Marc-Olivier Baradez

Rat Primary Hepatocytes Show Enhanced Performance and Sensitivity to Acetaminophen During Three-Dimensional Culture on a Polystyrene Scaffold Designed for Routine Use

The in vitro evaluation of hepatotoxicity is an essential stage in the research and development of new pharmaceuticals as the liver is one of the most commonly impacted organs during preclinical toxicity studies. Fresh primary hepatocytes in monolayer culture are the most commonly used in vitro model of the liver but often exhibit limited viability and/or reduction or loss of important liver-specific functions. These limitations could potentially be overcome using three-dimensional (3D) culture systems, but their experimental nature and limited use in liver toxicity screening and drug metabolism has impaired their uptake into commercial screening programs. In this study we use a commercially available polystyrene scaffold developed for routine 3D cell culture to maintain primary rat hepatocytes for use in metabolism and toxicity studies over 72 h. We show that primary hepatocytes retain their natural cuboidal morphology with significantly higher viability (>74%) than cells grown in monolayer culture (maximum of 57%). Hepatocytes in the 3D scaffolds exhibit differential expression of genes associated with phase I, II, and III drug metabolism under basal conditions compared with monolayer culture and can be induced to stably express significantly higher levels of the cytochrome-P450 enzymes 1A2, 2B1, and 3A2 over 48 h. In toxicity studies the hepatocytes in the 3D scaffolds also show increased sensitivity to the model toxicant acetaminophen. These improvements over monolayer culture and the availability of this new easy to use 3D scaffold system could facilitate the uptake of 3D technologies into routine drug screening programs.

Dynamic monitoring of metal oxide nanoparticle toxicity by label free impedance sensing.

The increased use of nanoparticles in industrial and medical products is driving the need for accurate, high throughput in vitro testing procedures to screen new particles for potential toxicity. While approaches using standard viability assays have been widely used, there have been increased reports of the interactions of nanoparticles with their soluble labels or optical readouts which raise concerns over the potential generation of false positive results. Here, we describe the use of an impedance spectroscopy approach to provide real-time reagent free detection of toxicity for a panel of metal oxide nanoparticles (ZnO, CuO, and TiO(2)). Using this approach, we show how impedance measurements can be used to track nanoparticle toxicity over time with comparable IC(50) values to those of standard assays (ZnO-55 μg/mL, CuO-28 μg/mL) as well as being used to identify a critical 6 h period following exposure during which the nanoparticles trigger rapid cellular responses. Through targeted analysis during this response period and the use of a novel image analysis approach, we show how the ZnO and CuO nanoparticles trigger the active export of intracellular glutathione via an increase in the activity of the ATP dependent MRP/1 efflux pumps. The loss of glutathione leads to increased production of reactive oxygen species which after 2.5 h triggers the cells to enter apoptosis resulting in a dose dependent cytotoxic response. This targeted testing strategy provides comprehensive information beyond that achieved with standard toxicity assays and indicates the potential for cell-nanoparticle interactions that could occur following in vivo exposure.

The use of multidimensional image-based analysis to accurately monitor cell growth in 3D bioreactor culture.

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells.

Quantification of Cells with Specific Phenotypes I: Determination of CD4+ Cell Count Per Microliter in Reconstituted Lyophilized Human PBMC Prelabeled with Anti-CD4 FITC Antibody

A surface‐labeled lyophilized lymphocyte (sLL) preparation has been developed using human peripheral blood mononuclear cells prelabeled with a fluorescein isothiocyanate conjugated anti‐CD4 monoclonal antibody. The sLL preparation is intended to be used as a reference material for CD4+ cell counting including the development of higher order reference measurement procedures and has been evaluated in the pilot study CCQM‐P102. This study was conducted across 16 laboratories from eight countries to assess the ability of participants to quantify the CD4+ cell count of this reference material and to document cross‐laboratory variability plus associated measurement uncertainties. Twelve different flow cytometer platforms were evaluated using a standard protocol that included calibration beads used to obtain quantitative measurements of CD4+ T cell counts. There was good overall cross‐platform and counting method agreement with a grand mean of the laboratory calculated means of (301.7 ± 4.9) μL−1 CD4+ cells. Excluding outliers, greater than 90% of participant data agreed within ±15%. A major contribution to variation of sLL CD4+ cell counts was tube to tube variation of the calibration beads, amounting to an uncertainty of 3.6%. Variation due to preparative steps equated to an uncertainty of 2.6%. There was no reduction in variability when data files were centrally reanalyzed. Remaining variation was attributed to instrument specific differences. CD4+ cell counts obtained in CCQM‐P102 are in excellent agreement and show the robustness of both the measurements and the data analysis and hence the suitability of sLL as a reference material for interlaboratory comparisons and external quality assessment.

Validation of high-throughput single cell analysis methodology

High-throughput quantitative polymerase chain reaction (qPCR) approaches enable profiling of multiple genes in single cells, bringing new insights to complex biological processes and offering opportunities for single cell-based monitoring of cancer cells and stem cell-based therapies. However, workflows with well-defined sources of variation are required for clinical diagnostics and testing of tissue-engineered products. In a study of neural stem cell lines, we investigated the performance of lysis, reverse transcription (RT), preamplification (PA), and nanofluidic qPCR steps at the single cell level in terms of efficiency, precision, and limit of detection. We compared protocols using a separate lysis buffer with cell capture directly in RT-PA reagent. The two methods were found to have similar lysis efficiencies, whereas the direct RT-PA approach showed improved precision. Digital PCR was used to relate preamplified template copy numbers to Cq values and reveal where low-quality signals may affect the analysis. We investigated the impact of calibration and data normalization strategies as a means of minimizing the impact of inter-experimental variation on gene expression values and found that both approaches can improve data comparability. This study provides validation and guidance for the application of high-throughput qPCR workflows for gene expression profiling of single cells.

Rapid phenotypic fingerprinting of cell products by robust measurement of ubiquitous surface markers

Flow cytometry is one of the most versatile and powerful approach for the quantitative analysis of cell suspensions. With widespread applications in basic and clinical research, its commonest use is in the detection of cell populations labelled against markers specific for a particular phenotype. In this study, we aimed to expand the potential of flow cytometry by describing a method based on robust automated quantification of ubiquitous cell surface markers. We demonstrate that automatic fluorescence standardization combined with whole cell population analysis yields highly reproducible results and can alleviate many of the difficulties associated with conventional analyses. This new generic approach is very flexible for quantifying the uniqueness of entire cell populations regardless of their composition. It provides quantitative phenotypic fingerprints rapidly, does not incorporate subjective factors, is more amenable to standardization, and is easily transferable across a wide diversity of applications, such as quality control for cell manufacture and authentication of cell products.

Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing

Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate feedback on process performance. This could help significantly improve cell therapy bioprocessing by allowing proactive decision-making based on real-time process data. Going forward, these types of in-line sensors also open up opportunities to improve bioprocesses further through concepts such as adaptive manufacturing.