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

ABSTRACT: 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.

SUBMITTER: Murschhauser A

PROVIDER: S-EPMC6345847 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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

Murschhauser Alexandra A Röttgermann Peter J F PJF Woschée Daniel D Ober Martina F MF Yan Yan Y Dawson Kenneth A KA Rädler Joachim O JO

Communications biology 20190124

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, ...[more]

PMID: 30701200

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Project description:IntroductionElectroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals.MethodsBased on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising.ResultsThe experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals.DiscussionThe proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.

Dataset Information

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

Publications

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

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