Research Article Open Access
A Quantitative Comparison of 12-Lead Electrocardiograms and In Vitro Field Potentials of Stem Cell-Derived Cardiomyocytes
Sari U. M. Vanninen1,2*#, Ville J. Kujala1#, Ilkka Porsti1,3 and Katriina Aalto-Setala1,2
1 Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
2Heart Hospital, Tampere University Hospital, Tampere, Finland
3Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
#These authors contributed equally to this work
*Corresponding author: Sari Vanninen, MD, University of Tampere, Faculty of Medicine and Health Technology, Tampere, Finland. Tel: +358 50 528 2291; E-mail: @
Received: June 8, 2019; Accepted: July 29, 2019; Published: August 1, 2019
Citation: Vanninen SUM, Kujala VJ, Ilkka P, Katriina AS (2019) A Quantitative Comparison of 12-Lead Electrocardiograms and In Vitro Field Potentials of Stem Cell-Derived Cardiomyocytes. Int J Stem Cells Res Ther 1(1): 1-8.
Objective: The human pluripotent stem cell -derived cardiomyocyte (hPSC-CMs) represent a novel biomedical technology that hold great promise for investigating cardiac differentiation and characteristics. The electrophysiological aspects of hPSC-CM can be studied using microelectrode arrays (MEAs). However, there are no reports about the relationship of electrophysiological characteristics between the human heart and hPSC-CMs.

Methods: We analyzed 12-lead electrocardiograms (ECG) of healthy individuals with low basic heart rate and they all performed stress exercise test to increase heart rate and to match the beating rate of hPSC-CMs. All participants were on sinus rhythm. The field potential (FP) recordings of hPSC-CMs were obtained with MEAs. We compared FP duration (FPD) to QT time of ECG and the effect of beating frequency on repolarization time was analyzed.

Results: The heart rate in ECG ranged from 39 to 120 beats per minute (bpm) and the beating hPSC-CMs clusters were selected to match this range. The uncorrected mean FPD value was 375 ms with a mean beating rate of 73 bpm, while the uncorrected mean QT was 397 ms with a mean heart rate 76 bpm. When FPD was corrected with Bazett’s formula, cFPD was 400 ms with standard deviation (SD) 64 ms. Volunteers had a mean QTc 432 ms with SD 39 ms. The duration of uncorrected and corrected QT and FPD were similar in ECG and MEA recordings in this beating range.

Conclusions: Our results further validate hPSC-CM clusters as proper in vitro models of the native human heart and demonstrate the quantitative relationship of corresponding components in ECG and cardiac MEA recordings.

Keywords: Electrocardiogram; Microelectrode array; Cardiac field potential; Cardiac parameters; Dynamics, QTc, cFPD Running title: Cardiac Dynamics;
The first stable human embryonic stem cell (hESC) lines were derived in 1998 [1] and first human induced pluripotent stem cell (iPSC) lines in 2007 [2]. Since then, these human pluripotent stem cells (hPSCs) have been used in a variety of applications. They are able to differentiate into cell types of all the three germ layers: ectoderm, mesoderm, and endoderm. Hence, one of their advantages is that they enable to study of the functionality of human cells and pathophysiology of genetic diseases in vitro without the need of biopsies of the diseased organ. Biopsy is potentially a risky procedure and the fully differentiated cells in the biopsy usually de-differentiate fast in cell culture conditions, as is the case with human cardiomyocytes [3, 4].

The hPSC-derived cardiomyocytes (hPSC-CMs) have been proposed as a source of scalable and comparable origin of human cardiomyocytes [5]. Using microelectrode arrays (MEAs) it is possible to record electrical activity of cultured cells. Field potentials (FPs) of beating cells provide detailed information about the origin and spread of excitation in the heart [6]. FP represents spread of excitation and the conduction velocity in MEA, and FP corresponds intrinsic action potential (AP). In addition to AP duration is same than FP duration (FPD) [7]. The MEA with hPSC-CMs represents a platform that enables mediumthroughput analysis of human cardiac tissue. This is of interest especially for basic research, but also for pharmaceutical industry because novel human models for preclinical safety testing are urgently needed. In order to be used in preclinical tests, however, more detailed knowledge on how hPSC-CMs correspond to the clinical ECG measurements is needed.

The hPSC-CMs represent a powerful platform to study human cardiac tissue and they hold a great promise for investigating cardiac differentiation and characteristics. These cardiomyocytes were for the first time reported just three years after the derivation of the hESC lines [8] and since that they have been extensively used in vitro for electrophysiological recordings [8 -10]. The FPD is analogous with the QT interval on the ECG [11].

The QT interval represents the duration of ventricular electrical systole, especially from the beginning of contraction to the end of relaxation [12]. In epidemiologic studies the abnormal duration of the QT interval has been found to identify individuals at increased risk of sudden cardiac death [13], therefore measurement of QT interval is important.

QT time is the interval of the beginning of ventricle activation (beginning of the Q wave) to the end of repolarization (end of the T wave), and RR is the time from onset of one QRS complex to another in seconds [12]. The QT interval is known to be ratedependent [14]. In order to be able to compare QT intervals of different beating rates, the Bazett’s formula (QTc = QT/√RR and cFPD = FPD/√PPI (equation 1)) has been created. This formula has also been widely used to correct FPD for beating rate of cardiomyocytes in MEA recordings [15,16]. However, we found no studies directly comparing the cardiac parameters between ECG and MEA recordings. Therefore, we compared sinus rhythm 12-lead ECG from healthy adult individuals to cardiac FP recordings of hPSC-CMs. Our aim was to investigate how well the ECG and cardiac FP recordings correspond to each other. This provided further information how well hPSC-CMs recapitulate the electrophysiological characteristics of the adult human heart. These results indicate similar electrophysiological field potential characteristics, which further validate the hPSC-CM clusters as reliable in vitro models of the human myocardium.

In this study we compared with FPD to QT time and PPI to RR, in this context, also the comparability of the cFPD and QTc formulas.
Materials and MethodsTop
Ethical Approval
The local Ethics Committee gave their approval for the study (R07110M and R08070).
Stem cell culture
H7H7 hESCs (WiCell) [1] were cultured on mitomycin C inactivated mouse embryonic fibroblasts (MEFs) in hES medium, which consisted of DMEM/F-12 (Invitrogen) supplemented with 20% KnockOut Serum Replacement (KO-SR), serum replacement (Invitrogen), 1% nonessential amino acids (Lonza), 2 mM L-glutamine (Glutamax, Invitrogen), 50 U/ml penicillin/ streptomycin (Lonza), 0.1 mM beta mercaptoethanol (Invitrogen), and 7.8 or 4 ng/ml basic fibroblast growth factor (R&D Systems). The hES medium was refreshed daily, and the stem cell colonies were passaged onto a new MEF layer once a week using 1 mg/ml collagenase IV (Invitrogen). The stem cell derived cardiomyocytes are called hPSC-CMs in the results.
Cardiomyocyte differentiation and plating on microelectrode arrays
The hPSCs were differentiated into cardiomyocyte clusters by co-culturing them with mouse visceral endoderm –like cells as described before [17]. Spontaneously beating hPSC-CM clusters were excised and plated onto FBS (Invitrogen) and 0.1% gelatine (Sigma-Aldrich) coated 6wellMEA200/30iR-Ti-mr MEAs (Multi Channel Systems MCS GmbH) in EB medium consisting of Knockout DMEM (KO DMEM) (Gibco Invitrogen, USA) supplemented with 20% foetal bovine serum (FBS) (Gibco Invitrogen, USA), 1% nonessential amino acids (Cambrex BioSciences, Verviers, Belgium), 1% L-glutamine (Invitrogen, USA), and 50 U/ml penicillin/ streptomycin (Cambrex BioSciences, Verviers, Belgium). Cells were fed three times per week.
Electrocardiogram recordings
ECGs were obtained from five normal body weight healthy and no smoking individuals (mean age 38.4 + 20 years, 3 males and two females). Nobody has left ventricular hypertrophy using ECG Sokolow-Lyon-criterions. Other clinical characteristics of the healthy volunteers are described in Table I. Individuals with low baseline beating rate were selected to have similar rage of beating rate in clinical and in vitro situations. Electrodes were positioned using standardized protocol at a paper speed of 50 mm/s and 10 mm/mV standardization. All participants were in sinus rhythm and with normal conduction, depolarization and repolarization times. The volunteers performed a stress exercise test on a cycle ergometer (GEMS IT CardioSoft V4.2).
Table 1: Clinical characteristics of the healthy volunteers.






















60 bpm

















after treatment
with radio-


























Abbreviations: Gender (M, male; F, female); beating rate, BR; millisecond, ms; * with levothyroxin euthyreotic
Before the stress test, 12-lead electrocardiography was recorded at rest until heart rate decreased to less than 50 bpm. Starting at 20 W the work rate was then increased 5 W manually by degrees. The workload was adjusted for each volunteer so that heart rate increased as smoothly as possible. Each test person pedaled continuously at a cadence of 60 rpm (revolutions per minute), and the exercise stress test was interrupted when the target heart rate (130 bpm) was reached. None of the participants had symptoms limiting their exercise.

RR and QT interval were measured on a single selected lead V2 or V3 because of the clearest T wave during exercise. Heart rate was determined by RR cycle lengths (ms) before measured QT interval. QT interval was manually measured from the beginning of the QRS complex to the tangent to the end of the T wave. Measurements were taken to the nearest 5 ms. The QT interval and heart rate measured by the analysis programs was used in the heart rate adjustment formulae from the Framingham Study, adjusting the measured QT intervals for heart rate using Bazett’s formula [18].
Field potential recordings with microelectrode arrays
FPs were recorded in 5% FBS containing EB medium from 9 hESC-CM clusters in room air with a USB-MEA amplifier (Multi Channel Systems MCS GmbH) using a 20 kHz sampling rate. MC_ rack 4.0.0 software (Multi Channel Systems MCS GmbH) was used for data acqusition. MEAs were covered with a gas-permeable membrane (ALA Scientific) during recordings to keep cultures sterile. Recordings were made at +37 °C using a TC02 heater controller.
Data analysis
The different parameters were measured manually from the ECG recordings and using Clampfit 10 software (Molecular Devices, Inc.) from the FP recordings. Heart rate (HR, beats per minute [bpm]), beating rate (BR, bpm), heart-rate corrected QT-interval (QTc, Bazett’s formula, equation 1), beating-rate corrected field potential duration (cFPD, Bazett’s formula, equation 1), RR interval (ECG) and peak-to-peak interval (PPI, FPs) were determined from the recordings.
QTc = QT/ √RR and cFPD = FPD/√PPI (equation 1) [19]
Where, QTc is the heart rate corrected QT interval, QT is the QT-interval in milliseconds, and RR is the time from onset of one QRS complex to another in seconds (often 60 bpm). The same formula was used for FP recordings as well, only then QTc became cFPD, QT was FPD and RR was PPI (determined as time between two depolarizing sodium [Na+] peaks). FPD (Figure 1) was determined as the time between the onset of initial deflection and return to baseline as described before [15]. FPD and PPI were measured in triplicate from each FP trace and the mean value was used for further analysis. The Poincaré maps were generated using Neuroexplorer 4 software (Nex Technologies).
Statistical analysis
Data are presented as mean ± standard deviation (SD). Statistical analyses were performed between the groups with two-tailed t-test using SPSS software (IBM).
The QT and RR as well as FPD and PPI values were measured from the ECG and MEA recordings, respectively (Table II). The healthy volunteers were chosen so that the baseline beating rate was low to match the slowest beating rate in hPSC-CMs and stress exercise test was performed to match the highest beating of the cells.

A characteristic hPSC-CM FP trace is presented in figure 1 with the different cardiac FPD and PPI along with the sodium (Na+), calcium (Ca2+), and potassium (K+) components depicted. Millivolt scale recording of hPSC-CMs is possible with the MEA platform and these cardiomyocytes exhibited stable beating rhythms (Figures 1 and 4). The corresponding QT and RR intervals are also presented in Figure 1.

The relationship of the dynamics of native QT interval/FPD and RR/PPI is presented in Figure 2. These parameters have a clear positive correlation. The coefficient of determination of approximately 0.71 for hPSC-CMs (linear fitting) in figure 1 shows that 71% of the FPD prolongation is explained by concurrent prolongation of the PPI. The coefficient of determination was about 0.68 for ECG.

Figure 3A demonstrated the relationship between QTc and cFPD measurements. The hPSC-CMs had a mean cFPD of 400 ms with s SD 64 ms. Volunteers had a mean QTc 432 ms, SD 39 ms. Uncorrected mean FPD value was 375 ms with a mean BR of 73 bpm. Uncorrected mean QT was 397 ms with a mean heart rate 76 bpm.

We investigated the relationship of electrical activation and beating rate clinically in the adult myocardium with ECG and in vitro with MEA recordings of hESC-derived cardiomyocytes. Figure 3B depicts the mean values of measured QT-to-RR (QT/ RR) and FPD-to-PPI (FPD/PPI) relationships in ECG and MEA recordings, respectively. Their FPD/PPI mean value was 0.44, ranging between 0.358-0.545. Volunteers´ QT/RR mean value was 0.485, ranging between 0.312-0.634.

Figure 4 shows the PPI dynamics of cardiac MEA recordings with Poincaré plots where each the duration of each PPI interval is plotted against the preceding PPI interval. The plots show stable beating dynamics just above or below 1 hertz (Hz), which is evident from the small scatter and linear relationship.
Figure 1: Characteristics of field potential (FP) (upper panel) and ECG (lower panel) recordings. A FP trace from one of the recording electrodes on the microelectrode array generated by the spontaneously beating human embryonic stem cell –derived cardiomyocytes. The field potential duration (FPD) and peak-to-peak interval (PPI) is also depicted in the first cardiac FP cycle along with the sodium (Na+), calcium (Ca2+), and potassium (K+) currents in the cardiac FP components.
We compared FPD with QT time and PPI with RR. An ECG is shown how heart rate is determined by RR cycle lengths (ms) before measured QT interval. QT interval is measured from the beginning of the QRS complex to the tangent to the end of the T wave.
Figure 2: Correlation of beating rate and QT and FPD duration. RR-interval (ms) of electrocardiograms is plotted against QT- intervals (ms) and peakto- peak interval (PPI, ms) of pluripotent stem cell –derived cardiomyocyte (hPSC-CM) recordings against field potential durations (FPD, ms). For hPSC-CMs linear fitting shows a value of approximately 0.71 for the coefficient of determination and for ECGs about 0.68.
Figure 3: Comparison on electrocardiogram (ECG) and field potential (FP) parameters. A) Rate-corrected QT interval (QTc) and field potential duration (cFPD) times (ms). B) The ratio of QT interval (ECG) or FPD microelectrode arrays (MEA) to duration of one beat (RR with ECG and PPI with MEA recordings). The ratio was calculated with all the intervals measured in milliseconds.
Figure 4: Beat rate dynamics within microelectrode arrays (MEA) recordings of human embryonic stem cell -derived cardiomyocytes (hESC-CM) clusters. Representative Poincaré maps of peak-to-peak (PPI) intervals. Poincaré maps show the relation of a given PPI (x-axis) to its preceding PPI (y-axis) in seconds. Tight grouping and linear relationship of the data points suggest stable beating dynamics in the field potential (FP) recordings.
Electrical properties of the heart are due to ion transfer across the cell membrane. in vivo, the transfer rate of potassium ions is dependent on the beating rate [20]. The repolarization time presented as the QT interval in the ECG is, thus, dependent on how fast the heart beats [14]. Because heart rate is the principal determinant of repolarization length, there are several formulas how to correct the measured QT interval so that QT intervals with different beating frequencies can be compared [12]. The simplest and most widespread approach to correct the QT interval is to divide its value by the square root of the preceding RR interval expressed in seconds (QTc= QT/√RR), i.e., by using Bazett’s formula [21].

The definition of normal QT time has been difficult because abnormal QT interval can be either “too long” or “too short”. Large population studies suggest that, for the adult population, normal QTc values for males are 350 to 450 ms and for females 360 to 460 ms [22]. Therefore, the upper limit for the normal QTc for both genders is 0.44 seconds [12]. Nevertheless, the Bazett’s correction overestimates the number of patients with a prolonged QT [23]. However, marked abnormalities of the QT interval may be caused by many different reasons and situations: genetic disorders (e.g., long/short QT syndrome), pharmacologic agents (e.g., antiarrhythmics, antipsychotics, antibiotics), electrolyte abnormalities (e.g., hypokalemia and hypomagnesemia), and their interactions [13]. Especially when pharmacologic agents cause QT changes, we can choose individual treatments for patients by combining evidence from In vivo and in vitro studies using hPSC-CM- techniques [5]. Disease-specific likewise patientspecific hPSC-CMs also function in the study of heart diseases such as arrhythmias and electrophysiologcal abnormalities in patients [24, 25].

It is important that we have methods to examine cardiomyocyte function that correlates with in vitro models of the native human myocardium. At the moment there are no direct reports about the relationship of intricate electrophysiological characteristics between the human heart and hPSC-CMs. Here we investigated the relationship of electrical activation and beating rate clinically using ECG and MEA recordings in vitro of hPSC-CMs, and we found that the results correlated well with each other.

With hPSC-CMs these same correction formulas have been used without any evidence whether the same formulas are effective during in vitro situations [11]. In this paper we chose control individuals with low baseline beating rate and performed a stress exercise test to obtain QT variation over the same beating frequency spectrum as obtained with spontaneously beating hPSC-CMs. The cells were cultured on MEA platform and the FPD, corresponding to QT in ECG, was measured. A strong correlation was observed between QT intervals and FPDs, and also the correction of these parameters using Bazett’s formulation gave similar results thus confirming that the ion fluxes in cell culture present with corresponding performance as they do in vivo in the heart.

The hPSC-CMs beating rates in our cell cultures varied from 55 (minimum) to 100 (maximum) bpm with the average of 73 bpm, which is in the same range as typically observed in human heart. Normally the QT interval of the heart decreases with increasing heart rate due to increase [14] in potassium flux across the cell membrane [20]. We compared the cardiac parameters between ECG and MEA recordings and could show that QT and RR parameters correlate excellently with the FPD and PPI results. In the volunteers who were used for comparison the heart rate varied 39-120 bpm with an average of 76 bpm (Table II and Figure 2).
Table 2: The cardiac parameters measured from the electrocardiogram (ECG) and microelectrode array (MEA) recordings. Parameters were QT and RR interval in ECG measurements and field potential duration (FPD) and peak-to-peak interval (PPI) in MEA measurements. The first measurement is heart rate 40 beats per minute (bpm), second measurement 80 and third measurement 120 bpm in the ECG.


(Volunteer/hiPSC-CM Cluster #)

first measurement

second measurement

third measurement

QT (Volunteer #1)




RR (Volunteer #1)




QT (Volunteer #2)




RR (Volunteer #2)




QT (Volunteer #3)




RR (Volunteer #3)




QT (Volunteer #4)




RR (Volunteer #5)




QT (Volunteer #5)




RR (Volunteer #5)





FPD (Cluster #1)




PPI (Cluster #1)




FPD (Cluster #2)




PPI (Cluster #2)




FPD (Cluster #3)




PPI (Cluster #3)




FPD (Cluster #4)




PPI (Cluster #4)




FPD (Cluster #5)




PPI (Cluster #5)




FPD (Cluster #6)




PPI (Cluster #6)




FPD (Cluster #7)




PPI (Cluster #7)




FPD (Cluster #8)




PPI (Cluster #8)




FPD (Cluster #9)




PPI (Cluster #9)




QT/RR value means how quickly and how much QT-interval changes when heart rate changes [26], corresponding to FPD/ PPI values in MEA. The over- or under correction of QTc may lead to significant and systematic bias with both false positive and false negative findings, whereas QT/RR patterns in all different subjects will be characterized by the same mathematical form. Population risk stratification studies investigating QT/RR patterns would also benefit from this mathematical description avoiding the heart rate influence [27]. QT/RR mean value of our volunteers was 0.433, ranging 0.40-0.46. The mean FPD/PPI value of 0.44 indicates that the basal FPD in relation to the time between individual beats corresponds with QT/RR values.

The coefficient of determination value of 0.71 is good for biological samples. Our results indicate that 71% of the FPD prolongation is explained by concurrent prolongation of the PPI. However, the FPD generally increased when PPI was prolonged. Taken together with the observation that hPSC-CMs demonstrated fairly stable PPI dynamics within recordings, we conclude that they are rather reliable models in terms of their electrophysiological aspects, especially when corrected for beating rate –dependent FPD modulation. Of note, in different individuals the predicted QT times may normally vary up to 90 ms even if the RR-cycle lengths are the same [12]. QT time variation between our volunteers was less than 60 ms at various RR cycle lengths. The coefficient of determination of our volunteers was about 0.68 for ECG.

The cFPD value of 400 ms for hPSC-CMs is within the physiological normal range for QTc values. Because uncorrected FPD was 375 ms at 73 bpm, the Bazett’s formula is adequately compensating for the rate-induced FPD shortening also in hESCCMs. Analogously, the volunteers had a mean QTc 432 ms and their uncorrected mean QT was 397 ms with a mean heart rate 76 bpm. However, heart rate of 60 bpm is the most optimal when using Bazett’s formula [21]. Usually the relationship between QT/RR adaptation and mean QTc values means that those subjects who show longer QTc intervals have steeper QT/RR patterns [19]. In our study we demonstrated that the relationship FPD/PPI adaptation and cFPD values functioned similarly to the relationship between QT/RR adaptation and QTc values.

In this study beat rate was a little slower than heart rate, therefore cFPD was a slightly shorter than QTc. It is also known that Bazett’s formula overcorrects the QT interval at high heart rates [12] and under-corrects QT time at low heart rates [18]. This probably is the reason for this small difference between our cFPD and QTc values. In this research we did not compare the different correction formulas. However, there is no consensus, which would be clinically the optimal formula (Fridericia’s or Bazett’s formula) [19]. Although Bazett’s formula is the most widely used for QTrate correction, AHA/ACCF/HRS has also recommended that linear regression functions rather than the Bazett’s formula be used for QT-rate correction [28]. However, the Bazett’s formula is easy to use and widely applied and thus it was chosen in this study to compare in vitro and In vivo electrical parameters in different beating rates.
QT time is beating rate dependent and QTc is the rate corrected QT interval adjusted for the changes due to beating rate and corresponding ion flux across the cell membrane. The same correction formula was equally applicable to the corresponding repolarization parameter (FPD) in hPSC-CMs and thus the same rate correction formula can be applied to electrical recordings in cell culture situation.
Henna Lappi and Markus Haponen are acknowledged for technical support. The work was financed by Business Finland, former TEKES, Academy of Finland, Finnish Foundation for Cardiovascular Research and Pirkanmaa Hospital District.
Conflict of Interest
There are no conflicts of interest by any of the authors of this manuscript
Competing interests
The authors declare that they have no competing interests.
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