A bioelectronic device for electric field treatment of wounds reduces inflammation in an in vivo mouse model.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

A pro-reparative bioelectronic device for controlled delivery of ions and biomolecules.

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

Deep learning classification for macrophage subtypes through cell migratory pattern analysis.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters.

Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and γ-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R2 = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

A system for bioelectronic delivery of treatment directed toward wound healing.

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, which have been suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and noise/background associated with the Immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, DnCNN, to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3,7,10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly we found a positive correlation (R 2 = 0.933) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters.

Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and γ-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

Implementing QR codes in academia to improve sample tracking, data accessibility, and traceability in multicampus interdisciplinary collaborations.

The growing number of multicampus interdisciplinary projects in academic institutions expedites a necessity for tracking systems that provide instantly accessible data associated with devices, samples, and experimental results to all collaborators involved. This need has become particularly salient with the COVID pandemic when consequent travel restrictions have hampered in person meetings and laboratory visits. Minimizing post-pandemic travel can also help reduce carbon footprint of research activities. Here we developed a Quick Response (QR) code tracking system that integrates project management tools for seamless communication and tracking of materials and devices between multicampus collaborators: one school of medicine, two engineering laboratories, three manufacturing cleanroom sites, and three research laboratories. Here we aimed to use this system to track the design, fabrication, and quality control of bioelectronic devices, in vitro experimental results, and in vivo testing. Incorporating the tracking system into our project helped our multicampus teams accomplish milestones on a tight timeline via improved data traceability, manufacturing efficiency, and shared experimental results. This tracking system is particularly useful to track device issues and ensure engineering device consistency when working with expensive biological samples in vitro and animals in vivo to reduce waste of biological and animal resources associated with device failure.

Analysis of localized cAMP perturbations within a tissue reveal the effects of a local, dynamic gap junction state on ERK signaling.

Beyond natural stimuli such as growth factors and stresses, the ability to experimentally modulate at will the levels or activity of specific intracellular signaling molecule(s) in specified cells within a tissue can be a powerful tool for uncovering new regulation and tissue behaviors. Here we perturb the levels of cAMP within specific cells of an epithelial monolayer to probe the time-dynamic behavior of cell-cell communication protocols implemented by the cAMP/PKA pathway and its coupling to the ERK pathway. The time-dependent ERK responses we observe in the perturbed cells for spatially uniform cAMP perturbations (all cells) can be very different from those due to spatially localized perturbations (a few cells). Through a combination of pharmacological and genetic perturbations, signal analysis, and computational modeling, we infer how intracellular regulation and regulated cell-cell coupling each impact the intracellular ERK response in single cells. Our approach reveals how a dynamic gap junction state helps sculpt the intracellular ERK response over time in locally perturbed cells.

A Toolkit for Rapid Modular Construction of Biological Circuits in Mammalian Cells.

The ability to rapidly assemble and prototype cellular circuits is vital for biological research and its applications in biotechnology and medicine. Current methods for the assembly of mammalian DNA circuits are laborious, slow, and expensive. Here we present the Mammalian ToolKit (MTK), a Golden Gate-based cloning toolkit for fast, reproducible, and versatile assembly of large DNA vectors and their implementation in mammalian models. The MTK consists of a curated library of characterized, modular parts that can be assembled into transcriptional units and further weaved into complex circuits. We showcase the capabilities of the MTK by using it to generate single-integration landing pads, create and deliver libraries of protein variants and sgRNAs, and iterate through dCas9-based prototype circuits. As a biological proof of concept, we demonstrate how the MTK can speed the generation of noninfectious viral circuits to enable rapid testing of pharmacological inhibitors of emerging viruses that pose a major threat to human health.

Non-Conventional Yeast Strains Increase the Aroma Complexity of Bread.

Saccharomyces cerevisiae is routinely used yeast in food fermentations because it combines several key traits, including fermentation efficiency and production of desirable flavors. However, the dominance of S. cerevisiae in industrial fermentations limits the diversity in the aroma profiles of the end products. Hence, there is a growing interest in non-conventional yeast strains that can help generate the diversity and complexity desired in today's diversified and consumer-driven markets. Here, we selected a set of non-conventional yeast strains to examine their potential for bread fermentation. Here, we tested ten non-conventional yeasts for bread fermentation, including two Saccharomyces species that are not currently used in bread making and 8 non-Saccharomyces strains. The results show that Torulaspora delbrueckii and Saccharomyces bayanus combine satisfactory dough fermentation with an interesting flavor profile. Sensory analysis and HS-SPME-GC-MS analysis confirmed that these strains produce aroma profiles that are very different from that produced by a commercial bakery strain. Moreover, bread produced with these yeasts was preferred by a majority of a trained sensory panel. These results demonstrate the potential of T. delbrueckii and S. bayanus as alternative yeasts for bread dough leavening, and provide a general experimental framework for the evaluation of more yeasts and bacteria.

Contribution of the tricarboxylic acid (TCA) cycle and the glyoxylate shunt in Saccharomyces cerevisiae to succinic acid production during dough fermentation.

Succinic acid produced by yeast during bread dough fermentation can significantly affect the rheological properties of the dough. By introducing mutations in the model S288C yeast strain, we show that the oxidative pathway of the TCA cycle and the glyoxylate shunt contribute significantly to succinic acid production during dough fermentation. More specifically, deletion of ACO1 and double deletion of ACO1 and ICL1 resulted in a 36 and 77% decrease in succinic acid levels in fermented dough, respectively. Similarly, double deletion of IDH1 and IDP1 decreased succinic acid production by 85%, while also affecting the fermentation rate. By contrast, double deletion of SDH1 and SDH2 resulted in a two-fold higher succinic acid accumulation compared to the wild-type. Deletion of fumarate reductase activity (FRD1 and OSM1) in the reductive pathway of the TCA cycle did not affect the fermentation rate and succinic acid production. The changes in the levels of succinic acid produced by mutants Δidh1Δidp1 (low level) and Δsdh1Δsdh2 (high level) in fermented dough only resulted in small pH differences, reflecting the buffering capacity of dough at a pH of around 5.1. Moreover, Rheofermentometer analysis using these mutants revealed no difference in maximum dough height and gas retention capacity with the dough prepared with S288C. The impact of the changed succinic acid profile on the organoleptic or antimicrobial properties of bread remains to be demonstrated.

Glycerol production by fermenting yeast cells is essential for optimal bread dough fermentation.

Glycerol is the main compatible solute in yeast Saccharomyces cerevisiae. When faced with osmotic stress, for example during semi-solid state bread dough fermentation, yeast cells produce and accumulate glycerol in order to prevent dehydration by balancing the intracellular osmolarity with that of the environment. However, increased glycerol production also results in decreased CO2 production, which may reduce dough leavening. We investigated the effect of yeast glycerol production level on bread dough fermentation capacity of a commercial bakery strain and a laboratory strain. We find that Δgpd1 mutants that show decreased glycerol production show impaired dough fermentation. In contrast, overexpression of GPD1 in the laboratory strain results in increased fermentation rates in high-sugar dough and improved gas retention in the fermenting bread dough. Together, our results reveal the crucial role of glycerol production level by fermenting yeast cells in dough fermentation efficiency as well as gas retention in dough, thereby opening up new routes for the selection of improved commercial bakery yeasts.

Dynamics of the Saccharomyces cerevisiae transcriptome during bread dough fermentation.

The behavior of yeast cells during industrial processes such as the production of beer, wine, and bioethanol has been extensively studied. In contrast, our knowledge about yeast physiology during solid-state processes, such as bread dough, cheese, or cocoa fermentation, remains limited. We investigated changes in the transcriptomes of three genetically distinct Saccharomyces cerevisiae strains during bread dough fermentation. Our results show that regardless of the genetic background, all three strains exhibit similar changes in expression patterns. At the onset of fermentation, expression of glucose-regulated genes changes dramatically, and the osmotic stress response is activated. The middle fermentation phase is characterized by the induction of genes involved in amino acid metabolism. Finally, at the latest time point, cells suffer from nutrient depletion and activate pathways associated with starvation and stress responses. Further analysis shows that genes regulated by the high-osmolarity glycerol (HOG) pathway, the major pathway involved in the response to osmotic stress and glycerol homeostasis, are among the most differentially expressed genes at the onset of fermentation. More importantly, deletion of HOG1 and other genes of this pathway significantly reduces the fermentation capacity. Together, our results demonstrate that cells embedded in a solid matrix such as bread dough suffer severe osmotic stress and that a proper induction of the HOG pathway is critical for optimal fermentation.

Nucleosomes affect local transformation efficiency.

Genetic transformation is a natural process during which foreign DNA enters a cell and integrates into the genome. Apart from its relevance for horizontal gene transfer in nature, transformation is also the cornerstone of today's recombinant gene technology. Despite its importance, relatively little is known about the factors that determine transformation efficiency. We hypothesize that differences in DNA accessibility associated with nucleosome positioning may affect local transformation efficiency. We investigated the landscape of transformation efficiency at various positions in the Saccharomyces cerevisiae genome and correlated these measurements with nucleosome positioning. We find that transformation efficiency shows a highly significant inverse correlation with relative nucleosome density. This correlation was lost when the nucleosome pattern, but not the underlying sequence was changed. Together, our results demonstrate a novel role for nucleosomes and also allow researchers to predict transformation efficiency of a target region and select spots in the genome that are likely to yield higher transformation efficiency.