High-throughput Screening Applications Research Articles

A Universal Plug-and-Play Approach to In Situ Multinuclear Magnetic Resonance Analysis of Electrochemical Phenomena in Commercial Battery Cells.

Advancing electrochemical energy storage devices relies on versatile analytical tools capable of revealing the molecular mechanisms behind the function and degradation of battery materials in situ. The nuclear magnetic resonance phenomenon plays a pivotal role in fundamental studies of energy materials and devices because of its exceptional sensitivity to local environments and the dynamics of many electrochemically relevant elements. The jelly roll architecture is one of the most energy-dense and, therefore, most popular concepts implemented in pouch, prismatic, and cylindrical Li- and Na-ion cells. Such widely commercialized designs, however, represent a significant obstacle for a range of powerful in situ magnetic resonance-based methodologies due to negligible radio frequency electromagnetic field penetration through conductive metal casings and current collectors. In this work, we introduce an experimental setup that enables direct RF wave transmission through the cell terminals and current collectors, and provides efficient excitation and detection of NMR signals. An RF adapter designed as a plug-and-play device effectively turns the battery cell into an NMR probe that can be tuned to a broad range of Larmor frequencies. Due to its exceptional sensitivity, versatility, and multinuclear capability, the proposed methodology is suitable for fundamental research and industrial high-throughput screening applications. In situ NMR lineshapes provide a direct quantitative description of the chemical environments of electrochemically active elements and enable new metrics for the accurate assessment of state-of-charge (SoC) and state-of-health (SoH). Specifically, in commercial pouch cells based on lithium cobalt oxide, lithium nickel manganese cobalt oxide, and sodium nickel iron manganese oxide chemistries, 7Li and 23Na NMR data unambiguously show electrochemical transformations between intercalated and metallic forms of charge carrier ions, and reveal anisotropic magnetic properties of the electrode coating.

Transfer learning for molecular property predictions from small datasets

Machine learning has emerged as a new tool in chemistry to bypass expensive experiments or quantum-chemical calculations, for example, in high-throughput screening applications. However, many machine learning studies rely on small datasets, making it difficult to efficiently implement powerful deep learning architectures such as message passing neural networks. In this study, we benchmark common machine learning models for the prediction of molecular properties on two small datasets, for which the best results are obtained with the message passing neural network PaiNN as well as SOAP molecular descriptors concatenated to a set of simple molecular descriptors tailored to gradient boosting with regression trees. To further improve the predictive capabilities of PaiNN, we present a transfer learning strategy that uses large datasets to pre-train the respective models and allows us to obtain more accurate models after fine-tuning on the original datasets. The pre-training labels are obtained from computationally cheap ab initio or semi-empirical models, and both datasets are normalized to mean zero and standard deviation one to align the labels’ distributions. This study covers two small chemistry datasets, the Harvard Organic Photovoltaics dataset (HOPV, HOMO–LUMO-gaps), for which excellent results are obtained, and the FreeSolv dataset (solvation energies), where this method is less successful, probably due to a complex underlying learning task and the dissimilar methods used to obtain pre-training and fine-tuning labels. Finally, we find that for the HOPV dataset, the final training results do not improve monotonically with the size of the pre-training dataset, but pre-training with fewer data points can lead to more biased pre-trained models and higher accuracy after fine-tuning.

Validation of Saliva as the Clinical Specimen Type for a University-Wide COVID-19 Surveillance Program.

At the beginning of the COVID-19 pandemic, the Georgia Institute of Technology made the decision to keep the university doors open for on-campus attendance. To manage COVID-19 infection rates, internal resources were applied to develop and implement a mass asymptomatic surveillance program. The objective was to identify infections early for proper follow-on verification testing, contact tracing, and quarantine/isolation as needed. Program success depended on frequent and voluntary sample collection from over 40,000 students, faculty, and staff personnel. At that time, the nasopharyngeal (NP) swab, not saliva, was the main accepted sample type for COVID-19 testing. However, due to collection discomfort and the inability to be self-collected, the NP swab was not feasible for voluntary and frequent self-collection. Therefore, saliva was selected as the clinical sample type and validated. A saliva collection kit and a sample processing and analysis workflow were developed. The results of a clinical sample-type comparison study between co-collected and matched NP swabs and saliva samples showed 96.7% positive agreement and 100% negative agreement. During the Fall 2020 and Spring 2021 semesters, 319,988 samples were collected and tested. The program resulted in maintaining a low overall mean positivity rate of 0.78% and 0.54% for the Fall 2020 and Spring 2021 semesters, respectively. For this high-throughput asymptomatic COVID-19 screening application, saliva was an exceptionally good sample type.

Efficient and robust reverse genetics system for bovine rotavirus generation and its application for antiviral screening

Unveiling the molecular mechanisms underlying rotavirus replication and pathogenesis has been hampered by the lack of a reverse genetics (RG) system in the past. Since 2017, multiple plasmid-based RG systems for simian, human, and murine-Like rotaviruses have been established. However, none of the described methods have supported the recovery of bovine rotaviruses (BRVs). Here, we established an optimized plasmid-based RG system for BRV culture-adapted strain (BRV G10P [15] BLR) and clinical isolates (BRV G6P[1] C73, G10P[11] HM26) based on a BHK-T7 cell clone stably expressing T7 polymerase. Furthermore, using this optimized RG system, we successfully rescued the reporter virus BRV rC73/Zs, rHM26/Zs and rBLR/Zs, harboring a genetically modified 1.8-kb segment 7 encoding full-length nonstructural protein 3 (NSP3) fused to ZsGreen, a 232-amino acid green fluorescent protein. Analysis of the stability of genomic insertions showed that the rC73/Zs and rBLR/Zs replicated efficiently and were genetically stable in seven rounds of serial passaging, while rHM26/Zs can be stabilized only up to the third generation, indicating that the BRV segment composition may influence the viral fitness. In addition, we adopted the recombinant reporter viruses for high-throughput screening application and discovered 12 candidates out of 1440 compounds with potential antiviral activities against rotavirus. In summary, this improved RG system of BRVs represents an important tool with great potential for understanding the molecular biology of BRV and facilitates the development of novel therapeutics and vaccines for BRV.

Fully automated OCT-based tissue screening system.

This study introduces an innovative optical coherence tomography (OCT) imaging system dedicated to high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep-learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

A multiplexed sensing system with multimodal readout electronics and thread-based electrochemical sensors for high throughput screening

High throughput screening (HTS) based on microwell (or bioreactor) arrays is a well-established approach for rapid screening and identification of lead molecules or target analytes for drug discovery and other applications in biotechnology. However, it has been challenging to scale up these platforms due to the lack of integrated sensors to monitor the microenvironment of each microwell in the array needed. In this work, we present a practical solution for a miniaturized multiplexed sensing system with integrated circuitry capable of performing continuous electrochemical potentiometric and amperometric measurements. The platform uses lightweight multiplexed electrochemical sensors implemented on thin textile threads, all of which share a single operational amplifier (op-amp) and a switch network for both voltage and current measurements. The resulting platform forgoes the need for large-area sensors or bulky multichannel potentiostat with a simple readout circuitry capable of multimodal measurements. The design and validation of the multiplexed sensing system have been reported in the context of measuring pH and oxygen (O2), which are crucial biomarkers for any HTS application. The thread-based pH and O2 sensors possess sensitivities of ∼65.87 mV/pH for the pH sensor (n = 3) and 0.6621 nA/mg L-1 for the O2 sensor (n = 3). This architecture is modular such that it can be easily extended to monitoring multiple biomarkers employing thread-based sensors and sharing the same readout circuitry, making it a practical and versatile tool for biotechnology research.

Data-science-guided calibration curve prediction of an MLCT-based ee determination assay for chiral amines

Circular dichroism (CD) based enantiomeric excess (ee) determination assays are optical alternatives to chromatographic ee determination in high-throughput screening (HTS) applications. However, the implementation of these assays requires calibration experiments using enantioenriched materials. We present a data-driven approach that circumvents the need for chiral resolution and calibration experiments for an octahedral Fe(II) complex (1) used for the ee determination of α-chiral primary amines. By computationally parameterizing the imine ligands formed in the assay conditions, a model of the circular dichroism (CD) response of the Fe(II) assembly was developed. Using this model, calibration curves were generated for four analytes and compared to experimentally generated curves. In a single-blind ee determination study, the ee values of unknown samples were determined within 9% mean absolute error, which rivals the error using experimentally generated calibration curves.

Adsorption Behaviour of Pb and Cd on Graphene Oxide Nanoparticle from First-Principle Investigations.

Graphene oxide (GO) is considered as a promising adsorbent material for the removal of metal from aqueous environments. Here, we have used the density functional theory (DFT) approach and a combination of parameters to characterise the interactions of GO with lead (Pb) and cadmium (Cd), i.e., typical harmful metals often found in water. Our model systems consist of a singly and doubly adsorbed neutral (Pb0, Cd0) and charged (Pb2+, Cd2+) atoms adsorbed on the GO nanoparticle of the chemical formula C30H14O15. We show that a single charged metal ion binds more strongly than a neutral atom of the same type. Moreover, to determine the possibility of multiple adsorptions of the GO nanoparticle, two metal atoms of the same species were co-adsorbed on its surface. We found a site-dependent adsorption energy such that when two atoms of the same specie are adsorbed at sites Si and Sj, the binding energy per atom depends on whether one of the two atoms is adsorbed firstly on the Si or Sj sites. Furthermore, the binding energy per atom for the two co-adsorbed atoms of the same specie (i.e., neutral or charged) is less than the binding energy of a singly adsorbed atom. This suggests that atoms may become less likely to be adsorbed on the GO nanoparticle when their concentration increases. We adduce the origin of this observation to be interplay between the metal-metal interaction on the one hand and GO-metal on the other, with the former resulting in less binding for the charged adsorbed metals in particular, due to repulsive interaction between two positively charged ions. The frontier molecular orbitals analysis and the calculated global reactivity descriptors of the respective GO-metal complexes revealed that all the GO-metal complexes have a smaller HOMO-LUMO gap (HLG) relative to that of pristine metal-free GO nanoparticle. This may indicate that although the GO-metal complexes are stable, they are less stable compared to metal-free GO nanoparticles. The negative values of the chemical potentials obtained for all the GO-metal complexes further confirm their stability. Our work differs from previous experimental studies in that those lacked details of the interaction mechanisms between GO, Pb and Cd, as well as previous theoretical studies which used limited numbers of parameters to characterise the GO-metal interactions. Rather, we present a set of parameters or descriptors which provide comprehensive physical and electronic characterisation of GO-metal systems as obtained via the DFT calculations. These parameters, along with those reported in previous studies, may find applications in rational design and high-throughput screening of graphene-based materials for water purification, as an example.

The role and application of high-throughput screening in the search for chemical compounds of therapeutic activity

With the development of medicine and the aging population, the demand for more effective therapies is escalating. A tool that facilitates the discovery and introduction of new therapeutic drugs is High-Throughput Screening (HTS). These tests, consisting of a wide set of various assays, allow testing hundreds of thousands of compounds in a short period of time. The aim is to accurately identify active compounds that could become potential therapeutic candidates in the pharmaceutical industry. HTS is the first step in the quest for potential drugs, therefore it is one of the crucial tests that determine whether a particular drug candidate will be discovered. In this review, different stages of high-throughput screening studies will be described, as well as methods utilized in these studies. The necessary steps in the optimization of these tests, selection of equipment, automation and key quality control parameters for reliably performed screening will also be presented.

Rational Design of a Highly Sensitive Carboxylesterase Probe and Its Application in High-Throughput Screening for Uncovering Carboxylesterase Inhibitors.

Tracking carboxylesterases (CESs) through noninvasive and dynamic imaging is of great significance for diagnosing and treating CES-related metabolic diseases. Herein, three BODIPY-based fluorescent probes with a pyridine unit quaternarized via an acetoxybenzyl group were designed and synthesized to detect CESs based on the photoinduced electron transfer process. Notably, among these probes, BDPN2-CES exhibited a remarkable 182-fold fluorescence enhancement for CESs within 10 min. Moreover, BDPN2-CES successfully enabled real-time imaging of endogenous CES variations in living cells. Using BDPN2-CES, a visual high-throughput screening method for CES inhibitors was established, culminating in the discovery of an efficient inhibitor, WZU-13, sourced from a chemical library. These findings suggest that BDPN2-CES could provide a new avenue for diagnosing CES-related diseases, and WZU-13 emerges as a promising therapeutic candidate for CES-overexpression pathological processes.

Development of a novel high-throughput culture system for hypoxic 3D hydrogel cell culture.

Animal models lack physiologic relevance to the human system which results in low clinical translation of results derived from animal testing. Besides spheroids or organoids, hydrogel-based 3D in vitro models are used to mimic the in vivo situation increasing the relevance while reducing animal testing. However, to establish hydrogel-based 3D models in applications such as drug development or personalized medicine, high-throughput culture systems are required. Furthermore, the integration of oxygen-reduced (hypoxic) conditions has become increasingly important to establish more physiologic culture models. Therefore, we developed a platform technology for the high-throughput generation of miniaturized hydrogels for 3D cell culture. The Oli-Up system is based on the shape of a well-plate and allows for the parallel culture of 48 hydrogel samples, each with a volume of 15µl. As a proof-of-concept, we established a 3D culture of gelatin-methacryloyl (GelMA)-encapsulated mesenchymal stem/stromal cells (MSCs). We used a hypoxia reporter cell line to establish a defined oxygen-reduced environment to precisely trigger cellular responses characteristicof hypoxia in MSCs. In detail, the expression of hypoxia response element (HRE) increased dependent on the oxygen concentration and cell density. Furthermore, MSCs displayed an altered glucose metabolism and increased VEGF secretion upon oxygen-reduction. In conclusion, the Oli-Up system is a platform technology for the high-throughput culture of hydrogel-based 3D models in a defined oxygen environment. As it is amenable for automation, it holds the potential for high-throughput screening applications such as drug development and testing in more physiologic 3D in vitro tissue models.

Efficient approach to cyclic scheduling of high throughput screening systems for bioengineering

As a standard technology, high throughput screening (HTS) is widely applied for the analysis and detection of new biological substances and drugs in pharmaceutical industries and life science research. Efficient scheduling of HTS systems is a crucial issue for reducing the cost of their operations. As a typical application of high throughput screening, this work studies the scheduling problem of the enzyme-linked immunoassay (ELISA) processes in HTS systems. For this process, it requires a one-microplate cyclic schedule and a microplate visits some processing resources multiple times, making the process deadlock-prone such that its scheduling problem is very challenging. To tackle this problem, the process is modeled by a kind of Petri nets. Based on this model, by analyzing the dynamic properties of the process, it is able to directly deduce an activity sequence to form an optimal cyclic schedule. In this way, it not only efficiently solves the challenging scheduling problem without building a mathematical programming model, but also the obtained schedule is very easy to understand and implement.

Abstract 3093: Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids

Abstract Although patient-derived tumor organoids (PDOs) better-represent patient tumors compared to traditional in vitro cell cultures, limitations of PDO analysis remain a major hurdle for high-throughput screening (HTS) applications. In particular, current PDO analysis fall short in at least 1 of 2 major criteria: 1) accurate recapitulation of patient tumor or 2) scalability. To address this, we have developed an automation-compatible protocol to satisfy these 2 major criteria to facilitate the adoption of PDOs for HTS applications1. Our novel protocol leverages live-cell imaging techniques to capture the dynamic interactions and intricate complexities of PDOs, which allowed for more precise and comprehensive analysis. To further enhance the accuracy and efficiency of our approach, we developed a fully automated image and data analysis pipeline (Orbits). Using label-free detection of organoids from brightfield images, Orbits integrates growth-rate-based drug response metrics with drug synergy metrics to significantly improve the identification of synergistic drug interactions. Here, present the results of our HTS protocol on 10 distinct drug combination strategies, specifically focusing on the repurposed drug candidate Auranofin, a Thioredoxin reductase inhibitor. We screened on 10 PDOs sourced from healthy lung, non-small cell lung cancer, and pancreatic ductal adenocarcinoma. Not only were we able to distinguishing cytostatic from cytotoxic drug responses at a single-organoid level, we identified drug candidates that can synergistically enhanced the efficacy of Auranofin in a tumor selective manner. Our study presents a major step forward in the field of PDO-based drug discovery, offering a robust protocol for HTS and automated data analysis. The implementation of our method will accelerate the discovery of effective therapeutic strategies, ultimately translating to improved patient outcomes. 1 Le Compte, M., et al JoVE 190 (2022) Citation Format: Abraham Lin, Maxim Le Compte, Tyler Gilcrest, Edgar Cardenas De La Hoz, Geert Roeyen, Jeroen Hendriks, Filip Lardon, Christophe Deben. Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3093.

XPORT ENTRAP: A droplet microfluidic platform for enhanced DNA transfer between microbial species

A significant hurdle for the widespread implementation and use of synthetic biology is the challenge of highly efficient introduction of DNA into microorganisms. This is especially a barrier for the utilization of non-model organisms and/or novel chassis species for a variety of applications, ranging from molecular biology to biotechnology and biomanufacturing applications. Common approaches to episomal and chromosomal gene editing, which employ techniques such as chemical competence and electroporation, are typically only amenable to a small subset of microbial species while leaving the vast majority of microorganisms in nature genetically inaccessible. To address this challenge, we have employed the previously described B. subtilis broad-host conjugation strain, XPORT, which was modularly designed for loading DNA cargo and conjugating such DNA into recalcitrant microbes. In this current work, we have leveraged and adapted the XPORT strain for use in a droplet microfluidic platform to enable increased efficiency of conjugation-based DNA transfer. The system named DNA ENTRAP (DNA ENhanced TRAnsfer Platform) utilizes cell-encapsulated water-in-oil emulsion droplets as pico-liter-volume bioreactors that allows controlled contacts between the donor and receiver cells within the emulsion bioreactor. This allowed enhanced XPORT-mediated genetic transfer over the current benchtop XPORT process, demonstrated using two different Bacillus subtilis strains (donor and receiver), as well as increased throughput (e.g., number of successfully conjugated cells) due to the automated assay steps inherent to microfluidic lab-on-a-chip systems. DNA ENTRAP paves the way for a streamlined automation of culturing and XPORT-mediated genetic transfer processes as well as future high-throughput cell engineering and screening applications.

A Stable Cell Line Co-expressing hTRPV1 and GCaMP6s: A Novel Cell-based Assay For High-throughput Screening of hTRPV1 Agonists.

Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel capable of integrating various noxious chemical and physical stimuli. Recently, human TRPV1 (hTRPV1) has attracted wide attention from researchers because it is closely related to pain, inflammation, temperature perception, and tumors. Our study was aimed at generating a stable cell line co-expressing hTRPV1 receptor and GCaMP6s calcium indicator protein and, based on this, developing high-throughput screening methods for targeting hTRPV1 agonists. The CHO-hTRPV1-GCaMP6s cell line stably expressing hTRPV1 and GCaMP6s was generated by co-transfection of hTRPV1 and GCaMP6s into Chinese hamster ovary (CHO) cells. The high-throughput screening methods were developed based on detecting the concentration of intracellular calcium ions ([Ca2+]i) by using chemically synthesized dyes and genetically encoded calcium indicator (GECI). Meanwhile, the sensitivity and adaptability of these methods in the evaluation of capsaicinoids were also compared. A stable cell line co-expressing hTRPV1 and GCaMP6s was generated and used to establish a functional high-throughput screening assay based on the measurement of [Ca2+]i by fluorometric imaging plate reader (FLIPR). The GECI exhibited a higher sensitivity and applicability than that of chemically synthesized dyes in detecting the changes in [Ca2+]i induced by capsaicin. The CHO-hTRPV1-GCaMP6s cell line was further used to detect the dose-dependent relationships of various hTRPV1 agonists (comparison of EC50 values: capsaicin (39 ± 1.67 nM) < nonivamide (67 ± 3.05 nM) < piperine (9222 ± 1851 nM)), and this order is consistent with the pharmacological properties of hTRPV1 activation by these agonists. The successful establishment of the CHO-hTRPV1-GCaMP6s cell lines and their application in high-throughput screening of hTRPV1 agonists.

Nanomaterial-based therapeutics for enhanced antifungal therapy.

The application of nanotechnology in antifungal therapy is gaining increasing attention. Current antifungal drugs have significant limitations, such as severe side effects, low bioavailability, and the rapid development of resistance. Nanotechnology offers an innovative solution to address these issues. This review discusses three key strategies of nanotechnology to enhance antifungal efficacy. Firstly, nanomaterials can enhance their interaction with fungal cells via ingenious surface tailoring of nanomaterials. Effective adhesion of nanoparticles to fungal cells can be achieved by electrostatic interaction or specific targeting to the fungal cell wall and cell membrane. Secondly, stimuli-responsive nanomaterials are developed to realize smart release of drugs in the specific microenvironment of pathological tissues, such as the fungal biofilm microenvironment and inflammatory microenvironment. Thirdly, nanomaterials can be designed to cross different physiological barriers, effectively addressing challenges posed by skin, corneal, and blood-brain barriers. Additionally, some new nanomaterial-based strategies in treating fungal infections are discussed, including the development of fungal vaccines, modulation of macrophage activity, phage therapy, the application of high-throughput screening in drug discovery, and so on. Despite the challenges faced in applying nanotechnology to antifungal therapy, its significant potential and innovation open new possibilities for future clinical antifungal applications.

Benchmarking DFT-based excited-state methods for intermolecular charge-transfer excitations.

Intermolecular charge-transfer is a highly important process in biology and energy-conversion applications where generated charges need to be transported over several moieties. However, its theoretical description is challenging since the high accuracy required to describe these excited states must be accessible for calculations on large molecular systems. In this benchmark study, we identify reliable low-scaling computational methods for this task. Our reference results were obtained from highly accurate wavefunction calculations that restrict the size of the benchmark systems. However, the density-functional theory based methods that we identify as accurate can be applied to much larger systems. Since targeting charge-transfer states requires the unambiguous classification of an excited state, we first analyze several charge-transfer descriptors for their reliability concerning intermolecular charge-transfer and single out the charge-transfer distance calculated based on the variation of electron density upon excitation (DCT) as an optimal choice for our purposes. In general, best results are obtained for orbital-optimized methods and among those, the maximum overlap method proved to be the most numerically stable variant when using the initial MOs as reference orbitals. Favorable error cancellation with optimally-tuned range-separated hybrid functionals and a rather small basis set can provide an economical yet reasonable wavefunction when using time-dependent density functional theory, which provides relevant information about the excited-state character to be used in the orbital-optimized methods. The qualitative agreement makes these fast calculations attractive for high-throughput screening applications.

PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy.

Microfluidic 3D cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost-effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, an approach to pattern fluid guides inside microchannels is introduced by establishing differential hydrophilicity using pressure-sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. Optimal coating conditions are identified using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open-chip prototype and the fully bonded device containing additional features for medium perfusion. The suitability of the device for dynamic 3D cell culture is tested by growing human hepatocytes in the device under controlled fluid flow for a 14-day period. Additionally, the study demonstrated the potential of using the device for pharmaceutical high-throughput screening applications, such as predicting drug-induced liver injury. The approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials.

NanoFIRE: A NanoLuciferase and Fluorescent Integrated Reporter Element for Robust and Sensitive Investigation of HIF and Other Signalling Pathways.

The Hypoxia Inducible Factor (HIF) transcription factors are imperative for cell adaption to low oxygen conditions and development; however, they also contribute to ischaemic disease and cancer. To identify novel genetic regulators which target the HIF pathway or small molecules for therapeutic use, cell-based reporter systems are commonly used. Here, we present a new, highly sensitive and versatile reporter system, NanoFIRE: a NanoLuciferase and Fluorescent Integrated Reporter Element. Under the control of a Hypoxic Response Element (HRE-NanoFIRE), this system is a robust sensor of HIF activity within cells and potently responds to both hypoxia and chemical inducers of the HIF pathway in a highly reproducible and sensitive manner, consistently achieving 20 to 150-fold induction across different cell types and a Z' score > 0.5. We demonstrate that the NanoFIRE system is adaptable via substitution of the response element controlling NanoLuciferase and show that it can report on the activity of the transcriptional regulator Factor Inhibiting HIF, and an unrelated transcription factor, the Progesterone Receptor. Furthermore, the lentivirus-mediated stable integration of NanoFIRE highlights the versatility of this system across a wide range of cell types, including primary cells. Together, these findings demonstrate that NanoFIRE is a robust reporter system for the investigation of HIF and other transcription factor-mediated signalling pathways in cells, with applications in high throughput screening for the identification of novel small molecule and genetic regulators.