Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics.

Live-cell Imaging of Single-Cell Arrays (LISCA) is a versatile method to collect time courses of fluorescence signals from individual cells in high throughput. In general, the acquisition of single-cell time courses from cultured cells is hampered by cell motility and diversity of cell shapes. Adhesive micro-arrays standardize single-cell conditions and facilitate image analysis. LISCA combines single-cell microarrays with scanning time-lapse microscopy and automated image processing. Here, we describe the experimental steps of taking single-cell fluorescence time courses in a LISCA format. We transfect cells adherent to a micropatterned array using mRNA encoding for enhanced green fluorescent protein (eGFP) and monitor the eGFP expression kinetics of hundreds of cells in parallel via scanning time-lapse microscopy. The image data stacks are automatically processed by newly developed software that integrates fluorescence intensity over selected cell contours to generate single-cell fluorescence time courses. We demonstrate that eGFP expression time courses after mRNA transfection are well described by a simple kinetic translation model that reveals expression and degradation rates of mRNA. Further applications of LISCA for event time correlations of multiple markers in the context of signaling apoptosis are discussed.

Single-cell kinetics of siRNA-mediated mRNA degradation.

RNA interference (RNAi) enables the therapeutic use of small interfering RNAs (siRNAs) to silence disease-related genes. The efficiency of silencing is commonly assessed by measuring expression levels of the target protein at a given time point post-transfection. Here, we determine the siRNA-induced fold change in mRNA degradation kinetics from single-cell fluorescence time-courses obtained using live-cell imaging on single-cell arrays (LISCA). After simultaneous transfection of mRNAs encoding eGFP (target) and CayRFP (reference), the eGFP expression is silenced by siRNA. The single-cell time-courses are fitted using a mathematical model of gene expression. Analysis yields best estimates of related kinetic rate constants, including mRNA degradation constants. We determine the siRNA-induced changes in kinetic rates and their correlations between target and reference protein expression. Assessment of mRNA degradation constants using single-cell time-lapse imaging is fast (<30 h) and returns an accurate, time-independent measure of siRNA-induced silencing, thus allowing the exact evaluation of siRNA therapeutics.

Erratum: Publisher Correction: Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection.

[This corrects the article DOI: 10.1038/s41540-018-0079-7.].

A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death.

The temporal context of cell death decisions remains generally hidden in ensemble measurements with endpoint readouts. Here, we describe a method to extract event times from fluorescence time traces of cell death-related markers in automated live-cell imaging on single-cell arrays (LISCA) using epithelial A549 lung and Huh7 liver cancer cells as a model system. In pairwise marker combinations, we assess the chronological sequence and delay times of the events lysosomal membrane permeabilization, mitochondrial outer membrane permeabilization and oxidative burst after exposure to 58 nm amino-functionalized polystyrene nanoparticles (PS-NH2 nanoparticles). From two-dimensional event-time scatter plots we infer a lysosomal signal pathway at a low dose of nanoparticles (25 µg mL-1) for both cell lines, while at a higher dose (100 µg mL-1) a mitochondrial pathway coexists in A549 cells, but not in Huh7. In general, event-time correlations provide detailed insights into heterogeneity and interdependencies in signal transmission pathways.

Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection.

Single-cell time-lapse studies have advanced the quantitative understanding of cellular pathways and their inherent cell-to-cell variability. However, parameters retrieved from individual experiments are model dependent and their estimation is limited, if based on solely one kind of experiment. Hence, methods to integrate data collected under different conditions are expected to improve model validation and information content. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.