Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional states

Combining cell lineage tracing with single-cell RNA sequencing can reconstruct a phylogenetic tree to integrate single-cell transcriptomic atlas. However, the internal nodes of this tree - representing ancestral cells - remain transcriptionally silent, preventing along-lineage longitudinal tracing of cell state dynamics. Here, we overcome this by reconstructing high-resolution zygote-to-larva developmental cell phylogenies for 15 zebrafish larvae, with directly measured transcriptomes of the terminal nodes (i.e., sampled cells). Leveraging a set of lineage-committed upregulated genes (LUGs), we developed LUG-encoded ancestral projection (LEAP), a novel phylogeny-based computational framework, and successfully imputed the transcriptional states of internal nodes of the phylogenies. This enabled, for the first time in a non-nematode organism, lineage-informed longitudinal analysis of cell state dynamics throughout development. Our analysis revealed a major, previously unappreciated wave of fate specializations associated with hatching, distinct from the well-characterized events of gastrulation. Furthermore, we uncovered abundant incipient cell states that are already fate-determined but exhibit minimal transcriptional differentiation, revealing a hidden layer of developmental fate specializations. In sum, by resolving ancestral transcriptional states of a reconstructed cell phylogeny, this work paves the way for constructing lineage-resolved cell atlases in complex organisms to characterize comprehensive cell state dynamics.

developmental biology↗

bioRxiv2026

Molecular basis for chikungunya virus recognition of a mosquito-specific receptor

Alphaviruses are arthropod-borne viruses that recognize cellular receptors in both vertebrate hosts and mosquito vectors to complete their transmission cycle, yet how they maintain recognition of receptors across evolutionarily divergent host species remains unresolved. Among alphaviruses, chikungunya virus (CHIKV), which is primarily vectored in urban settings by Aedes species mosquitoes, is the most widespread, and causes explosive outbreaks that can involve hundreds of thousands to millions of cases annually. The cell adhesion protein Lachesin is a mosquito-specific cellular receptor for CHIKV and multiple other arthritogenic alphaviruses. The envelope E2-E1 glycoproteins of these alphaviruses broadly recognize Lachesin orthologs from diverse mosquito species, but not other insects or arachnids. Lachesin genetic manipulation to prevent mosquito virus infection without interfering with endogenous receptor function could have a major impact for CHIKV control. Here, we determined high-resolution cryo-electron microscopy (cryo-EM) structures of alphaviruses bound to Aedes albopictus Lachesin. Comparative analysis of Lachesin-bound CHIKV, Semliki Forest virus (SFV), and Middelburg virus (MIDV) revealed that these three genetically divergent viruses use a similar surface to recognize Lachesin domain 1, but with reorganized E2-E1 glycoprotein contact residues. We show that a soluble Ae. albopictus Lachesin receptor decoy protein blocks the E2-E1-mediated entry of CHIKV and other arthritogenic alphaviruses into mammalian cells with greater breadth than a vertebrate receptor MXRA8 decoy and protects against lethal SFV challenge and CHIKV pathogenesis in murine models. Additionally, we identified a naturally occurring single residue Lachesin polymorphism that is found in some Anopheles (malaria vector) mosquitoes, and fully ablates CHIKV E2-E1 recognition, informing strategies for mosquito-targeted genetic interventions that could prevent mosquito vector infection and virus transmission. These findings define distinct determinants of receptor binding in mosquitoes and humans for arthritogenic alphaviruses, with implications for countermeasure development and outbreak preparedness.

microbiology↗

bioRxiv2026

Uncovering key genes related to potato tuber starch concentration from the tuber bulking, maturation stage to storage

Potato is the worlds fourth major staple crop, and tuber starch is a critical dietary energy source and a food processing raw material. In this study, hybrid progenies of the high-starch cultivar Huasheng No.7 and China main commercial varieties were used as materials, high-throughput sequencing was performed on tuber samples at tuber bulking, maturation and storage stages. Combined with bioinformatics analysis, parent-progeny resequencing, SNP screening, the regulatory mechanism of tuber starch metabolism was explored. The results showed significant stage-specific transcriptional reprogramming in potato tubers: few differentially expressed genes (DEGs) were identified at tuber bulking and maturation stages, but massive transcriptional changes occurred during storage. XET family genes regulated cell wall remodeling and carbon translocation across all stages, and WRKY transcription factors specifically controlled starch homeostasis in stored tubers. The DEGs at different stage were induced by the SNPs from their parent, and the allele from the high-starch parent, Huasheng 7 may contribute the high-starch allele to the offsprings. One key gene, Soltu.DM.02G019910, which encoded {beta}-glucosidase, have a negatively relationship with tuber starch concentration. The low-starch potato breeding tubers shown higher significantly {beta}-glucosidase activity than high-starch breeding population at maturation stage. This study clarifies the molecular regulatory network of potato tuber starch metabolism and screens core regulatory genes from the tuber bulking stage to tuber storage, thereby providing theoretical support and genetic resources for molecular breeding of high-starch and storage-resistant potato varieties.

plant biology↗

bioRxiv2026

A thalamostriatal brake counteracts cortical recruitment of striatal ensembles in levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinsons disease therapy, yet how upstream circuits recruit and restrain dyskinesia-linked striatal ensembles remains unclear. Using FosTRAP-based ensemble access in a unilateral 6-hydroxydopamine mouse model, we identified secondary motor cortex (M2) and parafascicular thalamus (PF) as dominant afferents with opposing functions. Projection-wide M2 activation promoted dyskinesia, whereas PF activation suppressed ongoing dyskinesia and shifted behavior toward non-dyskinetic states. Chronic levodopa reduced overall presynaptic terminal abundance while preserving putative contacts onto ensemble neurons, thereby increasing effective pathway-to-ensemble coupling. This remodeling followed distinct pathway rules: M2 contacts became spatially dispersed and biased toward NMDAR-mediated excitation, whereas PF inputs recruited stronger polysynaptic inhibition. Dyskinesia preferentially re-engaged ensemble-projecting M2 neurons, but reactivated PF neurons were topographically segregated from PF neurons directly innervating the ensemble. Accordingly, selective M2-to-ensemble stimulation promoted dyskinesia, whereas selective PF-to-ensemble stimulation was ineffective. Finally, ensemble-restricted Grin1 knockdown reduced peak dyskinesia and weakened M2-driven dyskinesia. These findings define LID as a targetable imbalance between cortical ensemble recruitment and thalamostriatal restraint.

neuroscience↗

bioRxiv2026

Recombination on 'non-recombining' sex chromosomes: empirical insights from the blackspotted stickleback

One hallmark of sex chromosome evolution is recombination suppression driven by inversions. The resulting non-recombining sex-linked regions accumulate deleterious mutations and functionally degenerate. The question therefore arises: why does selection not favor the reestablishment of recombination? Two (non-exclusive) explanations for this have been proposed: 1) selection to maintain linkage between the Primary Sex Determination Locus (PSDL) and sexually antagonistic loci - loci with alleles that are beneficial to one sex but detrimental to the other; and 2) the rarity of reversions - structural mutations that reverse the inversion that initially suppressed recombination. Empirical perspectives are sorely needed to evaluate the relevance of these models. Here, we characterised the intermediate stages of sex chromosome evolution in the blackspotted stickleback (Gasterosteus wheatlandi). We generated high-quality, phased assemblies and annotations for the X and Y chromosomes, as well as population-level sequencing. Using these data, we inferred distinct recombination loss events and their associated inversions. Most significantly, we find support from multiple lines of evidence for a [~]2 Mb recombination event that likely occurred via double crossover within an existing non-recombining inversion. This result highlights that rare recombination can occur within sex-linked inversions. Simulations suggest that such histories of gene flux may allow for a novel empirical framework, adapted from work on autosomal inversions, to infer evolutionary processes at work within sex-linked non-recombining regions and distinguish between models to explain the maintenance of recombination suppression on sex chromosomes. Significance StatementSex chromosomes have evolved countless times across eukaryotes and often lose the ability to recombine, causing them to degenerate over time. Why then, does recombination not restart to purge harmful mutations? By generating a high-quality genome assembly of the blackspotted stickleback, we discovered the strong empirical evidence of a sex chromosome undergoing a double-crossover event within a "non-recombining" region, which reset the degenerative process. This finding challenges the view that sex chromosome decay is a one-way street. Furthermore, using forward evolutionary simulations, we demonstrate that the genetic signatures left by such events could help identify the selective forces acting within non-recombining sex chromosomes, thereby explaining the maintenance of recombination suppression.

evolutionary biology↗

bioRxiv2026

pHaseMD4AI: Phase-Space Dynamics Dataset with Chemical and pH Perturbations for Physically and Kinetically Consistent Biomolecular AI

Protein function emerges from dynamic conformational ensembles and transitions that are challenging to characterize experimentally and computationally. Recent advances in generative AI have created new opportunities for learning molecular thermodynamics, kinetics, and conformational evolution directly from simulation data, but progress is limited by the availability of large-scale datasets that combine rigorous sampling, complete phase-space information, and diverse physicochemical perturbations. Here, we present pHaseMD4AI, a molecular dynamics dataset that combines a globally equilibrated peptide branch with a protein-scale constant-pH molecular dynamics (CpHMD) branch spanning hundreds of soluble proteins. The peptide branch includes a complete set of canonical tripeptide and tetrapeptide systems together with post-translationally modified (PTM) and protonation-state datasets, providing synchronized atomic coordinates (R), velocities (V), forces (F), and Markov state model-based kinetic annotations. An accompanying web portal (https://isb.zju.edu.cn/md4ai/) enables users to browse, visualize, and download trajectories, annotations, and metadata. As an example application, we demonstrate a sequence-based model that can predict residue-level equilibrium dihedral distributions from sequence. pHaseMD4AI provides a resource for developing and benchmarking molecular machine learning methods while supporting broader studies of biomolecular dynamics under sequence, post-translational modification, and protonation-state perturbations.

bioinformatics↗

bioRxiv2026

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

Single-cell transcriptomics technology offers unprecedented insights into molecular heterogeneity. However, capturing sample-level representations that reflect both systemic and cellular states remains challenging, especially when disease annotations are mostly available as coarse sample-level labels. Here, we introduce Phenoverse, an interpretable deep learning framework that learns sample-level disease state representations through cell type-aware residual encoding, prototype learning, and Perceiver-based aggregation. Applied to independent single-cell transcriptomic cohorts of COVID-19, Alzheimers disease, and systemic lupus erythematosus, totaling over 5 million cells, we demonstrate that learned sample representations enable disease state prediction and encode a continuous spectrum of disease severity on unseen data that correlate with multiple clinical and pathological measures, despite being trained solely on binary phenotype labels. Further, we demonstrate that trajectory-derived genes reveal cross-cohort molecular programs and show consistently higher reproducibility than traditional case-control comparisons. Finally, prototype learning provides intrinsic model interpretability and enables the characterization of cell type-specific disease states. Taken together, Phenoverse offers an interpretable disease-phenotyping approach to dissecting sample heterogeneity, and our results highlight its utility in translating complex single-cell transcriptomic data into patient-level biological insights.

bioinformatics↗

bioRxiv2026

The carbon-nitrogen metabolism gene E1OGDH1 influences maize root system architecture and nitrogen plasticity

Synthetic nitrogen fertilizers have greatly increased crop yields, yet much of the applied nitrogen is lost from agroecosystems and contributes to environmental pollution and higher economic costs. Improving nitrogen uptake efficiency (NUpE) benefits from understanding how root system architecture (RSA) governs soil nitrogen capture. Although root traits have seldom been explicit breeding targets, selection for variation in above-ground nitrogen accumulation has also likely shaped differences in RSA. The Illinois Protein Strain Recombinant Inbred population, derived from more than a century of divergent selection for seed protein concentration, offers a powerful resource for dissecting RSA variation. Using multi-year field phenotyping of excavated root crowns and genome-wide association analysis, we identified a quantitative trait locus on chromosome 10 containing E1OGDH1, which encodes the E1 subunit of the 2-oxoglutarate dehydrogenase (OGDH) complex. OGDH performs a key step in the tricarboxylic acid cycle that also modulates 2-oxoglutarate, an important entry point into nitrogen metabolism and a co-factor for enzymes involved in hormone and secondary product synthesis. Long-read sequencing of inbreds derived from the divergent IHP and ILP parental populations revealed promoter polymorphisms defining E1OGDH1 alleles and differed in E1OGDH1 expression in root tissue. Field experiments in IPSRI lines carrying IHP- or ILP-associated E1OGDH1 alleles showed differences in root architectural traits over two years. CRISPR-Cas9 knockout mutants confirmed a functional role for E1OGDH1 in whole-plant performance and nitrogen-responsive root development. Mutants were shorter, had reduced biomass, and exhibited altered architectural responses to soil nitrogen levels. Transcriptome analysis further showed that loss of E1OGDH1 altered basal and nitrogen-responsive expression of genes associated with root development and nitrogen uptake and metabolism. Together, these findings identify E1OGDH1 as a strong candidate quantitative regulator of maize RSA and nitrogen plasticity, suggesting that central carbon-nitrogen metabolic genes can contribute to root developmental responses relevant to NUpE.

plant biology↗

bioRxiv2026

Physiological Cross-Sectional Surfaces: A Method for Estimating Muscle Functional Capacity from 3D Digital Models of Fiber Architecture

Physiological cross-sectional area (PCSA) is defined as the summed cross-sectional area of all muscle fibers contracting in parallel and at optimal length. PCSA is widely used, yet the conventional equations used to compute PCSA were developed from two-dimensional (2D) interpretations of muscle architecture and may not accurately represent muscle fiber cross-sections in the case of real three-dimensional (3D) geometries of muscles. Related measures of functional cross-sectional area (FCSA) and geometric cross-sectional area (GCSA) were also developed and interpreted with simplified 2D representations. Using realistic 3D muscle architectures derived from medical imaging, we sought to investigate whether conventional definitions of PCSA, FCSA, and GCSA represent the summed cross-sectional areas of all parallel muscle fibers, the fundamental definition of PCSA. We found that none of these measures consistently represented this definition. Thus, we introduce the physiological cross-sectional surface (PCSS), a curved surface within a muscle volume that is everywhere perpendicular to the local fiber direction. We estimated PCSS in 3D muscle surface meshes reconstructed from MRI data, using fiber orientations derived from Laplacian fiber reconstruction. PCSS-derived estimates were compared with PCSA, FCSA, and GCSA across six muscles representing five architectural classes. PCSS differed from all conventional measures, with the magnitude and direction of disagreement depending on muscle architecture. PCSS-to-PCSA ratios ranged from 0.771 to 1.399, while GCSA underestimated PCSS by up to a factor of 2.256 in bi- and multipennate muscles and overestimated it in muscles with more uniform fiber arrangements. PCSS demonstrated high geometric fidelity (perpendicularity>0.987) and robustness to fiber density across a tenfold range (coefficients of variation 0.39-3.95%). These findings indicate that conventional cross-sectional area measures do not consistently account for all fiber cross-sections in parallel within realistic 3D muscle geometries. PCSS provides a geometrically rigorous alternative that may improve estimation of functional muscle capacity from subject-specific imaging data.

bioengineering↗

bioRxiv2026

Toward routine health phenotyping: High-throughput prediction of metabolic, immune, and inflammatory biomarkers from milk mid-infrared spectroscopy in early-lactation dairy cows

This study evaluated the potential of milk mid-infrared (MIR) spectroscopy, combined with routinely available on-farm variables, for predicting serum metabolic, immune, and inflammatory biomarkers in early-lactation cows. Data included 5,936 blood samples from 4,442 cows across 23 Australian dairy herds, with paired milk MIR spectra and serum measurements for up to 14 biomarkers. Prediction models were developed using partial least squares regression and evaluated using nested 10-fold random cross-validation and leave-one-herd-out validation. The results show that while basic herd-test data, including milk fat, protein, and lactose concentration, as well as on-farm variables, including DIM, calving age, breed, and herd could predict serum biomarkers, combining MIR spectra with these on-farm variables produced the best overall performance. In random cross-validation, blood urea nitrogen (BUN) was predicted most accurately (R2 = 0.78), while {beta}-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) showed moderate accuracy (R2 = 0.56 and 0.44, respectively). BUN also showed the strongest external validation performance, with leave-one-herd-out R2 = 0.58 and comparable accuracy for predicting records collected after 70 days in milk (R2 = 0.65). BHB and NEFA had moderate leave-one-herd-out accuracy but did not transfer beyond early lactation. Most other biomarkers showed low or inconsistent external validation performance. Overall, MIR spectroscopy combined with on-farm variables shows promise for routine prediction of BUN, BHB and NEFA, which can be used for monitoring and genetic evaluation of, for example, ketosis and energy deficit. Initial random cross-validation results for glucose, bilirubin and cholesterol were promising, but more data is needed to improve the prediction accuracy and robustness of the predictions. HighlightsO_LIMilk MIR can predict several serum biomarkers in early-lactation dairy cows. C_LIO_LIMIR-predicted blood urea nitrogen shows the greatest accuracy and robustness. C_LIO_LIMIR-predicted {beta}-hydroxybutyrate and nonesterified fatty acids show moderate accuracy. C_LIO_LIMost mineral, hepatic, and inflammatory biomarkers had limited accuracy. C_LIO_LIRoutine MIR phenotyping is most promising for BUN, BHB, and NEFA. C_LI SummaryMilk mid-infrared (MIR) spectroscopy and on-farm variables are evaluated as a high-throughput tool to predict health-related serum biomarkers in early-lactation dairy cows. The data include 5,936 paired blood and milk samples from 4,442 cows across 23 Australian dairy herds and up to 14 serum biomarkers. Prediction models are developed using partial least squares regression with nested random cross-validation and leave-one-herd-out validation. Blood urea nitrogen (BUN) shows the greatest and most transferable prediction accuracy across herds and lactation stages. {beta}-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are predicted with moderate accuracy, but only during early lactation. Random cross-validation results for glucose and bilirubin are promising, but larger datasets are needed for robust external validation. Most other mineral, hepatic, and inflammatory biomarkers show limited external prediction accuracy. These results indicate that MIR-based routine health phenotyping is most promising for BUN, BHB, and NEFA.

genetics↗

bioRxiv2026

BaiZe: A Multi-View Dynamic Framework for Simulating and Interpreting Cellular Responses Across Perturbation Contexts

Accurately predicting how cells respond to perturbations is important for understanding cellular regulation and prioritizing experimental interventions, yet existing models are often designed for specific perturbation types or biological contexts. Here we present BaiZe, a multi-view conditional state-transition framework that predicts the post-perturbation transcriptome from a control-state transcriptome together with genetic, chemical, temporal and optional chromatin-accessibility information. BaiZe models perturbation responses as context-dependent transitions between cellular states. BaiZe supports prediction across held-out cell states and genetic perturbations, unseen multi-gene combinations, chemical structures and doses, temporal stages and species contexts. Benchmarking across diverse perturbation settings demonstrates that BaiZe effectively recovers major transcriptional response programs under previously unseen conditions. Incorporating matched ATAC-seq context further improves selected state-transition predictions and enables model-based attribution of chromatin regions to response-associated genes and pathways. BaiZe also supports few-shot transfer of perturbation responses from human to mouse cellular systems and connects predicted transcriptomes to candidate morphology projections. To facilitate interpretation and use, BaiZe-Agent organizes response genes, pathways, chromatin evidence, cross-species predictions and projected phenotypes into traceable, queryable perturbation records. Together, BaiZe provides a broadly applicable framework for predicting and interpreting context-dependent cellular responses and for prioritizing hypotheses across diverse perturbation settings.

bioinformatics↗

bioRxiv2026

A novel AR molecular glue degrader via bivalent engagement of the N-terminal domain and RNF213-UBE2J2 complex

Targeted protein degradation enables the elimination of disease-relevant proteins through induced proximity between E3 ligases and substrates. Current androgen receptor (AR) therapies act via the ligand-binding domain (LBD) but fail against constitutively active LBD mutants or splice variants such as AR-V7. Here, we identify GZL626, a small-molecule degrader that induces proteasome-dependent degradation of both full-length AR and AR-V7. GZL626 suppresses AR transcriptional activity, inhibits proliferation of AR-positive prostate cancer cells, and blocks SARS-CoV-2 infection. Mechanistically, GZL626 binds the intrinsically disordered AR N-terminal domain (NTD) and promotes formation of a noncanonical RNF213-UBE2J2-AR ternary complex that drives AR ubiquitination and degradation. The AR DNA-binding domain (DBD) acts as a structural hub within this assembly. Distinct from PROTACs or classical molecular glues, GZL626 engages both AR and RNF213 bivalently, remodeling protein interactions to stabilize a functional E3-E2-substrate complex. These findings establish GZL626 as a first-in-class NTD degrader with potential to overcome resistance in advanced prostate cancer.

biophysics↗

bioRxiv2026

Multidimensional atlas of RNA-regulated proteins revealed by RNA-dependent thermal proteome profiling

RNA and proteins interact pervasively in cellular processes, yet the functional relevance of most RNA-binding proteins remains unknown. To address this, we developed RNA-dependent thermal proteome profiling (RTPP), a method that identifies RNA-regulated proteins (RRPs) by detecting changes in a proteins structural stability upon RNA binding, without requiring crosslinking or enrichment. Applying RTPP in HEK293T cells revealed 1,664 RRPs, including 257 previously undetected RNA-binding "orphans". One such orphan, SGK3, binds the lncRNA CASC15, facilitating its PtdIns(3)P binding, endosomal recruitment and kinase activity. We further generated a tissue-specific RRP atlas, identifying interactions unique to particular organs, including hippocampus-specific RRPs dysregulated in Alzheimers disease. Integrating RTPP with proximity labeling resolved RNA-binding heterogeneities across subcellular compartments. Live-cell RNase treatment also uncovered proteins associated with cell surface RNA, revealing that RO60 form nanoscale clusters with glycoRNA. RTPP provides a powerful approach to decipher functional RNA-protein interactions across multiple biological dimensions.

molecular biology↗

bioRxiv2026

The evolutionarily conserved APP-Spastin cooperation regulates endolysosomal homeostasis and apoptotic cell degradation

Efferocytosis, the recognition, engulfment, and degradation of apoptotic cells by phagocytes, is essential for tissue homeostasis and development, and its failure contributes to chronic inflammation and neurodegeneration. The amyloid precursor protein (APP), a central pathogenic factor in Alzheimers disease, retains physiological functions independent of amyloid production that remain poorly understood. Here, we identify a conserved, non-amyloidogenic role for APP in regulating apoptotic cell degradation via the endolysosomal pathway. Using Drosophila APPL as a model, structure-function analysis demonstrated that the intracellular internalization domain of APPL, but not its secreted ectodomain, is required for efficient apoptotic cell degradation. Immunoprecipitation coupled with mass spectrometry revealed a physical interaction between APPL and the microtubule severing ATPase Spastin, mediated by the microtubule-interacting and trafficking domain of Spastin. APPL interacts with Spastin on endosomal microtubules and modulates the dynamics of the Spastin-ESCRT-III complex, enabling Spastin to sever microtubules and promote endosomal tubule fission. Loss of APPL disrupts this process, causing aberrant endosomal tubulation and impaired lysosome biogenesis. Furthermore, it compromises the function of residual lysosomes, characterized by reduced acidity, diminished proteolytic activity, and increased lysosomal damage, which ultimately impairs the degradation of engulfed apoptotic cells. Critically, this phenotype is evolutionarily conserved in C. elegans and mice. Together, these findings establish a conserved APP-Spastin axis that regulates endolysosomal homeostasis and apoptotic cargo digestion. This reveals a critical non-amyloidogenic function of APP in maintaining tissue homeostasis through efficient efferocytosis, with broad implications for inflammatory and neurodegenerative disorders that warrant further investigation.

cell biology↗

bioRxiv2026

scSpark: an AI-assisted cloud platform for traceable interpretation of single-cell transcriptomic results

Single-cell RNA sequencing now routinely produces detailed maps of cell types and states, but interpreting a finished project remains harder than it should be. Once the analysis is done, the results are usually handed over as static reports, figure panels and supplementary tables. A biologist who later wants to revisit an annotation, recompute a cell-type proportion or check whether a pathway is specific to one group typically has to return to a bioinformatician rather than explore the data directly. We developed scSpark to close this gap. The platform takes the completed outputs of a single-cell project: cell annotations, embeddings, differential-expression tables, trajectories, cell-cell communication networks and enrichment results--and serves them through a web browser as an interactive workspace. Heavy computation stays upstream: scSpark indexes the precomputed objects under a single project structure and exposes them through six modules for cell annotation, differential analysis, trajectory exploration, cell-cell communication, functional interpretation and AI-assisted result interrogation. Every action in these modules, from a query to a label change, an export or an AI-generated summary, is linked to a specific project version, data object, parameter set and output file, so that any conclusion can be traced back to the evidence behind it. We illustrate the platform by reworking a published periodontitis dataset through this interface. scSpark does not replace upstream pipelines or expert judgement; it is a layer that makes their results easier to inspect, revise and reuse, and that turns a single-cell project from a one-off report into an interpretation others can follow and check. Significance StatementSingle-cell studies produce increasingly intricate maps of tissues, but those maps are hard to interrogate once they have been written up as static reports. scSpark tackles this post-analysis bottleneck by holding a projects annotations, marker evidence, differential results, pathways, communication networks, AI-generated summaries and publication-ready figures together in one workspace, where each item is linked to the data and settings that produced it. The platform is built to support expert decisions rather than to make them: its aim is to let researchers check, revise and reuse a result, and to see exactly how it was reached.

bioinformatics↗

bioRxiv2026

ROOTQUANT: AUTOMATED ROOT TRAIT QUANTIFICATION FROMMINIRHIZOTRON IMAGES USING DEEP LEARNING

AO_SCPLOWBSTRACTC_SCPLOWQuantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The models generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R2 of 0.90 and an RMSE of 2.9 mm for RL, and an R2 of 0.88 and an RMSE of 4.2 mm2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.

plant biology↗

bioRxiv2026

Transmembrane aminopeptidase Q (Taqpep) is a common mechanism in the establishment of periodic patterning in skin and intestine

Periodic patterns are a frequent motif in biology that occurs across diverse tissues and species. In mammals, pigmentation patterns such as zebra stripes or tiger stripes are well-known examples of periodic patterns; more recently, the myenteric plexus (MP) of the enteric nervous system (ENS), which controls gastrointestinal motility, has been found to exhibit a striped organization in humans and laboratory mice. In domestic cats and other felids, the Transmembrane aminopeptidase Q (Taqpep) gene plays a key role in color pattern establishment during skin development, but its patterning role has not been examined in other tissues. Here, we show that, in laboratory mice, Taqpep is required for normal patterning of developing hair follicles and the MP. Using both sequencing and histologic techniques, we found Taqpep is expressed in mesenchymal cells in embryonic skin and intestine directly adjacent to where periodic patterning occurs. We generated Taqpep mutant mice, which exhibit disrupted epidermal patterning akin to the changes in periodic coat patterning observed in Taqpep mutant cats. The intestine of Taqpep mutants has irregularly periodicity of enteric neuronal stripes, and enteric neurons in Taqpep mutants exhibit disrupted Wnt signaling. This work provides new insight into the mechanism of enteric neuronal patterning and identify Taqpep as a common and conserved mediator of periodic patterning across mammalian tissues and organisms. Author summaryPeriodic patterning is a frequent motif in biology. Examples include pigmentation patterning such as tiger stripes and, as recently identified in both mouse and human, the striped organization of enteric neurons in the myenteric plexus of the intestine. In domestic and wild cats, the Transmembrane aminopeptidase Q (Taqpep) gene is essential for the establishment of periodic patterning. Whether this gene plays a conserved role in periodic patterning across other tissues and species has yet to be explored. We found that Taqpep is expressed in mesenchymal cells in embryonic mouse skin and intestine at key locations and developmental stages to instruct periodic patterning. We next generated Taqpep mutant mice that exhibit disrupted periodic patterns in both developing skin follicles and in enteric neuron organization. Thus, Taqpep is essential in establishing periodic patterning in diverse mammals and tissues.

developmental biology↗

bioRxiv2026

In situ structural basis for the LHCSR3-mediated photoprotection of photosystem II

Energy-dependent quenching (qE) represents a critical mechanism for photosynthetic organisms to mitigate photodamage caused by excessive light. In green algae, LHCSR3 protein plays a central role in qE, activated by thylakoid lumen acidification and associated with photosystem II (PSII) to dissipate excess energy. Despite extensive efforts, the assembly and energy dissipation mechanisms of PSII-LHCSR3 have remained unknown. Here, we present the in situ structures of the PSII supercomplex embedded in the native thylakoid membranes of Chlamydomonas reinhardtii in both quenched (LHCSR3-bound) and unquenched (LHCSR3-free) states at near-atomic resolutions. Our results demonstrate that in high-light-acclimated cells, LHCSR3 binds to PSII peripheral antenna CP26 and associates with an extra LHCII trimer (eLHCII). Structural comparison of LHCSR3 with other light-harvesting complexes reveals possible protonation-induced conformational changes in LHCSR3 and rearrangements of its two pigment clusters, which potentially serve as quenching sites to dissipate excess energy transferred from CP26 and eLHCII. Our findings provide a direct visualization of how photoprotection is spatially organized in vivo and have implications for engineering natural and artificial photosynthetic systems with more dynamic photoprotection.

plant biology↗