Biology subjects

Huang, Z.

Publications and source records attributed to Huang, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

An SE(3)-equivariant Crystal Structure Prediction Framework for Prospective Identification and Development of Bioactive Nucleoside Self-Assembling Materials

Flexible organic small molecules can assemble into supramolecular biomaterials whose properties are intrinsically governed by their crystal structures, yet experimental structure determination remains difficult to scale during molecular modification and materials optimization. Crystal structure prediction (CSP) provides a potential solution, but its prospective power is limited for flexible molecules owing to incomplete conformational sampling and the difficulty of identifying experimentally realized structures from large candidate sets. Here, an SE(3)-equivariant deep-learning workflow, SE3CSP, is developed for organic crystal structure prediction. By learning molecular conformations, unit-cell parameters and packing patterns from experimentally resolved crystal structures and integrating these predictions with MACE-based structure optimization, SE3CSP establishes an end-to-end pipeline from two-dimensional molecular representations to three-dimensional crystal structures. Using nucleosides as representative flexible self-assembling building blocks, SE3CSP achieves an overall prediction accuracy of [~]57%, substantially outperforming the benchmark MACE-based Genarris 3.0 workflow ([~]14%). Furthermore, a prospective prediction strategy is developed in which an SE3CSP-predicted density window ({+/-} 0.15 g/cm3) is applied prior to energy ranking and structural deduplication, enabling all experimentally realized structure to be consistently ranked within the top 1-2% of all generated candidates. Beyond structure prediction, SE3CSP-derived energy landscapes provide insight into potential single-crystal-to-single-crystal transformations and enable the identification of a nucleoside supramolecular material with dynamic breathing porosity, which is further developed as an adsorptive platform for inflammatory mediator removal with excellent anti-inflammatory performance and biocompatibility. These results establish SE3CSP as a practical framework for prospective CSP and highlight its utility in guiding the discovery and design of bioactive self-assembled materials.

bioengineering↗

bioRxiv2026

M1 macrophage-mediated lymphangiogenesis aggravates liver fibrosis via MDK/YAP signaling pathway

BackgroundLymphangiogenesis plays a critical role in various liver diseases, yet its function in liver fibrosis remains controversial. This study aimed to explore the role of lymphangiogenesis in liver fibrogenesis and its underlying regulatory mechanisms. MethodsLiver fibrotic mice were established by carbon tetrachloride (CCl4) or Thioacetamide (TAA)-induced injection or bile duct ligation. Lymphatic vessels were marked by podoplain (Pdpn) staining in mice and D2-40 staining in clinical samples. Lymphatic vessels area and density were measured to indicate lymphangiogenesis. Multiplexing immunohistochemistry was used to detect co-localization of proteins. ResultsIn the present study, we first verified increased lymphangiogenesis in human and murine fibrotic livers. Afterwards, we identified VEGFC rather than VEGFD as the primary driver of lymphangiogenesis in liver fibrosis. Furthermore, we demonstrated that M1 macrophages serve as the major source of VEGFC. Founctional studies revealed that VEGFC-mediated lymphangiogenesis exacerbates hepatic fibrosis, while its inhibition alleviated fibrosis. Bioinformatic analysis uncovered Midkine (MDK) as a key downstream of lymphangiogenesis. Both in vivo and in vitro studies confirmed that exogenous MDK promotes liver fibrosis via activating hepatic stellate cells (HSCs), whereas MDK inhibition counteracts the profibrotic effects of VEGFC-induced lymphangiogenesis. Importantly, we discovered that MDK activates HSCs through the Hippo/YAP signaling pathway. ConclusionsM1 macrophage-mediated lymphangiogenesis aggravates liver fibrosis via MDK secretion, which activates HSCs. These findings provide novel insights into coordinated crosstalk between macrophages, lymphatic endothelial cells and HSCs in liver fibrosis and suggest lymphangiogenesis and MDK as potential therapeutic targets for fibrotic liver diseases.

molecular biology↗

bioRxiv2026

Longitudinal Awake Mouse Brain Imaging Using Functional Ultrasound and Functional Ultrasound Localization Microscopy

Ultrafast ultrasound offers a unique route to cross-scale neurovascular phenotyping by integrating functional ultrasound (fUS), ultrasound localization microscopy (ULM), and functional ULM (fULM). Yet the baseline variability, longitudinal stability, and biological safety of such multimodal imaging in awake animals remain insufficiently defined, limiting its use for detecting subtle disease-associated neurovascular changes. Here, an awake longitudinal fUS-ULM-fULM framework is established and validated in mice over five months. Structural vascularity, microvascular flow velocity, mesoscale hemodynamic responses, and microvascular functional responses are repeatedly quantified in the same animals during monthly imaging sessions. Across all metrics, no significant longitudinal drift is detected (p > 0.60). Structural and flow-derived measures are markedly more reproducible than functional readouts, with within-subject coefficients of variation of 5.1% for mean flow velocity and 7.3% for vascularity, compared with 25.0% for fUS-derived cerebral blood volume responses and 53.2% for fULM-derived microvascular functional responses. Mean flow velocity shows the strongest longitudinal consistency (ICC = 0.70) and the lowest detection threshold. Behavioral testing and GFAP/Iba1 staining further reveal no memory impairment or chronic neuroinflammation. This study defines quantitative baselines, reproducibility limits, and safety evidence for awake cross-scale ultrasound imaging, providing a reference framework for longitudinal neurovascular phenotyping in preclinical disease models.

neuroscience↗

bioRxiv2026

Genos-m: a foundation model for human-associated microbial genomes

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

bioinformatics↗

bioRxiv2026

Decoding Immune Reconstitution Failure in People Living with HIV through Single-cell Genomics

Immune non-responders (INRs), a subset of people living with HIV (PLWH), fail to achieve full immune reconstitution and remain at increased risk of morbidity, mortality, and non-AIDS-related illnesses. As the mechanisms underlying this impaired immune recovery remain poorly understood, we performed single-cell multi-omics profiling on peripheral blood mononuclear cells from 43 INRs, 47 immune responders and 53 healthy donors. Our dataset comprises 2,744,009 transcriptomes and 1,226,658 chromatin-accessibility profiles across 58 identified immune cell types. INRs exhibited markedly elevated inflammatory signaling, increased apoptotic activity, and dysregulation of immune-activating ligand-receptor interactions. Additionally, we identified 2,996 interaction cis-eGenes and 5,938 cis-caPeaks, and validated key cell type-specific cis-xQTL effects associated with immune reconstitution failure. Furthermore, we identified 3,551 sc-eQTLs and 872 cell-state interaction eQTLs (ieQTLs) in CD4+ T cells. A genotype-specific upregulation of OAS3 in INRs, likely regulated by STAT1, was identified based on these results. We developed scPRISM (single cell Predictive Reconstitution Immune Status Model), a multi-modal framework that integrates scRNA-seq and scATAC-seq data to perform the dual tasks of differentiating disease states and predicting gene expression from chromatin accessibility, thereby deciphering the cell type-specific cis-regulatory basis of the disease. Our study delineates the transcriptional and epigenomic landscape of immune dysregulation in INRs and provides new genetic insights into HIV-associated immune reconstitution failure. These findings offer a foundation for developing biomarkers and precision therapies to restore immune function in PLWH.

genetics↗

bioRxiv2026

Incomplete letter recognition is limited by cortical and not optical factors: Simulating the visual deficits of dementia in healthy adults

Incomplete letter recognition tasks are frequently used to detect visual deficits arising from neurodegenerative syndromes, including Posterior Cortical Atrophy (PCA; visual-variant Alzheimers disease). A recent development of this approach is the Graded Incomplete Letters Test (GILT), which measures recognition thresholds for letters degraded by removing pixelated sections (decreasing completeness). Although GILT thresholds are strongly elevated in PCA relative to typical adults, the precise cortical visual impairments underlying these deficits are unclear, as is the potential contribution from age-related optical limitations. We compared candidate cortical factors (crowding and global integration) with optical limitations (blur and low contrast) by simulating these factors in typical adults (n=6) viewing incomplete letter stimuli. Participants identified foveally presented letters (12 alternatives), with completeness varied using QUEST. At baseline, thresholds averaged [~]5% completeness. Optical factors were simulated by separately applying blur and lowered contrast. These factors had minimal effect on thresholds, except where blur/contrast levels approached visibility limits, where thresholds rose modestly but remained far below clinical levels in PCA. Cortical factors were simulated by increasing crowding (disruptions from clutter) through peripheral presentation, with global-integration impairments simulated by varying pixel size to alter the distribution of degradation (limiting spatial integration) or degrading letters dynamically with limited-lifetime pixels (limiting temporal integration). These manipulations substantially elevated thresholds, with combined crowding and global-integration impairments increasing thresholds to levels comparable with PCA. We conclude that impaired incomplete letter recognition is driven primarily by cortical rather than optical factors, and that neurodegenerative deficits may reflect the combined impact of multiple cortical limitations.

neuroscience↗

bioRxiv2026

Optimizing phenotype scale improves genetic analyses in large-scale biobanks

Large-scale biobanks have enabled increasingly complicated genetic analyses across thousands of phenotypes. However, studies rarely consider the appropriate phenotype measurement scale, a problem that can drastically affect inferences on genetic architecture. Here, we introduce SIQReg, a practical solution to this classical problem, which learns a data-driven phenotype scale by minimizing heterogeneity across phenotype quantiles. Applied to complex traits in UK Biobank, SIQReg rejects the default scale for 24/25 traits. Generally, SIQReg scales lie between default and logarithmic, indicating that default-scale traits are neither purely additive nor purely multiplicative. We show that SIQReg improves both non-additive and additive genetic analyses. SIQReg eliminates most non-additive genetic signals (such as 97% of vQTL and 76% of quantile-dependent TWAS genes), indicating they may be statistical artifacts, while preserving biologically plausible non-additive signals. Simultaneously, SIQReg improves power to detect additive signals, increasing GWAS loci, TWAS genes, and PGS prediction accuracy by 11%, 13%, and 10%, respectively, and identifies 50% more high-risk individuals. These gains replicate across ancestry groups. Our results establish SIQReg as a principled approach to phenotype scale transformation that improves genetic analyses of complex traits.

genetics↗

bioRxiv2026

IL-36γ Drives Th9 cell Differentiation via IκBζ to Sustain Antitumor Immunity

Th9/Tc9 cells polarized with TGF-{beta} and IL-4 exhibit superior antitumor efficacy, prompting extensive efforts to optimize their differentiation protocols and augment therapeutic potency. In the current study, we identified IL-36{gamma} as a potent cytokine that synergizes with TGF-{beta} to robustly drive Th9 differentiation both in the presence and absence of IL-4. IL-36{gamma}-programmed Th9 cell subsets exhibited phenotypic and transcriptional profiles identical to classic Th9 cells. Mechanistically, IL-36{gamma} drove Th9 cell differentiation through amplifying key signaling pathways such as STAT6, STAT5, and NF-{kappa}B that are essential for classic Th9 programming. Notably, we uncovered a novel regulatory axis wherein IL-36{gamma} upregulates the transactivation factor I{kappa}B{zeta}, which directly governs Th9 lineage specification. In in adoptive cell therapy (ACT) models, IL-36{gamma}-polarized Th9 cell subsets demonstrated enhanced antitumor efficacy, attributable to their sustained persistence, reduced exhaustion markers and stem-like/memory properties. Collectively, this study elucidates a previously unrecognized I{kappa}B{zeta}-dependent mechanism underpinning Th9 differentiation and highlights the translational potential of IL-36{gamma}-engineered Th9 cells as a valuable ACT strategy for refractory tumors.

immunology↗

bioRxiv2026

linearPOA: A parallel, memory-efficient framework for Partial Order Alignment with linear space complexity

Multiple sequence alignment (MSA) is a fundamental problem in computational bioinformatics, playing a critical role in genome biology, especially in long read sequencing and assembly. One solution for representing and solving MSA is Partial Order Alignment (POA), which employs Directed Acyclic Graphs (DAGs) to represent sequence relationships. However, when facing the ultra-long, error-prone reads (e.g., >100 kbps), existing POA algorithms with quadratic space complexity become impractical due to excessive memory consumption. This paper introduces the linearPOA, which based on divide-and-conquer strategy to solve the POA, aimed at saving memory compared to quadratic space complexity algorithms like SPOA, abPOA and TSTA. Particularly notable is its capability to save up to 102.74 times memory usage when aligning sequences with 100 kbp reads, compared to the abPOA method using non-heuristic methods. The algorithm was implemented within the linearPOA library, providing functionality for POA and foundational support for sequencing analysis, like error correction for reads. The linearPOA algorithm provides memory-efficient algorithms for long-read sequencing, especially in directly assembling long reads like 100 kbp reads. AvailabilityThe linearPOA library is freely available at https://github.com/malabz/linearPOA, and the data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.15637837. Supplementary informationSupplementary information are available at BioRxiv online.

bioinformatics↗

bioRxiv2026

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

bioinformatics↗

bioRxiv2026

Physics-inspired computational methods for spatial transcriptomics reveal a dysplasia-restricted premalignant basin and a density-asymmetric autocrine niche in oral mucosal carcinogenesis

Spatial transcriptomics is often interpreted with tools that do not explicitly encode tissue-scale physical priors such as finite ligand diffusion, density-based state stability, or self-exciting spatial recruitment. We introduce three compact computational methods and apply them to a 326,554-cell spatial atlas of human oral mucosal carcinogenesis spanning normal mucosa, hyperplasia, oral lichen planus, and dysplasia. DC3 (Diffusion-Constrained Cell Communication) replaces distance-blind ligand-receptor co-expression with a ligand-specific exponential distance kernel. It down-weights short-range ECM-class interactions by three to four orders of magnitude relative to traditional scoring, remains rank-stable under diffusion-length perturbation and dys2 removal (Spearman rho >= 0.93), and better matches COMMOT than traditional co-expression on the dys2 benchmark. A within-section label-permutation null indicates that only 0.5-6.6% of the top MRB-autocrine DC3 signal is attributable to spatial clustering alone. A log-density landscape, reported in nats rather than thermodynamic units, places the Malignant_risk_basal (MRB) cluster in a high-density epithelial region with a reverse barrier of 2.43 nats from stress-proliferative basal cells on the full atlas, 1.93 nats after dys2 removal, and 1.0-1.9 nats under Silverman/Scott bandwidth rules. MRB local neighborhood entropy is stable at 0.67-0.68 bits across bandwidth and PCA-dimension choices. A spatial Hawkes model separates immune-cell background density from self-excitation; apparent dysplasia-level adaptive-immune attenuation is not significant by 10,000 label permutations (all FDR q = 1.00) and is retained only as a section-level observation. External checks in CELLxGENE Census and Puram 2017 reposition the MRB signature as a dysplasia-restricted aberrant-basal-differentiation phenotype rather than a marker preserved in fully malignant OSCC. The methods are presented as lightweight, interpretable tools, and the biological findings as hypotheses requiring independent validation. Author summarySpatial transcriptomics records both gene expression and tissue position, but common analysis pipelines often treat signaling and cell-state structure as if distance and tissue architecture were secondary details. We developed three simple methods that make these assumptions explicit: DC3 scores cell-cell communication with ligand-specific diffusion distances, a log-density landscape summarizes where epithelial states are densely populated in expression space, and a spatial Hawkes model separates baseline immune density from local self-excitation. Applied to an oral mucosal carcinogenesis atlas, these methods highlight a dysplasia-enriched Malignant_risk_basal (MRB) population with short-range autocrine signaling and mixed epithelial neighborhoods. We also stress-test each claim by removing the MRB-rich section, varying model parameters, running permutation tests, benchmarking against COMMOT, and checking public external datasets. These tests narrow the biological interpretation: MRB is best viewed here as a dysplasia-restricted aberrant-basal-differentiation phenotype, not as a proven step toward fully malignant oral squamous cell carcinoma, and the apparent dysplasia-level immune-cascade attenuation is not statistically supported. The study therefore offers both a reusable computational toolkit and a deliberately constrained biological hypothesis for future validation.

bioinformatics↗

bioRxiv2026

CMS: Achieving Uniform and High-Quality Sequencing across Challenging Non-canonical Genomic Regions

High-throughput sequencing is essential in modern biological research, yet low-complexity sequences remain challenging as they form structurally complex, non-canonical (non-B) DNA conformations that impede sequencing enzyme read-through. This leads to a long-standing trade-off: maximizing coverage introduces false positives (FP), while stringent filtering causes coverage loss and false negatives (FN). To address this, we developed CMS (Cross Mountains and Seas) on GeneMind sequencing platforms by optimizing its chemistry and enzymatic systems to traverse these secondary structures with high fidelity. Benchmarking across whole-genome (WGS) and whole-exome (WES) sequencing demonstrates that CMS addresses the trade-off by simultaneously enhancing both coverage uniformity and accuracy, notably achieving an approximately 100-fold reduction in low-coverage bins for WGS and a 70% reduction in FN insertions/deletions (INDELs) within complex non-B regions. Specifically, a synthetic G-quadruplex (G4) motif sequencing experiment demonstrates that CMS maintains a 1:1 strand ratio, effectively handling G4-induced biases where benchmarked platforms exhibit extensive depletion. These findings establish CMS as a reliable technology for the precise characterization of structural-challenging but functional-essential genome regions.

genomics↗

bioRxiv2026

T lymphocyte regulatory cytokines predict frailty in older adults

Frailty is a multi-system syndrome causing increased susceptibility to health insults in older adults. Immune system dysregulation and inflammaging have emerged as mechanisms that may affect multiple organ systems in the frailty syndrome. This present study seeks to define the immune state in community-dwelling adults suffering from frailty. We evaluated a subgroup of 169 individuals enrolled in the Gut-brain Alzheimers disease Inflammation and Neurocognitive Study (GAINS). Participants in the GAINS study were scored for frailty using the Clinical Frail Scale. A panel of 27 inflammatory cytokines was analyzed from the serum of each participant. Frailty was present in 33 (19.5%) of the cohort, and was correlated with age, malnutrition, and cognitive assessments. Statistical analysis adjusting for clinical covariates revealed higher serum levels of IL-2, IL-10, and IL-17 in frail patients. Using machine learning classification, we developed a predictive model of frailty with strong discriminative performance (AUC 0.78). Individual element analysis via Shapley Additive Explanations (SHAP) revealed that inflammatory markers had the greatest influence on the model, and IL-7 was the single most important element in the prediction of frailty. Together, our data demonstrate a novel pattern in which T-cell regulatory inflammatory molecules as mediators of frailty, implicating cellular immunity as a potential mechanism of dysfunctional aging.

immunology↗

bioRxiv2026

Regression-based Modeling of Spearman's Rho for Longitudinal Metabolomics and Mental Wellness in Breast Cancer Patients

SO_SCPLOWUMMARYC_SCPLOWChemotherapy in breast cancer (BC) can substantially affect mental wellness. Advances in metabolomics enable comprehensive profiling of metabolic changes over time during and after treatment, offering insights into biological mechanisms linking chemotherapy to mental health outcomes. To study the association between metabolite profiles and mental wellness, correlation-based analyses are particularly useful. Spearmans rho is a widely used correlation measure and popular alternative to Pearsons correlation, since it also applies to non-linear association between variables. However, existing methods are not designed for longitudinal data and do not allow for covariate adjustments. In this paper, we propose a novel regression-based framework grounded in a class of semiparametric models, the functional response models, to extend this popular correlation measure to longitudinal settings with missing data under the missing at random assumption. This framework facilitates inferences about temporal changes in correlations over time and association of explanatory variables for such changes. We use simulation studies to evaluate performance of the approach with moderate sample sizes. We apply the approach to a one-year longitudinal substudy of the EPIGEN study to examine the longitudinal association between metabolite profiles and mental wellness in BC patients undergoing chemotherapy. The identified metabolites may serve as candidates for future in-depth bioinformatics analyses and translational investigations.

cancer biology↗

bioRxiv2026

Lipoengineering of Biomolecular Condensates Controls Material Properties and Multiphase Hierarchy to Guide Organoid Morphogenesis

Cells use post-translational modifications (PTMs) to reconfigure biomolecular condensates across length scales, space, and time.1,2 While charged PTMs are well-known electrostatic switches,3,4 how ubiquitous neutral PTMs shape condensate plasticity and hierarchy remains unclear. Here, we establish a set of design principles for using site-specific lipidation, a class of neutral hydrophobic PTMs, to rationally control properties and interactions of engineered biomolecular condensates. Through systematic analysis of over 80 lipidated synthetic intrinsically disordered proteins (IDPs), we uncovered two distinct axes of control. First, the interplay between the lipid and the local three-residue sequence of its attachment site acts as a programmable switch for cohesion--the homotypic interactions that define the material state of the condensed phase-- directing assemblies toward dynamic liquids, arrested gels, or ordered fibrillar solids. Second, the lipid, together with the global properties of the IDP scaffold, tunes adhesion--the heterotypic interactions that govern condensate miscibility and hierarchical organization. We harnessed these principles to rationally engineer complex, multi-phase architectures and create hybrid hydrogels with programmed microstructure and material properties that guide the morphogenesis of functional intestinal organoids. These findings establish a new framework for lipoengineering advanced biomaterials and provide a blueprint for dissecting structure-property relationships across diverse classes of PTMs.

bioengineering↗

bioRxiv2026

Synthetic budding morphogenesis by optogenetic receptor tyrosine kinase signaling

The mammalian kidney relies on a branched network of collecting ducts for fluid transport and homeostasis. Replicating this network in vitro would parallelize function in synthetic replacement kidneys, yet current organoids have limited branching capacity. Here, we establish a developmentally-informed strategy to control organoid budding through optogenetic control of a receptor tyrosine kinase, RET. We first show pharmacological manipulation of RET signaling controls the extent of branching in mouse embryonic kidneys and human stem cell-derived kidney organoids. Next, we develop an optogenetic RET receptor (optoRET) that signals in a ligand-independent manner via blue light-mediated clustering. Epithelial cells expressing optoRET reproduce stereotyped RET signaling, scattering, and symmetry breaking in response to blue light. Human kidney organoids undergo budding with controllable orientation in response to spatially patterned optoRET stimulation. Our results establish ligand-free optogenetic control of branching and inspire new synthetic biology strategies for epithelial organoid design. HighlightsGDNF-RET controls branching and tip cell state in mouse and human kidney tissues. OptoRET reproduces endogenous RET signaling and morphogenesis in cell lines. OptoRET enables ligand-free budding in human renal epithelial organoids. Spatially patterned optoRET stimulation controls budding orientation.

bioengineering↗

bioRxiv2026

SELECTIVE TRANSCRIPTOMIC VULNERABILITY OF MEMBRANE-INTEGRATED ARCHITECTURES DURING NEURAL TISSUE VITRIFICATION

Cryopreservation is essential for long-term storage of biological tissues. Yet, surprisingly, the precise molecular impact of cryopreservation on tissue transcriptomes remains poorly defined. This study provides the first resource of whole-genome transcriptomic changes following cryopreservation. This study used bulk RNA sequencing to examine how preservation method (snap freezing or vitrification) affects transcriptomes in mouse cerebral cortex and hippocampus. This allowed us to separate cryoprotectant-specific changes from cold induced-changes via snap freezing. In a subset of genes, tissues processed under vitrification conditions showed selective under-representation of a small but structurally coherent group of transcripts, with the hippocampus exhibiting greater vulnerability than the cortex. UniProt annotation revealed that affected transcripts were strongly enriched for proteins with membrane-associated, secretory-pathway, and multi-pass topologies, indicating that structurally complex membrane-integrated architectures are disproportionately sensitive to vitrification. Pathway-level analysis using iPANDA further showed that negative preservation scores in vitrified tissue clustered primarily within signal transduction and metabolic pathways, suggesting coordinated pathway-level disruption rather than global transcript loss. Together, these results demonstrate that vitrification conditions induce selective and structured molecular perturbations in neural tissue, defined by the under-recovery of transcripts associated with membrane and secretory pathway organization. This work highlights molecular vulnerability during vitrification and emphasizes the need for transcript-level evaluation when optimizing cryopreservation approaches for neural systems.

molecular biology↗

bioRxiv2026

Gene-First Identity Construction for Robust Cell Identification in Single-Cell Transcriptomics

The precise delineation of cell types is fundamental to single-cell transcriptomics, yet current clustering pipelines often violate an axiomatic principle: hierarchical consistency. Existing methods measure cell-to-cell distances within a fixed global feature space, disregarding the fact that biological distinctions are inherently context-dependent lineage separation requires different gene programs than subtype resolution. Mathematically, this implies that the similarity metric itself should not be a static functional, but a pair-dependent energy functional evaluated within a specific Hilbert subspace determined by the biological comparison at hand. The challenge lies in the fact that allowing pair-dependent metrics typically destroys the global geometric consistency required for downstream analysis, unless the family of Hilbert subspaces is given strong biological structure. To resolve this geometric dilemma, we introduce GeCCo (Gene Co-expression Constructed identity), which constructs identities by projecting cells onto a rigorously derived hierarchy of gene programs. To construct this hierarchy, GeCCo first quantifies Boolean regulatory logic via the{phi} coefficient, and subsequently employs a greedy topological inference to organize genes based on their synergistic and antagonistic relationships. Benchmarking on human immune atlases demonstrates that GeCCo achieves superior hierarchical consistency, ensuring that globally inferred cell identities rigorously match locally refined subtypes. Furthermore, in pancreatic endocrine progenitors, GeCCo resolves a hidden mitotic bridge state, suggesting a concentrated division phase prior to differentiation. Ultimately, GeCCo shifts the paradigm from ad hoc clustering to programmatic cell typing, offering a mathematically grounded framework for scalable atlases of cellular discovery.

bioinformatics↗