High-throughput Screening Research Articles

High-throughput screening identified pacritinib as a promising therapeutic approach to overcome lenvatinib resistance in hepatocellular carcinoma by targeting IRAK1

Lenvatinib resistance presents a significant challenge in the clinical management of advanced hepatocellular carcinoma (HCC). To address this issue, we established lenvatinib resistant HCC cells and performed high-throughput screening using FDA-approved anti-cancer drug library. Through quantitative selective drug sensitivity scoring (sDSS), pacritinib, a well-known JAK inhibitor, emerged as the top candidate, displaying the highest sDSS score among 219 compounds. We further demonstrated that pacritinib reduced the viability of a panel of HCC cell lines in a dose-dependent manner, while exhibiting minimal effects on normal liver cells. Interestingly, pacritinib, but not other JAK inhibitors such as ruxolitinib, upadacitinib, or filgotinib, acted synergistically with lenvatinib in HCC cells. In lenvatinib-resistant HCC cells, pacritinib significantly decreased proliferation and induced apoptosis. Rescue studies using IL-1 receptor-associated kinase 1 (IRAK1) overexpression and knockdown revealed that pacritinib's effects are mediated through IRAK1, with minimal impact on normal liver cells. In a xenograft model of lenvatinib-resistant HCC, oral administration of pacritinib significantly reduced tumor size without affecting body weight. Immunohistochemical analysis of tumor sections revealed that pacritinib reduced Ki67 staining and phosphorylated IRAK1. Our findings suggest that pacritinib may be a promising therapeutic option for the treatment of advanced HCC, particularly in patients who have developed resistance to lenvatinib.

Gene expression profiles, potential targets and treatments of cardiac remodeling.

Hypertensive and ischemic heart diseases have high morbidity all over the world, and they primarily contribute to heart failure associated with high mortality. Cardiac remodeling, as a basic pathological process in heart diseases, is mainly comprised of cardiac hypertrophy and fibrosis, as well as cell death which occurs especially in the ischemic cardiomyopathy. Myocardial remodeling has been widely investigated by a variety of animal models, including pressure overload, angiotensin II stimulation, and myocardial infarction. Pressure overload can cause compensatory cardiac hypertrophy at the early stage, followed by decompensatory hypertrophy and heart failure at the end. Recently, RNA sequencing and differentially expressed gene (DEG) analyses have been extensively employed to elucidate the molecular mechanisms of cardiac remodeling and related heart failure, which also provide potential targets for high-throughput drug screenings. In this review, we summarize recent advancements in gene expression profiling, related gene functions, and signaling pathways pertinent to myocardial remodeling induced by pressure overload at distinct stages, ischemia-reperfusion, myocardial infarction, and diabetes. We also discuss the effects of sex differences and inflammation on DEGs and their transcriptional regulatory mechanisms in cardiac remodeling. Additionally, we summarize emerging therapeutic agents and strategies aimed at modulating gene expression profiles during myocardial remodeling.

Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module’s constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4’s role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach.

Low lattice thermal conductivity induced by rattling-like vibration in Zintl phase Na2CaCdSb2 compound with high ZT from two-channel model

Zintl phase compounds, characterized by the traits of a phonon-glass and electron-crystal, stand out as promising candidates for thermoelectric applications. This study delves into the thermoelectric properties of Zintl phase Na2CaCdSb2 compound through a synergy of first-principles calculations, machine learning interatomic potential approach based on the two-channel model, and high-throughput screening calculations. With an indirect bandgap of 1.02 eV, Na2CaCdSb2 compoundpossesses an high carrier mobility. The high valence band degeneracy and anisotropic band (light-heavy band) lead to a high power factor for the p-type Na2CaCdSb2 compound. The strong fourth-order anharmonicity of the rattling-like Na atom manifests the low lattice thermal conductivity on the basis of two-channel model. The loosely bonding Na atom displays random rotations and dynamically disordered orientations, leading to the weakly disordered nature of Na2CaCdSb2 compound. The unique crystal structure of Na2CaCdSb2 compound coupled with the abundance of diffusion-like phonons play a pivotal role in the phonon transport. Considering the low lattice thermal conductivity and high power factor with the framework of two-channel model, the n-type and p-type Na2CaCdSb2 compounds exhibit optimal figure-of-merits (ZT) of 1.0 and 2.0 at 600 K, showcasing excellent thermoelectric performance. Meanwhile, an optimal ZT of 2.3 is achieved for p-type Na2CaCdSb2 compound at the same temperature along the x-direction. Our present study not only provides fundamental insights into the thermal and electronic transport properties of Na2CaCdSb2 compound under the two-channel model in combination with the four-phonon scattering and multiple carrier scattering rates, but also rationalizes the high-order phonon interactions and thermal transport properties under two-channel model.

Gas Chromatography Tandem Mass Spectrometry (GC-MS/MS) for High-Throughput Screening of Pesticides in Rice Samples Collected in Bangladesh and Saudi Arabia

Gas Chromatography Tandem Mass Spectrometry (GC-MS/MS) with modified QuEChERS sample preparation has been applied to the high-throughput screening of pesticide residuals in rice collected from Bangladesh and Saudi Arabia markets. Both countries consume high volumes of rice, which is a fundamental food for their populations. We report optimized sample preparation and mass spectrometry analysis protocols, which can be rapidly deployed in analytical laboratories. The screening of four groups (organophosphorus, synthetic pyrethroid, organonitrogen, and organochlorine) of a total of 115 pesticides can be performed within ~10 min using a matrix-matched calibration. For most compounds, the limits of detection and quantification (LOD/LOQ) are well below the maximum residue levels (MRLs) of the main regulators. The method generally demonstrates acceptable recovery values (91 compounds 75–125% and 10 compounds 30–75%). Out of 55 rice samples analyzed, 16 samples (29%) contained pesticide residues above LOQ. Four samples contained chlorpyrifos with concentrations ranging from 21.3 to 71.9 µg/kg, ten samples contained tebuconazole (34.7–69.0 µg/kg), and three samples contained pirimiphos methyl (10.7–20.7 µg/kg). The concentrations of the pesticide residues detected in these samples are well below MRL of FAO/WHO (chlorpyrifos, 500 µg/kg; tebuconazole, 1500 µg/kg; pirimiphos methyl, 7000 µg/kg).

Computational Study of Carbon Dioxide Capture by Tertiary Amines.

The reaction mechanisms and corresponding structure-activity relationships of tertiary amines with respect to CO2 capture have been investigated using density functional theory (DFT) calculations. The reaction mechanism for CO2 capture via base-catalyzed hydration to form bicarbonate is proposed to proceed in a single step involving proton transfer and the formation of a carbon-oxygen bond. Based on the height of the reaction barriers, we suggest that amines containing side chains with the ethyl group, along with a single hydroxyl group, and cyclic structures, are especially active for CO2 capture. The activation barrier is shown to be a descriptor for predicting the experimental CO2 loading values. To enhance the prediction accuracy for CO2 loading, we employ the sure-independence screening and sparsifying operator (SISSO) method, which can scan a large pool of mathematical terms stemming from combining DFT-derived descriptors to select the superior ones. Thus, we can predict the CO2 loading with acceptable accuracy from the obtained mathematical expression. Since the computational workload of applying this expression is negligible, this facilitates high-throughput screening and accelerates the design of tertiary amines for CO2 capture.

Activity-Based DNA-Encoded Library Screening for Selective Inhibitors of Eukaryotic Translation.

Small molecule probes exist for only ∼2% of human proteins because most lack functional binding pockets or cannot be assayed for high-throughput screening. Selective translation modulation circumvents canonical druggability and assay development constraints by using in vitro transcription-translation (IVTT) as a universal biochemical screening assay. We developed an IVTT activity assay by fusing a GFP reporter to various target gene sequences and screened the target sequences for inhibitors in microfluidic picoliter-scale droplets using a 5,348-member translation inhibitor DNA-encoded library (DEL). Screening a proof-of-concept PCSK9-GFP reporter yielded many hits; 6/7 hits inhibited PCSK9-GFP IVTT (IC50 1-20 μM), and the lead hit reduced PCSK9 levels in HepG2 cells. Preliminary selectivity was informed by counterscreening the DEL against a frameshift mutant PCSK9-GFP reporter. A plug-and-play approach to assay development and screening was demonstrated by scouting the DEL for activity using reporter genes of targets with difficult-to-assay or even unknown function (RPL27, KRASG12D, MST1, USO1). This microfluidic IVTT DEL screening platform could scale probe discovery to the human proteome and perhaps more broadly across the tree of life.

Atomistic simulations of polysaccharide materials for insights into their crystal structure, nanostructure, and dissolution mechanism

AbstractCrystalline polysaccharides are abundant in nature and can be transformed into highly functional materials. However, the molecular basis for the formation of higher-order structures remains unclear. Computer simulation is an advanced tool for modeling macromolecular structures, and the atomistic simulations provide valuable information on the crystalline polysaccharides. Fiber deformation, crystalline transition, and novel nanostructures of cellulose were characterized through molecular dynamics simulations and density functional theory calculations of models of molecular chain sheets extracted from the crystal structure of the cellulose polymorphs. Extended ensemble molecular dynamics simulations were applied to analyze the artificial crystal structure of non-natural amylose analog polysaccharides, revealing the hexagonal packing of double helices through the self-assembly of molecular chains dispersed in aqueous solution. Dissolution simulations of the cellulose and chitin crystalline fibers revealed that the anions of ionic liquids, with their solvation power, played a key role in the cleavage of intermolecular hydrogen bonds in the crystal structure, whereas the cations contributed to irreversible molecular chain dispersion. The good correlation between the actual solubility of polysaccharides and the predicted number of intermolecular hydrogen bonds prompted the development of a platform that combined simulations and machine learning for high-throughput screening of solvents for cellulose and chitin.

Establishment and Characterization of Testis Organoids with Proliferation and Differentiation of Spermatogonial Stem Cells into Spermatocytes and Spermatids.

Organoids play pivotal roles in uncovering the molecular mechanisms underlying organogenesis, intercellular communication, and high-throughput drug screening. Testicular organoids are essential for exploring the genetic and epigenetic regulation of spermatogenesis in vivo and the treatment of male infertility. However, the formation of testicular organoids with full spermatogenesis has not yet been achieved. In this study, neonatal mouse testicular cells were isolated by two-step enzymatic digestion, and they were combined with Matrigel and transplanted subcutaneously into nude mice. Histological examination (H&E) staining and immunohistochemistry revealed that cell grafts assembled to form seminiferous tubules that contained spermatogonial stem cells (SSCs) and Sertoli cells, as illustrated by the co-expression of PLZF (a hallmark for SSCs) and SOX9 (a marker for Sertoli cells) as well as the co-expression of UCHL1 (a hallmark for SSCs) and SOX9, after 8 weeks of transplantation. At 10 weeks of transplantation, SSCs could proliferate and differentiate into spermatocytes as evidenced by the expression of PCNA, Ki67, c-Kit, SYCP3, γ-HA2X, and MLH1. Notably, testicular organoids were seen, and spermatids were observed within the lumen of testicular organoids after 16 weeks of transplantation, as shown by the presence of TNP1 and ACROSIN (hallmarks for spermatids). Collectively, these results implicate that we successfully established testicular organoids with spermatogenesis in vivo. This study thus provides an excellent platform for unveiling the mechanisms underlying mammalian spermatogenesis, and it might offer valuable male gametes for treating male infertility.

Inhibition of poly(ADP-Ribosyl)ation reduced vascular smooth muscle cells loss and improves aortic disease in a mouse model of human accelerated aging syndrome

Hutchinson-Gilford progeria syndrome (HGPS) is an extremely rare genetic disorder associated with features of accelerated aging. HGPS is an autosomal dominant disease caused by a de novo mutation of LMNA gene, encoding A-type lamins, resulting in the truncated form of pre-lamin A called progerin. While asymptomatic at birth, patients develop symptoms within the first year of life when they begin to display accelerated aging and suffer from growth retardation, and severe cardiovascular complications including loss of vascular smooth muscle cells (VSMCs). Recent works reported the loss of VSMCs as a major factor triggering atherosclerosis in HGPS. Here, we investigated the mechanisms by which progerin expression leads to massive VSMCs loss. Using aorta tissue and primary cultures of murine VSMCs from a mouse model of HGPS, we showed increased VSMCs death associated with increased poly(ADP-Ribosyl)ation. Poly(ADP-Ribosyl)ation is recognized as a post-translational protein modification that coordinates the repair at DNA damage sites. Poly-ADP-ribose polymerase (PARP) catalyzes protein poly(ADP-Ribosyl)ation by utilizing nicotinamide adenine dinucleotide (NAD+). Our results provided the first demonstration linking progerin accumulation, augmented poly(ADP-Ribosyl)ation and decreased nicotinamide adenine dinucleotide (NAD+) level in VSMCs. Using high-throughput screening on VSMCs differentiated from iPSCs from HGPS patients, we identified a new compound, trifluridine able to increase NAD+ levels through decrease of PARP-1 activity. Lastly, we demonstrate that trifluridine treatment in vivo was able to alleviate aortic VSMCs loss and clinical sign of progeria, suggesting a novel therapeutic approach of cardiovascular disease in progeria.

B-291 Validation of a Simple Qualitative Immunoassay for 21 Drugs of Abuse in Meconium on a Single Biochip Array

Abstract Background Drugs use during pregnancy is a major social and medical issue, which may lead to significant pre- and post-natal complications, such as preterm birth and newborn mortality. Exposed newborns may have long-term behavioral, cognitive, and developmental problems. Accurate detection of in utero drug exposure is crucial for timely medical intervention for the newborn and the mother. Meconium is the ‘gold standard' matrix for detection of drugs in newborn which reflects drug exposure during pregnancy. This study aims to develop and optimize a clinical assay for simultaneous screening of 21 drugs in meconium using a custom biochip array. Methods Custom biochip array (Randox Laboratories Ltd.) included 21 discrete test regions (DTRs) for amphetamine, methamphetamine, opioids/opiates (targeting morphine, oxymorphone, oxycodone, hydrocodone, 6-monoacetyl morphine, tramadol, buprenorphine, fentanyl, and methadone), phencyclidine (PCP), cocaine (targeting benzoylecgonine), benzodiazepines (targeting oxazepam, lorazepam, and clonazepam), zolpidem, barbiturates (targeting phenobarbital), ethyl glucuronide, methylphenidate, and marijuana metabolite (targeting Delta-9-Carboxy-Tetrahydrocannabinol [THC-COOH]). Patient samples were obtained from the residual meconium samples that had been previously used for other tests. Blank meconium samples were spiked at in-house cutoff, negative and positive assay control levels. All samples were extracted in specimen diluent using Biotage® Lysera and centrifuged. The supernatant was analyzed on a fully automated Randox Evidence+® Analyzer. A robotic arm pipetted 60 µL sample on to a biochip and then the analyzer performed a competitive chemiluminescent immunoassay. The results were generated as relative light units (RLUs). Results The sample results were determined to be “positive” or “negative” based on average RLU of cutoff controls for each analyte on an assay. The intra-assay (CV ≤ 15%) and inter-assay (CV ≤ 18%) precision was 100% agreement for positive and negative controls and there was no observed carryover for any of the analytes. The sample recovery was excellent with minimal to no matrix suppression for all analytes. Accuracy was performed by comparing spiked analyte results from Randox Evidence+® Analyzer with the expected results and all the spiked samples matched (positive or negative). When authentic patient or spiked results were compared with external laboratory test results, some discordant results were obtained. However, the discrepancies could be explained based on the different cutoffs (e.g., the Randox assay had lower cutoffs) or the known assay cross reactivities within the analytes of same class. These deviations did not affect clinical utility of the assay. Conclusions We validated a novel and simple qualitative chemiluminescent immunoassay in meconium using a custom biochip array. Unlike other routine screening/ELISA methods, which require separate assays for detection of similar drug targets, the custom biochip array offers a convenient, rapid and efficient method that utilizes only a single sample preparation for the screening of all 21 drug targets. Therefore, this technology has potential to be utilized as high-throughput screening assay. Like all qualitative assays, the limitation of this test is that all positive results will need to be assessed quantitatively for a definitive interpretation.

Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning

Abstract Given the rapidly expanding pool of synthesized and hypothetical metal–organic frameworks (MOFs), testing every single material for SF6/N2 separation by iterative experimental methods or computationally demanding molecular simulations is not practical. In this study, we integrated high-throughput computational screening and machine learning (ML) approaches to evaluate SF6/N2 mixture adsorption and separation performances of over 25 000 different types of synthesized and hypothetical MOFs (hypoMOFs), representing the largest set of structures studied for SF6/N2 separation to date. SF6/N2 mixture adsorption data that we produced for synthesized MOFs using molecular simulations were utilized to develop ML models to accurately and quickly predict SF6 and N2 uptakes, SF6/N2 selectivities, SF6 working capacities, adsorbent performance scores, and regenerabilities of both synthesized and hypoMOFs. Results showed the MOF space that we studied exhibits very high SF6/N2 selectivities in the range of 1.8–4204 at 1 bar in addition to high SF6 working capacities between 0.04–5.68 mol kg−1 at an adsorption pressure of 1 bar and desorption pressure of 0.1 bar at room temperature. The top-performing MOF adsorbents for SF6/N2 mixture separation were identified to have Zn, Cu, Ni metals; terphenyl, pyridine, naphthalene linkers; and medium pore sizes. Our comprehensive computational approach offers a highly efficient alternative to brute-force computer simulations by enabling the rapid assessment of the MOF adsorbents for SF6/N2 separation and provides molecular insights into the key structural features of the most promising adsorbents.

Using Reporter Gene Assays to Screen and Identify Chemical Compounds that Modulate Estrogen Receptor Activity.

Estrogen receptor alpha (ERα) is a nuclear receptor that is expressed mainly in the breast, uterus, and ovary, among several other organs. ERα plays important roles in reproduction, mammary gland formation, and glucose homeostasis. Disruption of ERα may result in adverse outcomes, such as cancer, impaired fertility, and abnormal fetal growth. Therefore, identifying compounds that modulate ERα is of great interest due to their potential-endocrine disrupting capability and pharmaceutical applications. To rapidly test tens of thousands of compounds, high-throughput screening assays are essential. Here, we describe high-throughput screening methods, including plating and treatment of cells in 384-well and 1536-well plates and analysis of the resulting data. The two cell lines used, MCF7-VM7Luc4E2 and HEK293-ERα-bla, have been described previously. MCF7-VM7Luc4E2 cells are a stable luciferase reporter gene cell line expressing full-length endogenous estrogen receptor in the MCF7 cell line background, and HEK293-ERα-bla cells stably express an ERα ligand-binding domain/GAL4 DNA-binding domain fusion regulating a UAS β-lactamase reporter gene. These cell lines can be used to identify and confirm ERα modulators. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of a high-throughput ERα reporter gene assay with luminescence readout to identify activators and inhibitors of estrogen receptor α Basic Protocol 2: Use of an orthogonal assay with fluorescence readout to confirm potential estrogen receptor activators or inhibitors.

Exploration of novel boron nitride polymorphs: High-throughput screening combined with multi-task orbital crystal graph convolutional neural network (MT-OCGCN)

In response to the limitations of traditional material property prediction methods, we have developed a Multi-task Orbital Crystal Graph Convolutional Neural Network (MT-OCGCN). This innovative model allows for sharing parameters and computational resources, significantly improving prediction efficiency and accuracy. Experimental comparisons show that the model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties, this offers an efficient approach for computational materials screening and design. Combined with high-throughput screening has resulted in the discovery of 13 new boron nitride polymorphs in the P21/c phase, among which 7 are characterized as wide direct band gap semiconductors. Furthermore, we have comprehensively analyzed the mechanical, thermal, and electronic properties of these new boron nitride polymorphs. It can be asserted that MT-OCGCN has the capability to precisely forecast the physical properties of novel structures that are absent from current databases.

A high-volume resonator for L-band DNP-NMR

DNP-NMR and EPR experiments that operate at or greater than L-band (i.e., ν0(e−) = 1–2 GHz) are typically limited to maximum sample volumes of several hundred µL. These experiments rely on well-known resonator designs for DNP/EPR irradiation such as the loop-gap resonator and Alderman-Grant coil, where their maximum volumes limit further application to imaging experiments and high-throughput screening beyond L-band. Herein, we demonstrate a birdcage (BC) resonator design that can accommodate several mL of sample while operating around 1.5 GHz. The sample volume is maximized by using two identical BC resonators in a stacked configuration. Simulations are used to optimize the BC design and the performance is validated experimentally with liquid-state Overhauser-DNP-NMR experiments. This BC design exploits just the parasitic capacitance of conductive rings and features no fixed tuning capacitors. An enhancement of −77 is achieved on a 10 mM 4-Amino-TEMPO in H2O sample for a 5 mL sample volume. The associated sample heating is minimal due to the low-E-fields generated and the large sample mass with +3.4 K when driving 100 W for several seconds.

Nuclear envelope budding inhibition slows down progerin-induced aging process

Progerin causes Hutchinson-Gilford progeria syndrome (HGPS), but how progerin accelerates aging is still an interesting question. Here, we provide evidence linking nuclear envelope (NE) budding and accelerated aging. Mechanistically, progerin disrupts nuclear lamina to induce NE budding in concert with lamin A/C, resulting in transport of chromatin into the cytoplasm where it is removed via autophagy, whereas emerin antagonizes this process. Primary cells from both HGPS patients and mouse models express progerin and display NE budding and chromatin loss, and ectopically expressing progerin in cells can mimic this process. More excitingly, we screen a NE budding inhibitor chaetocin by high-throughput screening, which can dramatically sequester progerin from the NE and prevent this NE budding through sustaining ERK1/2 activation. Chaetocin alleviates NE budding-induced chromatin loss and ameliorates HGPS defects in cells and mice and significantly extends lifespan of HGPS mice. Collectively, we propose that progerin-induced NE budding participates in the induction of progeria, highlight the roles of chaetocin and sustained ERK1/2 activation in anti-aging, and provide a distinct avenue for treating HGPS.

Machine Learning-Driven precise design of stable OLED Materials: Predicting and enhancing Multi-State C-N bond dissociation energies

The intrinsic stability of organic light-emitting diode (OLED) materials critically hinges on the bond dissociation energies (BDEs) of fragile bonds, particularly C-N bonds. Despite the necessity of considering BDEs in multiple electronic states due to the complex working conditions of organic materials in OLED devices, comprehensive exploration remains challenging. Herein, we constructed a high-quality dataset, CNBDE-24, comprising nearly 1500 data points for BDEs across multiple electronic states, obtained through an active learning-guided density functional theory (DFT) calculation strategy. Machine learning models trained on the CNBDE-24 dataset exhibit excellent accuracy in predicting BDEs for OLED materials in neutral, triplet excited (T1), and anionic states, achieving an adjusted coefficient of determination exceeding 0.90 on the test set and bypassing the need for high-cost calculation. Moreover, utilizing neutral state BDEs as pre-training data significantly reduces model error and provides deeper insights into the relationships between different BDEs. We find that BDEs in neutral are influenced by the electronic properties of the nitrogen atom, as indicated by descriptors such as Gasteiger charges. Incorporating C-N bonds into aromatic systems and slightly dispersing N atom charges can inherently increase multi-state BDEs without altering luminescent properties. Additionally, anionic BDEs are determined by the extent of charge transfer during bond cleavage and can be regulated by modifying the C-side fragment. High-throughput virtual screening reveals a new stable purple emitter with a dominant 0–0 transition at 360 nm, which was validated through DFT and excited-state property evaluations. Strategies for enhancing BDEs in multiple resonance thermally activated delayed fluorescence materials without altering excited-state properties are also demonstrated.

Modeling tumor-immune interactions using hybrid spheroids and microfluidic platforms for studying tumor-associated macrophage polarization in melanoma

Tumor-associated macrophages (TAMs), as key components of tumor microenvironment (TME), exhibit phenotypic plasticity in response to environmental cues, causing polarization into either pro-inflammatory M1 phenotypes or immunosuppressive M2 phenotypes. Although TAM has been widely studied for its crucial involvement in the initiation, progression, metastasis, and immune regulation of cancer cells, there have been limited attempts to understand how the metastatic potentials of cancer cells influence TAM polarization within TME. Here, we developed a miniaturized TME model using a 3D hybrid system composed of murine melanoma cells and macrophages, aiming to investigate interactions between cancer cells exhibiting various metastatic potentials and macrophages within TME. The increase in spheroid size within this model was associated with a reduction in cancer cell viability. Examining macrophage surface marker expression and cytokine secretion indicated the development of diverse TMEs influenced by both spheroid size and the metastatic potential of cancer cells. Furthermore, a high-throughput microfluidic platform equipped with trapping systems and hybrid spheroids was employed to simulate the tumor-immune system of complex TMEs and for comparative analysis with traditional 3D culture models. This study provides insight into TAM polarization in melanoma with different heterogeneities by modeling cancer-immune systems, which can be potentially employed for immune-oncology research, drug screening, and personalized therapy. Statement of significanceThis study presents the development of a 3D hybrid spheroid system designed to model tumor-immune interactions, providing a detailed analysis of how melanoma cell metastatic potential influences tumor-associated macrophage (TAM) polarization. By utilizing a microfluidic platform, we are able to replicate and investigate the complex tumor-immune system of the tumor microenvironments (TMEs) under continuous flow conditions. Our model holds significant potential for high-throughput drug screening and personalized medicine applications, offering a versatile tool for advancing cancer research and treatment strategies.

Proteomic Profiling of Serum Extracellular Vesicles Identifies Diagnostic Signatures and Therapeutic Targets in Breast Cancer.

Analysis of extracellular vesicles (EV) is a promising noninvasive liquid biopsy approach for breast cancer detection, prognosis, and therapeutic monitoring. A comprehensive understanding of the characteristics and proteomic composition of breast cancer-specific EVs from human samples is required to realize the potential of this strategy. In this study, we applied a mass spectrometry-based, data-independent acquisition proteomic approach to characterize human serum EVs derived from patients with breast cancer (n = 126) and healthy donors (n = 70) in a discovery cohort and validated the findings in five independent cohorts. Examination of the EV proteomes enabled the construction of specific EV protein classifiers for diagnosing breast cancer and distinguishing patients with metastatic disease. Of note, TALDO1 was found to be an EV biomarker of distant metastasis of breast cancer. In vitro and in vivo analysis confirmed the role of TALDO1 in stimulating breast cancer invasion and metastasis. Finally, high-throughput molecular docking and virtual screening of a library consisting of 271,380 small molecules identified a potent TALDO1 allosteric inhibitor, AO-022, which could inhibit breast cancer migration in vitro and tumor progression in vivo. Together, this work elucidates the proteomic alterations in the serum EVs of breast cancer patients to guide the development of improved diagnosis, monitoring, and treatment strategies. Significance: Characterization of the proteomic composition of circulating extracellar vesicles in breast cancer patients identifies signatures for diagnosing primary and metastatic tumors and reveals tumor-promoting cargo that can be targeted to improve outcomes.

Empowering High-Throughput High-Content Analysis of Microphysiological Models: Open-Source Software for Automated Image Analysis of Microvessel Formation and Cell Invasion.

The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity. Our approach involved annotating over 1000 2mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models. The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC50 values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software. Our free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools. The online version contains supplementary material available at 10.1007/s12195-024-00821-2.