Optimization of Microparticle Reagents to Collect and Detect Antibody.

Bead reagents are used in a large number of assays in bioscience and biotechnology to collect and purify antibodies by immobilization. Bead-based immunoassays offer high-throughput analysis of multiple antibodies in a single sample. Although a variety of antibody-binding moieties on the collection beads have been studied, the physical and material properties of collection beads have not been optimized to isolate specific antibodies over a broad range of concentrations from complex environments containing cells. We present a study of how to optimally use microparticles coated with protein G to collect low concentrations of IgG antibodies from complex solutions. We study the impact of bead material, bead size, incubation time, and protein G density to more efficiently collect antibodies and detect specific antibodies via fluorescent antigen labeling. The minimum detectable limit and the minimum incubation time for antibody collection are used as metrics to evaluate the collection parameters. We found that larger silica beads can capture more antibodies from a low concentration of sample, with a minimum incubation time of 60 min to equilibrium binding, resulting in a minimum detectable concentration of antibodies of 26 nM. We show that simple biophysical optimization of antibody collection reagents can be used to improve the collection of low concentrations of antibodies in complex environments. We demonstrate that the technology may be useful for monitoring antibody secretions from hybridoma cultures.

Semi-Automated Computational Assessment of Cancer Organoid Viability Using Rapid Live-Cell Microscopy.

The creation of patient-derived cancer organoids represents a key advance in preclinical modeling and has recently been applied to a variety of human solid tumor types. However, conventional methods used to assess in vivo tumor tissue treatment response are poorly suited for the evaluation of cancer organoids because they are time-intensive and involve tissue destruction. To address this issue, we established a suite of 3-dimensional patient-derived glioma organoids, treated them with chemoradiotherapy, stained organoids with non-toxic cell dyes, and imaged them using a rapid laser scanning confocal microscopy method termed "Apex Imaging." We then developed and tested a fragmentation algorithm to quantify heterogeneity in the topography of the organoids as a potential surrogate marker of viability. This algorithm, SSDquant, provides a 3-dimensional visual representation of the organoid surface and a numerical measurement of the sum-squared distance (SSD) from the derived mass center of the organoid. We tested whether SSD scores correlate with traditional immunohistochemistry-derived cell viability markers (cellularity and cleaved caspase 3 expression) and observed statistically significant associations between them using linear regression analysis. Our work describes a quantitative, non-invasive approach for the serial measurement of patient-derived cancer organoid viability, thus opening new avenues for the application of these models to studies of cancer biology and therapy.

Establishment of patient-derived organoid models of lower-grade glioma.

BACKGROUND: Historically, creating patient-derived models of lower-grade glioma (LGG) has been challenging, contributing to few experimental platforms that support laboratory-based investigations of this disease. Although organoid modeling approaches have recently been employed to create in vitro models of high-grade glioma (HGG), it is unknown whether this approach can be successfully applied to LGG. METHODS: In this study, we developed an optimized protocol for the establishment of organoids from LGG primary tissue samples by utilizing physiologic (5%) oxygenation conditions and employed it to produce the first known suite of these models. To assess their fidelity, we surveyed key biological features of patient-derived organoids using metabolic, genomic, histologic, and lineage marker gene expression assays. RESULTS: Organoid models were created with a success rate of 91% (n = 20/22) from primary tumor samples across glioma histological subtypes and tumor grades (WHO Grades 1-4), and a success rate of 87% (13/15) for WHO Grade 1-3 tumors. Patient-derived organoids recapitulated stemness, proliferative, and tumor-stromal composition profiles of their respective parental tumor specimens. Cytoarchitectural, mutational, and metabolic traits of parental tumors were also conserved. Importantly, LGG organoids were maintained in vitro for weeks to months and reanimated after biobanking without loss of integrity. CONCLUSIONS: We report an efficient method for producing faithful in vitro models of LGG. New experimental platforms generated through this approach are well positioned to support preclinical studies of this disease, particularly those related to tumor immunology, tumor-stroma interactions, identification of novel drug targets, and personalized assessments of treatment response profiles.

De novo pyrimidine synthesis is a targetable vulnerability in IDH mutant glioma.

Mutations affecting isocitrate dehydrogenase (IDH) enzymes are prevalent in glioma, leukemia, and other cancers. Although mutant IDH inhibitors are effective against leukemia, they seem to be less active in aggressive glioma, underscoring the need for alternative treatment strategies. Through a chemical synthetic lethality screen, we discovered that IDH1-mutant glioma cells are hypersensitive to drugs targeting enzymes in the de novo pyrimidine nucleotide synthesis pathway, including dihydroorotate dehydrogenase (DHODH). We developed a genetically engineered mouse model of mutant IDH1-driven astrocytoma and used it and multiple patient-derived models to show that the brain-penetrant DHODH inhibitor BAY 2402234 displays monotherapy efficacy against IDH-mutant gliomas. Mechanistically, this reflects an obligate dependence of glioma cells on the de novo pyrimidine synthesis pathway and mutant IDH's ability to sensitize to DNA damage upon nucleotide pool imbalance. Our work outlines a tumor-selective, biomarker-guided therapeutic strategy that is poised for clinical translation.