is driven by a distinct molecular program: recurrent tumors downregulate primary developmental drivers (PTCH1, MYCBP2), canonical suppressors (FOS, PTEN), and chromatin regulators (HDAC2), while selectively upregulating post-transcriptional machinery (RBM8A), endosomal trafficking regulators (RAB5C), acetyltransferases (NAA15), and the m6A RNA demethylase ALKBH5. These findings reveal that medulloblastoma shifts from a primary oncogenic state toward post-transcriptional and transcriptomic survival mechanisms following surgery. Identifying persistent candidates within this conserved core framework provides a roadmap for next-generation precision immunotherapies.

cancer biology↗

bioRxiv2026

Controlling Molecular Transport through Nanopores by Dynamic Aperture Sizing

Molecular transport through a nanopore determines the information that can be recovered from a translocation signal, yet it remains difficult to control in conventional solid-state nanopores. Rapid translocation reduces the information content and fixed nanopore geometries limit the dimensionality of the signal. Here, we control molecular transport through the development of the pipette-elastomer interfacial nanopore (PEIN), a dynamically reconfigurable solid-state nanopore which addresses these limitations. A PEIN is formed by depressing a glass nanopipette into a soft elastomer, progressively constricting its aperture and enabling continuous control over aperture size, while retaining the simplicity and favourable noise characteristics of glass nanopipette sensing. Using the dynamic aperture size control, DNA velocities could be controlled over more than a twofold range, with dwell times two orders of magnitude greater than observed in glass nanopipettes. DNA-origami rulers further revealed a progressive reduction in polymer velocity during translocation, indicating that hydrodynamic drag alone is insufficient to model forces on the DNA polymer. Finally, by using single- and double-stranded DNA and gold nanoparticles as molecular standards, we demonstrate reversible, size-selective molecular gating with sub-nanometre control. These results establish the PEIN as an accessible platform for controlling molecular transport and probing the relationships among biopolymer conformation, nanoscale confinement and translocation dynamics.

biophysics↗

bioRxiv2026

Sequential Molecular Interactions Shape Aβ42 Aggregation, Propagation, and Toxicity

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

biochemistry↗

bioRxiv2026

RECON infers regions of interest from H&E images and reconstructs whole-slide molecular profiles at single-cell resolution

Spatial omics technologies resolve molecular expression and spatial architecture at single-cell resolution, but profiling whole slides remains costly. In practice, only a few regions of interest (ROIs) are profiled, leaving the rest of the tissue unmeasured. S2-omics was the first framework to unify ROI selection with out-of-ROI prediction, but it operates on superpixels rather than individual cells and predicts discrete cell types rather than continuous molecular profiles. Superpixel-based representations do not explicitly preserve cell boundaries, while categorical cell-type labels cannot quantify molecular expression within cells. Here we present RECON, a two-stage framework that performs ROI inference and whole-slide molecular reconstruction at single-cell resolution, predicting both continuous molecular profiles and discrete cell-type labels. In the first stage, RECON extracts morphological and microenvironmental features from individual cells to identify a representative ROI for spatially resolved single-cell molecular profiling. In the second stage, RECON trains deep learning models on molecular measurements acquired within the selected ROI and reconstructs transcriptomic or proteomic profiles for all remaining cells on the slide. Benchmarked against pathologist annotations, RECONs ROI selection outperforms the superpixel-based S2-omics approaches (IoU: 0.75 versus 0.64). For transcriptomics, refining the modeling unit from superpixels to single cells improves per-gene Pearson correlation by 22%. For proteomics, RECON surpasses the current state-of-the-art method, ROSIE, across all 16 markers, with a median per-cell Pearson correlation of 0.91 versus 0.84. Moreover, RECON delineates tumour boundaries and regions with distinct immune-cell densities, and highlights candidate tertiary lymphoid structures. Together, these results demonstrate that RECON enables informative ROI selection and whole-slide molecular reconstruction at single-cell resolution for both spatial transcriptomics and spatial proteomics.

bioinformatics↗

bioRxiv2026

Hidden molecular states of bacterial replicons beyond the chromosome-plasmid dichotomy

Bacterial genomes are organized into autonomous replicons, traditionally classified as either chromosomes or plasmids-a binary framework that underpins genome annotation and evolution models. Yet whether this binary framework captures the full diversity of replicon organization remains unclear. Here we show that bacterial replicons occupy three recurrent organizational states rather than two canonical categories. By integrating quantitative measures of chromosome-plasmid sequence affinity (plasmidness) across more than 72,000 replicons from 21 bacterial genera, we identify a distinct class-intermediate replicons-that occupies a positional and functional middle ground. These replicons are plasmid-sized, harbor substantial chromosomal sequence ancestry, and lack canonical replication signatures typically associated with either class. Multiple complementary molecular properties converge on this same state. Comparative genomic analyses reveal their enrichment near recurrent chromosome remodeling regions and reveal close evolutionary ties to conjugative and antimicrobial resistance plasmids. Metagenomic data further corroborate their presence across natural ecosystems. Together, these findings reveal a previously unrecognized replicon state and redefine bacterial genome organization beyond the chromosome-plasmid dichotomy.

microbiology↗

bioRxiv2026

Whole-body Super-resolution Functional and Molecular Imaging with Panoramic Photoacoustic-Ultrasound Tomography

Photoacoustic (PA) and ultrasound (US) imaging provide complementary molecular, functional, and anatomical contrasts. Here, we present a panoramic PA-US imaging platform that integrates multispectral PA computed tomography (PACT) along with reflection-mode and transmission-mode US imaging through a single shared full-ring ultrasound array. We employ an ultrafast planewave transmission scheme in reflection-mode US for power Doppler (PWD) imaging and ultrasound localization microscopy (ULM). Additionally, we use the transmission-mode US to reconstruct a spatially resolved speed of sound (SoS) map that corrects both PA and US reconstruction. Such correction sharpens the resolution of PACT, suppresses the artifacts of PWD, and improves microbubble localization of ULM. Elevational scanning further enables whole-body volumetric imaging with co-registered PA and US contrasts. The integrated system maps photoswitchable DrBphP1-expressing tumors alongside their blood perfusion and oxygenation environment. Applying the platform to monitor unilateral renal ischemia-reperfusion injury, we report that microvascular perfusion and renal oxygenation recover at different rates. Collectively, we demonstrate that the integrated PA-US imaging platform provides a unified framework for multiparametric study of anatomy, perfusion, microvascular flow, oxygenation, and molecular activities.

bioengineering↗

bioRxiv2026

Stochastic Biophysics of Cellular Radiosensitivity: From Molecular Noise and Repair Kinetics to Evolutionary Demographics

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and severe evolutionary bottlenecks. While classical phenomenological models, such as the Linear-Quadratic (LQ) framework, reliably predict macroscopic population survival, they obscure the intrinsic single-cell stochasticity that governs critical rare events like tumor recurrence or the emergence of radioresistant persisters. To bridge this divide, we develop a mathematically exact stochastic differential equation (SDE) framework that models continuous DSB induction and repair as a Feller square-root process. By deriving exact closed-form expressions for the foci moments, we establish a highly efficient Maximum Likelihood Estimation (MLE) pipeline that circumvents computationally exhaustive Monte Carlo simulations, allowing the direct extraction of deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. Integrating this kinetic model with a cumulative damage hazard via the Feynman-Kac formalism, our framework seamlessly recovers the classic macroscopic LQ survival topology from microscopic first principles. Furthermore, systematic sensitivity analysis uncovers a fundamental evolutionary duality: while initial physical damage operates additively, ultimate cellular fate is driven by a nonlinear survival response governed by the trade-off between the damage hazard rate and intrinsic molecular noise strength. Crucially, we demonstrate that this molecular noise inherently enhances population survival. Governed by Jensen's inequality, stochastic variance acts as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with transiently low damage loads. Ultimately, this exact stochastic framework bridges microscopic biophysics and macroscopic demographics, offering deep mechanistic insights into the evolutionary roots of radioresistance.

biophysics↗

bioRxiv2026

Ancient Somatosensory Circuit Architectures Employ Flexible Molecular Strategies

The extent to which conserved neural circuit architectures depend on shared molecular specification programs remains unclear. Here, we address this question by examining the somatosensory system of the little skate, Leucoraja erinacea, an early-diverging vertebrate that retains ancestral features of both finned and limb-based body plans. We show that core features of somatosensory circuit organization, including laminar organization of the spinal cord and dorsally restricted targeting of sensory afferents, are deeply conserved. Unexpectedly, the molecular programs specifying dorsal root ganglion (DRG) sensory subtypes diverge extensively from those of mammals. Although DRG neuron subtype specification and spinal connectivity rely on target-derived cues, skates employ distinct neurotrophin receptor and transcription factor identity codes. These findings support a model in which conserved spinal circuit architectures provide a stable scaffold that leverages flexible sensory neuron specification programs, enabling the evolutionary diversification of vertebrate somatosensory systems. HighlightsO_LIIntegrated analysis of spinal cord and DRG neuronal diversity in Leucoraja erinacea C_LIO_LILaminar organization of the dorsal spinal cord is an ancestral vertebrate feature C_LIO_LIDivergent neurotrophin receptor and transcription factor codes in sensory neurons C_LIO_LIConserved target-dependent regulation of sensory identity and connectivity C_LI

neuroscience↗

bioRxiv2026

Molecular and functional profiling distinguishes PACS1 syndrome variant from PACS1 loss-of-function in iNeurons

PACS1 syndrome is a rare neurodevelopmental disorder caused by a recurrent de novo missense variant (p.R203W) in the PACS1 protein. However, it remains unclear whether the p.R203W variant acts through a loss-of-function or alternative mechanism. Here, we used isogenic iPSC-derived neurons (iNs) to directly compare the effects of PACS1 p.R203W to complete loss of PACS1 function. Using a combination of proteomic, biochemical and electrophysiological approaches, we identified molecular and functional phenotypes associated with each genotype. While PACS1(+/R203W) and PACS1(-/-) iNs shared phenotypic abnormalities, the overall molecular and functional consequences of the p.R203W variant were distinct from those caused by PACS1 deficiency. Notably, PACS1(+/R203W) presented with unique proteomic and kinase signaling signatures and a shift in stimulus dependent excitability. These findings demonstrate that PACS1 syndrome is not caused by a simple loss of function and instead support a non-loss-of-function mechanism. Lastly, our interactome analysis suggests that the p.R203W variant retains aspects of canonical PACS1 function while acquiring novel molecular interactions that could contribute to PACS1 syndrome pathogenesis. Altogether, these findings provide a framework for future mechanistic studies and therapeutic development in PACS1 syndrome. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/747101v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d1522corg.highwire.dtl.DTLVardef@69e4dforg.highwire.dtl.DTLVardef@30eebcorg.highwire.dtl.DTLVardef@899b9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

A reproducibility-audit framework for generalizable versus dataset-specific molecular transition boundaries in Alzheimer's disease

Molecular staging of Alzheimer's disease (AD) increasingly defines transition boundaries along single-cell pseudo-progression trajectories, yet whether such boundaries reproduce across brain regions, cohorts and molecular modalities is rarely tested. We present a permutation-controlled audit that combines nine boundary-detection algorithms with a fixed marker panel and four orthogonal reproducibility axes-algorithmic consensus, region, cohort and modality. On synthetic data with planted ground-truth boundaries the audit reaches 100% sensitivity and 94% specificity, rejecting four distinct artefact classes each by a different axis. Applied to the Seattle Alzheimer's Disease Brain Cell Atlas middle temporal gyrus, it localizes a transition that is robust across algorithms and recovered in most cell types but does not generalize: its leading marker is attenuated or absent in prefrontal cortex, entorhinal cortex and cerebrospinal fluid, and an apparent cross-region conservation of glial metabolic genes proves to be a global-expression offset rather than a shared program. The same audit nonetheless certifies an externally validated marker (astrocytic PTGDS) as reproducible across regions and modalities, showing that it separates generalizable anchors from dataset-specific ones rather than rejecting all signals. We provide this four-axis audit as a transferable, code-available standard to apply before a trajectory boundary is read as a biological stage, in AD and other progressive proteinopathies.

neuroscience↗

bioRxiv2026

Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

genomics↗

bioRxiv2026

Multiparametric microenvironment sensing via distinct molecular equilibria in a single cyanine dye

Reading both physical and chemical properties of a microenvironment from a single fluorophore remains a challenge. Here we demonstrate that two coexisting molecular equilibria within one near-infrared cyanine, CyC4, encode two mechanistically distinct ratiometric reporting channels. A meso-amino group and a pendant carboxylate form a tunable intramolecular hydrogen bond that toggles the dye between closed (700 nm) and open (780 nm) emissive conformers. Time-dependent density functional theory (TD-DFT) calculations show that the hydrogen bond raises the LUMO and blue-shifts the emission, establishing the 700/780 emission ratio as a local reporter of hydrogen bonding and polarity. Independently, the chromophore self-associates under crowding- and cosolvent-rich conditions into an aggregate with a blue-shifted, H-type absorption signature near 530-540 nm and a distinct emission near 610 nm upon 540 nm excitation. The intensity of this aggregate band relative to the monomer emission (Ra) serves as a ratiometric reporter of crowding and self-association. Because the two channels arise from distinct molecular equilibria (intramolecular hydrogen bonding vs. intermolecular self-association) they are largely decoupled: a glycerol titration series confirms that the self-association channel (Ra) can be moved while the hydrogen-bonding channel stays essentially fixed. Applied to protein-PEG biomolecular condensates, the two ratios move oppositely with increasing salt, showing that the interior's chemical (polarity, hydrogen bonding) and physical (packing, self-association) environments co-vary across the salt series; a single CyC4 measurement thereby maps this coupled microenvironment, providing a general strategy for multiparametric, ratiometric sensing of crowded microenvironments.

biophysics↗

bioRxiv2026

Molecular basis of AMPA receptor labeling by ligand-directed acyl imidazole chemistry in living neurons

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.

biophysics↗

bioRxiv2026

Regulation of a Classical Allosteric Molecular Machine by an Intrinsically Disordered Domain: the C-termini of GroEL

The bacterial chaperonin GroEL is a canonical example of an ATP-dependent molecular machine that must couple ligand binding to productive conformational work. GroEL passes through a series of distinct structural shifts, driven by ATP binding and hydrolysis, which power a facilitated protein folding reaction. How the complex allostery of the GroEL oligomer creates a folding cycle that is both efficient and directional remains incompletely understood. Here, we combine variable-temperature native ion mass spectrometry with single-molecule FRET to examine how the intrinsically disordered, highly conserved GroEL C-terminal tails impact the allosteric behavior of a single GroEL ring. Our observations show that the C-terminal tails restrain the conformational dynamics of the GroEL ring, most likely through direct interactions with the upper apical domains of the GroEL subunits, a constraint that is progressively released as ATP binds. These results support a model in which the C-terminal tails act as an entropic regulator of the GroEL reaction cycle: transient interactions between the tails and GroEL apical domains restrain premature ring opening and tune the energetic threshold for productive engagement by the smaller GroES co-chaperonin. By linking disordered tail dynamics to the classically cooperative reorganization of the GroEL ring, this mechanism enforces an ordered allosteric cascade that minimizes wasteful formation of empty GroEL-GroES cavities. These findings reveal how the conformational properties of an intrinsically disordered element can be exploited to optimize the energetic efficiency and functional timing of a large allosteric machine.

biophysics↗

bioRxiv2026

3D Printed X-ray Compatible Microfluidics for Online Characterization of Hexosomes: A Synchrotron SAXS-on-Chip Study with Molecular Dynamics Insights

Online structural characterization during microfluidic lipid self-assembly is important for understanding and controlling the formation of nonlamellar liquid crystalline nanodispersions. Here, we report a 3D-printed, X-ray-compatible hydrodynamic flow-focusing microfluidic chip with variable channel dimensions, integrated with synchrotron small-angle X-ray scattering (SAXS), for position-resolved SAXS-on-chip monitoring of Ca2+-triggered hexosome formation. Hexosomes were produced under continuous flow by mixing ethanolic solutions of docosahexaenoic acid monoglyceride (MAG-DHA), the negatively charged phosphatidylglycerol DOPG, and -tocopherol with Ca2+-containing PIPES buffer. Online SAXS-on-chip measurements detected three Bragg reflections characteristic of the internal inverse hexagonal (H2) phase on a tens-of-milliseconds residence-time scale, revealing rapid structural evolution during microfluidic mixing. Complementary ex situ SAXS identified the DOPG/Ca2+ molar ratio as a key parameter modulating the direct vesicle-to-hexosome transformation and the compactness of the internal H2 nanostructures. Dynamic light scattering showed that the flow-rate ratio modulated nanoparticle size, yielding hexosomes with mean hydrodynamic diameters in the range of approximately 120-175 nm and polydispersity index values down to 0.14 at a total flow rate of 200 {micro}L min-1. Cryo-TEM revealed coexistence of hexosomes and vesicular nanostructures, highlighting morphological heterogeneity, while Coarse-Grained Molecular Dynamics simulations supported a central role of Ca2+-DOPG association in promoting a direct lamellar-H2 phase transition. Overall, this work shows that 3D-printed SAXS-compatible microfluidics can integrate continuous production with online structural characterization, providing a basis for future formulation and process optimization of drug-loaded cubosomes, hexosomes, and related nonlamellar liquid crystalline nanodispersions.

biophysics↗

bioRxiv2026

High-Resolution Subtyping of Pediatric Low-Grade Glioma Using an Integrated Meta-Clustering Framework

Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet lab approaches including molecular profiling and histopathological studies for pLGG characterization are time consuming, costly, and laborious. Recently, methods based on artificial intelligence (AI) or machine learning (ML) have been widely used for pLGG molecular categorization, but most of them can only identify two or three pLGG subtypes. To more comprehensively characterize the molecular subtypes of pLGG and their potential biological and therapeutic significance, we develop an integrated meta-clustering approach, namely Meta-pLGG, that can explore high resolution molecular subtypes and their transcriptional heterogeneity for pLGG. Specifically, we first performed multiple rounds of random projection (RP) to generate dimension-reduced feature vectors from pLGG transcriptomics data, each of which was subsequently clustered by different clustering algorithms including hierarchical clustering, K-means, Self-Organizing Maps (SOM), Non-negative Matrix Factorization (NMF), Gaussian Mixture Model (GMM), and Spectral Clustering, as base clustering methods. Then, to yield robust clustering performance, we integrated the clustering results of these RP based individual clustering algorithms by adopting a weighted meta-clustering (wMetaC) approach. Results based on 532 pLGG patients suggested that our proposed approach demonstrated superior stability and discriminative powers for higher resolution pLGG subtyping compared to conventional approaches. Based on consensus matrix analysis, we identified two major pLGG mega-subtypes, with one further subdivided into three subgroups and the other into two. Then, we performed cluster specific differential gene expression analysis, molecular pathway analysis, and gene-drug-disease association analysis. The results showed that the identified five subgroups exhibited significant subtype-specific transcriptomic heterogeneity. In summary, our meta-clustering approach demonstrated much higher performance and robustness in identifying higher resolution molecular subtypes of pLGG, revealing the molecular heterogeneity within pLGG and potentially providing new insights for more precise molecular subtyping and precision therapy.

bioinformatics↗