A high-throughput cigarette smoke-treated bronchosphere model for disease-relevant phenotypic compound screening.

Cigarette smoking (CS) is the leading cause of COPD, and identifying the pathways that are driving pathogenesis in the airway due to CS exposure can aid in the discovery of novel therapies for COPD. An additional barrier to the identification of key pathways that are involved in the CS-induced pathogenesis is the difficulty in building relevant and high throughput models that can recapitulate the phenotypic and transcriptomic changes associated with CS exposure. To identify these drivers, we have developed a cigarette smoke extract (CSE)-treated bronchosphere assay in 384-well plate format that exhibits CSE-induced decreases in size and increase in luminal secretion of MUC5AC. Transcriptomic changes in CSE-treated bronchospheres resemble changes that occur in human smokers both with and without COPD compared to healthy groups, indicating that this model can capture human smoking signature. To identify new targets, we ran a small molecule compound deck screening with diversity in target mechanisms of action and identified hit compounds that attenuated CSE induced changes, either decreasing spheroid size or increasing secreted mucus. This work provides insight into the utility of this bronchopshere model to examine human respiratory disease impacted by CSE exposure and the ability to screen for therapeutics to reverse the pathogenic changes caused by CSE.

"3D, human renal proximal tubule (RPTEC-TERT1) organoids 'tubuloids' for translatable evaluation of nephrotoxins in high-throughput".

The importance of human cell-based in vitro tools to drug development that are robust, accurate, and predictive cannot be understated. There has been significant effort in recent years to develop such platforms, with increased interest in 3D models that can recapitulate key aspects of biology that 2D models might not be able to deliver. We describe the development of a 3D human cell-based in vitro assay for the investigation of nephrotoxicity, using RPTEC-TERT1 cells. These RPTEC-TERT1 proximal tubule organoids 'tubuloids' demonstrate marked differences in physiologically relevant morphology compared to 2D monolayer cells, increased sensitivity to nephrotoxins observable via secreted protein, and with a higher degree of similarity to native human kidney tissue. Finally, tubuloids incubated with nephrotoxins demonstrate altered Na+/K+-ATPase signal intensity, a potential avenue for a high-throughput, translatable nephrotoxicity assay.

A biomimetic assay platform for the interrogation of antigen-dependent anti-tumor T-cell function.

Overcoming tumor-mediated immunosuppression and enhancing cytotoxic T-cell activity within the tumor microenvironment are two central goals of immuno-oncology (IO) drug discovery initiatives. However, exploratory assays involving immune components are often plagued by low-throughput and poor clinical relevance. Here we present an innovative ultra-high-content assay platform for interrogating T-cell-mediated killing of 3D multicellular tumor spheroids. Employing this assay platform in a chemical genomics screen of 1800 annotated compounds enabled identification of small molecule perturbagens capable of enhancing cytotoxic CD8+ T-cell activity in an antigen-dependent manner. Specifically, cyclin-dependent kinase (CDK) and bromodomain (BRD) protein inhibitors were shown to significantly augment anti-tumor T-cell function by increasing cytolytic granule and type II interferon secretion in T-cells in addition to upregulating major histocompatibility complex (MHC) expression and antigen presentation in tumor cells. The described biotechnology screening platform yields multi-parametric, clinically-relevant data and can be employed kinetically for the discovery of first-in-class IO therapeutic agents.

A cell-level quality control workflow for high-throughput image analysis.

BACKGROUND: Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected. RESULTS: We present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics. CONCLUSIONS: Our cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.

Dexamethasone inhibits respiratory syncytial virus-driven mucus production while increasing viral replication without altering antiviral interferon signaling.

Respiratory syncytial virus (RSV) infection can cause mucus overproduction and bronchiolitis in infants leading to severe disease and hospitalization. As a therapeutic strategy, immune modulatory agents may help prevent RSV-driven immune responses that cause severe airway disease. We developed a high throughput screen to identify compounds that reduced RSV-driven mucin 5AC (Muc5AC) expression and identified dexamethasone. Despite leading to a pronounced reduction in RSV-driven Muc5AC, dexamethasone increased RSV infection in vitro and delayed viral clearance in mice. This correlated with reduced expression of a subset of immune response genes and reduced lymphocyte infiltration in vivo. Interestingly, dexamethasone increased RSV infection levels without altering antiviral interferon signaling. In summary, the immunosuppressive activities of dexamethasone had favorable inhibitory effects on RSV-driven mucus production yet prevented immune defense activities that limit RSV infection in vitro and in vivo. These findings offer an explanation for the lack of efficacy of glucocorticoids in RSV-infected patients.