HCells Trilogy · Part II: Calcium Transients

来源：北京心动康达信息技术有限公司更新时间：2025-12-10 15:15:22 阅读量：37

导读：HCells Trilogy · Part II: Calcium Transients

Keywords: calcium transients; cardiomyocytes; cellular function; HCells; high-throughput multimodal cardiomyocyte functional detection system

In the previous article, we focused on “force”: how much traction cardiomyocytes can actually generate, how the time course and efficiency of contraction–relaxation behave, and how traction forces have taken the “witness stand” as key evidence in top-journal studies across cardiac, pulmonary, tumor, and immune systems. But if we shift the time axis half a step earlier, a natural question arises: where do these changes in mechanical output first start to move?

For cardiomyocytes, the trail almost always leads to calcium. Each arriving action potential is translated into a rapid rise and subsequent fall in intracellular Ca2?; the waveform, amplitude, and rhythm of every calcium transient leave their marks along the chain of excitation–contraction coupling. The swings of arterial pressure and the peaks and valleys of traction-force curves, in fact, all have to be traced back to these invisible calcium signals.

For this very reason, calcium transients are far more than a simple matter of “a bit brighter or dimmer”: they represent a functional backbone that connects electrical activity, ion channels, sarcoplasmic/endoplasmic reticulum calcium handling, and the contractile apparatus into a single chain. In many cardiovascular safety assessments and disease-model studies, the waveform and rhythm of calcium transients often “speak up” earlier than changes in morphology or traditional injury markers.

As the second part of the HCells Trilogy, this article follows that line back from “force” to “calcium” to examine what additional information calcium transients can bring to cardiomyocyte functional evaluation, and how they are recorded and leveraged in our HCells high-throughput multimodal cardiomyocyte functional detection system.

Calcium Transients: The Native Language of Cardiomyocyte Communication and Function

If traction force answers the question of “how much work cardiomyocytes ultimately perform,” then calcium transients are more like the native language these cells use to issue commands. For the myocardium, a full contraction–relaxation cycle never happens out of nowhere; it always unfolds along a fixed sequence of “electrical signal → calcium signal → mechanical contraction.” An action potential reaches the cell membrane and opens L-type calcium channels; a small amount of Ca2? enters the cell from the extracellular space, which in turn triggers the sarcoplasmic reticulum to release a larger amount of Ca2?. The intracellular free calcium concentration rises rapidly, Ca2? binds to troponin, the myofilaments begin to slide, and the cardiomyocyte completes a contraction. Calcium is then pumped back into the sarcoplasmic reticulum or extruded from the cell, the cell relaxes, and it is ready for the next activation.

Under the microscope, this entire process can be “compressed” into a single fluorescence trace that rises and falls over time—that is what we usually refer to as the calcium transient. Its peak amplitude reflects the amount of Ca2? mobilized during a contraction; the slopes of the upstroke and decay correspond to the efficiency of calcium release and clearance; and whether the waveform is symmetric or shows a prolonged tail is closely linked to sarcoplasmic reticulum function, the status of calcium pumps and exchangers, and a series of related molecular mechanisms. When this trace recurs with a stable rhythm, what we observe is regular cardiomyocyte contraction; once the waveform becomes distorted and the rhythm disorganized, it often signals that some link in the excitation–contraction coupling chain has begun to fail.

Unlike the simple question of whether a particular receptor or channel is expressed, calcium transients capture a dynamic process rather than a static “snapshot.” Even when the same channel is downregulated, the eventual functional consequences can be completely different: in some cases, the calcium peak is only slightly reduced while the rhythm can still be maintained; in others, the calcium signal develops delayed repolarization and triggered activity, directly increasing the risk of arrhythmias. From the molecular level alone, it is difficult to intuitively distinguish these differences, whereas on the calcium-transient waveform these details are often immediately recognizable. Therefore, in many studies of cardiac safety assessment and ion channels, calcium transients are regarded as a key “intermediate readout” that links electrical activity to mechanical contraction.

Precisely because calcium transients can simultaneously “hear the signals, visualize the process, and match the function,” they have gradually evolved from being merely a form of fluorescence imaging into an independent information pathway in cardiomyocyte functional evaluation. In the following sections, drawing on representative studies in cardiovascular drug safety and disease models, we will examine how researchers, in real experimental settings, make use of this “native language” of calcium transients to decode how cardiomyocytes work under different conditions.

Calcium Transients in Top-Tier Journals: More Than Just “A Little Brighter or Dimmer”

If we only look at it at the imaging level, calcium transients are easily reduced to “the cell lights up for a moment and then goes dark again.” However, over the past decade or so, numerous studies published in top cardiovascular journals have made it clear that what researchers truly care about is not that brief bright moment itself, but the entire time course of the calcium signal—when it begins to rise, how fast it rises, how high the peak is, whether the decay is clean and complete, and whether a relatively stable rhythm can be maintained over long-term recordings.

In the fields of cardiac safety and drug screening, calcium transients have almost become a standard functional readout for hiPSC-derived cardiomyocytes. Many studies continuously record calcium-transient waveforms from the same batch of cells before and after drug exposure, and then compare subtle changes such as whether the peak amplitude is reduced, the upstroke and decay are slowed, or the rhythm develops premature beats, pauses, or alternans in RR intervals. For some compounds, traditional toxicity markers may still look unremarkable, while the calcium transients have already shown abnormalities such as prolonged tails, double peaks, or trigger-like events. This often indicates that the electrophysiological and calcium-handling systems have entered a precarious, unstable state, sounding an “early alarm” for subsequent arrhythmic risk. In such studies, calcium transients provide an earlier and more nuanced warning curve than binary readouts like “alive or dead” or “presence or absence of a specific biomarker.”

Hwang H, Liu R, Maxwell JT, Yang J, Xu C. Machine learning identifies abnormal Ca2? transients in human induced pluripotent stem cell-derived cardiomyocytes. Scientific Reports 2020;10:16977. DOI: 10.1038/s41598-020-73801-x

This figure shows a typical Ca2? transient signal together with its first- and second-derivative traces, with 10 out of 14 peak-level parameters labeled around the peak (including ΔF, the left and right peak amplitudes A_l / A_r, the left and right peak durations D_l / D_r, as well as the extrema of the first derivative Dy_max / Dy_min and of the second derivative D2y_max / D2y_min, etc.). In other words, a calcium signal that would otherwise be described simply as “lighting up and then returning to baseline” is here decomposed into a complete set of quantifiable waveform features, which are further used to support SVM-based modeling for peak-state classification.

In disease models, calcium transients are repeatedly used to answer the question of “where exactly is this cardiomyocyte impaired.” In heart failure models, a typical pattern is reduced calcium peak amplitude, slower upstroke, and markedly prolonged decay, with the entire waveform taking on a “sluggish and exhausted” appearance. In hypertrophy and certain inherited cardiomyopathies, by contrast, the peak amplitude may be relatively preserved, while pronounced rhythm instability or repolarization-related abnormalities emerge in the waveform. In some channelopathies, the action potential may still look largely unchanged, whereas the calcium transients already exhibit frequent extra fluctuations or afterglow-like signals. For such studies, calcium transients are no longer a simple “on/off” brightness readout, but a functional fingerprint that can delineate the trajectory of pathological changes with single-cell precision.

Tatekoshi Y, Chen C, Shapiro JS, et al. Human induced pluripotent stem cell-derived cardiomyocytes to study inflammation-induced aberrant calcium transient. eLife 2024;13:RP95867. DOI: 10.7554/eLife.95867

The figure above shows that, in hiPSC-CMs treated with TNF-α or IFN-γ, the decay time and downstroke duration of calcium transients are markedly prolonged, so that the entire return phase is noticeably stretched out. When riociguat, sildenafil, PF-04447943, or dapagliflozin are co-applied, this inflammation-driven disturbance of calcium handling is corrected to varying degrees. The figure combines representative calcium-transient traces and their derivative curves with bar graphs summarizing decay times, providing a clear visual depiction of the functional sequence from “inflammatory cytokines → abnormal calcium transients → partial reversal by drug treatment.”

More importantly, many top-tier studies have begun to look at calcium transients alongside other dimensions of data: on one side lie the calcium waveforms and rhythm; on the other, traction-force traces, sarcomere-length changes, or the reprogramming signatures seen in transcriptomic and proteomic profiles. The questions researchers ask have also become more specific: when a given channel is knocked out or a particular drug is added, do changes in the calcium transient appear first, or is a drop in traction force seen earlier? Once calcium transients are clearly abnormal, can sarcomere geometry and shortening still “hold up” for a while? In the early stages of disease, is there a temporal sequence of “calcium changes first, force follows later”? These are questions that are hard to answer by looking only at static morphology or a single molecular marker, and calcium transients happen to provide the dynamic time axis that links “electrical activity–calcium–force.”

In this sense, when we look back at the calcium-transient data in those top journals, it becomes difficult to see them merely as “beautiful green or red fluorescence traces.” They are telling us how cardiomyocytes adjust their signaling rhythm under different drugs, genetic backgrounds, and pathological contexts, and how they redistribute “reserve” and “cost” among electrical activity, calcium handling, and mechanical contraction. It is precisely in this way of use that calcium transients have gradually grown from a “side parameter” of imaging into an independent information channel in cardiomyocyte functional studies. In the next section, we will return to our own experimental system and examine how, on HCells, we systematically “decode” this calcium signal.

How We Read Calcium Transients on HCells

In our own experimental system, the question becomes: how exactly do we “listen” to this calcium signal on HCells? In terms of measurement strategy, what HCells does is in fact quite straightforward: it interferes as little as possible with the cell’s intrinsic rhythm, uses highly stable imaging conditions, and continuously records the temporal changes of calcium fluorescence in single cardiomyocytes or small local cell clusters. For spontaneously beating hiPSC-CMs, we simply let them “speak” in their own rhythm; for models that require external pacing, we use the electrical stimulation module to set the pacing rate and then observe how calcium transients respond under a relatively controlled frequency. In all cases, what we ultimately obtain are calcium-fluorescence traces that rise and fall along a shared time axis.

At the data-processing level, HCells does more than simply drawing traces; it extracts a full set of parameters for every calcium transient. The most basic ones are the minimum/maximum fluorescence intensity and the relative fluorescence change (ΔF/F?), which answer the question of “how strong is a single calcium mobilization.” In the time dimension, we report the time required to reach 25%, 50%, 75%, and 95% of the peak, as well as the time it takes to decay from the peak down to these levels, to characterize the efficiency of calcium release and clearance. Going a step further, we can compute the velocities and time constants of the upstroke and decay, the decay time constant τ, and other metrics that describe in finer detail whether the waveform is “crisp and clean” or “prolonged and sluggish.”

These parameters are not collected just to pile up numbers, but to allow each “light up and return to baseline” event to be decomposed into a set of features that can be compared, modeled, and aligned with other dimensions of measurement.

To turn these analyses from “nice to look at” into “truly useful,” HCells incorporates several standardized designs in its acquisition and processing workflow. The acquisition software provides preset protocols tailored for calcium-transient recording, including recommended frame rates, exposure times, and recording durations, which users can fine-tune according to their experimental needs. The cruise-acquisition function allows the system to automatically move across multiple wells and fields of view according to a predefined list, enabling continuous recording from large numbers of cells. On the analysis side, adaptive filtering and adjustable low-pass filtering are supported, suppressing noise as much as possible while preserving genuinely meaningful waveform details. For multiple calcium transients recorded on the same plate, the software can batch-extract parameters and organize them into tables while retaining the corresponding raw traces, making it convenient to perform subsequent statistical analysis, clustering, or cross-comparison with gene expression and traction-force data.

Within this framework, calcium transients in HCells are no longer judged subjectively as “does it light up” or “does it look irregular,” but are treated as functional trajectories with clear time stamps and multidimensional features. For the same batch of cells, we can compare whether ΔF/F? decreases after drug treatment, whether decay time is prolonged, and whether beat-to-beat variability in rhythm increases. Across different substrate stiffnesses, culture conditions, or disease models, we can use a unified set of parameters to describe how the mode of calcium handling differs from one cell population to another.

Integrating Force and Calcium: From a Single Trace to a Complete Functional Chain

When traction force and calcium transients are still measured in separate experiments or on different platforms, we often end up drawing conclusions in parallel: on one side, “mechanical output has decreased,” and on the other, “calcium handling is abnormal,” yet it is difficult to answer on a single time axis “which changed first, which followed, and whether the two truly correspond.” One of the key design philosophies of HCells is precisely to acquire these two traces simultaneously—in the same batch of cells and under the same experimental conditions—so that the relationship between “force and calcium” is transformed from a conceptual link into a directly comparable fact.

In practical analysis, we usually start with the most straightforward questions. For example, under a given class of drug treatment, do the peak amplitude and the rise/decay times of the calcium transient change? Do the corresponding traction-force peak and strain energy decrease in parallel? If we see that “calcium transients remain largely normal, but traction force is already clearly weakened and mechanical power reduced,” suspicion tends to fall more heavily on the sarcomeric apparatus or the mechanical coupling machinery itself. Conversely, if traction force can still be maintained at a relatively stable level while calcium transients already show prolonged decay, rhythm disturbances, or extra small fluctuations, the problem is more likely rooted in ion-channel function and calcium-handling processes. When the two traces are examined side by side, many phenomena that used to be described only in terms of “impressions” can be broken down into much more concrete mechanistic hypotheses.

Taken together, the value of integrating “force and calcium” is not simply about adding one more trace to look at. It lies in being able to answer, within a single dataset, three questions at once: “Has function changed?”, “Where exactly has it changed?”, and “Did the signaling fail first, or did the terminal mechanical output fail first?”. In this way, our assessment of model quality, disease mechanisms, and drug effects can move beyond qualitative impressions and evolve into a functional chain of evidence that carries temporal order and causal clues.

Conclusion: From “Force–Calcium” to “Force–Calcium–Structure”

In the HCells Trilogy, the traction-force chapter and the calcium-transient chapter sketch two major perspectives on cardiomyocyte function: “terminal mechanical output” and “dynamic signaling process.” To truly complete this story, we still need a third piece of the puzzle—sarcomere dynamics. Only when sarcomeric-level contraction geometry is brought onto the same time axis and aligned with both traction force and calcium transients can we, in the very same batch of cells, simultaneously observe “how the signal arrives, how the structure moves, and how the force is ultimately delivered.” In the next article, we will shift to the viewpoint of the sarcomere to fill in this final missing link.

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