Biology subjects

Shen, L.

Publications and source records attributed to Shen, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimers disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearmans rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted {beta}-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.

neuroscience↗

bioRxiv2026

MethylCurate: Tool For Dataset Curation and Epigenetic Aging Clock Evaluation

SummaryDNA methylation datasets from public repositories such as NCBI Gene Expression Omnibus are central to the development and evaluation of epigenetic aging clocks, yet existing resources and tools do not fully resolve the bottlenecks of dataset retrieval and metadata harmonization. Current benchmarking frameworks often rely on static curated collections, support only a subset of available Gene Expression Omnibus studies, focus on specific tissues, or require substantial manual intervention when metadata fields and supplementary files are inconsistently structured across studies. We developed MethylCurate, an agentic AI framework that addresses these limitations by automating the retrieval of DNA methylation datasets from the Gene Expression Omnibus, harmonizing heterogeneous metadata, mapping datasets to a unified format, and enabling scalable evaluation of epigenetic aging clocks through an integrated, dialogue-driven workflow. Availability and ImplementationMethylCurate is implemented in Python and combines deterministic modules for Gene Expression Omnibus dataset retrieval, quality control, and clock evaluation with large language model-assisted agents for metadata extraction, metadata harmonization, and DNA methylation data parsing. Source code, documentation, and example workflows are available at: https://github.com/Travyse/methylcurate Contacttravyse.edwards@pennmedicine.upenn.edu Supplementary InformationSupplementary data are available at Bioinformatics online. Graphical AbstractMethylCurate is an agentic-AI framework that converts user-specified NCBI Gene Expression Omnibus DNA methylation datasets into standardized metadata, beta matrices, artifacts, logs, and aging clock benchmarking outputs through automated retrieval, quality control, metadata extraction, harmonization, and evaluation workflows. Figure generated with Biorender. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=51 SRC="FIGDIR/small/723515v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@197c0fborg.highwire.dtl.DTLVardef@1feace4org.highwire.dtl.DTLVardef@108b0d5org.highwire.dtl.DTLVardef@191a1b8_HPS_FORMAT_FIGEXP M_FIG C_FIG Key MessagesO_LIAutomated curation of DNA methylation datasets from the Gene Expression Omnibus. C_LIO_LIStandardized preprocessing and metadata harmonization. C_LIO_LIIntegrated benchmarking of epigenetic aging clocks. C_LI

bioinformatics↗

bioRxiv2026

Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-β Spread in Alzheimer's Disease

Understanding the pathogenesis of amyloid-{beta} pathology in Alzheimers Disease (AD) proves to be a challenge. In this work, we expand upon the application of network diffusion models (NDM) to study pathophysiological spread of amyloid-{beta} throughout white matter structural brain networks. We found that the NDM successfully recaptures subpopulation-level spatial patterns (Pearsons R=0.45-0.48, PFDR < 0.01) of amyloid-{beta} deposition in the Alzheimers Disease Neuroimaging Cohort at a regional level, but with drawbacks in mechanism interpretability. We then moved to an extended NDM framework (eNDM), including a protein synthesis term to better reflect the role of amyloid-{beta} metabolism, as well as including regional vulnerability using spatial transcriptomics from the Allen Human Brain Atlas to modulate the region-level rate parameters of the synthesis term. The novel gene eNDMs exhibited significant performance increases in Pearsons correlation (Steigers Z, PFDR < 0.10) over baseline NDM performance in mild cognitive impairment and AD groups using APOE, SORL1, and FGL2 for gene modulation. The results were robust and replicable when testing on an external cohort of the Alzheimers Disease Sequencing Project. The study thus demonstrates the importance of regional genetic vulnerability, in conjunction with network diffusion mechanisms, in improving the modelling and prediction of amyloid-{beta} pathophysiological spread.

bioinformatics↗

bioRxiv2026

Reconstructing True 3D Spatial Omics at Single-Cell Resolution

Capturing the three-dimensional (3D) organization of cells is essential for deciphering complex biological processes, yet comprehensive 3D spatial omics is severely hindered by the destructive nature of physical sectioning and the depth limitations of intact tissue imaging. Current computational methods rely on 2.5D stacking of discrete slices, which inherently disrupts tissue topology and fails to resolve continuous depth-dependent molecular gradients. To bridge this gap, we introduce DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW, an Optimal Transport flow matching framework that models tissue evolution as a continuous dynamic vector field. By solving the underlying probability flow ODEs, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW enables the direct extraction of uninterrupted, infinitely resolvable tissue states at arbitrary spatial depths. Using Deep STAR/RIBOmap 3D technologies, we demonstrate that DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW achieves improved 3D reconstruction fidelity relative to 2.5D approaches, yielding structures that more closely recapitulate native tissue microenvironments in real-world datasets. Across diverse spatial omics modalities, including spatial proteomics using imaging mass cytometry in human breast cancer and spatial transcriptomics using openST in head and neck squamous cell carcinoma metastatic lymph nodes, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW produces biologically interpretable and high-fidelity reconstructions across datasets. We evaluated the scalability and robustness of DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW on a large-scale mouse brain dataset, reconstructing a continuous 3D cellular atlas comprising 39 million cells within 41.6 hours. Systematic downstream characterization validated its ability to recapitulate consistent spatial architectures, cell-type distributions, transcriptomic patterns, and microenvironmental structures across brain regions. Collectively, these results demonstrate DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW as a generalizable and efficient solution for true 3D spatial reconstruction across scales and modalities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/721395v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a19624org.highwire.dtl.DTLVardef@188361forg.highwire.dtl.DTLVardef@199321corg.highwire.dtl.DTLVardef@a8f411_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

A High-Fidelity 3D Fluid-Structure Interaction Framework for Predictive Microfluidic Design

The commercial maturation of microfluidics remains bottlenecked by empirical prototyping and an absence of predictive digital design capabilities. Because optimizing advanced technologies such as passive particle separation fundamentally hinges on the precise coupling of fluid dynamics and particle mechanics, conventional two-dimensional or decoupled fluid simulations inherently fail to capture authentic multiscale behaviors. To bridge this gap, we establish a high-fidelity three-dimensional fluid-structure interaction framework combining a high-order Arbitrary Lagrangian-Eulerian mapping-based finite element method with a localized hierarchical dynamic mesh strategy. Engineered to accurately resolve complex multiscale hydrodynamics, this architecture utilizes deterministic lateral displacement structures as a stringent test case. Validated against experimental data for rigid microspheres and tumor cells, the framework predicts transport trajectories and critical separation diameters with sub-micron precision. Crucially, the simulation explicitly resolves the M-shaped spatial fluctuation of local size thresholds alongside the dynamic vertical migration of particles. Unveiling these hidden physical mechanisms provides a deterministic explanation for highly debated phenomena such as mixed-mode transport. By enabling the rigorous in silico evaluation of complex non-periodic architectures, this framework serves as a powerful instrument for predictive structural optimization. Such capabilities establish the essential infrastructure for microfluidic digital design, accelerating the transition from empirical trial-and-error to precision simulation-driven engineering.

bioengineering↗

bioRxiv2026

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

Intratumoral heterogeneity drives variable drug responses in cancer. Single-cell RNA sequencing (scRNA-seq) enables characterization of such heterogeneity and prediction of drug response at single-cell resolution. Accordingly, various computational models have been developed to infer drug response from scRNA-seq data. However, their performance, robustness, and generalizability across different biological contexts remain insufficiently evaluated. To address this gap, we benchmarked representative single-cell drug response prediction models using 26 curated datasets comprising over 760,000 cells across 12 cancer types and 21 therapeutic agents. We constructed balanced and imbalanced scenarios to reflect realistic drug-response label distributions. To address the lack of ground-truth labels in conventional scRNA-seq datasets, we incorporated lineage-tracing data with experimentally validated drug-response annotations, enabling evaluation in a clinically relevant pre-treatment prediction setting. Our results show that prediction performance was markedly higher in cell lines than in tissue samples. Under imbalanced conditions, most methods exhibited sharp performance declines, whereas scDEAL demonstrated the highest robustness. Independent validation using an in-house pancreatic ductal adenocarcinoma dataset further confirmed scDEALs robustness and ability to capture biologically meaningful state transitions. Label-substitution experiment revealed that this robustness was partially driven by the models specific training-label construction. However, benchmarking with lineage-tracing data revealed a fundamental limitation: most models capture drug-induced transcriptional changes but struggled to predict intrinsic resistance before treatment. In summary, our study defines the performance boundaries of current approaches and highlights their limitations in addressing intratumoral heterogeneity, class imbalance, and intrinsic resistance prediction, emphasizing the need for the next-generation single-cell drug response models with stronger clinical relevance.

bioinformatics↗

bioRxiv2026

Integrated analysis of stemness-associated immune modulatory circuits in squamous cell carcinomas

Emerging evidence indicates that a subset of cancer cells enriched for stemness-related gene signatures possess distinct immunomodulatory capacities, enabling these tumor-initiating stem cells (tSCs) to more effectively evade or resist anti-tumor immunity. Despite these advances, the tSC-specific molecular circuits orchestrating their specialized immune privilege program are not well defined. Here, in squamous cell carcinomas of the skin and oral cavity, we comprehensively delineate the unique immune-evasive properties of tSCs and dissect the transcriptional regulation shaping their immunomodulatory programs. By integrating transcriptome profiling, chromatin landscape mapping, genetic perturbation, and single-cell RNA sequencing, we found that the tSC-specific immune program is broadly governed by SOX2, a stemness-associated transcription factor. We demonstrate that SOX2 enables tSCs to sustain immature tumor-associated neutrophils (TANs) and subsequently trigger these myeloid cells to foster the development of tumor-associated macrophages (TAMs). This SOX2-directed tSC-TAN-TAM axis establishes a localized immunosuppressive niche for protecting tSC. SIGNIFICANCEHere, we uncover SOX2 as a master regulator that orchestrates conserved immune modulatory circuits in tSCs to sustain pro-tumor myeloid cell states. These findings place tSCs at the apex of immune landscape remodeling, asserting a central role of stemness-associated program in organizing the immunosuppressive tumor microenvironment.

cancer biology↗

bioRxiv2026

ProteinSage: From implicit learning to explicit structural constraints for efficient protein language modeling

While protein language models typically rely on sequence-only pretraining objectives, this approach often fails to capture structural regularities and demands large datasets. To address this, we introduce ProteinSage, a pretraining framework that learns protein representations under explicit structural constraints. ProteinSage incorporates structural signals via structure-guided masking and a causal objective designed to model longrange dependencies. This structure-constrained pretraining equips ProteinSage with transferable representations using less data and computation, yet achieves competitive or superior performance across diverse structure-aware and general protein modeling benchmarks. To determine whether these gains stem from genuine structural generalization rather than task-specific fitting, we applied ProteinSage to a structure-driven protein discovery task, focusing on proteins with multi-pass transmembrane helical architectures such as distantly related microbial rhodopsins. The model successfully identified six previously unannotated microbial rhodopsin homologs. Together, our work establishes structure-constrained pretraining as an effective pathway toward data-efficient and structurally faithful protein representation learning.

bioinformatics↗

bioRxiv2026

Coordinated topoisomerase function shapes the fluoroquinolone response of Chlamydia trachomatis

DNA supercoiling is essential for the developmental cycle of Chlamydia trachomatis, yet its role in shaping antibiotic responses remains poorly understood. We investigated how the fluoroquinolone moxifloxacin (Mox) influenced C. trachomatis growth across developmental stages with its distinct supercoiling levels. Early Mox exposure completely halted bacterial growth, whereas treatment during mid-cycle produced enlarged, persistent forms and abolished formation of infectious progeny. These stage-specific outcomes coincided with inhibition of DNA replication, depletion of DNA gyrase, and transcriptional repression of ompA and omcB, accompanied by preserved or elevated expression of the stress-responsive groESL1 operon. Mox also elicited compensatory downregulation of topoisomerase I (TopA), consistent with attempts to rebalance intracellular supercoiling. Together, these data demonstrate that fluoroquinolone susceptibility in C. trachomatis reflects stage-dependent supercoiling levels. Perturbation of supercoiling homeostasis drives developmental arrest and persistence phenotypes, highlighting coordinated gyrase-TopA activity as a key determinant of fluoroquinolone tolerance and a potential target for overcoming persistent infection. ImportanceC. trachomatis, a medically significant bacterial pathogen, can persist under antimicrobial pressure, complicating treatment strategy. This study links supercoiling homeostasis to fluoroquinolone tolerance, offering mechanistic insights into chlamydial adaptation to antibiotic stress and identifying potential targets to overcome persistence--an urgent challenge in global reproductive health.

microbiology↗

bioRxiv2026

Quinone reductase 2 reads H3 serotonylation to support neuronal maturation

Histone H3 Gln5 serotonylation (H3Q5ser) is a recently described posttranslational modification1 that plays important roles in guiding transcriptional permissiveness in brain and peripheral systems2-5. H3Q5ser has been implicated in diverse physiological and pathological processes ranging from neural differentiation1 to sensory processing6, circadian rhythmicity7, stress responsivity8, placental gene regulation9, and tumorigenesis10-19. Since H3Q5ser can occur in combination with H3 Lys4 trimethylation (H3K4me3), most mechanistic studies to date have focused on H3Q5sers roles in modulating H3K4me3 reader interactions, where it has been shown to potentiate TAF3/TFIID binding to H3K4me31,20,21 and inhibit the recruitment of K4me3 demethylases21; however, whether H3 serotonylation functions as an autonomous chromatin signaling mark through dedicated reader proteins has remained unknown. Here, using a combination of proteomic-, structural-, molecular-, epigenomic-, and cellular-based approaches, we demonstrate that the Quinone reductase 2 (QR2) enzyme reads H3Q5ser independently of H3K4me3. CRISPR-Cas9-mediated disruption of H3 serotonylation or QR2s binding to the mark in human induced pluripotent stem cell-derived neurons impairs the establishment of neuronal transcriptional programs, alters synaptic connectivity, and disrupts electrophysiological maturation. These findings thus uncover an H3 serotonylation-dependent chromatin signaling axis that is essential for human neurodevelopment.

molecular biology↗

bioRxiv2026

Instantaneous Beta Frequency Regulates Self-Generated Timing in Humans

Self-generated timing requires the brain to determine when to terminate an ongoing action without reference to external cues. Although beta-band oscillations have been implicated in temporal control, how their intrinsic dynamics shape moment-to-moment variability in internally generated time remains unclear. Here, combining scalp EEG with intracranial recordings in humans, we show that the instantaneous frequency of beta oscillations (13-30 Hz) provides a dynamic control signal for self-generated timing. During a self-paced time production task, higher instantaneous beta frequency reliably predicted longer produced durations, both within individuals on a trial-by-trial basis and across individuals. This relationship was beta-specific, frontoparietally distributed, robust to motor and spectral confounds, and validated by convergent intracranial recordings. These findings run counter to classical pacemaker-accumulator models and instead establish beta-frequency dynamics as a mechanism that delays action termination by increasing the density of intrinsic stabilizing control updates during self-generated timing.

neuroscience↗

bioRxiv2026

Cryo-electron tomography reveals paracellular claudin-15 pores at the tight junction

Tight junctions (TJs) are composed of anastomosing strands between epithelial cells. Members of the claudin family of proteins reside within TJ strands and either seal the paracellular space or assemble into charge and size-selective pathways. Functional studies suggest that claudin-mediated conductance pathways resemble traditional ion channels. However, such postulated pores have not been directly visualized. Using a model claudin deficient epithelium where exogenously introduced EGFP-CLDN15 is the only claudin family member expressed, our investigation sheds light on the arrangement and structure of the postulated claudin pores. Following correlative light and electron microscopical identification of TJs and cryo-electron tomography, we identified series of linearly distributed electron lucent features that locate between two closely apposed plasma membranes of adjacent cells. At these sites, the median spacing between adjacent features is 2.25 nm (IQR = 1.83), with a median 1.66 nm (IQR = 0.92) diameter. In contrast, such features were not observed in claudin deficient model epithelium with exogenous mCherry-ZO-1 expression. These findings agree with the postulated and extensively modeled claudin pores formed within the simple columnar epithelium. This provides the first direct evidence of paracellular pore organization and paves way for future biophysical investigation. SIGNIFICANCEBy combining correlative fluorescence imaging, FIB milling, and cryo-ET within an epithelial system restricted to a single claudin isoform, we were able to visualize repetitive, low-density pore features within CLDN15-containing tight junctions (TJs), structures not previously resolved in intact epithelia. These features were absent in claudin-negative controls and displayed placement and geometry consistent with CLDN15 X-ray crystallography and molecular dynamics models. Quantitative measurements of pore diameter, paracellular gap width, and pore spacing further support their assignment as CLDN15 pores. These findings establish a structurally validated platform for defining claudin pore ultrastructure and provide a foundation for future efforts to compare pore-forming and barrier-forming claudins, understand disease-associated junction remodeling, and guide therapeutic modulation of epithelial barrier function.

physiology↗

bioRxiv2026

A shared transcriptional network in the nucleus accumbens supports resilience to chronic stress across sex.

Although chronic stress increases the risk for depression, only a subset of exposed individuals develop psychiatric illness. The biological mechanisms that protect against depression remain incompletely understood, particularly at the molecular level. Here, we identify a transcriptional network in the nucleus accumbens (NAc), a central brain reward region, that supports stress resilience in both sexes and demonstrate the causal contribution of key hub genes. Using chronic social defeat stress, RNA-seq, and co-expression network analysis, we find sex-specific but overlapping gene modules linked to resilience, anchored by shared hub genes embedded within a common network architecture. Overexpression of these hub genes in stress-naive mice confers stress protection and induces a transcriptional state that is discrete from both susceptible and resilient profiles. These findings position resilience as a structured and targetable molecular phenotype and provide a basis for investigating sex-informed mechanisms of stress adaptation.

neuroscience↗

bioRxiv2026

Enhancing Pan-cancer Spatial Transcriptomics atSingle-cell Resolution with stPainter

Subcellular spatial transcriptomics technologies offer unprecedented views of tissue architecture but are fundamentally constrained by sparse gene panels and limited detection sensitivity. Current computational enhancement strategies typically rely on tissue-matched single-cell RNA sequencing (scRNA-seq) references and necessitate computationally intensive retraining for each dataset, impeding their scalability and clinical applicability. Here, we present STPAINTER, a conditional generative model that leverages a massive pretraining pan-cancer scRNA-seq atlas to universally enhance spatial transcriptomics data. Built upon a latent diffusion architecture with stochastic differential equation-guided generation, STPAINTER learns a universal manifold of cellular states to reconstruct genome-wide expression profiles from sparse spatial measurements. Uniquely, our pretraining paradigm enables zero-shot generalization and empowers downstream tasks by providing imputed transcriptomes and informative latent variables to enhance resolution at both the gene and cluster levels. Applied STPAINTER upon 6 spatial transcriptomics datasets of different cancer types, we demonstrate that our model empowers high-fidelity downstream analyses, including fine-grained subpopulation clustering and pathway enrichment. Furthermore, cross-validation with spatially resolved proteomics (CODEX) confirms the biological veracity of the imputed cellular landscapes. STPAINTER provides a robust, scalable framework for decoding complex tumor microenvironments without the need for auxiliary sequencing data.

bioinformatics↗

bioRxiv2026

iMyocyte: A Web-Based Cellular Automata Platform for Real-Time Demonstration of Cardiac Reentry

Cardiac arrhythmias arise from excitable-media dynamics that are often challenging to teach and visualize in real time. To address this, we developed iMyocyte, an interactive web-based platform for real-time demonstration of excitation wave propagation, conduction block, and reentry dynamics using a simplified cellular automaton model (i.e., a network of discrete cells that change state based on neighbouring cells). In iMyocyte, a network is built from student devices, each representing a model cell coupled to its neighbours to propagate excitation. Unlike traditional teaching materials, iMyocyte lets students manipulate key model parameters and immediately observe how these shape wave dynamics and reentry conditions. Pilot classroom deployments with undergraduate life science students at McGill University indicate high engagement and suggest improved conceptual clarity, motivating future work to improve the platforms usability, robustness, and visualization features, and to conduct formal learning assessment.

physiology↗

bioRxiv2026

Antigen presentation requirements for effective cDC1-based cancer immunotherapy

Type 1 conventional dendritic cells (cDC1s) are important for generating and sustaining antitumor immunity. Accordingly, the abundance of cDC1s in human tumors correlates with improved outcomes in cancer. Capitalizing on this role, we previously demonstrated that vaccination with in vitro-derived murine cDC1s elicits durable tumor control in multiple preclinical models; however, the immunological mechanisms underlying the efficacy of cDC1 vaccination remain unclear. Here, we examined whether in vitro-derived cDC1s resemble tumor-infiltrating DC populations and whether MHC-I and MHC-II antigen presentation contribute to cDC1-mediated tumor control following vaccination in melanoma. As expected, MHC-I- or MHC-II-deficiency had minimal impact on the transcriptional state of cDC1s in homeostasis or following stimulation with the adjuvant poly dI:dC. Moreover, in vitro-derived cDC1s cultured under steady-state conditions closely resembled tumor-infiltrating cDC1s, whereas their poly dI:dC-stimulated counterparts resembled CCR7+ tumor-infiltrating DC populations, also referred to as mregDCs or LAMP3+ DCs. Our data further show that both MHC-I and MHC-II contribute to tumor control upon cDC1 vaccination, and coexpression of MHC-I and MHC-II on the same cDC1 is necessary for a robust vaccine response. We also identified an important function for host cDC1s in supporting the efficacy of vaccination with in vitro-derived cDC1s, as judged by impaired tumor control in Irf8+32-/- mice, which lack endogenous cDC1s. Overall, these results indicate that effective antitumor responses depend on MHC-I and MHC-II antigen presentation by vaccine-delivered cDC1s, with additional contributions from host cDC1s. Key pointsO_LIIn vitro-generated cDC1s resemble intratumoral DC populations found in mice and humans. C_LIO_LIMHC-I and MHC-II antigen presentation by vaccine-delivered cDC1s contribute to antitumor efficacy. C_LIO_LICoexpression of MHC-I and MHC-II on the same cDC1 enhances vaccine responses. C_LIO_LIAntitumor responses reflect the activity of vaccine and endogenous cDC1s. C_LI

immunology↗

bioRxiv2026

Parthenogenote-Derived Brain Unveils the Critical Role of Paternal Genome in Neural Development

Genomic imprinting, an epigenetic mechanism that governs parent-of-origin-specific gene expression, is essential for mammalian development, yet its role in late-stage development remains unclear due to the lethality of parthenogenetic (Pg) embryos. Here, we establish cell replacement with parthenogenote-derived cells (CReP), a blastocyst complementation strategy that enables survival and tissue-specific contribution of Pg-derived cells. Brain-targeted CReP showed that Pg-derived cells can participate in neural development but fail to maintain neuronal-glial balance due to aberrant activation of Notch signaling caused by the loss of the paternally expressed gene Dlk1. Restoration of Dlk1 normalized Notch activity and rescued neuronal differentiation. These findings reveal a critical role of the paternal genome, through Dlk1-mediated regulation of Notch signaling, in ensuring neural stem cell expansion and balanced cell fate decisions. The CReP model provides a powerful platform for investigating genomic imprinting and parental genome contributions in development and disease.

developmental biology↗

bioRxiv2025

Ultrahigh-Throughput Forster Resonance Energy Transfer-Activated Droplet Sorting for Terminator Polymerase Engineering

Droplet microfluidic screening systems enable high-throughput, labor-saving enzyme directed evolution by employing fluorescence, absorbance, and Raman-activated sorting strategies for library screening. Forster resonance energy transfer (FRET) - a nanoscale technique for monitoring intramolecular/intermolecular conformational changes - is yet to be integrated into this process. We upgraded a single-channel sorter to a dual-channel one without redesigning the microscopy setup, which can monitor FRET signals during enzyme reactions in droplets at kilohertz rates. We applied this upgraded sorter to improve the incorporation efficiency of KOD DNA polymerase towards reversible terminator deoxyribonucleotides, a property crucial for its application in next-generation sequencing (NGS). Our data show that a single-round sorting can achieve 30-fold enrichment of active variants. Five KOD variants enabling 100-cycle single-end runs of DNA sequencing were identified using a novel cyclic reversible termination (CRT) substrate featuring a terminator group {approx} 5-fold bulkier than the classical azidomethyl moiety. We also engineered a metagenome-derived novel polymerase, and a variant achieving 90% terminator incorporation efficiency within 2 minutes was identified after two rounds of enrichment. In sum, we provide a practical setup for dual-channel FRET-based droplet sorting, and demonstrate its ability in terminator polymerases engineering, thereby broaden the scope of microfluidic applications.

biochemistry↗