Biotechnology and Bioengineering

Influenza A virus (IAV) defective interfering particles (DIPs) are regarded as promising broad-spectrum antivirals due to their high efficacy demonstrated in animal trials. Previously, an OP7 chimera DIP was generated that showed markedly enhanced antiviral efficacy compared to conventional IAV DIPs. In addition, a high-yield cell culture-based production process for OP7 chimera DIPs was established in perfusion mode. However, due to the technical complexity of this technology, we aimed to combine the advantages of perfusion in terms of yield with that of a simple and less complex batch process. In the present work, we established a fed-batch production process for OP7 chimera DIPs that allows a more straightforward implementation into existing production facilities. Using a feeding regime that was able to overcome the cell density effect, we achieved productive virus infection in stirred tank reactors until a cell density of 10E+6 cells/mL. Furthermore, we obtained a high virus titer of 3.8 log10(HAU/100 uL), corresponding to a 3.4-fold increase compared to a reference batch production. Moreover, the produced material demonstrated a significantly increased interfering bioactivity shown by an in vitro potency assay. Interestingly, this efficacy was similarly high as compared to OP7 chimera DIP material previously produced in perfusion mode, despite twofold lower virus titers. Finally, we demonstrate robustness of OP7 chimera DIP seed virus trains, which is significant step towards later pharmaceutical production. Further, corresponding GMP MDCK-PB2(sus) cell and OP7 chimera virus banks are now available for later biomanufacturing further use. In conclusion, the established fed-batch process offers a valid n additional option for pharmaceutical grade OP7 chimera DIP GMP manufacturing production under GMP conditions required for subsequent clinical testing and for later large-scale manufacturing.

Emerging efforts to engineer multicellular kidney tissues for drug discovery, disease modeling, and therapeutic repair require scalable generation of kidney organoids derived from human induced pluripotent stem cells (hiPSCs). Here, we report a method for generating kidney ureteric bud and collecting duct epithelial organoids in stirred tank bioreactors. We demonstrate that extracellular matrix microcarriers facilitate organoid expansion and are compatible with suspension bioreactor methods. The combination of cell-coated matrix microcarriers with stirred bioreactors promotes maturation marker expression compared to static controls comprising matrix-embedded organoids. Our method offers promise for generating epithelial organoids that serve as multicellular models and organ building blocks for scalable biofabrication.

Lack of robustness and high variability in cardiomyocyte (CM) differentiation from human induced pluripotent stem cells (hiPSCs) remain critical challenges for cardiac regenerative medicine and biological research. Guided by developmental principles of lineage specification, we investigated whether early WNT inhibition could expand the process window of CM differentiation and thereby improve robustness. To test this, we compared early WNT inhibition from days 1--5 with standard inhibition from days 3--5 following a 1-day CHIR99021 (CHIR) treatment for WNT activation. Focusing on CHIR concentration as a critical parameter, we found that early WNT inhibition substantially broadened the concentration window yielding [≥]80% differentiation efficiency across distinct hiPSC lines. Extending the analysis to a two-dimensional parameter space incorporating cell density revealed a markedly expanded [≥]80% process window. Temporal analysis of lineage-specific transcription factor expression showed that early WNT inhibition suppressed paraxial mesoderm (PM) specification, likely through MSGN1 suppression, in the range of high initial WNT activation that otherwise favored PM fate, thereby extending the process window. It also suppressed definitive endoderm (DE) specification within the expanded window, promoting a more favorable lateral plate mesoderm/cardiac fate. We further demonstrated substantially reduced variability under parameter perturbations and across wells and batches, accompanied by higher differentiation efficiency, without compromising CM phenotypes. These findings demonstrate early WNT inhibition as an effective strategy for expanding the process window of CM differentiation and improving robustness, while exemplifying the process engineering principle that a broader window can support more robust differentiation, with potential relevance to stem cell--based bioprocesses.

The human pancreas secretes digestive fluid that flows through an extensive ductal network before emptying into the duodenum. However, little is known about how hydrodynamic forces influence pancreatic function and development. We hypothesized that pancreatic cells respond to physiological levels of fluid shear stress. In this study, we developed a microfluidic system featuring reversible sealing between the device layer and the cell culture substrate, enabling (1) conventional open-well cell culture prior to fluid shear stress application and (2) selective patterning of proteins and cells for migration studies. Channel dimensions were chosen to support 24-hour perfusion using standard syringe pumps. Using this platform, we investigated effects of physiological shear stress levels representing basal and activated pancreatic function (2, 8 and 33 mPa) on human iPSC-derived pancreatic progenitor cells. Pancreatic progenitor cells exhibit graded sensitivity to shear stress at the transcriptomic level. Specifically, shear stress upregulated expression of cell cycle-related gene sets; endocrine-associated genes including NEUROG3; and Notch receptor ligands (DLL1, DLL3) in a magnitude-dependent manner. The exposure to unidirectional fluid flow also resulted in a reduction in YAP1 protein content, decreased WWTR1 gene expression, and downregulation of YAP/TAZ target genes. Furthermore, barrier-based migration assays under shear stress revealed that cell patches expanded their boundaries both along and against the flow, with greater distance in the direction of flow. Our system and findings may help lay the foundation for understanding biophysical regulation of developmental processes in a physiologically important, yet hard-to-access, epithelial model system.

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

Directed evolution enables the discovery of protein mutants with improved fitness through iterative rounds of selection and has been widely applied to adeno-associated virus (AAV) capsid engineering. Machine learning (ML) can augment this process by prioritising mutants for experimental validation, but whether its benefits outweigh the added cost of ML-designed library construction remains unclear. Here, we address this question by considering improvements in manufacturing efficiency of AAV capsids in the context of exosomal encapsulation, which is a technique for improving AAV immunogenicity. We generated a directed evolution dataset comprising 53,974 mutants across three rounds of selection and an independent assessment dataset of 472 ML-designed mutants with detailed profiling. Directed evolution alone yielded a 3-fold improvement, while ML-assisted directed evolution yielded a 41-fold improvement in manufacturing efficiency, demonstrating that ML-assisted directed evolution outperforms directed evolution alone. We show that data quality - specifically the number of selection rounds and the statistical power of mutant counts - has a greater impact on model performance than model architecture. Finally, we provide practical guidance on experimental design and implementation of ML tuning strategies that can augment model performance in protein engineering applications.

Synthetic endosymbiosis, the deliberate introduction of bacteria into eukaryotic host cells, provides a tractable model for how endosymbiotic relationships become established, and a route to cells with new metabolic capabilities that never evolved naturally in that lineage. Current methods for introducing bacteria into mammalian cells rely on microinjection, engineered invasion, listeriolysin-mediated phagosomal escape, or naturally permissive hosts. These methods are invasive, low-throughput, or costly, and most require the prospective endosymbiont to be genetically tractable, which excludes endosymbiotic bacteria which are of great interest as potential organelle precursors. Here we describe a protocol based on polyethylene glycol (PEG), long used to fuse mammalian cells, that delivers Escherichia coli into HeLa cells without genetic modification of either partner or the use of specialised equipment. We optimised the governing parameters in two stages: first, bacterial density, gentamicin concentration, and PEG molecular weight and concentration were optimised; then, medium composition and the timing of fusion and recovery. Across four parameters screened, 10% PEG-3350 applied for 4 min to HeLa cells 12 h after seeding, followed by 4 h in gentamicin-containing medium, gave the most consistent delivery. Under these conditions, confocal microscopy and z-stack reconstruction detected mCherry-tagged E. coli inside HeLa cells. Applying the same protocol to Candidatus Kinetoplastibacterium crithidii, a naturally occurring {beta}-proteobacterial endosymbiont of the trypanosomatid Angomonas deanei, we observed the endosymbiont inside the HeLa cells. This protocol offers an accessible entry point for constructing artificial endosymbioses.

Our prior works have demonstrated the therapeutic potential extracellular vesicles (EVs) derived from batch cultures of brain endothelial cells (BECs) owing to their ability to transfer mitochondria to injured recipient cells. However, conventional batch/flask cultures limit scalable and reproducible production of EVs. Here, we evaluated a CELLine AD1000 bioreactor as a platform for sustained production of EVs from mouse b.End3 BECs and compared bioreactor-derived (BR) EVs with EVs obtained conventional batch cultures (BT). Large EVs (lEVs) and small (sEVs) were isolated using a sequential centrifugation protocol and were characterized using dynamic light scattering, nanoparticle tracking analysis, microBCA assay, western blotting, proteomics analysis, and their ability to rescue bioenergetics in oxygen-glucose deprived (OGD) BECs. Bioreactor cultures maintained high cell viabilities (~90%) for up to eight weeks and produced approximately three- to four-fold greater EV protein yields than batch cultures. BR EVs showed reproducible particle characteristics consistent with EV subtypes derived from BT cultures. CD63, CD9 and Arf6 were detected in EV fractions, whereas the mitochondrial outer-membrane protein TOMM20 was enriched in lEVs. Proteomic analyses revealed that bioreactor-derived lEVs retained the mitochondrial cargo signature observed in batch-derived lEVs. Both bioreactor and batch EVs restored relative ATP levels in ODG-exposed BECs. These findings suggest that bioreactor BEC cultures are a scalable approach for generating functionally active BEC-EVs for downstream characterization and therapeutic development.

Fermentation is a process through which microorganisms are used to transform basic molecules into high value substances in various applications such as biopharma, food production, and biofuel generation. In such processes, monitoring the health of microbial cells is essential, as it is a key factor in the final product yield. Traditional methods of assessing the health of fermenter populations have involved manually extracting samples from the fermenter for benchtop testing, which lacked the accuracy and efficacy of real-time continuous measurements. This has opened the door for inline sensors to be integrated into fermenters for continuous measurement; including microfluidic based Electrochemical Impedance Spectroscopy (EIS). EIS utilizes a range of frequencies to gain insight into the characteristics of microbial populations, such as cell size and membrane capacitance. EIS cytometry, in particular, has emerged as a relevant tool, as it enables single-cell level detection in real-time. However, current EIS cytometry devices cannot be deployed directly within fermenters due to the high cell density conditions. This has limited EIS analysis in fermenters to bulk measurements averaged across a population. Here, we present a microfluidic chip that uses passive cell-flux-partitioning geometry to direct cells from high-density culture through a single-cell-constrained flow path containing integrated impedance-sensing electrodes. The proposed chip combines geometric channel design and embedded electrodes to enable continuous impedance measurements from cell-containing flow while operating at input cell densities on the scale of 10^9 CFU/mL. This work demonstrates a first step toward integrating single-cell-constrained microfluidic handling with impedance sensing for inline monitoring in industrial biomanufacturing.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

Polyhydroxyalkanoates (PHAs) are microbial polyesters that represent a sustainable alternative to fossil fuel-based plastics. These biodegradable polymers have properties similar to conventional plastics and can be produced from waste materials. The key enzyme in PHA synthesis is PHA synthase (PhaC) and despite its importance, only a few PhaCs from extremophilic microorganisms have been characterised in detail. Here we characterised PHA synthase enzyme from the thermophilic bacterium Caldimonas thermodepolymerans DSM15344, an attractive candidate for PHA production from lignocellulosic residues due to its unique properties such as utilisation of xylose-rich substrates and high PHA yields on this pentose sugar. PhaC was successfully expressed in the surrogate host E. coli BL21 (DE3) GOLD, purified to homogeneity and its enzymatic activity was confirmed in vitro. The optimal pH range and temperature were determined as 6.3-6.9 and 50 {degrees}C, respectively. The melting temperature of PhaC was detected at 57.83 {+/-} 0.04 {degrees}C and the enzyme storage stability was assessed. The tertiary structure, including the enzyme active site, was predicted using available bioinformatic tools and the predicted catalytic residues were supported by site-directed mutagenesis. This study represents the first biochemical characterisation of PHA synthase from C. thermodepolymerans, expanding the limited set of characterised thermophilic PhaC enzymes.

Functional protein micro/nanofibers integrate high specific surface areas with bioactive architectures but remain hampered low production throughput and severe processing instability. Focused rotary jet spinning (FRJS) shows promising to break these throughput constraints while enabling direct, conformal deposition onto complex, irregular substrates. However, navigating FRJS's narrow processing windows in proteins remains failure-prone without closed-loop experimental guidance. Here, we report FiberPro 1.0, a large language model driven multi agent framework that unites high-throughput spinning with real-time experimental feedback for autonomous design, execution, and optimization. Across three protein systems, FiberPro 1.0 converged on spinnable formulations within an average of two iterations. To demonstrate high-throughput conformal deposition in a translational setting, FiberPro designed a zein-based active packaging system applied directly onto diverse food matrices. The resulting conformal coating combined potent antibacterial activity with real-time freshness monitoring, markedly suppressing Escherichia coli and extending shelf life. This work connects autonomous AI reasoning with high-throughput processing, establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers.

Lymphatic endothelial cell (LEC) function is strongly tied to transitions between button- and zipper-like junctions that control fluid flow into vessels. Published methods quantifying junction morphology are limited to single cell analysis or use few quantitative metrics to describe junctions across LEC monolayers. Therefore, a novel 3-step image analysis pipeline was developed to identify new metrics that quantify differences in button- and zipper-like junctions in LECs. The pipeline used Cellpose, CellProfiler, and Fiji to analyze VE-cadherin expression across lymphatic vessels within a 3D microfluidic chip and quantify VE-cadherin radial intensity, number of objects, object size, branchpoints, end-points, and junction thickness. Tumor necrosis factor (TNF)- exposure produced but-ton-like junctions, and unexposed cells were zippered. Radial intensity was heightened at the cell membrane in zipper-like junctions while perinuclear intensity increased for but-ton-like junctions. Increased branchpoints, endpoints, objects per cell, and junction thick-ness were associated with junction buttoning, while increased object size was associated with junction zippering. Principle component analysis results reinforced these trends and identified metric combinations that represented each junctional state. Overall, this semi-automated pipeline quantified differences in VE-cadherin junction morphology across LEC monolayers and generated new quantitative metrics to expand the tools available for studying junction morphology in lymphatic vasculature.

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.

Cell fusion enables cell-based applications, yet systematic optimization remains constrained by low throughput (microfluidic devices) and manual operation (bulk protocols). Here, an automated microfluidic platform is presented for cell pairing and chemical fusion at a statistically relevant scale. High throughput is achieved by arranging ~350,000 deformability-based cell traps as concentric circular arrays in a radial chip. Using tumor and dendritic cells as a model for cancer vaccine, the device reproducibly co-occupies ~75% of traps (~250,000) of which ~140,000 hold exactly one cell of each type. This exceeds the yield of previously reported in hydrodynamic pairing arrays by more than an order of magnitude. A concentration step-up-and-down PEG profile yielded a fusion efficiency of ~20% (range 17.7-23.4%, n =3) at viability above 90% that results in producing at least ~50,000 hybrids per chip. This efficiency is comparable to that reported for PEG-based fusion in small-scale microfluidic devices but obtained at higher throughput and approximately four-fold higher than the conventional benchtop protocol. Fusion efficiencies were quantified by image analysis and validated by flow cytometry. By combining automation, large-area circular trap arrays and reagent control, the platform is useful for data-rich optimization of cell engineering processes previously constrained by limited microfluidic throughput.

Gelatin methacrylate (GelMA) hydrogels offer many advantageous properties such as excellent biocompatibility, tunable stiffness, and rapid fabrication, making them attractive materials for neural systems-on-a-chip devices used to study axonal outgrowth in three-dimensional (3D) environments. In this study, we evaluate properties of GelMA hydrogels crosslinked with visible blue light and application of these hydrogels for encapsulation of primary dorsal root ganglion (DRG) explants inside a custom microscale device. We selected low GelMA polymer concentrations of 3 % and 6 % w/v for hydrogel preparation to create soft materials and demonstrate the effect of degree of functionalization (DoF) on stiffness of GelMA in a blue light-induced crosslinking system. Additionally, a decrease in crosslinking efficiency and stiffness of GelMA was observed upon reconstitution of the polymer in DMEM cell culture media compared to PBS. To evaluate the axonal outgrowth, DRG explants were encapsulated in various GelMA formulations and cultured for 7 days. Following incubation, the explants were fixed and immunochemically labelled with anti-beta-III-tubulin antibody. We further describe an application of custom-developed semi-automated image analysis tool together with linear mixed effects modeling to evaluate axonal outgrowth across different GelMA formulations. Among the formulations tested in our study, 3 % GelMA 95 % DoF hydrogels supported the longest neurite outgrowth suggesting that matrix properties influenced neurite extension.