Methods to study the functionality of cardiomyocytes

Microelectrode arrays (MEAs) or multielectrode arrays are platforms than can be used to meas- ure the functionality of electrically active cell aggregates and sheets. These arrays provide plat- forms for electrophysiological studies of CMs and allow the recordings of the changes in the field potential (FP). FP resembles an ECG measurement (Reppel et al., 2004). The field poten- tial duration (FPD) can be measured from MEA recordings; the FPD correlates closely with the QT interval in the ECG. MEA signals reflect rapid depolarization, a plateau phase, and repolari- zation of the AP. (Halbach et al., 2006; Meyer et al., 2004)

The MEA platform consists of an MEA plate, which has a cell culture well that allows long- term cell culturing, and electrodes for recording electrical activity. MEA plates are manufac- tured with different electrode configurations, which can be used for different applications. The contact between the cells and the MEA electrode affects the signal, and current MEA electrodes are not sensitive enough to record signals from single CMs. The MEA method is usually used for cell aggregates or sheets, and it is a rather easy and fast method that is suitable for larger scale screenings.

CMs of various origins have been investigated with MEAs, e.g., NRCs (Berdondini et al., 2005), hESC-derived CMs (Binah et al., 2007; Kehat et al., 2001; Pekkanen-Mattila et al., 2009) and iPSC-derived CMs (Itzhaki et al., 2011a; Itzhaki et al., 2012; Lahti et al., 2012; Mehta et al., 2011). The MEA method has also been used extensively in in vitro drug testing (Braam et al., 2010; Caspi et al., 2009; Liang et al., 2010; Reppel et al., 2004).

30 2.4.4.2 Patch clamp method

Electrophysiology of single cells can be studied using a patch clamp, which is regarded as the gold standard method of cellular electrophysiology (Hamill et al., 1981; Sakmann and Neher, 1984). With this technique, a cardiac AP can be recorded from a single CM. To measure ion currents, a glass micropipette containing an electrode with ionic solution is brought into tightly sealed contact with the cell membrane. The tight gigaohm seal is formed between the cell mem- brane and the recording electrode by applying suction to the micropipette. This isolates the membrane patch from the external environment electronically and allows the measurement of changes in the membrane potential or ion channel currents at the level of single cells or even single channels across this patch of membrane. (Molleman, 2002)

Many variations of the patch clamp technique exist. In the whole-cell patch technique, cur- rent or voltage changes can be studied. In the voltage clamp method, the ion currents through the membranes of excitable cells are measured, and in the current-clamp method, changes in voltage across the plasma membrane are measured while the current is held constant. Another technique is the perforated patch method, where the cell membrane is ruptured with chemical agents such as amphotericin-B, thus generating many small holes in the membrane. (Hamill et al., 1981; Molleman, 2002) Traditional patch clamp is a very time consuming method, and in recent years, automated planar patch clamp technology has been developed to generate high- quality data with high-throughput capabilities (Milligan and Moller, 2013). Many automated patch clamp platforms have been developed during last years but this method is not yet selective enough to study iPSC-derived CMs accurately.

The patch clamp method have been utilized in studying the maturation of both hESC and IPSC derived CMs (Sartiani et al., 2007; Pekkanen-Mattila et al., 2010; Ma et al., 2011; Lundy et al., 2013). It has also been widely used in disease modeling studies to characterize the pheno- type of iPSC-derived CMs (Moretti et al., 2010; Yazawa et al., 2011; Itzhaki et al., 2011a; No- vak et al., 2012; Lahti et al., 2012; Jung et al., 2012; Itzhaki et al., 2012; Di Pasquale et al., 2013) as well as drug responses of these cells (Yazawa et al., 2011; Itzhaki et al., 2011a; Itzhaki et al., 2012).

31 2.4.4.3 Calcium imaging

Ca²⁺ imaging is a technique that quantitatively measures the fast cyclic increases and decreases in [Ca²⁺]i which causes the Ca²⁺ transients. The intracellular free Ca²⁺ levels are measured using cytoplasmic Ca²⁺ indicator dyes, which are fluorescent molecules that respond to the binding of Ca²⁺ ions by emitting fluorescent light. (Adams, 2010; Paredes et al., 2008)

Ca²⁺ indicators can be non-ratiometric or ratiometric. Non-ratiometric dyes such as Fluo-4 utilize only one wavelength and are optimal for detecting more than one fluorophore. One ad- vantage of non-ratiometric indicators is that an increase in fluorescence signal can be related directly to an increase in [Ca²⁺]i. However, fluorescence intensity depends on many factors such as probe concentration and acquisition conditions not related to [Ca²⁺]i. Ratiometric indicators such as Fura-2 utilize two wavelengths, and the amount of intracellular Ca²⁺ can be determined by the ratio between the two emission amplitudes, which show Ca²⁺ free- and Ca^2+-bound -forms of the indicator. The benefit of ratiometric indicators is that those indicators can be calibrated precisely to minimize the problems associated with uneven dye loading, dye leakage, photo- bleaching and cell volume variability. (Grynkiewicz et al., 1985; Paredes et al., 2008)

Indicator dyes usually contain an acetoxymethyl (AM) ester group, which makes the mole- cule lipophilic and which allows easy entrance into the cell. Once the indicator is in the cell, esterases cleave off the AM group, the dye is captured inside the cell, and carboxyl groups are exposed, which will be able to bind Ca²⁺. (Paredes et al., 2008)

Ca²⁺ imaging method have been utilized widely when studying CMs, for example to inves- tigate the basic Ca²⁺ cycling properties and maturity of hESC and iPSC-derived CMs (Itzhaki et al., 2011b; Lee et al., 2011; Lundy et al., 2013). Ca²⁺ cycling of iPSC-derived CMs has also been characterized in disease modeling of long QT syndrome (LQT) (Spencer et al., 2014) and more widely in CPVT disease with Ca²⁺ cycling defects (Di Pasquale et al., 2013; Fatima et al., 2011; Itzhaki et al., 2012; Jung et al., 2012; Zhang et al., 2013). In addition, the drug effects of diseased CMs have been evaluated with this method (Jung et al., 2012; Yazawa et al., 2011).

2.4.4.4 Analysis of the mechanical beating behavior

It has been shown that in addition to defective electrophysiology of a CM, genetic cardiac dis- eases alter also the mechanical beating behavior, like beating motion or contractile force (Chang et al., 2013; Kiviaho et al., 2015; Lahti et al., 2012). Therefore, novel approaches for analyzing

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the mechanical beating of the CMs have been developed. The electrophysiology, contractile force and mechanical beating behavior of a CM are connected, and the comparison of this data has been under investigation.(Laurila et al., 2015)

Mechanical beating behavior can be analyzed with different non-invasive methods. The force exerted by hPSC derived CMs during a contraction has been estimated with a traction force microscopy, where fluorescent beads are embedded in a cell culture substrate. Cell activity can be tracked with optical methods from the displacement of the fluorescent beads due to the cell contraction. (Hazeltine et al., 2012) Atomic force microscopy (AFM), where a small canti- lever is brought in contact with the beating CM to measure the vertical movement of the cell membrane, has also been used to measure beating behavior of hPSC derived CMs(Liu et al., 2012). Impedance assays, where cells are seeded on a plate containing electrodes, can detect the beating of CMs indirectly from the varying impedance signal based on mechanical movement of the cells (Peters et al., 2015). The video microscopy is a new and fast method for the analysis of mechanical CM beating behavior. Different light microscopy methods have been published in recent years, which all are based on a video recording of a beating cell or area, which is then analyzed by different computational methods. (Kamgoue et al., 2009; Hayakawa et al., 2012;

Ahola et al., 2014; Maddah et al., 2015; Laurila et al., 2015)