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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Profiling and modulating astrocyte borders at injected biomaterials in mice

Astrocyte border formation is a conserved neuroprotective response to neural tissue disruption, yet astrocyte border states at implanted biomaterials remain less well characterized than injury responses. Here, we developed the Astrocyte Border Characterization (ABC) Tool, which leverages a shear-thinning, injectable biomaterial to locally deliver astrocyte-specific RiboTag AAVs and small molecule regulators in the mouse striatum, enabling molecular profiling and phenotypic modulation of astrocyte border (AB) cells. Spatially precise delivery of AAV using the ABC Tool yielded enhanced specificity and robust RiboTag expression in AB cells from 7-70 days post injection. Temporal transcriptomic profiling of AB cells revealed predominantly acute, transient changes in genes governing dedifferentiation, proliferation, metabolic reprogramming, and inflammation regulation. Persistent changes accounted for only 14% of regulated genes but involved critical gain of functions in immune regulation and host defense that mirrored astrocyte border responses at chronic CNS injuries. Local delivery of indiscriminate or astrocyte-selective ablation molecules delayed, rather than prevented, border formation, ultimately yielding thicker astrocytes borders with increased inflammation and fibrosis at the biomaterial-tissue interface. Conversely, local delivery of {beta}-hydroxybutyrate (BHB) from the ABC Tool altered key aspects of the transcriptional reprogramming to attenuate chronic astrocyte reactivity and prevent biomaterial contraction without exacerbating inflammation or fibrosis. Our findings establish the ABC Tool as a bioassay for studying and manipulating astrocyte borders at implanted biomaterials and identify focal metabolic regulation as a strategy to modulate AB cell phenotypes and enhance the CNS biocompatibility of biomaterials.

neuroscience↗

bioRxiv2026

Data-driven spectroscopic dictionaries and detector-calibrated inference for photon-limited Raman hyperspectral imaging of living cells

Label-free Raman imaging of living cells is photon limited: at exposures compatible with cellular dynamics, single-pixel spectra carry about one count per channel on a dominant smooth background. We present an unmixing framework in which the decoder of a physics-constrained autoencoder is restricted to a data-driven spectroscopic dictionary: band centers,widths, and pseudo-Voigt shapes are measured from the dataset and fixed, and the network learns only nonnegative band amplitudes, a smooth B-spline background, and a per-pixel gain.First, on slit-scanning images of HeLa cells (532 nm) the dictionary yields spike-free component spectra that read as band tables, including a resonance-enhanced cytochrome-c-associated component matching literature spectra, and the most stable decomposition against the component number. Second, the dictionary and initialization calibrated at 1 s exposure perline transfer to 100 ms per line (12 s sweeps): cytochrome-c spectral identity survives a single sweep (correlation 0.92) while its map remains photon limited; the dictionary provides spectral physicality, and the transferred initialization prevents a structural collapse that global map correlations miss; in a measurement-derived phantom the dictionary estimator holds thecytochrome-c spectrum to 17-19{degrees} spectral angle at 100 ms, where classical factorizations and free decoders lose it (55-64{degrees}). Estimation on the count-equivalent detector output uses a calibrated shifted-Poisson quasi-likelihood. Third, evaluation must be time matched:correlation against a separately acquired reference saturates through slow specimen drift and acquisition mismatch rather than photon noise, and the self-consistency of learned denoisers is inflated by shared bias; time-matched self-consistency and independent cross-checks areproposed.

cell biology↗

bioRxiv2026

Zebrafish larval nitrogen excretion is flexible and resilient to loss of rhesus glycoproteins

Nitrogenous waste excretion is essential for all developmental stages of fish. Embryonic fish excrete urea, transitioning to cutaneous and later branchial ammonia excretion. In zebrafish, ammonia excretion involves rhesus glycoproteins Rhbg and Rhcgb in keratinocytes and ionocytes, but the developmental moment they appear in the gill remains unclear. Potential redundancy between Rhbg and Rhcgb in ammonia excretion is also not fully investigated, nor is the difference in response to low pH. We hypothesized that rhesus glycoproteins are partially redundant, and that they differ in their response to low pH as ammonia excretion enables ionocytes to exchange Na+ and H+ (Rh-NHE-metabolon). We predicted that a loss of rhbg or rhcgb induces compensatory responses. We characterized the transition from urea to branchial ammonia excretion from 0 to 8 days-post fertilization (dpf) and the response to pH 5.0 on the expression and localization of rhesus glycoproteins in control zebrafish and rhbg or rhcgb-crispants. Effects of high external ammonia (HEA, 500 M NH4Cl) and 10 mM HEPES-buffering were further characterized in rhcgb-crispants. Rhag and Rhbg appeared in the gill at 5 dpf, while Rhcgb appeared at 6 dpf. A loss of rhbg or rhcgb did not impact baseline N-excretion, illustrating that zebrafish can maintain ammonia excretion without the full complement of rhesus glycoproteins. We observed no compensatory increase in rhesus glycoproteins, but expression of the transporter hippocampus-abundant transcript 1b increased. HEA-exposed rhcgb-crispants switched to urea as primary nitrogen waste. Together, these findings underline the plasticity of the larval in dealing with nitrogenous waste.

physiology↗

bioRxiv2026

Tendon-derived injectable bio-instructive gel augments tenogenic differentiation of iMSC-SCX cells and extracellular matrix remodeling

In the US, 33 million musculoskeletal injuries have been reported per year, with 50% involving tendons and ligaments in both athletic and aging populations. Tendon repair often results in the formation of biomechanically inferior scar tissue rather than functional regeneration. Local cell therapy is garnering significant interest in tendon repair because it provides a targeted means to repopulate defects with potent therapeutic cells. Here, we developed a porcine tendon-derived thermoresponsive extracellular matrix hydrogel (TG) as an injectable, bio-instructive carrier for scleraxis-overexpressing induced mesenchymal stem cell derived tenocytes (iTenocytes), with the goal of improving cell retention and overall transplantation success. TG was compositionally distinct from purified collagen (PC, from rat tail type 1 collagen) and exhibited favorable material properties for a minimally invasive percutaneous strategy, including thermoresponsive gelation, shear-thinning injectability, retention at the injection site, and controlled biodegradation. In vitro, TG supported three-dimensional cell residence, promoted cell interconnectivity and redistribution at the matrix interface, and increased collagen type I release from iTenocytes compared to those embedded in PC. Transcriptomic and proteomic analyses further showed that TG enhanced programs associated with tenogenic maturation, extracellular matrix assembly, focal adhesion, mechano-transduction, and remodeling. In a rat Achilles tendon partial defect model, both optical imaging and histological analyses demonstrated retention of iTenocytes at the injection site. Additionally, confocal imaging demonstrated retention of iTenocytes within the defect site for up to 10 days. Together, these findings identify TG as a biofunctional injectable carrier that supports the tenogenic characteristics of iTenocytes and enhances their local persistence after transplantation.

cell biology↗

bioRxiv2026

PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria

Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.

microbiology↗

bioRxiv2026

Rewiring of Integrin Signaling and Cell-cycle Deregulation Drive SMARCB1-Deficient Epithelioid Sarcoma

Epithelioid sarcoma (EPS) is an aggressive soft-tissue sarcoma characterized by loss of the chromatin-remodeling subunit SMARCB1. The oncogenic programs driving EPS remain poorly understood. Through CRISPR loss-of-function screens, we identified conserved dependencies on integrin signaling components and cyclin-dependent kinases (CDKs). Genetic disruption of integrin subunit alpha V (ITGAV)-centered signaling impaired epithelioid cluster formation and reduced MYC expression. SMARCB1 re-expression phenocopied these effects and revealed that SMARCB1 loss selectively represses context-dependent integrin subunits while preserving an ITGAV-centered pro-survival axis, associated with altered BAF complex occupancy. Analysis of EPS cell lines and primary tumors revealed frequent genetic or epigenetic inactivation of CDKN2A/p16, indicating that loss of cell-cycle control is a key cooperating event in EPS development and providing a mechanistic rationale for targeting CDK4/6. Together, these findings establish integrin-driven oncogenic signaling coupled with disruption of cell-cycle control as a central oncogenic program in EPS and identify actionable therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

bioRxiv2026

Ex vivo glioblastoma migration phenotypes define clinical recurrence and tumor heterogeneity

Glioblastoma's pronounced migratory capacity underlies its diffuse invasion, presenting a formidable barrier to successful treatment. Ex vivo characterization of glioblastoma cells isolated from freshly resected clinical samples under physiologically relevant conditions revealed two distinct migratory phenotypes, Fast Migrating (FM) and Slow Migrating (SM). These phenotypes reflect distinct mechanosensitivity profiles and are associated with pharmacological responses that support the motor clutch model of cell migration. Analysis of genes associated with these phenotypes revealed a transcriptomic signature that closely associated with in vitro cell migration, histological invasion in patient specimens, and clinical survival. Single-nucleus RNA sequencing revealed that FM and SM cells coexist within a single glioblastoma, with FM cells enriched at the periphery and SM cells localized to the tumor core. Collectively, our study demonstrates the utility of ex vivo glioblastoma characterization, allowing decoding of tumor heterogeneity and clinical prognostication as well as providing a framework for deconvoluting the complex cancer phenotype.

cancer biology↗

bioRxiv2026

VDAC1 regulates stress-associated matrix localization of DJ-1 to support mitochondrial homeostasis and neuronal survival

DJ-1 is a redox-sensitive protein implicated in early-onset Parkinson's disease, and its mitochondrial localization protects against oxidative stress, but the mechanisms regulating its submitochondrial targeting and functional impact on mitochondrial integrity remain poorly understood. We identify voltage-dependent anion channel 1 (VDAC1) as a regulator of the submitochondrial distribution of DJ-1 during stress. Endogenous DJ-1 interacted with VDAC1, and loss of VDAC1 reduced stress-induced DJ-1 accumulation within the mitochondrial matrix. VDAC1-deficient neurons exhibited mitochondrial fragmentation, impaired oxidative phosphorylation, reduced ATP levels, altered reactive oxygen species (ROS) responses, and increased sensitivity to MPP+;. Matrix-targeted, but not outer-membrane-targeted, DJ-1 rescued basal, ATP-linked, and maximal respiration, improved mitochondrial morphology, and enhanced neuronal survival. ATP synthase inhibition also rapidly increased mitochondrial DJ-1, suggesting bioenergetic stress promotes its mitochondrial accumulation. Our findings identify compartment-specific localization as a key determinant of DJ-1 function and establish VDAC1-dependent matrix targeting as a critical mechanism supporting mitochondrial integrity during stress.

neuroscience↗

bioRxiv2026

Unbiased and scalable reduction of diverse bacterial genomes

The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.

synthetic biology↗

bioRxiv2026

Sequential Molecular Interactions Shape Aβ42 Aggregation, Propagation, and Toxicity

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

biochemistry↗

bioRxiv2026

Timing of transient darkness shapes carbon-nitrogen metabolism and sugar signaling in sugarcane

Fluctuating light is common in field environments. Yet, the mechanisms by which C4 crops coordinate carbon and nitrogen metabolism during short-term carbon deprivation remain poorly understood. Here, we imposed transient darkness at different phases of the diel cycle to assess how the timing of light loss affects photosynthesis, carbohydrate turnover, amino acid dynamics, and sugar-sensing pathways in commercial sugarcane leaves. Early-day darkness significantly impaired photosynthetic induction and revealed a temporal disconnect between stomatal and metabolic limitations, whereas midday and late-day treatments caused temporary, time-specific disruptions in carbon assimilation. These shifts altered the balance between sucrose preservation and catabolic mobilization, leading to treatment-dependent changes in starch reserves and free amino acids. Core circadian components largely maintained their phase relationships, but their amplitudes varied across treatments, consistent with partial decoupling from carbon status. Darkness also reorganized energy signaling, with SnRK1 and DIN6 responses associated with greater declines in sucrose. Notably, trehalose-pathway transcripts showed marked changes in network connectivity, with ScTPSIIG consistently emerging as a highly connected candidate associated with photosynthetic performance, water-use traits, sugar sensing, and amino acid metabolism. Overall, these results indicate that the timing of carbon limitation and residual sucrose availability shape distinct metabolic responses, while trehalose metabolism provides a candidate regulatory layer coordinating carbon-nitrogen adjustment during the diel cycle, highlighting class II TPS proteins as targets for functional investigation of metabolic resilience in sugarcane.

plant biology↗

bioRxiv2026

TheCellVision.org repository: expansion with high-content cell imaging projects on eukaryotic intracellular organization and DUB biology

High-content cell imaging approaches enable the systematic characterization of cellular function through the acquisition of multimodal information from large cohorts of live single cells. Yet, due to their scale and complexity, data acquired via such approaches are often challenging to meaningfully share across laboratories and effectively use for independent studies. Since its inception, the main purpose of TheCellVision.org repository has been to fill this gap, providing the research community with access to large-scale, multimodal single-cell datasets, in a structured, intuitive, and user-friendly way. Here, we report on the third major update of TheCellVision.org, which involves the expansion of the repository with the addition of data from two single-cell phenomics projects; the Intracellular Organization Dynamics project, which quantitatively maps changes in the morphology of 21 major subcellular structures in live yeast cells elicited by the systematic inhibition of essential genes, and the DUB Biology project, which describes changes in the concentration and localization of the budding yeast proteome in mutants of key deubiquitination enzymes (DUBs). With these additions, the repository now hosts six complementary high-content imaging projects which collectively explore the dynamics of intracellular organization and the proteome during changes in cell state and in response to environmental and genetic perturbations.

cell biology↗

bioRxiv2026

From concentration to export: resource contrasts and bee traits shape pollinator spillover to crops

Floral plantings can either concentrate bees or export them to adjacent crops, yet the ecological conditions influencing these outcomes remain unclear. Here, we develop a mathematical model as proof of concept for our previous integrative hypothesis: concentrator and exporter outcomes can arise as alternative, context-dependent outcomes of the same underlying resource-selection process. Using bees as a model and focusing specifically on spillover from floral plantings to crops, we identified resource-specific thresholds separating concentration- and export-favoring conditions. Our model translates differences in relative patch attractiveness into context-dependent concentration and export outcomes and generates resource-specific, testable predictions about the conditions favoring pollinator movement into crops. In our simulations, the concentrator-exporter transition occurred at a lower flowering-intensity contrast than at pollen or nectar contrasts, which suggests that flowering intensity may provide an initial cue for bee movement, whereas nectar and pollen rewards refine or sustain bee responses once crops are perceived as attractive. Spillover thresholds differed among resource contrasts, whereas response steepness varied across bee-trait and community scenarios. Under the model's trait-sensitivity formulation, predicted spillover probability responded more strongly to flowering contrast for specialists than for generalists; colony size amplified this response, whereas bee richness dampened it. Together, these patterns show how flowering and resource contrasts interact with bee traits and community context to shape predicted spillover. Our results confirm that the concentrator and exporter hypotheses can be understood as context-dependent outcomes of the same ecological process rather than as mutually exclusive alternatives. Experimental tests of the predicted thresholds conducted in the field could reveal when and where floral plantings are most likely to promote bee spillover to crops, potentially supporting crop pollination.

ecology↗

bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗

bioRxiv2026

DIFFERENTIAL PHOTOSYNTHETIC RESPONSES TO GLUFOSINATE AMMONIUM IN TWO GRASS WEEDS: Lolium multiflorum AND Echinochloa crus-galli.

Background: Weed control is one of the main challenges in agriculture today, particularly due to the increasing occurrence of herbicide-resistant populations. Among the most problematic species are Lolium multiflorum (L.) and Echinochloa crus-galli (L.) Beauv., for which glyphosate-resistant populations have been reported. In this context, glufosinate ammonium has emerged as an alternative for their control; however, its efficacy may vary depending on species and photosynthetic metabolism. Objective: The objective of this study was to evaluate the differential sensitivity of ryegrass (C3) and barnyardgrass (C4) to ammonium glufosinate by analyzing physiological responses associated with leaf senescence and photosystem II activity. Methods: Visual injury, chlorophyll fluorescence, and ammonium accumulation were assessed. Results: Results revealed a differential response between species. Barnyardgrass exhibited earlier symptom onset and a greater reduction in the quantum yield of photosystem II ({Phi}PSII), whereas ryegrass showed a slower senescence process. These differences indicate a higher sensitivity of barnyardgrass to glufosinate ammonium, possibly associated with its C4 photosynthetic metabolism. Conclusions: It is concluded that the effectiveness of glufosinate ammonium depends on the type of photosynthetic metabolism and on the ability of each species to cope with herbicide-induced oxidative stress. This information contributes to optimizing glufosinate ammonium use and to the development of management strategies aimed at delaying the evolution of herbicide resistance.

plant biology↗

bioRxiv2026

Combined Image-Based Profiling and Biochemical Analysis of GCaMP Overexpression Effects on Mammalian Cells

Protein-based fluorescent sensors are a powerful addition to the biology toolbox for their ability to be stably expressed within living organisms, tissues, cells, and subcellular compartments, with the capacity to report on the presence of specific target molecules or other analytes. At the same time, sensor components will unavoidably present opportunities for unintended interaction with endogenous cellular machinery, potentially confounding both sensor function and cell health. Interactions with host components may not be readily predictable during the sensor design process, especially when simultaneously optimizing many other sensor parameters such as fluorescence response, dynamic range, and kinetics. Characterizing effects of sensor expression on cells is currently a laborious ad hoc process; new methods to characterize the cell expression effects of sensors and their variants could dramatically improve sensor design pipelines, laying the groundwork to recognize potentially problematic expression side effects earlier in the iterative design and testing workflow. Here, we take a dual high-content imaging-based and biochemical approach to examine sensor interactions with native cell biology, focusing on the widely used GCaMP calcium sensor. We identify a morphology-based signature of the cellular effects of high sensor expression in a neuroblastoma cell line. Subsequently, we identify biochemical interactions between GCaMP and a component of the mammalian cytoskeleton and track morphological features in sensor-expressing cells that lack these structural components. Our findings present an entry point for engineering new minimally cross-reactive sensor versions given a contextual biological understanding of sensor overexpression. We anticipate that as this and related workflows are incorporated into sensor engineering pipelines, bioorthogonality can be more systematically assessed and prioritized in diverse sensor scaffolds.

cell biology↗

bioRxiv2026

Evolutionary stabilisation of stressful metabolism via integrated biocomputing and essential-gene metabolic locking circuits

Synthetic genetic circuits enable microbial differentiation from growth to production, yet metabolic burden, imbalance and toxicity frequently drive strain degeneration. Yeast strains engineered to produce different terpene products exhibited divergent genetic responses to metabolic stresses, but commonly underwent progressive loss of induction of synthetic GAL regulatory circuits, either across the entire population or within subpopulations. Using di- and tri-input biocomputing circuits, the essential glutamine synthetase gene GLN1 was coupled to GAL induction, thereby enabling stabilisation and evolutionary adaptation of the synthetic genetic circuits and stressful heterologous terpene synthetic pathways. The integrated biocomputing and metabolic coupling circuit systems not only prevent strain degeneration but also enable interrogation of non-degenerative evolutionary shifts, providing a platform for metabolic engineering optimisation.

synthetic biology↗