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bioRxiv2026

DNA Sequence-Programmed Protein Coronas Determine Intracellular Fate and Proteostatic Stress of Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) show promise for optical biosensing, imaging, and drug delivery, but turning them into safe, precision nanomedicine tools requires understanding how nanotube surface chemistry dictates recognition and processing by cells. Like other nanomaterials, carbon nanotubes acquire a biomolecular corona on contact with biological fluids, and corona identity is increasingly recognized as central to sensor performance and drug delivery efficacy. However, whether corona identity also governs the intracellular fate of carbon nanotubes remains largely unknown. Here, we show that the single-stranded DNA wrapping of (6,5)-enriched single-walled carbon nanotubes reprograms their protein corona, intracellular trafficking, and macrophage response. By profiling (AT)15, (GT)15, and (CT)15 wrapped SWCNTs, we show that the wrapping sequence programs both the protein corona and the resulting proteostatic stress on macrophages. Photoluminescence imaging and confocal Raman measurements reported that (AT)15 is internalized the most yet leaves the proteome and nanotube structure largely undisturbed, whereas (CT)15, taken up the least, undergoes the most aggressive intracellular degradation and drives the highest oxidative and proteostatic stress. Corona proteomics indicated that all three tested nanotubes form coronas with distinct functional identities that are responsible for divergent intracellular routes. Time-resolved intracellular proteomics combined with functional assays resolved how the host cell reorganizes its biomolecular complexity over time, including oxidative outputs, aside from a sequence-independent core response involving particle engagement, phagosomal sorting, and lysosomal processing. These findings provide mechanistic insight into nanomaterial-cell interactions and the wrapping sequence as a tunable, nucleotide-level design handle for controlling the intracellular fate of carbon nanomaterials, with potential implications for safe and effective nanomedicine platforms.

bioengineering↗

bioRxiv2026

Increased substrate complexity drives re-diversification and functional reorganization in simplified methanogenic consortia

Anaerobic digestion is a sustainable process for methane production that relies on complex microbial networks. While simplified enriched consortia offer a promising strategy to improve process control, excessive simplification can disrupt key functions and microbial partnerships, reducing community resilience. In this study, we investigated whether simplified methanogenic communities could re-diversify and maintain methane production when exposed to more complex substrates, namely butyrate and glucose. We also evaluated the effect of vitamin and amino acid supplementation on sustaining key methanogens and beneficial microbial partners. Three methanogenic communities were monitored over three months for methane production and microbial diversity while receiving butyrate and/or glucose, with different vitamin or amino acid supplements. Exposure to more complex substrates successfully restored the diversity of acidogenic and acetogenic populations, even after prolonged feeding with simple substrates, highlighting both the resilience of the simplified communities and the ecological importance of low-abundance taxa. However, the transition reduced process stability and methane production, likely due to substrate overloading. The results further suggest that substrate complexification should be introduced stepwise, promoting acetogenesis before acidogenesis. This fundamental study brings new light on which factors must be considered in the long-term goal of designing tailored-made consortia for anaerobic digestion.

bioengineering↗

bioRxiv2026

De novo designed single-domain antibodies protect against lethal cobra venom neurotoxicity in vivo

Generative protein design can now rapidly produce de novo binders with high affinity and functional activity against a wide range of targets, including lethal snake venom toxins. However, so far most reported successes rely on new-to-nature scaffolds with limited therapeutic precedent. Single-domain antibodies (VHHs) offer a clinically validated alternative scaffold that can bind and neutralize long-chain -neurotoxins, which are some of the most lethal components in snake venoms. Here we compare three recently established de novo design models with VHH-design capabilities (Germinal, RFantibody, and BoltzGen) for their ability to generate VHHs against the neurotoxin -cobratoxin from the monocled cobra (Naja kaouthia). Using standardized model inputs and evaluation criteria based on AlphaFold3 interface confidence (ipTM) and RMSD self-consistency, we find that Germinal was the only method to generate designs passing stringent in silico criteria for experimental testing. We therefore performed a larger Germinal design campaign employing three different VHH frameworks and experimentally validated 46 designs in vitro. Of these, 42 expressed as soluble proteins and we identified four binding hits derived from two of the three tested frameworks. Of the four binders, two lead candidates were further characterized and demonstrated high affinity (KDs of 4.1 nM and 10.8 nM), monomeric behavior and low polyreactivity, indicating favorable biophysical and developability properties, as well as functional toxin neutralization in vitro. To assess their therapeutic potential we investigated their ability to protect against -cobratoxin toxicity in vivo. Both candidates fully protected mice after -cobratoxin challenge, with 100% survival compared to a lethal control. One candidate also retained notable neutralization capacity against whole venom of Naja kaouthia with a survival of 56%, while the other protected 22% when tested in a rescue setting. Together, we demonstrate that de novo VHH design can generate high affinity single-domain antibodies with in vivo protection against lethal cobra venom neurotoxicity, and provide practical insights into method- and framework-dependent performance.

bioengineering↗

bioRxiv2026

Upcycling banana peduncle fibers into mycelium-based composites for sustainable packaging and thermal insulation

The growing concerns due to plastic pollution in India have intensified the search for sustainable materials. Mycelium-based composites (MBCs) have emerged as bio-based alternatives for packaging and thermal insulation applications. India, the worlds largest producer of bananas, generates significant quantities of banana biomass (~200 tons per hectare per year), much of which remains underutilized. The banana peduncle, the stalk that supports the fruit bunch, is one such underutilized biomass. In this study, banana peduncle fibers were used as the main substrate with Pleurotus ostreatus for the fabrication of MBCs. Banana peduncle fibers were mixed with wood shavings (10-50 wt%) to enhance the dimensional stability and structural integrity of the composites. The properties of developed MBCs such as density, shrinkage, moisture absorption, water absorption, morphology, compressive properties, and thermal conductivity were studied. The 90% banana peduncle fibers-10% wood shavings formulation showed the highest radial mycelial growth rate (7 mm/day). MBCs consisting of 100% banana peduncle fibers had volumetric shrinkage of 36%, while the incorporation of 30-50% wood shavings reduced shrinkage by approximately 17%. Among the formulations, MBCs containing 30% wood shavings had the highest compressive strength (4.82 MPa) and compressive modulus (1.78 MPa), whereas MBCs containing 50% wood shavings had the highest recovery (59.6%). In contrast, MBCs fabricated using 100% banana peduncle fibers had the lowest thermal conductivity (0.04 W/m. K). These results demonstrate that banana peduncle fibers are a promising lignocellulosic substrate for the development of MBCs for sustainable packaging and thermal insulation.

bioengineering↗

bioRxiv2026

Simple Feedback for Complex Movement: Capturing Whole-Limb Reorganization during Single-IMU Gait Retraining

Clinical gait retraining typically relies on multi-sensor arrays and high-dimensional feedback displays, imposing setup and interpretation burdens that limit routine clinical deployment. We developed a single-IMU visual biofeedback system that delivers real-time feedback of Lower Limb Trajectory Error (LLTE), a composite kinematic error metric integrating knee position and shank angle across the stance phase. Twenty able-bodied adults walked on a treadmill under two visual biofeedback targets (flexed-knee, extended-knee) while receiving either corrected (n=10) or uncorrected (n=8) feedback, where the correction accounted for limb orientation at initial contact. LLTE and stance-phase knee kinematics adapted consistently under the flexed-knee target for both feedback groups, with feedback formulation moderating the temporal trajectory of change. Adaptation toward the extended-knee target was limited, likely because participants were already operating near terminal knee extension and because the scalar error metric provided limited directional information for correction. Ankle range of motion (ROM) changed significantly across the stance phase under both target conditions, while hip ROM did not. Multiscale multivariate sample entropy (MSMVSE) increased monotonically with time scale across all conditions, with no statistically distinguishable difference between corrected and uncorrected feedback. These results suggest that single-IMU LLTE biofeedback can modify gait mechanics and that adaptation was expressed across multiple lower-limb segments rather than through changes at a single joint.

bioengineering↗

bioRxiv2026

HRV-GUI: A MATLAB Graphical User Interface for Heart Rate Variability Analysis and Validation Using Human, Rodent, and Clinical Diabetic Gastroparesis Data

Background and Objective: Heart rate variability (HRV) analysis provides a non-invasive method for quantifying autonomic modulation from electrocardiographic recordings. However, practical HRV analysis often depends on fragmented workflows, limited signal-quality review, and software tools optimized for either human or preclinical recordings, but not both. This study developed and evaluated HRV-GUI, a MATLAB-based graphical interface for electrocardiogram (ECG)-derived HRV analysis in translational biomedical research. Methods: The HRV-GUI integrates electrocardiographic and RR interval loading, human and rat analysis modes, preprocessing, segment selection, automated R-peak detection, manual peak correction, RR interval generation, multi-domain HRV computation, diagnostic visualization, result export, and session saving/loading. The software was evaluated using deterministic synthetic RR interval datasets, baseline recordings from healthy human controls and healthy rats, and a clinical use-case comparison between healthy controls and patients with diabetic gastroparesis. Results: The HRV-GUI produced expected outputs in synthetic RR validation tests, including constant RR sequences, alternating RR sequences, outlier-containing RR sequences, and low-frequency- or high-frequency-dominant sinusoidal RR modulation. The software generated physiologically plausible HRV profiles in both human and rat recordings. In the clinical use-case analysis, patients with diabetic gastroparesis showed higher heart rate and sympathetic index, together with lower respiratory sinus arrhythmia, absolute low- and high-frequency spectral power, standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), percentage of successive RR intervals differing by more than 50 ms (pNN50), Poincare short-term variability (SD1), and Poincare long-term variability (SD2) compared with healthy controls. Conclusions: HRV-GUI provides an integrated biomedical software workflow for ECG-derived HRV analysis. The validation results support its use for controlled RR testing, human and rodent ECG recordings, and clinical autonomic assessment in diabetic gastroparesis.

bioengineering↗

bioRxiv2026

Compression Sequencing enables ultra-sensitive and scalable scRNA-seq

Current sequencing methods are inefficient and bottlenecked by repeated sampling of highly abundant molecules, which dominate sequencing reads, limit assay throughput and sensitivity for rare targets. For example, single-cell RNA sequencing (scRNA-seq) can profile up to millions of cells, but remains severely constrained by sequencing cost, resulting in shallow gene coverage and high dropout rate. Here we report an information science-inspired method, Compression Sequencing, that tackles this fundamental inefficiency and enables highly improved (>100x) sequencing power. Our method works by performing an accurate and unbiased logarithmic transform on molecular abundances over a wide (5 logs) dynamic range, thus suppressing high-abundance targets and enriching rare ones, while maintaining quantitative accuracy. Applied to scRNA-seq libraries, our method allows ultra-sensitive detection of low-abundance transcripts (2-5x more UMIs), ultra-low sequencing cost (200x reduction), preserves accurate cell types and differential expression analysis over a 500-2,000 gene panel. In AML clinical samples, Compression Sequencing reproduces clinical diagnosis and additionally allows transcriptomic profiling at affordable cost (est. $10 per sample). Our approach thus enables ultra-sensitive and scalable single-cell analysis for large-scale functional genomics studies, drug discovery screens, AI cell model training, as well as affordable single-cell disease diagnostics.

bioengineering↗

bioRxiv2026

Readout-dependent time-of-flight weighting of absorption and flow in interferometric diffuse optics

Joint optical measurement of hemoglobin and blood-flow dynamics could provide a compact route to richer functional monitoring of tissue physiology. However, hybrid diffuse-optical systems commonly optimize photon time-of-flight (TOF) for individual readouts, even when absorption and flow are recovered from the same acquisition. Here, we asked whether a single TOF window can optimally preserve both signals. Dual-wavelength TOF-iSCOS recovered absorption, hemoglobin and relative blood-flow index (BFI) from the same interferometrically sensed optical field, while Early, Middle and Late photon windows provided progressively greater weighting of longer and deeper photon paths. During forearm cuff occlusion in 11 adults, absorption and flow showed markedly different TOF dependence: Late-gate BFI responses were retained, whereas absorption decreased to a median Late/Early ratio of 0.18. The resulting readout-by-TOF interaction was positive in all 11 participants (median 2.61 octaves, 95% bootstrap CI 2.44-3.22; exact sign-test P = 9.8 x 10-4). Readout-specific gate selection increased held-out response retention by 24.8% (95% CI 8.8-42.5%) relative to a single common gate. We then tested the approach during prefrontal working-memory activation in seven participants using a higher-load 2-back task relative to a low-load 0-back control. Late-gate BFI increased in all seven participants (+5.07%, 95% CI 1.46-8.68%; P = 0.014) and remained positive after adjustment using an Early-TOF signal, whereas hemoglobin responses were less consistent. Layered Monte Carlo modeling showed that the same detected photon histories acquire different absorption and dynamic-scattering sensitivity across TOF, linking the experimental effects to anatomy- and geometry-conditioned tissue weighting. Within the present geometry and photon budget, the central result is therefore not that one readout is intrinsically superior, but that absorption and flow recovered from the same optical field retain physiological information differently across photon time-of-flight.

bioengineering↗

bioRxiv2026

A Nanoheater-Integrated Fluorescence Lifetime Thermometer for Investigating Subcellular Heat Shock Factor 1 Responses

Subcellular thermal engineering provides a powerful approach for investigating and manipulating biological processes. However, existing subcellular heating platforms capable of combining spatially confined heating, quantitative thermometry and simultaneous imaging of cellular responses remain limited. We developed a quantitative nanoheater-thermometer (qNanoHT), a polymeric nanoparticle integrating a temperature-sensitive fluorescent, dye and a photothermal dye. qNanoHT determines local temperature from fluorescence lifetime using fluorescence lifetime imaging microscopy (FLIM), thereby reducing susceptibility to photobleaching, focal drift and variations in probe concentration compared with intensity-based methods. The platform enabled real-time measurement at a subcellular heat spot while the dynamics of heat shock factor 1 (HSF1) were monitored in living cells. Heating at a single intracellular site was sufficient to induce HSF1 foci. Foci induced by mild heating at approximately 38 {degrees}C dissolved after heating ceased, whereas those induced by stronger heating at approximately 41 {degrees}C persisted and were associated with caspase-3/7 activation and apoptosis. Notably, qNanoHT-mediated subcellular heating induced HSF1 foci at a lower measured temperature than uniform whole-cell heating approximately 38 {degrees}C versus 39 {degrees}C indicating that the spatial extent of heating influences the HSF1 activation threshold. qNanoHT therefore provides a quantitative platform for relating local intracellular temperature to cellular stress responses and subsequent cell fate.

bioengineering↗

bioRxiv2026

Feasibility of adopting water displacement and 3D scanning methods to quantify lung volume reduction in ex vivo models

Lung volume reduction is a treatment for chronic obstructive pulmonary disease (COPD) patients with severe emphysema. Currently, in vivo animal models are used as a first step to quantify the efficiency of novel lung volume reduction treatments. By quantifying lung volume reduction in ex vivo samples, animal testing may be reduced. This study proposes two new methods for quantifying lung volume reduction in ex vivo tissue: water displacement and three-dimensional (3D) scanning. Porcine lung lobes were inflated and treated with cyanoacrylate glue to simulate lung volume reduction. Volume changes were measured using a custom water displacement setup and in a ventilated setting using a handheld Artec Space Spider 3D surface scanner. Both techniques detected post-treatment volume reductions. Water displacement offers a simple, cost-effective approach for isolated lung lobes, while 3D scanning enables volumetric analysis of intact lungs under ventilated conditions. These methods provide a reliable intermediate step between in vitro and in vivo testing, reducing animal use and improving preclinical assessment of novel lung volume altering interventions.

bioengineering↗

bioRxiv2026

Modelling and measuring effects of shear stress in extrusion bioprinting of endothelial- epithelial cell co-cultures

Extrusion-based bioprinting enables the development of tissue-like constructs; however, the impact of printing-associated shear stress on cell viability and function remains a critical consideration. To address this, we developed a comprehensive workflow combining rheological characterization, computational fluid dynamics (CFD) modelling, and experimental validation to predict and assess shear stress effects during bioprinting. The rheological properties of gelatin methacryloyl (GelMA) at 5 % (w/v, 20 {degrees}C) and 10 % (30 {degrees}C) concentrations were modelled, comparing various non-Newtonian regression models. CFD simulations were validated using micro-particle image velocimetry, showing agreement between predicted and measured velocities. The impact of bioprinting-associated shear stress on cell viability was assessed using a co-culture of human umbilical vein endothelial cells and breast epithelial cells. Immediate post-printing analysis revealed increased apoptosis in GelMA 5 % (w/v, 20 {degrees}C), although 10 % (w/v) GelMA demonstrated higher shear stress levels compared to 5 % GelMA. After 1 day of culture in crosslinked hydrogels, apoptosis increased in extrusion pressure, demonstrating the impact of low levels of acute shear stress. This workflow provides a robust methodology for predicting acute shear stress impacts during bioprinting, laying the foundation for future optimization studies.

bioengineering↗

bioRxiv2026

Patterned alginate hydrogel spatially guides collagen fibrillogenesis, viscoelasticity and endothelial cell invasion

Angiogenesis following injury has been shown to be driven by fibrillar proteins of the extracellular matrix (ECM), such as collagen. However, the use of protein-based biomaterials presents some challenges, such as uncontrolled degradation and limited tuneability. We demonstrate how to create patterned interpenetrating networks (IPNs) based on covalently crosslinked alginate and physically crosslinked collagen that provide suitable mechanical properties to support migration of endothelial cells (ECs) in a spatially controlled manner. Low molecular weight alginate is functionalized with norbornene (N) or tetrazine (T), which enables two independent covalent crosslinking methods: UV-mediated and degradable crosslinks with matrix metalloproteinase (MMP) sensitive peptides (Deg) and slower spontaneous N:T non-degradable crosslinks (noDeg). Using photolithography, patterns in degradation, collagen fibrillogenesis, microarchitecture and matrix viscoelasticity are created. The potential of such 3D patterned alginate-collagen (Alg-Col) IPNs to spatially guide EC invasion and proliferation was tested in a microfluidics platform resembling an early healing setting. Only regions combining collagen fibrillogenesis, alginate degradability and viscoelasticity demonstrated EC cell invasion similar to the ones found in vivo following injury. The 3D patterned Alg-Col IPNs are compatible with microfluidics, offer an strategy to widen the applications of protein-based hydrogels and present a versatile platform for tissue engineering and disease modeling.

bioengineering↗

bioRxiv2026

Operando Failure Diagnosis and Performance Dynamics in Microbial Fuel Cells Treating Mine Waste

Bench-scale microbial fuel cells (MFCs) treating mining wastewater frequently exhibit operational variability and uncharacterized degradation that obscure true biocatalytic performance. To decouple genuine biological treatment effects from mechanical failures, this paper presents an integrated diagnostic framework validated on two bench-scale systems treating heavy-metal-rich gold mine tailings. The first system evaluates Micractinium inermum algal bio-augmentation (System 1), while the second compares Psychrobacter alimentarius- and Trichococcus patagoniensis-dominated anodic consortia (System 2). To overcome single-reactor constraints, the framework integrates paired time-series statistical modeling, an adaptive percentile-floor change-point detector, equivalent-circuit modeling, and baseline-corrected spectroscopy (XRD/FTIR). Applying the framework to these systems uncovers previously masked dynamics: statistical analysis demonstrates that algal biocatalysis provides no voltage advantage under stable operation (+0.17%) but increases output by 27.54% under diurnal perturbation, while periodicity analysis links these diurnal shifts to the chamber photoperiod. Furthermore, heavy-metal remediation (up to 97.7%) is governed by system-level physicochemical mechanisms rather than algal-specific processes. The change-point detector successfully isolates distinct failure modes, distinguishing a recoverable excursion from terminal structural collapse. Finally, equivalent-circuit modeling reveals that the superior power density of Trichococcus consortia is driven by combined improvements in internal resistance and open-circuit voltage. Ultimately, pairing statistical controls with automated fault detection resolves operational ambiguity, offering a scalable baseline for health monitoring in bio-electrochemical wastewater treatment.

bioengineering↗

bioRxiv2026

Delayed Tagging of ED-A Fibronectin-Mimetic Peptide in an RGD-Decorated Synthetic Matrix Induces Fibroblast-to-Myofibroblast Transition

Synthetic hydrogels with bioactive ligands have been utilized to develop 3D models to gain mechanistic insight into how discrete extracellular matrix (ECM) cues direct cell fate. While the RGD motif is ubiquitously present in healthy and diseased tissues, the EDGIHEL (EDG) sequence is present only in the extra domain A-containing fibronectin (ED-A FN), which is transiently deposited in the provisional matrix in the wound bed. Here, we explore the potential of covalently tethered EDG in conjunction with RGD to promote fibroblast-to-myofibroblast transition (FMT). Normal human lung fibroblasts (NHLFs) were maintained in bioorthogonally constructed, hyaluronan-based hydrogel (BOHAGel) with tethered RGD ligands. When EDG was introduced on day 0 during cell encapsulation, cellular expression of Toll-like receptor 4 (TLR4) was upregulated, and a pro-inflammatory matrix remodeling response was observed, but myofibroblast differentiation was not detected. To mimic the transition from a healthy to an injured state, we leveraged the temporal tunability of BOHAGel by supplementing cell culture media with trans-cyclooctene (TCO)-tagged EDG after cells were primed in the RGD environment for 8 days. As the TCO species diffused through the hydrogel, EDG was instantaneously coupled to the network through immobilized tetrazine functionalities. Delayed introduction of profibrotic EDG motifs increased mRNA levels of the myofibroblast marker (ACTA2), ECM proteins (COL1A1, COL3A1, FN1), and transforming growth factor beta1 (TGFbeta1) downstream targets (VEGFA, CTGF), as well as matrix remodeling enzymes (MMP2, TIMP1). These changes were accompanied by the formation of alpha-SMA stress fibers, confirming complete FMT. Delayed EDG conjugation also enhanced and reinforced alpha1 integrin expression. Importantly, removing the RGD signal from the gel failed to induce myofibroblast differentiation. Collectively, our results suggest that FMT depends on ligand identities and the timing of their emergence in engineered matrices.

bioengineering↗

bioRxiv2026

Multimodal Protein Retrieval via Joint Representation Learning from Sequences and Cryo-EM Density Maps

Aligning protein sequences with cryo-EM density maps remains challenging due to limited paired data, structural heterogeneity, varying map resolutions, and the presence of multiple conformational states. In this work, we propose a multimodal representation learning framework that learns a shared latent space between protein sequences and cryo-EM density maps for cross-modal retrieval. Our approach combines pretrained protein sequence embeddings with a volumetric cryo-EM encoder trained using self-supervised representation learning and transfer learning. The resulting model enables bidirectional retrieval between sequences and density maps while learning biologically meaningful structural representations. Experimental results demonstrate strong retrieval performance across both sequence-to-map and map-to-sequence tasks, achieving median retrieval ranks of 2--3 within a database of 3,275 cryo-EM maps. The learned embedding space shows a clear separation between matched and unmatched sequence--map pairs and remains robust across varying cryo-EM resolutions. Additionally, the model generalizes across species, successfully retrieving conserved mouse protein structures using human sequence embeddings. Our findings demonstrate that joint latent-space learning provides a promising direction for connecting protein sequences with cryo-EM structural representations, with potential applications in structural retrieval, protein annotation, and multimodal biological representation learning.

bioengineering↗