Neurons created from human induced pluripotent stem cells (hiPSCs) provide the capability of identifying biological mechanisms that underlie brain disorders. IPSC-derived human neurons, or iNs, hold promise for advancing precision medicine through drug screening, though it remains unclear to what extent iNs can support early-stage drug discovery efforts in industrial-scale screening centers. Despite several reported approaches to generate iNs from iPSCs, each suffer from technological limitations that challenge their scalability and reproducibility, both requirements for successful screening assays. We addressed these challenges by initially removing the roadblocks related to scaling of iNs for high throughput screening (HTS)-ready assays. We accomplished this by simplifying the production and plating of iNs and adapting them to a freezer-ready format. We then tested the performance of freezer-ready iNs in an HTS-amenable phenotypic assay that measured neurite outgrowth. This assay successfully identified small molecule inhibitors of neurite outgrowth. Importantly, we provide evidence that this scalable iN-based assay was both robust and highly reproducible across different laboratories. These streamlined approaches are compatible with any iPSC line that can produce iNs. Thus, our findings indicate that current methods for producing iPSCs are appropriate for large-scale drug-discovery campaigns (i.e. >10e 5 compounds) that read out simple neuronal phenotypes. However, due to the inherent limitations of currently available iN differentiation protocols, technological advances are required to achieve similar scalability for screens that require more complex phenotypes related to neuronal function.

Biomedical translational research has relied on two dimensional (2D) cell cultures for drug discovery over the decades, requiring cells to grow on a flat surface which does not always accurately model in vivo biological states. Three dimensional (3D) cell cultures, also known as 3D spheroids or organoids, grow as cellular tissues that are more physiologically relevant especially with respect to emulating cancer tumor-like structures [1]. While attractive, current methods for generating 3D spheroids has yet to replace 2D culturing methods used for drug discovery efforts that employ high-throughput screening (HTS), having limitations with scalability, reproducibility, and compatibility predominantly associated with conventional microtiter plate usage. Presented is a novel use of bead injection for the reproducible placement of spheroids and beads into high density microtiter plates of a 384- and 1536- well format.

Microplates are an essential tool used in laboratories for storing research materials and performing assays. Many types of laboratory automation exist that greatly reduce the effort needed to utilize microplates; however, there are cases where the use of such automation is not feasible or practical. In these instances, researchers must work in an environment where liquid handling operations are performed manually with handheld pipetting devices. This type of work is tedious and error-prone as it relies on researchers to manually track a significant amount of metadata, including transfer volumes, plate barcodes, well contents, and well locations. To address this challenge, we have developed an open-source, semiautomated benchtop system that facilitates manual pipetting using visual indicators. This device streamlines the process of identifying the location of wells so that the researcher can perform manual transfers in a more efficient, reliable, and accurate manner. This system utilizes a graphical user interface that allows the user to load worklists and then issues commands to illuminate wells of interest, providing a visual indicator for users to follow in real time. The software and hardware tools utilized for development, along with the implementation techniques used to produce this system, are described within.

Precision medicine is moving cancer treatment from etiological and histological parameter-based treatments to those that target specific key molecular drivers of disease in a time-resolved fashion. To arrive at actionable end points, representative subsamples of the disease tissue and multiparameter measurements are now being optimized to accurately profile human specimens at the single cell level to describe the relevant biological unit of disease. As these new biosensing and nanoscale measurement technologies mature and transition from basic research use to clinical research, it is important to determine pre-analytical variables that may significantly contribute to changes in readouts that can be optimized with appropriate quality control verification criteria versus those that cannot be further optimized in a clinical setting. The term pre-analytical variables refer to all factors that may affect a specimen or sample before it enters the analytical process. No matter how complete analytical and clinical validation for an assay is, if there is not confidence that the sample being analyzed actually reflects what is happening in the patient, then the results will be meaningless. Often times, especially in oncology and related fields, specimen collection, handling, and processing (CHP) variables are the most important of these pre-analytical variables to consider. This presentation will provide perspectives and lessons learned from national initiatives and public-private partnership efforts.