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Monitoring the intracellular calcium response to a dynamic hypertonic environment.

ABSTRACT: The profiling of physiological response of cells to external stimuli at the single cell level is of importance. Traditional approaches to study cell responses are often limited by ensemble measurement, which is challenging to reveal the complex single cell behaviors under a dynamic environment. Here we report the development of a simple microfluidic device to investigate intracellular calcium response to dynamic hypertonic conditions at the single cell level in real-time. Interestingly, a dramatic elevation in the intracellular calcium signaling is found in both suspension cells (human leukemic cell line, HL-60) and adherent cells (lung cancer cell line, A549), which is ascribed to the exposure of cells to the hydrodynamic stress. We also demonstrate that the calcium response exhibits distinct single cell heterogeneity as well as cell-type-dependent responses to the same stimuli. Our study opens up a new tool for tracking cellular activity at the single cell level in real time for high throughput drug screening.

SUBMITTER: Huang X

PROVIDER: S-EPMC4804238 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

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Project description:Dynamic processes, such as intracellular calcium signaling, are hallmark of cellular biology. As real-time imaging modalities become widespread, a need for analytical tools to reliably characterize time-series data without prior knowledge of the nature of the recordings becomes more pressing. The goal of this study is to develop a signal-processing algorithm for MATLAB that autonomously computes the parameters characterizing prominent single transient responses (TR) and/or multi-peaks responses (MPR). The algorithm corrects for signal contamination and decomposes experimental recordings into contributions from drift, TRs, and MPRs. It subsequently provides numerical estimates for the following parameters: time of onset after stimulus application, activation time (time for signal to increase from 10 to 90% of peak), and amplitude of response. It also provides characterization of the (i) TRs by quantifying their area under the curve (AUC), response duration (time between 1/2 amplitude on ascent and descent of the transient), and decay constant of the exponential decay region of the deactivation phase of the response, and (ii) MPRs by quantifying the number of peaks, mean peak magnitude, mean periodicity, standard deviation of periodicity, oscillatory persistence (time between first and last discernable peak), and duty cycle (fraction of period during which system is active) for all the peaks in the signal, as well as coherent oscillations (i.e., deterministic spikes). We demonstrate that the signal detection performance of this algorithm is in agreement with user-mediated detection and that parameter estimates obtained manually and algorithmically are correlated. We then apply this algorithm to study how metabolic acidosis affects purinergic (P2) receptor-mediated calcium signaling in osteoclast precursor cells. Our results reveal that acidosis significantly attenuates the amplitude and AUC calcium responses at high ATP concentrations. Collectively, our data validated this algorithm as a general framework for comprehensively analyzing dynamic time-series.

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Monitoring the intracellular calcium response to a dynamic hypertonic environment.

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