High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically,... Show moreHigh-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.KEYWORDS: Automated image analysis, functional t-test, functional regression model, functional principal component analysis, toxicologyShow less
Drug development requires physiologically more appropriate model systems and assays to increase understanding of drug action and pathological processes in individual humans. Specifically, patient... Show moreDrug development requires physiologically more appropriate model systems and assays to increase understanding of drug action and pathological processes in individual humans. Specifically, patient-derived cells offer great opportunities as representative cellular model systems. Moreover, with novel label-free cellular assays, it is often possible to investigate complex biological processes in their native environment. Combining these two offers distinct opportunities for increasing physiological relevance. Here, we review impedance-based label-free technologies in the context of patient samples, focusing on commonly used cell types, including fibroblasts, blood components, and stem cells. Applications extend as far as tissue-on-a-chip models. Thus, applying label-free technologies to patient samples can produce highly biorelevant data and, with them, unique opportunities for drug development and precision medicine.Show less
Lenzen, M.; Geschke, A.; Abd Rahmana, M.D.l; Xiao, Y.; Fry, J.; Reyes, R.; ... ; Yamano, N. 2017
