Biology subjects

Bao, Z.

Publications and source records attributed to Bao, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Ambient-Condition Visualization of Metalloenzyme Reactive Intermediates Enabled by the SHINERS Paradigm

Reactive metal-oxygen intermediates are central to metalloenzyme catalysis, yet their low populations and short lifetimes make vibrational characterization under aqueous reaction conditions challenging. Here, we establish shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) as a powerful vibrational approach for visualizing reaction-associated metal-ligand signatures of reactive metalloenzyme states. Across the heme and non-heme iron enzymes TauD, VioC, CYP119, and TqaLNc, isotope-sensitive features provide vibrational information on reactive coordination states. In TauD and VioC, isotope-sensitive features provide vibrational characterization of Fe(IV)=O intermediates. In CYP119, an isotope-sensitive Fe(IV)=O vibration is assigned to P450 Compound I generated by m-CPBA, including under lauric-acid turnover conditions. Most notably, in TqaLNc, combined 18O- and 15N-sensitive vibrational responses support formation of the proposed (R)HN-Fe(IV)=O coordination state and the dynamic-coordination mechanism underlying selective C-N bond formation. These results demonstrate that SHINERS can serve as a powerful technique for the in situ vibrational visualization of reactive intermediates and their coordination states in metalloenzyme catalysis.

biochemistry↗

bioRxiv2026

Direct electrochemical cortisol detection via transition-resolved interrogation at a defect-engineered graphene interface

Continuous tracking of cortisol is central to understanding human stress physiology, yet direct electrochemical detection without biological receptors remains challenging despite its promise for stable and dynamic sensing. Electrochemical reduction of cortisol typically occurs at highly negative potentials, where parasitic interfacial currents, hydrogen evolution, and substantial capacitive background overlap with the cortisol reduction signal, preventing accurate quantification. We overcome this barrier through an integrated material-measurement strategy combining a defect-engineered graphene interface with a transition-resolved interrogation (TRI) measurement strategy. First, we engineered polybenzimidazole-derived laser-induced graphene containing nitrogen-rich defects while suppressing oxygen-derived functionalities which reduced parasitic background currents within the same cathodic potential regime. Next, we designed TRI to leverage differences in the time-dependent evolution of overlapping cathodic processes to isolate a localized cortisol-associated electrochemical reduction transition. Derivative-domain projection coupled with background estimation enables its reliable quantification. This integrated sensing architecture enables sensitive, selective, and dynamic cortisol detection in both artificial and biological interstitial fluids at low nanomolar concentrations. The response remains reproducible across physiologically relevant variations in pH, ionic strength, temperature, and repeated cycling. Together, these capabilities provide a basis for continuous electrochemical cortisol monitoring for future study of stress physiology.

bioengineering↗

bioRxiv2026

Transient morphogenetic constraints organize self-assembling axon neighborhoods for robust yet flexible wiring

Nervous systems form wiring patterns that are reproducible across individuals. This reproducibility is thought to emerge from molecular encoding and developmental events, but their relative contributions remain unclear. We address this question in the C. elegans neuropil, where embryonic developmental dynamics and adult anatomy are resolved at single-cell resolution. We find that transient morphogenetic structures -- rosettes, corridor cells, pioneer axon scaffold -- restrict which axons make contact, shaping the neuropil into overlapping neighborhoods. This demonstrates how early events constrain wiring choices, but not whether they explain the resulting reproducibility. To explain, we use an agent-based model of stochastic innervation that recapitulates macro- and micro-level reproducibility, revealing a trade-off between physical constraint and molecular specificity that limits neighborhood size. Counterintuitively, less selective axons produce more reproducible wiring when constrained within neighborhoods. This trade-off lets nervous systems maximize reproducibility without having to molecularly encode every axon-contact, a strategy for robust yet flexible wiring.

neuroscience↗

bioRxiv2026

Life finds a way: Integrative phylogenomics resolves an overlooked bivalve order with chromosome fusion and mitochondrial translational-code evolution

Resolving deep phylogenetic relationships requires integrating multiple lines of evidence, as distinct evolutionary forces shape signals from different genomic markers. Here, we investigate the systematics of the controversial APPD lineage (Anomiidae, Placunidae, Plicatulidae, and, by inference, Dimyidae) within Pectinida sensu lato using phylogenomic, comparative genomic, transcriptomic, proteomic, and morphological approaches. Our analyses consistently recover APPD as a monophyletic lineage sister to Limida and Pectinoidea, divergent at [~]428 Mya. With three novel high-quality genomes, extensive progressive chromosomal fusions demonstrate a reduction in chromosome number of the APPD lineage (6-13), compared with an ancestral 20 molluscan linkage groups (MLGs). Accompanied by extensive intrachromosomal gene-order scrambling, we identify one functional centromere in Placuna vitream flanked by two vestigial centromeric remnants on a single chromosome, providing a potential resource for investigating centromere inactivation and neocentromere formation. Mitochondrial genomes of APPD lineage exhibit unprecedented plasticity in translational decoding: Pododesmus employs the invertebrate mitochondrial code; Heteranomia employs +1 translational frameshifting to bypass in-frame TAG codons, whereas in Anomia, Enigmonia, Placuna, and Plicatulidae, TAA is reassigned to tyrosine and confirmed by proteomic evidence, which supports mitochondrial frameshifting in APPD lineage and defines a novel translation table for bivalves. Integrating phylogenetic distinctiveness, deep divergence, extreme karyotypic restructuring, unique mitochondrial features, and morphological diagnosability, we elevate the APPD lineage into Anomiida ord. nov. This revision resolves long-standing uncertainties for Pectinida sensu stricto and Limida, and establishes the APPD lineage as a valuable system for investigating chromosome fusion, centromere evolution, codon reassignment, and translational recoding. ClassificationBiological Sciences; Evolution SIGNIFICANCE STATEMENTWe have re-examined a controversial group of marine bivalves (Anomiidae, Placunidae, Plicatulidae, and Dimyidae). Our integrative approach shows that these animals split from scallops and their relatives more than 428 million years ago and have undergone drastic chromosomal fusions that reduced their chromosome number from 20 to as few as 6. Additionally, some species evolved unusual ways of reading their mitochondrial genetic code, either reassigning the stop codon to tyrosine or using +1 translational frameshifting to skip stop signals. The combination of deep evolutionary time and genomic divergence warrants recognizing them as a new order, Anomiida ord. nov. This work, as a case study, demonstrates how chromosome fusion and genetic code variation contribute to invertebrate diversity.

evolutionary biology↗

bioRxiv2026

Repurposing the TIGR-Tas system for programmable transcription activation

Programmable transcription activators are central to functional genomics, gene therapy, and synthetic biology. However, application of the most versatile CRISPR activators (CRISPRa) is hampered by the bulky size and compact CRISPR systems are constrained by the requirement of long protospacer adjacent motifs (PAMs). To address these limitations, we repurposed the TIGR-Tas RNA-guided DNA-targeting system as a versatile platform for gene activation. By systematically optimizing the fusion architecture of a nuclease-inactivated TasR with gene activation domains, we achieved robust, specific, and multiplexable transcriptional activation of endogenous human genes. This TIGR activation (TIGRa) system could be a superior alternative to CRISPRa systems due to its extreme compactness and potentially PAM-independent targeting capability.

genetics↗

bioRxiv2026

Multiplex genome engineering in yeast using the TIGR-Tas system

Tandem Interspaced Guide RNA (TIGR)-TIGR-associated (Tas) systems are a newly discovered family of ultracompact, modular RNA-guided DNA-targeting proteins that function without a protospacer adjacent motif (PAM) requirement. Their utility as genome engineering tools in microbes remains unexplored. Here, we report the first functional implementation of TIGR-Tas in Saccharomyces cerevisiae for genome engineering. We show that TasR from Parcubacteria (ParTasR) can be programmed by user-defined tigRNAs to generate targeted DNA double-strand breaks at yeast endogenous loci. By co-delivering ParTasR with customized tigRNAs and donor templates, we achieved precise gene fragment deletion and targeted codon substitutions at multiple genomic loci. The multiplex genome engineering capability of this TIGR-Tas system was demonstrated through high-efficiency multiplex gene disruption and chromosomal assembly of a lycopene biosynthesis pathway while inactivating an endogenous gene. This work establishes TIGR-Tas as a valuable addition to the yeast genome engineering toolbox, particularly for applications requiring PAM-independent targeting or compact delivery.

synthetic biology↗

bioRxiv2026

Short-term dietary change rapidly remodels microbial community assemblages and reprogrammed systemic immune phenotypes

Diet is a major determinant of the gut microbiome and immune homeostasis, yet the extent to which short-term dietary interventions can remodel established microbial communities and reprogramme immune phenotypes following long-term western diet consumption remains poorly understood. Here, we investigated temporal dynamics of the gut microbiome, microbial metabolites, intestinal barrier function and local and systemic immune responses following diet switching. Mice were fed either a standard chow or a western diet for 8 weeks before remaining on these diets or switching to the alternate diet for 2 or 4 weeks. Long term consumption of chow and western diets resulted in distinct gut microbial communities and differences in intestinal permeability. Diet switching rapidly remodelled microbial community structure within two weeks, with substantial bidirectional changes in community composition. Despite these changes, the relative abundance of several taxa remained influenced by prior dietary exposure. In contrast, faecal SCFA profiles remained largely associated with long-term diet, indicating that microbial metabolic outputs were altered more slowly than microbial community composition. Mass cytometry revealed progressive remodelling of local (MLN) and systemic (PBMC and spleen) immune responses following dietary switching. Activation-associated immune phenotypes, including Ki67+ and PD-1+ B and T cells, inflammatory monocytes and ROR{gamma}t+ regulatory T cells, rapidly responded to diet switching, whereas overall B cells, regulatory T cells and effector memory T cells retained signatures of long-term dietary exposure. Together, these findings demonstrate distinct temporal dynamics across the diet-microbiome-immune axis, whereby gut microbial composition and immune activation states remain highly plastic, while microbial metabolic outputs and several memory and regulatory immune phenotypes exhibit persistent dietary imprinting. These findings highlight the potential utility of short-term dietary interventions to modulate host-microbiome interactions and immune homeostasis.

immunology↗

bioRxiv2026

EDTA v2: enabling scalable TE annotation in animal genomes

The Extensive de-novo TE Annotator (EDTA) automates transposable element annotation in plant genomes but lacks direct LINE/SINE detection, limiting its applicability to animal genomes. We present EDTA v2, which integrates LINE and SINE detection, completely rewrites TIR-Learner for deployability and scalability, and accelerates structural detectors by up to two orders of magnitude. Tested in 30 animal genomes from the Vertebrate Genomes Project Phase I, EDTA v2 bridges the non-LTR detection gap that has prevented automated TE annotation in animals.

genomics↗

bioRxiv2026

Dual histone methylation reader ZCWPW2 links histone methylation to initiation of meiotic recombination

Meiotic homologous recombination initiates with the formation of programmed DNA double-strand breaks (DSBs) by complexes comprising SPO11 and accessory proteins at discrete sites called recombination hotspots. In mammals, PRDM9-dependent H3K4me3 and H3K36me3 define recombination hotspots, but how these epigenetic characteristics determine the physiological DSB formation remains unknown. Here we show that dual histone methylation reader ZCWPW2 can recognize H3K4me3 and H3K36me3 marks in testis. The binding activity of ZCWPW2 to histone methylation is dependent on PRDM9 function. Moreover, we find the epigenetic writer-reader axis PRDM9-ZCWPW2 is essential for DSB formation at meiotic recombination hotspots, which may be partly explained by the finding that ZCWPW2 can physically interact with HORMAD1, IHO1, MEI4, and REC114. Finally, the absence of ZCWPW2 leads to disrupted chromosomal synapsis and recombination, thereby obstructing meiotic progression. Taken together, our findings provide new insights into how histone modifications and their associated regulatory proteins collectively regulate meiotic homologous recombination initiation.

cell biology↗

bioRxiv2026

S2F-agent: Skill-grounded agent for Sequence-to-Function computational genomics workflows

Sequence-to-function (S2F) models offer a revolutionary paradigm for genotype-phenotype mapping, yet their broader application is bottlenecked by the need for reliable orchestration and interpretation across a fragmented model ecosystem. While general-purpose language models can automate scientific workflows, they are not inherently grounded in the model-specific execution constraints required for robust S2F analysis. Here, we present S2F-Agent, a human-in-the-loop framework designed for the verifiable orchestration of the heterogeneous S2F ecosystems. The framework employs a contract-based harness to bridge model-specific capabilities (Skills) and model-agnostic biological objectives (Playbooks), seamlessly translating free-form biological requests into reliable execution and rigorous downstream interpretation. Evaluated on a benchmark of 54 query cases derived from published S2F workflows, S2F-Agent systematically outperformed general-purpose LLMs, demonstrating superior reliability accuracy in routing, groundedness, and end-to-end task execution success. We further demonstrate the robustness and scalability of S2F-Agent across model adaptation, variant interpretation, genome-scale functional profiling and personal-genome analysis. First, the agent autonomously adapts a genomic foundation model to quantitative chromatin profiles, resolving sequence features associated with primed and active regulatory states. Second, integrating multi-perspective variant effect predictions prioritized 42 high-priority candidate variants among CAD-associated variants (>16,000), and identified tissue-resolved regulatory mechanisms including the hepatic SORT1 axis. Third, genome-scale profiling of multiple traits GWAS atlas variants (>250,000) revealed pervasive context dependence in molecular consequences and regulatory architecture, highlighting the analytical focus toward fine-grained, tissue-specific regulatory variants. Finally, evidence-gated analysis of personal genomes expanded functional hypothesis generation beyond clinically annotated variants to thousands of prioritized candidates per individual while imposing explicit evidence-dependent boundaries on clinical claims. Collectively, these results establish S2F-Agent as a general framework for converting heterogeneous sequence-to-function capabilities into verifiable, scalable, and evidence-aware genomic analyses. By bridging the chasm between LLMs, specialized S2F ecosystems and rigorous genomic science, this framework democratizes the S2F paradigm for unlocking the full potential of these advanced models in real-world discoveries.

bioinformatics↗

bioRxiv2026

Machine Learning-Driven Multiplexed Biomarker Detection with Polymer-Enhanced Electrochemical Sensors

Biomarkers in sweat and saliva offer a promising avenue for non-invasive health monitoring. Electrochemical sensors have the potential to measure such biomarkers simultaneously. However, they are limited in discriminating individual biomarkers in mixtures, as redox potentials often overlap, resulting in current signatures that cannot be deconvoluted. This study focuses on differentiating biomarkers using orthogonal sensing materials combined with machine learning. We introduce a flexible electrochemical sensor array comprising carbon flower electrodes modified with poly(vinylidene fluoride) (PVDF) or poly(4-vinylpyridine) (P4VP) for the detection of estradiol (E2), ascorbic acid (AA), serotonin (5-HT), and melatonin (Mel). The two polymers act by altering the redox potential and current response of each biomarker, thereby enhancing signal diversity and enabling peak separation. Using multi-output regression models on 450 single and mixture measurements, the array accurately predicts concentrations (R2 = 0.95) over a wide dynamic range spanning nanomolar to micromolar levels. Polymer-resolved analysis reveals that PVDF-modifications enhance E2 and Mel detection, while P4VP-modifications improve AA and 5-HT quantification, highlighting the benefit of complementary orthogonal sensing electrodes. This finding is further supported by feature attribution analysis, which shows that the machine learning model relies on polymer-specific electrochemical signatures, directly linking improved performance to distinct polymer-analyte interactions. Overall, these results demonstrate that combining polymer-modified orthogonal electrodes with machine learning enables accurate, multiplexed sensing in complex mixtures, advancing selective detection strategies for future sensor platforms.

bioengineering↗

bioRxiv2026

An efficient and stable ascorbate/O2-driven route for L-DOPA synthesis by heme-dependent tyrosine hydroxylase

L-DOPA is a key therapeutic agent for Parkinsons disease, with growing demand due to global population aging. Here we report that heme-dependent tyrosine hydroxylase (TyrH) can utilize an ascorbate/O2 system--as an alternative to H2O2--to synthesize L-DOPA with markedly enhanced operational stability. While exogenous H2O2 rapidly inactivates TyrH within minutes, sodium ascorbate (NaAsc) enables sustained catalysis for up to 24 h, surpassing the H2O2-driven yield after only 30 min. UV-vis spectroscopy confirms that H2O2 readily degrades the heme center, whereas the heme remains intact in the presence of NaAsc. QM/MM simulations reveal that in situ generated H2O2 leads to the active species of Compound I for tyrosine hydroxylation. Through systematic optimization, we establish efficient reaction conditions (40 {micro}M TyrH, 1 mM L-Tyr, 100 mM NaAsc, pH 8.5, 40 {degrees}C), achieving >95% conversion of L-Tyr to L-DOPA within 2 h. This work not only provides a robust and sustainable biocatalytic route for L-DOPA production but also highlights the broader applicability of the ascorbate/O2 pathway in heme-enzyme catalysis.

biochemistry↗

bioRxiv2026

SPIN: A Scalable Bioinformatics Pipeline for Screening Pathogenicity Related Host-Pathogen Protein INteractions Using AlphaFold3

Traditional experimental approaches have greatly advanced our understanding of protein-protein interactions (PPIs) that govern host susceptibility or resistance. Nonetheless, the molecular characterization of microbial effectors and their cognate host targets remains challenging in many economically important plant pathosystems. AlphaFold3 (AF3) has transformed structural biology by achieving near-experimental accuracy in protein structure and complex prediction. A bioinformatics pipeline SPIN (Screening Pathogenicity-Related Host/Pathogen Protein INteractions) was developed for large-scale prediction of interactions between the host and pathogen-secreted proteins. SPIN integrates three core modules: pathogen and host protein preprocessing using bioinformatics tools (SignalP6.0, OrthoFinder, CD-HIT), AF3-based interaction modeling, supported by automated input generation and output filtering. However, AF3 training bias toward mammalian proteins necessitated careful evaluation in plant systems. Benchmarking against experimentally validated plant PPIs revealed that derivative metrics emphasizing interfacial geometry and residue-level contact (ipSAE and pDockQ) provide superior discrimination compared to native global confidence measures (pTM and ipTM), particularly for proteins with intrinsically disordered regions. A multi-metric confidence scoring framework combining pLDDT, PAE, ipSAE, and pDockQ, improved prediction reliability by enhancing recall and reduced false positives through robust assessment of structural confidence and interface quality. For proof-of-concept, SPIN was applied to examine the molecular landscape underlying two economically important diseases of citrus (Citrus L.) caused by Candidatus Liberibacter asiaticus and Ca. Phytoplasma citri. Both pathogens are phloem-limited and cause distinct symptoms, citrus greening and witches broom, respectively. AF3-predicted interactome data revealed conserved host colonization strategies alongside disease-specific molecular mechanisms, demonstrating the utility of SPIN for dissecting and supporting mechanistic studies in plant-pathogen interactions.

genomics↗

bioRxiv2026

Oncogenic RAS-driven α2 integrin induction under nutrient stress promotes cancer cell motility

Cancer metabolism rewiring is one of the hallmarks of cancer, enabling cancer cell survival in a nutrient deprived microenvironment. Key to this is nutrient scavenging where cancer cells rely on extracellular proteins, including extracellular matrix (ECM) components, to sustain their proliferation. ECM uptake is mediated by 2{beta}1 integrin, however it is not clear how this process is controlled by nutrient availability. Here we demonstrated that amino acid starvation promoted ECM internalisation, by inducing the expression of 2 integrin. Mechanistically, starvation-driven RAS/MAPK pathway activation in cells harbouring oncogenic RAS mutations and mTOR inhibition increased 2 integrin, while the GCN2-depedent integrated stress response was not required. Functionally, elevated 2 integrin levels promoted cell adhesion and migration in nutrient starved cells. Finally, 2 integrin was found upregulated in pancreatic tumours and correlated with poor prognosis in pancreatic adenocarcinoma patients. Together, these data indicate that the nutrient- starved pancreatic cancer microenvironment synergises with KRAS mutation to drive pancreatic cancer aggressiveness.

cancer biology↗

bioRxiv2026

Epigenome-informed prioritization of bivalent chromatin SNPs enhances genomic prediction robustness: a proof-of-concept study in Pacific white shrimp (Litopenaeus vannamei)

BackgroundGenomic selection (GS) has revolutionized animal breeding, spanning livestock sectors such as pigs and cattle to aquatic species like fish and shrimp. However, its broader application across these industries is often constrained by high genotyping costs and reduced predictive reliability across divergent populations or generations. Developing cost-effective, biologically informed genotyping strategies to overcome these limitations remains a critical goal in animal agriculture. Epigenetic annotations, particularly histone modifications, provide direct functional insights into regulatory elements underlying complex trait variation and represent a promising but underexplored resource for marker prioritization. ResultsHere, using the Pacific white shrimp (Litopenaeus vannamei) as a model organism, we conducted a proof-of-concept study integrating resequencing and phenotypic data from 972 individuals. We generated high-resolution epigenomic maps by profiling four histone marks (H3K4me1, H3K4me3, H3K27me3, and H3K27ac) across multiple embryonic stages and adult muscle tissue using CUT&Tag. These functional annotations were then leveraged to prioritize single nucleotide polymorphism (SNP) subsets for genomic prediction. Among the tested strategies, SNPs located in the muscle-specific bivalent promoter/enhancer (E6) state--characterized by the co-occurrence of active and repressive marks--consistently maximized prediction accuracy under the BayesA model. Notably, even at a moderate density (15k), E6-derived SNPs achieved prediction accuracies exceeding those obtained using substantially larger genome-wide SNP sets. Most importantly, in a challenging cross-population validation using an independent strain, the E6-derived SNP subset significantly improved prediction accuracy by 47.6% (increasing from 0.21 {+/-} 0.05 to 0.31 {+/-} 0.04, p < 0.05) compared to random subsets at equivalent density. ConclusionsThese results demonstrate that epigenetic annotation-guided SNP prioritization provides a biologically informed and cost-effective strategy to enhance genomic prediction accuracy and stability. This framework is broadly transferable across species and offers a practical strategy for designing low-density genotyping panels that reduce costs while maintaining reliable selection outcomes in large-scale breeding programs.

genetics↗

bioRxiv2026

Tuning selectivity of electrochemical sensors with polymer coatings

Electrochemical sensors are promising for health monitoring due to their high repeatability and sensitivity, particularly when nanostructured. Yet, their translation into real applications is hindered by limited selectivity in the absence of specific binding receptors: many biomarkers exhibit similar oxidation potentials, producing overlapping voltammetric signals that impede molecular discrimination. Here, we demonstrate that the oxidation potential of several small molecule biomarkers can be controlled through polymeric coatings, specifically poly(4-vinylpyridine), deposited onto glassy carbon electrodes. The polymer coating alters diffusion and adsorption characteristics, which ultimately lead to oxidation potential shifts of ascorbic acid and serotonin, enabling their separation of otherwise overlapping signals. These findings are supported by Chronocoulometry and Fourier-transform infrared spectroscopy analysis that reveal changes in diffusion coefficient, adsorbed charge, and hydrogen bonding that are likely responsible for the altered sensor performance. These findings can be expanded to further polymers and biomarkers, including estradiol and melatonin. Finally, we demonstrate that the same selectivity trends persist on nanostructured, stretchable carbon-flower electrodes, where the high surface area further enhances sensitivity. Collectively, these findings reveal polymer-controlled peak-potential tuning as a powerful and broadly applicable route toward highly selective electrochemical sensors, enabling molecular discrimination in complex mixtures and opening new avenues for sensor-array-based detection.

bioengineering↗

bioRxiv2025

A Single-cell Spatiotemporal Manifold of Tissue Morphology and Dynamics

Complex tissues are now characterizable at single-cell resolution, but the spatial logic underlying tissue organization remains challenging to access without effective single-cell spatial descriptors. We present a learned single-cell manifold of cell positions over time, with which one can measure tissue morphology and trace dynamics. Learning co-processes pairs of cell point clouds sampled along time using a Transformer encoder with inter-sample attention, a strategy that promotes efficient joint spatiotemporal learning. The manifold shows desirable properties of a general descriptor, e.g. interpretable cell type clusters, preserved local distances, and a pseudo-time axis, and enables common but challenging spatial reasoning tasks such as annotation of anatomical landmarks at cellular resolution and detection of subtle, transient phenotypes in large screens. Our study demonstrates a widely applicable cell-based learning strategy and representation for studying tissue biology.

developmental biology↗

bioRxiv2025

Sensitive Hormone and Neurotransmitter Detection with Carbon Flower Electrodes

Carbon-based electrochemical sensors have attracted substantial attention for continuous health monitoring due to their high surface area, wide potential window, capability for repeated measurements, and compatibility with soft wearable electronics. However, they face challenges when detecting target biomarker concentrations in the low nanomolar range (sensitivity) and differentiating them in a mixture (selectivity), limiting their applicability in real scenarios. Herein, we present sensitive and selective carbon flower sensors, fabricated via a facile and patternable spray-coating process on soft, stretchable substrates. The carbon flowers--obtained through the synthesis of polyacrylonitrile and subsequent heat treatments--exhibit unique hierarchical morphologies, high surface area, and excellent conductivity, ideal for mass transport and electrochemical detection. We demonstrate the detection of estradiol, serotonin, melatonin, dopamine, uric acid, and ascorbic acid with detection limits as low as sub-nanomolar. The carbon flower sensors exhibit good repeatability across 100 cycles and over several weeks, robustness to pH and salt variations, and excellent performance in complex bio-fluids such as artificial saliva. In mixtures containing up to four analytes, they differentiate individual molecules, demonstrating the selectivity of the carbon flowers. This combination of sensitivity, selectivity, and mechanical compatibility makes carbon flower sensors well-suited for biomolecular sensing in soft, skin-conformable wearable electronic patches.

bioengineering↗