Intracellular calcium imaging

Intracellular calcium imaging in hTERT RPE1 cells

Intracellular calcium was monitored in hTERT RPE1 cells synchronised at the G0 phase by serum deprivation (see “Synchronisation in G0 phase by serum deprivation”).

Fluo-4AM (Thermo Fischer Scientific) was used as intracellular calcium reporter in living cells (Gee et al., 2000). The binding to Ca²⁺ is only accompanied with

increase in the intensity of the emitted light, no wavelength shift in the emitted light is observed (Minta, Kao, & Tsien, 1989). Thus, the fluo-dyes are non-ratiometric and allow to detect only relative changes in cytosolic [Ca²⁺].

In brief, the hTERT RPE1 cells were plated onto 4-well µ-ibidi plates, and transfected with either Scr- or KCNH1-siRNA. The cells were then washed twice with warm growth medium with no FCS, and incubated with phenol red free DMEM medium supplemented with 1x GlutaMAX for 60 h. It should be noted that the number of the cells per well was adjusted to achieve a cell confluency of approximately 80 – 85% after the synchronisation. If allowed to reach full confluency, the growth of the cells would be contact inhibited (Bodnar et al., 1998; X. R. Jiang et al., 1999).

Figure 2.10: A schematic diagram de-picts the experimental design of intracellu-lar calcium imaging.

The synchronised cells were incu-bated with 10 µM Fluo-4AM in phenol red free DMEM medium supplemented with GlutaMAX and without FCS. The 4-well plate was placed for image acqui-sition in an environmental chamber with dark panels (Okolab) at 37^◦C and 5%

CO₂. Image acquisition started at 30 min after addition of Fluo4-AM, which was present throughout the entire dura-tion of the experiment.

All images were acquired using a Nikon TiE-Andor inverted microscope (Nikon) as described before. Fluo-4 was excited with the 488 nm laser line and emission was collected through a 525/30 emission filter. Laser power was set to 15%, and exposure time to 60 ms. In total, 10 positions per well were selected using the multipoint acquisition feature of NIS-Elements software. The timing of the image acquisition is depicted inFigure 2.10. First, the selected positions were imaged in the absence of FCS for 60 min at 10 s intervals. Afterwards, without removing the plate, 55 µl FCS was added to the imaged well to achieve 10% FCS final concentration.

After careful addition of FCS without disturbing the defined positions, imaging was immediately resumed and continued for an additional 60 min. The time interval

between the last image without FCS and the first image with FCS was fixed to 40 s throughout all the experiments.

Analysis of relative changes in intracellular [Ca²⁺]

The analysis of the obtained intracellular calcium imaging was done in three steps: i) processing of the recorded time-lapse series; ii) background subtraction and calculation of corrected total cell fluorescence (CTCF); iii) analysis of the oscillatory behaviour of the recorded signal.

Processing of time-lapse series. The changes in the intensity of the emitted light were measured using the NIS-Elements software (Nikon). In each recorded image, ten cells were manually selected as regions of interest (ROIs). Only the cells that stayed in the defined ROI throughout the experiment were used for analysis. In addition to sample ROIs, three background ROIs of constant size were identified. These background ROIs were later used for Mean Background Fluorescence calculation.

Once the ROIs were defined, using time measurement tool of NIS-Elements software the intensity values were measured for each ROI over time. The data was then exported into an Excel file.

Background subtraction and CTCF calculation. The generated Excel files were reorganised using R-studio open source software (RStudio, Inc). In brief, the script (kindly provided by J. Seidel; see Appendix) first reorganises the file by combining the recorded values for each ROI into following columns, e.g. Time ID, Time (sec), SUM of Intensities 1, Area of ROI 1. Afterwards, CTCF values are calculated using the following formula:

CTCF =Integrated Density – (Area of selected cell

∗Mean Background Fluorescence)

The CTCFs were calculated for each ROI and time point separately, and the resulting values were exported into a .txt file.

Analysis of the oscillatory behaviour of the recorded signal. The fluctuations in intracellular [Ca²⁺], also known as calcium oscillations, encode information on diverse biological processes. A cell is capable of differentiating these signals by decoding the frequency and the amplitude of the calcium oscillations (Parekh, 2011;

Samanta & Parekh, 2017; Smedler & Uhl´en, 2014). Calcium oscillations are reflected in fluctuations in Fluo-4 intensity that can be quantified.

The frequency of the oscillations of the acquired images was defined using an open source MATLAB-based tool “Spectral analysis of calcium oscillations”, version 3.0 (Uhlen, 2004). This tool deploys the Fourier transform principle: the time and frequency domains are swapped (Fourier, 1822), and as an output creates a power spectrum. The detailed explanation and the script can be found in Uhlen (2004). In brief, collected and calculated values from each ROI were divided into five temporal segments: i) total duration (-3600 – 3600 sec); ii) before FCS addition (-3600 – 00 sec); iii) after FCS addition (00 – 3600 sec); iv) the first peak observed immediately upon FCS addition (00 – 60 sec); and v) after FCS addition excluding the first peak (60 – 3600 sec). The power spectrum for each segment was generated and the mean

frequency value defined.

In addition to frequency, prominence and FWHM (full width at half maximum) were calculated for the above-listed segments in the frequency domain, and the total

Figure 2.11: Identification of peaks in the single-sided amplitude spectrum of in-tracellular [Ca²⁺].

number of peaks and total AUC (area under the curve) – in the time domain.

The prominence shows how much a given peak stands out of its neighbour peaks due to its intrinsic height and location relative to other peaks. Thus a low iso-lated peak can be more prominent than one that is higher but is an otherwise un-remarkable member of a tall range (The MathWorks, 2018). A new MATLAB-based script similarly to the above men-tioned one and using Fourier transform was developed. In brief, this second script first fits a linear function to the

“Before FCS” segment in order to com-pensate for the baseline drift in the collected data. This drift results from a combi-nation of on one hand photobleaching, and, on the other, continuous loading of the

cells with Fluo-4AM. The resulting line is extrapolated to the entire duration of the experiment and then subtracted from the measured values, resulting in a normalised CTCFs.

The peaks in the CTCF spectrum are identified and indexed with their time values using the “findpeaks” function in MATLAB. This function identifies local maxima (peaks) of the input signal vector by comparing valueiin the given spectrum with valuei−1 and valuei+ 1. If the valueiis larger than the two neighbours than it will be considered as a peak. In addition to the peak values, the function also returns a vector with the locations of the peaks in the time or frequency domain, the prominence and the FWHM (Figure 2.11). The AUC values in the CTCF spectrum are also defined.

The second part of the script repeats the aforementioned “Spectral analysis of calcium oscillations”, but this newly developed script identifies number, prominence and location (in relation to the frequency domain) of peaks, and also FWHM. The new script, given in the Appendix, is simplified due to its length and depicts the analysis of only one cell and segment.