Category Archives: Publications

EMG Analysis of Hip & Trunk Muscle Activity in Subjects With Gluteus Medius Weakness

Lee et al had integrated the Ultium EMG system with myoResearch software to investigate the effects of different side bridge exercises on the hip and trunk muscles in subjects with gluteus medius weakness in order to determine which variation would be best to help strengthen the gluteal muscles. Discover the application of EMG analysis into Hip and Trunk Muscle activity published in the Journal of Sport Rehabilitation by reading a snippet of the article below or clicking here for more!

Side bridge exercises strengthen the hip, trunk, and abdominal muscles and challenge the trunk muscles without the high lumbar compression associated with trunk extension or curls. Previous research using electromyography (EMG) reports that performance of the side bridge exercise highly activates the gluteus medius (Gmed). However, to the best of our knowledge, no previous research has investigated EMG amplitude in the hip and trunk muscles during side bridge exercise in subjects with Gmed weakness. The purpose of this study was to examine the EMG activity of the hip and trunk muscles during 3 variations of the side bridge exercise (side bridge, side bridge with knee flexion, and side bridge with knee flexion and hip abduction of the top leg) in subjects with Gmed weakness.

Learn more about how AEROBE’s EMG solutions such as the Ultium EMG sensor system can be transformative for sports rehabilitation research or for clinical applications by clicking here.

Combining EEG and eye tracking: a workflow for your lab experiment

Combining EEG and eye tracking can open new possibilities for your EEG analysis. If you would like to add eye tracking to your EEG setup but are unsure how to implement this, we have great news for you: Thanks to our new cooperation with Tobii Pro, Brain Products now offers complete out-of-the-box solutions for simultaneous EEG and eye tracking!

Abstract

This article intoduces how you can combine your EEG measurements with simultaneous eye tracking. We offer a full example workflow for a specific lab-based setup, while pointing to generally important aspects for a successful combination of EEG and eye tracking. For our setup, we are using the software Tobii Pro Lab for experimental control, the Tobii Pro Spectrum for recording eye tracking data, and the actiCHamp Plus to record EEG data in combination with our Photo Sensor. In the workflow, we describe how you can design your experiment while setting up shared event markers, how to perform the combined recordings, and how to merge both data streams in BrainVision Analyzer 2.

Boost your EEG research with simultaneous eye tracking!

In the last decade, the combination of eye tracking with measures of brain activity like EEG or fMRI has increased. But why would we want to take a closer look at the eyes when investigating brain activity?

Eye tracking offers two major sources of information:

The position of a person’s gaze gives us insight into the “open” focus their attention, and this information can be highly valuable for EEG research. With this gaze information, you will be able to tell if participants are focusing their attention on a target, when exactly their focus arrives and for how long it stays until shifting elsewhere. This will let you identify trials in which the participant was not paying attention and discard them from your analyses. Most importantly though, you gain precise timing information for your EEG analysis. Event-related potentials (ERPs) can, for example, be calculated with respect to the fixation on a stimulus (i.e., Fixation-Related Potentials), instead of the mere stimulus appearance on the screen. Research addressing topics like attentional processes, visual search, reading or social perception can highly benefit from gaze information.

Changes in pupil size can inform about cognitive and emotional experiences. The pupil reacts with short dilations (in the second range) to different stimuli. These “pupil responses” are a very sensitive physiological measure, and their magnitude reflects the intensity of the undergoing cognitive/emotional processes. Thereby, stimuli that are more emotionally arousing, or that demand higher cognitive effort cause larger pupil responses. By analyzing them, you may be able to check if your experimental manipulation was successful, or to even follow cognitive or emotional processes dynamically throughout your experiment. In combination with EEG, you could, for example, use the magnitude of pupil responses to weigh or categorize different trials in your experiment.

Adding eye tracking to your EEG setup will hence open a range of new possibilities for your research!

Figure 1. Combined EEG and eye tracking setup in a laboratory setting

Figure 1. Combined EEG and eye tracking setup in a laboratory setting

A workflow for your lab-based EEG & eye tracking experiment

Combining two different measures like EEG and eye tracking can be technically challenging, especially if they should be temporally aligned and analyzed together. The key here is setting up shared event markers/triggers that will appear in both the EEG and the eye tracking data at the same time. Note that not all trigger signals need to be shared. It is enough to have a few (at least two) shared event markers to align the data sets after recording. It is however crucial that an equal number of the shared events appear in both data sets, and that they mark common points in time. Therefore, we need to plan our setup and experiment with these shared trigger events in mind.

Event markers are usually generated by the software used for experimental control (like E-Prime®Presentation®Psychtoolbox or Tobii Pro Lab‘s Designer module). There are many ways to pass them on to your EEG and eye tracking recordings. The best setup for you will depend on your experimental software and the properties of the computer, EEG amplifier and eye tracker you are using.

Here, we want to show you one concrete example for setting up simultaneous EEG and eye tracking recordings. We are going to explain how you can record high-quality data in a lab-based setup using:

Figure 2. Example workflow for simultaneous EEG and eye tracking recordings

Figure 2. Example workflow for simultaneous EEG and eye tracking recordings

1. Design your experiment and set up shared event markers

Naturally, the workflow needs to start with designing and planning your experiment. If you use the Tobii Pro Lab software for the experimental design, it will allow you to set up the timeline of your experiment in a very intuitive way. Make sure the timeline always starts with a calibration and validation routine to accurately map and record gaze data. Next, you can add all sorts of stimuli to the timeline, e.g., pictures, text elements, videos, or groups of stimuli. You can find an introduction video on how to create a screen-based study with Tobii Pro Lab here, and further useful information here.

When designing your experiment, you need to set up shared event markers that will allow you to temporally align EEG and eye tracking data after recording. Note that you will need at least two markers of the same type appearing at the same time in both data streams. For example, you can send the first synchronization marker a few seconds before your task begins, and the last one a few seconds after the task finishes. This way your synchronization markers span the whole experiment, and you can align the EEG and eye tracking data sets completely.

(a) Marking events in the eye tracking data

The Pro Spectrum eye tracker can receive TTL trigger signals. However, in this specific example, we are using Tobii Pro Lab not only to present stimuli, but also to record eye tracking data. Therefore, all presented stimuli will be marked automatically as “Events” in the eye tracking data and you don’t need to worry about triggers.

(b) Marking events in the EEG data

To mark stimulus events in the EEG data, TTL hardware triggers are usually the preferred solution because they offer highly accurate timing. Tobii Pro Lab can send TTL pulses to mark stimulus events if your computer has a parallel port card available. However, for this scenario we will assume that you are working with a laptop that has no parallel port.

With a small workaround, you can still precisely record the stimulus timing in your EEG data by using a Photo Sensor. This small accessory detects changes in brightness that can be recorded alongside your EEG data. Simply attach the Photo Sensor to one corner of the presentation screen and modify your stimuli in a way that they differ in brightness in this very corner (see Figure 3). This way, the photo sensor will detect a change in brightness every time the next stimulus is presented. During later analysis, you can identify the stimulus onsets from the Photo Sensor signal. The timing of this solution is very precise because stimuli are detected by the Photo Sensor exactly when they appear on the screen.

 Tip: If you want to directly generate trigger events from your Photo Sensor, you can combine it with the Brain Products StimTrak! The StimTrak can convert the Photo Sensor signal into trigger pulses and pass them on to your EEG recording where they will appear as event markers. See this article for more information.

 Tip: If you want to identify different kinds of stimuli from your Photo Sensor signal, you can modify your stimuli with different shades of grey (this article offers a more detailed description).

Figure 3. Using a Photo Sensor to detect stimulus onsets. In this example, two checkerboard stimuli (A and B) are shown alternatingly on the presentation screen. Only Stimulus A displays a bright square in one corner. If the Photo Sensor is attached in this corner of the presentation screen, it will detect the change in brightness at every onset and offset of Stimulus A. During later data analysis, the photo sensor signal can be used to derive stimulus markers with very precise timing

Figure 3. Using a Photo Sensor to detect stimulus onsets. In this example, two checkerboard stimuli (A and B) are shown alternatingly on the presentation screen. Only Stimulus A displays a bright square in one corner. If the Photo Sensor is attached in this corner of the presentation screen, it will detect the change in brightness at every onset and offset of Stimulus A. During later data analysis, the Photo Sensor signal can be used to derive stimulus markers with very precise timing.

Here are a few additional options for alternative setups:

 Find a support article here about sending TTL trigger pulses, or read about our TriggerBox for sending trigger signals via USB port.

 If your EEG amplifier and your eye tracker have trigger ports and you can send TTL pulses, you can share the exact same triggers among both devices. Either split the trigger signal with a Y-cable, or use the practical trigger mirroring function of our actiCHamp Plus: this amplifier can receive 8-bit triggers and can immediately pass them on to your eye tracker!

 If you are using E-Prime® for presenting your experiment and have a screen-based Tobii eye tracker, you may be interested in the E-Prime extension for Tobii Pro Lab.

2. Prepare the eye tracking recordings

To set up your eye tracking recording, your Spectrum eye tracker needs to be connected and correctly set up in Tobii Pro Lab (find more information here). Once this is done, you will find everything you need in the “Record” tab of Tobii Pro Lab. Here, you should pay special attention to the sampling rate (or “sampling frequency”) with which you are recording the eye tracking data (click on the eye tracker symbol in the top left corner). Higher sampling rates allow you to assess not only fixations, saccades and even micro-saccades (see this article), but they also allow you to record the stimulus events with more temporal precision. Therefore, higher sampling rates are better for a more precise synchronization with the EEG data.

It is also important to set up the stimulus markers in Pro Lab with the highest temporal precision. You may encounter delays between the stimulus marker being registered in Pro Lab, and the stimulus actually appearing on the presentation screen. To reduce such delays, please make sure that the computer running Pro Lab matches the required specifications, and carefully follow these important tips to optimize your stimulus timing in Pro Lab. To find how you can determine this delay in your setup, and how you can account for it during recording, you can take a look at this Timing Guide.

Before recording data with an actual participant, you will need to run at least one test recording of your final task and make sure your current setup and the available stimulus events let you analyze everything of interest in Pro Lab’s “Analyze” tab. If all events are marked in Pro Lab and you are satisfied with their timing, you are all set for the eye tracking recordings.


3. Prepare the EEG Recordings

To prepare your EEG recordings, you will need to set up the actiCHamp Plus with the PowerUnit, and connect the Photo Sensor to one of the amplifier’s AUX channels. When preparing your workspace in BrainVision Recorder, make sure to also set up the respective AUX channel for recording the Photo Sensor signal. For the EEG data, we can use a higher sampling rate (for example 2000 Hz) to have a high temporal precision of the signal and a good synchronization with the eye tracking data.

When everything is set up, you will need to identify the correct position for the Photo Sensor on the presentation screen. For this, briefly start a test run of your experiment and attach the Photo Sensor to the monitor with an adhesive ring. Next, run a test EEG recording to make sure you can identify all necessary stimulus events in the recorded Photo Sensor signal. Present the full experiment while recording, then load the data in BrainVision Analyzer 2. If your setup contains the Photo Sensor in combination with a StimTrak, the stimulus events should already be marked in your EEG data. Otherwise, you can now use the “Level Trigger” transformation. Here, you can identify the optimal threshold value for your Photo Sensor data and extract the stimulus events from the Photo Sensor channel (see Figure 4).

Figure 4. Identify the stimulus onsets from the photo sensor channel with the Level Trigger transformation.

Figure 4. Identify the stimulus onsets from the photo sensor channel with the Level Trigger transformation.

Keep in mind that the shared synchronization events need to appear at the same time in both EEG and eye tracking data, and that there need to be an equal number of synchronization events present in both data sets. If necessary, you can use the “Edit Markers” transformation to rename or modify some events in your EEG data.

4. Record EEG and eye tracking data simultaneously

Now you are ready for the real data acquisition! Set up the EEG system and cap, use the prepared workspace and the Photo Sensor. To get ready for the eye tracking recordings, load the correct experiment in Tobii Pro Lab. Then have the participant sit in front of the eye tracker and presentation screen at the optimal distance. After double-checking that all settings are correct (see section “2. Prepare the eye tracking recordings” above), you can enter a name for your participant, and the “Record data” button will become available in Tobii Pro Lab.

When starting the recording in Tobii Pro Lab, follow the calibration and validation procedure until you are satisfied with accuracy and precision. Before you start the actual task, make sure to start your EEG recordings in time for the Photo Sensor to capture the first synchronization marker. Always keep an eye on the data streams in BrainVision Recorder and Tobii Pro Lab to make sure all data is recorded smoothly. When the task is finished, again make sure the Photo Sensor captured the last synchronization event before stopping the EEG recording.

 Tip: Make sure you do not stop or pause the EEG recordings before the task is fully finished, so you can later align the EEG and eye tracking data sets!

5. Analyze the eye tracking data

Now it’s time to analyze your eye tracking data in Tobii Pro Lab’s “Analyze” tab. It is good practice to start with some quality control (reviewing the recording and checking for data loss). Then you will be able to perform all kinds of analyses, export metrics or create graphics from your recorded gaze data. What may be most relevant for your combined EEG and eye tracking analysis is to identify times of interest or fixations in areas of interest in your eye tracking data.

When you are done with your eye tracking analysis, you can export the gaze and pupil data together with all identified event markers and import them into your EEG data. For this, use the “Data Export” option in Pro Lab and export the data in the Pro Lab Output File (PLOF) format.

6. Identify the event markers in your EEG data

After a brief quality control, you can extract all stimulus events from the Photo Sensor channel by using the “Level Trigger” transformation with the previously tested settings (see section “3. Prepare the EEG recordings” above). If necessary, modify the resulting markers so you can clearly identify the synchronization events that should be shared with the eye tracking recording.

 Tip: Be careful with segmenting your EEG data before importing the eye tracking data to make sure you don’t lose important synchronization markers!

7. Merge both data sets for combined EEG and eye tracking analysis

Finally, you can import the eye tracking data and the events you identified in your eye tracking analysis into your EEG recordings. At this time point, the sampling rates and the length of both data sets will likely be different, but BrainVision Analyzer 2 will now use the shared synchronization events to bring both data streams to the same timeline.

To merge the EEG and eye tracking data, open the EEG data containing the identified synchronization events. Next, use Analyzer’s Add Channels transform and select the previously exported eye tracking file under Import files. In the next window, you will need to select the shared synchronization markers which will be used to align both data sets. For the EEG data, they can be selected from the Markers in Active Node list, for the eye tracking data from the Markers in Import File list. If you click on the Details button, you will see if there is an equal number of synchronization markers in both data sets.

In the following dialogs, you will be able to select the specific channels and markers you would like to import. Finally, when you finish the Transformation, the eye tracking channels will appear underneath your EEG channels, and all selected event markers will be imported.

 Tip: You can find a full description of how to use the Add Channels transform in the BrainVision Analyzer 2 User Manual, and more information about its latest enhanced features here. However you can also always contact our Scientific Support team if you need help with BrainVision Analyzer 2.

Now that both data streams are temporally aligned, you can start analyzing them together! As mentioned in the introduction, you can discard data during which the subject was not focusing on areas of interest. Finally, you can also segment your EEG data based on fixations or other events you identified in your eye tracking analysis, and you can calculate Fixation-Related Potentials.

Conclusion

We hope this article provided you with helpful guidelines for your lab-based EEG and eye tracking setup, and that we could walk you through the most important steps for your recordings and analysis. Keep your eyes open for more articles as well as dedicated online events about our new eye tracking solutions!

Is it all in the knee?

Patellofemoral pain (PFP) is considered a mechanistic pain syndrome, originating from kinetic, anatomic or biomechanical dysfunction leading to nociceptive pain.

However, some data shows that not all pain expressions in patients with PFP can be causatively connected to a biomechanical impairment.

Researchers from the School of Health and Rehabilitation Sciences, The University of Queensland, Australia, have endeavored to clarify whether patients suffering from PFP have local or centrally altered sensory profiles.

Profiling patients vs. controls

One-hundred-and-fifty patients with PFP were recruited along with sixty one controls.
Quantitative sensory testing (QST) was performed on the most painful knee and on a remote site: the contralateral lateral epicondyle of the elbow. QST consisted of: mechanical and thermal sensory and pain thresholds, pressure pain thresholds (PPT), as well as mechanical temporal summation and conditioned pain modulation (CPM) with PPTs as test stimuli and cold pressor as the conditioning stimulus.

Medoc’s Pathway ATS, TSA2’s predecessor, was utilized for all thermal thresholds.

Questionnaires on kinesiophobia (TSK), self-efficacy (FESQ), catastrophizing (PCS), and anxiety and depression (HADS) were administered.

What was found

Interestingly, cold and heat pain thresholds were significantly lower for the patient group compared to the controls, both at the knee and the elbow, hinting at central sensitization. There were similar findings for the mechanical pain and pressure pain thresholds, but not for the sensory thermal/mechanical thresholds.
In pain modulation measures of temporal summation and CPM only temporal summation was significantly increased for the patient group.

Additional to this, higher prevalence of anxiety, depression and pain catastrophizing was found in the patient group as compared to the controls.

To conclude

The authors conclude that “Our discovery of thermal hyperalgesia offers new insight in terms of PFP mechanisms. Multi-modal hyperalgesia locally and at a remote site (elbow), reflected by greater sensitivity to heat, cold and pressure pain in our PFP group, could be construed as evidence of nociplastic pain.”
Physicians, physiotherapists and other clinicians treating patients with patellofemoral pain should take into account physiological, pain modulatory, and psychological changes, in order to holistically treat their patients.

Reference:

Maclachlan, L. R., Collins, N. J., Hodges, P. W., & Vicenzino, B. (2020). Psychological and pain profiles in persons with patellofemoral pain as the primary symptom. European Journal of Pain, 24(6), 1182-1196.

The Association Between Preoperative Pain Catastrophizing and Chronic Pain After Hysterectomy – Secondary Analysis of a Prospective Cohort Study

    Hon Sen Tan,1 Rehena Sultana,2 Nian-Lin Reena Han,3 Chin Wen Tan,1,4 Alex Tiong Heng Sia,1,4 Ban Leong Sng1,4
    1Department of Women’s Anaesthesia, KK Women’s and Children’s Hospital, Singapore; 2Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; 3Division of Clinical Support Services, KK Women’s and Children’s Hospital, Singapore; 4Anesthesiology and Perioperative Sciences Academic Clinical Program, SingHealth-Duke-NUS Medical School, Singapore
    Correspondence: Ban Leong Sng
    Department of Women’s Anaesthesia, KK Women’s and Children’s Hospital, 100 Bukit Timah Road 229899, Singapore
    Tel +65 6394 1077
    Email sng.ban.leong@singhealth.com.sg
    Purpose: Hysterectomy is associated with a high incidence of chronic post-hysterectomy pain (CPHP). Pain catastrophizing, a negative cognitive-affective response to pain, is associated with various pain disorders but its role in CPHP is unclear. We aimed to determine the association of high preoperative pain catastrophizing with CPHP development and functional impairment 4 months after surgery.
    Patients and Methods: Secondary analysis of a prospective cohort study of women undergoing abdominal/laparoscopic hysterectomy to investigate the association between high pain catastrophizing (pain catastrophizing scale, PCS≥ 20) with CPHP and associated functional impairment (defined as impairment with standing for ≥ 30 minutes, sitting for ≥ 30 minutes, or walking up or down stairs). CPHP and functional impairment were assessed via 4- and 6-month phone surveys.
    Results: Of 216 patients, 72 (33.3%) had high PCS, with mean (SD) of 30.0 (7.9). In contrast, 144 (66.7%) patients had low PCS, with mean (SD) of 9.0 (4.7). At 4 months, 26/63 (41.3%) patients in the high PCS group developed CPHP, compared to 24/109 (22.0%) in the low PCS group. At 6 months, 14/53 (26.4%) high PCS patients developed CPHP, compared to 10/97 (10.3%) patients with low PCS. High PCS was independently associated with CPHP at 4 months (OR 2.49 [95% CI 1.27 to 4.89], p=0.0082) and 6 months (OR 3.12 [95% CI 1.28 to 7.64], p=0.0126) but was not associated with functional impairment. High PCS≥ 20, presence of evoked mechanical temporal summation (MTS), and history of abdominal/pelvic surgery predict CPHP at 4 months with area under the curve (AUC) of 0.69. Similarly, PCS≥ 20 and increasing MTS magnitude predicted CPHP at 6 months with AUC of 0.76.
    Conclusion: High PCS was independently associated with CPHP. Future studies should identify other CPHP associated factors to formulate a risk-prediction model and investigate the effectiveness of early intervention for pain catastrophizers in improving pain-related outcomes.

Neuronavigation based 10 sessions of repetitive transcranial magnetic stimulation therapy in chronic migraine: an exploratory study

    Abstract

    Introduction: Chronic migraine is a disease of altered cortical excitability. Repetitive transcranial magnetic stimulation provides a novel non-invasive method to target the nociceptive circuits in the cortex. Motor cortex is one such potential target. In this study, we targeted the left motor cortex using fMRI-guided neuronavigation.

    Materials and methods: Twenty right-handed patients were randomized into real and sham rTMS group. Baseline subjective pain assessments were done using visual analog scale (VAS) and questionnaires: State-Trait Anxiety Inventory, Becks Depression Inventory, and Migraine Disability Assessment (MIDAS) questionnaire. Objectively, pain was assessed by means of thermal pain thresholds using quantitative sensory testing. For corticomotor excitability parameters, resting motor thresholds and motor-evoked potentials were mapped. For rTMS total, 600 pulses in 10 trains at 10 Hz with an intertrain interval of 60 s were delivered in each session. Ten such sessions were given 5 days per week over 2 consecutive weeks. The duration of each session was 10 min. Real rTMS was administered at 70% of Resting MT. All the tests were repeated post-intervention and after 1 month of follow-up. There are no studies reporting the use of fMRI-based TMS for targeting the motor cortex in CM patients.
    Results: We observed a significant reduction in the mean VAS rating, headache frequency, and MIDAS questionnaire in real rTMS group which was maintained after 1 month of follow-up.
    Conclusion: Ten sessions of fMRI-based rTMS over the left motor cortex may provide long-term pain relief in CM, but further studies are warranted to confirm our preliminary findings.
    Keywords: Chronic pain; Cortical excitability; Headache; Motor cortex stimulation; Neuromodulation; Quantitative Sensory test.

Stepwise increasing sequential offsets cannot be used to deliver high thermal intensities with little or no perception of pain

Abstract

Offset analgesia (OA) is the disproportionate decrease in pain experience following a slight decrease in noxious heat stimulus intensity. We tested whether sequential offsets would allow noxious temperatures to be reached with little or no perception of pain. Forty-eight participants continuously rated their pain experience during trials containing trains of heat stimuli delivered by Peltier thermode. Stimuli were adjusted through either stepwise sequential increases of 2°C and decreases of 1°C or direct step increases of 1°C up to a maximum of 46°C. Step durations (1, 2, 3, or 6 s) varied by trial. Pain ratings generally followed presented temperature, regardless of step condition or duration. For 6-s steps, OA was observed after each decrease, but the overall pain trajectory was unchanged. We found no evidence that sequential offsets could allow for little pain perception during noxious temperature presentation.

NEW & NOTEWORTHY Offset analgesia is the disproportionate decrease in pain experience following a slight decrease in noxious heat stimulus intensity. We tested whether sequential offsets would allow noxious temperatures to be reached with little or no perception of pain. We found little evidence of such overall analgesia. In contrast, we observed analgesic effects after each offset with long-duration stimuli, even with relatively low-temperature noxious stimuli.

INTRODUCTION

Offset analgesia (OA) was first described by Grill and Coghill (2002) and was defined as a disproportionate decrease in pain experience following a slight decrease in heat stimulus intensity. In a typical OA experiment, three successive periods (T1, T2, T3) each contain a continuous noxious stimulus. The first and last stimuli are of equal intensity, but the middle stimulus is slightly more intense (e.g., 45°C, 46°C, 45°C). The OA effect is revealed by a greater fall in reported pain intensity following a step back to the original noxious stimulus temperature compared with delivery of a continuous noxious stimulus temperature (e.g., 45°C, 45°C, 45°C).

Ergonomic Analysis of Workers During Cannabis Cultivation Activities to Reduce Musculoskeletal Injury

Winner: Lockheed Martin Best Project Award

Project Summary

Overview

The National Cannabis Risk Management Association (NCMRA) is interested in minimizing the strain undergone by cannabis workers, specifically at the trimming station, to reduce repetitive motion injuries and ensure worker safety. The team determined that creating an ergonomic table would best improve worker safety.

Objectives

– Characterize worker motion during cannabis trimming for the purpose of assessing musculoskeletal strain and to identify areas in need of improved methods and equipment.

– Propose and design standardized equipment (chair type and height, table shape and height, and clipper design) and methods to reduce musculoskeletal strain during trimming.

Approach

– Participate in weekly advisor and sponsor calls to gather information on the industry and discuss the plan of action.

– Research cannabis and ergonomic literature to familiarize with the current workplace setup.

– Use general workspace postural data to create the drawings and a Solidworks model of the ergonomic table.

– Design a two factor two-level experiment to analyze the standard and ergonomic table as well as the curved/straight blade trimmers.

– Collect data from the NCRMA that was collected using Noraxon’s software via sensors on various parts of the test subject’s body throughout the study.

– Analyze the anatomical angles and EMG activity collected for the standard and ergonomic table as well as the two trimmers.

Outcomes

The ergonomic table has shown improvements in the cervical spine, pelvis, and elbow flexion angles:
– The cervical spine showed a 50% decrease in average angle looking down (cervical flexion).

– The pelvic tilt decreased causing a reduction in noticeable lower back pain in the test subject.

– The elbow flexion angles are within the safe region 100% of the time when using the ergonomic table.

– The impact of straight blade trimmers and curved blade trimmers showed mixed results, but further studies would be more conclusive.

FDA clearance for MagVenture: 3 minute depression treatment

    For people suffering from severe depression, the road to remission just became a lot shorter: The treatment is known as Transcranial Magnetic Stimulation (TMS), and MagVenture has now, as the only company in the US, received FDA clearance for a newer and much faster treatment protocol which will cut down treatment time to just 3 minutes per session*. Before that, the required treatment time per session was up to 37 minutes.
    TMS has been FDA cleared for treatment-resistant major depressive disorder since 2008. Since then, over 1,000 psychiatric clinics have emerged in the US. Most private health insurance companies also cover the treatment. The relatively long treatment sessions have, however, not only limited the treatment capacity for TMS practices but also hindered a more widespread dissemination. Until now, each session has been up to 37 minutes long, with 20-30 sessions needed in total. The new treatment form, which is known as Theta Burst Stimulation (TBS), offers one significant advantage: Time. A TBS treatment session lasts only 3 minutes and thus has the potential to revolutionize the clinical field of TMS.
    “We have named it “Express TMS®” because that’s what it is: a treatment which is just as safe and effective for the treatment of depression as conventional TMS, only much, much faster. We are happy and proud to be the first in the US to receive an FDA clearance for this revolutionary treatment which is backed up by substantial scientific evidence. Our current treatment system, MagVenture TMS Therapy, can easily be upgraded with the new Express TMS option. This will enable our many customers to treat far more patients per day without having to invest in another TMS device. For people needing treatment, this will also be a huge benefit, as treatment will now take up less of their time,” says Kerry Rome, Vice President of Sales, MagVenture Inc.
    The new FDA cleared treatment protocol is based on a new clinical study, named the THREE-D trial, and led by a partnership of three leading research hospitals in Canada (CAMH, UHN, and UBC). It is the largest, double-blinded, randomized TMS trial to date, with 414 participants suffering from major depressive disorder. Response/remission rates were 32% for those receiving the TBS protocol, whereas 49% had an improvement in their depressive symptoms. These rates are similar to the standard, longer TMS protocol.

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Drug screening platform using human induced pluripotent stem cell‐derived atrial cardiomyocytes and optical mapping

ORIGINAL RESEARCH : Open Access

First published: 14 September 2020

Marvin G. Gunawan, Sarabjit S. Sangha, and Sanam Shafaattalab contributed equally to this study.

Funding information: Stem Cell Network; Canada Innovation Fund; Canadian Institutes of Health Research

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Abstract

Current drug development efforts for the treatment of atrial fibrillation are hampered by the fact that many preclinical models have been unsuccessful in reproducing human cardiac physiology and its response to medications. In this study, we demonstrated an approach using human induced pluripotent stem cell‐derived atrial and ventricular cardiomyocytes (hiPSC‐aCMs and hiPSC‐vCMs, respectively) coupled with a sophisticated optical mapping system for drug screening of atrial‐selective compounds in vitro. We optimized differentiation of hiPSC‐aCMs by modulating the WNT and retinoid signaling pathways. Characterization of the transcriptome and proteome revealed that retinoic acid pushes the differentiation process into the atrial lineage and generated hiPSC‐aCMs. Functional characterization using optical mapping showed that hiPSC‐aCMs have shorter action potential durations and faster Ca2+ handling dynamics compared with hiPSC‐vCMs. Furthermore, pharmacological investigation of hiPSC‐aCMs captured atrial‐selective effects by displaying greater sensitivity to atrial‐selective compounds 4‐aminopyridine, AVE0118, UCL1684, and vernakalant when compared with hiPSC‐vCMs. These results established that a model system incorporating hiPSC‐aCMs combined with optical mapping is well‐suited for preclinical drug screening of novel and targeted atrial selective compounds.

Significance statement

Current in vitro drug screening systems for treatment of atrial fibrillation are confounded by cell type heterogeneity, specificity, and translatability to human physiology. In this study, we developed a drug screening platform using human induced pluripotent stem cell‐derived atrial cardiomyocytes (hiPSC‐aCMs) and a multiwell optical mapping system. The high‐content optical mapping system reports on membrane voltage and Ca2+ transients which serve as critical biomarkers of cardiac function in vitro. The hiPSC‐aCMs generated by this protocol possess atrial‐specific molecular profiles, functional signatures, and pharmacological response. These findings demonstrate that the platform can be readily applied as a relevant preclinical model for drug screening for atrial fibrillation therapies.

1 INTRODUCTION

The advent of human induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs) has revolutionized the field of cardiac research. It has enabled the study of cardiac diseases in a patient‐specific and human‐relevant in vitro model system which provides a unique opportunity for clinical translation.1 Furthermore, the ability to differentiate chamber‐specific cardiomyocytes allows for a more precise study of cardiac disease physiology and pharmacology.

The cardiomyocytes of the lower (ventricles) and upper (atria) chambers have distinct characteristics that arise from differential developmental pathways. Previous work in vivo has shown that the expression patterns of retinoic acid and retinaldehyde dehydrogenase 2 (RALDH2) are important determinants of the atrial fate.25 These results were later recapitulated in a pivotal study by Lee and Protze et al6 who determined that atrial cardiomyocytes (aCMs) differentiated from human embryonic stem cells (hESCs) originate from a unique mesoderm characterized by robust RALDH2 expression. This study established an atrial differentiation protocol that included the addition of retinoic acid. Retinoic acid has also been utilized to selectively differentiate hESCs and hiPSCs into aCMs in other studies.610

The distinct properties of the atrial and ventricular cardiomyocytes are determined by the differential expression of unique sets of ion channels and other proteins that optimize their specific function. Drugs that target atrial ion channels selectively can therefore produce differences in pharmacological function in the two chambers. This atrial‐selective pharmacology is of utmost interest in the study and treatment of atrial‐specific diseases such atrial fibrillation (AF), which is the most common heart rhythm disorder. Investigating atrial‐selective pharmacology can assist and guide novel cardiac drug development as well as improving both safety and efficacy by avoiding potential toxic electrophysiologic effects on the ventricular chambers.

The differential pharmacology of stem cell‐derived aCMs was studied previously by Laksman et al7 who showed that flecainide can rescue the AF phenotype in a dish. Other studies have also studied the selective pharmacological effects of agents on hiPSC‐derived aCMs but have largely focused on proof‐of‐concepts using limited number of test compounds and standard measurement systems that are low in throughput.910 With a focus on translation, a preclinical model platform that characterizes pharmacological activity must capture the main cardiac functional signatures that most closely mimic and predict human cardiac physiology and drug responses. As such, we established in this study an in vitro assay platform by combining hiPSC‐derived atrial cardiomyocytes (hiPSC‐aCMs) and high‐content optical mapping, a noninvasive all‐optical system that simultaneously measures membrane potential (Vm) and Ca2+ transients at a high‐resolution in a monolayer tissue format.

We first demonstrate a selective hiPSC‐aCM differentiation protocol by modifying the well characterized GiWi protocol11 through the controlled introduction of retinoic acid. The recapitulation of the human atrial phenotype of the hiPSC‐aCMs was validated with assays that measure the expression of gene transcripts and proteins, as well as functional signatures. We then demonstrate the utility of our platform as an atrial‐selective drug screening tool by using existing clinical and experimental drugs. The model established in this study adds to our current understanding of the utility of stem cell‐derived cardiomyocytes in preclinical and translational research focused on screening new pharmacological agents.

2 METHODS AND MATERIALS

A detailed methods section is available in the Supplemental Information.

2.1 Maintenance and expansion of hiPSCs

hiPSCs (WiCell, IMR90‐1) were maintained and expanded in mTeSR1 medium and feeder‐free culture using 6‐well plates coated with Matrigel. Using Versene (EDTA), hiPSCs were passaged every 4 days or ~85% confluency at 1:15 ratio. Passaged hiPSCs were cultured with mTeSR1 supplemented with 10 μM Y27632 for the first 24 hours and the mTeSR1 was exchanged daily during cell culture maintenance.

2.2 Directed differentiation of hiPSCs into atrial and ventricular subtypes

hiPSC‐derived ventricular cardiomyocytes were differentiated by employing a modified GiWi protocol11 that we previously published.12 In brief, hiPSCs were seeded at a density of 87 500 cells/cm2. At day 0, differentiation was initiated using 12 μM CHIR99021. At day 3, the cells were incubated with 5 μM IWP‐4. At day 5, the media were refreshed with RPMI‐1640 supplemented with B27 minus insulin. At day 7, the medium was replaced with cardiomyocyte maintenance media (RPMI‐1640 supplemented with B27 with insulin). Thereafter, cardiomyocyte maintenance media were replaced every 4 days. For the atrial differentiation protocol, retinoic acid (RA) addition was first optimized in pilot studies (Figure S2 and S3) and determined to be 0.75 μM RA every 24 hours from days 4‐6.

2.3 Flow cytometry

hiPSC‐aCMs and hiPSC‐vCMs at Day 20‐30 postdifferentiation were dissociated into single cells as described in the Supplemental Information. The harvested cells were fixed in 4.1% PFA solution for 25 minutes and then washed and permeabilized in Saponin/FBS. Cells were subsequently incubated overnight in primary mouse‐cTnT (1:2000) and rabbit‐MLC2V (1:1000) antibodies. Subsequently, the cells were washed and incubated in secondary goat anti mouse Alexa‐488 (1:500) and goat anti rabbit Alexa‐647 (1:2000) antibodies for 1 hour, respectively. Cells were then washed and suspended in PBS for analysis. All analyses were performed using the BDJAZZ Fluorescence Activated Cell Sorter.

2.4 mRNA expression profiling

Gene expression profiling was conducted using multiplexed NanoString and real time quantitative PCR (qPCR). Pooled total RNA was used in both assays. The extracted RNA was reverse transcribed into cDNA which was used in the qPCR assay. Oligonucleotide sequences are described in Table S7. The multiplexed mRNA profiling was conducted using NanoString Technologies (Seattle, Washington) platform with a custom Codeset containing 250 gene probes. Analysis was performed on the Sprint instrument and nSolver analysis software with the Advanced Analysis module.

2.5 Atrial natriuretic peptide measurement

The levels of atrial natriuretic peptide (ANP) of hiPSC‐aCMs and ‐vCMs were measured by a competitive enzyme‐linked immunosorbent assay (ELISA) using a commercially available kit (Invitrogen, California). The assay was conducted according to the manufacturer’s protocol and was measured using a spectrophotometric plate reader.

2.6 Cardiomyocyte enrichment

For cardiac enrichment, hiPSC‐aCMs and ‐vCMs at day 20‐30 postdifferentiation were dissociated into single cells which were then enriched using a MidiMACS PSC‐derived Cardiomyocyte Isolation Kit (Miltenyi Biotec, Germany) according to the manufacturer’s protocol. Enriched hiPSC‐CMs were seeded on Matrigel‐coated 24‐well plates at a seeding density of 600 000 cells per well.

2.7 Patch‐clamp recordings

Single hiPSC‐aCMs and ‐vCMs were plated on gelatin (0.1%) and Geltrex (1:10) at 30 000 cells per well. After 48 hours in culture, glass electrodes were used to achieve the whole‐cell configuration with single hiPSC‐CMs and only cells with gigaohm seals were used for further analysis. The formulation for internal and external recordings solutions are outlined in the Supplemental Information. Current recordings were performed using the Axon Instruments 700B amplifier and digitized at 20 kHz. All recordings were performed at 33‐35°C as maintained. For pacing at 1 Hz, gradually increasing amounts of current were injected with a 1 ms pulse width until reliable action potentials (APs) were triggered. The maximal upstroke velocity was determined by calculating the maximum derivative and the resting membrane potential was measured during a 5 second epoch without spontaneous activity 1 minute after break‐in. Further details on data analysis are found in the Supplemental Information.

2.8 Optical mapping

Optical mapping recordings were performed on enriched monolayers of hiPSC‐aCMs and ‐vCMs cultured in a 24‐well plate format at Day 45‐60 postdifferentiation. Imaging experiments were conducted using Ca2+ Tyrode’s solution (formulation found in Supplemental Information). The hiPSC‐CMs were loaded with RH‐237, blebbistatin, and Rhod‐2AM sequentially before imaging as described.1213 Both RH‐237 and Rhod‐2AM were excited by 530 nm LEDs. Images were acquired at a frame rate of 100 frames/second by a sCMOS camera (Orca Flash 4.0V2, Hamamatsu Photonics, Japan) equipped with an optical splitter. The cells were paced using programmable stimulation. Data collection, image processing, and initial data analysis were accomplished using custom software. The multiwell optical mapping system was custom engineered in the lab based on a system as described previously.1213 Further details are found in the Supplemental Information.

2.9 Pharmacological analyses

The drugs used in this study are listed Table S8. Drug stocks were further diluted in Ca2+ Tyrode’s solution prior to pharmacological testing with the final DMSO concentration in the experimental solution not exceeding 0.03% (v/v). Drug effects were studied in serum‐free conditions (ie, Ca2+ Tyrode’s and drug only) at four doses by sequentially increasing the drug concentration in the same well with recordings at 20‐minute intervals.

2.10 Statistical analysis

Further details on data and statistical analysis can be found in the Supplemental Information. Unpaired t tests were conducted to compare two groups (ie, hiPSC‐aCMs vs hiPSC‐vCMs) in the analysis of qPCR, ELISA, patch clamp recordings, and optical mapping (baseline condition and normalized drug effects). Analysis of dose‐dependent effects was performed using one‐way ANOVA and Dunnett’s post hoc test. All data are presented as mean ± SEM unless noted otherwise. Significance level for all statistical analysis was set at p < .05 with the following notation: *p < .05, **p < .01, ***p < .001.

3 RESULTS

3.1 RA treatment drives cardiac differentiation into atrial phenotype

We first optimized the atrial differentiation protocol by altering the concentration and timing of retinoic acid (RA) based on the molecular signatures of atrial phenotype as measured by qPCR and flow cytometry (Figures S2 and S3). Higher dose of RA reduced cardiac differentiation efficacy defined by the decrease in the cTnT+ proportion of the total cell population as measured by flow cytometry (Figure S2A). The finalized protocol to generate hiPSC‐aCMs included RA addition at 0.75 μM every 24 hours on days 4, 5, and 6 (Figure 1A) which was found as a balance between sufficiently driving atrial differentiation as defined by decreased ventricular marker myosin light chain 2 ‐ ventricular paralog (MLC‐2v) while having no impact cardiac differentiation efficacy Figures S2 and S3).

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Directed differentiation of hiPSC‐derived atrial and ventricular CMs. A, Schematic depicting the atrial differentiation protocol. Doses of 0.75 μM retinoic acid (RA) were added to the cells every 24 hours on days 4, 5, and 6 with media exchanged to RPMI1640 + B27 with insulin at day 7. Cells were harvested for analysis at day 20. B, qPCR analysis of ventricular markers MYL2 and IRX4, cardiac marker NKX2.5, and atrial markers NPPAGJA5CACNA1DKCNA5, and KCNJ3. n = 3, unpaired t test, *p < .05. C, Flow cytometric analysis of cardiac troponin T (cTnT) and myosin light chain 2v (normalized to cTnT expression) in hiPSC‐aCMs and ‐vCMs. n = 4, unpaired t test, ***p < .001. D, Average beating rates of hiPSC‐aCMs and ‐vCMs from the day they begin to beat until day 20. n = 4 independent differentiation batches. E, Atrial Natriuretic peptide (ANP) concentration between hiPSCs, and hiPSC‐aCMs and ‐vCMs determined by competitive ELISA. n = 3 and n = 2 hiPSC lines, unpaired t test *p < .05, **p < .01, ***p < .001. Data are presented as mean ± SEM One n represents one independent differentiation batch

Compared with hiPSC‐vCMs, hiPSC‐aCMs were found to have no significant difference in pan cardiac phenotype. Expression of the pan cardiac transcript NKX 2.5 measured by qPCR was similar between hiPSC‐aCMs and ‐vCMs (Figure 1B), as was cardiac troponin T (cTnT) protein expression measured by flow cytometry (Figures 1C and S1). The protein expression of MLC‐2v was reduced in hiPSC‐aCMs compared with hiPSC‐vCMs (8.0 ± 1.1% vs 57.0 ± 0.5%; p < .05) (Figure 1C). Furthermore, hiPSC‐aCMs displayed higher concentrations (increased by 91%) of atrial natriuretic peptide (ANP) at 65 ± 2 compared with 34 ± 6 ng/mL in hiPSC‐vCMs as measured by ELISA (p < .05).

The qPCR assay revealed that atrial‐specific transcripts such as atrial natriuretic peptide (NPPA), connexin 40 (GJA5), the calcium channel CaV1.3 (CACNA1D), and the K+ channels Kv1.5 (KCNA5) and Kir3.1 (KCNJ3) transcripts were all expressed at a significantly higher levels in hiPSC‐aCMs compared with hiPSC‐vCMs (p < .05, Figure 1B). Another ventricular marker, IRX4, also had decreased expression in hiPSC‐aCMs (Figure 1B). Furthermore, consistent with previous studies,8101415 hiPSC‐aCMs started beating at day 10 or earlier and exhibited an increased beating frequency relative to hiPSC‐vCMs, which started beating around day 10‐12 postdifferentiation.

3.2 Gene expression analysis of hiPSC‐aCMs

We performed an extensive gene expression analysis of hiPSC‐aCMs and ‐vCMs using NanoString technology in which each mRNA copy was digitally counted for accurate and sensitive detection of gene expression.16 Five independent differentiation batches of each cardiac subtype were included in the analysis. The unsupervised hierarchical clustering analysis showed clear grouping of hiPSC‐aCM samples that were segregated relative to hiPSC‐vCMs (Figure 2A). The gene expression profile of the hiPSC‐vCM samples were more variable with 2 samples closer in distance to the hiPSC‐aCMs while three samples displayed clear segregation (Figure 2A). The overall difference in global gene expression and lineage between hiPSC‐aCMs and ‐vCMs was also captured in the principal component analysis (PCA, Figure S4A). Out of the 250 transcripts analyzed, 200 genes were detected above background noise defined by a threshold of 50 raw digital counts as determined by the negative controls of the assay. In the hiPSC‐aCMs, 14 and 27 genes were significantly upregulated and downregulated, respectively (Figure 2C). As expected, hiPSC‐aCMs displayed significantly higher expression profiles of atrial‐specific markers including atrial‐specific K+ channel Kv1.5 (KCNA5) and transcription factors (NR2F2 and TBX18) (Figure 2C). Meanwhile, hiPSC‐vCMs displayed higher expression of ventricular‐specific genes such as those encoding for contractile proteins MYL2MYH7, and the L‐type Ca2+ channel isoform Cav1.2 (CACNA1C) (Figure 2C). The genes encoding for the proteins in the sarcoplasmic reticulum complex such as TRDNCASQ2, and RYR2 were expressed in significantly lower amounts in the hiPSC‐aCMs samples (Figure 2C). Meanwhile, pan‐cardiac markers NKX2‐5 and TNNT2 were expressed at similar levels in both hiPSC‐aCMs and ‐vCMs, further corroborating the efficiency of the differentiation protocol (Figure S4B).

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Gene expression analysis of hiPSC‐aCMs and ‐vCMs using NanoString. Global gene expression pattern of hiPSC‐aCMs and ‐vCMs shown in A, heat map of the expression of the 250 genes across samples of hiPSC‐aCMs and ‐vCMs. The cluster dendrogram shows the unsupervised hierarchical clustering that was conducted using the agglomerative algorithm and the Euclidian distance criterion. B, Differentially expressed genes between hiPSC‐aCMs and ‐vCMs expressed in volcano plot shows 14 upregulated (red) and 27 downregulated (blue) genes in hiPSC‐aCMs. Solid horizontal line represents the Benjamini‐Hochberg false discovery rate (FDR) adjusted p‐value <.05 (−log10 = 1.3). Dashed vertical lines represent the arbitrary log2 fold change cut‐off of −0.5 and 0.5. C, Forty‐two differentially expressed genes identified from the statistical criteria of FDR adjusted p‐value <.05 and log2 fold change of <−0.5 and >0.5. Data are presented as mean ± SEM. n = 5 independent differentiation batches

3.3 Functional phenotyping of hiPSC‐derived atrial cardiomyocytes

We compared the electrophysiological characteristics of the differentiated hiPSC‐aCMs and ‐vCMs using whole‐cell patch clamp. Confirming our observations in tissue culture, the spontaneous beating rates were higher in the single hiPSC‐aCMs than in ‐vCMs (Figure 3A,C). Whole cell current clamp recordings demonstrated the ventricular‐like AP morphology of hiPSC‐vCMs with a clear and prolonged plateau phase while the AP of the hiPSC‐aCMs displayed atrial‐like morphology with a shorter action potential duration (APD) and a lack of prolonged plateau phase at both spontaneous beating rates (Figure 3B,D, left panel) and paced at 1 Hz (Figure 3B,D, right panel). No statistical differences were observed in the resting membrane potential and the maximum upstroke velocity of hiPSC‐aCMs and ‐vCMs. The APD at 50% (APD50) and 90% (APD90) of the peak voltage were significantly shorter in hiPSC‐aCMs than ‐vCMs at both spontaneous beating rates (APD50: 157 ± 16 ms vs 349 ± 35 ms, p‐value <.005; APD90: 249 ± 34 ms vs 484 ± 30 ms, p‐value <.005) and paced at 1 Hz (APD50: 157 ± 16 ms vs 264 ± 44 ms, p‐value <.05; APD90: 242 ± 22 ms vs 341 ± 48 ms, p‐value <.05).

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hiPSC‐aCMs and ‐vCMs have distinct electrophysiological characteristics. Single differentiated hiPSC‐aCMs and ‐vCMs were plated on gelatin and Geltrex after 30 days in culture. A, Whole cell current clamp recordings from a spontaneously beating hiPSC‐vCM. B, Recorded action potential (APs) demonstrates typical prolonged plateau phase in both spontaneous (left) and/or paced at 1 Hz (right). C, Current clamp recording from a spontaneously beating hiPSC‐aCM. D, Single AP from hiPSC‐aCM demonstrates shortened action potential duration (APD) and lack of prolonged plateau phase, spontaneous (left), paced at 1 Hz (right). E, The first differential of voltage recordings from hiPSC‐aCMs and ‐vCMs were used to calculate the maximal upstroke velocities. F, One minute after achieving the whole‐cell configuration, the average resting membrane potential was measured. G, Spontaneously beating and 1 Hz paced APs were assessed for duration at 50% of peak (APD50), and H, 90% of peak (APD90). Statistics were performed by unpaired t test. *p < .05, ***p < .005. Data are presented as mean ± SEM. Two differentiation batches were included in this analysis

We further assessed the functional properties of hiPSC‐aCMs and ‐vCMs using optical mapping with simultaneous measurement of APs and calcium transients (CaT). Like the patch clamp recordings, optical membrane voltage measurements revealed similar atrial‐like and ventricular‐like AP morphology in the hiPSC‐aCMs and ‐vCMs, respectively (Figure 4A). AP and CaT durations were quantified at early, mid, and late repolarization (APD20, APD50, and APD80) and Ca2+ decay (CaTD20, CaTD50, and CaTD80), respectively. These stages reflect different phases of ionic currents across the plasma membrane and the extrusion of Ca2+ handling mechanics.

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Functional phenotyping of hiPSC‐derived atrial and ventricular CMs using optical mapping. Representative average traces of A, action potential and B, Ca2+ transients of hiPSC‐aCMs and ‐vCMs electrically paced at 1 Hz. C, Electrical restitution curve measured at APD80 relative to the diastolic interval (DI). D, Quantification of early‐ (APD20), mid‐ (APD50), and late‐ (APD80) repolarization, unpaired t test, *p < .05, **p < .01. E, Quantification of early‐ (CaTD20), mid‐ (CaTD50), and late‐ (CaTD80) Ca2+ transient decay, unpaired t test, ***p < .001. F, Time to peak (TTP) of the Ca2+ transient, unpaired t test, ***p < .001. G, Time constant (τ) of Ca2+ decay, unpaired t test *p < .05. H, Maximum slope of the electrical restitution as shown in panel C, unpaired t test, *p < .05. Electrical restitution curves were measured under a variable rate pacing protocol (60‐200 bpm) as described in the Supplemental Information. n = 4 (four independent differentiation batches) and cardiac enriched hiPSC‐aCMs and ‐vCMs were analyzed in these set of experiments. Data are presented as mean ± SEM

For these experiments, both hiPSC‐aCMs and ‐vCMs were paced at 1 Hz. All measured levels of the APD were significantly shorter in hiPSC‐aCMs compared with hiPSC‐vCMs (APD20: 84 ± 8 ms vs 127 ± 6 ms, p < .05; APD50: 131 ± 12 ms vs 191 ± 8 ms, p < .01; APD80: 179 ± 16 ms vs 251 ± 12 ms, p < .05; Figure 4D). The overall CaTD of hiPSC‐aCMs was significantly shorter than that of hiPSC‐vCMs (CaTD20: 180 ± 12 ms vs 266 ± 12 ms, p < .001; CaTD50: 282 ± 18 ms vs 397 ± 16 ms, p < .001; CaTD80: 474 ± 27 ms vs 615 ± 18 ms, p < .001; Figure 4E). Compared with hiPSC‐vCMs, hiPSC‐aCMs displayed significantly faster CaT time‐to‐peak (hiPSC‐aCMs: 116 ± 7 ms vs hiPSC‐vCMs: 246 ± 10 ms, p < .05) and faster decay kinetics (τ; hiPSC‐aCMs: 350 ± 39 ms vs hiPSC‐vCMs: 671 ± 118 ms, p < .05) indicating that Ca2+ handling mechanics are accelerated in hiPSC‐aCMs (Figure 4F,G).

The direct comparison between whole‐cell patch clamp and optical mapping read‐outs paced at 1 Hz is shown in Figure S7. We observed no differences in the read‐outs of hiPSC‐aCMs at APD20 (optical: 84 ± 8 ms, patch: 98 ± 12 ms) and APD50 (optical: 131 ± 12 ms, patch: 169 ± 19 ms). However, APD80 of hiPSC‐aCMs measured by patch clamp was longer than the optical APD80 (253 ± 22 ms vs 179 ± 16 ms, p < .05). Similarly, both APD20 (216 ± 22 ms vs 127 ± 6 ms) and APD80 (393 ± 62 vs 251 ± 12 ms) of hiPSC‐vCMs measured by patch clamp were longer than the comparable optical measurements. APD50 of hiPSC‐vCMs did not show a statistical difference between the two assays (optical: 191 ± 8 ms, patch: 308 ± 60 ms).

Rate‐dependent properties are critical in cardiac function. A variable rate protocol (Figure S6) in which the hiPSC‐CMs were electrically paced with increasing frequency at every cycle was used to investigate the electrical restitution dynamics. The electrical restitution curve reflects the ability of the cardiac system to accommodate a higher pacing rate by progressive shortening of APD80 and is described as APD80 in relation to the diastolic interval (DI). Compared with hiPSC‐vCMs, the electrical restitution curve of the hiPSC‐aCMs displayed a flatter portion and did not show APD80 shortening at longer diastolic intervals (Figure 4F). The extensive shortening in APD80 started at shorter diastolic intervals for hiPSC‐aCMs (<275 ms) compared with hiPSC‐vCMs (<500 ms). The maximum slope of the restitution curve was higher in hiPSC‐vCMs compared with hiPSC‐aCMs (1.26 ± 0.08 vs 0.91 ± 0.04, p < .05; Figure 4G) indicating faster kinetics of APD in response to higher pacing rate.

3.4 In vitro screening for atrial‐selective pharmacology

We first established the utility of optical mapping to detect a pan‐cardiac pharmacological response by using dofetilide, a strong blocker of the rapid delayed rectifier K+ current (IKr),17 an ionic current expected to be present in both hiPSC‐aCMs and ‐vCMs.18 Dofetilide elicited a dose‐dependent response in both hiPSC‐aCMs and ‐vCMs. Compared with predrug baseline, dofetilide at 100 nM prolonged APD80 of both hiPSC‐aCMs from 182 ± 16 ms to 355 ± 24 ms (95 + 7% prolongation) and of hiPSC‐vCMs from 238 ± 20 ms to 319 ± 45 ms (34 ± 14% prolongation, p < .05; Table S1 and Figure 5C). The drug prolonged early‐repolarization (APD20) of hiPSC‐vCMs at 10 and 30 nM while having no effect on APD20 of hiPSC‐aCMs at all tested doses (Table S1). Additionally, CaTD50 and CaTD80 of both hiPSC‐aCMs and ‐vCMs were significantly prolonged in response to dofetilide (Table S1). However, hiPSC‐aCMs appeared to be more sensitive to dofetilide as the APD80 was significantly prolonged at the lowest tested dose of 3 nM (from to 182 ± 26 ms to 241 ± 26 ms, p < .05; Table S1) and displayed a larger dose‐response (Figure S8).

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The effects of dofetilide and nifedipine on action potential and Ca2+ transient of hiPSC‐aCMs and ‐vCMs. Representative traces of action potential and Ca2+ transients illustrating the effects of A, dofetilide and B, nifedipine on hiPSC‐aCMs and ‐vCMs. Higher drug doses are presented by a progressively darker shade. The effects of C, 4‐aminopyridine and D, nifedipine on normalized (percent change from predrug baseline) action potential duration (APD) and Ca2+ transient duration (CaTD); both parameters being measured at 20%, 50%, and 80%. Dashed line is the normalized predrug control presented as 0% change. n = 6 from six independent differentiation batches. hiPSC‐derived atrial cardiomyocytes (aCMs) are shown in red while hiPSC‐derived ventricular cardiomyocytes (vCMs) are presented in blue. Data are presented as mean ± SEM. Drug effects were compared between hiPSC‐aCMs and ‐vCMs at each dose using unpaired t test, *p < .05, **p < .001, ***p < .001. NS stands for not significant

Next, we demonstrated the functional differences in the ion channel profiles of hiPSC‐aCMs and ‐vCMs. We aimed to show that the ultrarapid outward current (IKur) produced by the channel Kv1.5 (KCNA5) was functional and specific to hiPSC‐aCMs, while the inward Ca2+ current (ICa,L) produced the voltage‐dependent L‐type Ca2+ channel CaV1.2 (CACNA1C) was functional and specific to hiPSC‐vCMs. We used two relatively selective compounds, 4‐aminopyridine (4AP) and nifedipine, to dissect the presence of functional IKur and ICaL, respectively. While nifedipine is also known to block Cav1.3, it is expected to have a preferential effect at lower concentrations on CaV1.2 based on the literature which indicates ~13‐fold higher block on CaV1.2 than CaV1.3.19

At the highest tested dose (300 nM), nifedipine significantly decreased APD50 of hiPSC‐vCMs from 170 ± 14 ms to 121 ± 16 ms (28 ± 4% shortening) and decreased CaTD50 from 357 ± 10 ms to 333 ± 23 ms (30 ± 3% shortening) (Figure 5D; Table S2). We observed a trend in APD50 shortening of hiPSC‐aCMs in response to increasing the nifedipine dose, but the drug elicited a significantly stronger dose‐dependent shortening in both APD and CaTD of hiPSC‐vCMs compared with hiPSC‐aCMs (Figures S8 and S9). Observing the percent change from predrug control, nifedipine induced differential response in overall APD and CaTD between hiPSC‐aCMs and ‐vCMs at 10, 100, and 300 nM (Figure 5D).

In hiPSC‐aCMs, 4AP prolonged APD and CaTD in a dose‐dependent manner with a statistically significant change starting at 30 μM (Figure 6A,C; Table S3). 4AP significantly prolonged early‐repolarization (APD20) of hiPSC‐aCMs by 46 ± 2% and 66 ± 2% at 50 and 100 μM, respectively (APD20 at baseline: 82 ± 8, at 50 μM: 120 ± 9 ms, at 100 μM: 131 ± 9 ms, p < .05) (Figure 6C and Table S3). In contrast, 4AP prolonged APD20 of hiPSC‐vCMs by 23 ± 4% (APD20 at baseline: 138 ± 8 ms, at 100 μM: 170 ± 9 ms) at the highest tested dose of 100 μM (Figure 6C and Table S3). hiPSC‐aCMs showed greater change in APD to relative to predrug control at all concentrations of 4AP compared with hiPSC‐vCMs (Table S3), This is corroborated by the steeper trend of the dose response relationship in hiPSC‐aCMs (Figure S8). Additionally, the overall CaTD of hiPSC‐aCMs were prolonged after exposure to 4AP at 10 μM while the drug had a significant effect on CaTD of hiPSC‐vCMs at 30 μM (CaTD50 elongation from baseline: 68 ± 2% vs 12 ± 2%, p < .05) (Table S3).

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The effects of 4‐aminopyridine (4AP) and AVE0118 on action potential and Ca2+ transient of hiPSC‐aCMs and ‐vCMs. Representative traces of action potential and Ca2+ transients illustrating the effects of A 4‐aminopyridine (4AP) and B, AVE0118 on hiPSC‐aCMs and ‐vCMs. Higher drug dose is presented by a progressively darker shade. The effects of C dofetilide and D, vernakalant on normalized (percent change from predrug baseline) action potential duration (APD), and B, Ca2+ transient duration (CaTD); both parameters being measured at 20%, 50%, and 80%. Dashed line is the normalized predrug control presented as 0% change. n = 6 from six independent differentiation batches. hiPSC‐derived atrial cardiomyocytes (aCMs) are shown in red while hiPSC‐derived ventricular cardiomyocytes (vCMs) are presented in blue. Data are presented as mean ± SEM. Drug effects were compared between hiPSC‐aCMs and ‐vCMs at each dose using unpaired t test, *p < .05, **p < .001, ***p < .001. NS stands for not significant

We then demonstrated the effectiveness of our drug screening platform in assessing the effects of experimental compounds designed to have targeted effects on atrial‐specific ion channels using AVE0118 and UCL1684.

AVE0118 is an experimental drug that blocks IKur, the G‐protein‐activated K+ current (IKAch), and the transient outward K+ current (Ito) at a similar dose range.20 Both IKur and IKAch are atrial‐specific ionic currents. AVE0118 prolonged mid‐ and late‐ repolarization (APD50 and APD80) of both hiPSC‐aCMs and ‐vCMs at the two highest tested doses (3 and 10 μM; Table S4). Similarly, AVE0118 had significant effects on CaTD50 and CaTD80 of hiPSC‐aCMs and ‐vCMs at all tested doses (Table S4). However, the APD50 and APD80 of hiPSC‐aCMs were significantly prolonged at a lower dose of 1 μM (control: 200 ± 14 ms, 1 μM: 244 ± 16 ms; Table S4). Furthermore, the atrial‐selective effects of the drug were demonstrated by a larger proportional prolongation in APD50 and APD80 of hiPSC‐aCMs compared with hiPSC‐vCMs at 1, 3, and 10 μM (APD; Figure 6D). Furthermore, AVE0118 induced a larger proportional prolongation in CaTD of hiPSC‐aCMs compared with hiPSC‐vCMs at all tested doses (Figure 6D). Early repolarization (APD20) of hiPSC‐aCMs also displayed a large dose‐dependent response (Figure S8) with a proportionally larger prolongation at 10 μM (63 ± 2% vs 43 ± 5%, p < .05; Figure 6D).

UCL1684 is purported to be a potent direct pore blocker of the small conductance Ca2+ activated K+ channel (SK channel)21 and was expected to induce a dose‐dependent atrial‐selective response. In hiPSC‐aCMs, UCL1684 treatment resulted in a significantly prolonged APD80 at 3 and 10 μM (from predrug control: 136 ± 11 ms to 3 μM: 188 ± 25 ms or 38 ± 5% prolongation, and to 10 μM: 206 ± 32 ms or 49 ± 11% prolongation, p < .05; Figure 7C and Table S5). UCL1684 prolonged CaTD80 of hiPSC‐aCMs at all tested doses (baseline: 300 ± 15 ms, at 0.3 μM: 372 ± 23 ms, at 1 μM: 387 ± 33 ms, at 3 μM: 413 ± 24 ms, at 10 μM: 416 ± 39 ms, p < .05; Table S5). In contrast, UCL1684 exposure showed no statistically significant effect on overall APD and CaTD of hiPSC‐vCMs. The sensitivity of hiPSC‐aCMs to UCL1684 was also reflected in the dose‐response relationship showing a prolongation APD80, in contrast to the minimal prolongation in APD80 of hiPSC‐vCMs (Figure S8).

image
The effects of UCL1684 and vernakalant on action potential and Ca2+ transient of hiPSC‐aCMs and ‐vCMs. Representative Vm and Ca2+ transients illustrating the effects of A, UCL1684 and B, vernakalant on hiPSC‐aCMs and ‐vCMs. Higher drug doses are presented by a progressively darker shade. The effects of C, AVE0118 and D, UCL1684 on normalized (percent change from predrug baseline) action potential duration (APD) and Ca2+ transient duration (CaTD); both parameters being measured at 20%, 50%, and 80%. Dashed line is the normalized predrug control presented as 0% change. n = 6 from six independent differentiation batches. hiPSC‐derived atrial cardiomyocytes (aCMs) are shown in red while hiPSC‐derived ventricular cardiomyocytes (vCMs) are presented in blue. Data are presented as mean ± SEM. Drug effects were compared between hiPSC‐aCMs and ‐vCMs using unpaired t test at each dose, *p < .05, **p < .001, ***p < .001. NS stands for not significant

Finally, we tested the effects of vernakalant which is a multi‐ion channel blocker that blocks the fast and late inward Na+ current (INa, INaL, respectively), the IKur, and the IKAch.22 The drug is used clinically for intravenous cardioversion of patients in AF23 and was expected to induce an atrial‐specific effect due to its IKur and IKAch blocking properties.

Vernakalant elicited a positive dose‐dependent response in both APD and CaTD of hiPSC‐aCMs with minimal measurable effects on hiPSC‐vCMs (Table S6; Figures S8 and S9). Vernakalant demonstrated atrial‐selectivity with statistically significant differences between APD and CaTD of hiPSC‐aCMs and ‐vCMs at doses of 3, 10, and 30 μM (Figure 7D). Compared with APD at baseline, vernakalant at 10 μM significantly prolonged APD20, APD50, and APD80 of hiPSC‐aCMs by 84 ± 6%, 70 ± 5%, and 77 ± 4%, respectively (Figure 7D). Additionally, vernakalant at 10 μM prolonged CaTD20, CaTD50, CaTD80 of hiPSC‐aCMs by 58 ± 4%, 50 ± 3%, 35 ± 5%, respectively (Figure 7D). At clinically relevant concentrations (30 μM), vernakalant greatly affected early repolarization of hiPSC‐aCMs (APD20 prolonged by 124 ± 8%; Figure 7D). At 30 μM, vernakalant prolonged APD80 of hiPSC‐vCM by 20 ± 7% (APD80: 238 ± 22 ms at baseline vs 289 ± 30 ms at 30 μM, p < .05; Figure 7D and Table S6). Except for APD80 prolongation at 30 μM, vernakalant had no statistically significant effect on overall APD and CaTD of hiPSC‐vCMs at the lower doses (Table S6).

4 DISCUSSION

In this study, we were successful in efficiently differentiating hiPSCs into a monolayer of cardiomyocytes with an atrial phenotype by modifying the GiWi protocol.11 We used multiple phenotypic approaches such as qPCR, digital multiplexed gene expression analysis with NanoString technology, flow cytometry, ELISA, voltage measurements with current clamp electrophysiology as well as simultaneous voltage and Ca2+ transient measurements with optical mapping to demonstrate a clear and distinct atrial phenotype. Unique to our study, we completed an in‐depth pharmacological analysis with simultaneous voltage and Ca2+ measurements to demonstrate the differential responses of these chamber‐specific cardiomyocytes, and their utility as a translational model in screening for the safety and efficacy of novel atrial‐specific compounds for the treatment of AF.

Our observations support previous data in showing that atrial specification is in part mediated by RA.681016 In our protocol, atrial differentiation was accomplished by adding 0.75 μM RA 24 hours after WNT inhibition, with a total exposure time of 72 hours. The generated hiPSC‐aCMs showed an atrial‐specific phenotype as validated at both protein and transcript levels with a decrease in ventricular‐specific and an increase in atrial‐specific markers. These results suggest that RA, at the dose and temporal exposure used in this study, maintains cardiac differentiation efficiency while pushing the differentiation process into an atrial lineage.

As a complementary assay, we used the NanoString digital multiplexed gene expression analysis to assess the expression of 250 genes custom‐curated from the existing literature. We found MYL2 and MYH7, markers of the ventricular phenotype, to be significantly differentially expressed between hiPSC‐aCMs and ‐vCMs, matching the gene expression pattern of native adult human right atrial and left ventricular tissues.24 Another ventricular‐specific marker KCNA425 which encodes for the Kv1.4 channel of the slow Ito was downregulated in hiPSC‐aCMs. Canonical atrial markers such as KCNA5 and NR2F2 were also confirmed to be differentially upregulated in hiPSC‐aCMs. Other markers of human atrial specificity such CXCR4GNAO1JAG1PLCB1, and TBX18 as retrieved from the GTEx database26 were upregulated in our hiPSC‐aCMs further demonstrating the effect of RA on driving the differentiation pathway into an atrial lineage.

MYL7, thought to be an atrial‐specific marker, was not found to have a significantly higher expression in hiPSC‐aCMs. The differential expression of MLC‐2a may however require additional maturation of the hiPSC‐CMs. Other studies1127 have shown a high expression in MLC‐2a at day 20 postdifferentiation and a subsequent decrease over time in culture systems generating predominantly ventricular hiPSC‐CMs. One study has shown a higher expression of MLC‐2a in hiPSC‐aCMs analyzed at a later date (earliest at day 60).6

Electrophysiological differences between atrial and ventricular phenotypes, in terms of voltage and Ca2+ handling, define their function and are critical to the development and determination of efficacy of atrial‐specific compounds. As demonstrated by whole‐cell patch clamp and optical mapping measurements, the hiPSC‐aCMs generated in this study exhibited atrial‐like AP and Ca2+ handling properties. Namely, the AP of hiPSC‐aCMs was significantly shorter, along with a lack of a prolonged plateau phase as opposed to the AP of hiPSC‐vCMs, an observation that is aligned with native cardiomyocyte electrophysiology.28 Similarly, the CaT of hiPSC‐aCMs had faster kinetics with a faster decay time as reflected by the differential expression of Ca2+ channel isoforms, further demonstrating the differential physiology between hiPSC‐aCMs and ‐vCMs.

In terms of APD measurements, we observed a good correlation between the patch clamp and optical mapping recordings for hiPSC‐aCMs. In hiPSC‐vCMs, however, the optical AP measurements were shorter overall than patch clamp recordings. This discrepancy may be attributed to the heterogeneity of our current ventricular differentiation protocol which generated predominantly ventricular cardiomyocytes but also contain a small proportion of nonventricular phenotypes (ie, atrial myocytes and nodal cells). Thus, the optical AP signals represents an average from about 300 000 cells in each 1 cm2 region of interest.

Another hallmark of cardiomyocyte function is rate‐dependence, as described by the electrical restitution curve.29 We observed that the electrical restitution properties were different between hiPSC‐aCMs and ‐vCMs. Compared with hiPSC‐vCMs, hiPSC‐aCMs displayed a steady‐state‐like property by undergoing minimal APD80 shortening in response to the lower ranges of the pacing protocol (cycle lengths of about 400‐1000 ms) indicating full recovery of ion channel kinetics at these pacing ranges. In contrast, the hiPSC‐vCMs displayed consistent APD80 shortening at the same pacing range. It is important to note that APD restitution curves are likely different when using the standard steady‐state extra stimulus protocol compared with dynamic pacing, particularly in cardiomyocytes with immature Ca2+ handling and memory.29 In relation to dynamic pacing protocol, hiPSC‐vCMs have steeper maximum slope of the restitution curve compared with hiPSC‐aCMs as steady‐state APD is the principal determinant of the slope of the ventricular restitution curve.30

The presence of specific ion channel currents (ie, IKur, IKAch, and ICaL) explain, in part, the functional differences between the two cardiac chamber subtypes, the expressions of which were already shown in our qPCR and NanoString assays. We used a series of compounds (4‐aminopyridine, dofetilide, vernakalant, AVE0118, UCL1684, and nifedipine) to demonstrate the function of atrial‐specific ionic currents in our model system and were able to show the expected chamber specific differences between hiPSC‐aCMs and ‐vCMs.

Dofetilide (DF) served as a positive control in our optical mapping assay as a clinically relevant drug which has a strong effect on IKr in both atria and ventricular CM.31 As expected, dofetilide affected the repolarization of both hiPSC‐aCMs and ‐vCMs, confirming the presence of IKr in both cell types. At clinically relevant doses of DF (3 and 10 nM), hiPSC‐aCMs displayed greater sensitivity to the drug indicating a larger proportional contribution of IKr in the AP of hiPSC‐aCMs relative to hiPSC‐vCMs. This may partly explain the effectiveness of the drug in the clinical treatment of AF. However, clinical use of the drug to treat AF is limited due to its tendency to induce QTc prolongation. This pro‐arrhythmic risk of TdP32 which was captured by the prolongation of APD80, an in vitro surrogate of QTc, in the hiPSC‐vCMs. This finding supports the utility of our optical mapping assay in predicting the risk of ventricular proarrhythmia in vitro.

The compound 4AP has been shown to selectively block Kv1.4 (Ito) and Kv1.5 (IKur)33 and is therefore expected to elicit a response in hiPSC‐aCMs at lower doses than in hiPSC‐vCMs as IKur (Kv1.5) is a strong functional indicator of atrial phenotype. Confirmation of the atrial expression of IKur channels was demonstrated by the stronger dose‐dependent hiPSC‐aCM AP prolongation to 4AP at all tested doses (10, 30, 50 and,100 μM) suggesting selective sensitivity of hiPSC‐aCMs to 4AP due to a greater expression of Kv1.5. The inhibitory effects of 4AP were observed at higher doses (50 and 100 μM) in hiPSC‐vCMs which can be attributed to the heterogeneous population, potential off‐target effects at these high doses, as well as baseline expression of Kv1.4 (Ito).

Using nifedipine, we demonstrated the functional differences in Ca2+ handling dynamics between hiPSC‐aCMs and ‐vCMs. Nifedipine elicited a dose‐dependent response in hiPSC‐vCMs demonstrating high sensitivity at 300 nM thereby confirming the functional presence of Cav1.2. In contrast, hiPSC‐aCMs were relatively insensitive to nifedipine showing no statistically significant differences in APD at all tested doses. This finding is further corroborated by the relatively decreased expression of CACNA1C (Cav1.2) in the hiPSC‐aCMs. This suggests that Ca2+ handling in hiPSC‐aCMs may be reliant on other voltage‐gated Ca2+ channels such as Cav1.3, as this Ca2+ channel is blocked less potently by nifedipine.34 Moreover, our qPCR assay confirmed that hiPSC‐aCMs had higher expression of CACNA1D (Cav1.3).

AVE0118 is an experimental K+ channel blocker (Ito, IKur, and IKr) that was predicted to demonstrate targeted effects in hiPSC‐aCMs. However, only a nuanced atrial specificity was observed in our assay. Although the effects were proportionally larger in hiPSC‐aCMs, AVE0118 prolonged early repolarization of both hiPSC‐aCMs and ‐vCMs in a similar fashion. The drug prolonged mid‐ and late‐repolarization at a lower dose (1 μM) in hiPSC‐aCMs showing minimal atrial specific effects. Interestingly, AVE0118 greatly affected Ca2+ handling in hiPSC‐aCMs compared with hiPSC‐vCMs with larger proportional prolongation of CaTD50 at all doses. These results were unexpected as AVE0118 is thought to be highly specific to hiPSC‐aCMs due to its IKur blocking component. Perhaps the observed mixed‐effects in both cell types is due to the drug binding to Ito (IC50: 3.4 μM) and IKr (IC50: 9.6 μM)35 which prolongs APD at the tested doses of 3 and 10 μM as genes encoding the channels producing the Ito (KCNA4) and IKr (KCNH2) were expressed in our hiPSC‐vCMs. The drug was also shown to be effective in terminating certain ventricular arrhythmias36 which was predicted based on our results of prolongation in the APD of hiPSC‐vCMs.

Next, we used UCL1684, a highly specific SK channel pore blocker, to assess the presence of functional SK channels in hiPSC‐aCMs. The SK channel has three paralogs but the SK3 channel variant (KCNN3) has been shown to be atrial‐specific and has been implicated in AF pathogenesis in several studies.3738 In this study, UCL1684 displayed high specificity toward hiPSC‐aCMs with a strong dose‐dependent response. The drug confirmed the presence of functional SK channels in hiPSC‐aCMs at 3 μM with a positive dose‐dependent response while having no effect on hiPSC‐vCMs at all tested doses (0.3, 1, 3, and 10 μM).

Vernakalant is touted as an atrial‐selective compound clinically approved for intravenous cardioversion of AF.39 Strikingly, out of all the tested drugs, vernakalant showed the most pronounced atrial‐selective effects even though it is a blocker of multiple ion channels (INa, IKur, and IK,Ach). Vernakalant prolonged APD and CaTD of hiPSC‐aCMs at three tested doses (3, 10, 30 μM). However, no statistically significant changes were observed in hiPSC‐vCMs at early‐ and mid‐ repolarization while the slight prolongation at APD80 at the clinically relevant dose (30 μM) may be attributed to the INa blocking component of vernakalant. This result further demonstrates the sensitivity of the assay in establishing atrial‐selective drug effects.

This study has several limitations. One limitation in our findings is that we cannot directly compare the results from qPCR and NanoString as both assays have fundamental differences in technical principles and statistical methodologies. Taken together, however, both assays show the global changes in cell type specific gene markers and further validate the role of retinoic acid in directing the cardiac differentiation process toward an atrial lineage. The main limitation in this field is the maturation state of the hiPSC‐CMs as they have an overall immature phenotype with some crucial differences compared with adult cardiomyocytes.40 Nonetheless, we were able to observe the stark differences in genetic, protein, as well as functional signatures of AP and CaT in the two generated chamber‐specific cell types. Additionally, maturation stage does not explain the differences in chamber‐specific phenotype as parallel batch differentiation and time‐in‐culture were incorporated in our study design. Most importantly, we were able to capture effects of drugs that were expected to have atrial‐specific properties in hiPSC‐aCMs.

5 CONCLUSION

The ability to differentiate hiPSC‐aCMs provides a unique opportunity to study atrial physiology and its pharmacologic responses in a human‐relevant in vitro model. We demonstrated an hiPSC‐based in vitro model that recapitulates the molecular and functional characteristics of the phenotype of native atrial tissue. Our platform adds to the repertoire of cardiac drug screening and can be readily applied in future efforts of atrial‐specific drug discovery.

ACKNOWLEDGMENTS

We would like to thank Salina Kung and Jennifer Yi for their help in designing the NanoString codeset. This work was financially supported by the Canadian Institutes of Health Research (G.F.T), the Canada Innovation Fund (G.F.T), the Stem Cell Network (G.F.T and Z.L.), and the Michael Smith Foundation (Z.L.).

CONFLICT OF INTEREST

The authors declared no potential conflicts of interest.

AUTHOR CONTRIBUTIONS

M.G.G., S.S.S.: conception and design, collection and assembly of data, data analysis and interpretation, manuscript writing; S.S.: experimental design support, data interpretation, manuscript writing; E.L.: designed and built the optical mapping system (hardware and software); D.A.H.‐W.: cell culture; V.J.B.: data collection, data analysis and interpretation, manuscript writing; Z.L., G.F.T.: conception of study, manuscript writing support and review, data interpretation, financial support.

Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis

Abstract

Background Implantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.

Methods Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.

Results Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%–100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.

Conclusion We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.

“It’s so Cute I Could Crush It!”: Understanding Neural Mechanisms of Cute Aggression

  • Graduate School of Education, University of California, Riverside, Riverside, CA, United States

The urge people get to squeeze or bite cute things, albeit without desire to cause harm, is known as “cute aggression.” Using electrophysiology (ERP), we measured components related to emotional salience and reward processing. Participants aged 18–40 years (n = 54) saw four sets of images: cute babies, less cute babies, cute (baby) animals, and less cute (adult) animals. On measures of cute aggression, feeling overwhelmed by positive emotions, approachability, appraisal of cuteness, and feelings of caretaking, participants rated more cute animals significantly higher than less cute animals.

There were significant correlations between participants’ self-report of behaviors related to cute aggression and ratings of cute aggression in the current study.

N200: A significant effect of “cuteness” was observed for animals such that a larger N200 was elicited after more versus less cute animals. A significant correlation between N200 amplitude and the tendency to express positive emotions in a dimorphous manner (e.g., crying when happy) was observed.

RewP: For animals and babies separately, we subtracted the less cute condition from the more cute condition. A significant correlation was observed between RewP amplitude to cute animals and ratings of cute aggression toward cute animals. RewP amplitude was used in mediation models.

Mediation Models: Using PROCESS (Hayes, 2018), mediation models were run. For both animals and babies, the relationship between appraisal and cute aggression was significantly mediated by feeling overwhelmed. For cute animals, the relationship between N200 amplitude and cute aggression was significantly mediated by feeling overwhelmed. For cute animals, there was significant serial mediation for RewP amplitude through caretaking, to feeling overwhelmed, to cute aggression, and RewP amplitude through appraisal, to feeling overwhelmed, to cute aggression. Our results indicate that feelings of cute aggression relate to feeling overwhelmed and feelings of caretaking. In terms of neural mechanisms, cute aggression is related to both reward processing and emotional salience.

Introduction

Cute aggression is defined as the urge some people get to squeeze, crush, or bite cute things, albeit without any desire to cause harm. Aragón et al. (2015) initially operationalized the phenomenon of “cute aggression” through individual self-reports while viewing cute stimuli. The authors investigated cute aggression using pictures of baby humans and animals via an online survey. Findings indicated that for infantile babies (e.g., images that had been altered to have large eyes and chubby cheeks; Sherman et al., 2013) and baby animals, there was a relationship between being overwhelmed by positive feelings and the expression of cute aggression (Aragón et al., 2015).