David Gerhold

Good cell culture practice for stem cells and stem-cell-derived models

The first guidance on Good Cell Culture Practice (GCCP) dates back to 2005. This document expands this to include aspects of quality assurance for in vitro cell culture focusing on the increasingly diverse cell types and culture formats used in research, product development, testing and manufacture of biotechnology products and cell-based medicines. It provides a set of basic principles of best practice that can be used in training new personnel, reviewing and improving local procedures, and helping to assure standard practices and conditions for the comparison of data between laboratories and experimentation performed at different times. This includes recommendations for the documentation and reporting of culture conditions. It is intended as guidance to facilitate the generation of reliable data from cell culture systems, and is not intended to conflict with local or higher level legislation or regulatory requirements. It may not be possible to meet all recommendations in this guidance for practical, legal or other reasons. However, when it is necessary to divert from the principles of GCCP, the risk of decreasing the quality of work and the safety of laboratory staff should be addressed and any conclusions or alternative approaches justified. This workshop report is considered a first step toward a revised GCCP 2.0.

Detection of Phospholipidosis Induction A Cell-Based Assay in High-Throughput and High-Content Format

Drug-induced phospholipidosis is characterized by the accumulation of intracellular phospholipids in cells exposed to cationic amphiphilic drugs. The appearance of unicentric or multicentric multilamellar bodies viewed under an electron microscope (EM) is the morphological hallmark of phospholipidosis. Although the EM method is the gold standard for detecting cellular phospholipidosis, this method has its drawbacks, including low throughput, high cost, and unsuitability for screening a large chemical library. In this study, a cell-based phospholipidosis assay has been developed using the LipidTOX Red reagent in HepG2 cells and miniaturized into a 1536-well plate format. To validate this assay for high-throughput screening (HTS), the LOPAC library of 1280 compounds was screened using a quantitative HTS platform. A group of known phospholipidosis inducers, such as amiodarone, propranolol, chlorpromazine, desipramine, promazine, clomipramine, and amitriptyline, was identified by the screen, consistent with previous reports. Several novel phospholipidosis inducers, including NAN-190, ebastine, GR127935, and cis-(Z)-flupentixol, were identified in this study and confirmed using the EM method. These results demonstrate that this assay can be used to evaluate and profile large numbers of chemicals for drug-induced phospholipidosis.

Characterization of three human cell line models for high‐throughput neuronal cytotoxicity screening

More than 75 000 man‐made chemicals contaminate the environment; many of these have not been tested for toxicities. These chemicals demand quantitative high‐throughput screening assays to assess them for causative roles in neurotoxicities, including Parkinsons disease and other neurodegenerative disorders. To facilitate high throughput screening for cytotoxicity to neurons, three human neuronal cellular models were compared: SH‐SY5Y neuroblastoma cells, LUHMES conditionally‐immortalized dopaminergic neurons, and Neural Stem Cells (NSC) derived from human fetal brain. These three cell lines were evaluated for rapidity and degree of differentiation, and sensitivity to 32 known or candidate neurotoxicants. First, expression of neural differentiation genes was assayed during a 7‐day differentiation period. Of the three cell lines, LUHMES showed the highest gene expression of neuronal markers after differentiation. Both in the undifferentiated state and after 7 days of neuronal differentiation, LUHMES cells exhibited greater cytotoxic sensitivity to most of 32 suspected or known neurotoxicants than SH‐SY5Y or NSCs. LUHMES cells were also unique in being more susceptible to several compounds in the differentiating state than in the undifferentiated state; including known neurotoxicants colchicine, methyl‐mercury (II), and vincristine. Gene expression results suggest that differentiating LUHMES cells may be susceptible to apoptosis because they express low levels of anti‐apoptotic genes BCL2 and BIRC5/survivin, whereas SH‐SY5Y cells may be resistant to apoptosis because they express high levels of BCL2, BIRC5/survivin, and BIRC3 genes. Thus, LUHMES cells exhibited favorable characteristics for neuro‐cytotoxicity screening: rapid differentiation into neurons that exhibit high level expression neuronal marker genes, and marked sensitivity of LUHMES cells to known neurotoxicants. Copyright

A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics

Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of “sentinel genes” adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.